A Model-based irrigation water consumption estimation. edited by Flavio Lupia

A Model-based irrigation water consumption estimation at farm level edited by Flavio Lupia INEA 2013

Istituto Nazionale di Economia Agraria A Model-based irrigation water consumption estimation at farm level edited by Flavio Lupia INEA 2013

Editor: Flavio Lupia Contributors: INEA Flavio Lupia - Foreword, Introduction, Glossary, Annex 1, Chapter 5, Paragraphs: 2.4, 3.4.2, 4.1, 4.2, 4.3, 4.4 and 4.5 Silvia Vanino - Paragraphs 3.2 and 3.3 Francesco De Santis - Annex 1, Paragraphs: 2.4, 4.1, 4.2 and 4.3 Filiberto Altobelli - Paragraph 2.5 Giuseppe Barberio - Chapter 5 Pasquale Nino - Paragraph 2.6 ISTAT Giampaola Bellini - Chapter 1 Giancarlo Carbonetti - Paragraph 4.1 Massimo Greco - Paragraph 3.1 Luca Salvati - Paragraph 3.4.1 IAS-CSIC Luciano Mateos - Paragraphs: 2.1, 2.2, 2.3, 2.4 and 4.2 CRA-CMA Luigi Perini - Paragraph 3.4.3 Free-lance consultants Nicola Laruccia - Paragraph 3.3 Disclaimer: This publication has been realized in the framework of the MARSALa project funded by Eurostat with the Grant Agreement No. 40701.2008.001008.140 (Grant Programme 2008 - Theme Pilot studies for estimating the volume of water used for irrigation ). Its content does not represent the official position of the European Commission and is entirely under the responsibility of the authors. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. Copyright 2013 by Istituto Nazionale di Economia Agraria, Roma. Editorial coordination: Benedetto Venuto Graphic design: Ufficio Grafico Inea (Barone, Cesarini, Lapiana, Mannozzi) Publish coordination: Roberta Capretti

Essentially, all models are wrong but some are useful. (George Edward Pelham Box)

Acknowledgments At the outset, it is my duty to acknowledge with gratitude the generous help received from the researchers and technicians belonging to the institutions involved during project life. I am grateful to INEA personnel, in particular: Isabella Salino and Mauro Santangelo for timely providing elaboration of the RICA database; Alfonso Scardera (INEA-Molise) for the advises during the design of the pilot areas questionnaire; Antonio Giampaolo and the personnel from INEA-Abruzzo for the design and implementation of the electronic survey on crop planting/harvesting date through GAIA website; Federica Floris (INEA-Sardegna) for supporting the activities in Sardegna and Cinzia Morfino for irrigation water consumption data collection; Giancarlo Peiretti (INEA-Piemonte), Sonia Marongiu (INEA-Veneto), Lucia Tudini (INEA-Toscana) and Roberto Lo Vecchio (INEA-Calabria) for the support during data collection on rice cultivation water use; Iraj Namdarian for the revision of the text and the useful hints. I would like to thank Michele Fiori (ARPA Sardegna) and Vittorio Marletto (ARPA Emilia-Romagna) for timely providing high resolution agrometeorological data. Special thanks are due to Maurizio Esposito from MiPAAF for the cooperation since the project proposal and for his full support and the useful suggestions during the data collection. I am also grateful to Carmelo Cicala from MiPAAF for the support and Costanzo Massari from MiPAAF that provided information about the state-of-the-art on soil databases in Italy. 5

Foreword This publication contains an exhaustive description of the developed methodological approach to estimate the irrigation water consumption at farm level in Italy by using the data collected though the 6 th General Agricultural Census realized by ISTAT in the period 2010-2012 1. In 2008, Eurostat awarded grants to 13 European Member States (MS) to develop methodologies for irrigation water consumption estimation that could be extended to all MS. This necessity arose from the EC-Regulation Nr.1166/2008 that binds all MS to provide, for each holding surveyed with the Statistics on Agricultural Production Methods (SAPM), an estimation of irrigation water consumption measured in cubic metres. The Italian grant, titled a Modelling Approach for irrigation water estimation at farm Level (MARSALa), has been leaded by INEA in partnership with the Instituto de Agricoltura Sostenibile-Consejo Superior de Investigaciones Cientificas (IAS-CSIC), the Spanish research institute based in Cordoba specialized in irrigation and agricultural sciences. IAS-CSIC cooperated with INEA for the realization of the work package (WP) dealing with the design and integration of the computational models (Models Design). The project lasted 22 months starting from July 2008 till May 2010 and it has been articulated in five WPs with different phases as depicted in the work breakdown structure (WBS) in Figure 1. The project plan is reported in Table 1. Figure 1. Project work breakdown structure with the five WPs and the relative phases marsala Models design Census questionnaire amendments Data collection Models calibration and validation Software implementation and testing Model A Agro-meteo database Pilot campaigns Module 1 Model B Crop characteristics database Calibration Module 2 Model C Soil database 1 The methodology has been developed in the framework of the Eurostat Grant Programme 2008 (Theme Pilot studies for estimating the volume of water used for irrigation ) with the Grant Agreement Nr. 40701.2008.001008.140 awarded to the Italian Institute for Agricultural Economics (INEA). 7

During the project, a collaboration has been established with the National Statistic Service of Greece (NSSG) which was carrying out a similar project in Greece. The collaboration allowed a sharing of knowledge, a comparison and a critical analysis of the two approaches, in particular for all those concerning country agricultural characteristics, territorial/environmental features and data availability. Table 1 - MArsALa project plan with the start and end dates by WP and phase. Activity Start End Project start 15/07/2008 15/07/2008 Census questionnaire amendments 1/09/2008 15/01/2009 Models design 15/09/2008 28/02/2010 Model A 15/09/2008 15/03/2009 Model B 15/09/2008 15/03/2009 Model C 1/10/2009 28/02/2010 Data collection 1/10/2009 1/05/2010 Agrometeorological database 1/10/2008 15/01/2009 Crop characteristics database 15/01/2009 30/06/2009 Soil database 1/02/2009 1/05/2010 Models calibration and validation 1/02/2010 1/05/2010 Pilot campaigns 15/10/2009 15/02/2010 Calibration 15/01/2010 1/05/2010 Software implementation and testing 15/12/2009 10/05/2010 Module 1 15/12/2009 28/02/2010 Module 2 15/12/2009 10/05/2010 Project end 14/05/2010 14/05/2010 The WP Models Design, the core activity of the action, has been aimed at the design and integration of three computational models: Model A, Model B and Model C. The models have been designed after an extensive analysis of the state-of-the-art and by taking into account the characteristics of the Italian agricultural farms as well as the constraints imposed by the main sources of information: the Census Questionnaire (CQ). The WP has been also addressed to the analysis and identification of the main input parameters required by the models. The input parameters have been used during the WP Census Questionnaire Amendments, which has been jointly carried out with ISTAT and focussed on the CQ structure analysis and definition of an amended version containing some changes and additional questions of fundamental importance for the models application. The amendments allowed a better extraction of the required parameters and, as consequence, a potentially more precise estimation. The WP Data Collection lasted almost for the entire duration of the project due to the difficulty of identification, analysis, collection and standardization of the input data required by the models. The creation of the soil parameters database for the whole Italian agricultural area has been the most complex phase. Indeed, the activity required a full inventory of the available Italian soil information and the development of a methodology to extract the soil parameters by considering several information such as topography (altitude and slope) and land use. 8

The WP Models Calibration has been addressed to the comparison of the simulated and actual irrigation water volumes used at farm level. Pilot campaigns have been realized in four Italian regions by submitting a questionnaire to a sample of almost 300 farms. Surveyors collected, in each farm, the same information reported in the CQ and in addition the measured and/or estimated water consumption of the farm irrigated crops. The WP Software Implementation and Testing has been devoted to the implementation of the three integrated models. The final system realized is made up of different computational modules (some dedicated to data pre-processing) and it works by using a set of databases containing all the input parameters. 9

Executive summary The MARSALa (Modelling Approach for irrigation water estimation at farm Level) project has been realized in the framework of the Eurostat Grant Programme 2008 (Theme Pilot studies for estimating the volume of water used for irrigation ) with the Grant Agreement awarded to the Italian Institute for Agricultural Economics (INEA). Aim of the project was to design a methodology for estimating, by implementing a computational model, the irrigation water consumption at farm level in Italy by using, as a key source of information, the 6 th General Agricultural Census 2010. The methodology has been applied to estimate the water consumption (in cubic meters) for the whole universe of the Italian irrigated farms as requested by EC-Regulation Nr.1166/2008. The methodology grounds on the development and integration of three models dealing with the main aspects related to the farm irrigation water consumption: the crops irrigation demand, the irrigation systems efficiency and the farmer irrigation strategy. Each model has been developed by considering the state-of-the-art methodologies, the limits imposed by the data availability and data resolution (climate, soil, crops characteristics and other statistics), the expert knowledge and the nature of the information to be collected by the Census. One of the main issues of the project has been the data collation as accurate as possible for the whole agricultural Italian area. In fact, the Italian framework is characterized by data usually produced with different standards and methodologies and managed by offices operating at different administrative levels. The MARSALa model has been calibrated with a sample of about 300 farms located in four Italian regions (Campania, Sardegna, Emilia-Romagna and Puglia), the farms sample has been designed to ensure the representativeness for the main Italian agricultural characteristics. The calibration phase has shown how accuracy and reliability of the simulated results are directly linked to the quality of the input data required by the three sub-models. The model developed has been implemented through a client-server architecture and is provided with the necessary routines to import and manage the required datasets as well as with all the input databases. The outputs produced by the model are the irrigation water consumption for each irrigated farm crops and the total irrigation farm consumption. 11

Table of contents Acknowledgements 5 Foreword 7 Executive summary 11 Introduction 15 chapter 1 The Irrigated Agriculture in Italy: an Analysis through fss Data 17 1.1 Historical trend of the irrigation phenomenon 17 1.2 Details on the irrigation phenomenon 20 chapter 2 Methodology for the Irrigation Water Consumption Estimation 25 2.1 State of the art on the estimation of irrigation water requirements 25 2.2 Crop Irrigation Requirements Model (Model A) 27 2.3 Irrigation Efficiency Model (Model B) 30 2.4 Irrigation Strategy Model (Model C) 32 2.5 Irrigation water consumption estimation for rice 38 2.6 Irrigation water consumption estimation for protected crops 45 chapter 3 Input Data Collection 49 3.1 The 6 th General Agricultural Census database 49 3.2 Crop characteristics database 53 3.3 Soil database 56 3.4 Agro-meteorological database 61 chapter 4 Models Calibration 67 4.1 Methodology for pilot areas definition and farms sample extraction 70 4.2 Pilot questionnaire for the model calibration 77 13

4.3 Pilot campaigns 79 4.4 Analysis of the model simulation results 90 4.5 Influence of the resolution of the agro-meteorological data on the simulation results 96 chapter 5 Software Implementation 99 5.1 Module architecture of the computational system1 99 5.2 Functions of the modules and sub-modules 100 Conclusions 103 References 107 Glossary 113 Acronyms and abbreviations 117 Annex 1: Rule-based approach for the definition of the farm irrigated land use 119 Annex 2: 6 th general agricultural census questionnaire (in italian language) 125 Annex 3: Pilot questionnaire and compilation guidelines (in italian language) 143 Annex 4: Database of mean irrigation water volumes used for rice 167 14

Introduction Agriculture is the main driving force in the management of water use. In the EU as whole, 24% of abstracted water is used in agriculture and, in particular, in some regions of southern Europe agriculture water consumption rises to more than 80% of the total national abstraction (EEA Report No 2/2009). Over the last two decades agricultural water use has increased driven both by the fact that farmers have seldom had to pay for the real cost of the water and for the old Common Agricultural Policy (CAP), having often provided subsides to produce water-intensive crops with low-efficiency techniques. As for the majority of the Mediterranean countries, irrigation represents for Italy one of the most relevant pressures on the environment in terms of use of water due to the occurrence of hot and dry season causing increased water demand to maintain the optimal growing conditions for some valuable crops species. Future scenarios are expected to be worse due to climate change that might intensify problems of water scarcity and irrigation requirements in the Mediterranean region (IPCC, 2007, Goubanova and Li, 2006, Rodriguez Diaz et al., 2007). Accurately estimating the irrigation demands (as well as those of the other water uses) is therefore a key requirement for more precise water management (Maton et al., 2005) and a large scale overview on European water use can contribute to developing suitable policies and management strategies. So far, the main policy objectives in relation to water use and water stress at EU level aim at ensuring a sustainable use of water resources (e.g. the 6th Environment Action Programme (EAP), 1600/2002/EC) and the Water Framework Directive (WFD), 2000/60/EC). Although in several areas are installed a wide variety of flow measurement devices, in most irrigation systems water measurements are not performed routinely. In addition, water measurement may be expensive or unfeasible. Even if measuring devices are installed, there are numerous reasons (from technical to socioeconomic) that prevent systematic measurements. Few information about irrigation water use are actually available for Italy, the fragmentation and the complex organization of public agencies combined with the private water abstraction prevent a complete accounting. Government reported figures result from indicative modelling studies (ISTAT, 2006); some research projects reported results derived from Geographic Information System (GIS) approaches at NUTS 2 1 and NUTS 3 2 level mainly for Southern Italy (Portoghese et al., 2005; Nino et al., 2009). This study, can contribute to the lack of irrigation water measurements by providing a model-based estimation of the irrigation water use at farm level. It reviews the state-ofthe-art on irrigation water requirements and presents an innovative methodology taking 1 Level 2 of the Nomenclature of Territorial Units for Statistics (NUTS) corresponds to the Regions. 2 Level 3 of the Nomenclature of Territorial Units for Statistics (NUTS) corresponds to the Provinces. 15

into account the crop water consumption, the irrigation application efficiency (as a function of irrigation distribution uniformity and irrigation depth) and the irrigation strategy adopted by farmers (generally tied to socioeconomic and environmental reasons). The report is organized into the following sections. The first chapter contains a description of the irrigated agriculture in Italy based on the analysis of Farm Structure Survey (FSS) data collected by ISTAT. The second chapter describes the methodology developed and the three integrated models. The third chapter reports the activity of data inventorying and collection for the input parameters, with particular focus on the methodology for the creation of the soil database with country coverage. The fourth chapter concerns with the models calibration, namely: farms sample selection, realization of the pilot campaigns and tuning of the models parameters. The last chapter outlines the activity related to the implementation of the models through the MARSALa software application with a brief description of the system architecture and the features. 16

CHAPter I The irrigated agriculture in Italy: an analysis through fss data Irrigation represents in Italy one of the most relevant pressures on environment in terms of use of water as in other Mediterranean countries where hot and dry season might create conditions for requirements of additional water to ensure the optimal growth for specific crops. A picture of the irrigation phenomenon in Italy is provided by ISTAT, who carried out a monitoring activity by collecting several data during the years through FSS data - at census and sample level - as required by European regulations and for national interest. At national level the following data are available: farms with irrigation activity, irrigable and irrigated surface, irrigated crops, irrigation system adopted and related irrigated area, source of water and supply methods. All those characters are strictly related to the water volumes used depending also on efficiency of water use that might be strongly affected by the adopted irrigation technologies. In the following a brief overview of the phenomenon is proposed 1. 1.1 Historical trend of the irrigation phenomenon Data collected in the last three decades referring to farms with irrigation and related irrigable and irrigated surfaces show different patterns: farms with irrigation registered a drop of almost 40% between year 1990 and 2007 (the phenomenon is related to the decrease registered also in the total number of farms); whereas irrigable and irrigated surface have been almost steady, accounting for 3,950,503 and 2,666,205 hectares in year 2007 respectively (see Table 1.1 and Figure 1.1). The almost constant difference between irrigable and irrigated area, with the first one always greater that the latter, can be explained by the following elements: recursive events of water shortage periods avoiding the full exploitation of the whole farm area equipped with irrigation systems (the phenomenon generally affects mainly the Southern regions); low efficiency of the irrigation systems and of the farm irrigation and conveyance network preventing the optimal usage of the irrigation water across the whole equipped surface; agronomic techniques (e.g. crop rotation) reducing the annually irrigated area. As shown by the following figures, Italian farms withdraw water from more than one source, are served according to various supply modalities, and adopt more than one irrigation system. 1 Data analysis performed by Simona Ramberti and Nicola Mattaliano (ISTAT). 17

Going into more detailed data, changes are evident in specific irrigation aspects (see Table 1.1). Regarding the use of water sources and delivering systems, data are comparable in pares: 1982 is comparable with 1990, and 2000 with 2003 where data are available. In terms of water source, between 1982 and 1990 farms resorting to Surface water bodies and Other sources increased (around 30%) more than farms resorting to Surface flowing water. Particularly, in year 2000, 233,010 farms uses Surface flowing water, whereas 531,853 farms resort to Other sources. In terms of delivering system Irrigation and land reclamation consortia resulted to be more widespread in year 2003 than in year 2000 to damage of the Other ways variable (including the self-supply). Figures for year 2003 show that 397,199 farms resort to the water from Other ways while 329,032 to Irrigation and land reclamation consortia. As regards the irrigation system, figures show that Micro-irrigation - a water saving irrigation system - registered a considerable increase in the decade between 1982 and 1990, rising from 28,208 farms using it to 113,577. With reference to the year 2007, data show that Border (or Superficial flowing water) and Furrows (or Lateral infiltration), Aspersion (or Sprinkler) and Micro-irrigation have comparable distribution among farms (respectively adopted by 193,682, 189,865 and 170,035 farms). Figure 1.1 - Irrigable and irrigated area for the years 1982, 1990, 2000, 2003, 2005 and 2007 (area in thousands of hectares). 8.000 7.000 6.000 Thousands of hectares 5.000 4.000 3.000 2.000 1.000 0 1982 1990 2000 2003 2005 2007 Irrigable area Irrigated area Year 18

Table 1.1 - Farms with irrigation and related surfaces by supply source and irrigation method expressed as absolute value and percentage over total farms with irrigation (Years 1982, 1990, 2000, 2003, 2005 and 2007). Irrigated farms / Irrigated surface / Water source / Irrigation method Irrigated farms Census survey (a) Sample survey (b) 1982 1990 2000 2003 2005 2007 a.v. % over total farms with irrigation a.v. % over total farms with irrigation a.v. % over total farms with irrigation a.v. % over total farms with irrigation a.v. % over total farms with irrigation Farms with irrigable surface n.a. 1,059,456 966,270 710,522 660,349 677,738 a.v. % over total farms with irrigation Farms with irrigated surface 834,424 934,640 731,082 622,541 503,461 563,663 Irrigated surface Irrigable area 2,780,614 3,881,772 3,892,202 3,977,206 3,972,666 3,950,503 Irrigated area 2,521,193 2,711,182 2,471,378 2,763,510 2,613,419 2,666,205 Farms irrigation method Superficial flowing water and lateral infiltration 241,366 28.9 377,579 35.6 322,313 44.1 213,603 34.3 183,990 36.5 193,682 34.4 Flood 73,533 8.8 48,095 4.5 7,439 1.0 23,235 3.7 13,973 2.8 14,838 2.6 Aspersion 533,423 63.9 583,183 55.0 333,711 45.6 221,402 35.6 170,477 33.9 189,865 33.7 Dripping 28,208 3.4 113,577 10.7 114,369 15.6 184,214 29.6 146,504 29.1 170,035 30.2 Other systems 23,406 2.8 28,164 2.7 31,373 4.3 45,691 7.3 35,682 7.1 44,967 8.0 Farms water souces (c) Surface flowing water 159,401 19.1 194,557 18.4 233,010 31.9 n.a. n.a. n.a. Surface water bodies 18,891 2.3 25,134 2.4 33,790 4.6 n.a. n.a. n.a. Other 341,738 41.0 456,401 43.1 531853 (d) 72.7 n.a. n.a. n.a. Delivering management (c): Irrigation and land reclamation Consortia 305,465 36.6 398,913 37.7 302,872 41.4 329,032 52.9 n.a. n.a. Other farms 32,477 3.9 31,037 2.9 35,071 4.8 27,015 4.3 n.a. n.a. Other ways 35,102 4.2 34,592 3.3 429325 (e) 58.7 397.199 (e) 63.8 n.a. n.a. Source: ISTAT, FSS - Year 1982, 1990, 2000, 2003, 2005, 2007 a.v.: absolute value n.a.: not available (a) National Universe (b) European Union Universe (c) Variables related to water sources and adopted delivering systems have been surveyed as source of water in surveys run in 1982 and 1990, whereas in years 2000 and 2003 sources and delivering management have been considered independent phenomena. (d) Includes the following source of water: aqueduct, groundwater, treated wastewater and rainfall basin. (e) Includes self-supply and other forms. 19

Irrigated crops changed also their pattern in the last three decades as showed in Table 1.2. An analysis of the individual crop trend revealed an increase for irrigated grain maize surface (19.1%) between 1982 and 2003, whereas rotational forage dramatically decreased (45.7%) in the same period of time. A decrease is also registered for the soybean cultivation (73.2% less surface compared to 1990), whereas vineyards rose 67.3%. With reference to the last available year 2003, the most irrigated crops, beside the other crops group accounting for 719,521 hectares, are grain maize with 666,723 hectares, followed by rotational forage with 353,261, showing that irrigated crops are mainly linked to livestock foodstuff production. Other relevant irrigated crops are in order of relevance - vineyards, fruit and berry plantations, and fresh vegetables (respectively with 266,330, 210,089 and 197,107 hectares). Table 1.2 - Number of farms with irrigation and irrigated area (in hectares) for the main crops (Years 1982, 1990, 2000 and 2003). Crop Farms Census year Sample survey 1982 1990 2000 2003 Irrigated area Farms Irrigated area Farms Irrigated area Farms Irrigated area Wheat - - 18,566 69,489 27,178 99,636 13,061 57,391 Grain maize 200,002 559,804 179,057 507,170 124,895 623,155 108,220 666,723 Potato - - 90,925 34,710 56,872 26,461 22,944 24,847 Sugar beet - - 18,684 81,965 15,282 81,532 14,271 83,203 Sunflower - - 3,841 18,537 2,526 14,260 1,839 7,399 Soybean - - 40,250 201,083 11,971 78,618 9,527 53,895 Fresh vegetables 264,015 217,607 223,873 233,587 152,293 191,012 102,292 197,107 Rotational forage 143,290 650,280 96,202 439,376 47,439 267,560 52,085 353,261 Vineyards 136,349 159,177 113,119 162,391 110,828 182,694 109,910 266,330 Citrus plantations 122,180 146,735 137,212 153,815 109,136 113,651 75,309 123,744 Fruit and berry plantations 82,511 144,329 117,355 199,059 108,974 189,175 88,545 210,089 Other crops 282,859 643,262 384,574 609,999 285,184 603,624 269,313 719,521 Total 934,427 2,521,193 934,840 2,711,182 731,082 2,471,378 622,541 2,763,510 Source: ISTAT, FSS - Years 1982,1990, 2000 and 2003. 1.2 details on the irrigation phenomenon 1.2.1 Farms with irrigation, irrigable and irrigated area Referring to irrigated and irrigable area the most recent data refers to year 2007 (Table 1.3). Figures show that farms with irrigable and irrigated area are concentrated mainly in the southern regions (respectively 52.5% and 54.7% over the total), whereas irrigable and irrigated area are mainly located in the northern regions (59.7 and 63.6% over the total). Irrigable area represents 30.7% of cultivated area at national level, the value rises to 50.1% in northern regions; whereas the irrigated area represents 20.7% of the total cultivated area at national level rising to 36% in the northern regions. 20

Table 1.3 Farms with irrigable and irrigated area by region (Year 2007). Region/Autonomous province (AP) Farms with irrigable area % over the total % over the total farms (a) Irrigable area % over the total % over cultivated area (b) Farms with irrigated area % over the total % over the total farms (a) Irrigated area % over the total % over the cultivated area (b) Piemonte 5.4 48.7 10.5 39.2 5.9 44.5 13.6 34.2 Valled Aosta 0.5 96.0 0.5 31.6 0.7 95.5 0.6 25.3 Lombardia 5.2 62.0 17.2 67.1 5.5 54.1 21.2 56.0 Trentino-Alto Adige 4.3 70.4 1.7 16.7 5.0 68.0 2.4 16.2 Bolzano (AP) 2.3 73.6 1.1 17.6 2.7 72.4 1.7 17.3 Trento (AP) 2.1 67.2 0.5 15.3 2.3 63.7 0.8 14.3 Veneto 11.2 52.3 12.0 57.2 9.0 35.1 11.2 36.1 Friuli-Venezia Giulia 1.4 40.6 2.5 42.2 1.7 39.3 3.1 35.4 Liguria 1.9 63.3 0.2 14.6 2.2 58.7 0.2 11.6 Emilia-Romagna 6.1 50.9 15.1 56.5 5.2 35.9 11.1 28.0 Toscana 4.0 34.2 3.0 14.7 3.1 22.2 1.8 5.8 Umbria 1.3 23.7 1.3 15.4 1.1 16.7 0.9 7.1 Marche 1.9 26.7 1.5 11.9 1.7 19.0 0.9 4.9 Lazio 4.0 26.8 3.6 20.7 4.2 23.3 3.2 12.7 Abruzzo 3.1 34.7 1.5 13.8 3.0 28.4 1.3 7.9 Molise 0.4 11.7 0.5 10.2 0.4 9.5 0.6 7.4 Campania 8.4 37.5 2.6 17.8 9.2 34.2 2.9 13.8 Puglia 13.6 37.5 10.5 34.8 13.3 30.6 10.2 22.7 Basilicata 2.7 31.9 2.0 14.4 2.9 28.6 1.7 8.3 Calabria 8.4 47.5 3.0 22.9 9.6 45.5 3.3 16.9 Sicilia 11.4 32.7 5.9 18.7 12.2 29.1 6.6 14.0 Sardegna 4.6 47.0 4.8 17.2 4.0 34.3 3.0 7.3 Italy 100.0 40.4 100.0 30.7 100.0 33.6 100.0 20.7 North 36.2 54.6 59.7 50.1 35.2 44.1 63.6 36.0 Centre 11.3 28.5 9.4 16.0 10.1 21.2 6.8 7.8 South 52.5 37.1 30.9 20.9 54.7 32.1 29.6 13.6 Source: ISTAT, FSS-Year 2007 (a) Farms with Utilised Agricultural Area (UAA) of trees for wood production (b) Cultivated area includes UAA and trees for wood production The analysis of the distribution of irrigated area by altimetric zone (Figure 1.2) shows a concentration (69%) in the plain areas and a minor distribution on hilly (24%) and mountainous areas (7%). Figure 1.2 Irrigated area by altimetric zone (Year 2007). Hill 3% Mountain 9% Plain 88% 21

1.2.2 Irrigation system Survey run in year 2007 collected information also on irrigated area by irrigation system. The irrigation system adopted is an important indicator for water use efficiency. Data presented in Table 1.4 show that Aspersion is the most widespread system (36.8% of the irrigated area) followed by Border/Furrows (30.6%). Micro-irrigation at national level covers 21.4 % of irrigated area, but in the southern regions - where very dry weather conditions and low water availability are quite common in the irrigation season - the percentage rises to 53.4%. Table 1.4 - Irrigated area by irrigation system and region (Year 2007). Data are expressed as percentage over the total irrigated area. Irrigation system Region/Autonomous province (AP) Border and Micro-irrigation Other Flood Aspersion Furrows Total Drip system Piemonte 59.8 33.2 4.9 1.8 1.6 0.8 Valle d Aosta 53.9-44.4 1.0 1.0 0.7 Lombardia 64.1 17.2 18.4 1.4 0.8 1.0 Trentino-Alto Adige 2.2 0.2 72.9 28.5 24.6 0.6 Bolzano (AP) 2.3 0.1 85.1 18.6 17.7 0.0 Trento (AP) 1.9 0.3 46.0 50.2 39.6 2.1 Veneto 23.7 0.9 64.6 5.3 3.0 7.6 Friuli-Venezia Giulia 12.2 0.0 80.1 3.8 2.0 4.1 Liguria 5.4 0.1 11.8 25.8 22.7 57.5 Emilia-Romagna 15.9 3.1 61.9 19.8 18.0 2.3 Toscana 10.0 0.4 66.4 26.4 24.6 2.5 Umbria 4.1 1.3 84.7 9.5 9.3 1.8 Marche 6.8 1.3 70.9 10.6 9.0 11.2 Lazio 5.4 2.0 66.6 21.7 15.2 4.8 Abruzzo 5.9 0.1 64.3 25.7 24.1 4.3 Molise 5.6-34.9 60.8 51.2 0.1 Campania 27.1 1.8 46.7 16.9 10.5 9.0 Puglia 5.8 1.0 13.8 75.4 61.6 5.9 Basilicata 12.9 0.2 27.1 49.3 27.3 10.5 Calabria 30.4 1.5 29.2 28.0 17.8 11.7 Sicilia 5.0 1.2 27.9 64.7 53.1 1.8 Sardegna 3.9 4.7 56.2 30.0 22.8 5.4 Italy 30.6 9.1 36.8 21.4 17.0 3.8 North 42.4 13.5 36.6 6.6 5.4 2.7 Centre 6.6 1.4 69.5 19.8 16.0 4.6 South 10.7 1.4 29.6 53.4 42.0 6.0 Source: ISTAT, FSS - Year 2007. The following table reports the distribution of the irrigation system adopted at farm level, the figure shows that a 76% of the irrigated area belongs to farms adopting only one irrigation system, 22.1% with two different irrigation systems, whereas only 1.9% with three and more irrigation systems. 22

Table 1.5 - Number of farms and relative irrigated area (hectares) by number of irrigation system (Year 2007). Number of irrigation systems Farms with UAA and/or wooden arboriculture Irrigated area Absolute values % Absolute values % 0 1,114,481 66.4 0.0 0.0 1 515,374 30.7 2,026,215 76.0 2 46,871 2.8 588,619 22.1 3 or more 1,417 0.1 51,371 1.9 Total 1,678,144 100.0 2,666,205 100.0 Source: ISTAT, FSS - Year 2007. 1.2.3 Irrigated crops Last available data on irrigated crops have been collected through the survey run in year 2003. Referring to irrigated crops an analysis has been performed to understand whether a specific crop grown in a specific farm is completely irrigated or not. Results show that rice and potato are the crops in which respectively 98.8% and 98.4% of the irrigated area is cultivated in farms where the crop is completely irrigated, for other crops such percentages are lower as for wheat and rotational forage where they reach values of 59.6% and 71.9%. Referring to permanent crops, 97.3% of the citrus plantations irrigated area is in farms where the crop is completely irrigated, whereas this value lowers to 75.6% for olive plantations (Figure 1.3). Figure 1.3 Cultivated and irrigated area (hectares) by crop (Source: ISTAT, FSS 2003). 800 700 THOUSANDS OF HECTARES 600 500 400 300 200 100 0 Wheat Mais Rice Potato Sugar beet Sunflower CROPS Soya bean Rotational forage Vine Olive Citrus Cultivated area Total irrigated area Irrigated area in farms where crop is completely irrigated 23

Table 1.6 - Farms with irrigated area by number of irrigated crops and irrigation system (Year 2003). Number of irrigation systems Irrigation system Farms One irrigated crop More than one irrigated crop Total Unique Border and Furrows 132,943 49,981 182,924 Flood 12,784 3,817 16,601 Aspersion 123,084 55,317 178,401 Micro-irrigation 30,407 6,453 36,860 Other system 29,206 6,711 35,917 More than one 102,277 69,561 171,838 Total 430,701 191,840 622,541 Source: ISTAT, FSS, Year 2003. The analysis performed on number of irrigation systems adopted at farm level and number of irrigated crops show that in many cases farms adopt more than one irrigation system (172 thousands farms over 622 thousands), among which 102 thousands irrigate only one crop and the remaining more than one. In terms of geographical distribution of the mentioned crops, data in Table 1.7 show that northern and southern regions differ quite a lot. Beside other crops, grain maize, rice, rotational forage, vineyards, fruit and berry plantations trees, and meadows are mostly widespread in northern regions, whereas fresh vegetables, vineyards, olive plantations, citrus plantations are mainly located in southern regions. Table 1.7 - Irrigated area (hectares) by crop and geographical region (Year 2003). Crop Geographical area North Centre South Italy Grain maize 616,220.24 37,607.74 12,894.81 666,722.79 Rice 247,017.52 266.02 2,417.43 249,700.98 Fresh vegetables 64,861.01 28,712.46 103,533.72 197,107.17 Rotational forage 244,690.83 32,345.31 76,225.31 353,261.45 Vineyards 95,743.10 11,618.17 158,969.00 266,330.26 Olive plantations 2,734.73 6,712.60 164,646.19 174,093.52 Citrus plantations 12.29 504.4 123,226.83 123,743.52 Fruit 130,336.25 15,259.17 64,493.93 210,089.36 Meadows 132,847.43 2,003.87 3,942.28 138,793.57 Other crops 206,367.98 59,755.40 117,544.18 383,667.53 Total 1,740,831.32 194,785.14 827,893.70 2,763,510.16 Source: ISTAT, FSS, Year 2003. 24

CHAPter II Methodology for the irrigation water consumption estimation 2.1. State-of-the-art on the estimation of irrigation water requirements Scientific research carried out during the first half of the 20th century generated a new set of indications for quantitative irrigation management. The water balance and the concepts of the upper and lower limits of the soil water readily available to the plants (Veihmeyer and Hendrickson, 1927) formed the basis of modern irrigation management. The equation developed by Penman (1948) for estimating a reference evapotranspiration and the combination of this concept with the one of crop coefficient (Doorembos and Pruitt, 1977a) improved the accuracy of the water budget for determining irrigation water requirements. This procedure is widely used today for irrigation systems design and management. The water balance provides irrigation schedules: target irrigation depths and dates, but then water has to be applied to the field with an irrigation system which can have a given efficiency. Irrigation system performance is quantified in terms of application efficiency and uniformity. The efficiency of the application system can be assessed as the ratio of water volume actually used to grow the crop relative to the volume of water at the head of the system. This is the conceptual construct applied by Israelsen (1950) who defined irrigation efficiency. Jensen (1993) proposed changing the name of this ratio to irrigation consumptive use coefficient. The term irrigation efficiency has been reserved for the same ratio but using all the beneficial uses of the diverted water as the numerator rather than just consumptive use (Burt et al. 1997). Note that the non-uniformity of application within a given field is not accounted for in the efficiency definitions. However, when or where the soil profile is not filled or filled in excess affects crop water deficit and irrigation efficiency. Irrigation uniformity has been expressed using non-dimensional coefficients: the uniformity coefficient of Christiansen (Christiansen, 1942), the Wilcox and Swailes uniformity coefficient (Wilcox and Swailes, 1947) and the distribution uniformity of Merriam and Keller (1978). Typical values of these coefficients may be associated to the most common irrigation systems (Burt et al., 2000). Irrigation uniformity has been considered for long time from the engineering perspective, but not for its agronomic implications. It was Wu (1988) who first established rational relationships between irrigation uniformity, efficiency, crop water requirements and crop water deficit. The development of Wu (1988) was later extended by Anyoji and Wu (1994), and it has been considered for the MARSALa approach, for the first time at the scale of a country. A milestone that followed the publications of Wu (1988) and Anyoji and Wu (1994), and that was simultaneous to the re-evaluation of efficiency and uniformity measures (Burt et al., 1997), was the adoption by FAO (Allen et al., 1998) of the Penman-Monteith equation (Monteith and Unsworth, 1990) to calculate reference evapotranspiration and the dual crop coefficient approach (Wright, 1982) for computing soil evaporation and crop transpi- 25

ration separately. This approach has gained remarkable popularity in the last decade, thus it has been adopted by MARSALa as state-of-the-art methodology. It is only recently that farmer behaviour against irrigation has been surveyed (Lorite et al., 2004) and modelled for the purpose of simulating irrigation demands at the scale of large irrigation schemes (Lozano and Mateos, 2008). A more general formulation of farmer irrigation strategies and its integration with crop water requirements and irrigation method has been developed in MARSALa and applied to the irrigated area in Italy. In summary, the MARSALa approach is based on up-to-date methodology that uses readily available information, plus information that may be collected through regular surveys and expert knowledge, to estimate irrigation water use and consumption in Italy. The methodology is based on the integration of three models dealing with the main aspects of the farm irrigation: Crop Irrigation Requirements Model (Model A), Irrigation Efficiency Model (Model B) and Irrigation Strategy Model (Model C). The framework of the MARSA- La methodology is depicted in Figure 2.1. Figure 2.1 - Framework of the MArsALa methodology: typology of the input data and models relationships. CROP statistics CROP PARAMETERS SOIL CLIMATE 2010 CENSUS MODEL A CROP IRRIGATION REQUIREMENT MODEL B IRRIGATION SYstem EFFICIENCY MODEL C IRRIGATION strategy IRRIGATION CONSUMPTION The three models estimate the irrigation consumption of the farm irrigated crops except for rice and protected crops, for which a separate approach is adopted (see paragraphs 2.5 and 2.6). 26

rice and protected crops. The irrigation consumption of the latter is computed by a separate methodology as described in the paragraph 2.5. In summary, the integration of the three mentioned computations provides the total irrigation consumption of the farm. (2) 2.2. Crop Irrigation Requirements Model (Model A) 2.2. crop Irrigation Requirements Model (Model A) The model accounts for the irrigation request of a single crop by considering the irrigation dates and depths through a daily root The zone model water accounts balance, for the the formulation irrigation in request (1): of a single crop by considering the irrigation dates and depths through a daily root zone water balance, the formulation in (1): (1) (1) - RZWD i and RZWD - RZWD i-1 are i the and root RZWD zone i-1 soil are water the deficit root zone days soil i water and i-1 deficit in mm; on days i and i-1 in mm; - Re i is the effective - Re rainfall i is the in effective mm on day rainfall i; in mm on day i; - I - I i is the irrigation in i is the irrigation in mm on day i; mm on day i; - ET i is the crop evapotranspiration in mm on day i; - ET i is the crop evapotranspiration in mm on day i; - RO i is the irrigation runoff in mm on day i; - RO i is the irrigation - D runoff mm on day i; i is the drainage in mm on day i. It is understood that the root zone is full of water (RZWD = 0) when its water content - D i is the drainage is at field in capacity, mm on day while i. it is empty when the water content is at the wilting point (see Figure 2.2). that The the root zone is water full of holding water capacity (RZWD = (RZWHC) 0) when its is water defined content as the is depth at field of water It is understood capacity, while it (within is empty the when root the zone) water between content field is at capacity the wilting and point wilting (see point. Figure 2.2). The root zone water holding capacity Runoff (RZWHC) of rain is defined water as is not the considered depth of water directly (within but the through root zone) the between concept field of effective capacity and wilting rainfall. point. It has been assumed moreover that runoff of irrigation water is negligible. Runoff of rain water Drainage is not considered of rain water directly is computed but through as the the concept excess of of effective the root rainfall. zone soil It has water been content assumed moreover over that field runoff capacity of irrigation the water given is day negligible. of the water balance. Drainage of irrigation water is dependent on the applied depth in relation to the required depth and the irrigation uniformity, this aspect is managed by Model B. Drainage of rain water is computed as the excess of the root zone soil water content over field capacity at the given day of the water balance. Drainage of irrigation water is dependent on the applied depth in relation to the required depth and the irrigation uniformity, this aspect is managed by Model B. Figure 2.2 - Characteristic soil water content in the reservoir analogy. Figure 2.2 - Characteristic soil water content in the reservoir analogy. Effective rainfall data are derived from the data acquired in agrometeorological stations. rainfall Evapotranspiration data are derived (ET, from mm) the is data computed acquired using in FAO agrometeorological methodology based stations. the con- Effective Evapotranspiration cepts (ET, of mm) crop is computed coefficient using and FAO reference methodology evapotranspiration based on the concepts (Doorembos of crop and coefficient Pruitt, 1977b). and reference evapotranspiration Reference evapotranspiration (Doorembos and Pruitt, (ETo, 1977b). mm) is Reference calculated evapotranspiration using the Penman-Monteith (ETo, mm) is equation Penman-Monteith (Monteith and Unsworth, equation (Monteith 1990, Cap. and 11; Unsworth, Allen et 1990, al., 1998) Cap. with 11; Allen data et of al., solar 1998) radiation, calculated using the with data of solar wind speed, radiation, air wind temperature speed, and air temperature relative humidity and relative acquired humidity in agrometeorological acquired in staagrometeorological stations. The crop coefficients are derived using the dual approach (Wright, 1982) in the form popularized by FAO (Allen et al., 1998). This approach separates crop transpiration from soil surface 27 evaporation as follows:

