A ModeL-bAsed irrigAtion wAter consuMption …dspace.crea.gov.it/bitstream/inea/601/1/MARSALa_Lupia.pdfcomputational model, the irrigation water consumption at farm level in Italy

INEA 2013

edited by Flavio Lupia

A ModeL-bAsed irrigAtion wAter consuMption estiMAtion At FArM LeveL

Istituto Nazionale di Economia Agraria

A Model-bAsed irrigAtion wAter consuMption estiMAtion

At fArM level

edited by Flavio Lupia

INEA 2013

Editor: Flavio Lupia

Contributors:

INEA

Flavio Lupia - Foreword, Introduction, Glossary, Annex 1, Chapter 5, Paragraphs: 2.4, 3.4.2, 4.1, 4.2, 4.3, 4.4 and 4.5

Silvia Vanino - Paragraphs 3.2 and 3.3

Francesco De Santis - Annex 1, Paragraphs: 2.4, 4.1, 4.2 and 4.3

Filiberto Altobelli - Paragraph 2.5

Giuseppe Barberio - Chapter 5

Pasquale Nino - Paragraph 2.6

ISTAT

Giampaola Bellini - Chapter 1

Giancarlo Carbonetti - Paragraph 4.1

Massimo Greco - Paragraph 3.1

Luca Salvati - Paragraph 3.4.1

IAS-CSIC

Luciano Mateos - Paragraphs: 2.1, 2.2, 2.3, 2.4 and 4.2

CRA-CMA

Luigi Perini - Paragraph 3.4.3

Free-lance consultants

Nicola Laruccia - Paragraph 3.3

Disclaimer:

“This publication has been realized in the framework of the MARSALa project funded by Eurostat with the Grant Agreement No. 40701.2008.001008.140 (Grant Programme 2008 - Theme “Pilot studies for estimating the volume of water used for irrigation”). Its content does not represent the official position of the European Commission and is entirely under the responsibility of the authors.”

“The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability.”

Copyright © 2013 by Istituto Nazionale di Economia Agraria, Roma.

Editorial coordination: Benedetto Venuto

Graphic design: Ufficio Grafico Inea (Barone, Cesarini, Lapiana, Mannozzi)

Publish coordination: Roberta Capretti

“Essentially, all models are wrong but some are useful.”

(George Edward Pelham Box)

5

Acknowledgments

At the outset, it is my duty to acknowledge with gratitude the generous help recei-ved 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 theRICA database;

•AlfonsoScardera(INEA-Molise)fortheadvisesduringthedesignofthepilotare-as questionnaire;

•AntonioGiampaoloandthepersonnelfromINEA-Abruzzoforthedesignandim-plementation of the electronic survey on crop planting/harvesting date through GAIA website;

•FedericaFloris(INEA-Sardegna)forsupportingtheactivitiesinSardegnaandCinziaMorfinoforirrigationwaterconsumptiondatacollection;

•GiancarloPeiretti (INEA-Piemonte),SoniaMarongiu (INEA-Veneto),LuciaTu-dini (INEA-Toscana) and Roberto Lo Vecchio (INEA-Calabria) for the supportduring data collection on rice cultivation water use;

•IrajNamdarianfortherevisionofthetextandtheusefulhints.

IwouldliketothankMicheleFiori(ARPASardegna)andVittorioMarletto(ARPAEmilia-Romagna)fortimelyprovidinghighresolutionagrometeorologicaldata.

SpecialthanksareduetoMaurizioEspositofromMiPAAFforthecooperationsin-ce the project proposal and for his full support and the useful suggestions during the data collection.

IamalsogratefultoCarmeloCicalafromMiPAAFforthesupportandCostanzoMassarifromMiPAAFthatprovidedinformationaboutthestate-of-the-artonsoildata-bases in Italy.

7

Foreword

Thispublicationcontainsanexhaustivedescriptionofthedevelopedmethodologi-cal approach to estimate the irrigation water consumption at farm level in Italy by using the data collected though the 6thGeneralAgriculturalCensusrealizedbyISTATintheperiod 2010-20121.

In2008,Eurostatawardedgrantsto13EuropeanMemberStates(MS)todevelopmethodologies for irrigation water consumption estimation that could be extended to allMS.ThisnecessityarosefromtheEC-RegulationNr.1166/2008thatbindsallMStoprovide,foreachholdingsurveyedwiththeStatisticsonAgriculturalProductionMeth-ods(SAPM),anestimationofirrigationwaterconsumptionmeasuredincubicmetres.

TheItaliangrant,titledaModellingApproachforirrigationwateReStimationatfArmLevel(MARSALa),hasbeenleadedbyINEAinpartnershipwiththeInstitutodeAgricolturaSostenibile-ConsejoSuperiordeInvestigacionesCientificas(IAS-CSIC),theSpanishresearchinstitutebasedinCordobaspecializedinirrigationandagriculturalsciences.IAS-CSICcooperatedwithINEAfortherealizationoftheworkpackage(WP)dealingwiththedesignandintegrationofthecomputationalmodels(ModelsDesign).

Theprojectlasted22monthsstartingfromJuly2008tillMay2010andithasbeenarticulatedinfiveWPswithdifferentphasesasdepictedintheworkbreakdownstruc-ture(WBS)inFigure1.TheprojectplanisreportedinTable1.

figure 1. project work breakdown structure with the five wps and the relative phases

Data collection

marsala

models designCensus

questionnaire amendments

models calibration and validation

software implementation

and testing

Model A

Model B

Model C

Agro-meteo database

Crop characteristics

database

Soil database

Pilot campaigns

Calibration

Module 1

Module 2

1 ThemethodologyhasbeendevelopedintheframeworkoftheEurostatGrantProgramme2008(Theme“Pilotstu-diesforestimatingthevolumeofwaterusedforirrigation”)withtheGrantAgreementNr.40701.2008.001008.140awardedtotheItalianInstituteforAgriculturalEconomics(INEA).

8

Duringtheproject,acollaborationhasbeenestablishedwiththeNationalStatisticServiceofGreece(NSSG)whichwascarryingoutasimilarprojectinGreece.Thecol-laboration allowed a sharing of knowledge, a comparison and a critical analysis of the two approaches, in particular for all those concerning country agricultural character-istics, territorial/environmental features and data availability.

table 1 - MArsAla project plan with the start and end dates by wp and phase.

Activity start end

Project start 15/07/2008 15/07/2008

Census questionnaire amendments 1/09/2008 15/01/2009

models design 15/09/2008 28/02/2010

model a 15/09/2008 15/03/2009

model B 15/09/2008 15/03/2009

model C 1/10/2009 28/02/2010

Data collection 1/10/2009 1/05/2010

agrometeorological database 1/10/2008 15/01/2009

Crop characteristics database 15/01/2009 30/06/2009

soil database 1/02/2009 1/05/2010

models calibration and validation 1/02/2010 1/05/2010

Pilot campaigns 15/10/2009 15/02/2010

Calibration 15/01/2010 1/05/2010

software implementation and testing 15/12/2009 10/05/2010

module 1 15/12/2009 28/02/2010

module 2 15/12/2009 10/05/2010

Project end 14/05/2010 14/05/2010

TheWP Models Design, the core activity of the action, has been aimed at the de-signand integrationof three computationalmodels:ModelA,ModelBandModelC.Themodelshavebeendesignedafteranextensiveanalysisofthestate-of-the-artandby 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).TheWPhasbeenalsoaddressedtotheanalysisandidentificationof themaininput parameters required by the models.

TheinputparametershavebeenusedduringtheWP Census Questionnaire Amend-ments,whichhasbeenjointlycarriedoutwithISTATandfocussedontheCQstructureanalysisanddefinitionofanamendedversioncontainingsomechangesandadditionalquestionsoffundamentalimportanceforthemodelsapplication.Theamendmentsal-lowed 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 thedifficultyofidentification,analysis,collectionandstandardizationoftheinputdatarequiredbythemodels.ThecreationofthesoilparametersdatabaseforthewholeItal-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 method-ology to extract the soil parameters by considering several information such as topogra-phy(altitudeandslope)andlanduse.

9

TheWP Models Calibration has been addressed to the comparison of the simulated and actual irrigation water volumes used at farm level. Pilot campaigns have been re-alizedinfourItalianregionsbysubmittingaquestionnairetoasampleofalmost300farms.Surveyorscollected,ineachfarm,thesameinformationreportedintheCQandinaddition the measured and/or estimated water consumption of the farm irrigated crops.

TheWP Software Implementation and Testing has been devoted to the implemen-tationofthethreeintegratedmodels.Thefinalsystemrealizedismadeupofdifferentcomputationalmodules(somededicatedtodatapre-processing)anditworksbyusinga set of databases containing all the input parameters.

11

executive summAry

TheMARSALa(ModellingApproachforirrigationwateReStimationatfArmLev-el)projecthasbeenrealizedintheframeworkoftheEurostatGrantProgramme2008(Theme“Pilotstudiesforestimatingthevolumeofwaterusedforirrigation”)withtheGrantAgreementawardedtotheItalianInstituteforAgriculturalEconomics(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 6thGeneralAgriculturalCensus2010.Themethodol-ogyhasbeenappliedtoestimatethewaterconsumption(incubicmeters)forthewholeuniverse of the Italian irrigated farms as requested by EC-Regulation Nr.1166/2008.

Themethodologygroundsonthedevelopmentandintegrationofthreemodelsdeal-ing with the main aspects related to the farm irrigation water consumption: the crops irrigationdemand,theirrigationsystemsefficiencyandthefarmerirrigationstrategy.Each model has been developed by considering the state-of-the-art methodologies, the limitsimposedbythedataavailabilityanddataresolution(climate,soil,cropscharac-teristicsandotherstatistics),theexpertknowledgeandthenatureoftheinformationtobe 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 char-acterizedbydatausuallyproducedwithdifferent standardsandmethodologiesandmanagedbyofficesoperatingatdifferentadministrativelevels.

TheMARSALamodelhasbeencalibratedwithasampleofabout300farmslo-catedinfourItalianregions(Campania,Sardegna,Emilia-RomagnaandPuglia),thefarms sample has been designed to ensure the representativeness for the main Italian agriculturalcharacteristics.Thecalibrationphasehasshownhowaccuracyandreli-ability of the simulated results are directly linked to the quality of the input data required by the three sub-models.

Themodeldevelopedhasbeenimplementedthroughaclient-serverarchitectureand is provided with the necessary routines to import and manage the required data-setsaswellaswithalltheinputdatabases.Theoutputsproducedbythemodelaretheirrigation water consumption for each irrigated farm crops and the total irrigation farm consumption.

13

tAble oF contents

Acknowledgements 5

Foreword 7

Executive summary 11

Introduction 15

ChaPter 1

the irrigAted Agriculture in itAly: An AnAlysis through Fss dAtA 17

1.1 Historical trend of the irrigation phenomenon 17

1.2 Details on the irrigation phenomenon 20

ChaPter 2

methodology For the irrigAtion wAter consumption estimAtion 25

2.1 State of the art on the estimation of irrigation water requirements 25

2.2 Crop Irrigation Requirements Model (Model A) 27

2.3 Irrigation Efficiency Model (Model B) 30

2.4 Irrigation Strategy Model (Model C) 32

2.5 Irrigation water consumption estimation for rice 38

2.6 Irrigation water consumption estimation for protected crops 45

ChaPter 3

input dAtA collection 49

3.1 The 6th General Agricultural Census database 49

3.2 Crop characteristics database 53

3.3 Soil database 56

3.4 Agro-meteorological database 61

ChaPter 4

models cAlibrAtion 67

4.1 Methodology for pilot areas definition and farms sample extraction 70

4.2 Pilot questionnaire for the model calibration 77

14

4.3 Pilot campaigns 79

4.4 Analysis of the model simulation results 90

4.5 Influence of the resolution of the agro-meteorological data on the simulation results 96

ChaPter 5

soFtwAre implementAtion 99

5.1 Module architecture of the computational system1 99

5.2 Functions of the modules and sub-modules 100

Conclusions 103

References 107

Glossary 113

Acronyms and abbreviations 117

Annex 1: Rule-basedapproachforthedefinitionofthefarmirrigatedlanduse 119

Annex 2: 6thgeneralagriculturalcensusquestionnaire(initalianlanguage) 125

Annex 3: Pilotquestionnaireandcompilationguidelines(initalianlanguage) 143

Annex 4: Databaseofmeanirrigationwatervolumesusedforrice 167

15

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 wa-ter 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 suit-able 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, wa-ter 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 pri-vate 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 Level2oftheNomenclatureofTerritorialUnitsforStatistics(NUTS)correspondstotheRegions.

2 Level3oftheNomenclatureofTerritorialUnitsforStatistics(NUTS)correspondstotheProvinces.

16

into account the crop water consumption, the irrigation application efficiency (as a func-tion 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.

•ThefirstchaptercontainsadescriptionoftheirrigatedagricultureinItalybasedon the analysis of Farm Structure Survey (FSS) data collected by ISTAT.

•Thesecondchapterdescribesthemethodologydevelopedandthethreeintegratedmodels.

•Thethirdchapterreportstheactivityofdatainventoryingandcollectionfortheinput 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 sam-ple selection, realization of the pilot campaigns and tuning of the models parameters.

•Thelastchapteroutlinestheactivityrelatedtotheimplementationofthemodelsthrough the MARSALa software application with a brief description of the system architecture and the features.

17

ChaPter I

the irrigAted Agriculture in itAly: An AnAlysis through Fss dAtA

Irrigation represents in Italy one of the most relevant pressures on environment in terms of use of water as in other Mediterranean countries where hot and dry season might create conditions for requirements of additional water to ensure the optimal growth for specific crops.

A picture of the irrigation phenomenon in Italy is provided by ISTAT, who carried out a monitoring activity by collecting several data during the years through FSS data - at census and sample level - as required by European regulations and for national interest. Atnationallevelthefollowingdataareavailable: farmswithirrigationactivity, irrigableand 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 irrigableandirrigatedsurfacesshowdifferentpatterns:farmswithirrigationregisteredadrop of almost 40% between year 1990 and 2007 (the phenomenon is related to the de-crease 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 irriga-ble and irrigated area, with the first one always greater that the latter, can be explained by thefollowingelements:

•recursiveeventsofwatershortageperiodsavoidingthefullexploitationofthewholefarm area equipped with irrigation systems (the phenomenon generally affects mainly the Southern regions);

•lowefficiencyoftheirrigationsystemsandofthefarmirrigationandconveyancenetwork preventing the optimal usage of the irrigation water across the whole equipped surface;

•agronomictechniques(e.g.croprotation)reducingtheannuallyirrigatedarea.

