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

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

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

4 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 IAS-CSIC Luciano Mateos - Paragraphs: 2.1, 2.2, 2.3, 2.4 and 4.2 CRA-CMA Luigi Perini - Paragraph 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 (Grant Programme 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

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

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7 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

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9 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 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 awarded to the Italian Institute for Agricultural Economics (INEA). 7

10 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/ /07/2008 Census questionnaire amendments 1/09/ /01/2009 Models design 15/09/ /02/2010 Model A 15/09/ /03/2009 Model B 15/09/ /03/2009 Model C 1/10/ /02/2010 Data collection 1/10/2009 1/05/2010 Agrometeorological database 1/10/ /01/2009 Crop characteristics database 15/01/ /06/2009 Soil database 1/02/2009 1/05/2010 Models calibration and validation 1/02/2010 1/05/2010 Pilot campaigns 15/10/ /02/2010 Calibration 15/01/2010 1/05/2010 Software implementation and testing 15/12/ /05/2010 Module 1 15/12/ /02/2010 Module 2 15/12/ /05/2010 Project end 14/05/ /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

11 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

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13 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 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

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15 Table of contents Acknowledgements 5 Foreword 7 Executive summary 11 Introduction 15 chapter 1 The Irrigated Agriculture in Italy: an Analysis through fss Data Historical trend of the irrigation phenomenon Details on the irrigation phenomenon 20 chapter 2 Methodology for the Irrigation Water Consumption Estimation State of the art on the estimation of irrigation water requirements Crop Irrigation Requirements Model (Model A) Irrigation Efficiency Model (Model B) Irrigation Strategy Model (Model C) Irrigation water consumption estimation for rice Irrigation water consumption estimation for protected crops 45 chapter 3 Input Data Collection The 6 th General Agricultural Census database Crop characteristics database Soil database Agro-meteorological database 61 chapter 4 Models Calibration Methodology for pilot areas definition and farms sample extraction Pilot questionnaire for the model calibration 77 13

16 4.3 Pilot campaigns Analysis of the model simulation results Influence of the resolution of the agro-meteorological data on the simulation results 96 chapter 5 Software Implementation Module architecture of the computational system 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

17 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

18 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

19 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 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

20 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 Irrigable and irrigated area for the years 1982, 1990, 2000, 2003, 2005 and 2007 (area in thousands of hectares) Thousands of hectares Irrigable area Irrigated area Year 18

21 Table 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) 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, , , , ,738 a.v. % over total farms with irrigation Farms with irrigated surface 834, , , , , ,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, , , , , , Flood 73, , , , , , Aspersion 533, , , , , , Dripping 28, , , , , , Other systems 23, , , , , , Farms water souces (c) Surface flowing water 159, , , n.a. n.a. n.a. Surface water bodies 18, , , n.a. n.a. n.a. Other 341, , (d) 72.7 n.a. n.a. n.a. Delivering management (c): Irrigation and land reclamation Consortia 305, , , , n.a. n.a. Other farms 32, , , , n.a. n.a. Other ways 35, , (e) (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

22 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 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 Irrigated area Farms Irrigated area Farms Irrigated area Farms Irrigated area Wheat ,566 69,489 27,178 99,636 13,061 57,391 Grain maize 200, , , , , , , ,723 Potato ,925 34,710 56,872 26,461 22,944 24,847 Sugar beet ,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 , ,083 11,971 78,618 9,527 53,895 Fresh vegetables 264, , , , , , , ,107 Rotational forage 143, ,280 96, ,376 47, ,560 52, ,261 Vineyards 136, , , , , , , ,330 Citrus plantations 122, , , , , ,651 75, ,744 Fruit and berry plantations 82, , , , , ,175 88, ,089 Other crops 282, , , , , , , ,521 Total 934,427 2,521, ,840 2,711, ,082 2,471, ,541 2,763,510 Source: ISTAT, FSS - Years 1982,1990, 2000 and details on the irrigation phenomenon 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

23 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 Valled Aosta Lombardia Trentino-Alto Adige Bolzano (AP) Trento (AP) Veneto Friuli-Venezia Giulia Liguria Emilia-Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna Italy North Centre South 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

24 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 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 Valle d Aosta Lombardia Trentino-Alto Adige Bolzano (AP) Trento (AP) Veneto Friuli-Venezia Giulia Liguria Emilia-Romagna Toscana Umbria Marche Lazio Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna Italy North Centre South Source: ISTAT, FSS - Year 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

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