A Model-Based Irrigation Water Consumption Estimation at Farm Level 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.”

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 Tu- dini (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.

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 6th General Agricultural Census realized by ISTAT in the period 2010-20121. 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 Meth- ods (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.

F igure 1. Project work breakdown structure with the five WPs and the relative phases

marsala

Census Software Models calibration Models design questionnaire Data collection implementation and validation amendments and testing

Agro-meteo Model A Pilot campaigns Module 1 database

Crop Model B characteristics Calibration Module 2 database

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.

T able 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 Amend- ments, 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 Ital- ian 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.

Executive summary

The MARSALa (Modelling Approach for irrigation wateR eStimation at fArm Lev- el) 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 6th 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.

Table of contents

Acknowledgements 5

Foreword 7

Executive summary 11

Introduction 15 c hapter 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 c hapter 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 c hapter 3 Input Data Collection 49 3.1 The 6th General Agricultural Census database 49 3.2 Crop characteristics database 53 3.3 Soil database 56 3.4 Agro-meteorological database 61 c hapter 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

c hapter 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: 6th 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 oc- currence 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 Frame- work 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 21 and NUTS 32 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-of- the-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 C HAPTer 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 technolo- gies. In the following a brief overview of the phenomenon is proposed1.

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 sav- ing 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).

F igure 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

s e

r 5.000 a t c e h f

o 4.000 s d n a s u

o 3.000 h T

2.000

1.000

0 1982 1990 2000 2003 2005 2007

Year Irrigable area Irrigated area

8.0 irrigation farms with % over total total % over 2007 n.a. n.a. n.a. n.a. n.a. n.a. a.v. 44,967 677,738 677,738 563,663 3,950,503 2,666,205 7.1 irrigation farms with % over total total % over 2005 n.a. n.a. n.a. n.a. n.a. n.a. ample survey (b) survey ample a.v. S 35,682 13,973 2.8 14,838 2.6 660,349 660,349 503,461 2,613,419 3,972,666

7. 3 3.7 irrigation farms with % over total total % over 2003 n.a. n.a. n.a. a.v. 23,235 710,522 710,522 213,603 34.3 183,990 36.5 193,682 34.4 622,541 3,977,206 2,763,510

1.0 4 4.1 72.7 source of water in surveys run in 1982 and 1990, whereas in years 2000 and 2003 sources 2000 and 2003 sources in years whereas in 1982 and 1990, run in surveys water of source phenomena. independent been considered have management and delivering basin. rainfall irrigation farms with (c) Variables related to water sources and adopted delivering systems have been surveyed as been surveyed have systems delivering and adopted sources water to related Variables (c) and wastewater treated groundwater, aqueduct, water: of source (d) Includes the following forms. and other (e) Includes self-supply % over total total % over 2000 7,439 a.v. 31,373 4.3 45,691 731,082 302,872 41.4 329,032 52.9 114,369 15.6 184,214 29.6 146,504 29.1 170,035 30.2 966,270 2,471,378 3,892,202 531853 (d)

2.7 3 7.7 10.7 irrigation farms with % over total total % over 1990 ensus survey (a) ensus survey a.v. C 456,401 43.1 934,640 2,711,182 3,881,772 1,059,456

41.0 irrigation farms with % over total total % over 1982 n.a. a.v. 35,102 4.2 34,592 3.3 429325 (e) 58.7 (e) 397.199 63.8 32,477 3.9 31,037 2.9 35,071 4.8 27,015 4.3 28,20823,406 3.4 2.8 113,577 28,164 73,533 8.8 48,095 4.5 341,738 241,366 28.9 377,579 35.6 322,313 305,465 36.6 398,913 533,423 63.9 583,183 55.0 333,711 45.6 221,402 35.6 170,477 33.9 189,865 33.7 834,424 2,521,193 2,780,614

rrigated rrigated I rms with irrigation and rms related with irrigation surfaces source supply by method and irrigation expressed as absoluteand percentage value a ter source / source ter F a W rrigation method rrigation face / face I uropean Union Universe uropean ms water souces (c) ms water ms irrigation method ms irrigation sur rrigated farms / rrigated elivering management (c): management elivering urface water bodies water urface 18,891 2.3 25,134 2.4 33,790 4.6 urface flowing water flowing urface 159,40119.1 194,557 18.4 233,010 31.9 uperficial flowing water water flowing uperficial spersion spersion ar ar I a rrigated surface rrigated rrigated farms rrigated Other ways Other farms Irrigation and land Irrigation Consortia reclamation D Other S F S Dripping Dripping Other systems A Flood and lateral infiltration and lateral F S Irrigated area Irrigated I area Irrigable Farms with irrigated surface with irrigated Farms I surface with irrigable Farms T - 1.1 ble 2005 and 2000, 2003, 2007). 1990, totalover 1982, farms (Years with irrigation 2007 2005, 2003, 2000, 1990, 1982, Year FSS - ISTAT, Source: value absolute a.v.: available not n.a.: (a) National Universe (b) E

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).

T able 1.2 - Number of farms with irrigation and irrigated area (in hectares) for the main crops (Years 1982, 1990, 2000 and 2003).

Census year Sample survey 1982 1990 2000 2003 Crop Irrigated Irrigated Irrigated Irrigated Farms Farms Farms Farms area area area 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 82,511 144,329 117,355 199,059 108,974 189,175 88,545 210,089 plantations 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.

1.2D etails 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 (Ta- ble 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 T able 1.3 Farms with irrigable and irrigated area by region (Year 2007). Farms with Irrigable area Farms with Irrigated area irrigable area irrigated area R egion/Autonomous % over % over % over % over % over % over % over % over the province (AP) the total the total the total cultivated the total the total the total cultivated farms (a) area (b) farms (a) 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 6 7.1 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 4 7. 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 4 4.1 63.6 36.0 Centre 11.3 28.5 9.4 16.0 10 .1 21.2 6.8 7. 8 South 52.5 3 7.1 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 moun- tainous areas (7%).

F igure 1.2 Irrigated area by altimetric zone (Year 2007).

Mountain 9% Hill 3%

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%.

T able 1.4 - Irrigated area by irrigation system and region (Year 2007). Data are expressed as percentage over the total irrigated area.

Irrigation system

R egion/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 1 7.7 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 2 7.1 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 2 7.1 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 1 7. 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 T able 1.5 - Number of farms and relative irrigated area (hectares) by number of irrigation system (Year 2007).

Number of Farms with UAA and/or wooden arboriculture Irrigated area irrigation systems 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).

F igure 1.3 Cultivated and irrigated area (hectares) by crop (Source: ISTAT, FSS 2003).

800

700

600 ARES

CT 500

400

300 THOUSANDS OF HE THOUSANDS OF 200

100

Mais Rice Vine Olive Wheat Potato Citrus forage Sugar beet Sunflower Soya bean Rotational

CROPS

Cultivated area Total irrigated area Irrigated area in farms where crop is completely irrigated

23 T able 1.6 - Farms with irrigated area by number of irrigated crops and irrigation system (Year 2003).

Number of Irrigation Farms irrigation systems system 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.

T able 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 C I HAPTer I 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 (Vei- hmeyer 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 per- spective, 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.

F igure 2.1 - Framework of the MARSALa methodology: typology of the input data and models relationships.

CROP CROP SOIL CLIMATE 2010 CENSUS STATISTICS PARAMETERS

MODEL A MODEL B MODEL C CROP IRRIGATION IRRIGATION IRRIGATION REQUIREMENT SYSTEM EFFICIENCY 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 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 MARSALa methodology is depicted in Figure 2.1.

Figure 2.1 - Framework of the MARSALa methodology: typology of the input data and models relationships.

The three models allow estimating the irrigation consumption of all the farm irrigated crops except for 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. Crop Irrigation Requirements Model (Model A) 2.2.C rop 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 rootThe zone model water accounts balance, for the theformulation 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 and RZWD are the root zone soil water deficit on days i and i-1 in mm; - RZWDi and RZWDi-1 are ithe root zone i-1soil water deficit on days i and i-1 in mm;

- Rei is the effective rainfall in mm on day i; - Rei is the effective rainfall in mm on day i;

- Ii is the irrigation in mm on day i; - Ii is the irrigation in mm on day i; - ETi is the crop evapotranspiration in mm on day i; - ETi is the crop evapotranspiration in mm on day i; - ROi is the irrigation runoff in mm on day i; - ROi is the irrigation runoff in mm on day i; - Di 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 - Di is the drainageis at field in mmcapacity, on day whilei. it is empty when the water content is at the wilting point (see Fig- It is understoodure 2.2). that Thethe rootroot zonezone is water full ofholding water capacity(RZWD = ( RZWHC0) when )its is waterdefined content as the is atdepth field of water capacity, while it(within is empty the when root the zone) water between content field is at thecapacity wilting and point wilting (see Figurepoint. 2.2). The root zone water holding capacity Runoff(RZWHC of) rainis defined water asis notthe considereddepth of water directly (within but the through root zone) the betweenconcept fieldof effective capacity and wiltingrainfall. point. It has been assumed moreover that runoff of irrigation water is negligible. Runoff of rain waterDrainage 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 moreoverover that field runoff capacity of irrigation at the water given is negligible.day of the water balance. Drainage of irrigation water is dependent on the applied depth in relation to the required depth and the irrigation uniform- Drainage of rain water is computed as the excess of the root zone soil water content over field capacity ity, this aspect is managed by Model B. 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. F igure 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 sta- Effective tions.rainfall Evapotranspiration data are derived (ETfrom, mm) the is datacomputed acquired using in FAO agrometeorological methodology based stations. on the con- Evapotranspirationcepts (ET , ofmm) crop is computedcoefficient using and FAO reference methodology evapotranspiration based on the concepts (Doorembos of crop and coefficient Pruitt, 1977b). and reference evapotranspirationReference evapotranspiration (Doorembos and Pruitt,(ETo, 1977b).mm) is Referencecalculated evapotranspiration using the Penman-Monteith (ETo, mm) is equa- calculated using thetion Penman-Monteith (Monteith and Unsworth, equation (Monteith 1990, Cap. and 11; Unsworth, Allen et 1990, al., 1998) Cap. with11; Allen data et of al., solar 1998) radiation, with data of solarwind speed,radiation, air windtemperature speed, andair temperaturerelative humidity and relativeacquired humidity in agrometeorological acquired in sta- agrometeorological stations. The crop coefficients are derived using the dual approach (Wright, 1982) in the form popularized by FAO (Allen et al., 1998). This approach separates27 crop transpiration from soil surface evaporation as follows:

(2)

where Kcb is the basal crop coefficient, Ke is the soil evaporation coefficient and Ks quantifies the reduction in crop transpiration due to soil water deficit. Therefore, crop transpiration (T, mm) is:

(3) and soil evaporation (E, mm) is:

- Di is the drainage in mm on day i. - Di 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 is at field It is understood that the root zone is full of water (RZWD = 0) when its water content is at field capacity, while it is empty when the water content is at the wilting point (see Figure 2.2). The root zone capacity, while it is empty when the water content is at the wilting point (see Figure 2.2). The root zone water holding capacity (RZWHC) is defined as the depth of water (within the root zone) between field water holding capacity (RZWHC) is defined as the depth of water (within the root zone) between field capacity and wilting point. capacity and wilting point. Runoff of rain water is not considered directly but through the concept of effective rainfall. It has been Runoff of rain water is not considered directly but through the concept of effective rainfall. It has been assumed moreover that runoff of irrigation water is negligible. assumed moreover that runoff of irrigation water is negligible. Drainage of rain water is computed as the excess of the root zone soil water content over field capacity 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 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. 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. Effective rainfall data are derived from the data acquired in agrometeorological stations. Evapotranspiration (ET, mm) is computed using FAO methodology based on the concepts of crop coefficient Evapotranspiration (ET, mm) is computed using FAO methodology based on the concepts of crop coefficient and reference evapotranspiration (Doorembos and Pruitt, 1977b). Reference evapotranspiration (ETo, mm) is and reference evapotranspiration (Doorembos and Pruitt, 1977b). Reference evapotranspiration (ETo, mm) is calculated using the Penman-Monteith equation (Monteith and Unsworth, 1990, Cap. 11; Allen et al., 1998) calculated using the Penman-Monteith equation (Monteith and Unsworth, 1990, Cap. 11; Allen et al., 1998) with data of solar radiation, wind speed, air temperature and relative humidity acquired in with data of solar radiation, wind speed, air temperature and relative humidity acquired in agrometeorological stations. The crop coefficients are derived using the dual approach (Wright, 1982) in the agrometeorological stations. The croptions. coefficients The crop are coefficients derived using are the derived dual approach using the (Wright, dual approach 1982) in the(Wright, 1982) in the form form popularized by FAO (Allen et al., 1998). This approach separates crop transpiration from soil surface form popularized by FAO (Allen etpopularized al., 1998). Thisby FAO approach (Allen separates et al., 1998). crop transpiration This approach from separatessoil surface crop transpiration from evaporation as follows: evaporation as follows: soil surface evaporation as follows:

(2) (2) (2)

K K K where Kcb is the basal crop coefficient,where Ke is cbthe is soil the evaporation basal crop coefficient coefficient, and Ke sis quantifies the soil theevaporation reduction coefficient and s where K is the basal crop coefficient, K is the soil evaporation coefficient and K quantifies the reduction in cropcb transpiration due to soilquantifies watere deficit. the reduction in crop transpirations due to soil water deficit. in crop transpiration due to soil water deficit. Therefore, crop transpiration (T, mm) is: Therefore, crop transpiration (T, mm) is: Therefore, crop transpiration (T, mm) is: (3) (3) (3)