e rainfall data are derived from the data acquired in agrometeorological stations. infall data are derived from the data acquired in agrometeorological stations. tion (ET, mm) is computed using FAO methodology based on the concepts of crop coefficient (ET, mm) is computed using FAO methodology based on the concepts of crop coefficient evapotranspiration (Doorembos and Pruitt, 1977b). Reference evapotranspiration (ETo, mm) is otranspiration (Doorembos and Pruitt, 1977b). Reference evapotranspiration (ETo, mm) is g the Penman-Monteith equation (Monteith and Unsworth, 1990, Cap. 11; Allen et al., 1998) e Penman-Monteith equation (Monteith and Unsworth, 1990, Cap. 11; Allen et al., 1998) f solar radiation, wind speed, air temperature and relative humidity acquired in lar radiation, wind speed, air temperature and relative humidity acquired in gical stations. The crop coefficients are derived using the dual approach (Wright, 1982) in the stations. The crop tions. coefficients The crop are coefficients derived using are the derived dual approach using the (Wright, dual approach 1982) in (Wright, the 1982) in the form ed by FAO (Allen et al., 1998). This approach separates crop transpiration from soil surface y FAO (Allen et popularized al., 1998). This by FAO approach (Allen separates et al., 1998). crop transpiration This approach from separates soil surface crop transpiration from follows: ws: soil surface evaporation as follows: (2) (2) (2) e basal crop coefficient, where K e is K cb the is soil the evaporation basal crop coefficient, and KK e s is quantifies the soil the evaporation reduction coefficient and K s sal crop coefficient, ration due to soil quantifies K water e is the deficit. the soil reduction evaporation in crop coefficient transpiration and K s quantifies due to soil the water reduction deficit. n due to soil water deficit. re, crop transpiration (T, Therefore, mm) is: crop transpiration (T, mm) is: op transpiration (T, mm) is: (3) (3) (3) (4) ration (E, mm) is: n (E, mm) is: and soil evaporation (E, mm) is: (4) (4) e variation of K cb is represented based on the values of K cb at the initial, middle and final stages of growth cycle and the duration of the initial, rapid growth, mid season, and late season (4) phases (4) (see The variation of K cb is represented based on the values of K cb at the initial, middle and final stages of (4) ation.3). the crop of K cb growth is represented cycle and based the duration on the values of the of initial, K cb at rapid the initial, growth, middle mid season, and final and stages late season of phases (see h bsequently he Figure cycle variation and 2.3)., the the of duration Kroot f K cb is represented cb is zone based The represented of depth the initial, variation (Zbased the values r ) could rapid of Kon be the growth, of cb Kis computed cb represented values mid of as season, Ka at the initial, cb function based at the and initial, middle on of late and the K final cb season middle : phases values and stages of final (see of K cb at stages the initial, of middle p growth cycle e and the duration and the of final duration initial, stages of rapid of the the initial, growth, crop growth rapid growth, mid season, cycle and and mid late the season, season duration and late phases of season the (see initial, phases rapid (see Subsequently, the root zone depth (Z growth, mid 2.3). r ) could be computed as a function of K cb : ently, the root zone season, depth and (Z r ) late could season be computed phases as (see a function Figure 2.3). of K cb : (5) ubsequently, the root Subsequently zone depth (Z, the root zone depth (Z the root zone depth (Z r ) could be computed r ) could be computed as a function as a function of K cb : r ) could of be K cb computed : as a function of K cb : (5) (5) r max and Z r min are the maximum effective root depth and the minimum effective root depth during (5) the (5) age of crop growth and K (5) where Z r max and Z r min are cb max the maximum value of K the maximum effective root cb. depth and the minimum effective root depth during the ained initial Z r min by are stage calculating the of maximum crop the growth where amount effective and Z of K root depth and the minimum effective root depth during the cb energy r and max the Z available maximum r min are the at value maximum the soil of surface K cb. effective as follows: root depth and the minimum effective cb the max maximum the maximum effective value root of Kdepth and the minimum effective root depth during the crop growth r max and Z are the maximum r min and are K effective root depth root during depth and the the initial cb. K minimum stage effective of crop growth root depth and during K cb max the the maximum value of K cb. tage e is obtained by calculating the amount of energy available at the soil surface as follows: of crop growth and K rowth y calculating and K the cb max the amount maximum cb the maximum value of K value of K cb. cb. K e is of obtained energy available by calculating at the soil the surface amount as follows: of energy available at the soil surface as tained by calculating follows: the amount of energy available at the soil surface as follows: ulating the amount of energy available at the soil surface as follows: (6) (6) (6) (6) r is a dimensionless evaporation reduction coefficient dependent on topsoil water depletion (Allen et ) and K (6) where c K max is the maximum value of K r is a dimensionless evaporation c following rainfall or irrigation. The value (6) of K reduction coefficient dependent on topsoil water e cannot be depletion (Allen et imensionless han the product evaporation f where reduction K r is a coefficient dimensionless dependent evaporation on topsoil reduction water depletion coefficient (Allen dependent et on topsoil al., 1998) and K ew K K r c is max a is dimensionless the maximum water c max is c the max, where f maximum ew is the fraction of the surface that is both exposed and value of K depletion evaporation value of (Allen reduction following et al., coefficient rainfall 1998) c following rainfall or irrigation. The value of K or and dependent irrigation. K c max is on The the topsoil value maximum water of K e depletion cannot value of be K (Allen c following e cannot be rainfall greater than the product f et e ionless evaporation reduction coefficient dependent on topsoil water depletion (Allen et 8) product and K is the maximum c f ew is Kthe c irrigation. ew K max maximum, where fthe c max, where f ew value is value the of Kfraction of K ew is the fraction of the surface that is both exposed and value of K c following rainfall c following e cannot of the rainfall be greater surface or irrigation. that than is the The both product value exposed f of K ew and K or irrigation. The value of K e cannot be e cannot c max, where f be ew is e wetted. stress coefficient, K s, is computed based the relative root zone water deficit as: the fraction of the soil surface that is both exposed and wetted. than the product f uct f ew K c max, where ew K f c max, where f ew is the fraction ew is the fraction of the soil surface that is both exposed and The stress coefficient, The stress K s, is coefficient, computed of the based K soil surface that is both exposed and s coefficient, K s, is computed based on the relative s, is computed on the relative based root on zone the water relative deficit root as: zone water deficit as: root zone water deficit as: [if RZWD i < (1-p) RZWHC] (7) he stress coefficient, K ficient, K s, is computed s, is computed based on the relative root zone water deficit as: based on the relative root zone [if [if water RZWD i deficit < (1-p) as: RZWHC] (7) [if RZWD i < (1-p) RZWHC] i < (1-p) RZWHC] (7) (7) [if RZWD [if RZWD i < (1-p) i < (1-p) RZWHC] (7) [if RZWD i i (1-p) (1-p) RZWHC] (7) (8) (8) [if RZWD i (1-p) RZWHC] (8) is the fraction of the RZWHC where [if RZWD p below is the i which (1-p) fraction transpiration RZWHC] of the RZWHC is reduced. below which transpiration (8) is reduced. [if RZWD [if RZWD i (1-p) RZWHC] i (1-p) RZWHC] (8) where p is the fraction of the RZWHC below which transpiration is reduced. (8) raction of the RZWHC below which transpiration is reduced. is the fraction of the RZWHC below which transpiration is reduced. n of the RZWHC below which transpiration is reduced. 28

Figure 2.3 - Basal crop coefficient (K cb ) and crop coefficient (K c ) curves. Irrigation is triggered in the water balance model when the soil water deficit in the root zone reaches the management allowed depletion (which is an output of Models B and C). The irrigation depth is determined by the root zone water deficit (Model A) the irrigation efficiency (Model B) and the irrigation strategy (Model C). The data required by Model A are: Agrometeorological data - Reference evapotranspiration (ETo) - Rainfall Soil data - Field capacity (alternatively: soil texture, bulk density and organic matter content, in order of priority) - Wilting point (alternatively: soil texture, bulk density and organic matter content, in order of priority) - Soil depth Crop data - Characteristic crop coefficients - Planting and harvesting dates - Duration of the growing phases Irrigation method schedule - Fraction of soil wetting - Rule for determining irrigation date or frequency (datum provided by Models B and C) - Deficit coefficient (datum provided by Models B and C) 29

Efficiency Model (Model B) Efficiency Model 2.3 (Model Irrigation B) Efficiency Model (Model B) application efficiency, The thus irrigation the irrigation application drainage efficiency, losses, depends thus on the irrigation irrigation system drainage losses, depends on ment application factors. efficiency, An irrigation irrigation thus system system the irrigation factors is characterized drainage losses, and management by its depends application irrigation factors. An uniformity. system irrigation The system is characterized by ment are considered factors. An in its the irrigation application management system uniformity. deficit is characterized coefficient. by The management If the its deficit application factors coefficient uniformity. are considered is high, The a in the management deficit coefficient. ield are will considered not receive in the the management water required deficit If the to deficit maintain coefficient. coefficient full evapotranspiration; If the deficit coefficient is high, a large on fraction the is contrary, high, a of the field will not receive field plication will not uniformity receive the the water is low water required as required well, then to to maintain a maintain significant full full part evapotranspiration; evapotranspiration; of the applied irrigation the contrary, on the will contrary, be if it is low and the pplication e, the application uniformity application efficiency is low will as well, uniformity be low. then a significant part of the applied irrigation will be is low as well, then a significant part of the applied irrigation will be e, the application efficiency will be low. picts the frequency lost distribution as drainage, of hence, the applied the depth application of irrigation efficiency (relative will to be the low. required picts d assuming the frequency that it follows distribution Figure a uniform 2.4 of depicts the statistical applied the depth frequency distribution. of irrigation distribution Often, (relative the normal of to the the distribution applied required depth of irrigation (relative irrigation it follows to the water a required uniform better statistical depth) than the across distribution. uniform the distribution. field Often, assuming the Although normal that distribution the it same follows a uniform statistical niformity ld assuming of that the niformity ne assuming of the a distribution. normal irrigation distribution water Often, better (Anyoji the than normal the and uniform Wu, distribution 1994). distribution. Dealing adjusts Although with to the non the uniform same uniformity of the irrigation er one and assuming the unavailability a water normal better distribution of more than precise the (Anyoji uniform information and distribution. Wu, does 1994). not Dealing justify Although (in with the the context same uniform analysis of could be done assuming a of normal more precise distribution information (Anyoji does and not Wu, justify 1994). (in the Dealing context with of the uniform distribu- more er and complex the unavailability model. more complex model. tion is simpler and the unavailability of more precise information does not justify (in the context of MARSALa) using a more complex model. Figure 2.4 - Frequency distribution of the applied depth of irrigation (relative to the required depth) across the field assuming that it follows a cumulated uniform distribution. - Frequency distribution of the applied depth of irrigation (relative to the required depth) across the 4 - Frequency field distribution assuming that of the it follows applied a depth cumulated of irrigation uniform (relative distribution. to the required depth) across the field assuming that it follows a cumulated uniform distribution. equired depth, three areas can be distinguished in the graph (see Figure 2.4): area A er equired that is depth, available three for areas crop can consumption, be distinguished area B in representing the graph (see the water Figure that 2.4): is lost area by A For a given required depth, three areas can be distinguished in the graph (see Figure C ter representing that is available the part for of crop the root consumption, zone that has area not B received representing any irrigation the water water. that Therefore, lost by C representing the 2.4): part area of the A root representing zone that has the not water received that any is available irrigation for water. crop Therefore, consumption, area B representing may the be water defined: lost Application by percolation Efficiency and (E area a ), Percolation C representing Coefficient the part (CP) of the root zone that has rmance indicators may be defined: Application Efficiency (E a ), Percolation Coefficient (CP) rmance indicators nt (CD). nt (CD). not received any irrigation water. Therefore, three irrigation performance indicators may be defined: Application Efficiency (E a ), Percolation Coefficient (CP) and Deficit Coefficient (CD). (9) (9) (9) (10) (10) (10) 30

(11) (11) (11) (11) (11) the (11) (11) the uniform the distribution, uniform the distribution, the above the indicators above may indicators be may be expressed be in expressed in the in the following the form following (Wu, form (Wu, e uniform distribution, the above indicators may be expressed in the following form (Wu, n uniform the uniform distribution, distribution, the above the above indicators indicators may may be expressed be expressed in the in following the following form form (Wu, (Wu, Based on the uniform distribution, the above indicators may be expressed in the following form (Wu, the uniform distribution, the above indicators may be expressed in the following form (Wu, 1988): (12) (12) (12) (12) (12) (12) (12) (13) (13) (13) (13) (13) (13) (13) (14) (14) (14) (14) (14) (14) are determined are by determined by the by the application the application where a uniformity and uniformity b are and and determined X and X is is the is the ratio the by ratio the between between application required required uniformity depth (14) and depth and and and X is the ratio between application between represents determined also represents by the also the link link between required Model uniformity Model depth B and B and and C since X C applied is it the since it is it is the ratio is depth. the inverse the between inverse X represents of of the required of the Relative the Relative also depth Irrigation Irrigation the and determined link between Model B and are determined by the rameter ameter computed by by application determined computed by Model the application uniformity by by C. uniformity and and is X the is ratio the ratio between between required required depth depth and presents also the link C between the Model since C. application C. it Model is the B inverse and C since of the it is Relative the inverse Irrigation of the Relative Supply Irrigation and presents (RIS) parameter computed by X represents also also the link the link between between Model Model uniformity and B and since and C since X it is is it the is inverse ratio the inverse between of the of Relative the required Relative Irrigation eter computed by Model depth and represents ribution ibution Uniformity Uniformity also the (DU) C. Irrigation rameter computed computed by Model link by (DU) Model between is C. is a C. is a measure C. measure Model of of of B how and evenly how C evenly since water it water is the soaks soaks inverse into into of the the the the ground ground Relative across across Irrigation a a field field meter tion and and Uniformity and computed is is defined is (DU) defined by as as as Model one one is minus a one The measure minus C. Distribution the the ratio of the how between ratio evenly between Uniformity the the water average the soaks average (DU) applied into applied is the a depth depth measure ground in in the in the across quarter the of quarter how a field of evenly of the of the the water soaks into the tribution tion Uniformity ess s water bution ess and Uniformity (DU) water and the Uniformity and the average (DU) is measure the applied is a measure of how average depth (DU) is applied in of a measure depth in the how evenly of in the whole evenly water how the field. water soaks evenly whole DU soaks into water field. DU can soaks DU can be into the ground can be expressed the ground across into be as the expressed as a across field and is defined as one ground minus across the ratio a between field during the average the irrigation applied depth and in is the defined quarter ground as a function across as of function one the a field of minus of a field of the ratio between ation and is f variation and defined on (CV) is defined as one variation of and is defined (CV) of the as minus of one minus the ratio as water the one (Warrick, ratio between between the average minus water 1983): the average applied applied depth depth in the in quarter the quarter of the water and the average the applied average depth applied in the the ratio (Warrick, depth whole field. between 1983): in the DU quarter can be of expressed the field as receiving a function of of the less water water and the and average the average applied applied depth depth in the in whole the whole the field. average field. DU DU can applied be can expressed be depth expressed in as the quarter as function a function of of less water and the average riation (CV) of the applied the s water and the average water depth (Warrick, depth in in the 1983): of of riation variation (CV) (CV) of the of applied the applied water water (Warrick, (Warrick, the 1983): whole whole 1983): field. field. DU DU can can be be expressed expressed as a as function a function of of the coefficient of variation (CV) of the variation applied water (CV)(Warrick, of the applied 1983): water (Warrick, 1983): (15) (15) (15) (15) (15) (15) eters meters a a and and b and b that that define that the define the uniform the frequency uniform distribution frequency can distribution can be can be then be calculated then as: calculated as: as: (15) ers a and b that define the uniform frequency distribution can be then calculated as: ers meters and a and that b that define define the uniform The the uniform parameters frequency frequency a distribution and distribution b that can define be can then be the then calculated uniform calculated as: frequency as: distribution can be then eters a and b that define calculated the uniform as: frequency distribution can be then calculated as: (16) (16) (16) (16) (16) (16) (16) (17) (17) (17) (17) (17) (17) s (17) is known is for known for the for the irrigation the system irrigation of system of concern, of CV, concern, b, CV, b, and b, and a and a can can be can be computed. be Model computed. C Model provides C Once DU is known for the irrigation system of concern, provides CV, b, and a can be computed. system Model and known d hence and for X) hence X) the from X) irrigation which from system CD which CD can CD can of be can be concern, computed be CV, (see computed b, and Equation (see a can 12). be Equation computed. With 12). the With the value Model the of value of C the of the provides required known the required f Model is known for the A, s Model A, the for irrigation known A, the irrigation the irrigation for the (I the irrigation (I i ) irrigation (I i ) and system of concern, C system i and irrigation of provides concern, CV, a and application CV, b, value and b, irrigation efficiency and of can RIS a be can (and computed. of concern, application (E be CV, b, and efficiency (E a can be (E a ) computed. hence Model X) a ) can computed. a can be can be computed. Model from provides which C be Finally, provides CD can be computed (see hence X) from which CD can be computed (see Equation 12). With the value of the required Model computed. (and hence C provides Finally, e ge will hence X) from ge be d hence will be obtained X) from which be as which CD can X) obtained as the from which as the product CD be can computed the I CD product i I be can be i E computed (see i computed E a. (see Equation Equation 12). 12). With With the value the value of the of required odel A, the irrigation Equation (I the required odel a. f Model A, the A, irrigation the irrigation (I i ) and irrigation 12). With application the value efficiency of the required (E i and (I i ) and irrigation irrigation a application application (see Equation efficiency efficiency 12). (E a ) can be depth, computed. output Finally, of Model A, the irrigation (I i ) a With (E can a ) the be can value be of the Finally, will be obtained as the and product irrigation I required will age odel will be A, obtained be the obtained irrigation as the as product the (I i ) product and i Eapplication i irrigation a. efficiency (E a ) can be computed. Finally, irrigation drainage will be obtained as I i a the E a. product application I i efficiency E a. (E a ) can be computed. Finally, e will be obtained as the product I i E a. 31

Figure 2.5 shows the relationship between deficit coefficient and application efficiency for various distribution uniformities. Figure 2.5 - Deficit coefficient vs. application efficiency for various distribution uniformities. The basic data required by Model B are: Irrigation method - Distribution Uniformity (DU) Irrigation strategy - Relative Irrigation Supply (RIS) 2.4. irrigation Strategy Model (Model C) The farm irrigation practice for a given agrarian year is the result of the farmer decision process concerning the total amount of water to provide to the crops and the start and the end of irrigation. Model C is intended to deal with the concept of the farmer irrigation strategy by taking into account some elements of the farm and the surrounding territory having a connection with the decision process of the irrigation activity. The irrigation strategy refers to the decision of the farmer in relation to the irrigation depth and frequency and to the degree of stress to which the crop will be subjected. This strategy depends on the crop type, but also on other factors such as the water availability, the irrigation method, the distribution system, the economic dependence on irrigated crops, the education and habits, the irrigation equipment, the size of the farm, etc. MARSALa considers two pivotal elements in the irrigation decision process: the water amount provided to the crops (the irrigation depth), modelled by the parameter Relative Irrigation Supply (RIS); the tolerable crops stress level (or the allowed depletion fraction), modelled by the parameter f1. 32

To compute f1 and RIS a set of rules and decision trees have been defined, the parameters are calculated for each crop and for each farm. The rules result from correlations found in the farm surveys and from expert knowledge. The decision trees have been built by using all the available information reported in the CQ along with some rules defined by expert knowledge, additional information about the territory where the farm is located and the relevance of each farm crop in case of water shortage is also taken into account. The values indicated in the decision trees are those imputed following an expert based criteria and they have been used as starting values during the calibration process. During calibration the values have been altered in order to reach a good agreement between the irrigation volumes collected during farm interviews and those simulated by the MARSALa model. 2.4.1. Relative Irrigation Supply (RIS) The Relative Irrigation Supply (RIS) can be defined as the ratio between the irrigation supply and irrigation requirements for obtaining the maximum yield for a given crop an it indicates how properly irrigation supply and demand are matched, the possible values are: RIS = 1, the perfect match between water supply and demand (the farm follows an efficient irrigation regime for the crop); RIS < 1, the crop is not receiving enough water (the farm pursue a crop irrigation deficit strategy; it can be a voluntary decision - e.g. for crop quality reasons - or it can be pushed by external factors such as water scarcity); RIS > 1, the crop is irrigated excessively, in this case a waterlogging can occur impacting negatively on yield (the farm has a low irrigation efficiency). To define the RIS values a decision tree has been built by using all the aspects having a strong relationship with the farm irrigation strategy that are collected through the CQ (see Figure 2.6). Starting from the root up to the leaves, the following elements have been taken into consideration. 1. Irrigation water source - the types of water sources reported in the CQ have been reclassified in two classes: Flexible (self-supply from groundwater and/or superficial sources; ILRC with delivery on-demand; other source); Unflexible (ILRC with delivery arranged by rotational turns) Since the CQ can register more than one irrigation water source, it has been established that the farm is assigned to the class Unflexible in case of only an ILRC with rotational schedule is reported while it is assigned to the class Flexible for all the other possible combination of water sources. We hypothesized that the membership of a farm to one of the two water source classes influences the farm irrigation strategy for a given crop. For instance, if the farm has water availability is conditioned to the turn defined by the ILRC (that is it available only in a given period of time and for a given duration). There is a strong probability that the farm will follow a strategy of low irrigation efficiency providing to the crops all the available water even though it is not necessary. 33

2. Irrigation system - the irrigation systems reported in the CQ have been aggregated in three classes: Infiltration-Flood (Border and Furrows + Flood); Aspersion; Micro-irrigation/Other (Micro-irrigation + Other system); The main assumption is that the irrigation systems have different distribution efficiency affecting the amount of water applied by the farmer to the crops. 3. Shortage - a binary variable (yes/no) indicating if the farm, for the agrarian year of analysis, has undergone water shortage that could have affected the crops irrigation water supply (e.g. by reducing the irrigation water applied). The information is not reported in the CQ, even though it has been inserted in the pilot areas questionnaire used for calibration. In general, to assign a value to the variable it would be required to know, the water stored in the reservoirs serving a given irrigation district that depends in turn on the climatic course of the reference year. In addition, climatic scenarios can be taken into account in the case of lack of detailed territorial information for determining the state of water shortage for a given area. A possible solution for the farms with irrigation water supplied by ILRCs could be the use of information from SIGRIAN: the database managed by INEA reporting information about the Italian ILRCs. In this case, it would be possible to identify all the municipalities (hence the farms) affected by water shortage for a given agrarian year. The farms with a self-supply irrigation water source are generally not affected by shortage since they manage to satisfy the crop water demand and, in case of farms having also an ILRC supply, they try to compensate for the ILRC water delivery deficit. In a shortage scenario, when the groundwater availability can be strongly affected, it would be necessary to make additional consideration such as the increase of the pumping costs that generally have a direct impact on the irrigation strategy of the farmer. Since during the agrarian year 2009-2010 there is no evidence of water shortage, the variable Shortage can be set to no during the run of MARSALa. 4. Irrigation Advisory System (IAS) - a binary variable (yes/no) taking into account the level of instruction of the farmer (degree or technical diploma in agricultural sciences) and/or the avail of the farm to any irrigation advisory services (information reported in the CQ). The main assumption is that farms having at least one of the two mentioned characteristics will likely pursue an efficient irrigation management. The decision tree allows to define the appropriate value for each crop by narrowing down the ranges moving from the root to the leaves. It can be noted as the RIS values of the left side of the tree are lower than those on the right due to the different flexibility of the irrigation water supply; moving down through the tree Drip/Other assumes lower values than Furrows/Basin and Sprinkler since the latter usually tend to apply a water amount greater than that required by the crop. Moreover in case of shortage the values tend always to be lower. 34

Figure 2.6 - The decision tree used to define the RIS values. 35

wed depletion fraction (f1) 2.4.2. Allowed depletion fraction (f1) to the paper FAO no. 56 (Allen et al., 1998) the Readily Available Water (RAW) that a crop h the roots is a fraction According of the Total to the Available paper Water FAO no. (TAW) 56 (Allen as defined et al., by 1998) the following the Readily Available Water (RAW) that a crop can extract through the roots is a fraction of the Total Available Water (TAW) as defined by the following equation: (18) (18) ority of the crops the fraction For the p majority takes values of the between crops 0.4 the and fraction 0.65. p takes values between 0.4 and 0.65. We defined the parameter f1 as the management depletion fraction allowed by the the parameter f1 as the management depletion fraction allowed by the farmer for a given farmer for a given crop; f1 ranges from 0 to 1 and it can be greater than, equal to or less om 0 to 1 and it can be greater than, equal to or less than p. The case f1 greater than p than p. The case f1 greater than p indicates that the crop suffers for water deficit. To assign a proper value of f1 to each crop, another decision tree has been built (see Figure 2.7). crop suffers for water deficit. To assign a proper value of f1 to each crop, another decision lt (see Figure 2.7). In part, the decision tree has some building blocks identical to those In part, the decision tree has some building blocks identical to those belonging to the RIS IS decision tree (e.g. Water supply, Irrigation system and Shortage (Enough water)). The decision tree (e.g. Water supply, Irrigation system and Shortage (Enough water)). The ks are: new inserted blocks are: ive tree - a binary variable Deficit (yes/no) olive taking tree into - a binary account variable the application (yes/no) of taking deficit into irrigation account the use of deficit s for olive pantations; irrigation techniques for olive pantations; rop - a binary variable (yes/no) Priority related crop to - the a binary rank attributed variable by (yes/no) the farmer related to crops to in the case rank attributed by the shortage when it has to farmer be decided to crops which in crops case will of water have top shortage priority when for irrigation. it has to The be decided which crops he variable is defined in will terms have of top membership priority for of the irrigation. crop to a The predefined value of list the of variable priority is defined in terms of lt-in in MARSALa. The membership crops list (see of Table the crop 2.1) has to a been predefined by list expert of priority judgment crops by built-in in MARSALa. o account the crop resistance The crops to water list (see stress Table condition, 2.1) has the maximum been defined level by of expert yield loss judgment by taking into e and the market conditions. account Rice the and crop protected resistance crops are to water not part stress of the condition, list being always the maximum level of yield ith the highest priority. loss acceptable and the market conditions. Rice and protected crops are not part the structure of the decision of the tree list it being is evident always as under irrigated no shortage with the condition highest the priority. values of es that the crop is irrigated Observing with the a certain structure frequency of the avoiding decision any tree stress it is phenomenon evident as under in no shortage condi- shortage condition. the Moreover values the of the values leaves of f1 indicates reflect the that characteristics the crop is of irrigated the irrigation with a certain frequency. in the case Flexible avoiding a major stress control phenomenon. of the water Moreover, volume the in the values soil can of f1 be reflect applied the by characteristics of the RAW) and the irrigation system water supply (e.g. each (e.g. irrigation in the case system Flexible has its a major own efficiency control of and the water volume in the ncy). soil can be applied by replenishing the RAW) and the irrigation system (e.g. each irrigation system has its own efficiency and application frequency). of the priority crops defined by expert judgment, the priorities are defined for two oups. Table 2.1 - List of the priority crops defined by expert judgment, the priorities are defined Crop group no. 1 Crop group no. 2 for two different crop groups. es, Citrus plantations Legumes Sunflower Crop group no. 1 Crop group no. 2 ers and ornamental plants Sorghum Table grapes, Fruit trees, Citrus plantations Legumes e plantations Nuts Tobacco Sunflower Permanent grassland Fresh vegetables, Flowers Other and crops ornamental plants Sorghum Grapes for wine, Olive plantations Nuts Maize, Sugar beet Permanent grassland Fodder Other crops 36

Figure 2.7 - The decision tree used to define the values of f1. 37

2.5. irrigation water consumption estimation for rice Italy is the European largest producer of rice. Rice cultivated area in 2009 was about 238,000 hectares (see Table 2.2) and the total raw production reached 1,500,000 tons. Generally the location of the rice cultivated areas reflects the large water availability and the efficiency of the water delivery network. Table 2.2 - Rice cultivated areas (in hectares) for each Italian province and region. Region Surface (ha) Province Surface (ha) Piemonte 121,667 Vercelli 73,666 Biella 3,978 Novara 34,924 Pavia 348 Alessandria 8,360 Cuneo 203 Torino 188 Lombardia 101,673 Pavia 84,871 Milano 13,501 Bergamo 6 Mantova 1,365 Lodi 1,930 Veneto 3,205 Padova and Vicenza 105 Rovigo 969 Venezia 254 Verona 1,877 Lazio 8-8 Friuli Venezia Giulia 2-2 Emilia Romagna 7,878 Bologna 193 Ferrara 7,276 Modena 355 Piacenza 13 Reggio Emilia 41 Sardegna 3,154 Cagliari and Oristano 3,154 Toscana 363 Grosseto and Siena 363 Calabria 508 Cosenza 508 Italy 238,458 Source: Ente Nazionale Risi Year 2009. Two types of preparation for rice fields can be found in Italy depending on soil characteristics, topography and size and distribution of farm parcels: one is widespread in the western Po Valley (Piemonte and Lombardia), the other in the eastern Po Valley (Mantova province and in the provinces of Emilia Romagna and Veneto). The first one is typical of farms with small extension and with parcels slope not negligible, in this case the area of the cultivation units called rooms is relatively small (i.e. 2 or 3 ha or even less). The second one is widespread in Veneto and Emilia where rice cultivated parcels have large surfaces 38

(i.e. between 10 and 12 ha), in this case they are already naturally flat and are bordered by large banks also used as dirt roads for accessing to the fields. Concerning irrigation techniques two are the main typologies employed: flooding or dry condition; these are often applied with several variations conditioning the management of an irrigated district. Flooding is the traditionally techniques employed in the whole rice territory of Padana Plain. It consists in covering the field with a water stratum ranging from 5 to 20 cm in depth, the technique is applied for the majority of the growing cycle (generally from the end of March till the end of October depending on the cultivar). Traditionally, seeds are spread over a field already flooded but, in the recent years seeding occurs on the dry field. In this case flooding occurs immediately after seeding, or in a later phase, after the application of the herbicides. Rice cultivated under dry conditions is based on a periodical irrigation where the cultivation rooms flooded with a water depth of 5-10 cm left to infiltrate till the complete absorption; this allows the full replenishment of water in the root zone. The length of flooding and drying periods is different depending on soil texture, the number of irrigations applied depends on rainfall that can reduce the number of irrigations required to complete the growing cycle. Rice can be grown without irrigation (rainfed) as other cereals, only where the pluviometric regime reaches a minimum threshold of 900-1000 mm in a time interval of 3-5 months. The optimal thermal conditions are between 18 and 33 degrees Celsius. 2.5.1. Methodology Although the MARSALa model can be applied to estimate the irrigation water consumption for rice, to better take into account the influence of the cropping techniques and the territorial characteristics on the irrigation water volumes applied to rice, a different approach was followed. The approach consists on the creation of a national database of the mean irrigation water volumes (measured in m 4 /ha) used for growing rice and by reporting data at municipality level. This was considered an optimal solution both in terms of software computational efficiency and reliability and accuracy of the estimated values. Database has been compiled by running a national survey in the Italian provinces (NUTS 3) where rice is cultivated. The activity was divided into the following steps: 1. inventorying of the municipality where rice is cultivated; 2. data collection on the irrigation water consumption through interviews with different subjects (ILRCs, RICA surveyors, etc.); 3. imputation of a mean irrigation water consumption to each municipality and creation of the database. In the first step the identification of the municipality with rice cultivation has been realized by using the 2009 data provided by the Ente Nazionale Risi (the official institute collecting national data about the surfaces used for rice cultivation). The database provided has been considered enough reliable since all farmers growing rice are obliged to communicate annually the cultivated areas with rice to the Ente Nazionale Risi. The database 39

reports surfaces and location (in terms of municipality and province) of rice cultivated areas, the allocation of a cultivated area to a municipality is based on the geographical location of the farm centre rather than the actual location of rice parcels. Through the second step the municipalities containing rice cultivated areas have been associated to the areas served by ILRCs in order to identify the main actors dealing with irrigation management to be considered as potential respondent for the survey. Irrigation water consumption data collection has been performed by interviewing both ILRCs technicians, that have an extensive knowledge of the areas served by the ILRCs and of the water consumptions, and RICA surveyors that carry out activities in the various Italian provinces where rice cultivation was identified. All the values collected through the interviews have to be considered as expert evaluation. Sardegna has been treated differently by exploiting more accurate data coming directly from measurement devices available for the irrigation district managed by the Oristanese ILRC. In the third step, the data collected have been processed in order to build a national database at municipality level. This required an harmonization of the data having different spatial resolution ranging from the data measured at farm level by measurement devices (Sardegna) to the data estimated by experts at municipality, ILRC or province level. In some municipalities, where interviews have not produced any estimation, the mean water consumption of the relative province or of the near provinces with similar characteristics has been attributed. The structure of the database is reported in Table 2.3, it contains the administrative reference of the areas with rice cultivation (region, province and municipality), the mean water consumption extrapolated at municipality level and a code indicating the source of the information reported. The unabridged version of the database is reported in Annex 4, the relative data are depicted at geographical level in the following figures. The values reported shows water consumption values varying among municipalities from a minimum of 1,500 m 3 /ha in Toscana to a maximum of 40,200 m 3 /ha in Lombardia. The strong variability can be explained by the diversity of soil, cultivar and irrigation techniques. The database will allow during the run of the MARSALa system to assign directly the water consumption to the farm parcels based on the mean value of water consumption relative to the municipality where the farm centre is located. Table 2.3 - Structure of the national database on the irrigation water volumes used for rice cultivation. Region Province Mean irrigation water use (m 3 /ha) Source Veneto Verona 15,000 1 Veneto Venezia 10,500 2 Toscana Siena 1,500 4 Lombardia Pavia 40,200 2 Emilia Romagna Bologna 9,033 6 Source 1: data provided by ILRC technicians at provincial level. Source 2: data provided by ILRC technicians at ILRC level. Source 3: data provided by ILRC technicians and RICA surveyors at municipality level. Source 4: data provided by ILRC technicians and RICA surveyors at farm level. Source 5: data provided by ILRC technicians at irrigation district. Source 6: data attributed as mean of the values of the nearby provinces with similar characteristics. 40

Figure 2.8 Areas of rice cultivation and mean values of irrigation water in Northern Italy. 41

Figure 2.9 - Areas of rice cultivation and mean values of irrigation water in Toscana region. 42

Figure 2.10 - Areas of rice cultivation and mean values of irrigation water in Sardegna region. 43

Figure 2.11 - Areas of rice cultivation and mean values of irrigation water in Calabria region 44

rigation water consumption estimation for protected crops 2.6. irrigation water consumption estimation for protected crops RSALa model has been considered not appropriate to assess water consumption in protected ation water consumption MARSALa estimation model for has protected been considered crops not appropriate to assess water consumption nt (greenhouses or crops under protective cover), for the following reasons: in protected environment (greenhouses or crops under protective cover), for the following p La evapotranspiration model has been reasons: estimation considered in not indoor appropriate microclimate to assess conditions water is consumption related to different in protected variables greenhouses ch as the outdoor or crops climate, under the crop protective type evapotranspiration of cover), greenhouse, for the the following estimation climate reasons: control in indoor strategy microclimate and the feedback conditions is related to tween the crop and the inside different microclimate; vapotranspiration estimation in indoor variables microclimate such conditions as the outdoor is related climate, to different the type variables of greenhouse, the climate concept of reference evapotranspiration control strategy (ETo) and the is also feedback somewhat between difficult the crop and delicate and the to inside be s the outdoor climate, the type of greenhouse, the climate control strategy and the feedback microclimate; plied to greenhouse crops water requirements, because hypothetical grass reference crop as en the crop and the inside microclimate; the concept of reference evapotranspiration (ETo) is also somewhat difficult and fined in FAO paper 56, (Allen et al.) is not commonly grown in greenhouse production (Baille A., ncept of reference evapotranspiration delicate to be (ETo) applied is also to greenhouse somewhat difficult crops water and delicate requirements, to be because hypothetical grass requirements, reference because crop as hypothetical defined in FAO grass paper reference 56, (Allen crop as et al.) is not commonly 94); d to greenhouse crops water paration of crop transpiration and soil surface evaporation is very difficult, if not impossible, due d in FAO paper 56, (Allen et grown al.) is in not greenhouse commonly grown production in greenhouse (Baille A., production 1994); (Baille A., lack of greenhouses soil properties data; ; sence of precipitation in a protected separation environment of crop transpiration that generally and participates soil surface in partial evaporation restoration is very difficult, if not tion of crop transpiration and impossible, soil surface due evaporation the lack of is greenhouses very difficult, soil if not properties impossible, data; due evapotraspirative losses. k of greenhouses soil properties data; e absence of precipitation in a protected environment that generally participates in simple of precipitation approaches in a have protected been developed environment in that the estimation generally participates of evapotranspiration in partial restoration based on the tion; partial restoration of evapotraspirative losses. potraspirative the role of losses. solar radiation in determining the evapotranspiration in the greenhouses has been in several works in the 60' More and simple 70' (Morris approaches et al., 1957, have been Lake developed et al., 1966, in StanhiIl the estimation and Álberts, of evapotranspiration mple approaches have based been the developed solar radiation; in the estimation the role of of evapotranspiration solar radiation in based determining on the illele, 1974), showing a strong correlation between daily evapotranspiration and solar irradiance. the evapotranspiration in the ; the role of solar radiation in determining greenhouses the has evapotranspiration been evidenced in in the several greenhouses works has in the been 60 and 70 (Morris et everal rence evapotranspiration works in the 60' and is closely 70' (Morris dependent et al., 1957, from the Lake environmental et al., 1966, StanhiIl conditions and inside Álberts, of the al., 1957, Lake et al., 1966, StanhiIl and Álberts, 1974, De Villele, 1974), showing a strong e le, such 1974), as temperature, showing a strong relative correlation humidity between and global daily radiation. evapotranspiration Since these and three solar climatic irradiance. variables correlation between daily evapotranspiration and solar irradiance. y correlated (at least in the greenhouse environment), a simple mathematical model that takes in ce evapotranspiration is Reference closely dependent evapotranspiration from the environmental is closely conditions dependent inside from of the the ion only the inner greenhouse global radiation can be applied. Based on that, the so called "solar environmental conditions relative inside humidity of the and greenhouse global radiation. such as Since temperature, these three relative climatic humidity variables ch as temperature, method, or "solarimeter" method has been developed which is a simple relationship giving the and global radiation. orrelated (at least in Since the greenhouse these three environment), climatic variables a simple are mathematical strongly correlated model that takes (at least in evapotranspiration the greenhouse if the outside global radiation (RGo) and the greenhouse in the greenhouse only the inner greenhouse environment), global radiation a simple can mathematical be applied. Based model on that, the takes so called in consideration "solar transmission (Kt), are known (Baille A., 1994): only the inner hod, or "solarimeter" greenhouse method has global been radiation developed can which be applied. is a simple Based relationship that, giving the so the called solar radiation otranspiration in the method, greenhouse or solarimeter if the outside method global has radiation been developed (RGo) and which the greenhouse is a simple relationship giving nsmission (Kt), are the known reference (Baille A., evapotranspiration 1994): in the greenhouse if the outside global radiation (RGo) and the greenhouse coefficient transmission (Kt), are known (Baille (19) A., 1994): (19) (19) is the reference evapotranspiration where: in mm day -1 ; ETo is the reference evapotranspiration in mm day -1 ; is the outside global radiation in MJ m -2 day -1 ; he reference evapotranspiration RGo is in the mm outside day -1 ; global radiation in MJ m -2 day -1 ; the latent heat of vaporization (2.5 MJ/kg H 2 0); λ is the latent he outside global radiation in MJ m -2 heat day -1 of vaporization (2.5 MJ/kg H ; 2 0); anges between 0.55 e 0.65 Kt (empirical ranges between data provided 0.55 from e 0.65 Prof. (empirical Pardossi, data University provided of Pisa). from Prof. Pardossi, University (CWR) of Pisa). (2.5 MJ/kg latent Water heat Requirement of vaporization depends H 2 0); from the evaporating surface, which is expressed as a s the between Leaf Area 0.55 Index e 0.65 (LAI) (empirical Crop of the Water data crop. provided Requirement from Prof. (CWR) Pardossi, depends University from of the Pisa). evaporating surface, which is r ater this Requirement consideration expressed (CWR) Equation depends 19 as assumes a function from the the following of evaporating the Leaf form: Area surface, Index which (LAI) is of expressed the crop. as a Leaf Area Index (LAI) of After the crop. this consideration Equation 19 assumes the following form: is consideration Equation 19 assumes the following form: (20) (20) 45 (20)

pirical coefficient ranging from 0.20 to 0.35. mpirical coefficient ranging from 0.20 to 0.35. pirical f the solar coefficient radiation ranging measurements from 0.20 are to 0.35. not available or are relative to sites distant from the of the solar radiation where measurements a is an are empirical not available coefficient or are ranging relative to from sites 0.20 distant to 0.35. from the f e the procedures, solar radiation based measurements on extraterrestrial are not radiation available and or are air relative temperature to sites differences distant from (Allen. the me procedures, based In cases extraterrestrial of the solar radiation and measurements air temperature are differences not available (Allen. e be procedures, applied for the based its estimation. extraterrestrial radiation and air temperature differences (Allen. or are relative to sites n be applied for distant the its estimation. be g also applied that for the the main its estimation. source from of the water greenhouse, is ground water, some and procedures, the lack of based rain-driven on extraterrestrial leakage, is radiation and air oduce ing also that the temperature main source of differences water is ground (Allen. water, R.G., and 1995), the lack can of be rain-driven applied for leakage, the its is estimation. g also in that the the calculation main source the Leaching of water is Fraction ground (LF): water, and the lack of rain-driven leakage, is roduce in the calculation Considering the Leaching also Fraction that the (LF): duce in the calculation the Leaching Fraction (LF): main source of water is ground water, and the lack of raindriven leakage, is necessary to introduce in the calculation the Leaching Fraction (LF): (21) (21) (21) (21) where: irrigation water salinity ECw (expressed is the irrigation as Electrical water Conductivity salinity (EC) (expressed in ms/cm); as Electrical Conductivity (EC) in he irrigation water ms/cm); salinity (expressed as Electrical Conductivity (EC) in ms/cm); ds irrigation the crop, water the salinity higher (expressed the value as the Electrical higher is the Conductivity crop resistance (EC) in to ms/cm); salinity. ends on the crop, the higher ECe depends the value on the the higher crop, is the the crop higher resistance the value to salinity. the higher is the crop resistance to ent ds on crops the crop, categories the higher salinity. the following the value values the higher of ECe is the can crop be resistance applied (empirical to salinity. data provided ssi, rent crops categories the following values of ECe can be applied (empirical data provided nt crops University categories of Pisa): the ossi, University of Pisa): For following different values crops of categories ECe can be the applied following (empirical values data of ECe provided can be applied (empirical ssi, fruit University vegetable; of data Pisa): provided from Prof. Pardossi, University of Pisa): r fruit fruit leaf vegetable; 2.5 for fruit vegetable; r leaf vegetable; leaf cut plants; vegetable; r cut 2.0 for leaf vegetable; cut pot plants; r pot plants; 1.8 for cut plants; pot Irrigation plants; Requirements (IR), quantity of water needed to satisfy the CWR, and allowing, he Irrigation Requirements 1.5 (IR), for pot quantity plants; uate of water needed to satisfy the CWR, and allowing, Irrigation leaching, Requirements to maintain (IR), the salinity quantity of of the water soil at needed lower to level satisfy than the those CWR, of toxicity and allowing, for the quate leaching, to maintain Hence, the the salinity Irrigation of the soil Requirements at lower level (IR), than those quantity of toxicity of water for the needed to satisfy the ate e expressed leaching, as: to maintain the salinity of the soil at lower level than those of toxicity for the be expressed as: CWR, and allowing, through an adequate leaching, to maintain the salinity of the soil at e expressed as: lower level than those of toxicity for the cultivation, can be expressed as: (22) (22) (22) (22) rms to be considered in the Irrigation Water Consumption (IWC) estimation are represented n terms to be considered in the Irrigation Water Consumption (IWC) estimation are represented rms distribution to be considered uniformity in The the coefficient, last Irrigation terms Water to Kt be and considered Consumption the efficiency in (IWC) the of Irrigation estimation the irrigation Water are represented system Consumption Ki (IWC) estimation sprinkler are represented coefficient, and 0.90 - Kt by 0.95 the and for irrigation the drip efficiency irrigation) distribution of adopted the irrigation uniformity in the greenhouses, system coefficient, Ki Kt and the effi- on are distribution uniformity coefficient, Kt and the efficiency of the irrigation system Ki distribution 0.6-0.7 uniformity quation s are 0.6-0.7 for sprinkler and 0.90-0.95 for drip irrigation) adopted in the greenhouses, are 0.6 is: - 0.7 for ciency sprinkler of the and irrigation 0.90-0.95 system for drip Ki (common irrigation) ranges adopted are in the 0.6 greenhouses, - 0.7 for sprinkler and 0.90-0.95 equation is: uation is: for drip irrigation) adopted in the greenhouses, hence the final equation is: (23) (23) (23) (23) Some experimental results obtained for a case study carried out in some pilot areas located in Toscana region are reported in Figure 2.12 and 2.13. erimental results obtained for a case study carried out in some pilot areas located in Toscana perimental ed results obtained for a case study carried out in some pilot areas located in Toscana rimental in Figure results 2.12 obtained and 2.13. for a case study carried out in some pilot areas located in Toscana rted in Figure 2.12 and 2.13. ed in Figure 2.12 and 2.13. 46