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 irriga-tion system.

1 DataanalysisperformedbySimonaRambertiandNicolaMattaliano(ISTAT).

18

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 inpares:1982iscomparablewith1990,and2000with2003wheredataareavailable.Interms of water source, between 1982 and 1990 farms resorting to Surfacewaterbodies and Other sources increased (around 30%) more than farms resorting to Surfaceflow-ing water. Particularly, in year 2000, 233,010 farms uses Surfaceflowingwater, 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 Superficialflowingwater) and Furrows (or Lateral infiltration), Aspersion (or Sprinkler) and Micro-irrigation have comparable distribution among farms (respectively adopted by 193,682, 189,865 and 170,035 farms).

figure 1.1 - irrigable and irrigated area for the years 1982, 1990, 2000, 2003, 2005 and 2007 (area in thousands of hectares).

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

1982 1990 2000 2003 2005 2007

Thou

sand

s of

hec

tare

s

Year Irrigable area Irrigated area

19

tabl

e 1.

1 -

farm

s w

ith

irri

gati

on a

nd r

elat

ed s

urfa

ces

by s

uppl

y so

urce

and

irri

gati

on m

etho

d ex

pres

sed

as a

bsol

ute

valu

e an

d pe

rcen

tage

ov

er to

tal f

arm

s w

ith

irri

gati

on (Y

ears

198

2, 1

990,

200

0, 2

003,

200

5 an

d 20

07).

irri

gate

d fa

rms

/ irr

igat

ed

surf

ace

/ wat

er s

ourc

e /

i rri

gati

on m

etho

d

cen

sus

surv

ey (a

)s

ampl

e su

rvey

(b)

1982

1990

2000

2003

2005

2007

a.v.

% o

ver t

otal

fa

rms

wit

h ir

riga

tion

a.v.

% o

ver t

otal

fa

rms

wit

h ir

riga

tion

a.v.

% o

ver t

otal

fa

rms

wit

h ir

riga

tion

a.v.

% o

ver t

otal

fa

rms

wit

h ir

riga

tion

a.v.

% o

ver t

otal

fa

rms

wit

h ir

riga

tion

a.v.

% o

ver t

otal

fa

rms

wit

h ir

riga

tion

irri

gate

d fa

rms

Farm

s w

ith

irri

gabl

e su

rfac

en.

a.1,

059,

456

966,

270

710,

522

660,

349

677,

738

Farm

s w

ith

irri

gate

d su

rfac

e83

4,42

493

4,64

073

1,08

262

2,54

1 50

3,46

1 56

3,66

3

irri

gate

d su

rfac

e

Irri

gabl

e ar

ea2,

780,

614

3,88

1,77

23,

892,

202

3,97

7,20

63,

972,

666

3,95

0,50

3

Irri

gate

d ar

ea2,

521,

193

2,71

1,18

22,

471,

378

2,76

3,51

02,

613,

419

2,66

6,20

5

farm

s ir

riga

tion

met

hod

sup

erfi

cial

flow

ing

wat

er

and

late

ral i

nfilt

rati

on24

1,36

628

.937

7,57

935

.632

2,31

344

.121

3,60

334

.318

3,99

036

.519

3,68

234

.4

Floo

d73

,533

8.8

48,0

954.

57,

439

1.0

23,2

353.

713

,973

2.8

14,8

382.

6

asp

ersi

on

533,

423

63.9

583,

183

55.0

333,

711

45.6

221,

402

35.6

170,

477

33.9

189,

865

33.7

Dri

ppin

g 28

,208

3.4

113,

577

10.7

114,

369

15.6

184,

214

29.6

146,

504

29.1

170,

035

30.2

Oth

er s

yste

ms

23,4

062.

828

,164

2.7

31,3

734.

345

,691

7.3

35,6

827.1

44,9

678.

0

farm

s w

ater

sou

ces

(c)

sur

face

flow

ing

wat

er15

9,40

119

.119

4,55

718

.423

3,01

031

.9n.

a.n.

a.n.

a.

sur

face

wat

er b

odie

s18

,891

2.3

25,1

342.

433

,790

4.6

n.a.

n.a.

n.a.

Oth

er34

1,73

841

.045

6,40

143

.153

1853

(d)

72.7

n.a.

n.a.

n.a.

del

iver

ing

man

agem

ent (

c):

Irri

gati

on a

nd la

nd

recl

amat

ion

Con

sort

ia30

5,46

536

.639

8,91

337

.730

2,87

241

.432

9,03

252

.9n.

a.n.

a.

Oth

er fa

rms

32,4

773.

931

,037

2.9

35,0

714.

827

,015

4.3

n.a.

n.a.

Oth

er w

ays

35,1

024.

234

,592

3.3

4293

25 (e

)58

.739

7.199

(e)

63.8

n.a.

n.a.

Sou

rce:

ISTA

T, F

SS

- Y

ear 1

982,

199

0, 2

000,

200

3, 2

005,

200

7a.

v.: a

bsol

ute

valu

en.

a.: n

ot a

vaila

ble

(a) N

atio

nal U

nive

rse

(b) E

urop

ean

Uni

on U

nive

rse

(c) V

aria

bles

rela

ted

to w

ater

sou

rces

and

ado

pted

del

iver

ing

syst

ems

have

bee

n su

rvey

ed a

s so

urce

of w

ater

in s

urve

ys ru

n in

198

2 an

d 19

90, w

here

as in

yea

rs 2

000

and

2003

sou

rces

an

d d

eliv

erin

g m

anag

emen

t hav

e be

en c

onsi

dere

d in

depe

nden

t phe

nom

ena.

(d) I

nclu

des

the

follo

win

g so

urce

of w

ater

: aqu

educ

t, gr

ound

wat

er, t

reat

ed w

aste

wat

er a

nd

rain

fall

basi

n.(e

) Inc

lude

s se

lf-su

pply

and

oth

er fo

rms.

20

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 de-creased (45.7%) in the same period of time. A decrease is also registered for the soybean cultivation (73.2% less surface compared to 1990), whereas vineyards rose 67.3%. With reference to the last available year 2003, the most irrigated crops, beside the other crops group accounting for 719,521 hectares, are grain maize with 666,723 hectares, followed by rotational forage with 353,261, showing that irrigated crops are mainly linked to livestock foodstuff production. Other relevant irrigated crops are – in order of relevance - vineyards, fruit and berry plantations, and fresh vegetables (respectively with 266,330, 210,089 and 197,107 hectares).

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

crop

census year sample survey

1982 1990 2000 2003

farmsirrigated

areafarms

irrigated area

farmsirrigated

areafarms

irrigated area

Wheat - - 18,566 69,489 27,178 99,636 13,061 57,391

Grain maize 200,002 559,804 179,057 507,170 124,895 623,155 108,220 666,723

Potato - - 90,925 34,710 56,872 26,461 22,944 24,847

sugar beet - - 18,684 81,965 15,282 81,532 14,271 83,203

sunflower - - 3,841 18,537 2,526 14,260 1,839 7,399

soybean - - 40,250 201,083 11,971 78,618 9,527 53,895

Fresh vegetables 264,015 217,607 223,873 233,587 152,293 191,012 102,292 197,107

rotational forage 143,290 650,280 96,202 439,376 47,439 267,560 52,085 353,261

Vineyards 136,349 159,177 113,119 162,391 110,828 182,694 109,910 266,330

Citrus plantations 122,180 146,735 137,212 153,815 109,136 113,651 75,309 123,744

Fruit and berry plantations

82,511 144,329 117,355 199,059 108,974 189,175 88,545 210,089

Other crops 282,859 643,262 384,574 609,999 285,184 603,624 269,313 719,521

total 934,427 2,521,193 934,840 2,711,182 731,082 2,471,378 622,541 2,763,510

Source: ISTAT, FSS - Years 1982,1990, 2000 and 2003.

1.2 details on the irrigation phenomenon

1.2.1 Farms with irrigation, irrigable and irrigated area

Referring to irrigated and irrigable area the most recent data refers to year 2007 (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 culti-vated area at national level rising to 36% in the northern regions.

21

table 1.3 farms with irrigable and irrigated area by region (Year 2007).

region/Autonomous province (Ap)

farms with irrigable area

irrigable area farms with irrigated area

irrigated area

% over the total

% over the total farms (a)

% over the total

% over cultivated

area (b)

% over the total

% over the total farms (a)

% over the total

% over the cultivated

area (b)

Piemonte 5.4 48.7 10.5 39.2 5.9 44.5 13.6 34.2

Valled’aosta 0.5 96.0 0.5 31.6 0.7 95.5 0.6 25.3

lombardia 5.2 62.0 17.2 67.1 5.5 54.1 21.2 56.0

trentino-alto adige 4.3 70.4 1.7 16.7 5.0 68.0 2.4 16.2

Bolzano (aP) 2.3 73.6 1.1 17.6 2.7 72.4 1.7 17.3

trento (aP) 2.1 67.2 0.5 15.3 2.3 63.7 0.8 14.3

Veneto 11.2 52.3 12.0 57.2 9.0 35.1 11.2 36.1

Friuli-Venezia Giulia 1.4 40.6 2.5 42.2 1.7 39.3 3.1 35.4

liguria 1.9 63.3 0.2 14.6 2.2 58.7 0.2 11.6

emilia-romagna 6.1 50.9 15.1 56.5 5.2 35.9 11.1 28.0

toscana 4.0 34.2 3.0 14.7 3.1 22.2 1.8 5.8

Umbria 1.3 23.7 1.3 15.4 1.1 16.7 0.9 7.1

marche 1.9 26.7 1.5 11.9 1.7 19.0 0.9 4.9

lazio 4.0 26.8 3.6 20.7 4.2 23.3 3.2 12.7

abruzzo 3.1 34.7 1.5 13.8 3.0 28.4 1.3 7.9

molise 0.4 11.7 0.5 10.2 0.4 9.5 0.6 7.4

Campania 8.4 37.5 2.6 17.8 9.2 34.2 2.9 13.8

Puglia 13.6 37.5 10.5 34.8 13.3 30.6 10.2 22.7

Basilicata 2.7 31.9 2.0 14.4 2.9 28.6 1.7 8.3

Calabria 8.4 47.5 3.0 22.9 9.6 45.5 3.3 16.9

sicilia 11.4 32.7 5.9 18.7 12.2 29.1 6.6 14.0

sardegna 4.6 47.0 4.8 17.2 4.0 34.3 3.0 7.3

italy 100.0 40.4 100.0 30.7 100.0 33.6 100.0 20.7

north 36.2 54.6 59.7 50.1 35.2 44.1 63.6 36.0

centre 11.3 28.5 9.4 16.0 10.1 21.2 6.8 7.8

south 52.5 37.1 30.9 20.9 54.7 32.1 29.6 13.6

Source: ISTAT, FSS-Year 2007(a) Farms with Utilised Agricultural Area (UAA) of trees for wood production(b) Cultivated area includes UAA and trees for wood production

The analysis of the distribution of irrigated area by altimetric zone (Figure 1.2) shows a concentration (69%) in the plain areas and a minor distribution on hilly (24%) and moun-tainous areas (7%).

figure 1.2 irrigated area by altimetric zone (Year 2007).

Hill 3%

Mountain 9%

Plain 88%

22

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 condi-tions and low water availability are quite common in the irrigation season - the percentage rises to 53.4%.

table 1.4 - irrigated area by irrigation system and region (Year 2007). data are expressed as percentage over the total irrigated area.

region/Autonomous province (Ap)

irrigation system

border and furrows

flood AspersionMicro-irrigation other

system total drip

Piemonte 59.8 33.2 4.9 1.8 1.6 0.8

Valle d’aosta 53.9 - 44.4 1.0 1.0 0.7

lombardia 64.1 17.2 18.4 1.4 0.8 1.0

trentino-alto adige 2.2 0.2 72.9 28.5 24.6 0.6

Bolzano (aP) 2.3 0.1 85.1 18.6 17.7 0.0

trento (aP) 1.9 0.3 46.0 50.2 39.6 2.1

Veneto 23.7 0.9 64.6 5.3 3.0 7.6

Friuli-Venezia Giulia 12.2 0.0 80.1 3.8 2.0 4.1

liguria 5.4 0.1 11.8 25.8 22.7 57.5

emilia-romagna 15.9 3.1 61.9 19.8 18.0 2.3

toscana 10.0 0.4 66.4 26.4 24.6 2.5

Umbria 4.1 1.3 84.7 9.5 9.3 1.8

marche 6.8 1.3 70.9 10.6 9.0 11.2

lazio 5.4 2.0 66.6 21.7 15.2 4.8

abruzzo 5.9 0.1 64.3 25.7 24.1 4.3

molise 5.6 - 34.9 60.8 51.2 0.1

Campania 27.1 1.8 46.7 16.9 10.5 9.0

Puglia 5.8 1.0 13.8 75.4 61.6 5.9

Basilicata 12.9 0.2 27.1 49.3 27.3 10.5

Calabria 30.4 1.5 29.2 28.0 17.8 11.7

sicilia 5.0 1.2 27.9 64.7 53.1 1.8

sardegna 3.9 4.7 56.2 30.0 22.8 5.4

italy 30.6 9.1 36.8 21.4 17.0 3.8

north 42.4 13.5 36.6 6.6 5.4 2.7

centre 6.6 1.4 69.5 19.8 16.0 4.6

south 10.7 1.4 29.6 53.4 42.0 6.0

Source: ISTAT, FSS - Year 2007.

The following table reports the distribution of the irrigation system adopted at farm level, the figure shows that a 76% of the irrigated area belongs to farms adopting only one ir-rigation system, 22.1% with two different irrigation systems, whereas only 1.9% with three and more irrigation systems.