(4) and soil evaporation (E, mm) is: and soil evaporation (E, mm) is: and soil evaporation (E, mm) is: (4) (4) The variation of Kcb is represented based on the values of Kcb at the initial, middle and final stages of the crop growth cycle and the duration of the initial, rapid growth, mid season, and late season phases(4) (see (4) The variation of Kcb is represented based on the values of Kcb at the initial,(4) middle and final stages of TheFigure variation 2.3). of Kcb is represented based on the values of Kcb at the initial, middle and final stages of the crop growth cycle and the duration of the initial, rapid growth, mid season, and late season phases (see the crop growth cycle and the duration of the initial, rapid growth, mid season, and late season phases (see SubsequentlyTheFigure variation 2.3). , theof K rootcb is zone Trepresentedhe depthvariation (Z basedr) couldof Kon be theis computed representedvalues of as K acb functionbasedat the initial,on of theKcb middle: values and of finalK at stages the initial,of middle FigureThe variation 2.3). of Kcb is represented based on the values ofcb Kcb at the initial, middle and final stages of cb the crop growth cycle and thefinal duration stages of of the the initial, crop rapidgrowth growth, cycle midand season,the duration and late of season the initial, phases rapid (see growth, mid the crop growth cycle and theSubsequently duration of ,the the initial, root zone rapid depth growth, (Zr) couldmid season, be computed and late as seasona function phases of K (seecb: SubsequentlyFigure 2.3). , the root zoneseason, depth and (Zr )late could season be computed phases as(see a function Figure 2.3).of Kcb : Figure 2.3). (5) Subsequently , the rootSubsequently zone depth (Zr ), couldthe root be computed zone depth as a( Zfunctionr) could of be K cbcomputed: as a function of Kcb: Subsequently , the root zone depth (Zr) could be computed as a function of Kcb: (5) (5) where Zr max and Zr min are the maximum effective root depth and the minimum effective root depth during(5) the (5) initial stage of crop growth and Kcb max the maximum value of Kcb. (5) where Zr max and Zr min are the maximum effective root depth and the minimum effective root depth during the where Z r max and Zr min are the maximum effective root depth and the minimum effective root depth during the Ke is obtainedinitial by stage calculating of crop thegrowthw hereamount andZ of K cb energyand max theZ available maximum are the at valuemaximumthe soil of surfaceKcb .effective as follows: root depth and the minimum effec- initial stage of crop growth and K the maximumr max valuer min of K . where Zr max and Zr min aretive cbthe maxroot maximum depth during effective the root initial depthcb stage and theof crop minimum growth effective and K root depththe maximum during the value of K . where Zr max and Zr min areK isthe obtained maximum by calculatingeffective root the depth amount and of the energy minimum available effective at the root soil depthsurface during ascb maxfollows: the cb initial stagee of crop growth and K the maximum value of K . initial Kstagee is obtained of crop growthby calculating and K the theamount maximumK isofcb obtainedmaxenergy value available ofby K calculating. at the soil thesurfacecb amount as follows: of energy available at the soil surface as cb max e cb Ke is obtained by calculatingfollows: the amount of energy available at the soil surface as follows: (6) Ke is obtained by calculating the amount of energy available at the soil surface as follows:

(6) (6) (6) where Kr is a dimensionless evaporation reduction coefficient dependent on topsoil water depletion (Allen et al., 1998) and K is the maximum value of K following rainfall or irrigation. The value of K cannot(6) be where cK max is a dimensionless evaporationc reduction coefficient dependent on topsoil(6) watere depletion (Allen et greater than the productr f × whereK , Kwhere is a fdimensionless is the fraction evaporation of the soil surfacereduction that coefficientis both exposed dependent and on topsoil where Kr is a dimensionlessal., 1998) and evaporation Kew is cthe maxreduction maximumr coefficientew value of dependent K following on topsoil rainfall water or irrigation. depletion The (Allen value et of K cannot be waterc max depletion (Allen et al., 1998)c and K is the maximum value of K followinge rainfall al., 1998)wetted. and Kc max is the maximum value of Kc following rainfall or irrigation.c max The value of Ke cannot be c where Krgreater is a dimensionless than the product evaporation few × K reductionc max, where coefficient few is the dependent fraction of on the topsoil soil watersurface depletion that is both (Allen exposed et and where Kr is a dimensionless evaporationor irrigation. reduction coefficientThe value dependent of K cannot on topsoil be greater water thandepletion the product(Allen et f × K , where f is greater than the product few × Kc max, where few is the fractione of the soil surface that is both exposed ewand c max ew al., 1998)Thewetted. stressand K coefficient,c max is the maximumKs, is computed value basedof Kc onfollowing the relative rainfall root or zone irrigation. water deficit The value as: of Ke cannot be al., 1998)wetted. and Kc max is the maximumthe value fraction of Kc offollowing the soil rainfall surface or that irrigation. is both The exposed value ofand Ke wetted.cannot be greater than the product few × Kc max, where few is the fraction of the soil surface that is both exposed and greater than the product few ×The K cstress max, where coefficient,The few stressis theKs, iscoefficient,fraction computed of thebased K ,s soili con omputedsurface the relative that based isroot both o nzone t heexposed water relative deficitand r oot as: zone water deficit as: wetted. s wetted. The stress coefficient, Ks, is computed based on the relative root zone water deficit as: [if RZWDi < (1-p) RZWHC] (7) The stress coefficient, Ks, is computed based on the relative root zone water deficit as: The stress coefficient, Ks, is computed based on the relative root zone [if [if RZWD waterRZWD deficit< (1- < p(1-p) )as: RZWHC RZWHC] ] (7) i i (7) [if RZWDi < (1-p) RZWHC] (7) [if RZWDi < (1-p) RZWHC] (7) [if RZWD[if[if RZWD RZWDi < (1-≥ p≥(1-) (1-p)RZWHCp) RZWHC RZWHC] ] ] (8) i i (7) (8)

[if RZWDi ≥ (1-p) RZWHC] (8) [if RZWD ≥ (1-p) RZWHC] (8) where p is the fraction of the RZWHCwhere pbelow is thei which fraction transpiration of the RZWHC is reduced. below which transpiration is reduced. [if RZWDi ≥ (1-p) RZWHC] (8) where p is the fraction [if RZWD of thei ≥ RZWHC (1-p) RZWHC below ]which transpiration is reduced. (8)

where p is the fraction of the RZWHC below which transpiration is reduced. where p is the fraction of the RZWHC below which transpiration is reduced. where p is the fraction of the RZWHC below which transpiration is reduced. 28

F igure 2.3 - Basal crop coefficient (Kcb) and crop coefficient (Kc) 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)

2.3. Irrigation Efficiency Model (Model B) 2.3. Irrigation Efficiency Model2.3 (Model irrigation B) Efficiency Model (Model B) The irrigation application efficiency,The thus irrigation the irrigation application drainage efficiency,losses, depends thus on the irrigation irrigation system drainage losses, depends on The irrigation application efficiency, thus the irrigation drainage losses, depends on irrigation system factors and management factors. Anirrigation irrigation system system factors is characterized and management by its application factors. An uniformity. irrigation The system is characterized by factors and management factors. An irrigation system is characterized by its application uniformity. The management factors are considered inits theapplication management uniformity. deficit coefficient. The management If the deficit factors coefficient are considered is high, a in the management def- management factors are considered in the management deficit coefficient. If the deficit coefficient is high, a large fraction of the field will not receiveicit coefficient.the water required If the to deficitmaintain coefficientfull evapotranspiration; is high, a largeon the fraction contrary, of the field will not receive large fraction of the field will not receive the water required to maintain full evapotranspiration; on the contrary, if it is low and the application uniformitythe water is low required as well, then to maintain a significant full part evapotranspiration; of the applied irrigation on the will contrary, be if it is low and the if it is low and the application uniformity is low as well, then a significant part of the applied irrigation will be lost as drainage, hence, the applicationapplication efficiency will uniformity be low. is low as well, then a significant part of the applied irrigation will be lost as drainage, hence, the application efficiency will be low. Figure 2.4 depicts the frequencylost distribution as drainage, of hence,the applied the depthapplication of irrigation efficiency (relative will to thebe low. required depth) Figureacross the2.4 fielddepicts assuming the frequency that it follows distributionFigure a uniform 2.4 of depictsthe statistical applied the depthfrequencydistribution. of irrigation distributionOften, (relativethe normal of to the thedistribution applied required depth of irrigation (rela- adjustsdepth) acrossto the thenon field uniformity assuming of thatthetive irrigationit follows to the watera requireduniform better statistical depth)than the acrossdistribution. uniform the distribution. fieldOften, assumingthe Althoughnormal that distribution the it same follows a uniform statistical analysisadjusts tocould the nonbe doneuniformity assuming of the a distribution. normalirrigation distribution water Often, better (Anyoji the than normal theand uniform Wu,distribution 1994). distribution. Dealing adjusts Although with to thethe nontheuniform same uniformity of the irrigation distributionanalysis could is simplerbe done and assuming the unavailability awater normal better distributionof more than precise the (Anyoji uniform information and distribution.Wu, does 1994). not Dealing justifyAlthough (inwith thethe the context same uniform analysisof could be done as- MARSALa)distribution isusing simpler a more and complex the unavailability model.suming aof normal more precise distribution information (Anyoji does and not Wu,justify 1994). (in the Dealing context with of the uniform distribu- MARSALa) using a 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.

F igure 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.

Figure 2.4 - Frequency distribution of the applied depth of irrigation (relative to the required depth) across the Figure 2.4 - Frequencyfield 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.

For a given required depth, three areas can be distinguished in the graph (see Figure 2.4): area A representingFor a thegiven water required that isdepth, available three for areas crop can consumption, be distinguished area B in representing the graph (seethe waterFigure that 2.4): is lostarea by A For a given required depth, three areas can be distinguished in the graph (see Figure percolationrepresenting and the area water C 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,is lost by 2.4): area A representing the water that is available for crop consumption, area B represent- threepercolation irrigation and performance area C representing indicators the may part beof defined:the root zoneApplication that has Efficiency not received (E any), Percolation irrigation water.Coefficient Therefore, (CP) ing the water lost by percolation and areaa C representing the part of the root zone that has andthree Deficit irrigation Coefficient performance (CD). indicators may be defined: Application Efficiency (Ea), Percolation Coefficient (CP) and Deficit Coefficient (CD). not received any irrigation water. Therefore, three irrigation performance indicators may

be defined: Application Efficiency (Ea), Percolation Coefficient (CP) and Deficit Coeffi- cient (CD).

(9) (9) (9)

(10) (10) (10)

(11) (11)(11) (11) (11) (11)

(11) Based on the uniform distribution, the above indicators may be expressed in the following form(11) (Wu, BasedBased onon thethe uniformuniform distribution,distribution, thethe aboveabove indicatorsindicators maymay bebe expressedexpressed inin thethe followingfollowing formform (Wu,(Wu, 1988):Based on the uniform distribution, the above indicators may be expressed in the following form (Wu, 1988):1988):Based on the uniform distribution, the above indicators may be expressed in the following form (Wu, 1988): Based on the uniform distribution, the above indicators may be expressed in the following form (Wu, 1988): Based on the uniform distribution, the above indicators may be expressed in the fol- 1988):Based on the uniform distribution, the above indicators may be expressed in the following form (Wu, lowing form (Wu, 1988): 1988): (12)(12) (12) (12) (12) (12) (12)

(12)

(13)(13) (13) (13) (13) (13) (13)

(13)

(14)(14) (14) (14) (14) (14) (14)

wherewhere a aand and b bare are determined determined by by the the application applicationwhere a uniformity anduniformity b are and anddetermined X X is is the the ratio ratioby thebetween between application required required uniformitydepth depth and and and X is the ratio be- where a and b are determined by the application uniformity and X is the ratio between required depth(14) and whereapplied a anddepth. b areX represents determined also by the the linktween application between required Modeluniformity depth B and andand C since Xapplied is itthe is theratiodepth. inverse between X representsof therequired Relative alsodepth Irrigation the and link between Model B and where appliedapplied a and depth.depth. b are XX representsdeterminedrepresents alsoalso by thethethe linklinkapplication betweenbetween uniformity ModelModel BB andand and CC X sincesince is the itit is isratio thethe inverseinversebetween ofof required thethe RelativeRelative depth IrrigationIrrigation and appliedSupplywhere depth. (RIS) a and Xparameter represents b are determined computed also the linkbyby Model thebetween application C. Model uniformityB and C since and it Xis isthe the inverse ratio ofbetween the Relative required Irrigation depth and appliedSupplySupply depth. (RIS)(RIS) X parameterparameterrepresents computedcomputedalso the link bybyC betweenModelModelsince C.C.it Model is the B inverse and C since of the it is Relative the inverse Irrigation of the Relative Supply Irrigation (RIS) parameter computed by Supplywhereapplied (RIS) a and depth. parameter b are X represents determined computed also byby the Modelthe link application C.between Modeluniformity B and and C sinceX is itthe is ratiothe inverse between of therequired Relative depth Irrigation and Supply (RIS)The parameterDistribution computed Uniformity by Model(DUModel) is C. aC. measure of how evenly water soaks into the ground across a field appliedSupply Thedepth.The (RIS) DistributionDistribution X parameter represents Uniformity Uniformitycomputed also the link by ((DUDU Model between)) isis aa C. measuremeasure Model ofofB andhowhow C evenlyevenly since itwaterwater is the soakssoaks inverse intointo of thethe the groundground Relative acrossacross Irrigation aa fieldfield duringThe the Distribution irrigation and Uniformity is defined ( DUas one) is minusa measure the ratioof how between evenly the water average soaks applied into the depth ground in the across quarter a field of the SupplyduringduringThe (RIS) the theDistribution irrigationirrigation parameter andUniformityand computed isis defineddefined (byDU as asModel) oneoneis a Theminusminus measureC. Distribution thethe of ratioratio how betweenbetween evenly Uniformity thethewater averageaverage soaks (DU) applied appliedinto is the a depthdepth measureground inin thetheacross of quarterquarter how a field evenly ofof thethe water soaks into the duringfield thereceiving irrigationThe Distribution less and water is definedand Uniformity the asaverage one (DU minus applied) is athe measure depthratio betweenin of the how whole theevenly average field. water DU applied soakscan be depthinto expressed the in theground asquarter a acrossfunction of the a fieldof duringfieldfield the receivingreceiving irrigation lessless and waterwater is defined andand thethe as averageaverage oneground minus appliedapplied across the ratio depthdepth a betweenfield inin thethe during whole wholethe average field.thefield. irrigation DUDUapplied cancan depthbebe and expressedexpressed in is the defined quarter asas aa functionfunction ofas the one ofof minus the ratio between fieldtheduring receivingcoefficientThe the Distribution lessirrigation of watervariation and andUniformity (is CVthe defined) average of the(DU as applied appliedone) is aminus measurewater depth the (Warrick, in ratioof the how betweenwhole 1983):evenly field. the water averageDU soaks can appliedbe into expressed the depth ground asin athe acrossfunction quarter a field ofof the fieldthethe receiving coefficientcoefficient less ofof water variationvariation and the ((CVCV average)) ofof thethe appliedapplied average waterdepthwater applied (Warrick,in(Warrick, the depthwhole 1983):1983): field.in the DU quarter can be ofexpressed the field as areceiving function of less water and the average theduring coefficientfield thereceiving irrigation of variation less and water is(CV defined and) of the the as average applied one minus appliedwater the (Warrick, depthratio between in 1983):the whole the average field. DU applied can be depth expressed in the quarteras a function of the of the coefficient of variation (CV) of the applied water depth (Warrick, in the 1983):whole field. DU can be expressed as a function of the coefficient of field the receiving coefficient less of watervariation and ( theCV )average of the applied applied water depth (Warrick, in the whole 1983): field. DU can be expressed as a function of variation (CV) of the applied water (Warrick, 1983): the coefficient of variation (CV) of the applied water (Warrick, 1983): (15) (15)(15) (15) (15) (15) (15) The parameters a and b that define the uniform frequency distribution can be then calculated as: TheThe parametersparameters aa andand bb thatthat definedefine thethe uniformuniform frequencyfrequency distributiondistribution cancan bebe thenthen calculatedcalculated as:as: (15) The parameters a and b that define the uniform frequency distribution can be then calculated as: The parameters a and b that define the uniform frequency distribution can be then calculated as: The parameters a and b that defineThe the uniform parameters frequency a and distribution b that define can be thethen calculateduniform frequency as: distribution can be then