Figure 2.12 - Monthly IWC (in mm) computed for different crop categories cultivated in greenhouses located in Toscana region. 250,00 Montly IWC for the main crops group 200,00 IWC (mm) 150,00 100,00 50,00 0,00 January February March April May June July August September October November Dicember IWR Fruit vegetable IWR Leaf vegetable IWR Cut plants IWR Pot plants Source: elaboration with data provided by Prof. Pardossi Figure 2.13 - Annual IWC (in thousands of m 3 /ha) computed for the different crop categories cultivated in greenhouses located in Toscana region. 12,0 10,0 Annual IWC (000 mc/ha) 8,0 6,0 4,0 2,0 0,0 Fruit vegetable Leaf vegetable Cut plants Pot plants Source: elaboration with data provided by Prof. Pardossi MARSALa has a proper computational routines implementing the IWR equation by using all the empirical parameters described and by pre-processing the information relative to the crops cultivated in greenhouses reported in the CQ. 47

ChaPter III Input data collection The accuracy reachable with the model simulations has always a direct relationship with the quality of the input data used. To this end, a lot of resources have been employed during the data collection phase in order to identify and inventory all the available Italian dataset useful for the irrigation consumption estimation and to enable all the administrative procedures required for the data acquisition from several institutions. The task has been particularly difficult since the whole country coverage is required and, in addition, the Italian context is characterized by data managed at different administrative levels (national, regional and local) by several institutions which follow different standards in terms of data quality, data collection, data storage, scale and resolution. For instance, the highest level of resolution for some data types (i.e. the agrometeorological and the soil data) can be only reached by acquiring all the dataset owned by each regional administration, but at the same time it entails the establishment of 21 relationships with the Italian regional administrations/autonomous provinces, without mentioning the enormous work necessary to harmonize the data at national level. Given the described context, the input data collection has been simplified whenever possible selecting principally data produced and managed by national institutions with a national coverage, accepting therefore an unavoidable loss of resolution (as in the case of the agrometeorological dataset). In the cases of lack of standardized data at national level an integration and standardization process of different sources has been carried out as in the case of the soil dataset. At the end of the data collection process and harmonization, all the geographical and statistical datasets have been reported at municipality level: the minimum computational unit for the model simulation. Hereafter a comprehensive description of the input dataset and the relative collection procedures is reported. 3.1. The 6 th General Agricultural Census database In Italy agriculture censuses have been taken since 1961, on decennial frequency, based on complete enumeration of agricultural holdings. The 6 th General Agricultural Census started in October 2010 and the official results will be released by the end of 2012. ISTAT is the institution responsible for the surveys and coordination of the Census network an data collection is carried out by enumerators through a face to face interview to the holders. The census covers all agricultural holdings where the Utilised Agricultural Area (UAA) for farming is greater than one hectare. A certain number of units with UUA less than one hectare are also included in the enumeration, according to the physical thresh- 49

olds applied at NUTS 2 level, in order to reach the 98% of total UUA and the 98% of the total number of the farm livestock units. The agricultural Census is carried out in conformity with two Regulations: Regulation (EC) n.1166/2008 of the European Parliament and of the Council of 19 November 2008 on Farm Structure Surveys (FSS) and the Survey on Agricultural Production Methods (SAPM). Council Regulation (EEC) No 357/79 of 5 February 1979 on statistical surveys of areas under vines. Italy carried out the survey on agricultural production methods at census level even if Regulation allows Member States to carry out it by sample. Therefore, all information on FSS and SAPM are collected by a single questionnaire. For the first time in Italy the Census is assisted by administrative information. The pre-census list has been prepared integrating different specific and general administrative sources. A sample survey on 80 municipalities has been carried out in October 2008 to check the quality of the pre-list and to define the rules to include the units from each administrative source to the definitive Census list. ISTAT avails itself of Regions and Municipalities for the field work. Around 10.000 enumerators recruited directly by Regions or Municipalities collected data by paper questionnaire. In alternative, the respondents have been given the choice to answer via web, through a controlled electronic questionnaire. Table 3.1 - Sections and boxes of the 6 th CQ. Section Box Detail Legal personality of the holding B Type of tenure and farming System B 1 General information Information Technology C Support for rural development B Landscape features A Land use B Organic farming (concerning crops) B Quality scheme production (concerning crops) C 2 Information for holdings with land Specific information on vineyards B Tillage methods A Soil conservation A Irrigation B Livestock B Organic farming (concerning animals) B 3 Information for holdings with animal Quality scheme production (concerning animals) C Animal grazing A Animal housing A Manure storage and application A 4 Localization Localization of the land and livestock at Municipality level C Labour force B 5 Labour force and other gainful Third partly job C activities of the holding Other gainful activities of the holdings B Equipment used for renewable energy production B 6 Economic information Income, self consumption and marketing C Farming accounting C A: production methods characteristics; B: FSS characteristics; C: national and sub national needs. 50

3.1.1 Census questionnaire amendments according to the MARSALa requirements MARSALa irrigation water estimation is performed by means of the integration of the results produced by three different models (A, B and C), each one uses a set of farm parameters and most of them are derivable from the CQ. The enumeration of all the necessary models parameters has been realized during CQ preparation. In addition a set of additional information beyond the scope of the Census has been proposed to be inserted in order to complete the requirements of the models and to ensure an improvement of the quality and accuracy of the models simulations. The amendment have been officially requested by INEA to ISTAT and a proper agreement has been established between the institutions to carry out the activities for the national irrigation water estimation in the framework of the Census. The proposed amendments are reported below (the CQ is reported in Annex 2): 1. registration of the number of cuts for the crop 8.10.a.45-Alfalfa (Erba medica) 2. registration of the seeding, planting, transplanting and harvesting date for each irrigated crop; 3. registration of irrigation information for every single crop, avoiding the aggregation of crops into groups or categories; 4. registration of the irrigation system used for each crop; 5. registration of the share of the crop surface irrigated by different irrigation systems (for crops irrigated with more than one irrigation system); 6. use of a detailed list of irrigation systems (e.g. eight typologies to fully identify the most common systems used in Italy); 7. inclusion of questions about the status of the farm irrigation network (i.e. restoration works realized, maintenance and overall quality); 8. inclusion of questions about the use of irrigation advisory services or any other technological apparatus for the crop irrigation demand estimation; 9. inclusion of questions about the delivery of irrigation water to the farm. Due to the necessity to limit the length of the CQ and to reduce the burden for the surveyor, only a subset of the proposed amendments have been finally accepted by ISTAT who acknowledged the following integration (see Figure 3.1). Insertion of a column for registering the crop irrigation system used for all the irrigated crops reported in 22.4-Crops irrigated almost once in the agrarian year 2009-2010 (Coltivazioni irrigate almeno una volta nell annata agraria 2009-2010). The irrigation system types are: - border and furrows (Scorrimento superficiale ed infiltrazione laterale) - flood (Sommersione) - aspersion (Aspersione a pioggia) - micro-irrigation (Microirrigazione) - other system (Altro sistema) Insertion of a question (question 22.7) relative to the use of irrigation advisory services and/or systems for determining the crop irrigation demand (Barrare la casella se l azienda utilizza sercvizi di consulenza irrigua e/o sistemi di determinazione del fabbisogno irriguo). 51

Insertion of additional questions in 22.6-Irrigation water source supply (Fonte di approvviggionamento dell acqua irrigua) about the type of delivery of irrigation water: - 22.6.4-Aqueduct, irrigation and land reclamation consortium or other irrigation body with delivery arranged by rotational turns (Acquedotto, consorzio di bonifica e irrigazione o altro ente irriguo con consegna a turno); - 22.6.5-Aqueduct, irrigation and land reclamation consortium or other irrigation body with delivery on-demand (Acquedotto, consorzio di bonifica e irrigazione o altro ente irriguo con consegna a domanda); - 22.6.6-Other source (Altra fonte). Figure 3.1 - The irrigation box (box 22) of the CQ with highlighted the main integrations realized to acquire additional farm information. 52

3.2. crop characteristics database The database of crop characteristics is the basic database used by Model A to simulate the crop irrigation requirement for each crop. The database has been compiled by collating available information for all the irrigated crops cultivated in Italy as precise as possible to ensure a good accuracy during simulation. During the collection phase, priority has been given to data produced in the framework of research projects which have carried out experimentation in Italian pilot areas, additional data have been retrieved from FAO paper no. 56 (Allen et al., 1998) and by literature review. Crop characteristics data (i.e. rooting depth, critical growth stage, rate of development and the amount of water that can be withdrawn from the soil profile without affecting production) can be considered a crucial element because they affect irrigation schedule for the maintenance of the optimum yield. For each irrigated crop the following parameters have been collected: planting and harvesting date, duration of the growing phases, crop coefficients (K cb ) for the initial/development/mature/final stage, crop height, minimum and maximum rooting depth and depletion fraction (p). Since climate in Italy is very different for geographical reasons, data has been collected for three macro-areas: Northern, Central and Southern Italy. Crops have been divided in four groups (see Table 3.2): Annual crops, Perennial crops, Fruit trees and Forage. Annual crops have characteristics that change with the growing seasons. MARSALa performs simulations on annual basis by considering the time range between January and December, therefore have been done some adjustments to the crops (e.g. Perennial crops) having the start of the growing stage in autumn. Crops sown in autumn has been therefore treated as if the growing cycle started in January by shrinking the length of the crop cycle and with the assumption that, generally, irrigation is not applied during November and December. Other types of adjustments have been applied to crops having the seeding stage differentiated between Northern/Central and Southern Italy (e.g. artichoke harvest is in March-April for North Italy and in autumn for South Italy). Fruit trees, such as peach and grapes, have roots which increase in depth year by year until they become more or less fixed in depth when trees reach maturity. Full-grown fruit trees have been considered with a growing phase long 365 days and with fixed root depth. Young fruit trees have the same characteristics of the full-grown except for the minimum rooting depth and for the crop coefficients which have been considered equal to the value assigned to the full-grown fruit trees decreased by 20%. For fruit trees, young and full-grown, a parameter called irrigation schedule has been added in the database, it defines the time range during which usually irrigation is applied, the lower bound of the range is the first of April and the upper bound is set to October or November depending on the crop type. Forages crops have been considered, also if they are long term, as the annual crops with a growing phase long 365 days and with a crop coefficient (K cb ) constant and equal to 0.72. 53

Table 3.2 - Extract of the Crop characteristics database. Crop group Crop Planting date (day/month) Kcb Crop cycle in North Italy (days) p Rooting depth (m) Crop height (m) North Central South Initial Mid End Start Develop. Mature Final Total Min Max Initial Mature Final Annual Barley 02/01 02/01 02/01 0,15 1,1 0,15 0 82 90 10 182 0,55 0,1 1,25 0,1 1,5 1,5 Oats 02/01 02/01 02/01 0,15 1,1 0,15 0 82 90 10 182 0.55 0.1 1.25 0.1 1.5 1.5 Maize 25/04 15/04 01/04 0,15 1,1 0,6 20 30 80 10 140 0.55 0.1 1.5 0.1 1.8 1.8 Potato 01/03 01/03 15/02 0,15 1,15 0,65 30 30 120 10 190 0.35 0.1 0.6 0.1 0.6 0.6 Carrot 01/03 15/02 15/02 0,15 1,05 0,8 60 45 60 30 195 0.35 0.1 0.7 0.1 1.5 1.5 Cotton 20/03 20/03 20/03 0,15 1,15 0,4 30 50 100 5 185 0.65 0.1 1.5 0.1 1.3 1.3 Sweet pepper 01/05 15/04 01/03 0,15 1 0,8 25 30 60 10 125 0.3 0.1 1 0.1 0.75 0.75 Spinach 02/01 02/01 02/01 0,15 0,9 0,85 0 0 5 10 15 0.2 0.1 0.5 0.1 0.8 0.8 Colza 02/01 02/01 02/01 0,15 0,95 0,25 0 55 45 5 105 0.1 0.1 0.6 0.6 Perennial Artichoke 15/04 15/04 15/11 0,15 1,35 0,7 0 0 80 25 105 0.45 0.1 0.9 0.1 1.2 1.2 Full-grown fruit trees Olive 02/01 02/01 02/01 0,6 0,6 0,6 90 30 170 74 364 0.65 1.5 1.5 1.5 1.5 1.5 Orange 02/01 02/01 02/01 0,65 0,7 0,65 91 60 150 63 364 0.5 1.5 1.5 2 2 2 Apple 02/01 02/01 02/01 0,1 0,95 0,75 80 30 180 74 364 0.5 1.2 1.2 2 2 2 Pear 02/01 02/01 02/01 0,1 0,95 0,75 74 30 160 100 364 0.5 1.2 1.2 2 2 2 Peach 02/01 02/01 02/01 0,45 0,86 0,6 64 45 160 95 364 0.5 1.4 1.4 2 2 2 Young fruit trees Olive 02/01 02/01 02/01 0,24 0,24 0,24 90 30 170 74 364 0.65 0.5 1.2 1.5 2 2 Orange 02/01 02/01 02/01 0,26 0,28 0,25 91 60 150 63 364 0.5 0.5 1.2 1.5 2 2 Apple 02/01 02/01 02/01 0,04 0,38 0,3 80 30 180 74 364 0.5 0.5 1 1.5 2 2 Pear 02/01 02/01 02/01 0,04 0,38 0,3 74 30 160 100 364 0.5 0.5 1 1.5 2 2 Peach 02/01 02/01 02/01 0,18 0,35 0,24 64 45 160 95 364 0.5 0.5 1 1.5 2 2 Forage Alfalfa 02/01 02/01 02/01 0,72 0,72 0,72 364 0.55 0.1 1.5 0.1 0.5 0.2 Rough grazings 02/01 02/01 02/01 0,72 0,72 0,72 364 0.6 0.1 1 0.1 0.5 0.2 54

3.2.1 The web survey on crops cycle To enhance the quality and the spatial resolution of the information contained in the crop characteristics database an additional survey on crops cycle has been performed through an electronic survey. The survey has been addressed to voluntary recipients belonging to the following categories: FADN surveyors, technicians working at public and private agricultural offices, agronomists and farmers. This allowed to gather additional information as accurate as possible from respondents that generally have a better understanding on crops cycle and their variations (e.g. harvesting and planting dates) with the agro-climatic zones and farming practices. The survey has been realized by using a web questionnaire, hosted at the INEA website (see Figure 3.2), the questionnaire contains a list of the main irrigated crops reported in the CQ (see Table 3.3), the list has been compiled by considering the most important Italian crops in terms of spatial extension at national level. The list contains also aggregated crops belonging to the same botanic family and/or with similar crop cycle. The electronic survey has been structured to collect crops data referred to an average agrarian at provincial level (NUTS 3) by discriminating among three altimetric zones: plain, hill and mountain. Table 3.3 - List of irrigated crops used for the web survey. Crop ID Crop 1 Winter wheat 2 Sorghum 3 Grain maize 4 Green maize 5 Potato 6 Sugar beet 7 Tobacco 8 Soybean 9 Rape 10 Sunflower 11 Alfalfa 12 Table tomato 13 Plum tomato 14 Eggplant and Pepper 15 Endive and Lettuce 16 Sweet melon and Water melon 17 Fennel 18 Cauliflower, Broccoli, Cabbage 19 Field bean, French bean, Peas 20 Artichoke 21 Strawberry 22 Spring grass The information collected throw the electronic survey are: name or other identification of the respondent (anonymous respondents are also allowed); professional category of the respondent (useful for further assessment of the accuracy of the answers during data analysis); 55

name of the province where the crop is cultivated; altimetric zone (plain/hill, mountain) where the crop is cultivated; crop seeding or transplanting date (month and decade); final crop harvesting date (month and decade). average number of crop cycles for fresh vegetables; prevailing FAO class for green maize; average number of cuts for alfalfa. The electronic questionnaire has been advertised to potential respondents thanks to the support of the INEA regional offices. Figure 3.2 - Screenshot of the electronic questionnaire hosted at the INEA website (http://www.rica.inea.it/marsala/). 3.3 soil database 3.3.1 State-of-the-art on soil data in Italy The collection of the soil data for the Italian agricultural territory is a necessary step for the simulations performed by Model A. The model requires three main soil parameters to compute the crop irrigation requirement: soil depth: defined as the maximum rooting depth bounded by the lithic or paralithic layer; water content at the field capacity: defined as weighted average on the rooting depth; water content at the wilting point: defined as weighted average on the rooting depth. 56

Table 3.4 - State-of-the-art on the soil maps availability and spatial resolution for each Italian region/autonomous province. Region/Autonomous province 1:250,000 scale 1:25,000-1:50,000 scale Bolzano (AP) not available available for some pilot areas Abruzzo available available for some pilot areas Basilicata available available for some pilot areas Calabria available available for some pilot areas Campania in progress available for some pilot areas Emilia-Romagna available available for the plain territory and few Apennine areas Friuli Venezia Giulia not available available for a portion of the plain territory Lazio not available information not available Liguria not available information not available Lombardia available available for the plain territory and some Alpine areas Marche available available for some pilot areas Molise available available for some pilot areas Piemonte available available for a portion of the plain territory Puglia available currently under review and updating Sardegna available available for some pilot areas Sicilia in progress available for some pilot areas Toscana available available for some pilot areas Trento (AP) not available available for some pilot areas Umbria not available available for some pilot areas Valle d Aosta not available available for some pilot areas Veneto available available for a portion of the plain territory In Italy, soil maps have been produced with different levels of details and methodologies by several entities without a national coordination with activities accomplished in a time span of some decades. The soil information currently available, with reference to the main historical periods of realization are described below: Monographs and studies realized either research institution or by regional offices in the framework of pilot projects. These documents are referred to the first Italian experiences in soil cartography. Even though the outcomes have been produced without a methodological coordination and have not been harmonized, they represented the stimulus and the basic knowledge that triggered the recent soil mapping activities. Regional soil maps of recognition (1:250,000 scale), realized at the beginning as autonomous activities by few pioneer regions (Sicilia, Sardegna, Emilia-Romagna) and later carried out, thanks to national funds (i.e. Programma Interregionale Agricoltura e Qualità ), by all the Italian regions (see Table 3.4). Inappropriately, though some methodological guidelines have been defined, each regions followed their own methodology (e.g. geographical reference system, survey methods, guidelines and description methods for the observations, generalization techniques, reporting guidelines, etc.). The result is the realization of regional soil maps that lack harmonization neither geometrically (for instance the mapped polygons never match along regional boundaries) nor semantically (the same label attributed to a particular object can assume several meanings in different maps). 57

Semi-detailed regional soil maps (1:25,000-1:50,000 scale). Some regions decided to realize a more detailed cartography with a more intensive surveying activity in comparison to the soil maps of recognition. The maps as usual lack of any harmonization and cover generally areas with intensive agriculture (e.g. Padano-Veneta valley) or with particular issues. Table 3.5 - Available soil maps with country-level coverage. Year Map Author Scale Description 1966 Carta dei Suoli d Italia (Italian Soil Map) F. Mancini et al., 1966 1:1,000,000 The map has been the first relevant study about the Italian soils. It has been based mainly more on the distribution of pedogenetic factors than on a systematic survey. 2003 Carta Ecopedologica d Italia (Italian Ecopedologic Soil Map) 2006 Badasuoli (Italian soil database) European Soil Office - JRC (Ispra) MiPAAF, CRA and the Regional Soil Services 1:250,000 The realization of the map has been linked to the activities carried out during the Carta della Natura (The Map of Nature) Project, under the Italian law 394/91 on protected areas, and the European Soil Database developed in the framework of the European Soil Information System (EUSIS). The objectives of the map are: characterization of the soils in terms of hydrological properties and erosion risk; analysis of the soil-vegetation relationship; analysis of the preservation aspects. 1:1,000,000 The soil database has been realized through the whole collection, integration and harmonization of the regional soil maps at 1:250,000 scale. As shown in Table 3.5, various soil maps are available at national level. Unfortunately, the analysis of the maps highlighted that none of them is suitable to provide directly the soil needed parameters without applying further elaboration and integration. As matter of fact: Carta dei Suoli d Italia was realized in 1966 following mainly naturalistic criteria, therefore it is short of enough numerical information to be used to derive the necessary soil variables. Carta Ecopedologica d Italia as well as Badasuoli, shows a big deal of inconsistencies both for the geographical and semantic part (the associated database) and inside the database, for instance, there are some undescribed cartographic units or some soil typological units without any observation. To determine the soil parameters, a proper methodology has been developed in order to integrate all the available data sources (soil maps and numerical information associated to each soil type) and later to compute the soil depth and the hydrologic retention properties. 58

3.3.2. Methodology for a country-level harmonized soil map The methodology has been developed by taking into account resolution, quality, accuracy and last but not the least ease of access and acquisition for the available data sources. Based on the mentioned elements a priority has been attributed to the following soil datasets (in the reported order): 1. soil maps at 1:25,000-1:50,000 scale produced by the Italian regional administrations; 2. soil maps at 1:250,000 scale produced by the Italian regional administrations; 3. Badasuoli; 4. Carta Ecopedologica d Italia. The methodology has been implemented through the following phases: 1. Acquisition of the available soil maps in digital format: a. Soil maps at 1:250,000 of the Southern Italian regions produced during a national research project carried out by INEA; b. Badasuoli; c. Carta Ecopedologica d Italia; d. Regional soil map of Emilia-Romagna region (1:250,000 scale for the Apenninic areas and 1:50,000 scale for the plain areas); e. Regional soil map of Lombardia region (1:250,000 scale for the Alpine areas and 1:50,000 scale for the plain areas); f. Regional soil map of Friuli Venezia Giulia region (1:50,000 scale); g. Regional soil map of Piemonte region (1:250,000 scale for the Alpine and Apenninic areas and 1:50,000 scale for the plain areas); h. Regional soil map of Marche region (1:250,000 scale). 2. Geometric harmonization of soil maps and realization of a unique national layer (in shapefile format with coordinate system UTM, WGS 84 datum, zone 32N); 3. Creation of a database containing the following tables: a. UC: list of all cartographic units with the relative source and reliability; b. SUOLI: list of the soils belonging to each cartographic unit; c. UC_SUOLI: relationship table between UC and SUOLI indicating the soils spreading for each cartographic unit expressed as percentage of cartographic unit surface; d. ORIZZONTI: table (see Table 3.6) containing, for each horizon of the representative profile (actual or hypothetical) of each type of soil, the basic information to be used to compute the soil depth, the field capacity and the wilting point. 4. Computation of the soil parameters. The computation of the soil parameters has been performed with a procedure developed to exploit additional information such as morphology and land use to associate the parameters spatially to sub-polygons belonging to the municipality polygons. In particular, the following variables have been considered: crop group (i.e. arable land and tree crops); morphology (i.e. areas above or below the slope threshold of 5 %). 59

The mentioned variables reduce the loss of accuracy of the model results caused by the uncertainty of the geographical location of the farm crops described in the CQ and allow to differentiate the soil parameters on a crop basis. Table 3.6 - Minimal set of characteristics collected in the table ORIZZonti to compute soil depth, field capacity and wilting point. Field name SUOLO NUMORIZZ TOPSOIL CODICE_ST TIPO PROFLSUP PROFLINF SCHELETRO sabbia LIMO ARGILLA SOSTORG Description Soil identification code Progressive number indicating the horizon in the representative soil profile 1: shallow horizon; 0: deep horizon Horizon label according to Soil Taxonomy Horizon type (value used to compute the hydrologic parameters) Horizon upper bound (cm) Horizon lower bound (cm) Rock fragments (> 2 mm) expressed as percentage of the volume Sand content expressed as percentage of the volume Silt content expressed as percentage of the volume Clay content expressed as percentage of the volume Organic matter content expressed as percentage of the volume The adopted procedure has been articulated in seven steps as described hereafter. 1. Creation of a slope vector layer with polygons belonging to the two slope classes (greater and less than 5%) by processing a 20 m resolution Digital Elevation Model (DEM). The vector layer has been produced after generalizing the slope grid to 500 m resolution and by removing manually the polygons too small and the polygons of flat areas localized at high altitude (i.e. plateaus and high-altitude grasslands). 2. Construction of a land use vector layer with polygons belonging to two land use classes: Agricultural areas and Non-agricultural areas. This step required the following sub-steps: a. Identification and acquisition of the latest up-to-date land use map (regional land use map at 1:25,000 scale for Lombardia and Emilia-Romagna; INEA CASI3 2005 1 for the Southern Italian regions and Corine Land Cover for the rest of Italy); b. Geoprocessing of the various land use vector layers by using GIS functions. 3. Identification, through a geometric intersection, of the agricultural soils and their distribution (in percentage) relative to the total agricultural area for municipality and slope class for each municipality and for the two slope classes. 4. Computation of the maximum rooting depth (horizons indicated as R or Cr) for each agricultural soil. 5. Computation of the parameters of the soil water retention curve of Van Genuchten by the Pedotransfer Functions (PTF) defined in the HYPRES project (Development 1. Land use map with focus on irrigated areas available for all the Southern Italian regions. Resolution is 1:50,000 for the irrigated land use and 1:100,000 for the others land use classes. 60

and use of a database of hydraulic properties of European soils) and of the water content at field capacity and wilting point for each horizon of the agricultural soils. 6. Computation of the weighted average on the entire rooting depth of the water content at the field capacity and at the wilting point for each agricultural soil. 7. Computation of the three soil parameters by a weighted average of the parameters of the single soils taking as weights their percentage of diffusion for the two slope classes for each municipality. Since tree crops generally require deeper soils, during the weighting average it has been assumed that: a. all the soils occurring in the various combination municipality-slope class are considered for the arable land; b. only soils having a depth greater than 70 cm are considered for the tree crops. The procedure allowed the creation of the soil database with the structure shown in Table 3.7 where, the soil parameters are computed for each combination municipalityslope class-agricultural land use. Table 3.7 - Soil database structure. Arable land Tree crops Areas with slope < 5% Areas with slope > 5% Areas with slope < 5% Areas with slope > 5% Municipality Soil Depth (cm) Field Capacity (m 3 /m 3 ) Wilting Point (m 3 /m 3 ) Soil Depth (cm) Field Capacity (m 3 /m 3 ) Wilting Point (m 3 /m 3 ) Soil Depth (cm) Field Capacity (m 3 /m 3 ) Wilting Point (m 3 /m 3 ) Soil Depth (cm) Field Capacity (m 3 /m 3 ) Wilting Point (m 3 /m 3 ) 3.4. Agrometeorological database In the past, meteorological observations have been carried out in Italy by the Meteorological Service of the Italian Air Force, the Central Office for Crop Ecology (CRA-CMA), the Ministry of Agricultural, Food and Forestry Policies (MiPAAF) and by the Central Hydrographical Service. With their large networks, the public bodies (institutions) guaranteed a rather good coverage of the national territory. The reform of national technical services, carried out at the end of 1990s, shifted the central hydrological network to the 20 administrative regions (NUTS 2 level). In addition, several agrometeorological services started meteorological observations at regional level since early 1980s. Finally, a plenty of meteorological networks with smaller numbers of working gauging stations continued to operate, especially in the northern regions, in that period throughout Italy. The monitoring potential of the networks is satisfactory due to the generally high-data quality, the complete national coverage and the quite acceptable spatial resolution of the gauging networks, even though there is a great deal of heterogeneity in the information collected. Today, three national actors collect and perform harmonization activities of agrometeorological data at country level: ISTAT, National Institute for the Protection and En- 61

vironmental Research (ISPRA) and CRA-CMA. The characteristics of the three databases are described in the following paragraphs. The CRA-CMA database has been chosen for MARSALa project following a trade-off among completeness, resolution and harmonization at national level. The variables taken into account have been precipitation and reference evapotranspiration (ETo) both measured in millimetres and with a daily temporal resolution. 3.4.1. ISTAT database Since 1926, ISTAT disseminates meteorological data collected from gauging stations located across Italy. Table 3.8 - The survey of meteorological networks in Italy. Administration level Service/Institution name Number of institutions Estimated number of working stations Average length of time series (years) National Meteorological services of Military Air Force 1 100 > 50 National CRA-CMA 1 200 > 50 National Corpo Forestale dello Stato 1 100 > 10 Regional Regional hydrological services 20 4,000 > 50 Regional Regional Agrometeorological Services 20 1,000 > 20 Sub-regional Agricultural consortia > 350 250 > 10 Provincial Agrometeorological services of provinces 10 200 > 15 Local National Council for Research (CNR) 20 > 50 > 30 Local Council for agricultural research (CRA) 50 200 > 30 Local Climatological and geophysical observatories > 20 100 > 40 Local Universities, agricultural schools, and other institutions > 20 > 50 > 20 Total > 500 > 6,250 > 50 Source: ISTAT In 2007 ISTAT carried out a research project entitled Meteo-climatic and hydrologic indicators. The aim was to implement a geographical data-warehouse with meteorological, agrometeorological, and hydrological daily values measured since 1951 from more than 6,000 gauging stations of several national, regional, and local institutions. The project was conducted within the partnership of the CRA-CMA and the Meteorological Service of the Italian Air Force. The survey involved more than 600 respondents such as meteorological services working at the national level, regional authorities and local institutions operating in the environmental field. The list of respondent has been compiled through Web searches, by collecting information through the national meteorological services and by interviewing experts working at the regional and local level. Data have been collected through a statistical survey in 2007-2008 by using software tools and data capturing. A geo-database has been developed in Oracle/ARCGIS environment in order to properly store the collected time series data for all the variables. A dedicated module is also available to calculate cli- 62

matic indicators for environmental surveillance in agriculture, public health, tourism and water use at both daily, week, month and year basis. 3.4.2. ISPRA database ISPRA, in the framework of the national environmental information system and in collaboration with several national and regional institutions developed the National System for the collection, elaboration and diffusion of climatological data of environmental interest (SCIA). The aim is to establish a common procedure for calculating, updating and representing climatological data among all the relevant institutions dealing with meteorological networks and observations to be used for representation of the state and trend of the Italian climate. The main meteo-climatic variables taken into account are: temperature, potential temperature, equivalent potential temperature, precipitation, relative humidity, wind, water balance, bio-climatological index, insulation, potential evapotranspiration, degreedays, fog and visibility, cloudiness, atmospheric pressure, global radiation. For each variable 10-days, monthly and annual indicators are calculated. The indicators undergo homogeneous validity controls agreed with the data owners from which the indicators are derived. Through SCIA Web site it is possible to display and download the main indicators calculated and stored into the system as tables, diagrams, bar charts and maps. Up to now, the indicators contained in the database have been calculated from the historical meteorological time series belonging to the synoptic stations of General Office for Meteorology (UGM), CRA-CMA, Regional Agency for Environmental Protection (ARPA)-Emilia Romagna and to the pluviometric station of National Service for Study of Waters and Seas (SIMN). Some of the synoptic stations are operated from a few years by Italian Company for Air Navigation Services (ENAV). 3.4.3. CRA-CMA database CRA-CMA database was realized in the framework of CLIMAGRI project (Perini, 2007). The database has been obtained through Objective Analysis 2 and is made up of a complete series of daily values of air temperature (minimum and maximum), rain, solar radiation, relative humidity and wind speed (10 meters asl) estimated for a regular grid of 544 nodes covering the whole Italian territory. Each node is the centroid of a meteorological cell with a side length of 30 km (see Figure 3.3). The mentioned variables allow to calculate the Reference Evapotranspiration (ETo). ETo is usually estimated using meteorological data and is related to standard conditions (namely a wide grass field where the growth and production processes are not limited by the water availability or any additional stress factors). Among the various methods available for ETo estimation, the Penman- 2. The Objective Analysis was performed by Finsiel in the framework of National Agricultural Information System (SIAN) of MIPAAF. The study was carried out during 1988-1990 and the results are published in the report SIAN Analisi climatologica e progettazione della Rete Agrometeorologica Nazionale (April 1990) and in the papers of A. Libertà and A. Girolamo, 1991 and 1992. 63

sl) estimated for a regular grid of 544 nodes covering the whole Italian territory. Each node is the centroid f a meteorological cell with a side length of 30 km (see Figure 3.3). The mentioned variables allow to alculate the Reference Evapotranspiration (ETo). ETo is usually estimated using meteorological data and is lated to standard conditions (namely a wide grass field where the growth and production processes are not mited by the water availability or any additional stress factors). Among the various methods available for To estimation, the Penman-Monteith formula revised by FAO is considered the most reliable and therefore the one used to Monteith build the database: formula revised by FAO is considered the most reliable and therefore is the one used to build the database: (1) (1) here ETo is the reference evapotranspiration [mm d -1 ], R n is the net radiation [MJ m -2 d -1 ], G is soil heat ux [MJ m -2 d -1 where ETo is the reference evapotranspiration [mm d -1 ], R ], γ is the psychrometric constant [0.066 kpa C -1 n is the net radiation [MJ m -2 d -1 ], G is the soil heat flux [MJ m ], -2 900 d -1 ], is γ a is conversion the psychrometric factor, (e s - e constant a ) presents the vapour [0.066 pressure kpa Cdeficit -1 ], 900 of is the a conversion air [kpa], T factor, is the mean (e air temperature [ C], Δ represents the s - e a ) represents the vapour pressure deficit ope of the saturation vapour pressure temperature relationship [kpa -1 ] and U 2 is the wind speed at 2 eters [m s -1 of the air [kpa], T is the mean air temperature [ C], Δ represents the slope of the saturation vapour pressure temperature relationship [kpa C -1 ] and U 2 ]. is the wind speed at 2 he data used to meters build the [m database s -1 ]. have been originated from the meteorological measures stored in the ational Agrometerological The data Database used (BDAN) to build 3 and the are database referred have to the been thirty-year originated period from 1961-1990, the meteorological which defined the measures conventional stored reference in the for National climatological Agrometerological analysis and Database comparisons (BDAN) by 3 and the are World referred to eteorological Organization the thirty-year (WMO). period 1961-1990, which is defined the conventional reference for climatological he spatio-temporal reconstruction analysis and of the comparisons meteorological by the variables World has Meteorological been performed Organization by the geo-statistical (WMO). riging with external drift The methodology. spatio-temporal The methodology reconstruction allows of to the estimate, meteorological within the variables considered has spatial been performed of a given by the geophysical geo-statistical variable Kriging starting with from external the actual drift data methodology. available (in this The case, methodology the omain, the values bserved data at allows the meteorological to estimate, stations), within the taking considered into account spatial the domain, statistical the properties values of of a given the spatiomporal dynamics variable of the starting variable: from the so the called actual structural data available model. The (in this basic case, hypothesis observed is to consider data at the the me- geophysical hysical variables teorological as regionalized stations), random taking variables into (Matheron, account the 1970 statistical and 1971). properties Meteorological of the spatio-temporal variables tisfy this requirement dynamics since of they are variable: space and the time-dependent. so called structural Statistically model. speaking, The basic meteorological hypothesis data is to consider the stations physical always variables show a as certain regionalized level of correlation. random variables (Matheron, 1970 and 1971). corded from neighbour Meteorological variables satisfy this requirement since they are space and time-dependent. aily meteorological data estimation on grid nodes has been performed through an independent estimation Statistically speaking, meteorological data recorded from neighbour stations always show f the climatic mean and the meteorological deviation according to the following relationship: a certain level of correlation. meteorological Daily meteorological measure = climate data estimation + meteorological on grid deviation nodes has been performed (2) through an here climate is independent a cyclic annual estimation constant (it of varies the climatic during the mean year, and but the it is meteorological constant among the deviation years) with according ood spatio-temporal to the continuity following and relationship: good agreement with the mean trend of the meteorological fields at noptic scale, it generally coincides with the climatic mean; meteorological deviation is the variation caused climate by the instantaneous meteorological and local measure meteorological = climate condition. + meteorological deviation (2) he Objective Analysis was performed where by climate Finsiel in the is framework a cyclic of annual National Agricultural constant Information (it varies System during (SIAN) the of MIPAAF. year, but The study it is was constant rried out during 1988-1990 and the results are published in the report SIAN Analisi climatologica e progettazione della Rete Agrometeorologica azionale (April 1990) among and in the the papers years) of A. Libertà with and good A. Girolamo, spatio-temporal 1991 and 1992. continuity and good agreement with the mean he National Agrometeorological trend of Database the meteorological (BDAN) was realized fields in the at framework synoptic of SIAN scale, and contains it generally the observations coincides provided with by CRA-CMA the climatic eteorological network mean; and others meteorological Italian services. deviation is the variation caused to climate by the instantaneous and local meteorological condition. 16 Kriging methodology assigns proper weighting coefficients to the data within the estimation neighbourhood of each grid node. The coefficients are calculated on the basis of the spatial continuity of the meteorological variable. Within the geographic analysis domain, the structural model of the variable is represented by an analytical function ex- 3. The National Agrometeorological Database (BDAN) was realized in the framework of SIAN and contains the observations provided by CRA-CMA meteorological network and others Italian meteorological services. 64

clusively dependent on distance, orientation and altitude difference between each pair of points (variogram function). Therefore, the estimation of meteorological variables at grid nodes, for a given time interval, has been produced by a weighted linear combination of the meteorological data of the stations belonging to the estimation neighbourhood. Figure 3.3 - The regular grid of 544 nodes used in the CRA-CMA database to report the meteorological variable (i.e. precipitation and ETo). 65