23

table 1.5 - number of farms and relative irrigated area (hectares) by number of irrigation system (Year 2007).

number of irrigation systems

farms with uAA and/or wooden arboriculture irrigated area

Absolute values % Absolute values %

0 1,114,481 66.4 0.0 0.01 515,374 30.7 2,026,215 76.02 46,871 2.8 588,619 22.13 or more 1,417 0.1 51,371 1.9total 1,678,144 100.0 2,666,205 100.0

Source: ISTAT, FSS - Year 2007.

1.2.3 Irrigated crops

Last available data on irrigated crops have been collected through the survey run in year 2003. Referring to irrigated crops an analysis has been performed to understand whether a specific crop grown in a specific farm is completely irrigated or not.

Results show that rice and potato are the crops in which respectively 98.8% and 98.4% of the irrigated area is cultivated in farms where the crop is completely irrigated, for other crops such percentages are lower as for wheat and rotational forage where they reach values of 59.6% and 71.9%. Referring to permanent crops, 97.3% of the citrus plantations irrigated area is in farms where the crop is completely irrigated, whereas this value lowers to 75.6% for olive plantations (Figure 1.3).

figure 1.3 cultivated and irrigated area (hectares) by crop (source: istAt, fss 2003).

0

100

200

300

400

500

600

700

800

WheatMais

Rice

Potato

Sugar beet

Sunflower

Soya bean

Rotatio

nal

fora

ge VineOliv

e

Citrus

CROPS

THO

US

AN

DS

OF

HE

CTA

RE

S

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

24

table 1.6 - farms with irrigated area by number of irrigated crops and irrigation system (Year 2003).

number of irrigation systems

irrigation system

farms

one irrigated crop More than one irrigated crop total

Unique Border and Furrows 132,943 49,981 182,924

Flood 12,784 3,817 16,601

aspersion 123,084 55,317 178,401

micro-irrigation 30,407 6,453 36,860

Other system 29,206 6,711 35,917

more than one 102,277 69,561 171,838

total 430,701 191,840 622,541

Source: ISTAT, FSS, Year 2003.

The analysis performed on number of irrigation systems adopted at farm level and number of irrigated crops show that in many cases farms adopt more than one irrigation system (172 thousands farms over 622 thousands), among which 102 thousands irrigate only one crop and the remaining more than one.

In terms of geographical distribution of the mentioned crops, data in Table 1.7 show that northern and southern regions differ quite a lot. Beside other crops, grain maize, rice, rotational forage, vineyards, fruit and berry plantations trees, and meadows are mostly widespread in northern regions, whereas fresh vegetables, vineyards, olive plantations, citrus plantations are mainly located in southern regions.

table 1.7 - irrigated area (hectares) by crop and geographical region (Year 2003).

crop geographical area

north centre south italy

Grain maize 616,220.24 37,607.74 12,894.81 666,722.79

rice 247,017.52 266.02 2,417.43 249,700.98

Fresh vegetables 64,861.01 28,712.46 103,533.72 197,107.17

rotational forage 244,690.83 32,345.31 76,225.31 353,261.45

Vineyards 95,743.10 11,618.17 158,969.00 266,330.26

Olive plantations 2,734.73 6,712.60 164,646.19 174,093.52

Citrus plantations 12.29 504.4 123,226.83 123,743.52

Fruit 130,336.25 15,259.17 64,493.93 210,089.36

meadows 132,847.43 2,003.87 3,942.28 138,793.57

Other crops 206,367.98 59,755.40 117,544.18 383,667.53

total 1,740,831.32 194,785.14 827,893.70 2,763,510.16

Source: ISTAT, FSS, Year 2003.

25

ChaPter II

methodology For the irrigAtion wAter consumption estimAtion

2.1. state-of-the-art on the estimation of irrigation water requirements

Scientific research carried out during the first half of the 20th century generated a new set of indications for quantitative irrigation management. The water balance and the concepts of the upper and lower limits of the soil water readily available to the plants (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 require-ments. This procedure is widely used today for irrigation systems design and management.

Thewaterbalanceprovidesirrigationschedules:targetirrigationdepthsanddates,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 effi-ciency 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 expressedusingnon-dimensionalcoefficients:theuniformitycoefficientofChristiansen(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 equa-tion (Monteith and Unsworth, 1990) to calculate reference evapotranspiration and the dual crop coefficient approach (Wright, 1982) for computing soil evaporation and crop transpi-

26

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 meth-od 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 sur-veys 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 thefarmirrigation:CropIrrigationRequirementsModel (Model A), IrrigationEfficiencyModel (Model B) and IrrigationStrategyModel (Model C). The framework of the MARSA-La methodology is depicted in Figure 2.1.

figure 2.1 - framework of the MArsAla methodology: typology of the input data and models relationships.

The three models estimate the irrigation consumption of the farm irrigated crops ex-cept for rice and protected crops, for which a separate approach is adopted (see paragraphs 2.5 and 2.6).

Model ACrOP IrrIGatION

reQUIremeNt

Model bIrrIGatION

sYstem eFFICIeNCY

Model cIrrIGatION strateGY

IrrIGatION CONsUmPtION

CrOPstatIstICs

CrOPParameters sOIl ClImate 2010 CeNsUs

27

2.2. crop irrigation requirements Model (Model A)

The model accounts for the irrigation request of a single crop by considering the ir-rigationdatesanddepthsthroughadailyrootzonewaterbalance,theformulationin(1):

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)

The model accounts for the irrigation request of a single crop by considering the irrigation dates and depths through a daily root zone water balance, the formulation in (1):

- RZWDi and RZWDi-1 are the root zone soil water deficit on days i and i-1 in mm;

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

- Ii is the irrigation 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;

(1) (1)

- RZWDi and RZWD

i-1 are the root zone soil water deficit on days i and i-1 in mm;

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

- Ii is the irrigation 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;

- 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 capacity, while it is empty when the water content is at the wilting point (see Fig-ure 2.2). The root zone water holding capacity (RZWHC) is defined as the depth of water (within the root zone) between field capacity and wilting point.

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.

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 de-pendent on the applied depth in relation to the required depth and the irrigation uniform-ity, this aspect is managed by Model B.

figure 2.2 - characteristic soil water content in the reservoir analogy.

Effective rainfall data are derived from the data acquired in agrometeorological sta-tions. Evapotranspiration (ET, mm) is computed using FAO methodology based on the con-cepts of crop coefficient and reference evapotranspiration (Doorembos and Pruitt, 1977b). Reference evapotranspiration (ETo, mm) is calculated using the Penman-Monteith equa-tion (Monteith and Unsworth, 1990, Cap. 11; Allen et al., 1998) with data of solar radiation, wind speed, air temperature and relative humidity acquired in agrometeorological sta-


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 water holding capacity (RZWHC) is defined as the depth of water (within the root zone) between field capacity and wilting point.


Drainage of rain water is computed as the excess of the root zone soil water content over field capacity at the given day of the water balance. Drainage of irrigation water is dependent on the applied depth in relation to the required depth and the irrigation uniformity, this aspect is managed by Model B.

Figure 2.2 - Characteristic soil water content in the reservoir analogy.

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 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) 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 form popularized by FAO (Allen et al., 1998). This approach separates 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:

28

tions. The crop coefficients are derived using the dual approach (Wright, 1982) in the form popularized by FAO (Allen et al., 1998). This approach separates crop transpiration from soilsurfaceevaporationasfollows:







(2)



(3)


(2)

where Kcb

is the basal crop coefficient, Ke is the soil evaporation coefficient and K

s

quantifies the reduction in crop transpiration due to soil water deficit.

Therefore, crop transpiration (T,mm)is:







(2)



(3)


(3)

and soil evaporation (E,mm)is:

(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 (see Figure 2.3).

Subsequently , the root zone depth (Zr) could be computed as a function of Kcb:

where Zr max and Zr min are the maximum effective root depth and the minimum effective root depth during the initial stage of crop growth and Kcb max the maximum value of Kcb.

Ke is obtained by calculating the amount of energy available at the soil surface as follows:

where Kr is a dimensionless evaporation reduction coefficient dependent on topsoil water depletion (Allen et al., 1998) and Kc max is the maximum value of Kc following rainfall or irrigation. The value of Ke cannot be greater than the product few × Kc max, where few is the fraction of the soil surface that is both exposed and wetted.

The stress coefficient, Ks, is computed based on the relative root zone water deficit as:

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

(5)

(6)

[if RZWDi < (1-p) RZWHC] (7)

[if RZWDi ≥ (1-p) RZWHC] (8)

(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 (see Figure 2.3).

Subsequently , the root zone depth (Zr) could be computed as a function of K

cb:

(4)









(5)

(6)



(5)

where Zr max

and Zr min

are the maximum effective root depth and the minimum effec-tive root depth during the initial stage of crop growth and K

cb max the maximum value of K

cb.

Ke is obtained by calculating the amount of energy available at the soil surface as

follows:

(4)









(5)

(6)



(6)

where Kr is a dimensionless evaporation reduction coefficient dependent on topsoil

water depletion (Allen et al., 1998) and Kc max

is the maximum value of Kc following rainfall

or irrigation. The value of Ke cannot be greater than the product f

ew × K

c max, where f

ew is

the fraction of the soil surface that is both exposed and wetted.

The stress coefficient, Ks,iscomputedbasedontherelativerootzonewaterdeficitas:

(4)









(5)

(6)



[if RZWDi<(1-p)RZWHC] (7)

(4)









(5)

(6)


[if RZWDi ≥ (1-p) RZWHC] (8) [if RZWDi ≥(1-p)RZWHC] (8)


29

figure 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 irriga-tion efficiency (Model B) and the irrigation strategy (Model C).

ThedatarequiredbyModelAare:

•Agrometeorologicaldata

- Reference evapotranspiration (ETo)

- Rainfall

•Soildata

- Fieldcapacity(alternatively:soiltexture,bulkdensityandorganicmattercon-tent, in order of priority)

- Wiltingpoint(alternatively:soiltexture,bulkdensityandorganicmattercontent,in order of priority)

- Soil depth

•Cropdata

- Characteristic crop coefficients

- Planting and harvesting dates

- Duration of the growing phases

•Irrigationmethodschedule

- 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)

30

2.3 irrigation efficiency Model (Model b)

The irrigation application efficiency, thus the irrigation drainage losses, depends on irrigation system factors and management factors. An irrigation system is characterized by its application uniformity. The management factors are considered in the management def-icit coefficient. If the deficit coefficient is high, a 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 uniformity 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 frequency distribution of the applied depth of irrigation (rela-tive to the required depth) across the field assuming that it follows a uniform statistical distribution. Often, the normal distribution adjusts to the non uniformity of the irrigation water better than the uniform distribution. Although the same analysis could be done as-suming a normal distribution (Anyoji and Wu, 1994). Dealing with the uniform distribu-tion is simpler and the unavailability of more precise information does not justify (in the context of MARSALa) using a more complex model.

figure 2.4 - frequency distribution of the applied depth of irrigation (relative to the re-quired 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):areaArepresentingthewaterthatisavailableforcropconsumption,areaB represent-ing the water lost by percolation and area C representing the part of the root zone that has not received any irrigation water. Therefore, three irrigation performance indicators may bedefined:ApplicationEfficiency(E

a),PercolationCoefficient(CP)andDeficitCoeffi-

cient(CD).

2.3. Irrigation Efficiency Model (Model B)

The irrigation application efficiency, thus the irrigation drainage losses, depends on irrigation system factors and management factors. An irrigation system is characterized by its application uniformity. The management factors are considered in the management deficit coefficient. If the deficit coefficient is high, a 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 uniformity 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 frequency distribution of the applied depth of irrigation (relative to the required depth) across the field assuming that it follows a uniform statistical distribution. Often, the normal distribution adjusts to the non uniformity of the irrigation water better than the uniform distribution. Although the same analysis could be done assuming a normal distribution (Anyoji and Wu, 1994). Dealing with the uniform distribution is simpler and the unavailability of more precise information does not justify (in the context of MARSALa) using a more complex model.

Figure 2.4 - Frequency distribution of the applied depth of irrigation (relative to the required depth) across the field assuming that it follows a cumulated uniform distribution.

For a given required depth, three areas can be distinguished in the graph (see Figure 2.4): area A representing the water that is available for crop consumption, area B representing the water that is lost by percolation and area C representing the part of the root zone that has not received any irrigation water. Therefore, three irrigation performance indicators may be defined: Application Efficiency (Ea), Percolation Coefficient (CP) and Deficit Coefficient (CD).

(9)

(10)

(9)

2.3. Irrigation Efficiency Model (Model B)

The irrigation application efficiency, thus the irrigation drainage losses, depends on irrigation system factors and management factors. An irrigation system is characterized by its application uniformity. The management factors are considered in the management deficit coefficient. If the deficit coefficient is high, a 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 uniformity 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 frequency distribution of the applied depth of irrigation (relative to the required depth) across the field assuming that it follows a uniform statistical distribution. Often, the normal distribution adjusts to the non uniformity of the irrigation water better than the uniform distribution. Although the same analysis could be done assuming a normal distribution (Anyoji and Wu, 1994). Dealing with the uniform distribution is simpler and the unavailability of more precise information does not justify (in the context of MARSALa) using a more complex model.

Figure 2.4 - Frequency distribution of the applied depth of irrigation (relative to the required depth) across the field assuming that it follows a cumulated uniform distribution.

For a given required depth, three areas can be distinguished in the graph (see Figure 2.4): area A representing the water that is available for crop consumption, area B representing the water that is lost by percolation and area C representing the part of the root zone that has not received any irrigation water. Therefore, three irrigation performance indicators may be defined: Application Efficiency (Ea), Percolation Coefficient (CP) and Deficit Coefficient (CD).

(9)

(10) (10)

31

Based on the uniform distribution, the above indicators may be expressed in the following form (Wu, 1988):

where a and b are determined by the application uniformity and X is the ratio between required depth and applied depth. X represents also the link between Model B and C since it is the inverse of the Relative Irrigation Supply (RIS) parameter computed by Model C.

The Distribution Uniformity (DU) is a measure of how evenly water soaks into the ground across a field during the irrigation and is defined as one minus the ratio between the average applied depth in the quarter of the field receiving less water and the average applied depth in the whole field. DU can be expressed as a function of the coefficient of variation (CV) of the applied water (Warrick, 1983):

The parameters a and b that define the uniform frequency distribution can be then calculated as:

Once DU is known for the irrigation system of concern, CV, b, and a can be computed. Model C provides a value of RIS (and hence X) from which CD can be computed (see Equation 12). With the value of the required depth, output of Model A, the irrigation (Ii) and irrigation application efficiency (Ea) can be computed. Finally, irrigation drainage will be obtained as the product Ii × Ea.