The parameters a and b that definecalculated the uniform as: frequency distribution can be then calculated as: (16) (16)(16) (16) (16) (16) (16)

(16)

(17) (17)(17) (17) (17) (17) (17)

Once DU is known for the irrigation system of concern, CV, b, and a can be computed. Model C provides(17) OnceOnce DUDU isis knownknown forfor thethe irrigationirrigationO systemsystemnce DU ofof is concern,concern, known CVCV for,, bb ,the, andand irrigation aa cancan bebe computed.computed. system ofModelModel concern, CC providesprovides CV, b, and a can be com- a avalue valueOnce of of RISDU RIS (andis (and known hence hence for X X)the )from from irrigation which which systemCD CD can can of be be concern, computed computed CV (see ,(see b, andEquation Equation a can 12).be 12). computed. With With the the value Modelvalue of ofC the theprovides required required a valueOnce Onceof DU RIS isDU (and known is knownhence for Xthe for) from irrigationthe irrigationwhichputed. system CD Model system can of beconcern,C of providescomputed concern, CV , a (seeCVb value, and, Equationb, aand of can RIS a becan 12). (and computed. be With computed. hence the Model valueX) Modelfrom ofC providesthe which C requiredprovides CD can be computed (see a valuedepth,depth, of output outputRIS (and of of Model henceModel A,X A,) thefrom the irrigation irrigationwhich CD ( I(i) Ican i)and and be irrigation irrigationcomputed application application (see Equation efficiency efficiency 12). With ( E(Ea)a )thecan can value be be computed. computed. of the required Finally, Finally, a valuedepth, of outputRIS (and of henceModel X A,) from the irrigationwhich CD ( Icani) and be irrigationcomputed application (see Equation efficiency 12). With (E athe) can value be computed.of the required Finally, a valueOnce of DURIS is(and known hence for X the) from irrigation Equationwhich systemCD 12).can of be Withconcern, computed the CV value (see, b, and Equationof thea can required 12).be computed. With depth, the valueModel output of C the provides of required Model A, the irrigation (Ii) depth,irrigationirrigation output drainage drainage of Model will will A, be bethe obtained obtained irrigation as as the (theIi) product andproduct irrigation I i I×i × E Ea. a application. efficiency (Ea) can be computed. Finally, depth,irrigation output drainageof Model will A, bethe obtained irrigation as ( theI ) and product irrigation Ii × E aapplication. efficiency (E ) can be computed. Finally, a valuedepth, of output RIS (and of Model hence XA,) fromthe irrigation whichandi irrigationCD (Ii )can and be irrigation computedapplication application (see efficiency Equation efficiency 12). (E ) aWith can (Ea) bethecan computed.value be computed. of the Finally,required Finally, irrigation drainage will irrigation drainage will be obtained as the product Ii × Ea. a irrigationirrigation drainage drainage will willbe obtained be obtained as the as product the product Ii × E Iia .× Ea. depth, output of Model A, the irrigationbe obtained (Ii) and irrigation as the productapplication Ii × efficiency Ea. (Ea) can be computed. Finally, irrigation drainage will be obtained as the product Ii × Ea.

31 Figure 2.5 shows the relationship between deficit coefficient and application efficiency for various distribution uniformities.

F igure 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)

I2.4. rrigation 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 im- pacting 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 evi- dence 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 values. e decision tree used to define the RIS h T i F - gure 2.6

35 2.4.2. Allowed depletion fraction (f1) 2.4.2. Allowed depletion fraction (f1) According to the paper FAO no. 56 (Allen et al., 1998) the Readily Available Water (RAW) that a crop can extract through the roots is a fractionAccording of the Total to the Available paper WaterFAO no. (TAW 56) (Allenas defined et al., by 1998)the following the Readily Available Water equation: (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)

For the majority of the crops the fractionFor 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 We defined 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 crop; f1 ranges from 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 as- indicates that the crop suffers for water deficit. To assign a proper value of f1 to each crop, another decision sign a proper value of f1 to each crop, another decision tree has been built (see Figure 2.7). tree has been built (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 belonging to the RIS 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 new inserted blocks are: new inserted blocks are: • Deficit olive tree - a binary variable• Deficit (yes/no) olive taking tree into- a binary account variable the application (yes/no) of takingdeficit intoirrigation account the use of deficit techniques for olive pantations; irrigation techniques for olive pantations; • Priority crop - a binary variable• (yes/no)Priority related crop to- thea binary rank attributed variable by (yes/no) the farmer related to crops to inthe case rank attributed by the of water shortage when it has to farmerbe decided to crops which in crops case will of waterhave top shortage priority when for irrigation. it has to The be decided which crops value of the variable is defined inwill terms have of topmembership priority forof theirrigation. crop to aThe predefined value of list the of variable priority is defined in terms of crops built-in in MARSALa. The membershipcrops list (see ofTable the crop2.1) has to abeen predefined defined by list expert of priority judgment crops by built-in in MARSALa. taking into account the crop resistanceThe crops to water list (seestress Table condition, 2.1) hasthe maximumbeen defined level byof yieldexpert loss judgment by taking into acceptable and the market conditions.account Rice theand cropprotected resistance crops are to waternot part stress of the condition, list being alwaysthe maximum level of yield irrigated with the highest priority. loss acceptable and the market conditions. Rice and protected crops are not part Observing the structure of the decisionof the tree list it beingis evident always as under irrigated no shortage with the condition highest the priority. values of the leaves indicates that the crop is irrigatedObserving with thea certain structure frequency of the avoiding decision any tree stress it is phenomenonevident as under in no shortage condi- comparison to the shortage condition.tion theMoreover values the of thevalues leaves of f1 indicates reflect the that characteristics the crop is of irrigated the irrigation with a certain frequency water supply (e.g. in the case Flexibleavoiding a major stress control phenomenon. of the water Moreover, volume thein thevalues soil canof f1 be reflect applied the by characteristics of the replenishing the RAW) and the irrigation system water supply(e.g. each (e.g. irrigation in the case system Flexible has its a majorown efficiency control of and the water volume in the application frequency). soil can be applied by replenishing the R AW) and the irrigation system (e.g. each irrigation system has its own efficiency and application frequency). Table 2.1 - List of the priority crops defined by expert judgment, the priorities are defined for two different crop groups. T able 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. Table grapes, Fruit trees, Citrus plantations Legumes Tobacco Sunflower Crop group no. 1 Crop group no. 2 Fresh vegetables, Flowers and ornamental plants Sorghum Table grapes, Fruit trees, Citrus plantations Legumes Grapes for wine, Olive plantations Nuts Tobacco Sunflower Maize, Sugar beet Permanent grassland Fodder Fresh vegetables, FlowersOther and cropsornamental plants Sorghum Grapes for wine, Olive plantations Nuts Maize, Sugar beet Permanent grassland Fodder Other crops

36 F igure 2.7 - The decision tree used to define the values of f1.

37 2.5.I rrigation 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.

T able 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 m4/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 com- municate 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. Irriga- tion 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 m3/ha in Toscana to a maximum of 40,200 m3/ha in Lombar- dia. 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.

T able 2.3 - Structure of the national database on the irrigation water volumes used for rice cultivation.

Region Province Mean irrigation water use (m3/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 aly. t I rthern o N i F gure 2.8 – Areas of rice and cultivation in water mean of values irrigation

41 scana region. o T i F gure - Areas 2.9 of rice and cultivation in water mean of values irrigation

42 rdegna region. a S i F - Areas of rice and in cultivation water mean of values irrigation gure 2.10

43 labria region a C i F - Areas of rice and in cultivation water mean of values irrigation gure 2.11

2.6. Irrigation water consumption estimation for protected crops 2.6.I rrigation water consumption estimation for protected crops MARSALa model has been considered not appropriate to assess water consumption in protected 2.6. Irrigation water consumptionMARSALa estimation model for has protected been considered crops not appropriate to assess water consumption environment (greenhouses or crops under protective cover), for the following reasons: in protected environment (greenhouses or crops under protective cover), for the following MARSALa• crop evapotranspiration model has beenreasons: estimationconsidered in not indoor appropriate microclimate to assess conditions water isconsumption related to different in protected variables environmentsuch (greenhouses as the outdoor or crops climate, under• the cropprotective type evapotranspiration of cover), greenhouse, for the the following estimation climate reasons: control in indoor strategy microclimate and the feedback conditions is related to between the crop and the inside microclimate; • crop evapotranspiration estimationd ifferentin indoor variables microclimate such conditions as the outdoor is related climate, to different the type variables of greenhouse, the climate • the concept of reference evapotranspiration (ETo) is also somewhat difficult and delicate to be such as the outdoor climate, the controltype of greenhouse,strategy and the the climate feedback control between strategy the and crop the andfeedback the inside microclimate; applied to greenhouse crops water requirements, because hypothetical grass reference crop as between the crop and the inside• the microclimate; concept of reference evapotranspiration (ETo) is also somewhat difficult and defined in FAO paper 56, (Allen et al.) is not commonly grown in greenhouse production (Baille A., • the concept of reference evapotranspirationdelicate to be(ETo applied) is also to greenhousesomewhat difficult crops water and delicaterequirements, to be because hypotheti- 1994); applied to greenhouse crops watercal grassrequirements, reference because crop as hypothetical defined in grassFAO paperreference 56, (Allencrop as et al.) is not commonly • separation of crop transpiration and soil surface evaporation is very difficult, if not impossible, due defined in FAO paper 56, (Allen etgrown al.) is in not greenhouse commonly grownproduction in greenhouse (Baille A., production 1994); (Baille A., the lack of greenhouses soil properties data; 1994); • absence of precipitation in • separationa protected environment of crop transpiration that generally and participates soil surface in partialevaporation restoration is very difficult, if not • separation of crop transpiration and soil surface evaporation is very difficult, if not impossible, due of evapotraspirative losses. impossible, due the lack of greenhouses soil properties data; the lack of greenhouses soil properties data; • absence of precipitation in a protected environment that generally participates in • absenceMore simpleof precipitation approaches in ahave protected been developedenvironment in thatthe estimationgenerally participates of evapotranspiration in partial restoration based on the partial restoration of evapotraspirative losses. solarof radiation; evapotraspirative the role oflosses. solar radiation in determining the evapotranspiration in the greenhouses has been evidenced in several works in the 60'More and simple 70' (Morris approaches et al., 1957,have beenLake developedet al., 1966, in StanhiIl the estimation and Álberts, of evapotranspiration More simple approaches have been developed in the estimation of evapotranspiration based on the 1974, De Villele, 1974), showingbased a strong on the correlation solar radiation; between the daily role evapotranspiration of solar radiation and in solar determining irradiance. the evapotranspira- solar radiation; the role of solar radiationtion in the in determininggreenhouses the has evapotranspiration been evidenced in in the several greenhouses works has in thebeen 60’ and 70’ (Morris et Reference evapotranspiration is closely dependent from the environmental conditions inside of the evidenced in several works in theal., 60' 1957, and Lake70' (Morris et al., et1966, al., 1957, StanhiIl Lake and et al.,Álberts, 1966, 1974,StanhiIl De and Villele, Álberts, 1974), showing a strong greenhouse such as temperature, relative humidity and global radiation. Since these three climatic variables 1974, De Villele, 1974), showingcorrelation a strong correlation between between daily evapotranspiration daily evapotranspiration and and solar solar irradiance. irradiance. are strongly correlated (at least in the greenhouse environment), a simple mathematical model that takes in Reference evapotranspiration is closely dependent from the environmental conditions inside of the consideration only the inner greenhouseReference global evapotranspirationradiation can be applied. is closely Based dependenton that, the sofrom called the "solar environmental condi- greenhouse such as temperature, relative humidity and global radiation. Since these three climatic variables radiation" method, or "solarimeter"tions inside method of thehas greenhousebeen developed such which as temperature, is a simple relationship relative humidity giving theand global radiation. are strongly correlated (at least in the greenhouse environment), a simple mathematical model that takes in reference evapotranspiration Sincein the thesegreenhouse three ifclimatic the outside variables global areradiation strongly (RGo correlated) and the greenhouse(at least in the greenhouse consideration only the inner greenhouse global radiation can be applied. Based on that, the so called "solar coefficient transmission (Kt), environment),are known (Baille a A.,simple 1994): mathematical model that takes in consideration only the inner radiation" method, or "solarimeter"greenhouse method has global been radiation developed can which be applied.is a simple Based relationship on that, givingthe so the called “solar radiation” reference evapotranspiration in themethod, greenhouse or “solarimeter” if the outside method global has radiation been developed(RGo) and whichthe greenhouse is a simple relationship giving coefficient transmission (Kt), are theknown 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) where:

ETo is the reference evapotranspirationwhere: in mm day-1; ETo is the reference evapotranspiration in mm day-1; where: RGo is the outside global radiation in MJ m-2 day-1; -2 -1 ETo is the reference evapotranspirationRGo is in the mm outside day-1; global radiation in MJ m day ; λ is the latent heat of vaporization (2.5 MJ/kg H20); λ is the latent-2 heat-1 of vaporization (2.5 MJ/kg H 0); RGo is the outside global radiation in MJ m day ; 2 Kt ranges between 0.55 e 0.65Kt (empirical ranges between data provided 0.55 frome 0.65 Prof. (empirical Pardossi, data University provided of Pisa). from Prof. Pardossi, Univer- λ is Cropthe latent Water heat Requirement of vaporizationsity (CWR)of Pisa).(2.5 MJ/kgdepends H 20);from the evaporating surface, which is expressed as a functionKt ranges of the between Leaf Area 0.55 Index e 0.65 (LAI) (empiricalCrop of the W datacrop.ater provided Requirement from Prof. (CWR) Pardossi, depends University from ofthe Pisa). evaporating surface, which is expressed as a function of the Leaf Area Index (LAI) of the crop. CropAfter Water this Requirementconsideration (CWR)Equation depends 19 assumes from the the following evaporating form: surface, which is expressed as a After this consideration Equation 19 assumes the following form: function of the Leaf Area Index (LAI) of the crop. After this consideration Equation 19 assumes the following form: (20) (20)

(20) 45

where a is an empirical coefficient ranging from 0.20 to 0.35. where a is an empirical coefficient ranging from 0.20 to 0.35. where aIn is cases an empirical of the solar coefficient radiation ranging measurements from 0.20 are to 0.35.not available or are relative to sites distant from the In cases of the solar radiation measurements are not available or are relative to sites distant from the greenhouse,In cases some of the procedures, solar radiation based measurements whereon extraterrestrial a is an are empirical not radiation available coefficient and or areair relativetemperature ranging to fromsites differences distant0.20 to from (Allen.0.35. the greenhouse, some procedures, based on extraterrestrial radiation and air temperature differences (Allen. greenhouse,R.G., 1995), somecan be procedures, applied for thebased its estimation.Inon casesextraterrestrial of the solar radiation radiation and measurementsair temperature aredifferences not available (Allen. or are relative to sites R.G., 1995), can be applied for the its estimation. R.G., 1995),Considering can be alsoapplied that for the the maindistant its estimation.source from of the water greenhouse, is ground water,some andprocedures, the lack of based rain-driven on extraterrestrial leakage, is radiation and air Considering also that thetemperature main source ofdifferences water is ground (Allen. water, R.G., and 1995), the lack can of be rain-driven applied for leakage, the its is estimation. necessaryConsidering to introduce also in that the the calculation main source the Leachingof water isFraction ground (LF):water, and the lack of rain-driven leakage, is necessary to introduce in the calculationConsidering the Leaching also Fraction that the (LF): main source of water is ground water, and the lack of rain- necessary to introduce in the calculation the Leaching Fraction (LF): driven leakage, is necessary to introduce in the calculation the Leaching Fraction (LF):

(21) (21) (21) (21)

where: where: where: where:ECw is the irrigation water salinityECw (expressed is the irrigation as Electrical water Conductivity salinity (EC)(expressed in mS/cm); as Electrical Conductivity (EC) in ECw is the irrigation watermS/cm); salinity (expressed as Electrical Conductivity (EC) in mS/cm); ECwECe dependsis the irrigation on the crop,water the salinity higher (expressed the value asthe Electrical higher is theConductivity crop resistance (EC) into mS/cm);salinity. ECe depends on the crop, the higherECe dependsthe value onthe thehigher crop, is the the crop higher resistance the value to salinity. the higher is the crop resistance to ECe depends on the crop, the higher the value the higher is the crop resistance to salinity. For different crops categoriessalinity. the following values of ECe can be applied (empirical data provided For different crops categories the following values of ECe can be applied (empirical data provided from Prof.For differentPardossi, crops University categories of Pisa): the following values of ECe can be applied (empirical data provided from Prof. Pardossi, University of Pisa):For different crops categories the following values of ECe can be applied (empirical from Prof.• 2.5 Pardossi, for fruit University vegetable; of data Pisa): provided from Prof. Pardossi, University of Pisa): • 2.5 for fruit vegetable; •• 2.52.0 forfor fruitleaf vegetable;vegetable; • 2.5 for fruit vegetable; • • 1.82.0 for for cut leaf plants; vegetable; • 2.0 for leaf vegetable; • 2.0 for leaf vegetable; • 1.8 for cut plants; •• 1.81.5 forfor cutpot plants;plants; • 1.5 for pot plants; • 1.8 for cut plants; •Hence, 1.5 for the pot Irrigation plants; Requirements (IR), quantity of water needed to satisfy the CWR, and allowing, • 1.5 for pot plants; through Hence,an adequate the Irrigation leaching, Requirements to maintain the (IR salinity), quantity of the of soilwater at neededlower level to satisfy than thosethe CWR, of toxicity and allowing, for the Hence, the Irrigation RequirementsHence, (IR), the quantity Irrigation of water R equirementsneeded to satisfy (IR) the, quantity CWR, and of allowing,water needed to satisfy the cultivation,through an can adequate be expressed leaching, as: to maintain the salinity of the soil at lower level than those of toxicity for the through an adequate leaching, toCWR, maintain and the allowing, salinity ofthrough the soil anat loweradequate level leaching,than those to of maintaintoxicity for the the salinity of the soil at cultivation, can be expressed as: cultivation, can be expressed as: lower level than those of toxicity for the cultivation, can be expressed as:

(22) (22) (22) (22)

The last terms to be considered in the Irrigation Water Consumption (IWC) estimation are represented The last terms to be considered in the Irrigation Water Consumption (IWC) estimation are represented by theThe irrigation last terms distribution to be considered uniformity inT hethe coefficient,last Irrigation terms WatertoKt be and consideredConsumption the efficiency in ( IWCthe of Irrigation) estimationthe irrigation W areater representedsystem Consumption Ki (IWC) estima- by the irrigation distribution uniformity coefficient, Kt and the efficiency of the irrigation system Ki by(common the irrigation ranges aredistribution 0.6 - 0.7 uniformityfortion sprinkler are represented coefficient, and 0.90 - Ktby0.95 theand for irrigation thedrip efficiency irrigation) distribution of adopted the irrigation uniformityin the greenhouses, system coefficient, Ki Kt and the effi- (common ranges are 0.6 - 0.7 for sprinkler and 0.90 - 0.95 for drip irrigation) adopted in the greenhouses, (commonhence the rangesfinal equation are 0.6 is:- 0.7 forciency sprinkler of the and irrigation 0.90 - 0.95 system for drip Ki (commonirrigation) rangesadopted are in the0.6 greenhouses,- 0.7 for sprinkler and 0.90 - 0.95 hence the final equation is: for drip irrigation) adopted in the greenhouses, hence the final equation is: 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.

Some experimental results obtained for a case study carried out in some pilot areas located in Toscana Some experimental results obtained for a case study carried out in some pilot areas located in Toscana regionSome are reported experimental in Figure results 2.12 obtained and 2.13. for a case study carried out in some pilot areas located in Toscana region are reported in Figure 2.12 and 2.13. region are reported in Figure 2.12 and 2.13.

46 F igure 2.12 - Monthly IWC (in mm) computed for different crop categories cultivated in greenhouses located in Toscana region.

Montly IWC for the main crops group

250,00

200,00

150,00

100,00 IWC (mm)

50,00

0,00

May July April June March August January October February November Dicember September

IWR Fruit vegetable IWR Leaf vegetable IWR Cut plants IWR Pot plants

Source: elaboration with data provided by Prof. Pardossi

F igure 2.13 - Annual IWC (in thousands of m3/ha) computed for the different crop categories cultivated in greenhouses located in Toscana region.

12,0

10,0

8,0

6,0

4,0

Annual IWC (000 mc/ha) 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.

CA H PTer 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 6th General Agricultural Census database

In Italy agriculture censuses have been taken since 1961, on decennial frequency, based on complete enumeration of agricultural holdings. The 6th General Agricultural Cen- sus 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.

T able 3.1 - Sections and boxes of the 6th 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 Quality scheme production (concerning animals) C 3 Information for holdings with animal 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 Labour force and other gainful Third partly job C 5 activities of the holding Other gainful activities of the holdings B Equipment used for renewable energy production B Income, self consumption and marketing C 6 Economic information 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).

F igure 3.1 - The irrigation box (box 22) of the CQ with highlighted the main integrations realized to acquire additional farm information.

52 3.2.C rop 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 shrink- ing 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 Oc- tober 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 inal F 2 2 2 2 2 2 2 2 2 2 1,5 1,5 1.5 1.5 1.8 1.8 1.5 1.5 1.3 1.3 1.2 1.2 0.50.5 0.2 0.2 0.6 0.6 0.8 0.8 0.6 0.6 (m) 0.75 0.75 rop height height rop C 2 2 2 2 2 2 22 2 2 2 2 0,1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 1.5 1.5 1.5 nitial Mature I 1 1 1.5 1.5 1.5 0.7 1.2 1.5 1.2 1.5 0.6 0.5 0.9 1,25 1.25 (m) ting depth ting depth 0,1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 0 .1 oo 1.5 1.5 1.5 1.51.2 1.5 1.2 1.5 1.5 1.21.4 1.2 1.4 Min Max R p tal o 364 0.55 364 0.6 T inal taly taly F I orth N Mature (days) lop. rop cycle in cycle rop C eve D 00 82 82 90 90 10 10 182 182 0,55 0.55 0 0 80 25 105 0.45 20 30 80 10 140 0.55 6030 45 50 60 100 30 5 195 185 0.35 0.65 25 30 60 10 125 0.3 tart 90 3080 170 30 74 180 364 74 0.65 364 0.5 74 30 160 10080 364 30 0.5 180 74 364 0.5 0.5 1 64 45 160 95 364 0.5 74 30 160 100 364 0.5 0.5 1 S nd 0,7 0,6 0,8 0,4 0,8 0,3 0,6 0,3 E 0,75 0,75 0,65 30 30 120 10 190 0.35 0,85 0 0 5 10 15 0.2 0,65 91 60 150 63 364 0.5 1 0,7 0,9 Kcb 1,05 1,35 0,95 0,25 0 55 45 5 105 0,95 0,95 0,35 0,24 64 45 160 95 364 0.5 0.5 1 0,1 0,1 0,6 0,6 0,6 0,150,15 1,1 1,1 0,15 0,15 0,15 1,1 0,15 1,15 0,15 1,15 0,15 0,15 0,15 0,15 0,15 0,18 0 0,72 0,72 0,72 nitial Mid I outh ,72 0,72 0,72 02/01 S entral C lanting date lanting date (day/month) P rop characteristics database. orth C 01/03 01/03 15/02 01/03 15/02 15/02 N 02/01 02/01 02/01 02/01 02/01 02/01 0,04 0,38 02/01 02/01 azings 02/01 02/01 02/01 le le rop aize 25/04 15/04 01/04 pp pp lfalfa Oats 02/01 02/01 02/01 Pear 02/01 02/01 02/01 Pear 02/01 02/01 02/01 0,04 0,38 C Olive 02/01 02/01 02/01 Olive 02/01 02/01 02/01 0,24 0,24 0,24 90 30 170 74 364 0.65 0.5 pinach 02/01 02/01 02/01 Colza 02/01 02/01 02/01 A A M Peach 02/01 02/01 02/01 0,45 0,86 Peach 02/01 02/01 02/01 Carrot Barley 02/01 02/01 02/01 Potato A Cotton 20/03 20/03 20/03 rtichoke 15/04 15/04 15/11 Orange 02/01 02/01 02/01 0,65 Orange 02/01 02/01 02/01 0,26 0,28 0,25 91 60 150 63 364 0.5 0.5 S tract of the A weet pepperweet 01/05 15/04 01/03 x ough gr S R E

rop group rop nnual a C A Perennial Full-grown Full-grown trees fruit Young Young trees fruit Forage T - 3.2 ble

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 under- standing 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.

T able 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.

F igure 3.2 - Screenshot of the electronic questionnaire hosted at the INEA website (http://www.rica.inea.it/marsala/).

3.3S oil 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 T able 3.4 - State-of-the-art on the soil maps availability and spatial resolution for each Italian region/autonomous province.

R egion/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.

T able 3.5 - Available soil maps with country-level coverage.

Year Map Author Scale Description

1966 Carta dei Suoli d’Italia F. Mancini et al., 1966 1:1,000,000 The map has been the first relevant (Italian Soil Map) 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 European Soil Office 1:250,000 The realization of the map has been (Italian Ecopedologic Soil - JRC (Ispra) linked to the activities carried out Map) 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.

2006 Badasuoli MiPAAF, CRA and 1:1,000,000 The soil database has been realized (Italian soil database) the Regional Soil through the whole collection, Services 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. Unfortunate- ly, 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 inconsist- encies 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 Apen- ninic 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 Apen- ninic 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.

T able 3.6 - Minimal set of characteristics collected in the table ORIZZONTI to compute soil depth, field capacity and wilting point.

Field name Description SUOLO Soil identification code NUMORIZZ Progressive number indicating the horizon in the representative soil profile TOPSOIL 1: shallow horizon; 0: deep horizon CODICE_ST Horizon label according to Soil Taxonomy TIPO Horizon type (value used to compute the hydrologic parameters) PROFLSUP Horizon upper bound (cm) PROFLINF Horizon lower bound (cm) SCHELETRO Rock fragments (> 2 mm) expressed as percentage of the volume SABBIA Sand content expressed as percentage of the volume LIMO Silt content expressed as percentage of the volume ARGILLA Clay content expressed as percentage of the volume SOSTORG 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 20051 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 municipality- slope class-agricultural land use.

T able 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% ) ) ) ) ) ) ) ) 3 3 3 3 3 3 3 3 /m /m /m /m /m /m /m /m unicipality 3 3 3 3 3 3 3 3 (cm) (cm) (cm) M d Capacity (m (m (m (m (m (m (m (m oil Depth oil Depth oil Depth oil Depth oil Depth S S S oil Depth (cm) oil Depth Wilting Point Wilting Point Wilting Point Wilting Point Wilting Point Fiel Field Capacity Field Field Capacity Field Capacity Field S

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 through- out 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.

T able 3.8 - The survey of meteorological networks in Italy.