In this way, the estimate takes into account also some of the main morphological and topographic factors affecting the meteorological events, such as the morphological elements of the Padana Plain (e.g. a distance measured along the North-South direction has a larger local meteorological variability and a greater climatic gradient than the same distance measured along the East-West direction), or the alignment of the Apennines with the coastline in Central Italy. It is obvious that the structural model depends on the period of year as well: during winter the meteorological events have larger temporal variations and spatial continuity, while in the summer time the spatial correlation among the measures is marke0,dly lower. The spatial continuity of the meteorological events affects the precision of data estimation at grid nodes; this implies that the vagueness of the estimation increases as the chaos of the spatio-temporal variations of the variable grows (low spatial correlation). The estimate variance, strongly dependent on the structural model, increases as the number of known data (number of measurement stations) and the unit dimension of the analysis grid (distance among nodes) decrease. However, Kriging is a correct estimation method: the mean estimation error is equal to zero and the deviation between the mean of the estimated and of the observed values tends to zero as the extension of the analysis domain increases. In other words, the numerical model provides a good reproduction of the macroscopic statistical properties of the meteorological events, while it loses some peculiarities and details appearing to the observer more uniform than the actual meteorological event. This difference, known as smoothing effect, increases with the estimated variance. Theory demonstrates that the physical complexity recreated by the numerical model is always lower than the observed event (statistical smoothing). The difference is cancelled only in the case of perfect estimation (estimation error variance equals to zero) and exact knowledge of the actual event. 66

CHAPter IV Model calibration A model calibration can be in general defined as the estimation and adjustment of the model parameters and constraints to improve the agreement between model output and a data set (Rykiel, 1996). The calibration of MARSALa model has been performed by comparing the simulated and the actual measured farm irrigation water consumption for a representative farms sample, by analyzing the irrigation water consumption for the irrigated crops in the agrarian year 2007-2008. The farms sample has been extracted by taking into account two constraints: budget resources and availability of on-farm measurement devices (necessary for the acquisition of the actual values of water consumption). The sample has 279 farms located in four different regions: Emilia-Romagna, Campania, Puglia and Sardegna (hereafter indicated also as pilot areas); the survey has been conducted by interviewer having skills in the agricultural field. The irrigation water sources of the selected farms can be very different, this is a common feature among the Italian irrigated farms. Three are the main typologies: 1. water distributed by a public service (e.g. ILRC) - the actual data on farm water consumption have been provided by the public entity managing the water delivery. During farms sample definition a preference has been given to farms equipped with measurement devices controlled by ILRCs (see Figure 4.1); 2. water abstracted from a private source (e.g. water abstracted by a pump from a well or from a superficial water source (see Figure 4.2)) - the actual data on water consumption has been registered from the measurement device if available (see Figure 4.3), otherwise (as in the majority of the cases) it has been estimated by the interviewer using information about the equipment used for water abstraction; 3. hybrid water source - both the previous irrigation water sources can be used by the farm. At the outset of the calibration phase a lot of consideration, along with a literature review, have been made about the possible sources of errors and inaccuracy (see Table 4.1) that can affect model performances. It has been deemed effective the approach of focussing the calibration only to Model C (hence by adjusting the model parameters concerning the farmer irrigation strategy) since the errors associated with the input data of models A and B have been considered not easily manageable or reducible. The approach is also considered a mean for offsetting the errors affecting associated models A and B. 67

Table 4.1 - Main limitations and inaccuracies affecting models input data. Input data Agrometeorology Soil (soil depth, wilting point and field capacity) Crop characteristics Irrigation system Errors and inaccuracy Since the grid used has a coarse resolution (30 km) the agrometeorological variables (precipitation and ETo) represents average values over very large agricultural areas. This entails that crops are associated with values probably different from the actual ones. Soil database has been realized by collating regional and local soil maps produced with different standards and resolution. In addition soil parameters have been estimated for two land use classes at municipality level by averaging the parameters of several soil profiles. Moreover, the CQ reports crops location in an approximate manner by indicating the municipality of the farm centre and the location of the main crops groups if the farm has parcels in other municipalities. The crops parameters collected are average values gathered from literature and past research projects, only few crops has been fully characterized by field experiments. CQ reports only the prevailing irrigation system (in terms of cultivated surface covered) for each crop or aggregation of crops. This is an approximation since crops can be irrigated with different irrigation systems having different efficiency. In addition no information about the conditions, materials, size, maintenance and management of the farm irrigation network are collected through the CQ, therefore any speculation on the influence of this characteristics on the irrigation efficiency can be performed. Figure 4.1 - Sardegna pilot area: example of on-farm measurement devices. The digital flowmeter (AcquaCard) is provided by the ILRC, water distribution is managed trough an electronic card with a predefined water amount purchased by the farmer at the beginning of each irrigation season. 68

Figure 4.2 - Sardegna pilot area: example of in-farm pond used as private source for irrigation, the water source can be often used in conjunction with the irrigation water provided by an ILRC to ensure the availability in case of water shortage. Figure 4.3 - Puglia pilot area: in-farm bore used as private source for irrigation water abstraction through a pumping system. 69

Figure 4.4 - Puglia pilot area: example of on-farm measurement device, a mechanical flowmeter (Woltmann meter). 4.1 Methodology for pilot areas definition and farms sample extraction Pilot areas definition and farms sample extraction has been carried out by a proper methodology according to farms statistics availability, Italian agriculture features and, above all, budget constraints. The methodology has been designed through the cooperation of ISTAT and INEA researchers. The sample has been defined by using a so-called reasoned sample method instead of a random sample, the choice has been determined by the wide variety of the Italian farms characteristics and budget constraints that limited the sample size. In fact, the extraction of a sample by using a random method along with a limited size, could lead to a sample geographically too dispersed without meeting the statistical representativeness and budget constraints. Conversely the use of a reasoned sample is preferable whenever is necessary to control the farms location across the territory and to ensure a statistical representativeness. The geographical location has been defined by selecting, according to a specific representativeness criterion, a group of Italian regions to locate the final farms sample. To achieve an high level of significance of the results produced by the sample, the eligible farms listed for each region have been stratified through the variables: Crop Water Requirement (CWR), irrigation system, farm size and irrigation water source. The variables have been deemed as those having the larger impact on the irrigation water consumption estimation and on the models sensitivity. Sample extraction has been realized by using farms data belonging to the following datasets. 2007 Italian FADN (RICA) database. The database has been selected for its wealth of information, especially for those required by the methodology, moreover the use 70

of FADN farms allowed to recruit FADN surveyors who have a deep knowledge of the pilot areas and the farms, facilitating the process of questionnaire submission. FSS 2003 database, the most up-to-date source with farms irrigation information ensuring a full representativeness of the Italian farms universe. The devised methodology can be broken down into six steps: 1. aggregation of the main irrigated crops with similar annual CWR into groups (Homogeneous Classes (HCs)); 2. computation of the dominant HC for each Italian region and selection of the pilot areas; 3. definition of the stratification variables; 4. definition of the sample size and sampling rate; 5. listing of the eligible farms for each region; 6. farms sample extraction. Step 1 - Aggregation of the main irrigated crops having similar annual crop water requirement into crops groups The aggregation of the main irrigated crops having similar annual CWR into the HCs (see Table 4.3) has been done by using the Table 4.2. reporting data gathered from literature and/or from research projects with field experiments carried out in Centre-Southern Italy. Five classes of annual CWR have been defined, rice has been treated as a separated class due to its peculiarities in water management. Table 4.2 - Average values/range of variability for the annual CWR for the main Italian irrigated crops (Source: literature and research projects results). Crop Annual CWR (m 3 /ha/year) Rice 15,000-20,000 Fodder 7,000 Maize 4,000-6,000 Sugar beet 4,500 Fruit trees 500-4,000 Citrus plantations 3,000 Soya 2,000-3,500 Sunflower 2,000-3,500 Potato 2,000 Vineyards 1,500 Olive plantations 1,500 Wheat 950 Table 4.3 - Aggregation of crops into the HCs based on the CWR values of Table 4.2 HC Crop Ranges of annual CWR (m 3 /ha/year) A wheat, vineyards, olive plantations 0-1,500 B potato, sunflower, soya 1,501-3,000 C citrus plantations, fruit trees 3,001 4,500 D sugar beet, maize, fodder 4501-7000 Rice rice 15,000 20, 000 71

Step 2 - Computation of the dominant HC for each Italian region and selection of the pilot areas The identification of the dominant HC for each region (see Table 4.4) has been done by computing the share of each HC as the ratio between the sum of irrigated surface of the HC crops and the irrigated surface of the region. The data used comes from the FSS 2003 dataset. Table 4.4 - Values of the HCs share for each region and identification of the dominant HCs. Region Share of HC Dominant HC A (%) B (%) C (%) D (%) Piemonte 0.38 3.02 11.93 84.67 D Valle d Aosta 49.56 13.41 36.92 0.11 A Lombardia 0.79 3.88 3.13 92.20 D Trentino-Alto Adige 26.84 0.33 69.08 3.75 C Bolzano 17.94 0.19 78.70 3.17 C Trento 36.41 0.48 58.74 4.37 C Veneto 17.98 9.76 11.34 60.92 D Friuli-Venezia Giulia 14.61 7.28 2.82 75.28 D Liguria 66.41 5.60 24.81 3.17 A Emilia-Romagna 7.88 3.42 34.47 54.23 D Toscana 24.37 2.41 22.76 50.46 D Umbria 8.25 4.32 7.05 80.38 D Marche 22.97 2.65 22.51 51.87 D Lazio 18.61 2.77 34.30 44.32 D Abruzzo 22.96 10.72 34.97 31.36 C Molise 53.13 4.49 13.66 28.72 A Campania 10.42 7.91 44.28 37.39 C Puglia 77.37 0.57 18.38 3.67 A Basilicata 35.56 0.01 52.81 11.62 C Calabria 25.32 5.83 57.33 11.52 C Sicilia 38.54 0.53 55.95 4.99 C Sardegna 23.49 0.57 28.38 47.57 D Pursuant to the identification of the HCs for each region, four pilot areas have been selected (see Table 4.5): Puglia (HC A), Campania (HC B and C), Sardegna and Emilia- Romagna (HC D). Selection has been primarily based on the presence of irrigation water measurement devices at farm level (e.g. measurement devices of the ILRCs or owned by the farmer). Table 4.5 -Association of the Italian regions to the HCs. HC A B C D Regions Puglia, Valle d Aosta, Liguria Campania Campania, Trentino-Alto Adige, Bolzano, Trento, Abruzzo, Basilicata, Calabria, Sicilia Sardegna, Emilia-Romagna, Piemonte, Lombardia, Veneto, Friuli-Venezia Giulia, Umbria, Marche, Lazio, Sardegna 72

As reported in Table 4.4, HC B is not covered by any of the Italian regions since none of them has a prevalence of the irrigated surface in the class. Nevertheless, in order to consider the class, Campania has been chosen as region representative both for HC B and HC C since it has the highest share of irrigated surface for the HC B among the pilot areas selected. Two different regions (Sardegna and Emilia-Romagna) have been selected to cover HC D in order to perform analysis on the model behaviour in regions with different agrometeorological trends and diverse irrigation water sources. As reported in paragraph 2.5, rice is mainly localized inside few and well defined areas and an average irrigation water consumption per hectare has been attributed. Step 3 - Definition of the stratification variables The definition of the stratification variables has been done by enumerating the main drivers having an impact on the farm irrigation water consumption and on the model sensitivity. The following variables have been selected trough expert judgment. Irrigation water source - two typologies have been considered: - ILRCs; - self-supply. Farm size - two farm size classes have been considered: - large farms (farms having the UAA greater than or equal to 20 ha); - small farms (farms having the UAA less than to 20 ha). Irrigation system (prevailing) - three types have been considered: - border and Furrows; - aspersion; - micro-irrigation. By multiplying the modality of each variable the total number of strata is 2*2*3 = 12. CWR can be considered an additional stratum and is intrinsically associated with the pilot areas selected, for instance Puglia has farms with land use made up mainly of crops belonging to class A. Step 4 - Definition of the farms sample size and the sampling rate Although farms sample size should always be defined in order to keep a good representativeness at national level, budget constraints of the project bounded the size to 250 farms. The farms reference universe has been identified by the 2007 Italian FADN and considering only the irrigated farms. The sampling rate has been computed as the ratio between the sample size and the population of the irrigated farms of the four regions. The sampling rate has been later used to define the size of the sub-samples for each region, as described in Step 5 and 6. Step 5 - Listing of the eligible farms for each region The identification of the farms for each region has been done in terms of representativeness of each farm for the relative HC. Only the farms having the ratio between the sum of the irrigated surface of the HC crops and the total farm irrigated surface above a given threshold, have been selected. Therefore, each stratum have been filled with farms hav- 73

ing primarily value of the ratio equal to 100%, whenever the stratum resulted empty the threshold has been progressively diminished down to 50%. However, to maintain a good statistical representativeness, empty strata have been always avoided by also charging surveyors to search for at least one farm with the specific characteristics required. The 2007 Italian FADN has at national level 15,492 farms while the total number located in the four regions is 3,700, the farms with irrigated surface greater than 0 ha are 1,889 (see Table 4.6), these are the eligible farms to be stratified through the variable defined in Step 3. Table 4.6 - Italian FAdn 2007: total number of farms for each pilot region and number of farms with irrigated surface greater than 0 ha. Pilot Area No. of farms Farms with irrigated surface greater than 0 ha Emilia Romagna 1,150 559 Campania 579 366 Puglia 911 500 Sardegna 1,060 464 Total 3,700 1,889 Before starting with the stratification it has been necessary to identify the three stratification variables in the Italian FADN, in some cases it has been also necessary to reclassify some information to ensure a complete matching. Concerning the variable Irrigation water source a proper matching table has been defined (see Table 4.7). Farms with a prevailing irrigation water source classified as Other in the Italian FADN have been not considered in the farm universe. Table 4.7 - Correspondence between the irrigation water source classes in the Italian FAdn and MARSALa. RICA Water delivered by a public service Lake or river Well Other MARSALa ILRC Self-supply Other Table 4.8 - Correspondence between the irrigation systems of the Italian FAdn and MARSALa. RICA Aspersion Border and Furrows Flood Drip Other system MARSALa Aspersion Infiltration-Flood Micro-irrigation Other 74

Regarding the variable Farm size, farms have been split in two categories: large farms (UUA greater than or equal to 20 ha); small farms, (UUA less than 20 ha). Table 4.9 - Size of the farms universe for each pilot area. Pilot area HC No. of farms Emilia Romagna D 237 Campania B 7 Campania C 175 Puglia A 346 Sardegna D 179 Total 944 Concerning the variable Irrigation system, the Italian FADN registers the prevailing system with five typologies, therefore they have been reclassified in terms of irrigation efficiency in four classes as reported in Table 4.8. Farms with the prevailing irrigation system classified as Other have been excluded from the universe with the mentioned exclusions, the final size of the universe from 1,889 turns to 944 farms (see Table 4.9). The stratification of the universe through the three variables for each region is reported in Table 4.10. Table 4.10 - Stratification of the farms universe for each pilot area based on the three stratification variables. Stratification variable No. of farms Irrigation water source Farm size (UUA) Irrigation system (prevailing) Emilia- Romagna Campania Puglia Sardegna Self-supply ILRC Greater than or equal to 20 ha Less than 20 ha Greater than or equal to 20 ha Less than 20 ha Micro-irrigation 10 8 56 2 Infiltration-Flood 6 2 2 Aspersion 40 3 11 29 Micro-irrigation 2 51 147 1 Infiltration-Flood 2 52 6 Aspersion 25 32 27 3 Micro-irrigation 7 5 22 7 Infiltration-Flood 18 2 2 Aspersion 82 3 1 110 Micro-irrigation 4 19 58 2 Infiltration-Flood 7 4 Aspersion 34 3 16 21 Total per region 237 182 346 179 Grand total 944 75

Step 6 - Farm sample extraction Since farm sample size cannot exceed the 250 units, farms extraction for each region and for each stratum has been done with a sampling rate of 26 % (see Table 4.11). The total number of farms to be investigated are more than the established sample size, in fact some farms have been added to avoid empty stratum. The final sample size for each region is reported in Table 4.12. Table 4.11 - Application of the sampling rate for each region and stratum. The number of farms reported in the column Step 5 are the eligible farms. Irrigation water source Self-supply Stratification variable Farm size (UUA) Greater than 20 ha Less than 20 ha Irrigation system (prevailing) Microirrigation Infiltration- Flood No. of farms Emilia-Romagna Campania Puglia Sardegna Step 5 Sample Step 5 Sample Step 5 Sample Step 5 Sample 10 3 8 3 56 15 2 1 6 2 2* 2 1 2 1 Aspersion 40 11 3 2 11 3 29 8 Microirrigation Infiltration- Flood 2 1 51 15 147 40 1 1 2 1 52 14 6 2 1* Aspersion 25 7 32 10 27 7 3 1 ILRC Greater than 20 ha Less than 20 ha Microirrigation Infiltration- Flood 7 2 5 2 22 6 7 2 18 5 2 2 1* 2 1 Aspersion 82 22 3 2 1 1 110 30 Microirrigation Infiltration- Flood 4 1 19 6 58 16 2 1 7 2 4 2 1* 1* Aspersion 34 9 3 2 16 4 21 6 Total per region 237 66 182 62 346 97 179 54 Grand total 944 Sample size 279 (*) Farm added to fill the empty stratum. Table 4.12 - Number of farms extracted for pilot area. Pilot area No. of farms Emilia Romagna 66 Campania 62 Puglia 97 Sardegna 54 Total 279 76

4.2 Pilot questionnaire for the model calibration Pilot surveys in the pilot areas have been conducted by submitting a Pilot Questionnaire (PQ) made up of the same questions on irrigation reported in the ISTAT CQ. Additional questions, not included in the CQ, (labelled hereafter as Supplementary questions) have been inserted in the PQ, the aim is twofold as described below. Checking the sensitivity of the models with or without specific and more precise farm information in comparison to the CQ and estimating the loss of results accuracy. The additional questions have been in part those initially proposed by INEA to be added into the CQ, but later they have been discarded by ISTAT to avoid an increment of length and complexity of the questionnaire. Trying to collect useful information on the pilot areas related to the irrigation farmers behaviour that can be used to increase the quality of Model C. The additional questions concerns, for instance, the irrigation management for organic farming, the decision on the start of irrigation, etc. Hereafter the description of the PQ structure is reported, the additional questions not contained into the CQ are clearly indicated. The PQ and the compilation guidelines in Italian language are reported in Annex 3. Title-page It contains the farm identification code, the farm typology (according to the HC code defined for each pilot areas) and the farm centre location (region, province, municipality and address). According to ISTAT, the farm centre is the geographical area where the majority of agricultural activity is carried out (i.e. the area where farm buildings or the majority of cadastral parcels are located). Section No.1 The section contains general information: 1. sex, date of birth and educational level of the farmer; 2. farm technological equipment and use of crop management systems; 3. farm size information (total surface; UUA; irrigable surface; surface effectively irrigated during the agrarian year; average irrigated surface during the last three years and number of farm plots). 4. farm irrigation water source: a. groundwater sources located inside or nearby the farm; b. superficial waters sources located inside the farm (natural or artificial ponds); c. superficial waters sources located outside the farm (lakes, rivers, streams, etc.); d. aqueduct, ILRC or other body with water delivery arranged by rotational turns; e. aqueduct, ILRC or other body with water delivery on-demand; f. other source; 5. name of the ILRC serving the farm [supplementary question]. 6. share of usage (%) of each irrigation water source [supplementary question]. 77

Section No.2 The section contains several questions addressed to evaluate the farmer irrigation management behaviour: 1. resort to irrigation advisory services; 2. strategy adopted for starting irrigation [supplementary question]; 3. recent farm irrigation network restoration [supplementary question]; 4. adequate water availability for the irrigated surface [supplementary question]; 5. use of irrigation water even after rain events [supplementary question]; 6. achievement of the maximum level of production for the main crops [supplementary question]; 7. list of crops irrigated with priority in case of water shortage events [supplementary question]; 8. amount of water applied to olive plantations in case of deficit irrigation (expressed as percentage of the crop water requirement) [supplementary question]; 9. irrigation strategy adopted for quality crops (i.e. Controlled Designation of Origin (DOC), Controlled and Guaranteed Designation of Origin (DOCG) and Typical Geographical Indication (IGP)) [supplementary question]; 10. irrigation strategy adopted for organic farming [supplementary question]. Section No.3 The section contains detailed information on farm land use and crop irrigation management for the groups: arable land, fruit trees and other crops. Beyond the HC for which the farm is representative, the whole farm land use has been also surveyed. The information collected are: 1. total and irrigated surface for each crop; 2. irrigation system adopted for each crop (in case of multiple type only that covering the largest surface is reported); 3. seeding/transplanting date and final harvesting date [supplementary question]; 4. starting and ending date of irrigation [supplementary question]; 5. number of irrigation applications during the irrigation season [supplementary question]; 6. average water supply during the irrigation season (m 3 /ha) for each crop [supplementary question]; 7. crop details: a. crop under protective cover (yes/no); b. quality production crop (i.e. DOC, DOCG and IGP) (yes/no). c. number of cycles for fresh vegetable [supplementary question] d. number of cuts for alfalfa [supplementary question]; e. FAO class number for maize [supplementary question]. The PQ has been provided along with the guidelines to the surveyors both in paper and electronic format (a Microsoft Access 2003 application has been developed). After quality checks PQ results have been loaded into a MySQL RDMS to streamline and make effective the next calibration phases. 78

4.3 pilot campaigns The pilot campaigns in the four regions (Emilia-Romagna, Campania, Sardegna and Puglia) have been carried out during the period November 2009 - March 2010. Four surveyors have been employed, selection has been done by considering as requisites: a degree in agricultural sciences, the experience in agricultural surveys and the knowledge/past work experience in the pilot areas. Hereafter an analysis of the farms data collected is reported with particular focus on: number of scheduled and interviewed farms and response rate; farm geographical location at municipality level; farm land use and irrigated crops surfaces. As reported in the paragraph 4.1, sample size is 279 but the final number of the farms interviews is 265, the misalignment (see Table 4.13 and Table 4.14) is due to: lack of farms in the region for a given typology; difficulties in arranging meeting with the farmers or unwillingness to collaborate. Table 4.13 - Number of scheduled and actual farms interviewed and response rate by pilot areas. Pilot area Scheduled farms Actual farms Response rate (%) Emilia Romagna 66 61 92.42 Campania 62 53 85.48 Puglia 97 100 103.09 Sardegna 54 51 94.44 Total 279 265 94.98 Table 4.14 - Number of scheduled and actual farms interviewed and response rate by stratification variable. Stratification variable Scheduled farms Actual farms Response rate (%) Total interviews Farm size (UUA) Irrigation water source Irrigation system (prevailing) Large 128 109 85.16 Small 151 156 103.31 ILRC 127 136 107.09 Self-supply 152 129 84.87 Micro-irrigation 115 122 106.09 Infiltration-Flood 39 21 53.85 Aspersion 125 122 97.60 265 265 265 The interviewed farms have an overall cultivated surface of 4,802 ha in the agrarian year 2007-2008 (see Table 4.15), whereof 4,682 ha irrigated (97% of the cultivated area). 79

Table 4.15 - Total and irrigated surface of the farms sample (surface in hectares and in percentage over the total cultivated surface of the sample). Crop Total surface 80 Irrigated surface (ha) % (ha) % Alfalfa 5.50 0.17 5.00 0.16 Artichoke 49.61 1.52 49.61 1.56 Asparagus 17.00 0.52 17.00 0.54 Barley 0.20 0.01 0.20 0.01 Basil 0.20 0.01 0.20 0.01 Broccoli 37.00 1.13 37.00 1.17 Carrot 1.50 0.05 1.50 0.05 Cauliflower, cabbage 99.60 3.05 99.60 3.14 Celery 8.00 0.25 8.00 0.25 Chard 2.00 0.06 2.00 0.06 Grain maize 477.69 14.65 466.49 14.71 Corn for silage 475.65 14.59 475.65 15.00 Eggplant 4.50 0.14 4.50 0.14 Endive and lettuce 113.65 3.49 113.65 3.58 Fennel 35.80 1.10 35.80 1.13 Flowers and ornamental plants 0.15 0.00 0.15 0.00 Forage legume 628.52 19.28 564.02 17.79 French bean 33.50 1.03 33.50 1.06 Grass 143.50 4.40 143.50 4.53 Horticultural greenhouses 0.88 0.03 0.88 0.03 Italian chicory or chicory for greens 0.50 0.02 0.50 0.02 Onion 10.00 0.31 10.00 0.32 Other cereals grass 35.52 1.09 35.52 1.12 Other oilseeds 28.00 0.86 28.00 0.88 Other seeds 6.30 0.19 6.30 0.20 Parsley 0.20 0.01 0.20 0.01 Pea (dry or fresh) 40.00 1.23 40.00 1.26 Pepper 11.14 0.34 11.14 0.35 Permanent grassland 20.12 0.62 18.12 0.57 Plum tomato 519.48 15.93 519.48 16.38 Potato 121.60 3.73 121.60 3.83 Rice 76.00 2.33 76.00 2.40 Sorghum 7.00 0.21 7.00 0.22 Spinach 20.32 0.62 20.32 0.64 Strawberry 0.57 0.02 0.57 0.02 Sugar beet 90.03 2.76 80.03 2.52 Sweet melon 8.91 0.27 8.91 0.28 Table tomato 124.70 3.82 123.70 3.90 Water melon 0.50 0.02 0.50 0.02 Winter wheat 4.89 0.15 4.89 0.15 Total Arable land 3,260.23 67.89 3,171.03 67.72 Almond 2.50 0.16 2.50 0.17 Apple 7.50 0.49 7.00 0.46 follow >>

>> follow Total surface Irrigated surface Crop (ha) % (ha) % Apricot 2.10 0.14 2.10 0.14 Clementine 1.00 0.06 1.00 0.07 Quality wine (DOC/DOCG) 34.41 2.23 34.41 2.28 Table grapes 82.65 5.36 82.65 5.47 Other wines 574.67 37.26 574.67 38.01 Hazel 1.50 0.10 1.50 0.10 Kiwifruit 65.00 4.21 65.00 4.30 Nectarine 35.00 2.27 35.00 2.32 Olives for oil production 504.24 32.70 475.25 31.44 Orange 4.00 0.26 4.00 0.26 Other crops in greenhouses 1.75 0.11 1.75 0.12 Other temperate fruits 2.00 0.13 1.00 0.07 Peach 172.69 11.20 172.69 11.42 Pear 28.94 1.88 28.94 1.91 Plum 13.70 0.89 13.70 0.91 Table olives 7.05 0.46 7.05 0.47 Walnut 1.50 0.10 1.50 0.10 Total Tree crops 1,542.20 32.11 1,511.71 32.28 Grand Total 4,802.43 100.00 4,682.74 100.00 As reported in Table 4.15, forage crops cover an irrigated surface of 1,743 ha (56% ca. of the total cultivated surface), fresh vegetables (except tomato) have an irrigated surface of 692 ha (22% ca. of the irrigated arable land). Among the arable land, the main irrigated crops are: grain maize with about 1,000 ha (30% ca. of the irrigated surface), tomato (table and plum) with about 640 ha (20% ca. of the irrigated surface). The main irrigated tree crops are: grapes (wine and table use) with about 680 ha (45% ca. of the irrigated surface), olives for oil production with about 475 (30% ca. of the irrigated surface) and fruit trees with about 290 ha (19% ca. of the irrigated surface). Overall, the reported land use can be considered representative of the main Italian irrigated crops and suitable for an appropriate models calibration. The next paragraphs describe the main characteristics of the farms surveyed in terms of the stratification variables. The geographical maps depict the number of interviewed farms for each municipality and the 30 km resolution agrometeorological grid used to associate a reference agrometeorological station to each municipality as described in the paragraph 3.4. 4.3.1 Emilia Romagna pilot area Emilia-Romagna region has been selected as representative of the HC D (sugar beet, maize and fodder) in addition to Sardegna region. During the campaign 61 out of 66 farms have been interviewed (see Table 4.16), the difference is due to the difficulties identifying farms representative for the class. Interview have been carried out in the period October - February 2009, the number of farms interviewed is reported by province in Table 4.17. 81

Figure 4.5 - Emilia-Romagna pilot area: meteo-cells of the agrometeorological grid and number of actual farms interviewed by municipality. Table 4.16 - Emilia-Romagna pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable. Stratification variable Farm size (UUA) Water source Prevailing irrigation system Scheduled farms Actual farms Response Rate (%) Large 45 38 84.44 Small 21 23 109.52 ILRC 41 44 107.32 Self-supply 25 17 68.00 Micro-irrigation 7 6 85.71 Infiltration-Flood 10 4 40.00 Aspersion 49 51 104.08 Total interviews 61 61 61 Table 4.17 - Emilia-Romagna pilot area: number of actual farms interviewed by province. Province Interviewed farms Bologna 7 Ferrara 5 Forlì-Cesena 1 Modena 11 Parma 7 Piacenza 6 Ravenna 7 Reggio nell Emilia 16 Rimini 1 Total 61 82

The overall cultivated area for the agrarian year 2007-2008 of the regional sample (see Table 4.19) is 1,323 ha, whereof 1,235 ha of irrigated crops. Forages crops (maize and alfalfa) prevail and cover an irrigated surface of about 656 ha (61% ca. of the total irrigated surface of the regional sample). Plum tomato is also relevant with about 200 ha (19% ca. of the irrigated surface of the regional sample); fruit trees category is dominated by pears and grapes (wine and table use). Table 4.18 - Emilia-Romagna pilot area: average values for the main dimensional variables. Variable Average values (ha) Total surface 46.95 UUA 42.52 Irrigable surface 42.08 Irrigated surface in the agrarian year 2007-2008 25.84 Irrigated surface in the last three years 20.62 Table 4.19 - Emilia-Romagna pilot area: total and irrigated crops surface of the regional sample (surface in ha and in percentage over the total cultivated surface of the regional sample). Crop Total surface Irrigated surface (ha) (%) (ha) (%) Barley 0.20 0.02 0.20 0.02 Cauliflower, cabbage; 4.00 0.35 4.00 0.38 Grain maize 281.49 24.44 270.29 25.40 Corn for silage 96.05 8.34 96.05 9.03 Flowers and ornamental plants 0.15 0.01 0.15 0.01 Forage legume 354.72 30.79 290.22 27.27 French beans 32.00 2.78 32.00 3.01 Horticultural greenhouses 0.88 0.08 0.88 0.08 Italian chicory or chicory for greens 0.50 0.04 0.50 0.05 Onion 8.00 0.69 8.00 0.75 Other cereals grass 7.02 0.61 7.02 0.66 Other seeds 6.30 0.55 6.30 0.59 Pea (dry or fresh) 40.00 3.47 40.00 3.76 Permanent grassland 20.12 1.75 18.12 1.70 Plum tomato 202.53 17.58 202.53 19.03 Potato 33.00 2.86 33.00 3.10 Sorghum 7.00 0.61 7.00 0.66 Strawberry 0.07 0.01 0.07 0.01 Sugar beet 44.57 3.87 34.57 3.25 Sweet melon 8.41 0.73 8.41 0.79 Winter wheat 4.89 0.42 4.89 0.46 Total Arable land 1,151.90 87.03 1,064.2 86.11 Apple 6.00 3.50 6.00 3.50 Apricot 0.40 0.23 0.40 0.23 Quality wine (DOC/DOCG) 13.81 8.05 13.81 8.05 Other wines 79.17 46.13 79.17 46.13 Peach 18.2 10.60 18.2 10.60 Pear 44.34 25.84 44.34 25.84 Plum 9.70 5.65 9.7 5.65 Total Tree crops 171.62 12.97 171.62 13.89 Grand Total 1,323.00 100.00 1,235.82 100.00 83

4.3.2 Campania pilot area Campania region has been selected as representative for two HCs: B (potato, sunflower and soya) and C (Citrus plantations and fruit trees). Interviews have been carried out in the period October 2009 - February 2010. During the campaign, 53 out of 62 farms have been interviewed (see Table 4.20), the difference is due to difficults to identify farms representative for the class, the number of farms interviewed is reported by province in Table 4.21. Figure 4.6 - Campania pilot area: meteo-cells of the agrometeorological grid and number of actual farms interviewed by municipality. Table 4.20 - Campania pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable. Stratification variable HC B Response Scheduled Actual Rate (%) farms farms HC C Scheduled farms Actual farms Response Rate (%) Total interviews Farm size (UUA) Water source Prevailing irrigation system Large 6 2 33.33 7 7 100.00 Small 6 3 50.00 43 41 95.35 ILRC 6 2 33.33 10 13 130.00 Self-supply 6 3 50.00 40 35 87.50 Micro-irrigation 4 3 75.00 22 24 109.09 Infiltration-Flood 4 2 50.00 16 9 56.25 Aspersion 4 0.00 12 15 125.00 53 53 53 84

The total cultivated area of the regional sample is 688 ha ca. (see Table 4.23) and, in the agrarian year 2007-2008, is almost completely irrigated (686 ha ca.). Table 4.21 - Campania pilot area: number of the actual farms interviewed by province. Province Interviewed farms Benevento 1 Caserta 1 Napoli 23 Salerno 27 Benevento 1 Total 53 Table 4.22 - Campania pilot area: average values for the main dimensional variables. Variable Average values (ha) HC B HC C Total surface 55.20 8.77 UUA 53.84 8.15 Irrigable surface 36.44 8.11 Irrigated surface in the agrarian year 2007-2008 34.24 7.68 Irrigated surface in the last three years 34.84 7.75 Table 4.23 - Campania pilot area: total and irrigated surface of the cultivated crops of the regional sample (surface in hectares and in percentage over the total cultivated surface of the regional sample). Crop Total surface Irrigated surface (ha) (%) (ha) (%) Artichoke 9.80 1.88 9.80 1.88 Basil 0.20 0.04 0.20 0.04 Cauliflower, broccoli, cabbage 64.60 12.37 64.60 12.37 Grain maize 4.00 0.77 4.00 0.77 Eggplant 2.00 0.38 2.00 0.38 Endive and lettuce 113.65 21.77 113.65 21.77 Fennel 7.80 1.49 7.80 1.49 French bean 1.50 0.29 1.50 0.29 Onion 2.00 0.38 2.00 0.38 Other oilseeds 28.00 5.36 28.00 5.36 Parsley 0.20 0.04 0.20 0.04 Pepper 3.15 0.60 3.15 0.60 Plum tomato 78.95 15.12 78.95 15.12 Potato 87.10 16.68 87.10 16.68 Spinach 5.00 0.96 5.00 0.96 Strawberry 0.50 0.10 0.50 0.10 Table tomato 113.70 21.78 113.70 21.78 Total Arable land 522.15 75.87 522.15 76.04 Apple 1.50 0.90 1.00 0.61 Apricot 1.66 1.00 1.66 1.01 follow >> 85

>> follow Crop Total surface Irrigated surface (ha) (%) (ha) (%) Hazel 1.50 0.90 1.50 0.91 Kiwifruit 65.00 39.15 65.00 39.51 Nectarine 35.00 21.08 35.00 21.27 Other crops in greenhouses 1.75 1.05 1.75 1.06 Other temperate fruits 2.00 1.20 1.00 0.61 Peach 51.52 31.03 51.52 31.31 Pear 0.60 0.36 0.60 0.36 Plum 4.00 2.41 4.00 2.43 Walnut 1.50 0.90 1.50 0.91 Total Tree crops 166.03 24.13 164.53 23.96 Total 688.18 100.00 686.68 100.00 4.3.3 Puglia pilot area Puglia region is representative for HC A (wheat, vineyards and olive plantations), surveyors interviewed more farms than those scheduled (100 interviews out of a sample of 97), but some typologies have been not identified (see Table 4.24). Interviews have been carried out in the period October 2009 - January 2010, all the farms are localized in the provinces of Foggia (73 farms) and Bari (27 farms). Figure 4.7 - Puglia pilot area: meteo-cells of the agrometeorological grid and number of actual farms interviewed by municipality. 86

Table 4.24 - Puglia pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable. Stratification variable Farm size (UUA) Water source Prevailing irrigation system Scheduled farms Actual farms Response Rate (%) Large 27 20 74.07 Small 70 80 114.29 ILRC 29 38 131.03 Self-supply 68 62 91.18 Micro-irrigation 77 87 112.99 Infiltration-Flood 5 3 60.00 Aspersion 15 10 66.67 Total interviews 100 100 100 Table 4.25 - Puglia pilot area: average values for the main dimensional variables. Variable Average (ha) Total surface 18.23 UUA 18.26 Irrigable surface 14.09 Irrigated surface in the agrarian year 2007-2008 12.12 Irrigated surface in the last three years 11.29 The overall cultivated surface is 1,715 ha, whereof 1,168 ha irrigated (68% ca. of the total) in the agrarian year 2007-2008 (see Table 4.26). Table 4.26 - Puglia pilot area: total and irrigated surface of the cultivated crops of the regional sample (surface in hectares and in percentage over the total cultivated surface of the regional sample). Crop Total surface Irrigated surface (ha) (%) (ha) (%) Artichoke 3.40 0.20 3.40 0.29 Asparagus 17.00 0.99 17.00 1.46 Broccoli 9.00 0.52 9.00 0.77 Cauliflower, cabbage 16.00 0.93 16.00 1.37 Eggplant 5.00 0.29 5.00 0.43 Fennel 8.00 0.47 8.00 0.68 Pepper 2.50 0.15 2.50 0.21 Plum tomato 64.30 3.75 64.30 5.50 Spinach 1.32 0.08 1.32 0.11 Sugarbeet 17.00 0.99 17.00 1.46 Table tomato 10.00 0.58 9.00 0.77 Winter wheat 417.08 24.31 0.00 0.00 Total Arable land 570.6 33.26 152.52 13.05 Almond 2.50 0.15 2.5 0.21 Quality wine (DOC/DOCG) 16.60 0.97 16.6 1.42 Table grapes 83.65 4.88 83.65 7.16 Other wines 557.8 32.51 443.6 37.97 Olives for oil production 431.63 25.16 416.69 35.66 Peach 45.75 2.67 45.75 3.92 Table olives 7.05 0.41 7.05 0.60 Total Tree crops 1,144.98 66.74 1,015.84 86.95 Grand Total 1,715.58 100.00 1,168.36 100.00 87

As reported in the Table 4.26 vineyards and olive trees overall cover 63% ca. of the total cultivated surface and the 82% ca. of the irrigated surface moreover, 97% ca. and 83% ca. of the cultivated surface of olive trees and vineyards respectively are irrigated. Among the arable land the dominant crops are winter wheat, that is not irrigated and covers the majority of the surface (24% ca.), and plum tomato with 64 ha ca. of irrigated surface. 4.3.4 Sardegna pilot area Sardegna region, as Emilia-Romagna, has been identified as representative for the HC D (sugar beet, maize and fodder). During the campaign, 51 out of 54 farms have been interviewed (see Table 4.27), the difference is due to difficults to identify farms representative for the class. The number of farms interviewed is reported by province in Table 4.28, interviews have been conducted in the period October 2009 - February2010 Figure 4.8 - Sardegna pilot area: meteo-cells of the agrometeorological grid and number of actual farms interviewed by municipality. 88