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(11)

Based on the uniform distribution, the above indicators may be expressed in the fol-lowingform(Wu,1988):






(11)

(12)

(13)

(14)

(15)

(16)

(17)

(12)






(11)

(12)

(13)

(14)

(15)

(16)

(17)

(13)






(11)

(12)

(13)

(14)

(15)

(16)

(17)

(14)

where a and b are determined by the application uniformity and X is the ratio be-tween required depth and applied depth. X represents also the link between Model B and C since it is the inverse of the RelativeIrrigationSupply(RIS) parameter computed by Model C.

The DistributionUniformity(DU) is a measure of how evenly water soaks into the ground across a field during the irrigation and is defined as one minus the ratio between the average applied depth in the quarter of the field receiving less water and the average applied depth in the whole field. DU can be expressed as a function of the coefficient of variation (CV)oftheappliedwater(Warrick,1983):






(11)

(12)

(13)

(14)

(15)

(16)

(17)

(15)

The parameters a and b that define the uniform frequency distribution can be then calculatedas:






(11)

(12)

(13)

(14)

(15)

(16)

(17)

(16)






(11)

(12)

(13)

(14)

(15)

(16)

(17) (17)

Once DU is known for the irrigation system of concern, CV, b, and a can be com-puted. Model C provides a value of RIS (and hence X) from which CD can be computed (see Equation 12). With the value of the required depth, output of Model A, the irrigation (I

i)

and irrigation application efficiency (Ea) can be computed. Finally, irrigation drainage will

be obtained as the product Ii × E

a.

32

Figure 2.5 shows the relationship between deficit coefficient and application effi-ciency for various distribution uniformities.

figure 2.5 - deficit coefficient vs. application efficiency for various distribution uniformities.

ThebasicdatarequiredbyModelBare:

•Irrigationmethod

- Distribution Uniformity (DU)

•Irrigationstrategy

- Relative Irrigation Supply (RIS)

2.4. irrigation strategy Model (Model c)

The farm irrigation practice for a given agrarian year is the result of the farmer deci-sion 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 tak-ing into account some elements of the farm and the surrounding territory having a connec-tion 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 irriga-tion equipment, the size of the farm, etc.

MARSALaconsiderstwopivotalelementsintheirrigationdecisionprocess:

•thewateramountprovidedtothecrops(theirrigationdepth),modelledbythepa-rameter RelativeIrrigationSupply (RIS);

•thetolerablecropsstresslevel(orthealloweddepletionfraction),modelledbytheparameter f1.

33

To compute f1 and RIS a set of rules and decision trees have been defined, the pa-rameters 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 be-tween the irrigation volumes collected during farm interviews and those simulated by the MARSALa model.

2.4.1. Relative Irrigation Supply (RIS)

The RelativeIrrigationSupply (RIS) can be defined as the ratio between the irriga-tion 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,theperfectmatchbetweenwatersupplyanddemand(thefarmfollowsanefficient irrigation regime for the crop);

•RIS<1,thecropisnotreceivingenoughwater(thefarmpursueacropirrigationdeficit 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,thecropisirrigatedexcessively,inthiscaseawaterloggingcanoccurim-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 reclassifiedintwoclasses:

•Flexible (self-supply from groundwater and/or superficial sources; ILRC with de-livery 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 estab-lished that the farm is assigned to the class Unflexible in case of only an ILRC with rota-tional 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.

34

2. Irrigation system - the irrigation systems reported in the CQ have been aggre-gatedinthreeclasses:

•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 effi-ciency 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 irriga-tion 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 ques-tionnaire 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 addi-tion, 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 theuseofinformationfromSIGRIAN:thedatabasemanagedbyINEAreportinginformation 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 in-crease 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. IrrigationAdvisorySystem(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 (infor-mation 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.

35

figu

re 2

.6 -

the

dec

isio

n tr

ee u

sed

to d

efine

the

RIS

val

ues.

36

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 fraction of the Total Available Water (TAW)asdefinedbythefollowingequation:

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 fraction of the Total Available Water (TAW) as defined by the following equation:

For the majority of the crops the fraction p takes values between 0.4 and 0.65.

We defined the parameter f1 as the management depletion fraction allowed by the farmer for a given 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 indicates that the crop suffers for water deficit. To assign a proper value of f1 to each crop, another decision tree has been built (see Figure 2.7). In part, the decision tree has some building blocks identical to those belonging to the RIS decision tree (e.g. Water supply, Irrigation system and Shortage (Enough water)). The new inserted blocks are:

• Deficit olive tree - a binary variable (yes/no) taking into account the application of deficit irrigation techniques for olive pantations;

• Priority crop - a binary variable (yes/no) related to the rank attributed by the farmer to crops in case of water shortage when it has to be decided which crops will have top priority for irrigation. The value of the variable is defined in terms of membership of the crop to a predefined list of priority crops built-in in MARSALa. The crops list (see Table 2.1) has been defined by expert judgment by taking into account the crop resistance to water stress condition, the maximum level of yield loss acceptable and the market conditions. Rice and protected crops are not part of the list being always irrigated with the highest priority.

Observing the structure of the decision tree it is evident as under no shortage condition the values of the leaves indicates that the crop is irrigated with a certain frequency avoiding any stress phenomenon in comparison to the shortage condition. Moreover the values of f1 reflect the characteristics of the irrigation water supply (e.g. in the case Flexible a major control of the water volume in the soil can be applied by replenishing the RAW) 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.

Crop group no. 1 Crop group no. 2 Table grapes, Fruit trees, Citrus plantations Legumes Tobacco Sunflower Fresh vegetables, Flowers and ornamental plants Sorghum Grapes for wine, Olive plantations Nuts Maize, Sugar beet Permanent grassland Fodder Other crops

(18) (18)

For the majority of the crops the fraction p takes values between 0.4 and 0.65.

We defined the parameter f1 as the management depletion fraction allowed by the farmer for a given 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 indicates that the crop suffers for water deficit. To as-sign a proper value of f1 to each crop, another decision tree has been built (see Figure 2.7). In part, the decision tree has some building blocks identical to those belonging to the RIS decision tree (e.g. Watersupply,IrrigationsystemandShortage(Enoughwater)). The newinsertedblocksare:

•Deficitolivetree - a binary variable (yes/no) taking into account the use of deficit irrigation techniques for olive pantations;

•Priority crop - a binary variable (yes/no) related to the rank attributed by the farmer to crops in case of water shortage when it has to be decided which crops will have top priority for irrigation. The value of the variable is defined in terms of membership of the crop to a predefined list of priority crops built-in in MARSALa. The crops list (see Table 2.1) has been defined by expert judgment by taking into account the crop resistance to water stress condition, the maximum level of yield loss acceptable and the market conditions. Rice and protected crops are not part of the list being always irrigated with the highest priority.

Observing the structure of the decision tree it is evident as under no shortage condi-tion the values of the leaves indicates that the crop is irrigated with a certain frequency avoiding stress phenomenon. Moreover, the values of f1 reflect the characteristics of the irrigation water supply (e.g. in the case Flexible a major control of the water volume in the soil can be applied by replenishing the RAW) 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.

crop group no. 1 crop group no. 2

table grapes, Fruit trees, Citrus plantations legumes

tobacco sunflower

Fresh vegetables, Flowers and ornamental plants sorghum

Grapes for wine, Olive plantations Nuts

maize, sugar beet Permanent grassland

Fodder Other crops

37

figure 2.7 - the decision tree used to define the values of f1.

38

2.5. irrigation water consumption estimation for rice

Italy is the European largest producer of rice. Rice cultivated area in 2009 was about 238,000 hectares (see Table 2.2) and the total raw production reached 1,500,000 tons. Generally the location of the rice cultivated areas reflects the large water availability and the efficiency of the water delivery network.

table 2.2 - rice cultivated areas (in hectares) for each italian province and region.

region surface (ha) province surface (ha)

Piemonte 121,667 Vercelli 73,666

Biella 3,978

Novara 34,924

Pavia 348

alessandria 8,360

Cuneo 203

torino 188

lombardia 101,673 Pavia 84,871

milano 13,501

Bergamo 6

mantova 1,365

lodi 1,930

Veneto 3,205 Padova and Vicenza 105

rovigo 969

Venezia 254

Verona 1,877

lazio 8 - 8

Friuli Venezia Giulia 2 - 2

emilia romagna 7,878 Bologna 193

Ferrara 7,276

modena 355

Piacenza 13

reggio emilia 41

sardegna 3,154 Cagliari and Oristano 3,154

toscana 363 Grosseto and siena 363

Calabria 508 Cosenza 508

italy 238,458

Source: Ente Nazionale Risi – Year 2009.

Two types of preparation for rice fields can be found in Italy depending on soil char-acteristics,topographyandsizeanddistributionoffarmparcels:oneiswidespreadinthewestern 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

39

(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 twoare themain typologies employed:floodingor dry condition; these are often applied with several variations conditioning the manage-ment 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 flood-ing 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 plu-viometric 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 con-sumption 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 munici-pality level. This was considered an optimal solution both in terms of software computa-tional efficiency and reliability and accuracy of the estimated values.

Database has been compiled by running a national survey in the Italian provinces (NUTS3)wherericeiscultivated.Theactivitywasdividedintothefollowingsteps:

1. inventorying of the municipality where rice is cultivated;

2. data collection on the irrigation water consumption through interviews with dif-ferent subjects (ILRCs, RICA surveyors, etc.);

3. imputation of a mean irrigation water consumption to each municipality and crea-tion 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 EnteNazionaleRisi (the official institute collecting national data about the surfaces used for rice cultivation). The database provid-ed has been considered enough reliable since all farmers growing rice are obliged to com-municate annually the cultivated areas with rice to the EnteNazionaleRisi. The database

40

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 inter-views 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.

table 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.

41

figu

re 2

.8 –

Are

as o

f ric

e cu

ltiv

atio

n an

d m

ean

valu

es o

f irr

igat

ion

wat

er in

nor

ther

n it

aly.

42

figu

re 2

.9 -

Are

as o

f ric

e cu

ltiv

atio

n an

d m

ean

valu

es o

f irr

igat

ion

wat

er in

tos

cana

reg

ion.

43

figu

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.10

- A

reas

of r

ice

cult

ivat

ion

and

mea

n va

lues

of i

rrig

atio

n w

ater

in s

arde

gna

regi

on.

44

figu

re 2

.11

- A

reas

of r

ice

cult

ivat

ion

and

mea

n va

lues

of i

rrig

atio

n w

ater

in c

alab

ria

regi

on

45

2.6. irrigation water consumption estimation for protected crops

MARSALa model has been considered not appropriate to assess water consumption in protected environment (greenhouses or crops under protective cover), for the following reasons:

•cropevapotranspirationestimationinindoormicroclimateconditionsisrelatedtodifferent variables such as the outdoor climate, the type of greenhouse, the climate control strategy and the feedback between the crop and the inside microclimate;

• theconceptofreferenceevapotranspiration(ETo) is also somewhat difficult and delicate to be applied to greenhouse crops water requirements, because hypotheti-cal grass reference crop as defined in FAO paper 56, (Allen et al.) is not commonly grown in greenhouse production (Baille A., 1994);

• separationofcroptranspirationandsoilsurfaceevaporationisverydifficult,ifnotimpossible, due the lack of greenhouses soil properties data;

•absenceofprecipitationinaprotectedenvironmentthatgenerallyparticipatesinpartial restoration of evapotraspirative losses.

More simple approaches have been developed in the estimation of evapotranspiration based on the solar radiation; the role of solar radiation in determining the evapotranspira-tion in the greenhouses has been evidenced in several works in the 60’ and 70’ (Morris et al., 1957, Lake et al., 1966, StanhiIl and Álberts, 1974, De Villele, 1974), showing a strong correlation between daily evapotranspiration and solar irradiance.

Reference evapotranspiration is closely dependent from the environmental condi-tions inside of the greenhouse such as temperature, relative humidity and global radiation. Since these three climatic variables are strongly correlated (at least in the greenhouse environment), a simple mathematical model that takes in consideration only the inner greenhouse global radiation can be applied. Based on that, the so called “solar radiation” method, or “solarimeter” method has been developed which is a simple relationship giving the reference evapotranspiration in the greenhouse if the outside global radiation (RGo) and the greenhouse coefficient transmission (Kt),areknown(BailleA.,1994):

2.6. Irrigation water consumption estimation for protected crops


• crop evapotranspiration estimation in indoor microclimate conditions is related to different variables such as the outdoor climate, the type of greenhouse, the climate control strategy and the feedback between the crop and the inside microclimate;

• the concept of reference evapotranspiration (ETo) is also somewhat difficult and delicate to be applied to greenhouse crops water requirements, because hypothetical grass reference crop as defined in FAO paper 56, (Allen et al.) is not commonly grown in greenhouse production (Baille A., 1994);

• separation of crop transpiration and soil surface evaporation is very difficult, if not impossible, due the lack of greenhouses soil properties data;

• absence of precipitation in a protected environment that generally participates in partial restoration of evapotraspirative losses.

More simple approaches have been developed in the estimation of evapotranspiration based on the solar radiation; the role of solar radiation in determining the evapotranspiration in the greenhouses has been evidenced in several works in the 60' and 70' (Morris et al., 1957, Lake et al., 1966, StanhiIl and Álberts, 1974, De Villele, 1974), showing a strong correlation between daily evapotranspiration and solar irradiance.

Reference evapotranspiration is closely dependent from the environmental conditions inside of the greenhouse such as temperature, relative humidity and global radiation. Since these three climatic variables are strongly correlated (at least in the greenhouse environment), a simple mathematical model that takes in consideration only the inner greenhouse global radiation can be applied. Based on that, the so called "solar radiation" method, or "solarimeter" method has been developed which is a simple relationship giving the reference evapotranspiration in the greenhouse if the outside global radiation (RGo) and the greenhouse coefficient transmission (Kt), are known (Baille A., 1994):

(19)

where:

ETo is the reference evapotranspiration in mm day-1;

RGo is the outside global radiation in MJ m-2 day-1;

λ is the latent heat of vaporization (2.5 MJ/kg H20);

Kt ranges between 0.55 e 0.65 (empirical data provided from Prof. Pardossi, University of Pisa).