Administration Service/Institution name Number of Estimated Average level institutions number of length of working time series stations (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 Sys- tem 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, degree- days, 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 Of- fice 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 Analysis2 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 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 Analysis2 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-Monteith formula revised by FAO is considered the most reliable and therefore is the one used toMonteith build the database:formula revised by FAO is considered the most reliable and therefore is the one used to build the database:

(1) (1)

-1 -2 -1 where ETo is the reference evapotranspiration [mm d ], R is the net radiation [MJ m -1d ], G is the soil heat -2 -1 where ETo is the reference evapotranspirationn [mm d ], R is the net radiation -1 n flux [MJ m d ], γ is the-2 psychrometric-1 constant [0.066 kPa °C ],-2 900-1 is a conversion factor, (e - e ) [MJ m d ], G is the soil heat flux [MJ m d ], γ is the psychrometrics constanta represents the vapour pressure deficit-1 of the air [kPa], T is the mean air temperature [°C], Δ represents the [0.066 kPa °C ], 900 is a conversion factor, (es - ea) represents the vapour pressure deficit slope of the saturation vapour pressure temperature relationship [kPa °C-1] and U is the wind speed at 2 -1 of the air [kPa], T is the mean air temperature [°C], Δ represents2 the slope of the satura- meters [m s ]. -1 tion vapour pressure temperature relationship [kPa °C ] and U2 is the wind speed at 2 The data used tometers build the [m database s-1]. have been originated from the meteorological measures stored in the National AgrometerologicalThe dataDatabase used (BDAN) to build3 and the are database referred haveto the been thirty-year originated period from 1961-1990, the meteorological which is defined the measuresconventional stored reference in the forNational climatological Agrometerological analysis andDatabase comparisons (BDAN) by3 and the are World referred to Meteorological Organizationthe thirty-year (WMO). period 1961-1990, which is defined the conventional reference for climatological analysis and comparisons by the World Meteorological Organization (WMO). The spatio-temporal reconstruction of the meteorological variables has been performed by the geo-statistical Kriging with external driftThe methodology. spatio-temporal The methodologyreconstruction allows of tothe estim meteorologicalate, within the variables considered has spatial been per- domain, the valuesformed 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 observed data at allowsthe meteorological to estimate, stations),within the taking considered into account spatial the domain, statistical the properties values of ofa giventhe spatio- geophysical temporal dynamicsvariable of the startingvariable: fromthe so the called actual structural data available model. The(in thisbasic case, hypothesis the observed is to consider data at the the me- physical variablesteorological as regionalized stations), random taking variables into (Matheron,account the 1970 statistical and 1971). properties Meteorological of the spatio-temporal variables satisfy this requirementdynamics since o fthey the arevariable: space andthe time-dependent. so called structural Statistically model speaking,. The basic meteorological hypothesis data is to con- recorded from neighboursider the stations physical always variables show aas certain regionalized level of correlation. random va riables (Matheron, 1970 and 1971). Meteorological variables satisfy this requirement since they are space and time-dependent. Daily meteorologicalStatistically data estimation speaking, on gridmeteorological nodes has been data performed recorded through from neighbour an independent stations estimation always show of the climatic meana certain and the level meteorological of correlation. deviation according to the following relationship: meteorologicalDaily meteorological measure = climat datae estimation+ meteorological on grid deviation nodes has been performed(2) through an where climate is independenta cyclic annual estimation constant (it of varies the climatic during the mean year, and but theit is meteorologicalconstant among thedeviation years) with according good spatio-temporalto the continuity following and relationship: good agreement with the mean trend of the meteorological fields at synoptic scale, it generally coincides with the climatic mean; meteorological deviation is the variation caused to climate by the instantaneousmeteorological and local measuremeteorological = climate condition. + meteorological deviation (2)

2 The Objective Analysis was performedwhere by climate Finsiel in the is frameworka cyclic of annual National Agriculturalconstant Information (it varies System during (SIAN )the of MIPAAF. year, but The studyit is was constant carried out during 1988-1990 and the results are published in the report SIAN “Analisi climatologica e progettazione della Rete Agrometeorologica Nazionale” (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 3The National Agrometeorologicaltrend of Databasethe meteorological (BDAN) was realized fields in the frameworkat synoptic of SIAN scale, and contains it generally the observations coincides provided bywith CRA-CMA the climatic meteorological networkmean and others; meteorological Italian meteorological 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.

F igure 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 C VHAPTer I 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, Campa- nia, 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 T able 4.1 - Main limitations and inaccuracies affecting models input data.

Input data Errors and inaccuracy

Agrometeorology 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 Soil database has been realized by collating regional and local soil maps produced (soil depth, wilting point with different standards and resolution. In addition soil parameters have been es- and field capacity) timated 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. Crop characteristics The crops parameters collected are average values gathered from literature and past research projects, only few crops has been fully characterized by field experiments. Irrigation system 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.

F igure 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 F igure 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.

F igure 4.3 - Puglia pilot area: in-farm bore used as “private source” for irrigation water abstraction through a pumping system.

69 F igure 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.

T able 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 (m3/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

T able 4.3 - Aggregation of crops into the HCs based on the CWR values of Table 4.2

HC Crop Ranges of annual CWR (m3/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.

T able 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. 0 5 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).

T able 4.5 -Association of the Italian regions to the HCs.

HC Regions A Puglia, Valle d’Aosta, Liguria B Campania C Campania, Trentino-Alto Adige, Bolzano, Trento, Abruzzo, Basilicata, Calabria, Sicilia D 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.

T able 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 Irri- gation 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.

T able 4.7 - Correspondence between the irrigation water source classes in the Italian FADN and MARSALa.

RICA MARSALa

Water delivered by a public service ILRC

Lake or river Self-supply Well

Other Other

T able 4.8 - Correspondence between the irrigation systems of the Italian FADN and MARSALa.

RICA MARSALa

Aspersion Aspersion

Border and Furrows Infiltration-Flood Flood

Drip Micro-irrigation

Other system 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).

T able 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 asOther 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.

T able 4.10 - Stratification of the farms universe for each pilot area based on the three stratification variables.

Stratification variable No. of farms

Irrigation Farm size Irrigation system Emilia- Campania Puglia Sardegna water source (UUA) (prevailing) Romagna

Micro-irrigation 10 8 56 2 Greater than or Infiltration-Flood 6 2 2 equal to 20 ha Aspersion 40 3 11 29 Self-supply Micro-irrigation 2 51 147 1

Less than 20 ha Infiltration-Flood 2 52 6

Aspersion 25 32 27 3

Micro-irrigation 7 5 22 7 Greater than or Infiltration-Flood 18 2 2 equal to 20 ha Aspersion 82 3 1 110 ILRC Micro-irrigation 4 19 58 2

Less than 20 ha 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.

T able 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.

Stratification variable No. of farms Irrigation Farm Irrigation Emilia-Romagna Campania Puglia Sardegna water size system source (UUA) (prevailing) Step 5 Sample Step 5 Sample Step 5 Sample Step 5 Sample Micro- 10 3 8 3 56 15 2 1 irrigation Greater Infiltration- 6 2 2* 2 1 2 1 than 20 ha Flood Aspersion 40 11 3 2 11 3 29 8 Self-supply Micro- 2 1 51 15 147 40 1 1 irrigation Less Infiltration- 2 1 52 14 6 2 1* than 20 ha Flood Aspersion 25 7 32 10 27 7 3 1

Micro- 7 2 5 2 22 6 7 2 irrigation Greater Infiltration- 18 5 2 2 1* 2 1 than 20 ha Flood Aspersion 82 22 3 2 1 1 110 30 ILRC Micro- 4 1 19 6 58 16 2 1 irrigation Less Infiltration- 7 2 4 2 1* 1* than 20 ha Flood 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.

T able 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 Question- naire (PQ) made up of the same questions on irrigation reported in the ISTAT CQ. Addi- tional 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 (m3/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 4.3P ilot 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.

T able 4.13 - Number of scheduled and actual farms interviewed and response rate by pilot areas.

Pilot area Scheduled Actual Response rate farms farms (%)

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

T able 4.14 - Number of scheduled and actual farms interviewed and response rate by stratification variable.

Stratification variable Scheduled Actual Response rate Total farms farms (%) interviews

Large 128 109 85.16 Farm size (UUA) 265 Small 151 156 103.31

ILRC 127 136 107.09 Irrigation water source 265 Self-supply 152 129 84.87

Micro-irrigation 115 122 106.09

Irrigation system (prevailing) Infiltration-Flood 39 21 53.85 265

Aspersion 125 122 97.60

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 T able 4.15 - Total and irrigated surface of the farms sample (surface in hectares and in percentage over the total cultivated surface of the sample).

Total surface Irrigated surface Crop (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. 0 0 0.21 7. 0 0 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. 0 0 0.46

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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. 0 5 0.46 7. 0 5 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 F igure 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 Scheduled Actual farms Response Total farms Rate (%) interviews Large 45 38 84.44 Farm size (UUA) 61 Small 21 23 109.52 ILRC 41 44 107.32 Water source 61 Self-supply 25 17 68.00 Micro-irrigation 7 6 85.71 Prevailing irrigation system Infiltration-Flood 10 4 40.00 61 Aspersion 49 51 104.08

T able 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).

T able 4.18 - Emilia-Romagna pilot area: average values for the main dimensional variables.

Veariabl 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

T able 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. 0 2 0.61 7. 0 2 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. 0 0 0.61 7. 0 0 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.

F igure 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 HC C Response Total Rate (%) Rate (%) interviews Scheduled Actual Scheduled Actual farms farms farms farms

Farm size Large 6 2 33.33 7 7 100.00 53 (UUA) Small 6 3 50.00 43 41 95.35

ILRC 6 2 33.33 10 13 130.00 Water source 53 Self-supply 6 3 50.00 40 35 87.50

Micro-irrigation 4 3 75.00 22 24 109.09 Prevailing irrigation Infiltration-Flood 4 2 50.00 16 9 56.25 53 system Aspersion 4 0.00 12 15 125.00

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.).

T able 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

T able 4.22 - Campania pilot area: average values for the main dimensional variables.

Veariabl 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 7.7 5

T able 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 >>

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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).

F igure 4.7 - Puglia pilot area: “meteo-cells” of the agrometeorological grid and number of actual farms interviewed by municipality.

86 T able 4.24 - Puglia pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable.

Stratification variable Scheduled Actual farms Response Total farms Rate (%) interviews Large 27 20 74.07 Farm size (UUA) 100 Small 70 80 114.29 ILRC 29 38 131.03 Water source 100 Self-supply 68 62 91.18 Micro-irrigation 77 87 112.99 Prevailing irrigation system Infiltration-Flood 5 3 60.00 100 Aspersion 15 10 66.67

T able 4.25 - Puglia pilot area: average values for the main dimensional variables.

Veariabl 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).

T able 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 7.1 6 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. 0 5 0.41 7. 0 5 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

F igure 4.8 - Sardegna pilot area: “meteo-cells” of the agrometeorological grid and number of actual farms interviewed by municipality.

88 T able 4.27 - Sardegna pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable.

Stratification variable Scheduled Actual farms Response Total farms Rate (%) interviews Large 43 42 97.67 Farm size (UUA) 51 Small 11 9 81.82 ILRC 41 39 95.12 Water source 51 Self-supply 13 12 92.31 Micro-irrigation 5 2 40.00 Prevailing irrigation system Infiltration-Flood 4 3 75.00 51 Aspersion 45 46 102.22

T able 4.28 - Sardegna pilot area: number of the actual farms interviewed by province.

Province Interviewed farms Cagliari 4 Oristano 15 Sassari 32 Total 51

T able 4.29 - Sardegna pilot area: average values for the main dimensional variables.

Veariabl 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.

T able 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 (m3/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 Grapes for DOC wine wine DOC for Grapes

10 Grapes for wine wine for Grapes

99 Grapes for table use use table for Grapes

21 Plum Plum

7 Spinach Spinach

3 Sorghum Sorghum

1 Seeds Seeds

Celery Celery Chicory Chicory

1 1 Permanent grassland grassland Permanent

3 Forage legume legume Forage

50 Tomato for table table for Tomato

8 Plum tomato tomato Plum

32 Peach Peach

42 Pear Pear

12 Pepper Pepper

2 Potato Potato

12 Olive for table use use table for Olive

2 Olive oil oil Olive

Walnut Walnut

Hazel Hazel Nectarine Nectarine

1 1 1

ter melon melon ter Wa

3 Apple Apple

4 Eggplant Eggplant

Crop Almond Almond

1 Mais a maturazione cerosa cerosa maturazione a Mais

25 Maize Maize

37 Endive and lettuce lettuce and Endive

10 Strawberry Strawberry

2 Fennel Fennel

8 French bean bean French

Alfalfa Alfalfa

ter melon melon ter Wa Clementine Clementine

1 1 1 Onion Onion

5 Cauliflower, cabbage cabbage Cauliflower,

8 Carrot Carrot

1 Artichoke Artichoke

10 Broccoli Broccoli

6 Chard Chard

1 Sugar beet beet Sugar

13 Asparagus Asparagus

Orange Orange Other single-crop cereals cereals single-crop Other

2 2 2 Other cereals grass grass cereals Other

8 mber of surveys for each crop irrigated (546 are the crops surveyed distributedfarms). 265 in Apricot

5 u Kiwifruit Kiwifruit N 2 0

80 60 40 20

120 100 Number of surveys for each crop crop each for surveys of Number i F gure 4.9 -

Grapes for DOC wine wine DOC for Grapes

Grapes for wine wine for Grapes

Grapes for table use use table for Grapes

Plum Plum

Spinach Spinach

Sorghum Sorghum

Seeds Seeds

Celery Celery

Chicory Chicory

Permanent grassland grassland Permanent

rage legume legume rage Fo

tremes differences between the Tomato for table table for Tomato x

E Plum tomato tomato Plum

Peach Peach

Pear Pear

Pepper Pepper

tato tato Po

Olive for table use use table for Olive

Olive oil oil Olive

Walnut Walnut

Hazel Hazel

Nectarine Nectarine

Water melon melon Water

Apple Eggplant Eggplant

Crop Almond Almond

Mais a maturazione cerosa cerosa maturazione a Mais

Maize Maize

Endive and lettuce lettuce and Endive

Strawberry Strawberry

Fennel Fennel

French bean bean French

Alfalfa

Water melon melon Water

Clementine Clementine

Onion Onion

cabbage cabbage , Cauliflower

t t ro Car

Artichoke Artichoke Broccoli Broccoli

Minimum rd Cha

Sugar beet beet Sugar

Asparagus Asparagus

Orange Orange

Other single-crop cereals cereals single-crop Other Other cereals grass grass cereals Other

Maximum

Apricot Apricot Kiwifruit Kiwifruit 0 00 00 .0 .0

5.000 5.000

25.000 25.000 20 15.000 10

/ha) (m volume water Irrigation 3 i maximum must values be considered and minimum outliers. F consumption water - Maximum irrigation registered and minimum the interviews. during gure 4.10

92 T able 4.31 - Campania pilot area: comparison between the simulated and measured irrigation water volume for each crop.