Table 4.27 - Sardegna pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable. Stratification variable Farm size (UUA) Water source Prevailing irrigation system Scheduled farms Actual farms Response Rate (%) Large 43 42 97.67 Small 11 9 81.82 ILRC 41 39 95.12 Self-supply 13 12 92.31 Micro-irrigation 5 2 40.00 Infiltration-Flood 4 3 75.00 Aspersion 45 46 102.22 Total interviews 51 51 51 Table 4.28 - Sardegna pilot area: number of the actual farms interviewed by province. Province Interviewed farms Cagliari 4 Oristano 15 Sassari 32 Total 51 Table 4.29 - Sardegna pilot area: average values for the main dimensional variables. Variable Average values (ha) Total surface 75.37 UUA 69.22 Irrigable surface 36.07 Irrigated surface for the agrarian year 2007-2008 23.10 Irrigated surface for the last three years 24.44 The overall cultivated surface of the regional sample is 1,129 ha and, in the agrarian year 2007-2008, is completely irrigated. As reported in Table 4.30, the prevailing land use is arable land, in particular forage crops that cover more than 90% of the total irrigated surface. Table 4.30 - Sardegna pilot area: total and irrigated surface of the cultivated crops of the regional sample (surface in hectares and in percentage over the total cultivated surface of the regional sample). Crop Total surface Irrigated surface (ha) (%) (ha) (%) Alfalfa 5.50 0.49 5.50 0.49 Artichoke 3.00 0.27 3.00 0.27 Carrot 1.50 0.13 1.50 0.13 Grain maize 182.20 16.27 182.20 16.27 Corn for silage 379.60 33.89 379.60 33.89 Forage legume 273.80 24.44 273.80 24.44 Grass 143.50 12.81 143.50 12.81 follow >> 89

>> follow Crop Total surface Irrigated surface (ha) (%) (ha) (%) Other cereals grass 28.50 2.54 28.50 2.54 Plum tomato 23.00 2.05 23.00 2.05 Potato 1.50 0.13 1.50 0.13 Rice 76.00 6.79 76.00 6.79 Sweet melon 0.50 0.04 0.50 0.04 Table tomato 1.00 0.09 1.00 0.09 Water melon 0.50 0.04 0.50 0.04 Total Arable land 1,120.10 99.20 1,120.10 99.20 Clementine 1.00 11.11 1.00 11.11 Quality wine (DOC/DOCG) 4.00 44.44 4.00 44.44 Orange 4.00 44.44 4.00 44.44 Total Tree crops 9.00 0.80 9.00 0.80 Grand Total 1,129.10 100 1,129.10 100.00 4.4 Analysis of the model simulations results Data collected through the pilot surveys on the 265 farms provided us a good variety of irrigated crops cultivated by farms having diverse characteristics in terms of the stratification variables (crop, irrigation source, farm size and irrigation system). Irrigated crops data have been used as input for MARSALa to simulate the irrigation water consumption and to later allow a comparison between simulated and actual values of water consumption in order to calibrate the model. The overall crops sample size is 546, Figure 4.9 reports an histogram showing the number of surveys for each crop. Figure 4.10 reports an histogram with the maximum and minimum volume (m 3 /ha) registered for each irrigated crop surveyed. The width of the range of the irrigation volume can be explained with the variability of the territorial characteristics and farm features. On the other hand, values of the volumes particularly extremes must be considered outliers caused by errors in data collection, malfunctioning of the measurement device or errors in the estimation of the water volumes during the interviews. The extremes values have been not used in the calibration phase. As mentioned before, calibration has been carried out through the adjustment of Model C parameters (RIS and f1) by comparing, for each crop, the simulated and measured irrigation water volumes. An example of the comparison between the simulation performed by MARSALa and the actual measured values for the crops surveyed is reported in Table 4.31. During calibration Model C parameters have been adjusted until the difference between measured and simulated values was around 10-15%. 90

Figure 4.9 - Number of surveys for each irrigated crop (546 are the crops surveyed distributed in 265 farms). 120 100 80 60 40 20 0 2 5 8 2 2 2 13 1 6 10 1 8 5 1 1 1 4 8 2 10 37 25 1 2 4 3 1 1 1 71 2 12 2 12 42 32 8 50 7 3 1 1 2 1 3 21 99 10 Kiwifruit Apricot Other cereals grass Other single-crop cereals Orange Asparagus Sugar beet Chard Broccoli Artichoke Carrot Cauliflower, cabbage Onion Clementine Water melon Alfalfa French bean Fennel Strawberry Endive and lettuce Maize Mais a maturazione cerosa Almond Eggplant Apple Water melon Nectarine Hazel Walnut Olive oil Olive for table use Potato Pepper Pear Peach Plum tomato Tomato for table Forage legume Number of surveys for each crop Chicory Celery Seeds Sorghum Spinach Plum Permanent grassland Grapes for table use Grapes for wine Grapes for DOC wine Crop 91

Figure 4.10 - Maximum and minimum irrigation water consumption registered during the interviews. Extremes differences between the maximum and minimum values must be considered outliers. 25.000 20.000 15.000 10.000 5.000 0 Kiwifruit Apricot Other cereals grass Other single-crop cereals Orange Asparagus Sugar beet Chard Broccoli Artichoke Carrot Cauliflower, cabbage Onion Clementine Water melon Alfalfa French bean Fennel Strawberry Endive and lettuce Maize Mais a maturazione cerosa Almond Eggplant Apple Water melon Nectarine Hazel Walnut Olive oil Olive for table use Potato Pepper Pear Peach Plum tomato Tomato for table Forage legume Permanent grassland Chicory Celery Seeds Sorghum Spinach Plum Grapes for table use Grapes for wine Grapes for DOC wine Irrigation water volume (m 3 /ha) Maximum Minimum Crop 92

Table 4.31 - Campania pilot area: comparison between the simulated and measured irrigation water volume for each crop. Farm ID Crop ID Municipality Crop name Simulated volume (m 3 /ha) Measured volume (m 3 /ha) 23 447 Acerra Potato 592.30 1400.00 22 527 Acerra Hazel 6880.00 360.00 21 446 Acerra Cauliflower, cabbage and broccoli 2603.00 2000.00 36 467 Afragola Potato 4613.40 4000.00 36 468 Afragola Endive and lettuce 2217.90 1250.00 36 469 Afragola Endive and lettuce 2217.90 1250.00 35 464 Afragola Potato 4613.40 6720.00 35 465 Afragola Endive and lettuce 1970.00 2000.00 32 457 Afragola Potato 570.60 4200.00 32 458 Afragola Cauliflower, cabbage and broccoli 1982.90 2800.00 45 496 Angri Plum tomato 7032.90 5000.00 45 497 Angri Fennel 3140.90 300.00 44 492 Angri Onion 7051.80 3500.00 44 494 Angri Table tomato 5748.40 8000.00 58 3411 Battipaglia Plum tomato 5573.10 1800.00 58 3412 Battipaglia Endive and lettuce 1470.90 1600.00 39 476 Battipaglia Endive and lettuce 2423.60 3000.00 20 329 Battipaglia Peach 4982.30 1200.00 20 330 Battipaglia Nectarine 5171.00 1400.00 20 331 Battipaglia Plum 5170.70 1200.00 20 332 Battipaglia Actinidia 6934.00 2857.00 38 474 Bellizzi Table tomato 5452.20 4500.00 38 475 Bellizzi Endive and lettuce 1473.00 4500.00 37 470 Capaccio Artichoke 567.10 1440.00 37 472 Capaccio Grain maize 6263.90 2520.00 18 439 Capaccio Grain maize 5500.10 3000.00 18 441 Capaccio Endive and lettuce 2277.10 1250.00 18 442 Capaccio Fennel 2482.10 1250.00 17 432 Capaccio Potato 4831.90 2500.00 17 434 Capaccio French bean 5780.90 10500.00 17 435 Capaccio Plum tomato 7609.90 4500.00 17 436 Capaccio Cauliflower, cabbage and broccoli 2744.80 1000.00 17 437 Capaccio Fennel 4112.80 5000.00 34 462 Casoria Potato 587.20 4200.00 34 463 Casoria Cauliflower, cabbage and broccoli 2610.30 2100.00 31 455 Eboli Endive and lettuce 1472.80 2400.00 28 451 Eboli Artichoke 523.50 1000.00 93 follow >>

>> follow Farm ID Crop ID Municipality Crop name Simulated volume (m 3 /ha) Measured volume (m 3 /ha) 27 337 Eboli Kiwifruit 7202.00 2000.00 33 459 Frattamaggiore Potato 5232.80 3000.00 33 460 Frattamaggiore Plum tomato 7858.60 5000.00 33 461 Frattamaggiore Endive and lettuce 2558.60 1600.00 16 326 Giugliano in Campania Peach 6608.80 2506.00 16 327 Giugliano in Campania Plum 6743.90 1400.00 16 328 Giugliano in Campania Apricot 6717.80 1600.00 15 324 Giugliano in Campania Peach 6744.30 2260.00 15 325 Giugliano in Campania Apricot 6717.80 2700.00 14 323 Giugliano in Campania Peach 5368.10 8000.00 12 321 Giugliano in Campania Peach 6744.30 5000.00 10 318 Giugliano in Campania Peach 5368.10 2160.00 9 316 Giugliano in Campania Peach 5589.60 1100.00 8 314 Giugliano in Campania Peach 5508.50 2025.00 8 428 Giugliano in Campania Table tomato 6144.90 16500.00 7 311 Giugliano in Campania Peach 6457.10 6000.00 7 424 Giugliano in Campania Strawberry 4945.30 21000.00 5 305 Giugliano in Campania Peach 6608.80 1500.00 5 306 Giugliano in Campania Apricot 6717.80 3600.00 4 304 Giugliano in Campania Peach 6457.10 1200.00 11 319 Mugnano di Napoli Peach 6374.90 3745.00 42 486 Nocera Inferiore Table tomato 5775.20 11000.00 42 487 Nocera Inferiore Eggplant 6045.90 5500.00 42 488 Nocera Inferiore Fennel 1951.80 1800.00 42 489 Nocera Inferiore Endive and lettuce 1478.60 1600.00 52 510 Pagani Plum tomato 5678.20 4800.00 52 511 Pagani Fennel 1998.10 2000.00 13 322 Qualiano Peach 6632.40 1235.00 25 333 San Salvatore Telesino Apple 4300.00 2500.00 25 334 San Salvatore Telesino Pear 4303.30 3150.00 43 490 San Valentino Torio Onion 6991.40 3500.00 43 491 San Valentino Torio Table tomato 5770.80 8000.00 26 450 Santa Maria la Fossa Tomato for table 6958.30 3000.00 The results reported show clearly a difference between the simulated and measured irrigation volumes moreover, as mentioned before, the irrigation volumes can be very different for the same crop among different farms due to the variability of environmental conditions (precipitation, ETo and soil properties), irrigation system and farmers irrigation strategy. 94

Figure 4.11 - Campania pilot area: comparison between simulated and measured irrigation water volumes for a selection of crops (Crop group no. 1). 9.000 8.000 Irrigation water volume (m 3 /ha) 7.000 6.000 5.000 4.000 3.000 2.000 1.000 0 Actinidia Apple Apricot Apricot Apricot Hazel Kiwifruit Nectarine Peach Peach Peach Peach Peach Peach Crop Peach Peach Peach Peach Peach Peach Peach Pear Plum Plum Simulated Volume Measured Volume Figure 4.12 - Campania pilot area: comparison between simulated and measured irrigation water volumes for a selection of crops (Crop group no. 2). 8.000 7.000 Irrigation water volume (m 3 /ha) 6.000 5.000 4.000 3.000 2.000 1.000 0 Artichoke Artichoke Cauliflower Cauliflower Cauliflower Cauliflower Eggplant Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Endive and lettuce Fennel Fennel Fennel Fennel Fennel Onion Onion Crop Simulated Volume Measured Volume 95

Figure 4.13 - Campania pilot area: comparison between simulated and measured irrigation water volumes for a selection of crops (Crop group no. 3). 18.000 16.000 Irrigation water volume (m 3 /ha) 14.000 12.000 10.000 8.000 6.000 4.000 2.000 0 Corn Corn French bean Plum tomato Plum tomato Plum tomato Plum tomato Plum tomato Potato Potato Potato Potato Potato Potato Potato Tomato for table Tomato for table Tomato for table Tomato for table Tomato for table Tomato for table Simulated Volume Measured Volume Crop 4.5 influence of the resolution of the agrometeorological data on the simulation results In order to analyze the impact of the agrometeorological data resolution on MARSALa simulations results, an exercise has been carried out by using data with different resolution. The test has been performed before calibration by comparing the model results obtained for some crops. These crops belong to the Sardegna farms sample since agrometeorological data for some municipality have been kindly provided by the Hydrometeoclimatic Department of the Regional Environmental Protection Agency of Sardegna (ARPAS). The exercise has been realized by comparing the simulation results produced by the following datasets: CRA-CMA dataset (the default database used by MARSALa) - values of precipitation and ETo are referred to an agrometeorological grid with 30 km resolution; farms are associated with the meteo-cell of the municipality where farms centres is located. ARPAS dataset - values of precipitation and ETo associated with the farms are those belonging to the meteorological stations having the smallest distance from the farms centres. The variability of the values of precipitation and ETo between the two dataset is particularly evident even by simply comparing the two datasets. Figure 4.14 shows the difference between the balance of precipitation and ETo (i.e the simple difference P - ETo) 96

computed with CRA-CMA and ARPAS data for the stations of Olmedo, Dolianova, Ozieri and Palmas that are the closest to the selected farms centres. The difference is larger for the balance computed on yearly basis than for the balance on half-yearly basis (April- September). Figure 4.14 - Sardegna pilot area: difference between the balance of precipitation and ETo simulated by using the CRA-CMA and the ArpAS data for the year 2008 (both the annual and the half-yearly balance is computed, the half-yearly balance is relative to the period April-September). 300 250 200 P -ETO (mm) 150 100 50 0 OLMEDO DOLIANOVA OZIERI PALMAS Annual (P - Eto) Half yearly ( P -Eto) Meteorological stations Figure 4.15 reports the difference between the simulated and measured irrigation volumes for corn and corn for silage cultivated by farms located close to the Ozieri meteorological station. The comparison of the two results shows how the availability of agrometeorological data can improve the model performances. In fact, the simulation realized with the dataset having higher resolution (ARPAS) produces on average irrigation volumes closer to the measured volumes. In addition, the model appear strongly sensitive to the resolution and quality of the agrometeorological data. Ultimately, the exercise showed how the availability of agrometeorological dataset with better resolution allow to perform more precise simulation and also to realize finer calibration of the model. 97

Figure 4.15 - Sardegna pilot area: comparison between the difference of simulated and measured water volume for corn and corn for silage computed by using the ArpAS and CRA-CMA datasets. 2.500 Simulated volume - Measured volume (m 3 /ha) 2.000 1.500 1.000 500 0 ALGHERO ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARDARA MARRUBIU MORES MORES MORES MORES NULVI OZIERI OZIERI OZIERI OZIERI OZIERI SASSARI SASSARI SASSARI TULA TULA Municipality ARPAS CRA-CMA 98

CHAPter V Software implementation 5.1 Architecture of the computational system The three models (A, B and C) have been implemented through a software application with a client-server architecture (see Figure 5.1). The client, a Microsoft Windows application written in C# programming language, deals mainly with data importing, processing and storage. The server manages the input and output databases and all the models parameters (see Table 5.1). Table 5.1 - List of the databases managed at server-side by MArsALa. Database name Agro-Meteo Crops Soil Farm Land use Irrigation water consumption Description Database of daily values of precipitation and reference evapotranspiration (ETo), both in mm, relative to each municipality, the data are generated by processing the CRA-CMA database. Database of the crops characteristics (e.g. roots depth, length of the growing stages, etc.) reported for the three geographical macro areas North, Centre and South Italy. Database storing, for every agricultural areas of the Italian municipalities, the soil parameter: soil depth, wilting point and field capacity. Database of the farms information useful for running the models extracted from the CQ database provided by ISTAT. Database storing, for each farm, the characteristics of the irrigated land use generated by Module 1. The data (e.g. irrigation system, crop type, irrigated surface, geographical localization) are used by Module 2. Database storing, for each farm irrigated crop, the irrigation water consumption computed by the model along with additional information such as crop irrigated surface, irrigation system and geographical localization. The Database Management System (RDBMS) used is the open-source software MySQL version 5.1, the client-server connection and communication is ensured by a MySQL connector. The client application has three modules: Module 1, Sub-module 1.1 and Module 2. 99

Figure 5.1 - MArsALa software application: structure of the client-server architecture. 5.2 Functions of the modules and sub-modules Module 1 rebuilds the farm irrigated land use by processing the information collected by the CQ (see Annex 2). The module results are reported in a database storing for each farm crop the irrigated surface, the irrigation system and the geographical location (i.e. the municipality). The data subsequently feed Module 2 for the computation of the irrigation water consumption for each crop and, by further aggregation, for each farm. Module 1 creates the irrigated farm land use by using a set of decision rules using the various information reported in a series of interlinked CQ boxes (the rules are reported in Annex 1). In addition, since often the box no.22 reports the irrigation data of the farm for aggregation of crops, a disaggregation procedure is required in order to build an irrigated land uses made up of single crops (the graphical user interface of the procedure is depicted in Figure 5.2). The procedure performs a weighted allocation of the irrigated surface of the crop groups to the single crops, the irrigation system reported for the groups is, by definition, the most frequently used for, therefore is associated directly to the single crops. An example is the group Other arable land crops (Altri seminativi) for which the CQ reports the total irrigated surface and the disaggregation procedure allocates it to the single crops by using the information of the crops belonging to the category Arable land (Seminativi) 100

reported by the box no. 8. A more complex disaggregation case is represented for the Fresh vegetables (Ortive in piena aria) for which a full listing of the crops belonging to the group is not reported by box no.8 which distinguishes only between Table tomato (Pomodoro da mensa), Plum tomato (Pomodoro da industria) and Other fresh vegetables (Altre ortive). In this case, since no information is reported by the CQ for the subgroup Other fresh vegetables, the disaggregation procedure uses additional data such as the annual crop statistics published by ISTAT at NUTS 3 level to split the subgroup into the single irrigated crops. The allocation weight for each irrigated crops is defined as a share over the total surface of irrigated crops at NUTS 3 level. Module 1 performs another important task: the distribution of the farm irrigated land use to the municipality where the farm parcels are located. It is, indeed, well known that farms might have cultivated parcels spread over different municipalities even though often all the information are reported to the municipality where the farm centre is located. The territorial distribution is feasible since the CQ has a section reporting the extension and location, at municipality level (Sezione IV - Ubicazione dei terreni e degli allevamenti aziendali), for the following five crop groups: Arable land (Seminativi), Vineyards (Vite), Other permanent crops (Coltivazioni legnose agrarie escluso la vite), Kitchen gardens (Orti familiari) and Permanent grassland and pastures (Prati permanenti e pascoli). The territorial distribution is based on the proportional allocation of the irrigated surfaces indicated in the box no.22 to the different farm parcels by using the data reported for the five mentioned crop groups for each municipality where the parcels are located. Sub-module 1.1 assigns the parameters RIS and f1 to each farm crop by using the two decision trees as required by Model C. Module 2 performs the final computation by using the data stored in the databases listed in Table 2.1, irrigation water consumption is computed for each farm crop and stored along with the aggregated value of consumption for the farm in the Irrigation water consumption database, the graphical user interface is depicted in Figure 5.3. Figure 5.2 - Graphical user interface (in Italian language) of Module 1 for the allocation of irrigated surface of crop groups to the single crops. 101

Figure 5.3 - Graphical user interface (in Italian language) of Module 2. The great deal of controls allows the full management of the different parameters contributing to the estimation of the crops water consumption. 102

Conclusions The methodology proposed allows to perform an estimation of the irrigation water consumption at farm level by using a models-based approach based on the integration of three models related to the three main aspects of irrigation: crop irrigation demand (Model A), irrigation system efficiency (Model B) and, last but not the least, farmer irrigation strategy (Model C). The models have been implemented through a software application that will be used for the estimation of irrigation water consumption for the Italian irrigated farms universe. Farms data will be provided by the 6 th General Agriculture Census 2010, with reference to the agrarian year 2009-2010; all the other required input parameters are included into the MARSALa software by a set of built-in database. The system provides a models-based estimation of the irrigation water consumption for all the farm crops except for rice and protected crops (e.g. greenhouses) for which a separate methodology has been defined. The simulation of irrigation water used by each censued farm will be performed by using the agrometeorological data relative to the agrarian year 2009-2010. Beyond the models development phase, the creation of the input database can be considered the more challenging phase due to the difficulties in data inventorying and collection. In particular, the acquisition of soil and climate data for the whole Italian agricultural areas has requested numerous efforts in terms of data harmonization for the different sources. In addition it has required the establishment of relationships with the different institutions and organizations, at different administrative levels, producing and managing the data. MARSALa model has been calibrated and tested for the year 2008 by using a sample of nearly 300 farms located into four Italian pilot regions: Emilia-Romagna, Campania, Puglia and Sardegna. Farms sample selection was carried out by defining a proper methodology aimed to satisfy the budget constraints and the representativeness of the Italian agricultural characteristics; the main drivers affecting the crop irrigation consumption in the Italian farms have been also considered. The simulation results, obtained prior the calibration (by using only Model A and B integrated, therefore without considering the farmer irrigation strategy), showed that the irrigation water volumes estimated have often values quite different from the volumes measured or extrapolated by the surveyors during the farms interviews. The difference can be explained by taking into account the resolution of the territorial data used (agrometeorological and soil data), the generalization of some information about the farms and, above all, by the farmer irrigation strategy. The latter can be considered an important driver being the resultant of the application of the farmer knowledge and the response to external factors (e.g. water availability, water source, market conditions, etc.). Calibration has been therefore realized by acting exclusively on Model C parameters to better define, for each investigated farm, the farmer behaviour and at the same time to compensate for the inaccuracy of the input data. 103

During calibration a series of exercises were conducted to test the sensitivity of the models, results highlighted that simulation results are mainly affected, in order of importance, by the values of the following parameters: 1. crop characteristics (in particular crop coefficient); 2. precipitation and ETo; 3. soil parameters. Consequently, the accuracy of the simulation results suffers from the quality and resolution of the input data relative both to the single farm and to the territorial characteristics, whose parameters are extrapolated at municipality level: the minimum geographical unit for the computations. Ultimately, the results obtained for the pilot areas allowed to identify the main weaknesses elements affecting the quality and accuracy of the simulation, they are summarized below. Lack of some important farm details in the Census questionnaire. Results shows that better results could be achieved if the following information would be collected: - data on crop cycle for each irrigated crop (seeding/planting and harvesting date, number of cycles for horticultural crops); - geographical location of each crop - it would allow to precisely associate each crop to the underneath soil and to the closest meteo-cell ; - indication of all the different irrigation system used for the same crop - by defining the share of usage (in percentage) - it would be possible to consider the irrigation application efficiency for each irrigation system; - information about the farm irrigation network (e.g. age of the pipelines, construction materials and recent restoration of the network, dimensions, management etc.) - in this case a better definition of the irrigation water distribution efficiency could be realized. Lack of an harmonized and centralized database of agrometeorological data with a good spatial resolution covering the whole country (e.g. grid of meteo-cell with a cell size of 5 km or lower, such as the resolution of the data generally produced and managed at regional level). Data with higher resolution would allow to associate more realistic values of precipitation and ETo to each crop during simulation. The available grid has a spatial resolution of 30 Km therefore, the values of the variable are averaged for large areas hardly representing the real meteorological condition of the various agricultural areas strongly influenced by the topographic and morphological characteristics of the territory. Some tests, performed before the calibration in the Sardegna pilot area, with high resolution dataset showed how the simulation are closer to the field measurements. This leads to the conclusion that models calibration would improve significantly if agrometeorological dataset were available for all the pilot areas. Low quality of the soil information and lack of an harmonized national map with enough spatial resolution. The national scenario is characterized by soil information produced and managed at regional level that are not harmonized and standardized across the country. Each region uses different production methodology, physical-chemical analysis, scale, resolution, legends, etc.; this causes a strong variability on the simulation results across the country. 104

Improvements on the accuracy of the results can be only achieved by ameliorating the aspects mentioned above but, it is beyond the scope of the MARSALA project and, above all, it would entail the use of additional financial resources. Overall, the results provided by MARSALa simulations can be considered acceptable for the estimation of irrigation water consumption for the whole Italian farms universe, by taking into account the limits imposed by the data collected with the Census questionnaire and the dataset available at country level. The results that will be produced will allow Italy to comply with the requirements of the Regulation Nr.1166/2008 that binds all MS to provide, for each holding surveyed with the Statistics on Agricultural Production Methods (SAPM), an estimation of irrigation water consumption. 105

References Introduction Regulation (EC) no 1166/2008 of the European Parliament and of the Council of 19 November 2008 on farm structure surveys and the survey on agricultural production methods and repealing Council Regulation (EEC) No 571/88. 1600/2002/EC - Parliament and Council of the European Union (2002), Decision No 1600/2002/EC of the European Parliament and of the Council of 22 July 2002 laying down the Sixth Community Environment Action Programme. OJ L 242/1-15. CEC, Brussels. 2000/60/EC - Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000, establishing a framework for Community action in the field of water policy [Official Journal L 327, 22.12.2001], amended by the following Act: Decision No. 2455/2001/EC of the European Parliament and the Council, of 20 November 2001 [Official Journal L 331, 15.12.2001]. EEA Report No 2/2009, Water resources across Europe - confronting water scarcity and drought, ISSN 1725-9177 Goubanova K., Li L., (2006), Extremes in temperature and precipitation around the Mediterranean in an ensemble of future climate scenario simulations, Global and Planetary Change, doi:10.1016/j.globaplacha.2006.11.012. IPCC, (2007), Climate Change (2007): The Physical Science Basis - Summary for Policymakers, contribution of WGI to the 4th Assessment Report of the IPCC, Geneva. ISTAT, (2006), Water resources assessment and water use in agriculture, Collana Essay, n. 18, ISBN 9788845813641 Maton L., Leenhardt D., Goulard M., Bergez J.-E., (2005), Assessing the irrigation strategies over a wide geographical area from structural data about farming systems, Agricultural systems 86, 293-311, doi:10.1016/j.agsy.2004.09.010. Nino P., Vanino S., (2009), Uso del suolo e stima dei fabbisogni irrigui nelle aree non servite da reti collettive dei Consorzi di Bonifica nelle Regioni Meridionali, INEA- Rapporto Irrigazione - ISBN 978-88-8145-174-6. Portoghese I.; Uricchio V.F., Vurro M., (2005), A GIS Tool for Hydrogeological Balance Evaluation at Regional Scale in Semi-Arid Environments, Computer & Geosciences, 31, 15-27 Rodriguez Diaz J.A., Weatherhead E.K., Knox J.W., Camacho E., (2007), Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain, Regional Environmental Change 7, 149-159. 107

Chapter I ISTAT, (2008), Relazioni tra agricoltura e ambiente: dalle statistiche agli indicatori, Statistica in breve, gennaio 2008), http://www.istat.it/salastampa/comunicati/non_calendario/20080128_00/ ISTAT, (2007), Water resources assessment and water use in agriculture, Collana Essays, n. 18 ISBN 9788845813641. ISTAT, (2007), Agrienvironmental Indicators: Methodologies, Data Needs and Availability, Collana Essays, n. 16. ISTAT, (2003), Principali fattori agricoli di pressione sull ambiente, ISTAT, Roma, Argomenti, n. 27. ISTAT, Indagine di struttura dell azienda agricola. Anni vari. ISTAT, Statistiche ambientali. Annuario. Anni vari Chapter II Allen, R.G., (1995), Evaluation of procedures for estimating mean monthly solar radiation from air temperature, Report submitted to the United Nations Food and Agricultural Organization (FAO), Rome Italy. Allen R.G., Pereira L.S, Raes D., Smith M., (1998), Crop evapotranspiration. Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56, Rome, Italy. Anonymous, 2000. Directive of the European Parliament and of the Council 2000/60/EC Establishing a Framework for Community Action in the Field of Water Policy. Official Journal L 327, 1 73. Anyoji H., Wu I.P., (1994), Normal distribution water application for drip irrigation schedules, Transaction of the ASAE, 37:159-164. Baille A., (1994), Principle and methods for predicting crop water requirement in greenhouse environments, Cahier Options Méditerranéennes vol. 31:177-187 Bonciarelli F., Bonciarelli, U., (2003), Coltivazioni Erbacee, Edagricole Scolastico Burt C.M., Clemmens A.J., Bliesner R., Merriam J.L., Hardy L., (2000), Selection of irrigation methods for agriculture, On-farm Irrigation Committee, American Society of Civil Engineers, Reston, VI, US, pp 129. Burt C.M., Clemmens A.J., Strelkoff T.S., Solomon K.H., Bliesner R.D., Hardy L.A., Howell T.A., Eisenhauer D.E., (1997), Irrigation performance measures: efficiency and uniformity, Journal of Irrigation and Drainage Engineering, 123:423-442. Christiansen J.E., (1942), Irrigation by sprinkling. University of California, Berkeley Agric Exp Sta Bull 670. De Villele O., (1974), Besoins en eau des cultures sous serre.. Essai de conduite de l arrosage en fonction de l ensoleillement, Acta Horticulturae, 35: 123-39. Doorembos J., Pruitt W.O., (1977), Crop water requirements. FAO Irrigation and Drainage Paper No.24, Rome, Italy. Doorenbos J., Pruitt W.O., (1977), Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper No. 24. Rome, Italy 108

Israelsen O.W., (1950), Irrigation principles and practices. Wiley, New York, p471 Jensen M.E., (1993), Impacts of Irrigation and Drainage on the Environment, Fifth Gulhati Memorial Lecture. Proc. 15th ICID Congress. The Hague, Netherlands. Jensen M.E., Harrison D.S., Korven H.C., Robinson F.E., (1980), The role of irrigation in food and fibre production, in: M.E. Jensen, ed., Design and operation of farm irrigation systems, Am Soc Agr Eng, p 15-41 (revised printing, 1983, 841 pp) Lake J. V., Postlethwaithe J. D., Slack G., Edwards R. J., (1966), Seasonal variation in the transpiration of glasshouses plants, Agric. Meteorology, 3: 167-196. Lorite I.J., Mateos L., Fereres E., (2004), Evaluating irrigation performance in a Mediterranean environment, variability among crops and farmers, Irrigation Science, 23:85-92. Lozano D., L. Mateos., (2008), Usefulness and limitations of decision support systems for improving irrigation scheme management, Agricultural Water Management 95:409-418. Merriam J.L., Keller J., (1978), Farm irrigation system evaluation: A guide for management, 3rd ed. Utah State Univ, Logan, 285 p Monteith J. L., Unsworth M. H., (1990), Principles of environmental physics, 2ª ed., Edward Arnold, London, UK. Morris L. G., Neale F. E. and Postlethwaithe J. D., (1957), The transpiration of glasshouse crops and its relationship to the incoming radiation, J. Agric. Eng. Research, 2 : 11 1-22 Penman, H.L., (1948), Natural evaporation from open water, bare soil and grass, Proceedings of the Royal Society, 193:120-146. Ayers R.S., Westcot D.W., (1985), FAO Irrigation and drainage paper 29 Rev. 1 Reprinted 1989, 1994 Replogle J.A., Clemmens A.J., Bos M.G., (1990), Measuring irrigation water, in G.J. Hoffman, T.A. Howell, K.H. Solomon, eds., Management of farm irrigation systems. American Society of Agricultural Engineers, St. Joseph, MI, US, p. 313-370. Sirjacobs M., (1987), Mise au point d un bac de surface variable pour la conduite de l irrigation localisée, Bull. Rech. Agronomiques de Gembloux, 22 : 133-42. Stanhill G., Albert J. S., (1974), Solar radiation and water loss from glasshouse roses, J. Soc. Hort. Sci., 99: 107-10 US Bureau of Reclamation, (1997), Water Measurement Manual, 3rd Ed. US Government Printing Office, Denver, CO, USA. Veihmeyer F.J., Hendrickson A.H., (1927), Soil moisture conditions in relation to plant growth, Plant Physiology, 2:71-78. Warrick A.W., (1983), Interrelationships of irrigation uniformity terms, Journal of Irrigation and Drainage Engineering, 109:317-332. Wilcox J.C., Swailes G.E., (1947), Uniformity of water distribution by some under tree orchard sprinklers, Sci. Agric., 127:565-583 Wright J.L., (1982), New evapotranspiration crop coefficients, J. Irrig. and Drain. Div., 108:57-74. Wu I.P., (1988), Linearized water application function for drip irrigation schedules, Transactions of the ASAE, 31:1743-1749. Zucaro R., Tudini L., (2008), Rapporto Sullo Stato Dell irrigazione in Toscana, INEA, Roma. 109

Chapter III APAT, (2007), Annuario dei dati ambientali, Roma. Costantini E. A. C., Gardin L., Pagliai M., (2000), Advances in soil survey, monitoring and applications in Italy, in The European Soil Information System, (Eds. ESB and FAO), World Soil Resources Reports 91, FAO, Roma p. 97-101. EUROPEAN SOIL BUREAU, (1998), The soil geographical data base of Europe at scale 1:1,000,000, JRC, Ispra, Varese, Italy. FAO, (1978), Soil map of the world, FAO, Roma, Italy. ISTAT (2005). Statistiche meteorologiche. Anni 2000-2002. Roma (Annuario n. 29). Libertà A., Girolamo A., (1991), Geostatistical analysis of the average temperature fields in North Italy in the period 1961 to 1985, Science de la Terre Sér. Inf. Nancy, pp. 1 36. Libertà A., Girolamo A., (1992), Time Coregionalization model for the analysis of meteorological fields: an application in northern Italy, Science de la Terre, Sèr. Inf. Nancy, pp. 93 119. Mancini F., (1966). Carta dei Suoli d Italia, Comitato per la Carta dei Suoli, Firenze, Italy. Matheron G., (1970), Krigeage Universal pour une dérive aléatoire, Scientific notes from Centre de Géostatistique de l Ecole des Mines de Paris. Matheron G., (1971), La théorie des variables Régionalisées et ses applications, Scientific notes from Centre de Géostatistique de l Ecole des Mines de Paris. Perini L., (2004), Atlante agroclimatico, CRA-UCEA & MIPAF, Roma. Perini L., Salvati L., Ceccarelli T. et al., (2007), Atlante agroclimatico II Scenari di cambiamento climatico, CRA-UCEA & MIPAF, Roma. Perini L., Salvati L., Ceccarelli T., Motisi A., Marra F.P., Caruso T., (2007), Atlante agroclimatico Scenari di cambiamento climatico, UCEA, Roma. Collana Climagri n. 52 (Atlante + CD), 72 pag. ISBN 88-901472-8-8. Salvati L., Libertà A., Brunetti A., (2005), Bio-climatic evaluation of drought severity: a computational approach using dry spells, Biota, 5, 67-77. Chapter IV Rykiel E.J.Jr., (1996), Testing ecological models: the meaning of validation, Ecological Modeling, 90:229-244. Glossary Brouwer C., Goffeau A., Heibloem M., (1985), Irrigation Water Management: Training Manual No. 1 - Introduction to Irrigation, FAO - Food and Agriculture Organization of the United Nations. http://www.fao.org/docrep/r4082e/r4082e00.htm#contents Hanson B., Grattan S. R., Fulton A., (1999), Agricultural Salinity and Drainage, Water Management Series Publication Number 3375, Division of Agriculture and Natural Resources. http://cati.csufresno.edu/cit/drainagemanual/content/glossary.pdf 110

Web sites http://www.istat.it/ambiente/ http://www.istat.it/agricoltura/ http://www.netafim.com/glossary#i http://www.irrigation.org/ http://www.scia.sinanet.apat.it/ http:/www.enterisi.it/ http://www.flow-aid.wur.nl/uk/ http://www.estsesia.it/ 111

Glossary Aspersion or sprinkler irrigation The water is led to the field through a pipe system in which the water is under pressure. The spraying is accomplished by using several rotating sprinkler heads or spray nozzles or a single gun type sprinkler. It simulates an artificial rainfall. Available Water Content (AWC) The amount of water stored in the soil at field capacity minus the water that will remain in the soil at wilting point. It measures the amount of water actually available to the plant. It depends greatly on the soil texture and structure. Basins or Flood irrigation A kind of surface irrigation. Basins are horizontal, flat plots of land, surrounded by small dykes or bunds. The banks prevent the water from flowing to the surrounding fields. Basin irrigation is commonly used for rice grown on flat lands or in terraces on hillsides. Trees can also be grown in basins, where one tree usually is located in the centre of a small basin. Border or superficial flowing water irrigation The field to be irrigated is divided into strips (also called borders or borderstrips) by parallel dykes or border ridges. The water is released from the field ditch onto the border through gate structures called outlets. The water can also be released by means of siphons or spiles. The sheet of flowing water moves down the slope of the border, guided by the border ridges. Crop coefficient (Kc) The ratio of the crop evapotranspiration (Etc) to the reference evapotranspiration (ETo), and its represents an integration of the effects of four primary characteristics that distinguish the crop from reference grass. These characteristics are: crop height, albedo (reflectance) of the crop-soil surface, canopy resistance and evaporation from soil, especially exposed soil. (FAO paper no.56 (Allen et al., 1998)) Crop Water Requirement (CWR) or crop water need The depth or volume of water needed to meet the maximum evapotranspiration rate of the crop when soil water is not limiting for a given planting area and period (excluding leaching fraction). Digital Elevation Model (DEM) A digital representation of a continuous variable over a two-dimensional surface by a regular array of z values referenced to a common datum. Digital elevation models are typically used to represent terrain relief. Depletion fraction (p) Average fraction of Total Available Soil Water (TAW) that can be depleted from the root zone before moisture stress (reduction in ET) occurs. The possible value belongs to the interval [0-1]; p is a function of the evaporation power of the atmosphere. Distribution Uniformity (DU) A measure (%) of how uniformly water is applied over a field, calculated as the minimum depth of applied water, divided by the average depth of applied water, multiplied by 100. Drip/Trikle/Micro-irrigation The water is led to the field through a pipe system. On the field, next to the row of plants or trees, a tube is installed. At regular intervals, near 113

the plants or trees, a hole is made in the tube and equipped with an emitter. The water is supplied slowly, drop by drop, to the plants through these emitters. Evapotranspiration or Crop Evapotranspiration The rate of water loss through transpiration from vegetation plus evaporation from the soil surface or from standing water on the soil surface, expressed as mm/day or m 3 /day. Field capacity Field capacity has been defined as the soil moisture state when, 48 hours after saturation or heavy rain, all downward movement of water has ceased. It is the water content retained at low suctions (5-33kPa) depending on soil type, and is the upper limit of plant available water. Furrows or lateral infiltration irrigation A kind of surface irrigation where water runs along narrow ditches dug on the field between the rows of crops as it moves down the slope of the field. Gross Irrigation Water Requirements (GIWR) The quantity of water to be applied in reality, taking into account water losses and other, i.e. leaving storage in the soil for anticipated rainfall, harvest, etc. Irrigable area The maximum area which could be irrigated in the reference year using the equipment and the quantity of water normally available on the holding. Irrigated area Area of crops which have actually been irrigated at least once during the 12 months prior to the survey date. Irrigation efficiency A measure of the portion of total applied irrigation water beneficially used - as for crop water needs, frost protection, salt leaching, and chemical application - over the course of a season. Generally it can be calculated as beneficially used water divided by total water applied, multiplied by 100. Irrigation system Physical components (pumps, pipelines, valves, nozzles, ditches, gates, siphon tubes, turnout structures) and management used to apply irrigation water by an irrigation method. All equipment required to convey water to or within the design area. Set of components which includes (may include) the water source, water distribution network, control components and possibly other irrigation equipment. Leaf Area Index (LAI) Index defined as the one sided green leaf area per unit ground area in broadleaf canopies, or as the projected needleleaf area per unit ground area in needle canopies. Leaching fraction The fraction of infiltrated irrigation water that percolates below the plant root zone. For this unit to be meaningful, it needs to specify the time over which the leaching fraction is measured and the depth interval over which it is calculated. Lithic contact The boundary between soil and a coherent underlying material. Cracks that can be penetrated by roots are few, and their horizontal spacing is 10 cm or more. The underlying material must be sufficiently coherent when moist to make hand-digging with a spade impractical, although the material may be chipped or scraped with a spade. The material below a lithic contact must be in a strongly cemented or more cemented rupture-resistance class. Commonly, the material is indurated. Net Irrigation Water Requirement (NIWR) Actual amount of applied irrigation water stored in the soil for plant use or moved through the soil for leaching salts. Also includes water applied for crop quality and temperature modification; i.e. frost control, cooling plant foliage and fruit. Application losses, such as evaporation, runoff, and 114

deep percolation, are not included. It is expressed in millimetres per year or in m 3 / ha per year (1 mm = 10 m 3 /ha). Paralithic contact The contact between soil and paralithic materials (defined below) where the paralithic materials have no cracks or the spacing of cracks that roots can enter is 10 cm or more. Paralithic materials Relatively unaltered materials that have an extremely weakly cemented to moderately cemented rupture-resistance class. Cementation, bulk density, and the organization are such that roots cannot enter, except in cracks. Paralithic materials have, at their upper boundary, a paralithic contact if they have no cracks or if the spacing of cracks that roots can enter is 10 cm or more. Commonly, these materials are partially weathered bedrock or weakly consolidated bedrock, such as sandstone, siltstone, or shale. Paralithic materials can be used to differentiate soil series if the materials are within the series control section. Pedotransfer Function (PTF) The term used in soil science literature, which can be defined as predictive functions of certain soil properties from other more available, easily, routinely, or cheaply measured properties. The most readily available data come from soil survey, such as field morphology, soil texture, structure and ph. Pedotransfer functions add value to this basic information by translating them into estimates of other more laborious and expensively determined soil properties. These functions fill the gap between the available soil data and the properties which are more useful or required for a particular model or quality assessment. Pedotransfer functions utilize various regression analysis and data mining techniques to extract rules associating basic soil properties with more difficult to measure properties. Probably because of the particular difficulty, cost of measurement, and availability of large databases, the most comprehensive research in developing PTFs has been for the estimation of water retention curve and hydraulic conductivity. Readily Available Water (RAW) The water (in mm) that a plant can easily extract from the soil. The soil moisture held between field capacity and a nominated refill point for unrestricted growth. In this range of soil moisture, plants are neither waterlogged or water-stressed. Plant roots will continue to take water from the soil after the refill point is reached, but this water is not as readily available and the crop finds it difficult to extract. If the soil dries to the permanent wilting point, the plant can no longer remove any water from it: some water may still be present but is completely unavailable. Readily Evaporable Water (REW) The maximum total depth of water that can be evaporated when moisture is transported to the soil surface at a rate sufficient to supply the potential rate of evaporation, which, in turn, is governed by energy availability at the soil surface. Reference crop evapotranspiration or reference evapotranspiration (ETo) The evapotranspiration rate from an hypothetical grass reference crop with specific characteristics, not short of water. The concept of the reference evapotranspiration was introduced to study the evaporative demand of the atmosphere independently of crop type, crop development and management practices. The only factors affecting ETo are climatic parameters. Consequently, ETo is a climatic parameter and can be computed from weather data. ETo expresses the evaporating power of the atmosphere at a specific location and time of the year and does not consider the crop characteristics and soil factors. 115