Crop Water Requirement (CWR) depends from the evaporating surface, which is expressed as a function of the Leaf Area Index (LAI) of the crop.

After this consideration Equation 19 assumes the following form:

(20)

(19)

where:




Kt ranges between 0.55 e 0.65 (empirical data provided from Prof. Pardossi, Univer-sity of Pisa).

Crop Water Requirement (CWR) depends from the evaporating surface, which is expressed as a function of the LeafAreaIndex(LAI) of the crop.

AfterthisconsiderationEquation19assumesthefollowingform:

2.6. Irrigation water consumption estimation for protected crops


• crop evapotranspiration estimation in indoor microclimate conditions is related to different variables such as the outdoor climate, the type of greenhouse, the climate control strategy and the feedback between the crop and the inside microclimate;

• the concept of reference evapotranspiration (ETo) is also somewhat difficult and delicate to be applied to greenhouse crops water requirements, because hypothetical grass reference crop as defined in FAO paper 56, (Allen et al.) is not commonly grown in greenhouse production (Baille A., 1994);

• separation of crop transpiration and soil surface evaporation is very difficult, if not impossible, due the lack of greenhouses soil properties data;

• absence of precipitation in a protected environment that generally participates in partial restoration of evapotraspirative losses.

More simple approaches have been developed in the estimation of evapotranspiration based on the solar radiation; the role of solar radiation in determining the evapotranspiration in the greenhouses has been evidenced in several works in the 60' and 70' (Morris et al., 1957, Lake et al., 1966, StanhiIl and Álberts, 1974, De Villele, 1974), showing a strong correlation between daily evapotranspiration and solar irradiance.

Reference evapotranspiration is closely dependent from the environmental conditions inside of the greenhouse such as temperature, relative humidity and global radiation. Since these three climatic variables are strongly correlated (at least in the greenhouse environment), a simple mathematical model that takes in consideration only the inner greenhouse global radiation can be applied. Based on that, the so called "solar radiation" method, or "solarimeter" method has been developed which is a simple relationship giving the reference evapotranspiration in the greenhouse if the outside global radiation (RGo) and the greenhouse coefficient transmission (Kt), are known (Baille A., 1994):

(19)

where:




Kt ranges between 0.55 e 0.65 (empirical data provided from Prof. Pardossi, University of Pisa).

Crop Water Requirement (CWR) depends from the evaporating surface, which is expressed as a function of the Leaf Area Index (LAI) of the crop.

After this consideration Equation 19 assumes the following form:

(20) (20)

46

where a is an empirical coefficient ranging from 0.20 to 0.35.

In cases of the solar radiation measurements are not available or are relative to sites distant from the greenhouse, some procedures, based on extraterrestrial radiation and air temperature differences (Allen. R.G., 1995), can be applied for the its estimation.

Considering also that the main source of water is ground water, and the lack of rain-driven leakage, is necessary to introduce in the calculation the LeachingFraction(LF):



Considering also that the main source of water is ground water, and the lack of rain-driven leakage, is necessary to introduce in the calculation the Leaching Fraction (LF):

(21)

where:

ECw is the irrigation water salinity (expressed as Electrical Conductivity (EC) in mS/cm);

ECe depends on the crop, the higher the value the higher is the crop resistance to salinity.

For different crops categories the following values of ECe can be applied (empirical data provided from Prof. Pardossi, University of Pisa):

• 2.5 for fruit vegetable; • 2.0 for leaf vegetable; • 1.8 for cut plants; • 1.5 for pot plants;

Hence, the Irrigation Requirements (IR), quantity of water needed to satisfy the CWR, and allowing, through an adequate leaching, to maintain the salinity of the soil at lower level than those of toxicity for the cultivation, can be expressed as:

(22)

The last terms to be considered in the Irrigation Water Consumption (IWC) estimation are represented by the irrigation distribution uniformity coefficient, Kt and the efficiency of the irrigation system Ki (common ranges are 0.6 - 0.7 for sprinkler and 0.90 - 0.95 for drip irrigation) adopted in the greenhouses, hence the final equation is:

(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.

(21)

where:



For different crops categories the following values of ECe can be applied (empirical dataprovidedfromProf.Pardossi,UniversityofPisa):

•2.5forfruitvegetable;

•2.0forleafvegetable;

•1.8forcutplants;

•1.5forpotplants;

Hence, the Irrigation Requirements (IR), quantity of water needed to satisfy the CWR, and allowing, through an adequate leaching, to maintain the salinity of the soil at lowerlevelthanthoseoftoxicityforthecultivation,canbeexpressedas:




(21)

where:






(22)


(23)


(22)

The last terms to be considered in the IrrigationWaterConsumption(IWC) estima-tion are represented by the irrigation distribution uniformity coefficient, Kt and the effi-ciency of the irrigation system Ki (common ranges are 0.6 - 0.7 for sprinkler and 0.90 - 0.95 fordripirrigation)adoptedinthegreenhouses,hencethefinalequationis:




(21)

where:






(22)


(23)


(23)


47

figure 2.12 - Monthly iwc (in mm) computed for different crop categories cultivated in greenhouses located in toscana region.

Source: elaboration with data provided by Prof. Pardossi

figure 2.13 - Annual iwc (in thousands of m3/ha) computed for the different crop catego-ries cultivated in greenhouses located in toscana region.

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 rela-tive to the crops cultivated in greenhouses reported in the CQ.

0,00

50,00

100,00

150,00

200,00

250,00

IWC

(mm

)

Montly IWC for the main crops group

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

Janu

ary

Febr

uary

Mar

ch

April

May

June July

Augu

st

Sept

embe

r

Nov

embe

r

Dice

mbe

r

Octo

ber

Ann

ual I

WC

(000

mc/

ha)

0,0

2,0

4,0

6,0

8,0

10,0

12,0

Pot plantsCut plantsLeaf vegetableFruit vegetable

49

ChaPter III

input dAtA collection

The accuracy reachable with the model simulations has always a direct relationship with the quality of the input data used. To this end, a lot of resources have been employed during the data collection phase in order to identify and inventory all the available Italian dataset useful for the irrigation consumption estimation and to enable all the administra-tive 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 (na-tional, 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 statisticaldatasetshavebeenreportedatmunicipalitylevel:the“minimumcomputationalunit” 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 net-work 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-

50

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.

TheagriculturalCensusiscarriedoutinconformitywithtwoRegulations:

•Regulation(EC)n.1166/2008oftheEuropeanParliamentandoftheCouncilof19November 2008 on Farm Structure Surveys (FSS) and the Survey on Agricultural Production Methods (SAPM).

•CouncilRegulation(EEC)No357/79of5February1979onstatisticalsurveysofareas 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 ques-tionnaire. In alternative, the respondents have been given the choice to answer via web, through a controlled electronic questionnaire.

table 3.1 - sections and boxes of the 6th cQ.

section box detail

1 General information

legal personality of the holding B

type of tenure and farming system B

Information technology C

support for rural development B

landscape features a

2 Information for holdings with land

land use B

Organic farming (concerning crops) B

Quality scheme production (concerning crops) C

specific information on vineyards B

tillage methods a

soil conservation a

Irrigation B

3 Information for holdings with animal

livestock B

Organic farming (concerning animals) B

Quality scheme production (concerning animals) C

animal grazing a

animal housing a

manure storage and application a

4 localization localization of the land and livestock at municipality level C

5labour force and other gainful activities of the holding

labour force B

third partly job C

Other gainful activities of the holdings B

equipment used for renewable energy production B

6 economic informationIncome, self consumption and marketing C

Farming accounting C

A: production methods characteristics; B: FSS characteristics; C: national and sub national needs.

51

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 nec-essary 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.

Theproposedamendmentsarereportedbelow(theCQisreportedinAnnex2):

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 ir-rigated 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. restora-tion 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).

• Insertionof acolumn for registering thecrop irrigation systemused forall theirrigated 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).Theirrigationsystemtypesare:

- border and furrows (Scorrimentosuperficialeedinfiltrazionelaterale)

- flood (Sommersione)

- aspersion (Aspersione a pioggia)

- micro-irrigation (Microirrigazione)

- other system (Altro sistema)

• Insertionof aquestion (question22.7) relative to theuseof irrigationadvisoryservices and/or systems for determining the crop irrigation demand (Barrarelacasellasel’aziendautilizzasercvizidiconsulenzairriguae/osistemidideter-minazionedelfabbisognoirriguo).

52

• Insertionofadditionalquestionsin22.6-Irrigationwatersourcesupply(Fonte di approvviggionamentodell’acquairrigua) 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,consorziodibon-ificaeirrigazioneoaltroenteirriguoconconsegnaaturno);

- 22.6.5-Aqueduct, irrigation and land reclamation consortium or other irrigation body with delivery on-demand (Acquedotto,consorziodibonificaeirrigazioneo altro ente irriguo con consegna a domanda);

- 22.6.6-Other source (Altra fonte).

figure 3.1 - the irrigation box (box 22) of the cQ with highlighted the main integrations realized to acquire additional farm information.

53

3.2. crop characteristics database

The database of crop characteristics is the basic database used by Model A to simu-late the crop irrigation requirement for each crop.

The database has been compiled by collating available information for all the ir-rigated 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 develop-ment 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.

Foreachirrigatedcropthefollowingparametershavebeencollected:plantingandharvesting date, duration of the growing phases, crop coefficients (K

cb) for the initial/de-

velopment/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 col-lectedforthreemacro-areas:Northern,CentralandSouthernItaly.Cropshavebeendi-videdinfourgroups(seeTable3.2):Annualcrops,Perennialcrops,FruittreesandForage.Annual crops have characteristics that change with the growing seasons.

MARSALa performs simulations on annual basis by considering the time range be-tween 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.

54

tabl

e 3.

2 -

ext

ract

of t

he c

rop

char

acte

rist

ics

data

base

.

cro

p gr

oup

cro

pp

lant

ing

date

(d

ay/m

onth

)K

cbc

rop

cycl

e in

nor

th it

aly

(day

s)p

roo

ting

dep

th

(m)

cro

p he

ight

(m

)

nor

thc

entr

als

outh

init

ial

Mid

end

sta

rtd

evel

op.

Mat

ure

fina

lto

tal

Min

Max

init

ial

Mat

ure

fina

l

ann

ual

Bar

ley

02/0

102

/01

02/0

10,

151,

10,

150

8290

1018

20,

550,

11,

250,

11,

51,

5

Oat

s02

/01

02/0

102

/01

0,15

1,1

0,15

082

9010

182

0.55

0.1

1.25

0.1

1.5

1.5

mai

ze25

/04

15/0

401

/04

0,15

1,1

0,6

2030

8010

140

0.55

0.1

1.5

0.1

1.8

1.8

Pot

ato

01/0

301

/03

15/0

20,

151,

150,

6530

3012

010

190

0.35

0.1

0.6

0.1

0.6

0.6

Car

rot

01/0

315

/02

15/0

20,

151,

050,

860

4560

3019

50.

350.

10.

70.

11.

51.

5

Cot

ton

20/0

320

/03

20/0

30,

151,

150,

430

5010

05

185

0.65

0.1

1.5

0.1

1.3

1.3

sw

eet

pepp

er01

/05

15/0

401

/03

0,15

10,

825

3060

1012

50.

30.

11

0.1

0.75

0.75

spi

nach

02/0

102

/01

02/0

10,

150,

90,

850

05

1015

0.2

0.1

0.5

0.1

0.8

0.8

Col

za02

/01

02/0

102

/01

0,15

0,95

0,25

055

455

105

0.1

0.1

0.6

0.6

Per

enni

ala

rtic

hoke

15/0

415

/04

15/1

10,

151,

350,

70

080

2510

50.

450.

10.

90.

11.

21.

2

Full-

grow

n fr

uit

tree

s O

live

02/0

102

/01

02/0

10,

60,

60,

690

3017

074

364

0.65

1.5

1.5

1.5

1.5

1.5

Ora

nge

02/0

102

/01

02/0

10,

650,

70,

6591

6015

063

364

0.5

1.5

1.5

22

2

app

le02

/01

02/0

102

/01

0,1

0,95

0,75

8030

180

7436

40.

51.

21.

22

22

Pea

r02

/01

02/0

102

/01

0,1

0,95

0,75

7430

160

100

364

0.5

1.2

1.2

22

2

Pea

ch02

/01

02/0

102

/01

0,45

0,86

0,6

6445

160

9536

40.

51.

41.

42

22

Youn

g fr

uit

tree

sO

live

02/0

102

/01

02/0

10,

240,

240,

2490

3017

074

364

0.65

0.5

1.2

1.5

22

Ora

nge

02/0

102

/01

02/0

10,

260,

280,

2591

6015

063

364

0.5

0.5

1.2

1.5

22

app

le02

/01

02/0

102

/01

0,04

0,38

0,3

8030

180

7436

40.

50.

51

1.5

22

Pea

r02

/01

02/0

102

/01

0,04

0,38

0,3

7430

160

100

364

0.5

0.5

11.

52

2

Pea

ch02

/01

02/0

102

/01

0,18

0,35

0,24

6445

160

9536

40.

50.

51

1.5

22

Fora

gea

lfal

fa02

/01

02/0

102

/01

0 ,72

0,72

0,72

364

0.55

0.1

1.5

0.1

0.5

0.2

rou

gh g

r azi

ngs

02/0

102

/01

02/0

10,

720,

720,

7236

40.

60.