F arm ID Crop ID Municipality Crop name Simulated Measured volume volume (m3/ha) (m3/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

follow >>

93 >> follow

F arm ID Crop ID Municipality Crop name Simulated Measured volume volume (m3/ha) (m3/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 F igure 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

7. 000 /ha) 3 6.000

5.000

4.000

3.000 Irrigation water volume (m

2.000

1.000

0 Pear Pear Plum Plum Apple Hazel Peach Peach Peach Peach Peach Peach Peach Peach Peach Peach Peach Peach Peach Peach Apricot Apricot Apricot Kiwifruit Actinidia Nectarine Crop

Simulated Volume Measured Volume

F igure 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

6.000 /ha) 3

5.000

4.000

3.000

2.000 Irrigation water volume (m

1.000

0 Onion Onion Fennel Fennel Fennel Fennel Fennel Fennel Eggplant Artichoke Artichoke Cauliflower Cauliflower Cauliflower Cauliflower 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

Crop

Simulated Volume Measured Volume

95 F igure 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

14.000 /ha) 3

12.000

10.000

8.000

6.000 Irrigation water volume (m

4.000

2.000

0 tato tato tato tato tato tato tato Corn Corn Po Po Po Po Po Po Po French bean Plum tomato Plum tomato Plum tomato Plum tomato Plum tomato Tomato for table for Tomato table for Tomato table for Tomato table for Tomato table for Tomato table for Tomato

Simulated Volume Measured Volume Crop

4.5I nfluence 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 Depart- ment 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).

F igure 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

150 P -ETO (mm)

100

0 ODEMLO AVONAILOD IREIZO SAMLAP

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 F igure 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 /ha) 3

2.000

1.500

1.000

500 Simulated volume - Measured (m

0 TULA TULA NULVI NULVI OZIERI OZIERI OZIERI OZIERI OZIERI OZIERI MORES MORES MORES MORES ARDARA SASSARI SASSARI SASSARI ALGHERO ALGHERO ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA ARBOREA MARRUBIU Municipality ARPAS CRA-CMA

98 C HAPTer 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).

T able 5.1 - List of the databases managed at server-side by MARSALa.

Database name Description

Database of daily values of precipitation and reference evapotranspiration Agro-Meteo (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 Crops 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 soil parameter: soil depth, wilting point and field capacity.

Database of the farms information useful for running the models extracted Farm from the CQ database provided by ISTAT.

Database storing, for each farm, the characteristics of the irrigated land use 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 Irrigation water consumption 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 F igure 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. Mod- ule 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 (Po- modoro 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.

F igure 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 F igure 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 6th 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, Campa- nia, 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 sum- marized 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 con- clusion 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

I ntroduction

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107 C hapter I

ISTAT, (2008), Relazioni tra agricoltura e ambiente: dalle statistiche agli indicatori, Statis- tica in breve, gennaio 2008), http://www.ISTAT.it/salastampa/comunicati/non_calen- dario/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, Argomen- ti, n. 27. ISTAT, Indagine di struttura dell’azienda agricola. Anni vari. ISTAT, Statistiche ambientali. Annuario. Anni vari

C hapter II

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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 Mediterra- nean 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”, Proceed- ings 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. Hoff- man, T.A. Howell, K.H. Solomon, eds., Management of farm irrigation systems. Ameri- can 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 Irriga- tion and Drainage Engineering, 109:317-332. Wilcox J.C., Swailes G.E., (1947), “Uniformity of water distribution by some under tree or- chard 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”, Trans- actions of the ASAE, 31:1743-1749. Zucaro R., Tudini L., (2008), Rapporto Sullo Stato Dell’irrigazione in Toscana, INEA, Roma.

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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.

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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 Man- agement Series Publication Number 3375, Division of Agriculture and Natural Resourc- es. http://cati.csufresno.edu/CIT/DrainageManual/Content/glossary.pdf

110 W eb 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 lim- iting 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 m3/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 re- ality, 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 m3/ ha per year (1 mm = 10 m3/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. Pedotrans- fer 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 International (or Intergovernmental) Panel on Climatic Change ISPRA (ex APAT) National Institute for the Protection and Environmental Research ISTAT Italian National Statistics Institute JRC Joint Research Centre LAU Local Administrative Unit MATTM Ministry of the Environment, Land and Sea MiPA AF Ministry of Agricultural, Food and Forestry Policies MS Member States NIWR Net Irrigation Water Requirements NSSG National Statistic Service of Greece NUTS Nomenclature of Territorial Units for Statistics OECD Organization for Economic Cooperation and Development PDO Protection Designation of Origin PGI Protected Geographical Identification PQ Pilot Questionnaire PTF Pedotransfer Function R AW Readily Available Water REW Readily Evaporable Water RICA (Italian FADN) Rete d’Informazione Contabile Agricola RIS Relative Irrigation Supply RZWD Root Zone Water Deficit RZWHC Root Zone Water Holding Capacity SAPM Statistics on Agricultural Production Methods SCIA National System for the collection, elaboration and diffusion of climatological data of environmental interest SIAN National Agricultural Information System SIGRIAN Sistema Informativo per la Gestione delle Risorse Idriche in Agricoltura SIMN National Service for Study of Waters and Seas SINA National Information System for Environmental Monitoring TAW Total Available Water TEW Total Evaporable Water UAA Utilised Agricultural Area or Agricultural Area (AA) UGM General Office for Meteorology UTC Universal Coordinated Time WBS Work Breakdown Structure WFD Water Framework Directive WMO World Meteorological Organization WP 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 (Frut- ta 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 6th general agricultural census questionnaire (in italian language)

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Numero identiﬁcativo Istat 6°Censimento generale dell’agricoltura 24 OTTOBRE 2010 (art. 17 del decreto legge 25 settembre 2009, n. 135, convertito con modiﬁcazioni dalla legge 20 novembre 2009, n. 166) Sistema statistico nazionale Istituto nazionale di statistica

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

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B ESITO DELLA RILEVAZIONE 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) B.1 AZIENDA RILEVATA 1 (compilare sempre il presente questionario) d. Terreni destinati a soli orti familiari 5 o allevamenti per autoconsumo B.2 AZIENDA IN LISTA NON RILEVATA o aziende esclusivamente forestali (compilare solo il riquadro in bianco a pagina 14 del questionario) e. Soggetto che non ha mai esercitato 6 a. Irreperibilità del conduttore 2 attività agricola b. Rifiuto 3 f. Terreni agricoli definitivamente abbandonati 7 c. Altra motivazione 4 o destinati ad altro uso o aziende (specificare……………………………………………………………………) esclusivamente zootecniche che hanno totalmente dismesso l’attività senza cessione ad altri caso g: compilare il riquadro D indicando le notizie dell’azienda/e g. Azienda agricola interamente affittata, 8 che ha/hanno acquisito i terreni o gli allevamenti ceduta, assorbita, fusa o smembrata caso h: compilare il riquadro D indicando le notizie dell’azienda h. Unità da ricondurre ad azienda già in lista o già intervistata esistente (doppione) 9

CC CESSIONICESSIONI PARZIALIPARZIALI (in(in caso caso di di risposta risposta al al quesito quesito B.1) B.1) L’azienda ha ceduto parzialmente terreni agricoli o allevamenti ad altra/e azienda/e nell’annata agraria 2009/2010? In caso di risposta SI compilare il riquadro D indicando le notizie dell’azienda/e che ha/hanno acquisito parzialmente i terreni o gli SI 1 allevamenti NO 2

DD UNITÀUNITÀ COLLEGATE COLLEGATE ALLEALLE AZIENDEAZIENDE ININ LISTALISTA (da(da compilarecompilare perper ii casicasi B.3g, B.3h e per risposta SISI alal riquadroriquadro C)C) Cognome e nome della persona fisica Indirizzo, Comune e Provincia CUAA o Codice fiscale della persona o denominazioneCognome e nome della della società persona o ente fisica Indirizzo, Comune e Provincia CUAA ofisica Codice o della fiscale società della persona o denominazioneche conduce l’azienda della società o ente o entefisica che oconduce 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 Per i Comuni con catasto tavolare elencati nell’appendice B e quelli elencati nell’appendice B del libretto d’istruzioni del libretto d’istruzioni

a a a Sez. censuaria Foglio di mappa catastale Sez. censuaria Particella catastale / Tipo

Per i Comuni con catasto a foglio aperto elencati nell’appendice B Per i Comuni delle province di Trento e Bolzano del libretto d’istruzioni

a a a Comune catastale Particella catastale / Tipo Sez. censuaria 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

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sezione I Notizie generali sull’azienda

1 FORMA GIURIDICA 6 INFORMATIZZAZIONE DELL’AZIENDA (è ammessa una sola risposta)

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

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

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sezione II segue Utilizzazione dei terreni (annata agraria 2009 - 2010)

9 COLTIVAZIONI LEGNOSE AGRARIE Gli ORTI FAMILIARI sono piccole superﬁci utilizzate prevalentemente per la coltivazione di or- SUPERFICIE taggi e piante arboree (vite, olivo, fruttiferi) sparse, SUPERFICIE Cod. Di cui in anche in consociazione tra loro, la cui produzione Cod. Totale è destinata esclusivamente al consumo del con- produzione duttore e della sua famiglia (autoconsumo) Ettari Are Ettari Are Ettari Are

9.1 Vite (2) 55 ORTI FAMILIARI 85 per autoconsumo 9.2 Olivo per la produzione di 10 a. Olive da tavola 56 11 PRATI PERMANENTI E PASCOLI b. Olive per olio 57 11.1 Prati permanenti (utilizzati) 86 9.3 Agrumi 11.2 Pascoli (utilizzati) a. Arancio 58 a. Pascoli naturali 87 b. Mandarino 59 b. Pascoli magri 88 11.3 TOTALE PRATI PERMANENTI E c. Clementina e suoi ibridi 60 89 PASCOLI UTILIZZATI d. Limone 61 11.4 PRATI PERMANENTI E PASCOLI e. Altri agrumi 62 NON PIÙ DESTINATI ALLA PRO- 90 9.4 Fruttiferi DUZIONE, AMMESSI A BENEFI- CIARE DI AIUTI FINANZIARI a. Frutta fresca di origine temperata SUPERFICIE AGRICOLA - Melo 63 12 UTILIZZATA (SAU) 91 - Pero 64 Somma dei punti 8.13, 9.8, 10, 11.3 e 11.4 - Pesco 65 - Nettarina (pesca noce) 66 13 ARBORICOLTURA DA LEGNO - Albicocco 67 13.1 Pioppeti 92 - Ciliegio 68 13.2 Altra arboricoltura da legno 93 - Susino 69 13.3 TOTALE ARBORICOLTURA 94 - Fico 70 DA LEGNO - Altra frutta 71 14 BOSCHI b. Frutta fresca di origine sub-tropicale 14.1 Boschi a fustaia 95 - Actinidia (kiwi) 72 14.2 Boschi cedui 96 - Altra frutta 73 14.3 Altra superﬁcie boscata 97 c. Frutta a guscio 14.4 TOTALE BOSCHI 98 - Mandorlo 74 SUPERFICIE AGRARIA - Nocciolo 75 15 NON UTILIZZATA 99 Esclusi i terreni a riposo indicati al 76 - Castagno punto 8.12 - Noce 77 ALTRA SUPERFICIE - Altra frutta 78 16 Aree occupate da fabbricati, cortili, 100 strade poderali, stalle, superfici a 9.5 Vivai funghi, ecc. a. Fruttiferi 79 XXX X SUPERFICIE TOTALE b. Piante ornamentali 80 XXX X 17 DELL’AZIENDA 101 c. Altri 81 XXX X Somma dei punti 12, 13.3, 14.4, 15 e 16 9.6 Altre coltivazioni legnose agrarie 82 18 FUNGHI (compresi gli alberi di Natale) 2 (coltivati in grotte, Cod. SUPERFICIE INVESTITA (m ) sotterranei o in appositi 9.7 Coltivazioni legnose 102 83 edifici) agrarie in serra ha Cod. SUPERFICIE DI BASE (m2) 9.8 TOTALE COLTIVAZIONI 84 19 SERRE LEGNOSE AGRARIE 103 ha

(2) La superficie totale deve coincidere con quella indicata al punto COLTIVAZIONI ENERGETICHE SUPERFICIE 20 Cod. 21.5 di pagina 6 (colture utilizzate per la produzione Ettari Are di energia) 20.1 Soggette a contratto di coltivazione 104

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sezione II Notizie particolari sulla vite

21 NATURA DELLA PRODUZIONE 21.1 Uva per la produzione di vini SUPERFICIE INVESTITA A VITE SECONDO L’ANNO DI IMPIANTO a denominazione di origine SUPERFICIE controllata (vini DOC) TOTALE Posteriore Da settembre Da settembre Da settembre Da settembre Anteriore e controllata e garantita Cod. A VITE ad agosto 2004 ad 2000 ad 1990 ad 1980 ad al settembre (vini DOCG) 2007 agosto 2007 agosto 2004 agosto 2000 agosto 1990 1980 VITIGNI (denominazione) Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are 1……

1……

1…… TOTALE………………………………. 1999

21.2 Uva per la produzione di altri vini

VITIGNI (denominazione) 2……

2……

2…… TOTALE………………………………. 2999

21.3 Uva da tavola 3999 21.4 Viti non innestate 4001

21.5 TOTALE PARZIALE (1) Cod. QUINTALI (somma dei dati ai punti 21.1, 4002 21.9 TOTALE UVA DA VINO RACCOLTA 21.2, 21.3 e 21.4) 21.9.1 Per la produzione di vini 5001 21.6 Viti madri da portinnesto 4003 DOC e DOCG 21.7 Barbatelle 4004 21.9.2 Per la produzione di altri vini 5002 21.8 TOTALE SUPERFICIE A VITE (somma dei dati ai punti 21.5, 4999 21.6 e 21.7)

(1) Deve coincidere con la superﬁcie totale del punto 9.1 di pagina 5.