RICA or Italian FADN The Italian network information system for gathering annually accountancy data from farms for the determination of incomes and business analysis of agricultural holdings. The field observation survey does not coincide with the universe of farms, but includes only those which due to their size could be considered commercial. The methodology applied aims to provide representative data along three dimensions: region, economic size and type of farming. In Italy the FADN is based on a farm sample, structured to represent the different production types and sizes on the national territory. Soil depth Depth of soil profile from the top to parent material or bedrock or to the layer of obstacles for roots. It differs significantly for different soil types. It is one of basic criterions used in soil classification. Soils can be very shallow (less than 25 cm), shallow (25 cm-50 cm), moderately deep (50 cm-90 cm), deep (90cm-150 cm) and very deep (more than 150 cm). Subirrigation Application of irrigation water below the ground surface by raising the water table to within or near the root zone. Synoptic station A station at which meteorological observations are made for the purposes of synoptic analysis. The observations are made at the main synoptic times of 0000, 0600, 1200, 1800 UTC and normally at the intermediate synoptic hours of 0300, 0900, 1500, 2100 UTC and are entered into a coded format for dissemination. Transpiration Transpiration consists of the vaporization of liquid water contained in plant tissues and the vapour removal to the atmosphere. The vaporization occurs within the leaf, namely in the intercellular spaces, and the vapour exchange with the atmosphere is controlled by the stomatal aperture. Nearly all water taken up is lost by transpiration and only a tiny fraction is used within the plant. Total Available Water (TAW) The volume of water (in mm) in a soil that can be utilised by plant roots, its magnitude depends on the type of soil and the rooting depth. It is the amount of water released between in situ field capacity and the permanent wilting point. Total Evaporable Water (TEW) The maximum total depth of water that can be evaporated from the surface soil layer. Water retention curve The relationship between the water content (or soil moisture), θ, and the soil water potential (tendency of water to move from one area to another due to osmosis, gravity, mechanical pressure, or matrix effects such as surface tension), ψ. This curve is characteristic for different types of soil, and is also called the soil moisture characteristic. It is used to predict the soil water storage, water supply to the plants ( field capacity) and soil aggregate stability. Due to the hysteretic effect of water filling and draining the pores, different wetting and drying curves may be distinguished. Wilting point Soil moisture content when the rate of absorption of water by plant roots is too slow to maintain plant turgidity and permanent wilting occurs. The average moisture tension at the outside surface of the moisture film around soil particles when permanent wilting occurs is 15 atmospheres or 1500kPa. 116

Acronyms and abbreviations AM The Italian Air Force AP Autonomous Province APAT see ISPRA ARPA Regional Agency for Environmental Protection ARPAS Hydrometeoclimatic Department of the Regional Environmental Protection Agency of Sardinia AWC Available Water Content BDAN Banca Dati Agrometeorologica Nazionale CAP Common Agricultural Policy CISIS Centro Interregionale per i Sistemi informatici, geografici e statistici CLC CORINE Land Cover CNR National Research Council CQ Census Questionnaire CRA Agricultural Research Council CRA-ABP Research Centre for Agrobiology and Pedology CRA-CMA (ex CRA-UCEA) Central Office for Crop Ecology CSIC see IAS-CSIC CWR Crop Water Requirement DBMS Database Management System DOC Controlled Designation of Origin DOCG Controlled and Guaranteed Designation of Origin DU Distribution Uniformity EAP (EU) Environmental Action Plan EAP European Action Programs in the Field of the Environment EC European Commission EDP Electronic Data Processing EEA European Environment Agency ENAV Italian Company for Air Navigation Services EU European Union EUROSTAT Statistical Office of the European Union FADN Farm Accountancy Data Network FAO Food and Agriculture Organization of the United Nations FSS Farm Structure Surveys GIS Geographic Information Systems GIWR Gross Irrigation Water Requirements IAS-CSIC Instituto de Agricoltura Sostenibile Consejo Superior de Investigaciones Cientificas IGT Typical Geographical Indication ILRC Irrigation and Land Reclamation Consortium 117

IPCC ISPRA ISTAT JRC LAU MATTM MiPAAF MS NIWR NSSG NUTS OECD PDO PGI PQ PTF RAW REW RICA RIS RZWD RZWHC SAPM SCIA SIAN SIGRIAN SIMN SINA TAW TEW UAA UGM UTC WBS WFD WMO WP International (or Intergovernmental) Panel on Climatic Change (ex APAT) National Institute for the Protection and Environmental Research Italian National Statistics Institute Joint Research Centre Local Administrative Unit Ministry of the Environment, Land and Sea Ministry of Agricultural, Food and Forestry Policies Member States Net Irrigation Water Requirements National Statistic Service of Greece Nomenclature of Territorial Units for Statistics Organization for Economic Cooperation and Development Protection Designation of Origin Protected Geographical Identification Pilot Questionnaire Pedotransfer Function Readily Available Water Readily Evaporable Water (Italian FADN) Rete d Informazione Contabile Agricola Relative Irrigation Supply Root Zone Water Deficit Root Zone Water Holding Capacity Statistics on Agricultural Production Methods National System for the collection, elaboration and diffusion of climatological data of environmental interest National Agricultural Information System Sistema Informativo per la Gestione delle Risorse Idriche in Agricoltura National Service for Study of Waters and Seas National Information System for Environmental Monitoring Total Available Water Total Evaporable Water Utilised Agricultural Area or Agricultural Area (AA) General Office for Meteorology Universal Coordinated Time Work Breakdown Structure Water Framework Directive World Meteorological Organization Work Package 118

Annex 1 rule-based approach for the definition of the farm irrigated land use The following set of decision rules are implemented by the Module 1 to perform both the disaggregation of the irrigated surface of the crop groups into the corresponding single crops and the territorial distribution of the farm crops. General rule no. 1 If the farm is made up of several land parcels located in different municipalities the irrigated surface of each crop, computed by the application of the following rules for the disaggregation, must be distributed territorially. The territorial distribution is performed by allocating, in a proportional manner, the irrigated surface of each crop according to the corresponding crop groups surface reported in the CQ section Sezione IV - Ubicazione dei terreni e degli allevamenti aziendali. General rule no. 2 According to ISTAT, whenever different irrigation systems are used for each crop or crop group of the box no.22, the reported irrigation system is always that serving the largest cultivated surface. During the disaggregation procedure the irrigation system reported for the crop groups is assigned directly to all the relative single crops. Rule no. 1 The following crops are not aggregated therefore, they are reported directly with the relative irrigated surface and irrigation system to the farm irrigated land use: 22.4.b-Grain maize (Mais da granella); 22.4.e-Potato (Patata); 22.4.f -Sugar beet (Barbabietola da zucchero); 22.4.g-Rape and turnip rape (Colza e ravizzone) 22.4.h-Sunflower (Girasole); 22.4.m-Green maize (Mais verde); 22.4.p-Permanent grassland and pastures (Prati permanenti e pascoli); 22.4.u-Other permanent crops (Altre coltivazioni legnose agrarie). In addition the following consideration have been done: the irrigated surface reported in 22.4.m-Green maize is the sum of the irrigated surface of 8.10.b.47-Corn grass (Mais in erba) and 8.10.b.48-Corn for silage (Mais a maturazione cerosa) since the crops can be considered equivalent therefore, the disaggregation procedure is not required. the irrigated surface reported in 22.4.p-Permanent grassland and pastures is the sum of the irrigated surface of 11.1.86-Permanent grassland (Prati permanenti), 119

11.2.a.87-Pasture and meadow (Pascoli naturali) and 11.2.b.88-Rough grazings (Pascoli magri), since the crops can be considered equivalent the disaggregation procedure is not required. the irrigated surface reported in 22.4.u-Other permanent crops can be different from that of 9.6.82-Other permanent crops, since the latter includes other tree crops, however the analysis of the Other permanent crops definition indicates that the other tree crops can be considered generally not irrigated, therefore the disaggregation procedure is not required. Rule no. 2 The irrigated surface in 22.4.a-Cereals for the production of grain (Cereali per la produzione di granella) is the sum of the irrigated surface of 8.1.a-Common wheat and spelt (Frumento tenero o spelta), 8.1.b-Durum wheat (Frumento duro), 8.1.c-Rye (Segale), 8.1.d- Barley (Orzo), 8.1.e-Oat (Avena), 8.1.h-Sorghum (Sorgo), 8.1.i (Altri cereali). Among these, only Sorghum has the highest chance to be irrigated in Italy, therefore the irrigated surface in 22.4.a is attributed wholly to the latter up to the saturation of the surface reported in 8.1.h, the residual share is slit proportionally among the other mentioned crops. Rule no. 3 The irrigated surface in 22.4.c-Rice (Riso) is not treated by the disaggregation procedure since the irrigation water consumption estimation is carried out by using the methodology described in paragraph 2.5. Rule no. 4 The irrigated surface in 22.4.d-Dried pulses (Legumi secchi) is split proportionally among 8.2.a-Peas (Pisello), 8.2.b-Field beans (Fagiolo secco), 8.2.c (Fava), 8.2.d-Sweet lupins (Lupino dolce) and 8.2.e-Other dried pulses (Altri legumi secchi). Rule no. 5 The irrigated surface in 22.4.i-Fibre crops (Piante tessili) is split proportionally among 8.6.c.20-Cotton (Cotone), 8.6.c.21-Flax (Lino), 8.6.c.22-Hemp (Canapa) and 8.6.c.23-Other fibre crops (Altre piante tessili). Rule no. 6 The irrigated surface in 22.4.l-Fresh vegetables in outdoor (Ortive in piena aria) is the sum of the irrigated surface of 8.7.a.31-Tomato for table in open field (Pomodoro da mensa in coltivazioni di pieno campo), 8.7.a.32-Plum tomato in open field (Pomodoro da industria in coltivazioni da pieno campo), 8.7.a.33-Other fresh vegetables in open field (Altre ortive in coltivazioni da pieno campo), 8.7.b.34-Table tomato in market gardening (Pomodoro da mensa in orti stabili ed industriali) and 8.7.b.35-Other fresh vegetables in market gardening (Alre ortive in orti stabili ed industriali). The disaggregation procedure for the crop group is based on the following steps. The irrigated surface in 22.4.l is split proportionally among two subgroups made up of crops considered equivalent: Tomato (8.7.a.31, 8.7.a.32 and 8.7.b.34) and Other horticultural crops (8.7.a.33 and 8.7.b.35). 120

The surface allocated to the subgroup Other horticultural crops is split proportionally among a set of fresh vegetables in outdoor made up of crops with the lager diffusion in Italy. The proportional splitting is performed by taking into account the fresh vegetable surfaces reported in the ISTAT Crop Statistics produced annually at provincial level (NUTS3), the province is selected according to the farm centre location. Rule no. 7 The irrigated surface reported in 22.4.n-Other green fodder (Altre foraggere avvicendate) is the sum of the irrigated surface of 8.10.a.45-Alfalfa (Erba medica), 8.10.a.46-Other grassland (Altri prati avvicendati), 8.10.b.49-Other cereals grass (Altri erbai monofiti di cereali) and 8.10.b.50-Other grass (Altri erbai), therefore it is split proportionally among these crops. The crop characteristics of Alfalfa are considered equivalent to Other grassland as well as those of Other cereals grass and Other grass. Rule no. 9 The irrigated surface reported in 22.4.o-Other arable land crops (Altri seminativi) is the sum of the irrigated surface of the following crops: 8.5-Fodder roots and brassicas (Piante sarchiate da foraggio); 8.6.a.18-Tobacco (Tabacco); 8.6.a.19-Hops (Luppolo); 8.6.d.26-Soybean (Soia); 8.6.d.27-Linseed (Semi di lino); 8.6.d.28-Other oil seed crops (Altre piante di semi oleosi); 8.6.e.29-Aromatic plants, medicinal and culinary plants (Piante aromatiche, medicinali, spezie e da condimento); 8.6.f.30-Other industrial crops (Altre piante industriali); 8.8.a.39-Flowers and ornamental plants in open fields (Fiori e piante ornamentali in piena aria); 8.11-Seeds (Sementi); 8.12.a.52-Fallow land without any subsidies (Terreni a riposo non soggetti a regime di aiuto) 8.12.a.53-Fallow land subject to the payment of subsidies, with no economic use (Terreni a riposo soggetti a regime di aiuto) The irrigated surface of Other arable land crops is split proportionally, up to the saturation of the cultivated surface, among the crops Tobacco, Soybean, Flowers and ornamental plants in open fields, the only considered irrigated in Italy. The residual surface is split proportionally among Fodder roots and brassicas, Hops, Linseed, Other oilseeds crops, Aromatic plants, medicinal and culinary plants and Other industrial crops. Rule no. 10 The irrigated surface in 22.4.q-Vineyards (Vite) is the sum of the surfaces reported in 9.1-Vineyards: 21.1.1999-Quality wine (Uva per la produzione di vini a denominazione di origine controllata (DOC) e controllata garantita (DOCG)); 121

21.2.2999-Other wines (Uva per la produzione di altri vini); 21.3.3999-Table grapes (Uva da tavola); 21.4.4001-Ungrafted wines (Viti non innestate). It is assumed that irrigation is a priority for some categories in the following order (the surface of new plantings of a given crop is the difference between the total crop surface and the crop surface in production): 1. Ungrafted wines; 2. New plantings of Quality wine, Other wines and Table grapes; 3. Surface in production of Quality wine, Other wines and Table grapes. Therefore, the procedure to create the irrigated land use for Vineyards is the following: 1. The irrigated surface in 22.4.q is allocated to Ungrafted wines; 2. The residual surface is split proportionally among new plantings of Quality wine, Other wines and Table grapes; 3. The residual is split proportionally among Quality wine, Other wines and Table grapes in production. Rule no. 11 The irrigated surface in 22.4.r-Olive plantations (Olivo) is the sum of the irrigated surface of 9.2.56-Table olives (Olive da tavola) and 9.2.57-Olives for oil production (Olive per olio). In general, it is assumed that for tree crops the irrigation is applied with priority to new plantings and later to the crops in production, therefore the following procedure is defined for distributing the irrigated surface: 1. the irrigated surface is split proportionally between new plantings of Olives for oil production and Table olives; 2. the residual surface is split proportionally between Table olives and Olives for oil production in production. Rule no. 12 The irrigated surface in 22.4.s-Citrus plantations (Agrumi) is split proportionally among 9.3.a-Orange tree (Arancio), 9.3.b-Mandarin tree (Mandarino), 9.3.c-Clementine tree (Clementina), 9.3.d-Lemon tree (Limone) and 9.3.e-Other citrus plantations (Altri agrumi). Rule no. 13 The irrigated surface in 22.4.t Fruit and berry plantations (Fruttiferi) is the sum of the irrigated surface of the crops reported in the groups 9.4.a- Fruit of temperate climate zones (Frutta fresca di origine temperata), 9.4.b- Fruit of subtropical climate zones (Frutta fresca di origine sub-tropicale) and 9.4.c-Nuts (Frutta a guscio). It is assumed that irrigation is applied according to the following priorities: 1. The irrigated surface is allocated proportionally to new planting Fruit of subtropical climate zones and Fruit of subtropical climate zones; 2. The residual is split proportionally between Fruit of temperate climate zones and Fruit of subtropical climate zones in production; 122

3. The residual is assigned to new plantings of Nuts; 4. The residual is assigned to Nuts in production. Rule no. 14 The irrigated surface in 22.4.v-Short rotation coppice (Arboricoltura da legno) is split proportionally between 13.1-Poplar (Pioppeti) and 13.2-Other trees for wood (Altra arboricoltura da legno). Rule no. 15 Although the crops under protective cover (i.e. low (not-accessible) cover, under glass or other (accessible) cover, such as greenhouses or fixed or mobile high cover (glass or rigid or flexible plastic)) are not reported in the box 22.4, they are generally always irrigated in Italy. The total irrigated surface of the crops under protective cover is the sum of the following crops surfaces: 8.7.a.36-Table tomato under glass (Pomodoro da mensa in serra), 8.7.a.37-Other fresh vegetables under glass (Altre ortive in serra), 8.7.a.38-Fresh vegetables under low (not-accessible) protective cover (Ortive protette in tunnel, campane, ecc.); 8.8.b.40-Flowers and ornamental plants under glass (Fiori e piante ornamentali protetti in serra), 8.8.b.41-Flowers and ornamental plants under low (not-accessible) protective cover (Fiori e piante ornamentali protetti in tunnel, campane, ecc.); 9.7-Permanent crops under glass (Coltivazioni legnose agrarie in serra). A dedicated routine has been implemented for the estimation of the water consumption (see paragraph 2.6). 123

Annex 2 6 th general agricultural census questionnaire (in italian language)

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 9:59 Pagina 1 Sistema statistico nazionale Istituto nazionale di statistica 6 Censimento generale dell agricoltura 24 OTTOBRE 2010 Numero identificativo Istat (art. 17 del decreto legge 25 settembre 2009, n. 135, convertito con modificazioni dalla legge 20 novembre 2009, n. 166) QUESTIONARIO DI AZIENDA AGRICOLA A NOTIZIE ANAGRAFICHE, RESIDENZA O SEDE LEGALE DEL CONDUTTORE Nel caso di notizie diverse da quelle prestampate o di aziende da intervistare non presenti nella lista, riportare nei riquadri verdi sottostanti le notizie nuove, le variazioni o le integrazioni. Cognome e nome della persona fisica o denominazione della società o ente che conduce l azienda Codice Unico di Azienda Agricola (CUAA) o Codice fiscale della persona fisica o della società o ente che conduce l azienda Indirizzo (Via/Piazza/Località e numero civico) C.A.P. Denominazione Comune Codice Istat Denominazione Provincia Codice Istat Numero di telefono 1 Numero di telefono 2 E-mail Indirizzo sito web Mod. Istat CEAGR 127

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 9:59 Pagina 2 B ESITO DELLA RILEVAZIONE B.1 AZIENDA RILEVATA 1 (compilare sempre il presente questionario) B.2 AZIENDA IN LISTA NON RILEVATA (compilare solo il riquadro in bianco a pagina 14 del questionario) a. Irreperibilità del conduttore 2 b. Rifiuto 3 c. Altra motivazione 4 (specificare ) caso g: compilare il riquadro D indicando le notizie dell azienda/e che ha/hanno acquisito i terreni o gli allevamenti caso h: compilare il riquadro D indicando le notizie dell azienda già in lista o già intervistata B.3 AZIENDA IN LISTA NON ESISTENTE O DOPPIONE (compilare solo il riquadro in bianco a pagina 14 del questionario; per i casi g ed h, riempire anche il riquadro D) d. Terreni destinati a soli orti familiari 5 o allevamenti per autoconsumo o aziende esclusivamente forestali e. Soggetto che non ha mai esercitato 6 attività agricola f. Terreni agricoli definitivamente abbandonati 7 o destinati ad altro uso o aziende esclusivamente zootecniche che hanno totalmente dismesso l attività senza cessione ad altri g. Azienda agricola interamente affittata, 8 ceduta, assorbita, fusa o smembrata h. Unità da ricondurre ad azienda esistente (doppione) 9 C CESSIONI PARZIALI (in caso di di risposta al al quesito B.1) L azienda ha ceduto parzialmente terreni agricoli o allevamenti ad altra/e azienda/e nell annata agraria 2009/2010? SI 1 NO 2 In caso di risposta SI compilare il riquadro D indicando le notizie dell azienda/e che ha/hanno acquisito parzialmente i terreni o gli allevamenti D UNITÀ COLLEGATE ALLE AZIENDE IN LISTA (da (da compilare compilare per per i casi casi B.3g, B.3g, B.3h B.3h e per per risposta risposta SI SI al al riquadro riquadro C) C) Cognome e nome della persona fisica Indirizzo, Comune e Provincia CUAA o Codice fiscale della persona o denominazione Cognome e nome della della società persona o ente fisica Indirizzo, Comune e Provincia CUAA o fisica Codice o della fiscale società della persona o denominazione che conduce l azienda della società o ente o ente fisica che o conduce della società l azienda che conduce l azienda o ente che conduce l azienda E UBICAZIONE DEL CENTRO AZIENDALE Questo riquadro deve essere compilato solo se l ubicazione del centro aziendale è diversa dalla residenza o dalla sede legale del conduttore Per centro aziendale si intende il complesso dei fabbricati connessi all attività aziendale situato entro il perimetro dei terreni aziendali oppure, in assenza di fabbricati, il luogo che identifica la maggior parte della superficie aziendale Indirizzo (Via/Piazza/Località e numero civico del centro aziendale) C.A.P. Denominazione Comune Codice Istat Denominazione Provincia Codice Istat Telefono fisso (prefisso e n.) Per tutti i Comuni esclusi quelli di Trento e Bolzano e quelli elencati nell appendice B del libretto d istruzioni Per i Comuni con catasto tavolare elencati nell appendice B del libretto d istruzioni a a a Sez. censuaria Foglio di mappa catastale Sez. censuaria Particella catastale Tipo / Per i Comuni delle province di Trento e Bolzano Comune catastale Particella catastale Tipo / a Per i Comuni con catasto a foglio aperto elencati nell appendice B del libretto d istruzioni a Sez. censuaria a Foglio e Particella catastale Il centro aziendale è localizzato a meno di 5 km dalla residenza o sede legale del conduttore? 1 SI 2 NO 2 128

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 9:59 Pagina 3 sezione I Notizie generali sull azienda 1 FORMA GIURIDICA (è ammessa una sola risposta) 1.1 Azienda individuale 01 1.2 Società semplice 02 1.3 Altra società di persone (S.n.c., S.a.s., ecc.) 03 1.4 Società di capitali (S.p.a., S.r.l., ecc.) 04 1.5 Società cooperativa 05 1.6 Amministrazione o Ente pubblico 06 (Stato, Regioni, Province, Comuni, ecc.) 1.7 Ente (Comunanze, Università, Regole, ecc.) 07 o Comune che gestisce proprietà collettive 1.8 Ente privato senza fini di lucro 08 1.9 Altra forma giuridica 09 (specificare......) 2 3 CORPI AZIENDALI DI TERRENO 3.1 Corpi che costituiscono l azienda n. 5 SISTEMA DI CONDUZIONE 2.1 Forma di conduzione (è ammessa una sola risposta) a. Conduzione diretta del coltivatore 01 b. Conduzione con salariati (in economia) 02 c. Altra forma di conduzione 03 (specificare......) 2.2 Titolo di possesso dei terreni a. Proprietà, usufrutto, ecc. b. Affitto c. Uso gratuito 2.3 TOTALE I TOTALI della Superficie Totale e della SAU devono essere uguali ai corrispondenti dati riportati ai punti 17 e 12, pagina 5 ELEMENTI DEL PAESAGGIO AGRARIO Indicare la presenza di elementi lineari del paesaggio Cod. SUPERFICIE SUPERFICIE AGRICOLA TOTALE UTILIZZATA (SAU) Ettari Are Ettari Are Sottoposti a manutenzione durante gli ultimi tre anni c 4 STATO DI ATTIVITÀ DELL AZIENDA 4.1 Nell annata agraria 2009/2010 l unità agricola è stata: a) Attiva 1 b) Temporaneamente inattiva 2 (compilare solo il riquadro in bianco a pagina 14 del questionario) Di nuova realizzazione negli ultimi tre anni 5.1 Siepi 01 1 2 5.2 Filari di alberi 02 1 2 5.3 Muretti 03 1 2 6 INFORMATIZZAZIONE DELL AZIENDA 6.1 L azienda dispone di computer e/o altre attrezzature informatiche 1 SI 2 NO per fini aziendali? Se SI rispondere al punto 6.1.1 e successivi, se NO passare al punto 6.2 e successivi 6.1.1L azienda usa normalmente proprie attrezzature informatiche per: a. Servizi amministrativi (contabilità, paghe, ecc.) 1 SI 2 NO b. Gestione informatizzata di coltivazioni 1 SI 2 NO c. Gestione informatizzata degli allevamenti 1 SI 2 NO 6.2 L azienda utilizza normalmente la rete Internet per le proprie attività? 1 SI 2 NO 6.3 L azienda ha un sito web oppure una o più pagine su Internet? 1 SI 2 NO 6.4 L azienda fa commercio elettronico per: a. La vendita di prodotti e servizi aziendali 1 SI 2 NO b. L acquisto di prodotti e servizi 1 SI 2 NO 7 SOSTEGNO ALLO SVILUPPO RURALE 7.1 Indicare se l azienda ha beneficiato di una o più delle seguenti misure nel corso del 2008-2009-2010 a. Insediamento di giovani agricoltori (misura 112) 01 b. Utilizzo di servizi di consulenza (misura 114) 02 c. Ammodernamento 03 delle aziende agricole (misura 121) d. Accrescimento del valore aggiunto 04 dei prodotti agricoli e forestali (misura 123) e. Cooperazione per lo sviluppo 05 di nuovi prodotti, processi e tecnologie nel settore agricolo e alimentare e in quello forestale (misura 124) f. Rispetto delle norme basate sulla 06 legislazione comunitaria (misura 131) g. Partecipazioni degli agricoltori ai sistemi 07 di qualità alimentare (misura 132) h. Indennità a favore degli agricoltori 08 delle zone montane (misura 211) i. Indennità a favore degli agricoltori 09 delle zone caratterizzate da svantaggi naturali diverse da zone montane (misura 212) l. Indennità Natura 2000 (misura 213) 10 m.indennità connesse alla Direttiva Quadro 11 2000/60/CE sulle acque (misura 213) n. Pagamenti agro-ambientali (misura 214) 12 di cui nel quadro dell agricoltura biologica 13 di cui nel quadro dell agricoltura integrata 14 o. Pagamenti per il benessere degli animali 15 (misura 215) p. Sostegno agli investimenti non produttivi 16 (misura 216) q. Diversificazione in attività non agricole 17 (misura 311) r. Incentivazione di attività turistiche (misura 313) 18 3 129

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 10:00 Pagina 4 sezione II Informazioni per aziende con terreni A questa sezione (pagine 4, 5, 6 e 7) devono rispondere le aziende con terreni NOTA: Le aziende esclusivamente zootecniche che abbiano ricoveri per animali devono comunque indicare le superfici relative a questi fabbricati a pagina 5, al punto 16 Altra superficie Utilizzazione dei terreni (annata agraria 2009-2010) 8 SEMINATIVI 8.1 Cereali per la produzione di granella (1) Cod. a. Frumento tenero e spelta 01 b. Frumento duro 02 c. Segale 03 d. Orzo 04 e. Avena 05 f. Mais (escluso mais in erba e a maturazione cerosa da indicare al punto 8.10b) 06 g. Riso 07 h. Sorgo 08 i. Altri cereali 09 8.2 Legumi secchi (1) a. Pisello (proteico e secco) 10 b. Fagiolo secco 11 c. Fava 12 d. Lupino dolce 13 e. Altri legumi secchi 14 8.3 Patata (1) 15 8.4 Barbabietola da zucchero 16 8.5 Piante sarchiate da foraggio 17 8.6 Piante industriali a. Tabacco 18 b. Luppolo 19 c. Piante tessili - Cotone 20 - Lino 21 - Canapa 22 - Altre piante tessili 23 d. Piante da semi oleosi (1) - Colza e ravizzone 24 - Girasole 25 - Soia 26 - Semi di lino 27 - Altre piante di semi oleosi 28 e. Piante aromatiche, medicinali, spezie e da condimento 29 f. Altre piante industriali 30 SUPERFICIE COLTIVAZIONE PRINCIPALE Ettari Are segue SEMINATIVI 8.7 Ortive In piena aria Cod. a. In coltivazioni di pieno campo - Pomodoro da mensa 31 - Pomodoro da industria 32 - Altre ortive 33 b. In orti stabili ed industriali - Pomodoro da mensa 34 - Altre ortive 35 Protette a. In serra - Pomodoro da mensa 36 - Altre ortive 37 b. In tunnel, campane, ecc. 38 8.8 Fiori e piante ornamentali a. In piena aria 39 b. Protetti - In serra 40 - In tunnel, campane, ecc. 41 8.9 Piantine a. Orticole 42 b. Floricole ed ornamentali 43 c. Altre piantine 44 8.10 Foraggere avvicendate (1) a. Prati avvicendati - Erba medica 45 - Altri prati avvicendati 46 b. Erbai - Mais in erba 47 - Mais a maturazione cerosa 48 - Altri erbai monofiti di cereali 49 - Altri erbai 50 8.11 Sementi 51 8.12 Terreni a riposo a. Non soggetti a regime di aiuto 52 b. Soggetti a regime di aiuto (buone condizioni agronomiche e ambientali) 53 8.13 TOTALE SEMINATIVI 54 SUPERFICIE COLTIVAZIONE PRINCIPALE Ettari Are (1) Comprese le superfici destinate alle produzioni di sementi 4 130

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 10:00 Pagina 5 sezione II segue Utilizzazione dei terreni (annata agraria 2009-2010) 9 COLTIVAZIONI LEGNOSE AGRARIE Cod. 9.1 Vite (2) 55 9.2 Olivo per la produzione di a. Olive da tavola 56 b. Olive per olio 57 9.3 Agrumi a. Arancio 58 b. Mandarino 59 c. Clementina e suoi ibridi 60 d. Limone 61 e. Altri agrumi 62 9.4 Fruttiferi 9.5 Vivai a. Frutta fresca di origine temperata - Melo 63 - Pero 64 - Pesco 65 - Nettarina (pesca noce) 66 - Albicocco 67 - Ciliegio 68 - Susino 69 - Fico 70 - Altra frutta 71 b. Frutta fresca di origine sub-tropicale - Actinidia (kiwi) 72 - Altra frutta 73 c. Frutta a guscio - Mandorlo 74 - Nocciolo 75 - Castagno 76 - Noce 77 - Altra frutta 78 Totale SUPERFICIE Di cui in produzione Ettari Are Ettari Are a. Fruttiferi 79 XXX X b. Piante ornamentali 80 XXX X c. Altri 81 XXX X 9.6 Altre coltivazioni legnose agrarie (compresi gli alberi di Natale) 9.7 Coltivazioni legnose agrarie in serra 9.8 TOTALE COLTIVAZIONI LEGNOSE AGRARIE 82 83 84 Gli ORTI FAMILIARI sono piccole superfici utilizzate prevalentemente per la coltivazione di ortaggi e piante arboree (vite, olivo, fruttiferi) sparse, anche in consociazione tra loro, la cui produzione è destinata esclusivamente al consumo del conduttore e della sua famiglia (autoconsumo) 10 11 ORTI FAMILIARI per autoconsumo FUNGHI (coltivati in grotte, sotterranei o in appositi edifici) Cod. PRATI PERMANENTI E PASCOLI 11.1 Prati permanenti (utilizzati) 86 11.2 Pascoli (utilizzati) a. Pascoli naturali 87 b. Pascoli magri 88 11.3 TOTALE PRATI PERMANENTI E 89 PASCOLI UTILIZZATI 11.4 PRATI PERMANENTI E PASCOLI NON PIÙ DESTINATI ALLA PRO- 90 DUZIONE, AMMESSI A BENEFI- CIARE DI AIUTI FINANZIARI SUPERFICIE AGRICOLA UTILIZZATA (SAU) 91 Somma dei punti 8.13, 9.8, 10, 11.3 e 11.4 12 13 85 ARBORICOLTURA DA LEGNO 13.1 Pioppeti 92 13.2 Altra arboricoltura da legno 93 13.3 TOTALE ARBORICOLTURA DA LEGNO 94 14 BOSCHI 14.1 Boschi a fustaia 95 14.2 Boschi cedui 96 14.3 Altra superficie boscata 97 14.4 TOTALE BOSCHI 98 SUPERFICIE AGRARIA NON UTILIZZATA 99 Esclusi i terreni a riposo indicati al punto 8.12 ALTRA SUPERFICIE Aree occupate da fabbricati, cortili, 100 strade poderali, stalle, superfici a funghi, ecc. SUPERFICIE TOTALE DELL AZIENDA 101 Somma dei punti 12, 13.3, 14.4, 15 e 16 15 16 17 18 19 SERRE SUPERFICIE Ettari Are Cod. SUPERFICIE INVESTITA (m 2 ) 102 ha Cod. SUPERFICIE DI BASE (m 2 ) 103 ha (2) La superficie totale deve coincidere con quella indicata al punto 21.5 di pagina 6 20 COLTIVAZIONI ENERGETICHE (colture utilizzate per la produzione di energia) Cod. 20.1 Soggette a contratto di coltivazione 104 SUPERFICIE Ettari Are 5 131

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 10:00 Pagina 6 sezione II Notizie particolari sulla vite 21 NATURA DELLA PRODUZIONE 21.1 Uva per la produzione di vini a denominazione di origine controllata (vini DOC) e controllata e garantita (vini DOCG) VITIGNI (denominazione) Cod. 1 1 1 1 1 1 1 1 1 1 1 1 1 TOTALE. 1999 21.2 Uva per la produzione di altri vini VITIGNI (denominazione) 2 2 2 2 2 2 2 2 2 2 2 2 2 TOTALE. 2999 SUPERFICIE TOTALE A VITE Posteriore ad agosto 2007 SUPERFICIE INVESTITA A VITE SECONDO L ANNO DI IMPIANTO Da settembre 2004 ad agosto 2007 Da settembre 2000 ad agosto 2004 Da settembre 1990 ad agosto 2000 Da settembre 1980 ad agosto 1990 Anteriore al settembre 1980 Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are 21.3 Uva da tavola 3999 21.4 Viti non innestate 4001 21.5 TOTALE PARZIALE (1) (somma dei dati ai punti 21.1, 4002 21.2, 21.3 e 21.4) 21.6 Viti madri da portinnesto 4003 21.7 Barbatelle 4004 21.8 TOTALE SUPERFICIE A VITE (somma dei dati ai punti 21.5, 4999 21.6 e 21.7) 21.9 TOTALE UVA DA VINO RACCOLTA 21.9.1 Per la produzione di vini DOC e DOCG Cod. 5001 21.9.2 Per la produzione di altri vini 5002 QUINTALI (1) Deve coincidere con la superficie totale del punto 9.1 di pagina 5. 6 132

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 10:00 Pagina 7 sezione II Metodi di produzione agricola (annata agraria 2009-2010) 22 IRRIGAZIONE (esclusa l irrigazione di soccorso) 23 22.1 Superficie irrigabile 22.2 Superficie effettivamente irrigata 02 22.3 Superficie media irrigata nelle ultime 3 annate agrarie 22.4 Coltivazioni irrigate almeno una volta nell annata Cod. agraria 2009-2010 a. Cereali per la produzione di granella 01 (escluso mais e riso) b. Mais da granella 02 c. Riso 03 d. Legumi secchi 04 e. Patata 05 f. Barbabietola da zucchero 06 g. Colza e ravizzone 07 h. Girasole 08 i. Piante tessili 09 l. Ortive in piena aria 10 m. Mais verde (in erba ed a maturazione cerosa) 11 n. Altre foraggere avvicendate 12 o. Altri seminativi (tabacco, fiori, ecc.) 13 p. Prati permanenti e pascoli 14 q. Vite 15 r. Olivo 16 s. Agrumi 17 t. Fruttiferi 18 u. Altre coltivazioni legno se agrarie 19 v. Arboricoltura da legno 20 22.5 TOTALE SUPERFICIE IRRIGATA (deve corrispondere al punto 22.2) Cod. Ettari Are 01 03 SUPERFICIE Codice IRRIGATA Sistema di Ettari Are irrigazione (1) 21 XXXXX (1) Indicare il codice del sistema di irrigazione unico o prevalente. 1 Scorrimento superficiale ed infiltrazione laterale 2 Sommersione 3 Aspersione (a pioggia) 4 Microirrigazione 5 Altro sistema 22.6 Fonte di approvvigionamento dell acqua irrigua (è ammessa una sola risposta) - Acque sotterranee all interno o nelle vicinanze dell azienda 01 - Acque superficiali all interno dell azienda (bacini naturali ed artificiali) 02 - Acque superficiali al di fuori dell azienda (laghi, fiumi o corsi d acqua) 03 Acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo - con consegna a turno 04 - con consegna a domanda 05 - Altra fonte 06 22.7 Barrare la casella se l azienda utilizza servizi di consulenza irrigua e/o sistemi di determinazione del fabbisogno irriguo 01 AGRICOLTURA BIOLOGICA E PRODUZIONI DI QUALITÀ DOP E IGP Coltivazioni (Annata agraria 2009-2010) SUPERFICIE BIOLOGICA: Superficie agricola utilizzata in cui si applicano metodi di produzione biologica certificati o in fase di conversione secondo le norme comunitarie o nazionali SUPERFICIE DOP E IGP: Superficie principale o secondaria per la quale l azienda è controllata e certificata dal competente organismo di controllo 23.1 Coltivazioni Cod. a. Cereali 01 b. Legumi secchi 02 c. Patata 03 SUPERFICIE BIOLOGICA SUPERFICIE DOP E IGP Ettari Are Ettari Are d. Barbabietola da zucchero 04 XXX XX e. Piante da semi oleosi 05 XXX XX f. Ortive 06 g. Foraggere avvicendate 07 XXX XX h. Prati permanenti e Pascoli (esclusi pascoli magri) 08 XXX XX i. Vite 09 XXX XX l. Olivo 10 m. Agrumi 11 n. Fruttiferi 12 o. Altre coltivazioni (tabacco, fiori, piante 13 aromatiche, ecc) 23.2 TOTALE 14 di cui Superficie agricola utilizzata in fase di conversione al biologico 15 XXX XX 24 LAVORAZIONE DEL TERRENO Indicare le lavorazioni effettuate sui SEMINATIVI Cod. SUPERFICIE Ettari Are 24.1 Lavorazione convenzionale 01 (aratura) 24.2 Lavorazione di conservazione 02 (a strisce, verticale, a porche permanenti) 24.3 Nessuna lavorazione 03 La somma dei codici 01, 02 e 03 deve essere minore o uguale a quanto riportato al punto 8.13 di pagina 4 25 CONSERVAZIONE DEL SUOLO 25.1 Copertura invernale del suolo a SEMINATIVI Cod. a. Colture invernali (ad esempio frumento autunno-vernino) 01 b. Colture di copertura o intermedie 02 c. Residui colturali (ad esempio stoppie, paglia, pacciame) 03 d. Nessuna copertura 04 25.2 Avvicendamento dei SEMINATIVI a. Monosuccessione 05 b. Avvicendamento libero 06 c. Piano di rotazione 07 SUPERFICIE Ettari Are La somma dei codici da 01 a 04 e dei codici da 05 a 07 deve essere minore o uguale a quanto riportato al punto 8.13 di pag. 4 25.3 Inerbimento controllato delle superfici a COLTIVAZIONI 08 LEGNOSE AGRARIE 7 133