11

0.1

0.5

0.2

55

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 be-longing to the followingcategories:FADNsurveyors, techniciansworkingatpublic andprivate 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 aver-ageagrarianatprovinciallevel(NUTS3)bydiscriminatingamongthreealtimetriczones:plain, hill and mountain.

table 3.3 - list of irrigated crops used for the web survey.

crop id crop1 Winter wheat2 sorghum3 Grain maize4 Green maize5 Potato6 sugar beet7 tobacco8 soybean9 rape

10 sunflower11 alfalfa12 table tomato13 Plum tomato14 eggplant and Pepper15 endive and lettuce16 sweet melon and Water melon17 Fennel18 Cauliflower, Broccoli, Cabbage19 Field bean, French bean, Peas20 artichoke21 strawberry22 spring grass

Theinformationcollectedthrowtheelectronicsurveyare:

•nameorotheridentificationoftherespondent(anonymousrespondentsarealsoallowed);

•professionalcategoryoftherespondent(usefulforfurtherassessmentoftheac-curacy of the answers during data analysis);

56

•nameoftheprovincewherethecropiscultivated;

•altimetriczone(plain/hill,mountain)wherethecropiscultivated;

•cropseedingortransplantingdate(monthanddecade);

•finalcropharvestingdate(monthanddecade).

•averagenumberofcropcyclesforfreshvegetables;

•prevailingFAOclassforgreenmaize;

•averagenumberofcutsforalfalfa.

The electronic questionnaire has been advertised to potential respondents thanks to the support of the INEA regional offices.

figure 3.2 - screenshot of the electronic questionnaire hosted at the ineA website (http://www.rica.inea.it/marsala/).

3.3 soil database

3.3.1 State-of-the-art on soil data in Italy

The collection of the soil data for the Italian agricultural territory is a necessary step for the simulations performed by Model A. The model requires three main soil parameters tocomputethecropirrigationrequirement:

• soildepth:definedasthemaximumrootingdepthboundedbythelithicorpara-lithic layer;

•water content at the field capacity: defined as weighted average on the rootingdepth;

•water content at the wilting point: defined as weighted average on the rootingdepth.

57

table 3.4 - state-of-the-art on the soil maps availability and spatial resolution for each italian region/autonomous province.

region/Autonomous province 1:250,000 scale 1:25,000 - 1:50,000 scale

Bolzano (aP) not available available for some pilot areas

abruzzo available available for some pilot areas

Basilicata available available for some pilot areas

Calabria available available for some pilot areas

Campania in progress available for some pilot areas

emilia-romagna available available for the plain territory and few apennine areas

Friuli Venezia Giulia not available available for a portion of the plain territory

lazio not available information not available

liguria not available information not available

lombardia available available for the plain territory and some alpine areas

marche available available for some pilot areas

molise available available for some pilot areas

Piemonte available available for a portion of the plain territory

Puglia available currently under review and updating

sardegna available available for some pilot areas

sicilia in progress available for some pilot areas

toscana available available for some pilot areas

trento (aP) not available available for some pilot areas

Umbria not available available for some pilot areas

Valle d’aosta not available available for some pilot areas

Veneto available available for a portion of the plain territory

In Italy, soil maps have been produced with different levels of details and methodolo-gies 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“historicalperiods”ofrealizationaredescribedbelow:

•Monographsandstudiesrealizedeitherresearchinstitutionorbyregionalofficesin 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 repre-sented the stimulus and the basic knowledge that triggered the recent soil mapping activities.

•Regionalsoilmapsofrecognition(1:250,000scale),realizedatthebeginningasautonomous 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, guide-lines and description methods for the observations, generalization techniques, re-porting 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).

58

•Semi-detailedregionalsoilmaps(1:25,000-1:50,000scale).Someregionsdecidedto 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 harmo-nization and cover generally areas with intensive agriculture (e.g. Padano-Veneta valley) or with particular issues.

table 3.5 - Available soil maps with country-level coverage.

Year Map Author scale description

1966 Carta dei Suoli d’Italia

(Italian soil map)

F. mancini et al., 1966 1:1,000,000 the map has been the first relevant study about the Italian soils. It has been based mainly more on the distribution of pedogenetic factors than on a systematic survey.

2003 Carta Ecopedologica d’Italia

(Italian ecopedologic soil map)

european soil Office - JrC (Ispra)

1:250,000 the realization of the map has been linked to the activities carried out during the Carta della Natura (the map of Nature) Project, under the Italian law 394/91 on protected areas, and the european soil Database developed in the framework of the european soil Information system (eUsIs). the objectives of the map are:• characterization of the soils in

terms of hydrological properties and erosion risk;

• analysis of the soil-vegetation relationship;

• analysis of the preservation aspects.

2006 Badasuoli

(Italian soil database)

miPaaF, Cra and the regional soil services

1:1,000,000 the soil database has been realized through the whole collection, integration and harmonization of the regional soil maps at 1:250,000 scale.

As shown in Table 3.5, various soil maps are available at national level. 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 mat-teroffact:

•CartadeiSuolid’Italia was realized in 1966 following mainly naturalistic criteria, therefore it is short of enough numerical information to be used to derive the nec-essary soil variables.

•CartaEcopedologicad’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 reten-tion properties.

59

3.3.2. Methodology for a country-level harmonized soil map

The methodology has been developed by taking into account resolution, quality, ac-curacy 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 soildatasets(inthereportedorder):

1. soilmapsat1:25,000-1:50,000scaleproducedbytheItalianregionaladminis-trations;

2. soilmapsat1:250,000scaleproducedbytheItalianregionaladministrations;

3. Badasuoli;

4. CartaEcopedologicad’Italia.

Themethodologyhasbeenimplementedthroughthefollowingphases:

1. Acquisitionoftheavailablesoilmapsindigitalformat:

a. Soilmapsat1:250,000oftheSouthernItalianregionsproducedduringana-tional research project carried out by INEA;

b. Badasuoli;

c. CartaEcopedologicad’Italia;

d. Regional soil map of Emilia-Romagna region (1:250,000 scale for the Apen-ninicareasand1:50,000scalefortheplainareas);

e. RegionalsoilmapofLombardiaregion(1:250,000scale fortheAlpineareasand1:50,000scalefortheplainareas);

f. RegionalsoilmapofFriuliVeneziaGiuliaregion(1:50,000scale);

g. RegionalsoilmapofPiemonteregion(1:250,000scalefortheAlpineandApen-ninicareasand1:50,000scalefortheplainareas);

h. RegionalsoilmapofMarcheregion(1:250,000scale).

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. Creationofadatabasecontainingthefollowingtables:

a. UC:listofallcartographicunitswiththerelativesourceandreliability;

b. SUOLI:listofthesoilsbelongingtoeachcartographicunit;

c. UC_SUOLI: relationship table between UC and SUOLI indicating the soilsspreading for each cartographic unit expressed as percentage of cartographic unit surface;

d. ORIZZONTI:table(seeTable3.6)containing, foreachhorizonof therepre-sentative profile (actual or hypothetical) of each type of soil, the basic infor-mation 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 devel-oped 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, thefollowingvariableshavebeenconsidered:

•cropgroup(i.e.arablelandandtreecrops);

•morphology(i.e.areasaboveorbelowtheslopethresholdof5%).

60

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 al-low to differentiate the soil parameters on a crop basis.

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

field name 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 followingsub-steps:

a. Identification and acquisition of the latest up-to-date land use map (region-allandusemapat1:25,000scaleforLombardiaandEmilia-Romagna;INEACASI3 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 VanGenuchtenby the Pedotransfer Functions (PTF) defined in the HYPRES project (Development

1. LandusemapwithfocusonirrigatedareasavailableforalltheSouthernItalianregions.Resolutionis1:50,000forthe irrigated land use and 1:100,000 for the others land use classes.

61

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 con-tent 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 beenassumedthat:

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.

table 3.7 - soil database structure.

mun

icip

alit

y

arable land tree crops

areas with slope < 5% areas with slope > 5% areas with slope < 5% areas with slope > 5%

soi

l Dep

th

(cm

)

Fiel

d C

apac

ity

(m3 /m

3 )

Wilt

ing

Poi

nt

(m3 /m

3 )

soi

l Dep

th (c

m)

Fiel

d C

apac

ity

(m3 /m

3 )

Wilt

ing

Poi

nt

(m3 /m

3 )

soi

l Dep

th

(cm

)

Fiel

d C

apac

ity

(m3 /m

3 )

Wilt

ing

Poi

nt

(m3 /m

3 )

soi

l Dep

th

(cm

)

Fiel

d C

apac

ity

(m3 /m

3 )

Wilt

ing

Poi

nt

(m3 /m

3 ) 3.4. Agrometeorological database

In the past, meteorological observations have been carried out in Italy by the Meteorological Service of the Italian Air Force, the Central Office for Crop Ecology (CRA-CMA), the Ministry of Agricultural, Food and Forestry Policies (MiPAAF) and by the Central Hydrographical Service. With their large networks, the public bodies (institu-tions) 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 net-work to the 20 administrative regions (NUTS 2 level). In addition, several agrometeoro-logical 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 reso-lution 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 agro-meteorologicaldataatcountrylevel:ISTAT,NationalInstitutefortheProtectionandEn-

62

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 meas-ured in millimetres and with a daily temporal resolution.

3.4.1. ISTAT database

Since 1926, ISTAT disseminates meteorological data collected from gauging stations located across Italy.

table 3.8 - the survey of meteorological networks in italy.

Administration level

service/institution name number of institutions

estimated number of

working stations

Average length of

time series (years)

National meteorological services of military air Force 1 100 > 50

National Cra-Cma 1 200 > 50

National Corpo Forestale dello stato 1 100 > 10

regional regional hydrological services 20 4,000 > 50

regional regional agrometeorological services 20 1,000 > 20

sub-regional agricultural consortia > 350 250 > 10

Provincial agrometeorological services of provinces 10 200 > 15

local National Council for research (CNr) 20 > 50 > 30

local Council for agricultural research (Cra) 50 200 > 30

local Climatological and geophysical observatories > 20 100 > 40

local Universities, agricultural schools, and other institutions > 20 > 50 > 20

total > 500 > 6,250 > 50

Source: ISTAT

In 2007 ISTAT carried out a research project entitled “Meteo-climatic and hydrologic indicators”. The aim was to implement a geographical data-warehouse with meteorological, agrometeorological, and hydrological daily values measured since 1951 from more than 6,000 gauging stations of several national, regional, and local institutions. The project was conducted within the partnership of the CRA-CMA and the Meteorological Service of the Italian Air Force.

The survey involved more than 600 respondents such as meteorological services working at the national level, regional authorities and local institutions operating in the environmental field. The list of respondent has been compiled through Web searches, by collecting information through the national meteorological services and by interviewing experts working at the regional and local level. Data have been collected through a statisti-cal 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-

63

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 NationalSys-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 meteoro-logical networks and observations to be used for representation of the state and trend of the Italian climate.

Themainmeteo-climaticvariablestakenintoaccountare:temperature,potentialtemperature, 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 vari-able 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 “meteoro-logical 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 mete-orological 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. TheObjective Analysis was performed by Finsiel in the framework of National Agricultural Information System (SIAN)ofMIPAAF.Thestudywascarriedoutduring1988-1990andtheresultsarepublishedinthereportSIAN“Analisi climatologica e progettazione della Rete Agrometeorologica Nazionale” (April1990)and in thepapersofA.LibertàandA.Girolamo,1991and1992.

64

Monteith formula revised by FAO is considered the most reliable and therefore is the one usedtobuildthedatabase:

16

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 to build the database:

(1)

where ETo is the reference evapotranspiration [mm d-1

], Rn is the net radiation [MJ m-2

d-1

], G is the soil heat flux [MJ m

-2d

-1], γ is the psychrometric constant [0.066 kPa °C-1], 900 is a conversion factor, (es - ea)

represents the vapour pressure deficit of the air [kPa], T is the mean air temperature [°C], Δ represents the slope of the saturation vapour pressure temperature relationship [kPa °C-1] and U2 is the wind speed at 2 meters [m s

-1].

The data used to build the database have been originated from the meteorological measures stored in the National Agrometerological Database (BDAN)3 and are referred to the thirty-year 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 drift methodology. The methodology allows to estimate, within the considered spatial domain, the values of a given geophysical variable starting from the actual data available (in this case, the observed data at the meteorological stations), taking into account the statistical properties of the spatio-temporal dynamics of the variable: the so called structural model. The basic hypothesis is to consider the physical variables as regionalized random variables (Matheron, 1970 and 1971). Meteorological variables satisfy this requirement since they are space and time-dependent. Statistically speaking, meteorological data recorded from neighbour stations always show a certain level of correlation.

Daily meteorological data estimation on grid nodes has been performed through an independent estimation of the climatic mean and the meteorological deviation according to the following relationship:

meteorological measure = climate + meteorological deviation (2)

where climate is a cyclic annual constant (it varies during the year, but it is constant among the years) with good spatio-temporal continuity and 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 instantaneous and local meteorological condition.

2The 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. 3The 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.

(1)

where ETo is the reference evapotranspiration [mm d-1], Rn is the net radiation

[MJ m-2d-1], G is the soil heat flux [MJ m-2d-1], γ is the psychrometric constant [0.066 kPa °C-1], 900 is a conversion factor, (e

s - e

a) represents the vapour pressure deficit

of the air [kPa], T is the mean air temperature [°C], Δ represents the slope of the satura-tion vapour pressure temperature relationship [kPa °C-1] and U

2 is the wind speed at 2

meters [m s-1].

The data used to build the database have been originated from the meteorological measures stored in the National Agrometerological Database (BDAN)3 and are referred to the thirty-year period 1961-1990, which is defined the conventional reference for climato-logical analysis and comparisons by the World Meteorological Organization (WMO).

The spatio-temporal reconstruction of the meteorological variables has been per-formed by the geo-statistical Kriging with external drift methodology. The methodology allows to estimate, within the considered spatial domain, the values of a given geophysical variable starting from the actual data available (in this case, the observed data at the me-teorological stations), taking into account the statistical properties of the spatio-temporal dynamicsofthevariable:thesocalledstructural model. The basic hypothesis is to con-sider the physical variables as regionalizedrandomvariables (Matheron, 1970 and 1971). Meteorological variables satisfy this requirement since they are space and time-dependent. Statistically speaking, meteorological data recorded from neighbour stations always show a certain level of correlation.

Daily meteorological data estimation on grid nodes has been performed through an independent estimation of the climatic mean and the meteorological deviation according tothefollowingrelationship:

meteorologicalmeasure=climate+meteorologicaldeviation (2)

where climate is a cyclic annual constant (it varies during the year, but it is constant among the years) with good spatio-temporal continuity and 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 instantaneous and local meteorological condition.