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sezione II Metodi di produzione agricola (annata agraria 2009 - 2010)

AGRICOLTURA BIOLOGICA E 22 IRRIGAZIONE (esclusa l’irrigazione di soccorso) 23 PRODUZIONI DI QUALITÀ DOP E IGP Cod. Ettari Are Coltivazioni (Annata agraria 2009-2010) SUPERFICIE BIOLOGICA: Superficie agricola utilizzata in cui si applicano me- 22.1 Superficie irrigabile 01 todi di produzione biologica certificati o in fase di conversione secondo le 22.2 Superficie effettivamente irrigata 02 norme comunitarie o nazionali SUPERFICIE DOP E IGP: Superficie principale o secondaria per la quale 22.3 Superficie media irrigata nelle l’azienda è controllata e certificata dal competente organismo di controllo 03 ultime 3 annate agrarie SUPERFICIE SUPERFICIE SUPERFICIE Cod. BIOLOGICA DOP E IGP 22.4 Coltivazioni irrigate almeno Codice 23.1 Coltivazioni una volta nell’annata Cod. IRRIGATA Sistema di Ettari Are Ettari Are agraria 2009-2010 Ettari Are irrigazione (1) a. Cereali 01 a. Cereali per la produzione b. Legumi secchi 02 di granella 01 c. Patata 03 (escluso mais e riso) d. Barbabietola da zucchero 04 XXX XX b. Mais da granella 02 e. Piante da semi oleosi 05 XXX XX c. Riso 03 f. Ortive 06 d. Legumi secchi 04 g. Foraggere avvicendate 07 XXX XX e. Patata 05 h. Prati permanenti e Pascoli f. Barbabietola da zucchero 06 08 XXX XX (esclusi pascoli magri) g. Colza e ravizzone 07 i. Vite 09 XXX XX h. Girasole 08 l. Olivo 10 i. Piante tessili 09 m. Agrumi 11 l. Ortive in piena aria 10 n. Fruttiferi 12 m. Mais verde (in erba ed a 11 o. Altre coltivazioni maturazione cerosa) (tabacco, fiori, piante 13 n. Altre foraggere aromatiche, ecc) 12 avvicendate 23.2 TOTALE 14 o. Altri seminativi di cui Superficie agricola utilizzata in 13 15 XXX XX (tabacco, fiori, ecc.) fase di conversione al biologico p. Prati permanenti e pascoli 14 q. Vite 15 24 LAVORAZIONE DEL TERRENO Indicare le lavorazioni effettuate SUPERFICIE r. Olivo 16 Cod. sui SEMINATIVI Ettari Are s. Agrumi 17 24.1 Lavorazione convenzionale 01 t. Fruttiferi 18 (aratura) u. Altre coltivazioni legnose 24.2 Lavorazione di conservazione 19 02 agrarie (a strisce, verticale, a porche permanenti) v. Arboricoltura da legno 20 24.3 Nessuna lavorazione 03 22.5 TOTALE SUPERFICIE La somma dei codici 01, 02 e 03 deve essere minore o uguale a IRRIGATA 21 XXXXX quanto riportato al punto 8.13 di pagina 4 (deve corrispondere al punto 22.2) (1) Indicare il codice del sistema di irrigazione unico o prevalente. 25 CONSERVAZIONE DEL SUOLO 1 Scorrimento superficiale ed 3 Aspersione (a pioggia) 25.1 Copertura invernale del suolo a SUPERFICIE Cod. infiltrazione laterale 4 Microirrigazione SEMINATIVI Ettari Are 2 Sommersione 5 Altro sistema a. Colture invernali 01 (ad esempio frumento autunno-vernino) 22.6 Fonte di approvvigionamento dell’acqua irrigua b. Colture di copertura o intermedie 02 (è ammessa una sola risposta) c. Residui colturali 03 - Acque sotterranee all’interno o nelle vicinanze dell’azienda 01 (ad esempio stoppie, paglia, pacciame) - Acque superficiali all’interno dell’azienda (bacini naturali ed artificiali) 02 d. Nessuna copertura 04 - Acque superficiali al di fuori dell’azienda (laghi, fiumi o corsi d’acqua) 03 25.2 Avvicendamento dei SEMINATIVI Acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo a. Monosuccessione 05 - con consegna a turno 04 b. Avvicendamento libero 06 - con consegna a domanda 05 c. Piano di rotazione 07 - Altra fonte 06 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 22.7 Barrare la casella se l’azienda utilizza servizi 25.3 Inerbimento controllato delle di consulenza irrigua e/o sistemi di superfici a COLTIVAZIONI 08 01 determinazione del fabbisogno irriguo LEGNOSE AGRARIE

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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 Cod. CAPI 33 SUINI Cod. CAPI 26.1 Di età inferiore a 1 anno 33.1 Di peso inferiore a 20 kg 25 a. Maschi 01 33.2 Da 20 kg a meno di 50 kg 26 b. Femmine 02 33.3 Da ingrasso di 50 kg e più 26.2 Da 1 anno a meno di 2 anni a. Da 50 kg a meno di 80 kg 27 a. Maschi 03 b. Da 80 kg a meno di 110 kg 28 b. Femmine 04 c. Da 110 kg e più 29 26.3 Di 2 anni e più 33.4 Da riproduzione di 50 kg e più a. Maschi 05 a. Verri 30 b. Femmine b. Scrofe montate 31 - Giovenche (manze) da allevamento 06 c. Altre scrofe 32 - Giovenche (manze) da macello 07 33.5 TOTALE SUINI 33

- Vacche da latte 08 Cod. CAPI 34 AVICOLI - Altre vacche (da carne o da lavoro) 09 34.1 Polli da carne 34 26.4 TOTALE BOVINI 10 34.2 Galline da uova 35 Cod. CAPI 27 BUFALINI 34.3 Tacchini 36 27.1 Annutoli (vitelli bufalini) 11 34.4 Faraone 37 27.2 Bufale 12 34.5 Oche 38 27.3 Altri bufalini 13 34.6 Altri allevamenti avicoli 39 27.4 TOTALE BUFALINI 14 34.7 TOTALE AVICOLI 40

Cod. CAPI 28 EQUINI Cod. CAPI 35 CONIGLI 28.1 Cavalli 15 35.1 Fattrici 41 28.2 Altri equini (asini, muli, bardotti, ecc.) 16 35.2 Altri conigli 42 28.3 TOTALE EQUINI 17 35.3 TOTALE CONIGLI 43 Cod. CAPI SE L’AZIENDA POSSIEDE ALLEVAMENTI DIVERSI 36 STRUZZI DA BOVINI, BUFALINI O EQUINI INDICARE 36.1 TOTALE STRUZZI 44 L’azienda possiede allevamenti 1 SI 2 NO Cod. 29 per autoconsumo? 37 ALTRI ALLEVAMENTI NUMERO ALVEARI L’azienda possiede allevamenti 37.1 Api 30 destinati alla vendita? 1 SI 2 NO 45 se SI indicare i soli capi destinati alla vendita ai punti da 31 a 37 37.2 Altri allevamenti 46 XXX se NO passare al punto 38 AGRICOLTURA BIOLOGICA E PRODUZIONI 38 DI QUALITÀ DOP E IGP - ALLEVAMENTI CAPI Cod. BIOLOGICI DOP e IGP 31 OVINI 38.1 Allevamenti Cod. ALLEVAMENTI Capi Capi 31.1 Pecore BIOLOGICI: a. Bovini 01 Capi di bestiame allevati con metodi di a. Da latte 18 02 b. Bufalini produzione biologica e b. Altre 19 c. Equini 03 XXX certiﬁcati secondo le norme comunitarie 31.2 Altri ovini 20 d. Ovini 04 o nazionali esclusi quelli e. Caprini 05 in fase di conversione 31.3 TOTALE OVINI 21 al biologico f. Suini 06 Cod. CAPI ALLEVAMENTI 32 CAPRINI g. Avicoli 07 DOP E IGP: 32.1 Capre 22 h. Conigli 08 XXX Capi per i quali l’azienda è controllata i. Api 09 10 32.2 Altri caprini 23 e certiﬁcata dal competente l. Altri allevamenti organismo di controllo 32.3 TOTALE CAPRINI 24 (incl. Struzzi) 11

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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 SUPERFICIE UTILIZZATA NUMERO TOTALE (prati permanenti, pascoli TIPOLOGIA DEI TERRENI A PASCOLO Cod. DI ANIMALI AL e foraggere avvicendate) NUMERO MESI PASCOLO Ettari Are 39.2 Terreni aziendali 01 39.3 Terreni di altre aziende 02 39.4 Terreni di proprietà collettive 03 In caso di risposta al punto 39.4 indicare la denominazione del Comune o dell’Ente gestore dei terreni appartenenti a proprietà collettive …………………………………………………………………………………………………………………………………………………………………………………………………………………………… 40 TIPOLOGIA DI STABULAZIONE DEL BESTIAME Numero medio Numero medio 40.1 Vacche da latte e Bufale Cod. Cod. di animali (1) di animali (1) 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 03 b. A terra al chiuso 13 lettiera (produzione di letame) c. In gabbia (tutti i tipi) 14 d.In stabulazione libera senza uso 04 di lettiera (produzione di liquame) c1. In gabbia con nastro di 15 asportazione delle deiezioni 40.2 Altri Bovini e Bufalini c2. In gabbia con fossa di stoccaggio a. In stabulazione con uso di 16 05 di deiezioni liquide lettiera (produzione di letame) c3. In gabbia con fossa di stoccaggio b. In stabulazione senza uso di 17 06 di deiezioni solide lettiera (produzione di liquame) 40.5 Polli da carne 40.3 Suini a. A terra con accesso all’esterno 18 a. Su fessurato (o grigliato) parziale 07 b. A terra al chiuso 19 b. Su fessurato (o grigliato) totale 08 (1) Il numero medio di animali può non coincidere con il numero di capi c. Su lettiera permanente 09 dichiarati a pagina 8.

41 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

ACCUMULO PLATEA VASCA LAGUNA EFFLUENTI ZOOTECNICI Cod. IN CAMPO Coperta Scoperta Coperta Scoperta Coperta Scoperta 41.2 Letame (incluso pollina) 01 1 2 3 XXX XXX XXX XXX 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 SAU TRATTATA CON EFFLUENTI ZOOTECNICI (Indicare la superﬁcie trattata secondo le seguenti applicazioni): Cod. EFFLUENTI ZOOTECNICI Ettari Are 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 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

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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 PROVINCIA Codice ISTAT Denominazione Codice ISTAT Denominazione COMUNE COMUNE Codice ISTAT Denominazione Codice ISTAT Denominazione

SUPERFICIE SUPERFICIE Cod. Cod. 1 COLTIVAZIONI (SEZ. II) Ettari Are 1 COLTIVAZIONI (SEZ. II) Ettari Are a. Seminativi (punto 8.13) 01 a. Seminativi (punto 8.13) 01 b. Vite (punto 9.1) 02 b. Vite (punto 9.1) 02

c. Coltivazioni legnose agrarie, escluso vite 03 c. Coltivazioni legnose agrarie, escluso vite 03 (punto 9.8 meno punto 9.1) (punto 9.8 meno punto 9.1) 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. CAPI 2 ALLEVAMENTI (SEZ. III) Cod. CAPI a. Bovini e Bufalini (punto 26.4 + 27.4) 01 a. Bovini e Bufalini (punto 26.4 + 27.4) 01

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 e. Presenza altri allevamenti 05 05 (punti 28, 35, 36, 37) (punti 28, 35, 36, 37)

Riquadro N° Riquadro N°

PROVINCIA PROVINCIA Codice ISTAT Denominazione Codice ISTAT Denominazione COMUNE COMUNE Codice ISTAT Denominazione Codice ISTAT Denominazione

2 ALLEVAMENTI (SEZ. III) Cod. CAPI 2 ALLEVAMENTI (SEZ. III) Cod. CAPI a. Bovini e Bufalini (punto 26.4 + 27.4) 01 a. Bovini e Bufalini (punto 26.4 + 27.4) 01

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 e. Presenza altri allevamenti 05 05 (punti 28, 35, 36, 37) (punti 28, 35, 36, 37)

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sezione IV Ubicazione dei terreni e degli allevamenti aziendali

Riquadro N° Riquadro N°

PROVINCIA PROVINCIA Codice ISTAT Denominazione Codice ISTAT Denominazione COMUNE COMUNE Codice ISTAT Denominazione Codice ISTAT Denominazione

2 ALLEVAMENTI (SEZ. III) Cod. CAPI 2 ALLEVAMENTI (SEZ. III) Cod. CAPI a. Bovini e Bufalini (punto 26.4 + 27.4) 01 a. Bovini e Bufalini (punto 26.4 + 27.4) 01

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 e. Presenza altri allevamenti 05 05 (punti 28, 35, 36, 37) (punti 28, 35, 36, 37)

Riquadro N° Riquadro N°

PROVINCIA PROVINCIA Codice ISTAT Denominazione Codice ISTAT Denominazione COMUNE COMUNE Codice ISTAT Denominazione Codice ISTAT Denominazione

2 ALLEVAMENTI (SEZ. III) Cod. CAPI 2 ALLEVAMENTI (SEZ. III) Cod. CAPI a. Bovini e Bufalini (punto 26.4 + 27.4) 01 a. Bovini e Bufalini (punto 26.4 + 27.4) 01

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 e. Presenza altri allevamenti 05 05 (punti 28, 35, 36, 37) (punti 28, 35, 36, 37)

NOTA: LA SOMMA DELLE COLTIVAZIONI E DEGLI ALLEVAMENTI DEI VARI RIQUADRI DEVE COINCIDERE CON QUANTO RIPORTATO NELLE SEZIONI II E III

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sezione V Lavoro ed attività connesse (annata agraria 2009 - 2010)

ALTRE ATTIVITÀ FAMIGLIA DEL LAVORO SVOLTO IN AZIENDA REMUNERATIVE (attività agricole e connesse) 43 CONDUTTORE E PARENTI EXTRA-AZIENDALI Compilare sempre se è stata data risposta a pagina 3 - ANNO DI % del tempo

Cod. SESSO (4)