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:36 Pagina 8 sezione III Informazioni per aziende con allevamenti A questa sezione (pagine 8 e 9) devono rispondere solo le aziende con allevamenti o quelle con terreni che applicano effluenti di origine animale (punto 42 a pagina 9) Le aziende che siano temporaneamente prive di animali alla data del 24 ottobre 2010 o che abbiano cessato completamente la propria attività zootecnica prima del 24 ottobre 2010 devono comunque compilare i punti 39, 40, 41 e 42 di pagina 9 Consistenza degli allevamenti al 24 ottobre 2010 26 BOVINI 26.1 Di età inferiore a 1 anno Cod. a. Maschi 01 b. Femmine 02 26.2 Da 1 anno a meno di 2 anni a. Maschi 03 b. Femmine 04 26.3 Di 2 anni e più a. Maschi 05 b. Femmine - Giovenche (manze) da allevamento 06 - Giovenche (manze) da macello 07 - Vacche da latte 08 - Altre vacche (da carne o da lavoro) 09 26.4 TOTALE BOVINI 10 27 BUFALINI Cod. 27.1 Annutoli (vitelli bufalini) 11 27.2 Bufale 12 27.3 Altri bufalini 13 27.4 TOTALE BUFALINI 14 28 EQUINI Cod. 28.1 Cavalli 15 28.2 Altri equini (asini, muli, bardotti, ecc.) 16 28.3 TOTALE EQUINI 17 SE L AZIENDA POSSIEDE ALLEVAMENTI DIVERSI DA BOVINI, BUFALINI O EQUINI INDICARE L azienda possiede allevamenti 29 per autoconsumo? 30 destinati alla vendita? L azienda possiede allevamenti 1 SI 1 SI CAPI se SI indicare i soli capi destinati alla vendita ai punti da 31 a 37 se NO passare al punto 38 Cod. 31 OVINI 31.1 Pecore a. Da latte 18 b. Altre 19 31.2 Altri ovini 20 31.3 TOTALE OVINI 21 32 CAPRINI Cod. 32.1 Capre 22 32.2 Altri caprini 23 32.3 TOTALE CAPRINI 24 CAPI CAPI 2 NO 2 NO CAPI CAPI 33 SUINI Cod. 33.1 Di peso inferiore a 20 kg 25 33.2 Da 20 kg a meno di 50 kg 26 33.3 Da ingrasso di 50 kg e più a. Da 50 kg a meno di 80 kg 27 b. Da 80 kg a meno di 110 kg 28 c. Da 110 kg e più 29 33.4 Da riproduzione di 50 kg e più a. Verri 30 b. Scrofe montate 31 c. Altre scrofe 32 33.5 TOTALE SUINI 33 34 AVICOLI Cod. 34.1 Polli da carne 34 34.2 Galline da uova 35 34.3 Tacchini 36 34.4 Faraone 37 34.5 Oche 38 34.6 Altri allevamenti avicoli 39 34.7 TOTALE AVICOLI 40 35 CONIGLI Cod. 35.1 Fattrici 41 35.2 Altri conigli 42 35.3 TOTALE CONIGLI 43 36 STRUZZI Cod. 36.1 TOTALE STRUZZI 44 37 ALTRI ALLEVAMENTI 37.1 Api 45 37.2 Altri allevamenti 46 BIOLOGICI Capi DOP e IGP Capi Cod. CAPI CAPI CAPI CAPI NUMERO ALVEARI XXX AGRICOLTURA BIOLOGICA E PRODUZIONI 38 DI QUALITÀ DOP E IGP - ALLEVAMENTI 38.1 Allevamenti Cod. a. Bovini 01 b. Bufalini 02 c. Equini 03 XXX d. Ovini 04 e. Caprini 05 f. Suini 06 g. Avicoli 07 h. Conigli 08 XXX i. Api 09 10 l. Altri allevamenti (incl. Struzzi) 11 ALLEVAMENTI BIOLOGICI: Capi di bestiame allevati con metodi di produzione biologica e certificati secondo le norme comunitarie o nazionali esclusi quelli in fase di conversione al biologico ALLEVAMENTI DOP E IGP: Capi per i quali l azienda è controllata e certificata dal competente organismo di controllo 8 134

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 9 sezione III Metodi di gestione degli allevamenti (nell annata agraria 2009-2010) 39 PASCOLO 39.1 L azienda ha avuto animali al pascolo? 1 SI 2 NO In caso di risposta negativa passare al punto 40 TIPOLOGIA DEI TERRENI A PASCOLO Cod. 39.2 Terreni aziendali 01 39.3 Terreni di altre aziende 02 39.4 Terreni di proprietà collettive 03 40 41 NUMERO TOTALE DI ANIMALI AL PASCOLO SUPERFICIE UTILIZZATA (prati permanenti, pascoli e foraggere avvicendate) Ettari Are NUMERO MESI In caso di risposta al punto 39.4 indicare la denominazione del Comune o dell Ente gestore dei terreni appartenenti a proprietà collettive TIPOLOGIA DI STABULAZIONE DEL BESTIAME 40.1 Vacche da latte e Bufale Cod. Numero medio di animali (1) Cod. a. In stabulazione fissa con uso di d. Su pavimento pieno 10 01 lettiera (produzione di letame) e. All aperto 11 b. In stabulazione fissa senza uso 02 40.4 Galline ovaiole di lettiera (produzione di liquame) a. A terra con accesso all esterno 12 c. In stabulazione libera con uso di lettiera (produzione di letame) 03 b. A terra al chiuso 13 c. In gabbia (tutti i tipi) 14 d. In stabulazione libera senza uso di lettiera (produzione di liquame) 04 40.2 Altri Bovini e Bufalini a. In stabulazione con uso di lettiera (produzione di letame) 05 b. In stabulazione senza uso di lettiera (produzione di liquame) 06 40.3 Suini a. Su fessurato (o grigliato) parziale 07 b. Su fessurato (o grigliato) totale 08 c. Su lettiera permanente 09 c1. In gabbia con nastro di asportazione delle deiezioni 15 c2. In gabbia con fossa di stoccaggio di deiezioni liquide 16 c3. In gabbia con fossa di stoccaggio di deiezioni solide 17 40.5 Polli da carne a. A terra con accesso all esterno 18 b. A terra al chiuso 19 Numero medio di animali (1) MODALITÀ DI STOCCAGGIO PER TIPOLOGIA DI EFFLUENTI ZOOTECNICI GENERATI IN AZIENDA 41.1 L azienda adotta modalità di stoccaggio degli effluenti zootecnici? 1 SI 2 NO in caso di risposta negativa passare al punto 42 EFFLUENTI ZOOTECNICI Cod. ACCUMULO IN CAMPO PLATEA VASCA LAGUNA Coperta Scoperta Coperta Scoperta Coperta Scoperta XXX XXX XXX XXX 41.2 Letame (incluso pollina) 01 1 2 3 41.3 Colaticcio (urine) 02 XXX XXX XXX 4 5 6 7 41.4 Liquame (feci + urine) 03 XXX XXX XXX 4 5 6 7 42 APPLICAZIONE DEGLI EFFLUENTI ZOOTECNICI DI ORIGINE ANIMALE EFFLUENTI ZOOTECNICI (Indicare la superficie trattata secondo le seguenti applicazioni): (1) Il numero medio di animali può non coincidere con il numero di capi dichiarati a pagina 8. 42.1 Spandimento di letame solido 01 di cui 42.1.1 Spandimento di letame con incorporazione immediata (entro 4 ore) 02 42.2 Spandimento di liquame e colaticcio (inclusa fertirrigazione) 03 di cui 42.2.1 Spandimento di liquame o colaticcio con incorporazione immediata (entro 4 ore) o iniezione profonda 04 42.2.2 Spandimento di liquame o colaticcio con incorporazione (aratura) entro le 24 ore 05 Cod. SAU TRATTATA CON EFFLUENTI ZOOTECNICI Ettari Are 42.2.3 Spandimento di liquame o colaticcio a raso in bande o iniezione poco profonda o fertirrigazione 06 Indicare la percentuale di effluenti zootecnici portati al di fuori dell azienda sul totale prodotto dall azienda (venduti o rimossi per uso diretto come fertilizzanti o per processi di trattamento) % 42.3 Percentuale di letame portato al di fuori dell azienda sul totale letame prodotto 07 42.4 Percentuale di liquame portato al di fuori dell azienda sul totale liquame prodotto 08 9 135

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 10 sezione IV Ubicazione dei terreni e degli allevamenti aziendali Tutti i terreni aziendali e/o gli allevamenti sono localizzati nel Comune del centro aziendale? 1 SI 2 NO Se SI passare alla sezione successiva, se NO compilare ciascun riquadro sottostante per ogni Comune in cui sono localizzate le coltivazioni e/o gli allevamenti (se i Comuni sono più di 8 utilizzare fogli aggiuntivi) Riquadro N (Riferito al comune del centro aziendale) Riquadro N PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are b. Vite (punto 9.1) 02 b. Vite (punto 9.1) 02 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 d. Orti familiari (punto 10) 04 d. Orti familiari (punto 10) 04 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 1.1 SAU (punto 12) 06 1.1 SAU (punto 12) 06 f. Arboricoltura da legno (punto 13.3) 07 f. Arboricoltura da legno (punto 13.3) 07 g. Totale boschi (punto 14.4) 08 g. Totale boschi (punto 14.4) 08 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 1.2 SUPERFICIE TOTALE (punto 17) 10 1.2 SUPERFICIE TOTALE (punto 17) 10 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI b. Suini (punto 33.5) 02 b. Suini (punto 33.5) 02 c. Ovi-caprini (punto 31.3 + 32.3) 03 c. Ovi-caprini (punto 31.3 + 32.3) 03 d. Avicoli (punto 34.7) 04 d. Avicoli (punto 34.7) 04 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 Riquadro N Riquadro N PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are b. Vite (punto 9.1) 02 b. Vite (punto 9.1) 02 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 d. Orti familiari (punto 10) 04 d. Orti familiari (punto 10) 04 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 1.1 SAU (punto 12) 06 1.1 SAU (punto 12) 06 f. Arboricoltura da legno (punto 13.3) 07 f. Arboricoltura da legno (punto 13.3) 07 g. Totale boschi (punto 14.4) 08 g. Totale boschi (punto 14.4) 08 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 1.2 SUPERFICIE TOTALE (punto 17) 10 1.2 SUPERFICIE TOTALE (punto 17) 10 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI b. Suini (punto 33.5) 02 b. Suini (punto 33.5) 02 c. Ovi-caprini (punto 31.3 + 32.3) 03 c. Ovi-caprini (punto 31.3 + 32.3) 03 d. Avicoli (punto 34.7) 04 d. Avicoli (punto 34.7) 04 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 10 136

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 11 sezione IV Riquadro N Ubicazione dei terreni e degli allevamenti aziendali Riquadro N PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are b. Vite (punto 9.1) 02 b. Vite (punto 9.1) 02 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 d. Orti familiari (punto 10) 04 d. Orti familiari (punto 10) 04 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 1.1 SAU (punto 12) 06 1.1 SAU (punto 12) 06 f. Arboricoltura da legno (punto 13.3) 07 f. Arboricoltura da legno (punto 13.3) 07 g. Totale boschi (punto 14.4) 08 g. Totale boschi (punto 14.4) 08 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 1.2 SUPERFICIE TOTALE (punto 17) 10 1.2 SUPERFICIE TOTALE (punto 17) 10 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI b. Suini (punto 33.5) 02 b. Suini (punto 33.5) 02 c. Ovi-caprini (punto 31.3 + 32.3) 03 c. Ovi-caprini (punto 31.3 + 32.3) 03 d. Avicoli (punto 34.7) 04 d. Avicoli (punto 34.7) 04 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 Riquadro N Riquadro N PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione PROVINCIA COMUNE Codice ISTAT Codice ISTAT Denominazione Denominazione 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are 1 COLTIVAZIONI (SEZ. II) Cod. a. Seminativi (punto 8.13) 01 SUPERFICIE Ettari Are b. Vite (punto 9.1) 02 b. Vite (punto 9.1) 02 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 c. Coltivazioni legnose agrarie, escluso vite (punto 9.8 meno punto 9.1) 03 d. Orti familiari (punto 10) 04 d. Orti familiari (punto 10) 04 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05 1.1 SAU (punto 12) 06 1.1 SAU (punto 12) 06 f. Arboricoltura da legno (punto 13.3) 07 f. Arboricoltura da legno (punto 13.3) 07 g. Totale boschi (punto 14.4) 08 g. Totale boschi (punto 14.4) 08 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 h. Super. non utiliz. e altra super. (punto 15 + 16) 09 1.2 SUPERFICIE TOTALE (punto 17) 10 1.2 SUPERFICIE TOTALE (punto 17) 10 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI 2 ALLEVAMENTI (SEZ. III) Cod. a. Bovini e Bufalini (punto 26.4 + 27.4) 01 CAPI b. Suini (punto 33.5) 02 b. Suini (punto 33.5) 02 c. Ovi-caprini (punto 31.3 + 32.3) 03 c. Ovi-caprini (punto 31.3 + 32.3) 03 d. Avicoli (punto 34.7) 04 d. Avicoli (punto 34.7) 04 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 e. Presenza altri allevamenti (punti 28, 35, 36, 37) 05 NOTA: LA SOMMA DELLE COLTIVAZIONI E DEGLI ALLEVAMENTI DEI VARI RIQUADRI DEVE COINCIDERE CON QUANTO RIPORTATO NELLE SEZIONI II E III 11 137

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 12 sezione V Lavoro ed attività connesse (annata agraria 2009-2010) 43 FAMIGLIA DEL CONDUTTORE E PARENTI Compilare sempre se è stata data risposta a pagina 3 - Forma giuridica, al punto 1.1 od al punto 1.2 (solo in Cod. SESSO caso di società semplice costituita esclusivamente o in parte da familiari o parenti che svolgono lavoro in azienda) o per altre forme giuridiche comprendenti persone legate da vincoli di parentela. 43.1 Conduttore (16 anni e più - responsabile 101 1 giuridico ed economico dell azienda) 2 43.2 Coniuge 201 1 2 F 43.3 Altri componenti della famiglia (16 anni e più) che lavorano in azienda xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 301 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 302 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 303 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 304 1 2 F 43.4 Altri componenti della famiglia che non lavorano in azienda (compresi i minori di 16 anni) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 401 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 402 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 403 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 404 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 405 1 2 F 43.5 Parenti del conduttore che lavorano in azienda (16 anni e più) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 501 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 502 1 2 F xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 503 1 2 F ANNO DI NASCITA CITTADINANZA (1) CONDIZIONE PROFESIONALE (2) Numero giorni LAVORO SVOLTO IN AZIENDA (attività agricole e connesse) Media ore giornaliera % del tempo dedicato ad attività connesse elencate al quesito 48 di pagina 13 ALTRE ATTIVITÀ REMUNERATIVE EXTRA-AZIENDALI Tempo dedicato (3) Settore di attività prevalente (4) 1 2 a a M F 19 b a a c b c 3 1 2 a a M 19 b a a c b c 3 M 19 b a a c b c 1 2 3 a a M 19 b a a c b c 1 2 3 a a M 19 b a a c b c 1 2 3 a a M 19 b a a c b c 1 2 3 a a M da a a a M da a a a M da a a a M da a a a M da a a a M 19 b a a c b c 1 2 3 a a M 19 b a a c b c 1 2 3 a a M 19 b a a c b c 1 2 3 a a M 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 504 1 2 F 43.6 TOTALE GIORNATE DI LAVORO DELLA MANODOPERA FAMILIARE 601 g (1) Italiana = 1; Altro Paese Unione Europea = 2; Paese Extra-Unione Europea = 3 (2) Occupato = 1; Disoccupato alla ricerca di nuova occupazione = 2; In cerca di prima occupazione = 3; Casalingo/a = 4; Studente = 5; Ritirato dal lavoro = 6; In altra condizione = 7 (3) Per un tempo maggiore di quello dedicato all azienda = 1; Per un tempo minore a quello dedicato all azienda = 2; Nessun tempo (nessuna attività extra-aziendale) = 3 (4) Agricoltura = 1; Industria = 2; Commercio, alberghi e pubblici esercizi = 3; Servizi (esclusa la Pubblica Amministrazione) = 4; Pubblica Amministrazione = 5 (5) Imprenditore = 1; Libero professionista = 2; Lavoratore in proprio = 3; Dirigente = 4; Impiegato = 5; Operaio = 6; Altro = 7 Posizione (5) 44 Cod. ALTRA MANODOPERA AZIENDALE IN FORMA CONTINUATIVA In forma continuativa: persone che nell annata agraria di riferimento hanno lavorato continuativamente nell azienda, indipendentemente dalla durata settimanale del lavoro. Vi rientrano anche le persone che non hanno lavorato per tutto il periodo per uno dei seguenti motivi: condizioni particolari di produzione dell azienda, servizio militare, malattia, infortunio, ecc. CONTRATTO (1) SESSO 701 a 1 2 F 702 a 1 2 F 703 a 1 2 F 704 a 1 2 F 705 a 1 2 F 706 a 1 2 F 707 a 1 2 F 708 a 1 2 F 709 a 1 2 F 710 M a 1 2 ANNO DI NASCITA CITTADINANZA (2) Numero giorni LAVORO SVOLTO IN AZIENDA (attività agricole e connesse) Media ore giornaliera % del tempo dedicato ad attività connesse elencate al quesito 48 di pagina 13 M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c 19 b a c b c F 44.1 TOTALE GIORNATE DI LAVORO IN FORMA CONTINUATIVA...cod. 602 g (1) A TEMPO INDETERMINATO: Dirigente = 1, Impiegato = 2, Operaio = 3; A TEMPO DETERMINATO: Dirigente = 4, Impiegato = 5, Operaio = 6, Altro (esempio soci di società di persone) = 7 (2) CITTADINANZA: Italiana = 1, Altro Paese Unione Europea = 2, Paese Extra Unione Europea = 3 12 Cod. CONTRATTO (1) SESSO 711 a 1 2 F 712 a 1 2 F 713 a 1 2 F 714 a 1 2 F 715 a 1 2 F 716 a 1 2 F 717 a 1 2 F 718 a 1 2 F 719 a 1 2 F 720 M a 1 2 ANNO DI NASCITA CITTADINANZA (2) Numero giorni LAVORO SVOLTO IN AZIENDA (attività agricole e connesse) Media ore giornaliera % del tempo dedicato ad attività connesse elencate al quesito 48 di pagina 13 M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c M 19 b a c b c 19 b a c b c F 138

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 13 sezione V segue Lavoro ed attività connesse (annata agraria 2009-2010) 45 ALTRA MANODOPERA AZIENDALE IN FORMA SALTUARIA Persone che non hanno lavorato continuativamente nell annata agraria 2009-2010, es: assunte per singole fasi lavorative, per lavori di breve durata, stagionali o saltuari Cod. a. Maschi 11 b. Femmine 21 TOTALE 31 47 italiana NUMERO PERSONE CITTADINANZA Altro Paese U.E. Paese extra U. E. TOTALE Lavoro svolto in azienda (attività agricole e connesse) N. giornate convertite in gg. di 8 ore NOTIZIE SUL CAPO AZIENDA (da compilare sempre) 47.1 Quale dei soggetti già dichiarati ai punti 43 o 44 di pagina 12 svolge anche la funzione di capo azienda (indicare il codice)? 47.2 Titolo di studio (il più elevato) 48 % del tempo dedicato ad attività connesse c a. Nessuno 01 b. Licenza di scuola elementare 02 c. Licenza di scuola media inferiore 03 ATTIVITÀ REMUNERATIVE CONNESSE ALL AZIENDA 48.1 Se nell azienda sono state svolte attività remunerative diverse da quelle agricole, ma ad essa connesse, precisare se trattasi di: Cod. a. Agriturismo 01 b. Attività ricreative e sociali 02 c. Fattorie didattiche 03 d. Artigianato 04 e. Prima lavorazione dei prodotti agricoli 05 f. Trasformazione di prodotti vegetali 06 g. Trasformazione di prodotti animali 07 h. Produzione di energia rinnovabile 08 i. Lavorazione del legno (taglio, ecc.) 09 l. Acquacoltura 10 m. Lavoro per conto terzi utilizzando mezzi di produzione dell azienda - attività agricole 11 - attività non agricole 12 n. Servizi per l allevamento 13 o. Sistemazione di parchi e giardini 14 p. Silvicoltura 15 q. Produzione di mangimi completi e complementari 16 r. Altre attività (specificare ) 17 48.2 Indicare quale delle attività sopra elencate è la più remunerativa in termini economici (indicare il codice) b 48.3 Indicare il peso percentuale dell attività sopra indicata (punto 48.2) rispetto al totale delle attività % elencate al punto 48.1 (indicare un valore percentuale) c 46 LAVORATORI NON ASSUNTI DIRETTAMENTE DALL AZIENDA Cod. TOTALE 41 Italiana NUMERO PERSONE CITTADINANZA Altro Paese U.E. Paese extra U. E. TOTALE Indirizzo agrario Altro tipo d. Diploma di qualifica che non 04 05 permette accesso universitario (2-3 anni) e. Diploma di scuola media superiore f. Laurea o diploma universitario 06 08 07 09 47.3 Il capo azienda ha frequentato negli ultimi 12 mesi corsi di formazione professionale? 1 SI 2 NO 49 CONTOTERZISMO (giornate di lavoro convertite in giornate di 8 ore) CONTOTERZISMO ATTIVO 49.1 Indicare le giornate di lavoro svolte con mezzi meccanici propri presso altre aziende agricole e CONTOTERZISMO PASSIVO 49.2 Indicare se l azienda ha usufruito di lavoro effettuato con persone e mezzi extra-aziendali 1 SI 2 NO Se SI indicare: 49.2.1 Giornate di lavoro effettuate in azienda e 49.2.2 - di cui da altre aziende agricole e 49.3 Tipo di operazioni effettuate SUPERFICIE Cod. in azienda Ettari Are AFFIDAMENTO COMPLETO (di una o più coltivazioni) 01 AFFIDAMENTO PARZIALE a. Aratura 02 b. Fertilizzazione 03 c. Semina 04 d. Raccolta meccanica e prima lavorazione di vegetali 05 e. Altre operazioni per le coltivazioni 06 f. Altre operazioni non sulle superfici (specificare ) 07 PRODUZIONE DI MANGIMI PER IL REIMPIEGO IN AZIENDA Lavoro svolto in azienda (attività agricole e connesse) N. giornate convertite in gg. di 8 ore % del tempo dedicato ad attività connesse 50 50.1 Nell azienda sono stati prodotti mangimi completi e complementari per il reimpiego in azienda? 1 SI 2 NO 51 IMPIANTI PER LA PRODUZIONE DI ENERGIA RINNOVABILE (sia per la vendita che per il reimpiego in azienda) 51.1 L azienda possiede impianti per la produzione di energia rinnovabile? 1 SI 2 NO In caso di risposta NO passare al punto 52 51.2 In caso di risposta SI indicare la tipologia di impianto per tipo di fonte energetica a. Eolica 01 b. Biomassa 02 - tra cui biogas 03 13 c. Solare 04 d. Idroenergia 05 e. Altre fonti di energia rinnovabile (specificare ) 06 139

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 14 sezione VI Altre informazioni (annata agraria 2009-2010) 52 CONTABILITÀ Indicare se l azienda ha: a. Contabilità forfettaria 01 b. Contabilità ordinaria 02 c. Nessuna contabilità 03 53 RICAVI Indicare la percentuale di ricavi lordi provenienti da % a. Vendita di prodotti aziendali 01 c b. Altre attività remunerative connesse all azienda 02 c c. Pagamenti diretti 03 c TOTALE PERCENTUALE 1 0 0 54 AUTOCONSUMO 54.1 La famiglia del conduttore consuma i prodotti aziendali? 1 SI 2 NO Se SI 54.1.1 Indicare se l azienda autoconsuma a. Tutto il valore della produzione finale 01 b. Oltre il 50% del valore della produzione finale 02 c. Il 50% o meno del valore della produzione finale 03 55 COMMERCIALIZZAZIONE DEI PRODOTTI AZIENDALI (in termini percentuali per canale di commercializzazione) Cod. VENDITA DIRETTA AL CONSUMATORE In azienda Fuori azienda VENDITA AD ALTRE AZIENDE AGRICOLE VENDITA AD IMPRESE INDUSTRIALI LE INFORMAZIONI RIPORTATE NEL QUESTIONARIO SONO STATE OTTENUTE 1. Con intervista di: - Conduttore o legale rappresentante 01 - Coniuge 02 - Altro familiare 03 - Parente 04 - Altro lavoratore dell azienda 05 - Altra persona di fiducia 06 2. Con altro metodo 07 VENDITE AD IMPRESE COMMERCIALI VENDITA O CONFERIMENTO AD ORGANISMI ASSOCIATIVI 55.1 Prodotti vegetali % % % % % % a. Cereali 01 100 b. Piante industriali e proteiche 02 100 c. Ortive e patate 03 100 d. Frutta compresi agrumi 04 100 e. Uva da vino 05 100 f. Uva da tavola 06 100 g. Olive 07 100 h. Florovivaismo 08 100 i. Foraggi 09 100 55.2 Prodotti animali l. Animali vivi 10 100 m. Latte 11 100 n. Altri 12 100 55.3 Prodotti trasformati o. Vino e mosto 13 100 p. Olio 14 100 q. Formaggi e altri prodotti lattierocaseari 15 100 r. Altri prodotti di origine animale 16 100 s. Altri prodotti di origine vegetale 17 100 55.4 Prodotti forestali 18 100 TOTALE % Dichiaro di essere stato intervistato dal rilevatore: L INTERVISTATO Dichiaro che i dati sono stati rilasciati in conformità alle istruzioni ricevute IL RILEVATORE Dichiaro di aver revisionato il questionario IL REVISORE (Firma) (Firma) (Firma) h Codice rilevatore Data Data.. 14 140

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 15 PROMEMORIA PER IL REVISORE Principali controlli di compatibilità del questionario Segnare i riquadri per ogni regola di revisione verificata in caso contrario indicare nelle annotazioni i problemi riscontrati 1) Notizie anagrafiche, residenza o sede legale del conduttore: deve essere sempre presente (prestampato o corretto) la spazio relativo al CUAA o codice fiscale del conduttore. 2) Esito della rilevazione: deve sempre essere data una risposta ed una sola ai punti da 1 a 9 del quadro B. 3) Azienda rilevata attiva: un azienda rilevata (punto B.1 a pagina 2), attiva (punto 4a a pagina 3) deve aver dichiarato almeno un informazione nella sezione II (aziende con terreni) e/o sezione III (aziende con allevamenti) e nella sezione V (lavoro). 4) Centro aziendale: devono essere sempre presenti le informazioni sull ubicazione del centro aziendale se diverse dalla residenza o sede legale del conduttore indicate a pagina 1. 5) Forma giuridica e sistema di conduzione: deve sempre essere data una risposta ed una sola ai quesiti 1 (forma giuridica) e 2 (sistema di conduzione) di pagina 3. 6) Forma giuridica e lavoro: se la forma giuridica è azienda individuale (punto 1.1 a pagina 3) allora deve sempre esistere manodopera familiare (punto 43 a pagina 12). 7) Forma giuridica e lavoro: se la forma giuridica è una di quelle comprese tra i punti 1.3 ed 1.8 a pagina 3 allora deve sempre esistere altra manodopera al punto 44 (pagina 12). 8) Superficie totale: il punto 2.3 (pagina 3) deve essere uguale al punto 17 (pagina 5). 9) Superficie agricola utilizzata: il punto 2.3 (pagina 3) deve essere uguale al punto 12 (pagina 5). 10) Vite: La superficie totale del punto 9.1 (pagina 5) deve essere uguale a quella del punto 21.5 (pagina 6). 11) Ubicazione dei terreni e degli allevamenti: deve essere sempre data una risposta alla prima domanda a pagina 10 sulla localizzazione dei terreni e/o degli allevamenti dell azienda. 12) Ubicazione dei terreni e degli allevamenti: la somma delle superfici totali indicate al punto 1.2 di ciascun riquadro comunale di pagina 10 e 11 deve essere uguale al punto 17 (pagina 5). 13) Capo azienda: deve essere sempre data una risposta al punto 47.1 a pagina 13. 14) Attività remunerative connesse all azienda: se è stata data almeno una risposta al punto 49 (pagina 13) allora deve esistere almeno una risposta alle colonne relative a % del tempo dedicato ad attività connesse nella Sezione Lavoro (pagine 12 e/o 13). 15) Codice rilevatore: deve essere sempre indicato il codice rilevatore a pagina 14. ULTERIORI CONTROLLI DI REVISIONE SONO PRESENTI NEL LIBRETTO D ISTRUZIONE PER LA RILEVAZIONE ANNOTAZIONI 15 141

Agricoltura_2Col_pp_8_16:Agricoltura_Tric_pp_8_16 6-05-2010 9:37 Pagina 16 SEGRETO STATISTICO, OBBLIGO DI RISPOSTA, TUTELA DELLA RISERVATEZZA E DIRITTI DEGLI INTERESSATI L esecuzione del 6 Censimento generale dell agricoltura, ai sensi dell art. 17 del d.l. 25 settembre 2009, n. 135 - convertito con modificazioni dalla l. 20 novembre 2009, n. 166 - assolve agli obblighi di rilevazione stabiliti dal Regolamento (CE) n. 1166/2008 del Consiglio e del Parlamento europeo, del 19 novembre 2008, relativo alle statistiche strutturali sulle aziende agricole e dal Regolamento (CE) n. 357/79 del Consiglio e del Parlamento europeo, del 5 febbraio 1979, e successive modificazioni, relativo alla rilevazione di base sulle superfici viticole. Il 6 Censimento generale dell agricoltura è previsto dal Programma statistico nazionale 2008-2010 - Aggiornamento 2009-2010 (codice IST-02112) ed inserito nell elenco delle rilevazioni che comportano obbligo di risposta per i soggetti privati, a norma dell art. 7 del d.lgs. 6 settembre 1989, n. 322, approvato con DPR 15 novembre 2009. La mancata fornitura dei dati richiesti mediante il questionario di rilevazione, accertata dai competenti Uffici di censimento, comporta l applicazione delle sanzioni amministrative ai sensi degli artt. 7 e 11 del d.lgs. 6 settembre 1989, n. 322, e successive modificazioni e integrazioni, e del DPR 31 dicembre 2009. I dati raccolti sono tutelati dal segreto statistico e saranno trattati nel rispetto della normativa in materia di protezione dei dati personali (d.lgs. 30 giugno 2003, n. 196 e Codice di deontologia e di buona condotta per i trattamenti di dati personali a scopi statistici e di ricerca scientifica effettuati nell ambito del Sistema statistico nazionale). I coordinatori e i rilevatori, inoltre, in quanto incaricati di pubblico servizio, sono tenuti all osservanza del segreto di ufficio ai sensi dell art. 326 del codice penale. I medesimi dati potranno essere utilizzati, anche per successivi trattamenti, esclusivamente per scopi statistici dai soggetti del Sistema statistico nazionale, nonché dagli uffici di censimento ai sensi del Regolamento di esecuzione, ed essere comunicati per finalità di ricerca scientifica alle condizioni e secondo le modalità previste dall art. 7 del Codice di deontologia per i trattamenti di dati personali effettuati nell ambito del Sistema statistico nazionale. La diffusione dei dati potrà avvenire anche in forma disaggregata in conformità a quanto previsto dall art. 4, comma 2, del citato Codice di deontologia. Titolare della rilevazione censuaria è l Istituto nazionale di statistica via Cesare Balbo, 16 00184 ROMA. I responsabili del trattamento dei dati sono, per le fasi di rispettiva competenza, il Direttore centrale della Direzione dei censimenti generali (DCCG) dell Istat e i responsabili degli Uffici di censimento, ai quali è possibile rivolgersi anche per quanto riguarda l esercizio dei diritti dell interessato. Principiali riferimenti normativi - Decreto legislativo 6 settembre 1989, e successive modificazioni e integrazioni - Norme sul Sistema statistico nazionale e sulla riorganizzazione dell Istituto nazionale di statistica ; - Decreto legislativo 30 giugno 2003, n. 196, e successive modificazioni e integrazioni - Codice in materia di protezione dei dati personali ; - Codice di deontologia e di buona condotta per i trattamenti di dati personali a scopi statistici e di ricerca scientifica effettuati nell ambito del Sistema statistico nazionale (allegato A.3 del d.lgs. 30 giugno 2003, n. 196); - Decreto del Presidente del Consiglio dei Ministri 3 agosto 2009 - Approvazione del Programma statistico nazionale triennio 2008-2010. Aggiornamento 2009-2010 (S.O. n. 186 alla G.U. 13 ottobre 2009 - serie gen. - n. 238); - Decreto del Presidente della Repubblica 15 novembre 2009 - Elenco delle rilevazioni statistiche rientranti nel Programma statistico nazionale 2008-2010 - Aggiornamento 2009-2010, che comportano l obbligo di risposta da parte dei soggetti privati, a norma dell art. 7 del decreto legislativo 6 settembre 1989 n. 322 (G.U. 14 dicembre 2009 - serie gen.- n. 290); - Decreto del Presidente della Repubblica 31 dicembre 2009 - Elenco delle rilevazioni statistiche, comprese nel Programma statistico nazionale per il triennio 2008-2010, aggiornamento 2009-2010, per le quali per l anno 2010 la mancata fornitura dei dati configura violazione dell obbligo di risposta, ai sensi dell art. 7 del decreto legislativo 6 settembre 1989, n. 322 (G.U. 17 marzo 2010 - serie gen. - n. 63). Stampa: Rubbettino Industrie Grafiche ed Editoriali 142

Annex 3 Pilot questionnaire and compilation guidelines (in italian language)

Specifiche tecniche Questionario Aziende Agricole 1. descrizione Questionario 1.1 frontespizio Riportare negli appositi spazi: - il nome del rilevatore; - la data di rilevazione dell azienda; - il codice RICA dell azienda se disponibile. Sul questionario cartaceo, riportare il codice di rilevazione generato automaticamente dal database. Selezionare la tipologia di azienda intervistata secondo le caratteristiche di: - ordinamento prevalente irriguo; - la fonte di approvvigionamento idrico prevalente; - la SAU aziendale; - il sistema di irrigazione utilizzato prevalentemente in azienda. Riguardo alla tipologia aziendale, riferirsi all Allegato 2 per le tipologie aziendali interessate. Riportare inoltre: - il nominativo del conduttore o la denominazione della società o ente che gestisce l azienda. - gli elementi utili per l identificazione del centro aziendale. 1.2 Sezione 1 Notizie generali sull azienda 1.2.1 Notizie sul conduttore Per conduttore si intende la persona che di fatto gestisce l azienda in loco e cioè la persona fisica che assicura la gestione corrente e quotidiana. Il rilevatore dovrà indicare per il conduttore le seguenti informazioni: - sesso; - anno di nascita; - titolo di studio ultimato. Il rilevatore dovrà indicare al punto 1.3 il più elevato titolo di studio conseguito dal conduttore distinguendo, per la laurea ed il diploma di scuola media superiore, tra indirizzo agrario e indirizzo di altro tipo. 145

- Indicare se il conduttore ha frequentato corsi di formazione professionale inerenti l agricoltura. Nel caso l azienda sia costituita in società o ente, indicare le notizie della persona che di fatto gestisce l azienda. 1.2.2 Informatizzazione aziendale Rispondere se l azienda utilizza e dispone di attrezzatura informatiche proprie per la gestione delle coltivazioni. 1.2.3 Superfici aziendali Riportare le informazioni richieste sulle superfici e sui corpi aziendali costituenti l azienda. In particolare verificare che: - la SAU; - la superficie irrigabile; - la superficie irrigata; - la superficie media irrigata negli ultimi tre anni. Verificare la congruenza delle superfici. Per superficie irrigabile, si intende la superficie aziendale che nel corso dell annata agraria di riferimento potrebbe essere irrigabile in base alla potenzialità degli impianti a disposizione dell azienda ed alla quantità di acqua disponibile. L annata agraria di riferimento è l annata agraria 2007-2008. 1.2.4 Fonte di approvvigionamento Specificare quale è la fonte o le fonti di approvvigionamento dell acqua irrigua e per ciascuna di esse indicare la percentuale di utilizzo durante la stagione. è distinto l autoapprovviggionamento per derivazione diretta da corpi d acqua superficiali o sotterranei, senza vincoli per quanto riguarda le modalità di presa e di utilizzazione dell acqua situati nel proprio fondo o nelle vicinanze, dall approvvigionamento tramite consorzi di bonifica con consegna a turno o a domanda. Nel caso in cui l azienda si approvvigioni da consorzio di bonifica, indicare il nome del consorzio. 1.2.5 Impianti di sollevamento utilizzati per l approvvigionamento In questa sezione, il rilevatore riporterà informazioni sugli impianti per il sollevamento dell acqua dalla fonte. In particolare: 146

- la potenza complessiva (in kw) delle pompe utilizzate (la somma delle potenze di ogni singola pompa); - il consumo elettrico annuo totale (in kwh) delle pompe utilizzate (inteso come somma dei consumi delle varie pompe); - le ore di funzionamento totale delle pompe nell annata. 1.3 Sezione 2 Gestione dell acqua In questa sezione, il rilevatore dovrà indicare alcune caratteristiche generali sulla gestione dell acqua di irrigazione tenute dal conduttore. In particolari, tali informazioni riguardano: 2.1) Servizi di consulenza irrigua. Si intendono per servizi di consulenza irrigua, l utilizzo da parte del conduttore di servizi gratuiti o a pagamento, offerti da società od enti pubblici di ricerca, regione, provincia, assessorati, associazioni di categoria o produttori, ecc. per la determinazione del fabbisogno idrico delle colture o altre informazioni utili per la sua determinazione. Nel caso l azienda utilizzi dei servizi di consulenza irrigua, specificare quali. 2.2) Indicare se ci sono stati ammodernamenti della rete idrica aziendale (approvvigionamento, trasporto e distribuzione) negli ultimi 10 anni; 2.3) Indicazione del momento di intervento irriguo; 2.4) indicare se l azienda ha aderito alle indennità connesse alla Direttiva Quadro 2000/60/CE sulle acque (misura 213 del PSR) 2.5) indicazione della disponibilità dell acqua necessaria al fabbisogno idrico colturale; 2.6) Indicare, per le aziende con approvvigionamento da consorzio di bonifica con fornitura a turno, nel caso in cui piova nel momento dell irrigazione turnata se irriga o continua ad irrigare normalmente. 2.7) 2.8) Indicare, le colture che, in caso di mancanza d acqua in una annata agraria media, vengono irrigate preferibilmente; indicare per le colture arboree, se si tratta di un primo impianto. Per l elenco delle colture vedere Allegati 2, 3 e 4. 2.9) Indicare sinteticamente la strategia adottata per l irrigazione di prodotti di qualità (DOC, DOCG, DOP, IGP) in relazione al disciplinare di produzione. 2.10) Indicare sinteticamente la strategia adottata per l irrigazione di colture in regime di agricoltura biologica o di produzione integrata. 2.11) Indicare se l oliveto è sottoposto a stress idrico controllato; nel caso positivo indicare la percentuale di irrigazione applicata rispetto al reintegro della quantità totale di acqua evapotraspirata. 2.12) Note sintetiche generali sulla gestione dell acqua per l irrigazione. 147