Kriging methodology assigns proper weightingcoefficients 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)wasrealizedintheframeworkofSIANandcontainstheobserva-tionsprovidedbyCRA-CMAmeteorologicalnetworkandothersItalianmeteorologicalservices.

65

clusively dependent on distance, orientation and altitude difference between each pair of points (variogram function). Therefore, the estimation of meteorological variables at grid nodes, for a given time interval, has been produced by a weighted linear combination of the meteorological data of the stations belonging to the estimation neighbourhood.

figure 3.3 - the regular grid of 544 nodes used in the crA-cMA database to report the meteorological variable (i.e. precipitation and ETo).

66

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.

Itisobviousthatthestructuralmodeldependsontheperiodofyearaswell:duringwinter 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 es-timation 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,Krigingisacorrectestimationmethod:themeanestimationerrorisequalto 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 nu-merical 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.

67

ChaPter IV

model cAlibrAtion

A model calibration can be in general defined as “the estimation and adjustment of the model parameters and constraints to improve the agreement between model output and a data set” (Rykiel, 1996).

The calibration of MARSALa model has been performed by comparing the simulated and the actual measured farm irrigation water consumption for a representative farms sample, by analyzing the irrigation water consumption for the irrigated crops in the agrar-ian year 2007-2008. The farms sample has been extracted by taking into account two con-straints:budgetresourcesandavailabilityofon-farmmeasurementdevices(necessaryforthe acquisition of the actual values of water consumption).

Thesamplehas279farmslocatedinfourdifferentregions:Emilia-Romagna,Campa-nia, Puglia and Sardegna (hereafter indicated also as pilot areas); the survey has been con-ducted 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 irrigatedfarms.Threearethemaintypologies:

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 deliv-ery. 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 avail-able (see Figure 4.3), otherwise (as in the majority of the cases) it has been esti-mated 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.

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table 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 vari-ables (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 depth, wilting point and field capacity)

soil database has been realized by collating regional and local soil maps produced with different standards and resolution. In addition soil parameters have been es-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 experi-ments.

Irrigation system CQ reports only the prevailing irrigation system (in terms of cultivated surface cov-ered) for each crop or aggregation of crops. this is an approximation since crops can be irrigated with different irrigation systems having different efficiency. In ad-dition no information about the conditions, materials, size, maintenance and man-agement of the farm irrigation network are collected through the CQ, therefore any speculation on the influence of this characteristics on the irrigation efficiency can be performed.

figure 4.1 - sardegna pilot area: example of on-farm measurement devices. the digital flowmeter (Acquacard) is provided by the ilrc, water distribution is managed trough an electronic card with a predefined water amount purchased by the farmer at the beginning of each irrigation season.

69

figure 4.2 - sardegna pilot area: example of in-farm pond used as “private source” for irrigation, the water source can be often used in conjunction with the irrigation water provided by an ilrc to ensure the availability in case of water shortage.

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

70

figure 4.4 - puglia pilot area: example of on-farm measurement device, a mechanical flowmeter (woltmann meter).

4.1 Methodology for pilot areas definition and farms sample extraction

Pilot areas definition and farms sample extraction has been carried out by a proper methodology according to farms statistics availability, Italian agriculture features and, above all, budget constraints. The methodology has been designed through the coopera-tion 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 statis-tical 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 pro-duced by the sample, the eligible farms listed for each region have been stratified through thevariables:CropWaterRequirement(CWR),irrigationsystem,farmsizeandirrigationwater 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.

•2007ItalianFADN(RICA)database.Thedatabasehasbeenselectedforitswealthof information, especially for those required by the methodology, moreover the use

71

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.

•FSS2003database,themostup-to-datesourcewithfarmsirrigationinformationensuring a full representativeness of the Italian farms universe.

Thedevisedmethodologycanbebrokendownintosixsteps:

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 litera-ture and/or from research projects with field experiments carried out in Centre-Southern Italy. Five classes of annual CWR have been defined, rice has been treated as a separated class due to its peculiarities in water management.

table 4.2 - Average values/range of variability for the annual cwr for the main italian irri-gated 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

table 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

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Step 2-ComputationofthedominantHCforeachItalianregionandselectionofthe pilot areas

The identification of the dominant HC for each region (see Table 4.4) has been done by computing the share of each HC as the ratio between the sum of irrigated surface of the HC crops and the irrigated surface of the region. The data used comes from the FSS 2003 dataset.

table 4.4 - values of the Hcs share for each region and identification of the dominant Hcs.

region share of Hc dominant Hc

A (%) b (%) c (%) d (%)

Piemonte 0.38 3.02 11.93 84.67 D

Valle d’aosta 49.56 13.41 36.92 0.11 a

lombardia 0.79 3.88 3.13 92.20 D

trentino-alto adige 26.84 0.33 69.08 3.75 C

Bolzano 17.94 0.19 78.70 3.17 C

trento 36.41 0.48 58.74 4.37 C

Veneto 17.98 9.76 11.34 60.92 D

Friuli-Venezia Giulia 14.61 7.28 2.82 75.28 D

liguria 66.41 5.60 24.81 3.17 a

emilia-romagna 7.88 3.42 34.47 54.23 D

toscana 24.37 2.41 22.76 50.46 D

Umbria 8.25 4.32 7.05 80.38 D

marche 22.97 2.65 22.51 51.87 D

lazio 18.61 2.77 34.30 44.32 D

abruzzo 22.96 10.72 34.97 31.36 C

molise 53.13 4.49 13.66 28.72 a

Campania 10.42 7.91 44.28 37.39 C

Puglia 77.37 0.57 18.38 3.67 a

Basilicata 35.56 0.01 52.81 11.62 C

Calabria 25.32 5.83 57.33 11.52 C

sicilia 38.54 0.53 55.95 4.99 C

sardegna 23.49 0.57 28.38 47.57 D

Pursuant to the identification of the HCs for each region, four pilot areas have been selected(seeTable4.5):Puglia(HCA),Campania(HCBandC),SardegnaandEmilia-Romagna (HC D). Selection has been primarily based on the presence of irrigation water measurement devices at farm level (e.g. measurement devices of the ILRCs or owned by the farmer).

table 4.5 -Association of the italian regions to the Hcs.

Hc 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

73

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 - Definitionofthestratificationvariables

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 sen-sitivity. The following variables have been selected trough expert judgment.

•Irrigationwatersource - twotypologieshavebeenconsidered:

- ILRCs;

- self-supply.

•Farmsize-twofarmsizeclasseshavebeenconsidered:

- large farms (farms having the UAA greater than or equal to 20 ha);

- small farms (farms having the UAA less than to 20 ha).

•Irrigationsystem (prevailing)-threetypeshavebeenconsidered:

- border and Furrows;

- aspersion;

- micro-irrigation.

Bymultiplyingthemodalityofeachvariablethetotalnumberofstratais2*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 - Definitionofthefarmssamplesizeandthesamplingrate

Although farms sample size should always be defined in order to keep a good repre-sentativeness 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 representa-tiveness 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-

74

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 sur-veyors to search for at least one farm with the specific characteristics required.

The 2007 Italian FADN has at national level 15,492 farms while the total number located in the four regions is 3,700, the farms with irrigated surface greater than 0 ha are 1,889 (see Table 4.6), these are the eligible farms to be stratified through the variable defined in Step 3.

table 4.6 - italian fAdn 2007: total number of farms for each pilot region and number of farms with irrigated surface greater than 0 ha.

pilot Area no. of farms farms with irrigated surface greater than 0 ha

emilia – romagna 1,150 559

Campania 579 366

Puglia 911 500

sardegna 1,060 464

total 3,700 1,889

Before starting with the stratification it has been necessary to identify the three stratification variables in the Italian FADN, in some cases it has been also necessary to reclassify some information to ensure a complete matching. Concerning the variable 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.

table 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 riverself-supply

Well

Other Other

table 4.8 - correspondence between the irrigation systems of the italian fAdn and MArsAla.

ricA MArsAla

aspersion aspersion

Border and FurrowsInfiltration-Flood

Flood

Drip micro-irrigation

Other system Other

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Regarding the variable Farmsize,farmshavebeensplitintwocategories:

•largefarms(UUAgreaterthanorequalto20ha);

•smallfarms,(UUAlessthan20ha).

table 4.9 - size of the farms universe for each pilot area.

pilot area Hc no. of farms

emilia – romagna D 237

Campania B 7

Campania C 175

Puglia a 346

sardegna D 179

total 944

Concerning the variable Irrigation system, the Italian FADN registers the prevailing system with five typologies, therefore they have been reclassified in terms of irrigation efficiency in four classes as reported in Table 4.8. Farms with the prevailing irrigation system classified as Other have been excluded from the universe with the mentioned exclusions, the final size of the universe from 1,889 turns to 944 farms (see Table 4.9). The stratification of the universe through the three variables for each region is reported in Table 4.10.

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

stratification variable no. of farms

irrigation water source

farm size (uuA)

irrigation system (prevailing)

emilia- romagna

campania puglia sardegna

self-supply

Greater than or equal to 20 ha

micro-irrigation 10 8 56 2

Infiltration-Flood 6 2 2

aspersion 40 3 11 29

less than 20 ha




IlrC

Greater than or equal to 20 ha




less than 20 ha


Infiltration-Flood 7 4


total per region 237 182 346 179

grand total 944

76

Step 6 - Farm sample extraction

Since farm sample size cannot exceed the 250 units, farms extraction for each region and for each stratum has been done with a sampling rate of 26 % (see Table 4.11). The total number of farms to be investigated are more than the established sample size, in fact some farms have been added to avoid empty stratum. The final sample size for each region is reported in Table 4.12.

table 4.11 - Application of the sampling rate for each region and stratum. the number of farms reported in the column “step 5” are the eligible farms.

stratification variable no. of farms

irrigation water source

farm size (uuA)

irrigation system (prevailing)

emilia-romagna campania puglia sardegna

step 5 sample step 5 sample step 5 sample step 5 sample

self-supply

Greater than 20 ha

micro- irrigation

10 3 8 3 56 15 2 1

Infiltration-Flood

6 2 2* 2 1 2 1

aspersion 40 11 3 2 11 3 29 8

less than 20 ha

micro- irrigation

2 1 51 15 147 40 1 1

Infiltration-Flood

2 1 52 14 6 2 1*

aspersion 25 7 32 10 27 7 3 1

IlrC

Greater than 20 ha

micro- irrigation

7 2 5 2 22 6 7 2

Infiltration-Flood

18 5 2 2 1* 2 1

aspersion 82 22 3 2 1 1 110 30

less than 20 ha

micro- irrigation

4 1 19 6 58 16 2 1

Infiltration-Flood

7 2 4 2 1* 1*

aspersion 34 9 3 2 16 4 21 6

total per region 237 66 182 62 346 97 179 54

grand total 944

sample size 279

(*) Farm added to fill the empty stratum.

table 4.12 - number of farms extracted for pilot area.

pilot area no. of farms

emilia – romagna 66

Campania 62

Puglia 97

sardegna 54

total 279

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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 Supplementaryquestions) have been inserted in the PQ, the aim is twofold as described below.

•Checkingthesensitivityofthemodelswithorwithoutspecificandmoreprecisefarm information in comparison to the CQ and estimating the loss of results accu-racy. 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.

•Tryingtocollectusefulinformationonthepilotareasrelatedtotheirrigationfarm-ers 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, municipal-ity 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

Thesectioncontainsgeneralinformation:

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. farmirrigationwatersource:

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].

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SecTion no.2

The section contains several questions addressed to evaluate the farmer irrigation managementbehaviour:

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 [supplemen-tary question];

7. list of crops irrigated with priority in case of water shortage events [supplemen-tary 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 man-agementforthegroups:arableland,fruittreesandothercrops.BeyondtheHCforwhichthe farm is representative, the whole farm land use has been also surveyed. The informa-tioncollectedare:

1. total and irrigated surface for each crop;

2. irrigation system adopted for each crop (in case of multiple type only that cover-ing 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 [supple-mentary question];

7. cropdetails:

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.

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4.3 pilot campaigns

The pilot campaigns in the four regions (Emilia-Romagna, Campania, Sardegna and Puglia) have been carried out during the period November 2009 - March 2010. Four survey-orshavebeenemployed,selectionhasbeendonebyconsideringasrequisites:adegreeinagricultural sciences, the experience in agricultural surveys and the knowledge/past work experience in the pilot areas.

Hereafterananalysisofthefarmsdatacollectedisreportedwithparticularfocuson:

•numberofscheduledandinterviewedfarmsandresponserate;

• farmgeographicallocationatmunicipalitylevel;

• farmlanduseandirrigatedcropssurfaces.

As reported in the paragraph 4.1, sample size is 279 but the final number of the farms interviewsis265,themisalignment(seeTable4.13andTable4.14)isdueto:

• lackoffarmsintheregionforagiventypology;

•difficultiesinarrangingmeetingwiththefarmersorunwillingnesstocollaborate.

table 4.13 - number of scheduled and actual farms interviewed and response rate by pilot areas.

pilot area scheduled farms

Actual farms

response rate

(%)

emilia – romagna 66 61 92.42

Campania 62 53 85.48

Puglia 97 100 103.09

sardegna 54 51 94.44

total 279 265 94.98

table 4.14 - number of scheduled and actual farms interviewed and response rate by stratification variable.

stratification variable scheduled farms

Actual farms

response rate

(%)

total interviews

Farm size (UUa)large 128 109 85.16

265small 151 156 103.31

Irrigation water sourceIlrC 127 136 107.09

265self-supply 152 129 84.87

Irrigation system (prevailing)

micro-irrigation 115 122 106.09

265Infiltration-Flood 39 21 53.85

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).