Forma giuridica, al punto 1.1 od al punto 1.2 (solo in (5) NASCITA dedicato ad attività (3) caso di società semplice costituita esclusivamente o in Numero Media ore connesse elencate parte da familiari o parenti che svolgono lavoro in CONDIZIONE giorni giornaliera Tempo al quesito 48 attività CITTADINANZA (1) PROFESIONALE (2)

azienda) o per altre forme giuridiche comprendenti per- di Settore dedicato Posizione sone legate da vincoli di parentela. di pagina 13 prevalente 43.1 Conduttore (16 anni e più - responsabile 101 1 M 2 F 19 1 2 3 giuridico ed economico dell’azienda) b a a c b c a a 43.2 Coniuge 201 1 M 2 F 19 b a a c b c 1 2 3 a a 43.3 Altri componenti della famiglia (16 anni e più) che lavorano in azienda xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 301 1 M 2 F 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 302 1 M 2 F 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 303 1 M 2 F 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 304 1 M 2 F 19 b a a c b c 1 2 3 a a 43.4 Altri componenti della famiglia che non lavorano in azienda (compresi i minori di 16 anni) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 401 1 M 2 F da a a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 402 1 M 2 F da a a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 403 1 M 2 F da a a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 404 1 M 2 F da a a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 405 1 M 2 F da a a a 43.5 Parenti del conduttore che lavorano in azienda (16 anni e più) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 501 1 M 2 F 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 502 1 M 2 F 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 503 1 M 2 F 19 b a a c b c 1 2 3 a a xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 504 1 M 2 F 19 b a a c b c 1 2 3 a a 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

44 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. LAVORO SVOLTO IN AZIENDA LAVORO SVOLTO IN AZIENDA (attività agricole e connesse) (attività agricole e connesse) % del tempo % del tempo dedicato ad dedicato ad Cod. SESSO Media Cod. SESSO Media Numero attività connesse Numero attività connesse giorni ore ore giornaliera elencate giorni giornaliera elencate CONTRATTO (1) al quesito 48 CONTRATTO (1)

ANNO DI NASCITA al quesito 48 ANNO DI NASCITA CITTADINANZA (2) CITTADINANZA CITTADINANZA (2) CITTADINANZA di pagina 13 di pagina 13 701 a 1 M 2 F 19 b a c b c 711 a 1 M 2 F 19 b a c b c 702 a 1 M 2 F 19 b a c b c 712 a 1 M 2 F 19 b a c b c 703 a 1 M 2 F 19 b a c b c 713 a 1 M 2 F 19 b a c b c 704 a 1 M 2 F 19 b a c b c 714 a 1 M 2 F 19 b a c b c 705 a 1 M 2 F 19 b a c b c 715 a 1 M 2 F 19 b a c b c 706 a 1 M 2 F 19 b a c b c 716 a 1 M 2 F 19 b a c b c 707 a 1 M 2 F 19 b a c b c 717 a 1 M 2 F 19 b a c b c 708 a 1 M 2 F 19 b a c b c 718 a 1 M 2 F 19 b a c b c 709 a 1 M 2 F 19 b a c b c 719 a 1 M 2 F 19 b a c b c 710 a 1 M 2 F 19 b a c b c 720 a 1 M 2 F 19 b a c b c 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

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sezione V segue Lavoro ed attività connesse (annata agraria 2009 - 2010)

ALTRA MANODOPERA AZIENDALE LAVORATORI NON ASSUNTI 45 IN FORMA SALTUARIA 46 DIRETTAMENTE DALL’AZIENDA 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

NUMERO PERSONE Lavoro svolto in azienda NUMERO PERSONE Lavoro svolto in azienda (attività agricole (attività agricole CITTADINANZA e connesse) CITTADINANZA e connesse) Cod. % del tempo Cod. % del tempo Altro Paese N. giornate Altro Paese N. giornate TOTALE dedicato ad TOTALE dedicato ad italiana Paese extra convertite in Italiana Paese extra convertite in attività attività U.E. U. E. gg. di 8 ore U.E. U. E. gg. di 8 ore connesse connesse a. Maschi 11 b. Femmine 21 TOTALE 41 TOTALE 31

Indirizzo agrario Altro tipo 47 NOTIZIE SUL CAPO AZIENDA (da compilare sempre) d. Diploma di qualifica che non 04 � 05 � 47.1 Quale dei soggetti già dichiarati ai punti 43 o 44 di permette accesso universitario pagina 12 svolge anche la funzione di capo azienda (2-3 anni) e. Diploma di scuola media superiore 06 07 (indicare il codice)? � � c f. Laurea o diploma universitario 08 09 47.2 Titolo di studio (il più elevato) � � a. Nessuno 01 � 47.3 Il capo azienda ha frequentato negli ultimi 12 mesi corsi di formazione b. Licenza di scuola elementare 02 � professionale? 1 � SI 2 � NO c. Licenza di scuola media inferiore 03 �

48 ATTIVITÀ REMUNERATIVE CONNESSE 49 CONTOTERZISMO ALL’AZIENDA (giornate di lavoro convertite in giornate di 8 ore) 48.1 Se nell’azienda sono state svolte attività remunerative CONTOTERZISMO ATTIVO diverse da quelle agricole, ma ad essa connesse, pre- 49.1 Indicare le giornate di lavoro svolte con mezzi meccanici cisare se trattasi di: Cod. propri presso altre aziende agricole e a. Agriturismo 01 � CONTOTERZISMO PASSIVO b. Attività ricreative e sociali 02 � 49.2 Indicare se l’azienda ha usufruito di lavoro effettuato c. Fattorie didattiche 03 � con persone e mezzi extra-aziendali 1 � SI 2 � NO d. Artigianato 04 � e. Prima lavorazione dei prodotti agricoli 05 � Se SI indicare: f. Trasformazione di prodotti vegetali 06 � 49.2.1 Giornate di lavoro effettuate in azienda e 49.2.2 - di cui da altre aziende agricole g. Trasformazione di prodotti animali 07 � e h. Produzione di energia rinnovabile 08 � 49.3 Tipo di operazioni effettuate SUPERFICIE Cod. i. Lavorazione del legno (taglio, ecc.) 09 � in azienda Ettari Are l. Acquacoltura 10 � AFFIDAMENTO COMPLETO 01 m. Lavoro per conto terzi utilizzando mezzi di (di una o più coltivazioni) produzione dell’azienda AFFIDAMENTO PARZIALE - attività agricole 11 � a. Aratura 02 - attività non agricole 12 � b. Fertilizzazione 03 n. Servizi per l’allevamento 13 � c. Semina 04 o. Sistemazione di parchi e giardini 14 � d. Raccolta meccanica e prima p. Silvicoltura 15 05 � lavorazione di vegetali q. Produzione di mangimi completi e complementari 16 � e. Altre operazioni per le coltivazioni 06 r. Altre attività (specificare……………………………) 17 � f. Altre operazioni non sulle superfici 48.2 Indicare quale delle attività sopra elencate è (specificare………………………………) 07 � la più remunerativa in termini economici (indicare il codice) PRODUZIONE DI MANGIMI b 50 PER IL REIMPIEGO IN AZIENDA 48.3 Indicare il peso percentuale dell’attività sopra 50.1 Nell’azienda sono stati prodotti indicata (punto 48.2) rispetto al totale delle attività % mangimi completi e complementari elencate al punto 48.1 (indicare un valore percentuale) c per il reimpiego in azienda? 1 � SI 2 � NO

IMPIANTI PER LA PRODUZIONE DI ENERGIA RINNOVABILE 51 (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 � c. Solare 04 � b. Biomassa 02 � d. Idroenergia 05 � - tra cui biogas 03 � e. Altre fonti di energia rinnovabile (specificare…………………………………………) 06 �

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sezione VI Altre informazioni (annata agraria 2009 - 2010)

52 CONTABILITÀ 54 AUTOCONSUMO

Indicare se l’azienda ha: 54.1 La famiglia del conduttore consuma i prodotti aziendali? a. Contabilità forfettaria 01 b. Contabilità ordinaria 02 1 SI 2 NO c. Nessuna contabilità 03 Se SI 53 RICAVI 54.1.1 Indicare se l’azienda autoconsuma a. Tutto il valore della produzione finale 01 Indicare la percentuale di ricavi lordi provenienti da % b. Oltre il 50% del valore della produzione a. Vendita di prodotti aziendali 01 c finale 02 b. Altre attività remunerative connesse all’azienda 02 c c. Il 50% o meno del valore della produzione c. Pagamenti diretti 03 c finale 03 TOTALE PERCENTUALE 1 0 0

COMMERCIALIZZAZIONE DEI PRODOTTI AZIENDALI 55 (in termini percentuali per canale di commercializzazione)

VENDITA O VENDITA DIRETTA VENDITA AD VENDITA AD VENDITE AD CONFERIMENTO AD AL CONSUMATORE ALTRE AZIENDE IMPRESE IMPRESE TOTALE Cod. ORGANISMI In azienda Fuori azienda AGRICOLE INDUSTRIALI COMMERCIALI % 55.1 Prodotti vegetali ASSOCIATIVI % % % % % % 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

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

Dichiaro di essere stato intervistato Dichiaro che i dati sono stati rilasciati in Dichiaro di aver revisionato il questionario dal rilevatore: conformità alle istruzioni ricevute IL REVISORE L’INTERVISTATO IL RILEVATORE

…………………………………………………………………… ……………………………………………………………………… …………………………………………………………………… (Firma) (Firma) (Firma)

h Data …………………… Data ………………….. Codice rilevatore

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

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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 Con- siglio 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 - Ag- giornamento 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 Uf- fici 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 te- nuti 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 Regola- mento 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 rien- tranti 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.D escrizione 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 in- teressate. 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 qua- lità (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 ap- plicato 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 successi- vamente 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 m3. - Il volume stagionale distribuito espresso in m3.

148 1.4.2 Coltivazioni legnose agrarie

1.4.3 Altre coltivazioni

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 calenda- rio 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’a- zienda) 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 Q UestionARIO AZIENDE AGRICOLE

Rilevatore

Data rilevazione

Codice RICA

Codice Progressivo regionale

TIPOLOGIA DI AZIENDA

barbabietola, mais, coltivazioni foraggere 

patata, girasole, soia  Ordinamento prevalente irriguo agrumi, frutteti, ortive 

frumento, vite, olivo 

Acqua pubblica  Fonte di approvvigionamento prevalente Autoapprovvigionamento 

Grande (> 20 ha)  Superficie (SAU) Piccola (< 20 ha) 

Microirrigazione 

Sistema di irrigazione prevalente 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

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.I IMP ANti DI SOLLEVAMENTO UTILIZZATI PER L’APPROVVIGIONAMENTO

Potenza totale (kW) Consumo elettrico medio annuo Ore di funzionamento totali totale (kWh)

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 (m3)

Volume medio adacquate (m3)

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) a H irrigata uperficie S a H totale uperficie S Coltura Uso del suolo

3 a agraria 2007 - 2008) IONE Z Comune SE (annat 3.1 Seminativi

160 Volume stagionale totale (m3)

Volume medio adacquate (m3)

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) a H Nuovo irrigata impianto a H Nuovo impianto uperficie S a H irrigata a H In produzione In produzione Coltura Comune 3.2 Coltivazioni legnose agrarie Coltivazioni 3.2

161 Volume totale stagionale (m3)

Volume medio adacquate (m3)

Durata irrigazione (h)

n. adacquate

Sistema di irrigazione (vedi

codici) o sistema ltr A 5 Produzione biologica o produzione integrata (SI/NO

Data fine irrigazione

Data inizio irrigazione a H irrigata uperficie S a ale H ot T icroirrigazione (goccia, manichetta forata, ecc) forata, manichetta (goccia, icroirrigazione uperficie spersione (a pioggia) spersione S A M 4 Coltura ndicare il codice del sistema di irrigazione unico o prevalente di irrigazione il codice del sistema ndicare I corrimento superficiale ed infiltrazione laterale ed infiltrazione superficiale corrimento ommersione 3 S S 2 Comune (1) 1 3.3 Altre coltivazioni (pascoli da ed Altrearboricoltura 3.3 legno) coltivazioni

162 AGI LLE AT

T ipologia di aziende

Regione Emilia – Romagna | Ordinamento prevalente irriguo: barbabietola, mais, coltivazioni foraggere

Fonte prevalente Dimensione Sistema irrigazione prevalente N° aziende Microirrigazione 3 Grande Infiltrazione - scorrimento 2 (> 20 ha) Aspersione 11 Autoapprovvigionamento Microirrigazione 1 Piccole Infiltrazione – scorrimento 1 (<20 ha) Aspersione 7 Microirrigazione 2 Grande Infiltrazione – scorrimento 5 (> 20 ha) Aspersione 22 Pubblica 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 Microirrigazione 1 Grande Infiltrazione - s corrimento 1 (> 20 ha) Aspersione 1 Autoapprovvigionamento Microirrigazione 1 Piccole Infiltrazione – scorrimento 1 (<20 ha) Aspersione 1 Microirrigazione 1 Grande Infiltrazione – scorrimento 1 (> 20 ha) Aspersione 1 Pubblica 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 Microirrigazione 2 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 1 Autoapprovvigionamento Microirrigazione 14 Piccole Infiltrazione - scorrimento 13 (<20 ha) Aspersione 9 Microirrigazione 1 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 1 Pubblica 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 Microirrigazione 15 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 3 Autoapprovvigionamento Microirrigazione 40 Piccole Infiltrazione - scorrimento 2 (<20 ha) Aspersione 7 Microirrigazione 6 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 1 Pubblica Microirrigazione 16 Piccole Infiltrazione - scorrimento 1 (<20 ha) Aspersione 4 TOTALE 97

Regione Sardegna | Ordinamento prevalente irriguo: barbabietola, mais, coltivazioni foraggere

Fonte prevalente Dimensione Sistema irrigazione prevalente N° aziende Microirrigazione 1 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 8 Autoapprovvigionamento Microirrigazione 1 Piccole Infiltrazione - scorrimento 1 (<20 ha) Aspersione 1 Microirrigazione 2 Grande Infiltrazione - scorrimento 1 (> 20 ha) Aspersione 30 Pubblica 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 (m3/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 L ombardia 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 DÁdda 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 SantÁngelo 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 DÁrio 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 L ombardia 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 L ombardia 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 SantÁlessio con Vialone 40200 2 Lombardia Pavia SantÁngelo 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 dÁrese 40200 2 Lombardia Pavia Torre dÍsola 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 dÁrdenghi 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 W ater Resource Management

ISBN 978-88-8145-289-7