1.4 Sezione 3 Uso del suolo (annata agraria 2007-2008) La sezione è divisa in 3 sottosezioni per seminativi, coltivazioni legnose ed altre colture. Riportare esclusivamente sole le colture irrigate. In generale per le coltivazioni riportare le caratteristiche prevalenti. Ad esempio, se una coltura è irrigata con due sistemi di irrigazione, riportare il sistema di irrigazione applicato sulla maggioranza della superficie. 1.4.1 Seminativi Per ogni coltura presente il rilevatore dovrà inserire le seguenti informazioni: - il comune in cui si trova la coltura; - il nome della coltura. L elenco delle colture definite per questa sottosezione è riportato nell Allegato 3. - La superficie totale e la superficie irrigata; - barrare la casella nel caso la coltura sia la coltivazione principale. Per coltivazione principale si intende la sola praticata su una data superficie nel corso dell annata agraria di riferimento. Questa domanda è presente solo nel caso dei seminativi. - Barrare la casella nel caso la coltura non sia praticata in piena aria. Nel caso la coltura sia protetta in serra od in tunnel o campane, riportare successivamente solo il volume di acqua totale utilizzato durante la stagione. - Barrare la casella nel caso la coltura sia destinata a produzioni di qualità (DOC, DOCG, DOP, IGP); - barrare la casella nel caso la coltivazione della coltura avvenga in regime di agricoltura biologica o di produzione integrata; - Nella casella dei dettagli, riportare alcune informazioni specifiche riguardo: ~ per le specie ortive: indicare il numero di cicli colturali che vengono effettuati; ~ per il mais da granella o da insilato: indicare la classe FAO utilizzata; ~ per l erba medica: indicare il numero di tagli. - Solo per i seminativi in piena aria, inserire la data di inizio semina e fine raccolta e la data di inizio e fine irrigazione. Queste date si riferiscono ad una annata media. Verificare la congruenza delle date inserite. - Indicare il sistema di irrigazione unico o prevalente per quella specifica coltura. Il sistema di irrigazione è codificato nel box alla fine della sezione. - Il numero di adacquate praticate durante il periodo d irrigazione. - La durata media delle adacquate praticate. - Il volume medio distribuito per intervento espresso in m 3. - Il volume stagionale distribuito espresso in m 3. 148

1.4.2 Coltivazioni legnose agrarie Per ogni coltura presente il rilevatore dovrà inserire le seguenti informazioni: - il comune in cui si trova la coltura; - il nome della coltura. L elenco delle colture definite per questa sottosezione è riportato nell Allegato 4. - Indicare la superficie in produzione e la superficie in produzione irrigata. - Indicare la superficie di nuovo impianto e la superficie di nuovo impianto irrigata. - barrare la casella nel caso la coltura sia destinata a produzioni di qualità (DOC, DOCG, DOP, IGP). - barrare la casella nel caso la coltivazione della coltura avvenga in regime di agricoltura biologica o di produzione integrata. - Inserire la data di inizio e fine irrigazione. Verificare la congruenza delle date inserite. - Indicare il sistema di irrigazione unico o prevalente per quella specifica coltura. - Il sistema di irrigazione è codificato nel box alla fine della sezione. - Il numero di adacquate praticate durante il periodo d irrigazione. - La durata media delle adacquate praticate. - Il volume medio distribuito per intervento espresso in m 3. - Il volume stagionale distribuito espresso in m 3. 1.4.3 Altre coltivazioni Per ogni coltura presente il rilevatore dovrà inserire le seguenti informazioni: - il comune in cui si trova la coltura; - il nome della coltura. L elenco delle colture definite per questa sottosezione è riportato nell Allegato 5. - Indicare la superficie totale e la superficie irrigata. - barrare la casella nel caso la coltivazione della coltura avvenga in regime di agricoltura biologica o di produzione integrata; - Inserire la data di inizio e fine irrigazione. Verificare la congruenza delle date inserite. - Indicare il sistema di irrigazione unico o prevalente per quella specifica coltura. Il sistema di irrigazione è codificato nel box alla fine della sezione. - Il numero di adacquate praticate durante il periodo d irrigazione. - La durata media delle adacquate praticate. - Il volume medio distribuito per intervento espresso in m 3. - Il volume stagionale distribuito espresso in m 3. 149

2. inserimento DAti Per una corretta visualizzazione del database è necessaria l installazione di Access 2007 o il visualizzatore di Access 2007. E possibile scaricare il visualizzatore a questo indirizzo: http://www.microsoft.com/downloads/details.aspx?familyid=d9ae78d9-9dc6-4b38-9fa6-2c745a175aed&displaylang=it Per l installazione seguire la normale procedura guidata. 1. Dalla pagina iniziale (start) scegliere aggiungi azienda per cominciare l inserimento dati di una azienda o vedi aziende per visualizzare il riepilogo delle aziende già inserite. 2. Nella pagina Frontespizio compilare tutti i campi inserendo la data dal calendario che appare cliccando nella casella oppure inserendo direttamente la data nel formato gg/mm/aaaa (es. 23/01/1978). 150

3. In Notizie Generali Parte 1 fare attenzione alla compilazione dei valori delle superfici. 4. Nella pagina Notizie Generali Parte 2 è importante inserire le percentuali dell acqua in modo da raggiungere obbligatoriamente il 100% distribuendo i valori nei vari campi. 151

5. Gestione dell acqua è costituita da vari campi e da due campi che si attivano solo se viene spuntata la casella L azienda utilizza servizi di consulenza irrigua? e In caso di mancanza di acqua, irriga soltanto certe colture?. 6. Uso del Suolo: questa è la maschera generale per l inserimento delle coltivazioni. Per ogni tipo di coltura c è un bottone che permette l inserimento di un nuovo record (nuova coltura) e un bottone per visualizzare ed eventualmente modificare le coltivazioni inserite. 152

7. Uso del Suolo: Seminativi. Premendo il tasto inserisci si aprirà una nuova maschera per l inserimento dei seminativi. E importante seguire una specifica procedura per l inserimento dati e terminare la compilazione di ogni campo prima di creare un nuovo record altrimenti le informazioni non saranno salvate. Basta compilare seguendo queste priorità: 1) comune, 2) coltura, 3) superficie totale, 4) superficie irrigata, 5) i campi restanti nella tabella in basso, 6-7) inserire tutte le date richieste, 8) specificare il tipo di irrigazione e terminare compilando i campi restanti. Solo dopo aver compilato ogni campo della maschera si potrà procedere creando un nuovo record. Fondamentalmente bisogna compilare prima la tabella in basso poi i campi in alto. 8. Uso del Suolo: Coltivazioni Legnose. La procedura è simile a quella per i seminativi, è importante compilare tutti i campi della tabella in basso e tutti i campi nella parte alta. 9. Uso del Suolo: Altre Colture. La procedura è simile a quella per i seminativi, è importante compilare tutti i campi della tabella in basso e tutti i campi nella parte alta. 153

10.Riepilogo Azienda. Da questa maschera è possibile modificare la maggior parte dei dati inseriti durante la procedura guidata. Per attivare le modifiche bisogna premere il tasto modifica altrimenti non sarà possibile editare i campi. 154

3. DOCUMentAZione FOTOGRAficA Sarebbe utile scattare delle fotografie degli elementi più significativi sulla pratica irrigua aziendale come: contatori/misuratori di volumi di acqua dell azienda (qualora presenti) sorgente irrigua (pozzo/canale/presa di rete) impianti di sollevamento (pompe/...) associazione coltura - sistema di irrigazione (se attualmente in coltivazione nell azienda) Per legare univocamente le fotografie all azienda, si suggerisce denominare le varie fotografie con il codice relativo alla rilevazione ovvero quello generato automaticamente dal database ed annotato sulla copia cartacea del questionario. 155

QuestionArio AZiende Agricole Rilevatore Data rilevazione Codice RICA Codice Progressivo regionale tipologia DI AZIENDA Ordinamento prevalente irriguo barbabietola, mais, coltivazioni foraggere patata, girasole, soia agrumi, frutteti, ortive frumento, vite, olivo Fonte di approvvigionamento prevalente Acqua pubblica Autoapprovvigionamento Superficie (SAU) Grande (> 20 ha) Piccola (< 20 ha) Sistema di irrigazione prevalente Microirrigazione Pioggia Infiltrazione - scorrimento notizie DEL CONDUTTORE Cognome e nome della persona fisica o denominazione della società o ente che gestisce l azienda UBICAZIONE DEL CENTRO AZIENDALE Luogo dove viene svolta la maggior parte o l intera attività agricola (località dove sono presenti fabbricati rurali o la maggior parte delle particelle aziendali) Regione Provincia Comune Indirizzo 156

SEZIONE 1 Notizie generali sull azienda 1. NOTIZIE SUL CONDUTTORE 1.1 Sesso M F 1.2 Anno di nascita 1.3 Titolo di studio (il più elevato) Indirizzo agrario Altro tipo a) Laurea o diploma universitario b) Diploma di scuola media superiore c) Diploma di qualifica che non permette l accesso universitario (2-3 anni) d) Licenza di scuola media inferiore e) Licenza di scuola elementare f) Nessuno 1.4 Il conduttore ha frequentato negli ultimi 12 mesi corsi di formazione professionale Sì NO 2. INFORMAtiZZAZione DELL AZiendA 2.1 L azienda dispone di personal computer e/o altre attrezzature informatiche per fini aziendali? Sì NO 2.2 Se SI Gestione informatizzata di coltivazioni Sì NO 3. SUPERFICI Ha 3.1 Superficie totale dell azienda 3.2 Superficie agricola utilizzata (SAU) 3.3 Superficie irrigabile 3.4 Superficie effettivamente irrigata nell annata 3.5 Superficie media irrigata negli ultimi 3 anni 3.6 Corpi che costituiscono l azienda 4. FONTE DI ApprovvigionAMento DELL ACQUA IRRIGUA (sono ammesse risposte multiple) (sì/no) % utilizzo - acque sotterranee all interno o nelle vicinanze dell azienda - acque superficiali all interno dell azienda (bacini naturali e artificiali) - acque superficiali al di fuori dell azienda (laghi, fiumi o corsi d acqua) - acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo con consegna a turno - acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo con consegna a domanda - altra fonte (specificare) 157

Se l azienda si approvvigiona da un Consorzio di Bonifica indicare il nome del Consorzio. 5. IMpiAnti DI SOLLEVAMento UTILIZZAti PER L ApprovvigionAMento Potenza totale (kw) Consumo elettrico medio annuo totale (kwh) Ore di funzionamento totali Sezione 2 Notizie generali sull azienda 2.1 L azienda utilizza servizi di consulenza irrigua? Sì No Se Sì, quali 2.2 Su cosa basa il momento di intervento irriguo? (ammessa una sola risposta) Disponibilità idrica Sì NO Andamento climatico Sì NO Esperienza, metodi empirici Sì NO Modelli telematici Sì NO 2.3 Ci sono stati ammodernamenti della rete idrica aziendale negli ultimi 10 anni? Sì No 2.4 L azienda aderisce alle indennità connesse alla Direttiva Quadro 2000/60/CE sulle acque (Misura 213 del PSR) Sì No 2.5 L azienda dispone di tutta l acqua necessaria per soddisfare il fabbisogno idrico colturale? Sì No 2.6 Se piove ed ha il turno di irrigazione, irriga ugualmente? Sì No 158

2.7 Le colture principali sono irrigate per ottenere la massima produzione potenziale? Sì No 2.8 In caso di mancanza d acqua, irriga soltanto certe colture? Sì No Se Sì, quali? (Specificare nel caso sia una arborea di nuovo impianto) Coltura Arboree primo impianto (SI/NO) 2.9 Se la sua azienda coltiva prodotti di qualità (DOC, DOCG, DOP, IGP), specificare la strategia di irrigazione prevista. 2.10 Se la sua azienda coltiva prodotti in regime di agricoltura biologica o in regime di produzione integrata, specificare la strategia di irrigazione prevista. 2.11 L oliveto viene sottoposto a stress idrico controllato? Sì No Se Sì in quale percentuale rispetto al reintegro totale dell acqua evapotraspirata? 2.12 Annotazioni generali sulla gestione dell acqua per irrigazione. 159

Volume stagionale totale (m 3 ) Volume medio adacquate (m 3 ) Durata adacquate (h) n. adacquate Sistema di irrigazione (vedi codici) Data fine irrigazione Data inizio irrigazione Data fine raccolta Data inizio semina /trapianto Dettagli Produzione biologica o produzione integrata (SI/NO Produzione di qualità (SI/NO) Coltura protetta (SI/NO) Coltivazione principale (SI/NO) SEZIONE 3 Uso del suolo (annata agraria 2007-2008) 3.1 Seminativi Comune Coltura Superficie totale Superficie irrigata Ha Ha 160

Volume stagionale totale (m 3 ) Volume medio adacquate (m 3 ) Durata adacquate (h) n. adacquate Sistema di irrigazione (vedi codici) Data fine irrigazione Data inizio irrigazione Produzione biologica o produzione integrata (SI/NO Produzione di qualità (SI/NO) 3.2 Coltivazioni legnose agrarie Comune Coltura Superficie In produzione In produzione irrigata Nuovo impianto Nuovo impianto irrigata Ha Ha Ha Ha 161

Volume totale stagionale (m 3 ) Volume medio adacquate (m 3 ) Durata irrigazione (h) n. adacquate 3.3 Altre coltivazioni (pascoli ed arboricoltura da legno) Sistema di irrigazione (vedi codici) Produzione biologica o produzione integrata (SI/NO Data fine irrigazione Data inizio irrigazione Superficie Superficie irrigata Totale Comune Coltura Ha Ha (1) Indicare il codice del sistema di irrigazione unico o prevalente 1 Scorrimento superficiale ed infiltrazione laterale 3 Aspersione (a pioggia) 5 Altro sistema 2 Sommersione 4 Microirrigazione (goccia, manichetta forata, ecc) 162

AllegAti Tipologia di aziende Regione Emilia Romagna Ordinamento prevalente irriguo: barbabietola, mais, coltivazioni foraggere Fonte prevalente Dimensione Sistema irrigazione prevalente N aziende Autoapprovvigionamento Microirrigazione 3 Grande Infiltrazione - scorrimento 2 (> 20 ha) Aspersione 11 Microirrigazione 1 Piccole Infiltrazione scorrimento 1 (<20 ha) Aspersione 7 Pubblica Microirrigazione 2 Grande Infiltrazione scorrimento 5 (> 20 ha) Aspersione 22 Microirrigazione 1 Piccole Infiltrazione scorrimento 2 (<20 ha) Aspersione 9 TOTALE 66 Regione Campania Ordinamento prevalente irriguo: patata, girasole, soia Fonte prevalente Dimensione Sistema irrigazione prevalente N aziende Autoapprovvigionamento Microirrigazione 1 Grande Infiltrazione - s corrimento 1 (> 20 ha) Aspersione 1 Microirrigazione 1 Piccole Infiltrazione scorrimento 1 (<20 ha) Aspersione 1 Pubblica Microirrigazione 1 Grande Infiltrazione scorrimento 1 (> 20 ha) Aspersione 1 Microirrigazione 1 Piccole Infiltrazione scorrimento 1 (<20 ha) Aspersione 1 TOTALE 12 Regione Campania Ordinamento prevalente irriguo: agrumi, frutteti, ortive Fonte prevalente Dimensione Sistema irrigazione prevalente N aziende Autoapprovvigionamento Microirrigazione 2 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 1 Microirrigazione 14 Piccole Infiltrazione - scorrimento 13 (<20 ha) Aspersione 9 Pubblica Microirrigazione 1 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 1 Microirrigazione 5 Piccole Infiltrazione - scorrimento 1 (<20 ha) Aspersione 1 TOTALE 50 163

Regione Puglia (classe A>50%) Ordinamento prevalente irriguo: vite, olivo Fonte prevalente Dimensione Sistema irrigazione prevalente N aziende Autoapprovvigionamento Pubblica Grande (> 20 ha) Piccole (<20 ha) Grande (> 20 ha) Piccole (<20 ha) Microirrigazione 15 Infiltrazione - scorrimento 1 Aspersione 3 Microirrigazione 40 Infiltrazione - scorrimento 2 Aspersione 7 Microirrigazione 6 Infiltrazione - scorrimento 1 Aspersione 1 Microirrigazione 16 Infiltrazione - scorrimento 1 Aspersione 4 TOTALE 97 Regione Sardegna Ordinamento prevalente irriguo: barbabietola, mais, coltivazioni foraggere Fonte prevalente Dimensione Sistema irrigazione prevalente N aziende Autoapprovvigionamento Microirrigazione 1 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 8 Microirrigazione 1 Piccole Infiltrazione - scorrimento 1 (<20 ha) Aspersione 1 Pubblica Microirrigazione 2 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 30 Microirrigazione 1 Piccole Infiltrazione - scorrimento 1 (<20 ha) Aspersione 6 TOTALE 54 Elenco delle colture seminativi ID Descrizione 1 Frumento tenero 2 Frumento duro 3 Segale 4 Orzo 5 Avena 6 Mais 7 Riso 8 Sorgo 9 Cereali minori 10 Pisello (proteico e secco) o fresco 11 Fagiolo fresco o secco 12 Fava fresca o secca 13 Lupini 14 Ceci 15 Lenticchie 164

16 Patata 17 Barbabietola da zucchero 18 Piante sarchiate da foraggio 19 Tabacco 20 Luppolo 21 Cotone 22 Lino 23 Canapa 24 Altre piante tessili 25 Colza e ravizzone 26 Girasole 27 Soia 28 semi di lino 29 altre piante di semi oleosi 30 Erbe Officinali 31 Altre piante industriali 32 Pomodoro da mensa 33 Pomodoro da industria 34 Melanzana 35 Peperone 36 Insalate (indivia riccia e scarola, lattuga) 37 Insalate (indivia riccia e scarola, lattuga) 38 Radicchio o Cicoria 39 Melone 40 Cocomero 41 Cetriolo da mensa 42 Cetriolo da sottaceti 43 Zucchina 44 Finocchio 45 Carota 46 Broccoletto di rapa 47 Cavolo cappuccio 48 Cavolo verza 49 Cavolo di Bruxelles 50 Altri cavoli 51 Cavolfiore e cavolo broccolo 52 Fava fresca o secca 53 Fagiolino 54 Fagiolo fresco o secco 55 Pisello (proteico e secco) o fresco 56 Asparago 57 Aglio e scalogno 58 Cipolla 59 Carciofo 60 Fragola 61 Prezzemolo 62 Basilico 63 Sedano 64 Spinacio 65 Zucca 66 Barbabietola da orto 67 Altre ortive da pieno campo 68 Serre colture ortive 69 Fiori e piante ornamentali in pieno campo 70 Serre per fiori e piante ornamentali 71 Fiori e piante ornamentali in tunnel o campane 72 Piantine orticole 73 Floricole ed ornamentali 74 Altre piantine 165

75 Prati di leguminose 76 Altri prati avvicendati 77 Mais in erba 78 Mais a maturazione cerosa 79 altri erbai monifiti di cereali 80 altri erbai 81 Sementi 82 Terreni a riposo non soggetti a regime di aiuto 83 Terreni a riposo soggetti a regime di aiuto (buone condizioni agronomiche e ambientali) Elenco delle colture coltivazioni legnose agrarie ID Descrizione 84 Vite da tavola irrigua 85 Vite da vino da tavola 86 Vite da vino DOC 87 Olive da tavola 88 Olive da olio 89 Arancio 90 Mandarino 91 Clementina 92 Limone 93 Bergamotto 94 Melo 95 Pero 96 Pesco 97 Nettarina 98 Albicocca 99 Ciliegio 100 Susino 101 Fico 102 Diospiro 103 Frutteto misto 104 Altra frutta temperata 105 Actinidia 106 Altra frutta di origine sub tropicale 107 Mandorlo 108 Nocciolo 109 Castagno 110 Noce 111 Altra frutta a guscio 112 Vivai fruttiferi 113 Vivai piante ornamentali 114 Altri vivai 115 Altre Coltivazioni legnose agrari in serra (compresi gli alberi di Natale) 152 Viti innestate Elenco delle colture altre coltivazioni ID Descrizione 116 Orti familiari 117 Prati permanenti 118 Pascoli naturali 119 Pascoli magri 120 Prati permanenti non più destinati alla produzione.. 121 Pioppeti 122 Altra arboricoltura da legno 166

Annex 4 Database of mean irrigation water consumption used for rice cultivation Region Province Municipality Irrigation water volume Source (m 3 /ha) Piemonte Biella Brusnengo 13000 6 Piemonte Biella Castelletto Cervo 13000 6 Piemonte Biella Cavaglia 13000 6 Piemonte Biella Dorzano 13000 6 Piemonte Biella Gifflenga 13000 6 Piemonte Biella Massazza 13000 6 Piemonte Biella Masserano 13000 6 Piemonte Biella Mottalciata 13000 6 Piemonte Biella Salussola 13000 6 Piemonte Biella Villanova Biellese 13000 6 Piemonte Cuneo Barge 13000 6 Piemonte Cuneo Bra 13000 6 Piemonte Cuneo Cherasco 13000 6 Piemonte Cuneo Costigliole Saluzzo 13000 6 Piemonte Cuneo Envie 13000 6 Piemonte Cuneo Moretta 13000 6 Piemonte Cuneo Morozzo 13000 6 Piemonte Cuneo Saluzzo 13000 6 Piemonte Cuneo Savigliano 13000 6 Piemonte Novara Barengo 11000 1 Piemonte Novara Bellinzago Novarese 11000 1 Piemonte Novara Biandrate 11000 1 Piemonte Novara Borgolavezzaro 11000 1 Piemonte Novara Briona 11000 1 Piemonte Novara Caltignaga 11000 1 Piemonte Novara Cameri 11000 1 Piemonte Novara Casalbeltrame 11000 1 Piemonte Novara Casaleggio Novara 11000 1 Piemonte Novara Casalino 11000 1 Piemonte Novara Casalvolone 11000 1 Piemonte Novara Castellazzo Novarese 11000 1 Piemonte Novara Cerano 11000 1 Piemonte Novara Galliate 11000 1 Piemonte Novara Garbagna Novarese 11000 1 Piemonte Novara Granozzo con Monticello 11000 1 Piemonte Novara Landiona 11000 1 Piemonte Novara Mandello Vitta 11000 1 Piemonte Novara Momo 11000 1 Piemonte Novara Nibbiola 11000 1 Piemonte Novara Novara 11000 1 Piemonte Novara Recetto 11000 1 Piemonte Novara Romentino 11000 1 Piemonte Novara San Nazzaro Sesia 11000 1 Piemonte Novara San Pietro Mosezzo 11000 1 Piemonte Novara Sillavengo 11000 1 Piemonte Novara Sozzago 11000 1 167

Piemonte Novara Terdobbiate 11000 1 Piemonte Novara Tornaco 11000 1 Piemonte Novara Trecate 11000 1 Piemonte Novara Vespolate 11000 1 Piemonte Novara Vicolungo 11000 1 Piemonte Novara Vinzaglio 11000 1 Piemonte Torino Borgaro Torinese 13000 6 Piemonte Torino Calluso Cavour 13000 6 Piemonte Torino Chivasso 13000 6 Piemonte Torino Rivarolo Canavese 13000 6 Piemonte Torino San Benigno Canavese 13000 6 Piemonte Torino San Raffaele Cimena 13000 6 Piemonte Torino Scalenghe 13000 6 Piemonte Torino Settimo Torinese 13000 6 Piemonte Torino Verolengo 13000 6 Piemonte Vercelli Albano Vercellese 15000 1 Piemonte Vercelli Arborio 15000 1 Piemonte Vercelli Asigliano Vercellese 15000 1 Piemonte Vercelli Balocco 15000 1 Piemonte Vercelli Bianze 15000 1 Piemonte Vercelli Borgovercelli 15000 1 Piemonte Vercelli Buronzo 15000 1 Piemonte Vercelli Caresana 15000 1 Piemonte Vercelli Caresana Blot 15000 1 Piemonte Vercelli Carisio 15000 1 Piemonte Vercelli Casanova Elvo 15000 1 Piemonte Vercelli Cigliano 15000 1 Piemonte Vercelli Collobiano 15000 1 Piemonte Vercelli Costanzana 15000 1 Piemonte Vercelli Crova 15000 1 Piemonte Vercelli Desana 15000 1 Piemonte Vercelli Fontanetto Po 15000 1 Piemonte Vercelli Formigliana 15000 1 Piemonte Vercelli Gattinara 15000 1 Piemonte Vercelli Ghislarengo 15000 1 Piemonte Vercelli Greggio 15000 1 Piemonte Vercelli Lamporo 15000 1 Piemonte Vercelli Lenta 15000 1 Piemonte Vercelli Lignana 15000 1 Piemonte Vercelli Livorno Ferraris 15000 1 Piemonte Vercelli Motta dei Conti 15000 1 Piemonte Vercelli Olcenengo 15000 1 Piemonte Vercelli Oldenico 15000 1 Piemonte Vercelli Palazzolo Vercellese 15000 1 Piemonte Vercelli Pertengo 15000 1 Piemonte Vercelli Pezzana 15000 1 Piemonte Vercelli Prarolo 15000 1 Piemonte Vercelli Quinto Vercellese 15000 1 Piemonte Vercelli Rive 15000 1 Piemonte Vercelli Roasio 15000 1 Piemonte Vercelli Ronsecco 15000 1 Piemonte Vercelli Rovasenda 15000 1 Piemonte Vercelli Salasco 15000 1 Piemonte Vercelli Sali Vercellese 15000 1 Piemonte Vercelli San Germano Vercellese 15000 1 Piemonte Vercelli San Giacomo Vercellese 15000 1 Piemonte Vercelli Santhia 15000 1 Piemonte Vercelli Stroppiana 15000 1 168

Piemonte Vercelli Tricerro 15000 1 Piemonte Vercelli Trino Vercellese 15000 1 Piemonte Vercelli Tronzano 15000 1 Piemonte Vercelli Villarboit 15000 1 Piemonte Vercelli Villata 15000 1 Piemonte Alessandria Balzona 15000 1 Piemonte Alessandria Borgo San Martino 15000 1 Piemonte Alessandria Casale Monferrato 15000 1 Piemonte Alessandria Castellazzo Bormida 15000 1 Piemonte Alessandria Frassineto sul Po 15000 1 Piemonte Alessandria Giarole 15000 1 Piemonte Alessandria Isola San Antonio 15000 1 Piemonte Alessandria Masio 15000 1 Piemonte Alessandria Morano sul Po 15000 1 Piemonte Alessandria Occimiano 15000 1 Piemonte Alessandria Oviglio 15000 1 Piemonte Alessandria Pomano Monferrato 15000 1 Piemonte Alessandria Sezzadio 15000 1 Piemonte Alessandria Ticinetto 15000 1 Piemonte Alessandria Valmaccca 15000 1 Piemonte Alessandria Villanova 15000 1 Lombardia Milano Abbiategrasso 40200 1 Lombardia Milano Albairate 40200 1 Lombardia Milano Assago 40200 1 Lombardia Milano Basiglio 40200 1 Lombardia Milano Besate 40200 1 Lombardia Milano Binasco 40200 1 Lombardia Milano Boffalora Sopra Ticino 40200 1 Lombardia Milano Buccinasco 40200 1 Lombardia Milano Busto Garolfo 40200 1 Lombardia Milano Calvignasco 40200 1 Lombardia Milano Carpiano 40200 1 Lombardia Milano Casarile 40200 1 Lombardia Milano Casorezzo 40200 1 Lombardia Milano Cassinetta di Lugagnano 40200 1 Lombardia Milano Cernusco sul naviglio 40200 1 Lombardia Milano Cisliano 40200 1 Lombardia Milano Colturano 40200 1 Lombardia Milano Corbetta 40200 1 Lombardia Milano Cusago 40200 1 Lombardia Milano Gaggiano 40200 1 Lombardia Milano Gudo Visconti 40200 1 Lombardia Milano Lacchiarella 40200 1 Lombardia Milano Locate Triulzi 40200 1 Lombardia Milano Magenta 40200 1 Lombardia Milano Mediglia 40200 1 Lombardia Milano Melegnano 40200 1 Lombardia Milano Milano 40200 1 Lombardia Milano Morimondo 40200 1 Lombardia Milano Motta Visconti 40200 1 Lombardia Milano Noviglio 40200 1 Lombardia Milano Opera 40200 1 Lombardia Milano Ozzero 40200 1 Lombardia Milano Pieve emanuele 40200 1 Lombardia Milano Robecchetto con Induno 40200 1 Lombardia Milano Robecco sul Naviglio 40200 1 Lombardia Milano Rosate 40200 1 Lombardia Milano Rozzano 40200 1 169

Lombardia Milano San donato Milanese 40200 1 Lombardia Milano San giuliano Milanese 40200 1 Lombardia Milano San zenone al Lambro 40200 1 Lombardia Milano Sesto San Giovanni 40200 1 Lombardia Milano Settimo Milanese 40200 1 Lombardia Milano Trezzano sul Naviglio 40200 1 Lombardia Milano Tribiano 40200 1 Lombardia Milano Truccazzano 40200 1 Lombardia Milano Vermezzo 40200 1 Lombardia Milano Vernate 40200 1 Lombardia Milano Villa Cortese 40200 1 Lombardia Milano Vizzolo Predabissi 40200 1 Lombardia Milano Zelo Surrigone 40200 1 Lombardia Milano Zibido San Giacomo 40200 1 Lombardia Lodi Casaletto Lodigiano 40200 1 Lombardia Lodi Caselle Lurani 40200 1 Lombardia Lodi Cavenago D Adda 40200 1 Lombardia Lodi Codogno 40200 1 Lombardia Lodi Cornegliano Laudense 40200 1 Lombardia Lodi Galgagnano 40200 1 Lombardia Lodi Graffignana 40200 1 Lombardia Lodi Lodi 40200 1 Lombardia Lodi Lodivecchio 40200 1 Lombardia Lodi Mulazzano 40200 1 Lombardia Lodi Orio Litta 40200 1 Lombardia Lodi Ospedaletto Lodigiano 40200 1 Lombardia Lodi Ossago 40200 1 Lombardia Lodi Pieve Fissiraga 40200 1 Lombardia Lodi Sant Angelo Lodigiano 40200 1 Lombardia Lodi Secugnago 40200 1 Lombardia Lodi Senna Lodigiana 40200 1 Lombardia Lodi Tavazzano con Villavesco 40200 1 Lombardia Lodi Valera Fratta 40200 1 Lombardia Lodi Villanova Sillaro 40200 1 Lombardia Lodi Zelo Buon Persico 40200 1 Lombardia Mantova Bigarello 30000 1 Lombardia Mantova Castel D Ario 30000 1 Lombardia Mantova Castelbelforte 30000 1 Lombardia Mantova Guidizzolo 30000 1 Lombardia Mantova Mantova 30000 1 Lombardia Mantova Ostiglia 30000 1 Lombardia Mantova Porto Mantovano 30000 1 Lombardia Mantova Roncoferraro 30000 1 Lombardia Mantova Roverbella 30000 1 Lombardia Mantova San Giorgio di Mantova 30000 1 Lombardia Mantova Sustinente 30000 1 Lombardia Mantova Villimpenta 30000 1 Lombardia Pavia Alagna 40200 2 Lombardia Pavia Albonese 40200 2 Lombardia Pavia Albuzzano 40200 2 Lombardia Pavia Badia Pavese 40200 2 Lombardia Pavia Bascape 40200 2 Lombardia Pavia Bastida Pancarana 40200 2 Lombardia Pavia Battuda 40200 2 Lombardia Pavia Belgioioso 40200 2 Lombardia Pavia Bereguardo 40200 2 Lombardia Pavia Borgarello 40200 2 Lombardia Pavia Borgo San Siro 40200 2 170

Lombardia Pavia Bornasco 40200 2 Lombardia Pavia Breme 40200 2 Lombardia Pavia Bressana Bottarone 40200 2 Lombardia Pavia Candia Lomellina 40200 2 Lombardia Pavia Carbonara al Ticino 40200 2 Lombardia Pavia Casorate Primo 40200 2 Lombardia Pavia Cassolnovo 40200 2 Lombardia Pavia Castello D Agogna 40200 2 Lombardia Pavia Castelnovetto 40200 2 Lombardia Pavia Cava Manara 40200 2 Lombardia Pavia Ceranova 40200 2 Lombardia Pavia Ceretto Lomellina 40200 2 Lombardia Pavia Cergnago 40200 2 Lombardia Pavia Certosa di Pavia 40200 2 Lombardia Pavia Chignolo Po 40200 2 Lombardia Pavia Cilavegna 40200 2 Lombardia Pavia Confienza 40200 2 Lombardia Pavia Copiano 40200 2 Lombardia Pavia Corteolona 40200 2 Lombardia Pavia Costa De Nobili 40200 2 Lombardia Pavia Cozzo Lomellina 40200 2 Lombardia Pavia Cura Carpignano 40200 2 Lombardia Pavia Dorno 40200 2 Lombardia Pavia Ferrera Erbognone 40200 2 Lombardia Pavia Filighera 40200 2 Lombardia Pavia Frascarolo 40200 2 Lombardia Pavia Galliavola 40200 2 Lombardia Pavia Gambarana 40200 2 Lombardia Pavia Gambolo 40200 2 Lombardia Pavia Garlasco 40200 2 Lombardia Pavia Genzone 40200 2 Lombardia Pavia Gerenzago 40200 2 Lombardia Pavia Giussago 40200 2 Lombardia Pavia Gravellona Lomellina 40200 2 Lombardia Pavia Gropello Cairoli 40200 2 Lombardia Pavia Inverno e Monteleone 40200 2 Lombardia Pavia Landriano 40200 2 Lombardia Pavia Langosco 40200 2 Lombardia Pavia Lardirago 40200 2 Lombardia Pavia Linarolo 40200 2 Lombardia Pavia Lomello 40200 2 Lombardia Pavia Magherno 40200 2 Lombardia Pavia Marcignago 40200 2 Lombardia Pavia Marzano 40200 2 Lombardia Pavia Mede 40200 2 Lombardia Pavia Mezzana Bigli 40200 2 Lombardia Pavia Mezzana Rabattone 40200 2 Lombardia Pavia Mezzanino 40200 2 Lombardia Pavia Miradolo Terme 40200 2 Lombardia Pavia Mortara 40200 2 Lombardia Pavia Nicorvo 40200 2 Lombardia Pavia Olevano di Lomellina 40200 2 Lombardia Pavia Ottobiano 40200 2 Lombardia Pavia Palestro 40200 2 Lombardia Pavia Parona 40200 2 Lombardia Pavia Pavia 40200 2 Lombardia Pavia Pieve Albignola 40200 2 Lombardia Pavia Pieve del Cairo 40200 2 171

Lombardia Pavia Pieve porto Morone 40200 2 Lombardia Pavia Pizzarrosto Pezzana 40200 2 Lombardia Pavia Robbio 40200 2 Lombardia Pavia Rognano 40200 2 Lombardia Pavia Roncaro 40200 2 Lombardia Pavia Rosasco 40200 2 Lombardia Pavia San Genesio ed Uniti 40200 2 Lombardia Pavia San Giorgio di Lomellina 40200 2 Lombardia Pavia San Martino Siccomario 40200 2 Lombardia Pavia San Zenone Po 40200 2 Lombardia Pavia Sannazzaro de Burgondi 40200 2 Lombardia Pavia Santa Cristina e Bissone 40200 2 Lombardia Pavia Sant Alessio con Vialone 40200 2 Lombardia Pavia Sant Angelo Lomellina 40200 2 Lombardia Pavia Sartirana Lomellina 40200 2 Lombardia Pavia Scaldasole 40200 2 Lombardia Pavia Semiana 40200 2 Lombardia Pavia Siziano 40200 2 Lombardia Pavia Sommo 40200 2 Lombardia Pavia Spessa 40200 2 Lombardia Pavia Suardi 40200 2 Lombardia Pavia Torre Beretti e Castellaro 40200 2 Lombardia Pavia Torre dei Negri 40200 2 Lombardia Pavia Torre d Arese 40200 2 Lombardia Pavia Torre d Isola 40200 2 Lombardia Pavia Torrevecchia Pia 40200 2 Lombardia Pavia Travaco Siccomario 40200 2 Lombardia Pavia Trivolzio 40200 2 Lombardia Pavia Tromello 40200 2 Lombardia Pavia Trovo 40200 2 Lombardia Pavia Valeggio Lomellina 40200 2 Lombardia Pavia Valle Lomellina 40200 2 Lombardia Pavia Valle Salimbene 40200 2 Lombardia Pavia Velezzo Lomellina 40200 2 Lombardia Pavia Vellezzo Bellini 40200 2 Lombardia Pavia Vidigulfo 40200 2 Lombardia Pavia Vigevano 40200 2 Lombardia Pavia Villa Biscossi 40200 2 Lombardia Pavia Villanova d Ardenghi 40200 2 Lombardia Pavia Villanterio 40200 2 Lombardia Pavia Vistarino 40200 2 Lombardia Pavia Voghera 40200 2 Lombardia Pavia Zeccone 40200 2 Lombardia Pavia Zeme 40200 2 Lombardia Pavia Zerbo 40200 2 Lombardia Pavia Zerbolo 40200 2 Lombardia Pavia Zinasco 40200 2 Lombardia Bergamo Antegnate 30000 1 Veneto Padova Bagnoli di Sopra 15000 1 Veneto Padova Codevigo 15000 1 Veneto Rovigo Porto Tolle 10500 2 Veneto Rovigo Porto Viro 10500 2 Veneto Rovigo Salara 10500 2 Veneto Rovigo Taglio di Po 10500 2 Veneto Venezia Eraclea 10500 2 Veneto Venezia Mira 10500 2 Veneto Vicenza Arzignano 12750 2 Veneto Vicenza Grumolo delle Abbadesse 12750 6 172

Veneto Vicenza Lonigo 12750 6 Veneto Verona Bovolone 15000 1 Veneto Verona Casaleone 15000 1 Veneto Verona Cerea 15000 1 Veneto Verona Erbe 15000 1 Veneto Verona Gazzo Veronese 15000 1 Veneto Verona Isola della Scala 15000 1 Veneto Verona Mozzecane 15000 1 Veneto Verona Nogara 15000 1 Veneto Verona Nogarole Rocca 15000 1 Veneto Verona Oppeano 15000 1 Veneto Verona Palu 15000 1 Veneto Verona Salizzole 15000 1 Veneto Verona Sorga 15000 1 Veneto Verona Trevenzuolo 15000 1 Veneto Verona Vigasio 15000 1 Emilia-Romagna Bologna Malalbergo 9033.3 6 Emilia Bologna Medicina 9033.3 6 Emilia Bologna Molinella 9033.3 6 Emilia Bologna San Pietro in Casale 9033.3 6 Emilia Ferrara Argenta 15000 2 Emilia Ferrara Berra 15000 2 Emilia Ferrara Bondeno 15000 2 Emilia Ferrara Codigoro 15000 2 Emilia Ferrara Comacchio 15000 2 Emilia Ferrara Copparo 15000 2 Emilia Ferrara Ferrara 15000 2 Emilia Ferrara Goro 15000 2 Emilia Ferrara Jolanda di Savoia 15000 2 Emilia Ferrara Lagosanto 15000 2 Emilia Ferrara Massa Fiscaglia 15000 2 Emilia Ferrara Mesola 15000 2 Emilia Ferrara Mezzogoro (Codigoro) 15000 2 Emilia Ferrara Ostellato 15000 2 Emilia Ferrara Tresigallo 15000 2 Emilia Modena Carpi 8500 2 Emilia Modena Novi di Modena 8500 2 Emilia Piacenza Castelvetro Piacentino 9033.3 6 Emilia Reggio Emilia Gualtieri 3600 2 Emilia Reggio Emilia Guastalla 3600 2 Toscana Grosseto Grosseto 8000 2 Toscana Siena Murlo 1500 4 Sardegna Cagliari Muravera 12000 1 Sardegna Cagliari San Gavino Monreale 12000 1 Sardegna Oristano Cabras 14000 2 Sardegna Oristano Nurachi 14000 2 Sardegna Oristano Oristano 14000 2 Sardegna Oristano Palmas Arborea 14000 2 Sardegna Oristano San Vero Milis 14000 2 Sardegna Oristano Santa Giusta 14000 2 Sardegna Oristano Siamaggiore 14000 2 Sardegna Oristano Simaxis 14000 2 Sardegna Oristano Tramatza 14000 2 Sardegna Oristano Zeddiani 14000 2 Calabria Cosenza Cassano allo Ionio 8750 2 Calabria Cosenza Corigliano Calabro 8750 2 Calabria Cosenza Sibari (Cassano allo Ionio) 8750 2 Calabria Cosenza Villapiana 8750 2 173

Printing CSR Centro Stampa e Riproduzione s.r.l. Via di Pietralata, 157-00158 Roma Printing completed in June 2013

Research is now able to analyze and evaluate, within an integrated and multidisciplinary approach, all activities related to natural resources and their sustainable management thanks to a growing integration between agricultural, environmental and energy policies. In these publications), INEA focuses its research and analysis on the protection of natural resources and their sustainable management, in environmental and agricultural policies methods of analysis for decision support. The use of water resources in agriculture plays a strategic role in the priority issues for the future and INEA has become since the nineties a point of scientific and technical reference for the activities of study, research and support carried out on irrigation water and the monitoring of national irrigation systems. Furthermore, INEA has a key role for the investments in irrigation and public spending in the sector. Specific searches have been done on economic instruments, pricing policies on water and climate change scenarios for the irrigation sector. Water Resources is part of a series of publications produced by INEA Environmental and Agricultural Policy which emphasizes the importance of water in agriculture. Series EnvironmentAL and agricultural policy Water Resource Management ISBN 978-88-8145-289-7