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

croptotal surface irrigated surface

(ha) % (ha) %

alfalfa 5.50 0.17 5.00 0.16

artichoke 49.61 1.52 49.61 1.56

asparagus 17.00 0.52 17.00 0.54

Barley 0.20 0.01 0.20 0.01

Basil 0.20 0.01 0.20 0.01

Broccoli 37.00 1.13 37.00 1.17

Carrot 1.50 0.05 1.50 0.05

Cauliflower, cabbage 99.60 3.05 99.60 3.14

Celery 8.00 0.25 8.00 0.25

Chard 2.00 0.06 2.00 0.06

Grain maize 477.69 14.65 466.49 14.71

Corn for silage 475.65 14.59 475.65 15.00

eggplant 4.50 0.14 4.50 0.14

endive and lettuce 113.65 3.49 113.65 3.58

Fennel 35.80 1.10 35.80 1.13

Flowers and ornamental plants 0.15 0.00 0.15 0.00

Forage legume 628.52 19.28 564.02 17.79

French bean 33.50 1.03 33.50 1.06

Grass 143.50 4.40 143.50 4.53

horticultural greenhouses 0.88 0.03 0.88 0.03

Italian chicory or chicory for greens 0.50 0.02 0.50 0.02

Onion 10.00 0.31 10.00 0.32

Other cereals grass 35.52 1.09 35.52 1.12

Other oilseeds 28.00 0.86 28.00 0.88

Other seeds 6.30 0.19 6.30 0.20

Parsley 0.20 0.01 0.20 0.01

Pea (dry or fresh) 40.00 1.23 40.00 1.26

Pepper 11.14 0.34 11.14 0.35

Permanent grassland 20.12 0.62 18.12 0.57

Plum tomato 519.48 15.93 519.48 16.38

Potato 121.60 3.73 121.60 3.83

rice 76.00 2.33 76.00 2.40

sorghum 7.00 0.21 7.00 0.22

spinach 20.32 0.62 20.32 0.64

strawberry 0.57 0.02 0.57 0.02

sugar beet 90.03 2.76 80.03 2.52

sweet melon 8.91 0.27 8.91 0.28

table tomato 124.70 3.82 123.70 3.90

Water melon 0.50 0.02 0.50 0.02

Winter wheat 4.89 0.15 4.89 0.15

total Arable land 3,260.23 67.89 3,171.03 67.72

almond 2.50 0.16 2.50 0.17

apple 7.50 0.49 7.00 0.46

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81

croptotal surface irrigated surface

(ha) % (ha) %

apricot 2.10 0.14 2.10 0.14

Clementine 1.00 0.06 1.00 0.07

Quality wine (DOC/DOCG) 34.41 2.23 34.41 2.28

table grapes 82.65 5.36 82.65 5.47

Other wines 574.67 37.26 574.67 38.01

hazel 1.50 0.10 1.50 0.10

Kiwifruit 65.00 4.21 65.00 4.30

Nectarine 35.00 2.27 35.00 2.32

Olives for oil production 504.24 32.70 475.25 31.44

Orange 4.00 0.26 4.00 0.26

Other crops in greenhouses 1.75 0.11 1.75 0.12

Other temperate fruits 2.00 0.13 1.00 0.07

Peach 172.69 11.20 172.69 11.42

Pear 28.94 1.88 28.94 1.91

Plum 13.70 0.89 13.70 0.91

table olives 7.05 0.46 7.05 0.47

Walnut 1.50 0.10 1.50 0.10

total tree crops 1,542.20 32.11 1,511.71 32.28

grand total 4,802.43 100.00 4,682.74 100.00

As reported in Table 4.15, forage crops cover an irrigated surface of 1,743 ha (56% ca. of the total cultivated surface), fresh vegetables (except tomato) have an irrigated surface of 692 ha (22% ca. of the irrigated arable land).

Amongthearableland,themainirrigatedcropsare:grainmaizewithabout1,000ha(30% ca. of the irrigated surface), tomato (table and plum) with about 640 ha (20% ca. of the irrigatedsurface).Themainirrigatedtreecropsare:grapes(wineandtableuse)withabout680 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 vari-ables. The geographical maps depict the number of interviewed farms for each municipality and the 30 km resolution agrometeorological grid used to associate a reference agromete-orological 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 prov-ince in Table 4.17.

>> follow

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figure 4.5 - emilia-romagna pilot area: “meteo-cells” of the agrometeorological grid and number of actual farms interviewed by municipality.

table 4.16 - emilia-romagna pilot area: number of scheduled and actual farms inter-viewed and response rate by stratification variable.


Actual farms response rate (%)

total interviews


61small 21 23 109.52

Water sourceIlrC 41 44 107.32

61self-supply 25 17 68.00

Prevailing irrigation system



aspersion 49 51 104.08

table 4.17 - emilia-romagna pilot area: number of actual farms interviewed by province.

province interviewed farms

Bologna 7

Ferrara 5

Forlì-Cesena 1

modena 11

Parma 7

Piacenza 6

ravenna 7

reggio nell’emilia 16

rimini 1

total 61

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The overall cultivated area for the agrarian year 2007-2008 of the regional sample (see Table 4.19) is 1,323 ha, whereof 1,235 ha of irrigated crops. Forages crops (maize and alfalfa) prevail and cover an irrigated surface of about 656 ha (61% ca. of the total irrigated surface of the regional sample). Plum tomato is also relevant with about 200 ha (19% ca. of the irrigated surface of the regional sample); fruit trees category is dominated by pears and grapes (wine and table use).

table 4.18 - emilia-romagna pilot area: average values for the main dimensional variables.

variable Average values (ha)

total surface 46.95

UUa 42.52

Irrigable surface 42.08

Irrigated surface in the agrarian year 2007-2008 25.84

Irrigated surface in the last three years 20.62

table 4.19 - emilia-romagna pilot area: total and irrigated crops surface of the regional sam-ple (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 seeds 6.30 0.55 6.30 0.59

Pea (dry or fresh) 40.00 3.47 40.00 3.76

Permanent grassland 20.12 1.75 18.12 1.70

Plum tomato 202.53 17.58 202.53 19.03

Potato 33.00 2.86 33.00 3.10

sorghum 7.00 0.61 7.00 0.66

strawberry 0.07 0.01 0.07 0.01

sugar beet 44.57 3.87 34.57 3.25

sweet melon 8.41 0.73 8.41 0.79

Winter wheat 4.89 0.42 4.89 0.46


apple 6.00 3.50 6.00 3.50

apricot 0.40 0.23 0.40 0.23


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

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4.3.2 Campania pilot area

Campania regionhasbeenselectedas representative for twoHCs:B (potato, sun-flower and soya) and C (Citrus plantations and fruit trees). Interviews have been carried out in the period October 2009 - February 2010. During the campaign, 53 out of 62 farms have been interviewed (see Table 4.20), the difference is due to difficults to identify farms representative for the class, the number of farms interviewed is reported by province in Table 4.21.

figure 4.6 - campania pilot area: “meteo-cells” of the agrometeorological grid and num-ber 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 rate (%)

Hc c response rate (%)

total interviews

scheduled farms

Actual farms

scheduled farms

Actual farms

Farm size (UUa)

large 6 2 33.33 7 7 100.0053

small 6 3 50.00 43 41 95.35

Water sourceIlrC 6 2 33.33 10 13 130.00

53self-supply 6 3 50.00 40 35 87.50


micro-irrigation 4 3 75.00 22 24 109.09

53Infiltration-Flood 4 2 50.00 16 9 56.25

aspersion 4 0.00 12 15 125.00

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The total cultivated area of the regional sample is 688 ha ca. (see Table 4.23) and, in the agrarian year 2007-2008, is almost completely irrigated (686 ha ca.).

table 4.21 - campania pilot area: number of the actual farms interviewed by province.


Benevento 1

Caserta 1

Napoli 23

salerno 27

Benevento 1

total 53

table 4.22 - campania pilot area: average values for the main dimensional variables.


Hc b Hc c

total surface 55.20 8.77

UUa 53.84 8.15

Irrigable surface 36.44 8.11

Irrigated surface in the agrarian year 2007-2008 34.24 7.68

Irrigated surface in the last three years 34.84 7.75

table 4.23 - campania pilot area: total and irrigated surface of the cultivated crops of the regional sample (surface in hectares and in percentage over the total cultivated surface of the regional sample).


(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

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86


(ha) (%) (ha) (%)

hazel 1.50 0.90 1.50 0.91

Kiwifruit 65.00 39.15 65.00 39.51

Nectarine 35.00 21.08 35.00 21.27

Other crops in greenhouses 1.75 1.05 1.75 1.06

Other temperate fruits 2.00 1.20 1.00 0.61

Peach 51.52 31.03 51.52 31.31

Pear 0.60 0.36 0.60 0.36

Plum 4.00 2.41 4.00 2.43

Walnut 1.50 0.90 1.50 0.91

total tree crops 166.03 24.13 164.53 23.96

total 688.18 100.00 686.68 100.00

4.3.3 Puglia pilot area

Puglia region is representative for HC A (wheat, vineyards and olive plantations), surveyors interviewed more farms than those scheduled (100 interviews out of a sample of 97), but some typologies have been not identified (see Table 4.24). Interviews have been carried out in the period October 2009 - January 2010, all the farms are localized in the provinces of Foggia (73 farms) and Bari (27 farms).

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

>> follow

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table 4.24 - puglia pilot area: number of scheduled and actual farms interviewed and re-sponse rate by stratification variable.



total interviews


100small 70 80 114.29


100self-supply 68 62 91.18





table 4.25 - puglia pilot area: average values for the main dimensional variables.

variable Average (ha)

total surface 18.23

UUa 18.26


Irrigated surface in the agrarian year 2007-2008 12.12

Irrigated surface in the last three years 11.29

The overall cultivated surface is 1,715 ha, whereof 1,168 ha irrigated (68% ca. of the total) in the agrarian year 2007-2008 (see Table 4.26).

table 4.26 - puglia pilot area: total and irrigated surface of the cultivated crops of the re-gional sample (surface in hectares and in percentage over the total cultivated surface of the regional sample).


(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


table grapes 83.65 4.88 83.65 7.16

Other wines 557.8 32.51 443.6 37.97

Olives for oil production 431.63 25.16 416.69 35.66

Peach 45.75 2.67 45.75 3.92

table olives 7.05 0.41 7.05 0.60

total tree crops 1,144.98 66.74 1,015.84 86.95

grand total 1,715.58 100.00 1,168.36 100.00

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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 representa-tive for the class. The number of farms interviewed is reported by province in Table 4.28, interviews have been conducted in the period October 2009 - February2010

figure 4.8 - sardegna pilot area: “meteo-cells” of the agrometeorological grid and num-ber of actual farms interviewed by municipality.

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table 4.27 - sardegna pilot area: number of scheduled and actual farms interviewed and response rate by stratification variable.



total interviews


51small 11 9 81.82


51self-supply 13 12 92.31





table 4.28 - sardegna pilot area: number of the actual farms interviewed by province.


Cagliari 4

Oristano 15

sassari 32

total 51

table 4.29 - sardegna pilot area: average values for the main dimensional variables.


total surface 75.37

UUa 69.22


Irrigated surface for the agrarian year 2007-2008 23.10

Irrigated surface for the last three years 24.44

The overall cultivated surface of the regional sample is 1,129 ha and, in the agrarian year 2007-2008, is completely irrigated. As reported in Table 4.30, the prevailing land use is arable land, in particular forage crops that cover more than 90% of the total irrigated surface.

table 4.30 - sardegna pilot area: total and irrigated surface of the cultivated crops of the regional sample (surface in hectares and in percentage over the total cultivated surface of the regional sample).


(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

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(ha) (%) (ha) (%)


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


Clementine 1.00 11.11 1.00 11.11


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 strati-fication 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 out-liers 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 meas-ured irrigation water volumes. An example of the comparison between the simulation per-formed 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%.

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table 4.31 - campania pilot area: comparison between the simulated and measured irrigation water volume for each crop.

farm id crop id Municipality crop name simulated volume (m3/ha)

Measured volume (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


35 464 afragola Potato 4613.40 6720.00


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

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farm id crop id Municipality crop name simulated volume (m3/ha)

Measured volume (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








8 428 Giugliano in Campania table tomato 6144.90 16500.00


7 424 Giugliano in Campania strawberry 4945.30 21000.00




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 dif-ferent 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.

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figure 4.11 - campania pilot area: comparison between simulated and measured irrigation water volumes for a selection of crops (crop group no. 1).

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figure 4.12 - campania pilot area: comparison between simulated and measured irrigation water volumes for a selection of crops (crop group no. 2).

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figure 4.13 - campania pilot area: comparison between simulated and measured irriga-tion water volumes for a selection of crops (crop group no. 3).

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4.5 influence of the resolution of the agrometeorological data on the simulation results

In order to analyze the impact of the agrometeorological data resolution on MARSALa simulations results, an exercise has been carried out by using data with different resolution. The test has been performed before calibration by comparing the model results obtained for some crops. These crops belong to the Sardegna farms sample since agrometeorological data for some municipality have been kindly provided by the Hydrometeoclimatic Depart-ment of the Regional Environmental Protection Agency of Sardegna (ARPAS).

The exercise has been realized by comparing the simulation results produced by the followingdatasets:

• CRA-CMAdataset(thedefaultdatabaseusedbyMARSALa)-valuesofprecipita-tion andETo are referred to an agrometeorological grid with 30 km resolution; farms are associated with the “meteo-cell” of the municipality where farms cen-tres is located.

• ARPASdataset -valuesofprecipitationandETo 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 andETo (i.e the simple difference P -ETo)

97

computed with CRA-CMA and ARPAS data for the stations of Olmedo, Dolianova, Ozieri and Palmas that are the closest to the selected farms centres. The difference is larger for the balance computed on yearly basis than for the balance on half-yearly basis (April-September).

figure 4.14 - sardegna pilot area: difference between the balance of precipitation and ETo simulated by using the crA-cMA and the ArpAs data for the year 2008 (both the annual and the half-yearly balance is computed, the half-yearly balance is relative to the period April-september).

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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 mete-orological 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 data-set 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 simula-tion and also to realize finer calibration of the model.

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figure 4.15 - sardegna pilot area: comparison between the difference of simulated and measured water volume for corn and corn for silage computed by using the ArpAs and crA-cMA datasets.

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ChaPter V

soFtwAre implementAtion

5.1 Architecture of the computational system

The three models (A, B and C) have been implemented through a software applica-tion 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, pro-cessing and storage. The server manages the input and output databases and all the models parameters (see Table 5.1).

table 5.1 - list of the databases managed at server-side by MArsAla.

database name description

agro-meteoDatabase of daily values of precipitation and reference evapotranspiration (eto), both in mm, relative to each municipality, the data are generated by processing the Cra-Cma database.

CropsDatabase of the crops characteristics (e.g. roots depth, length of the growing stages, etc.) reported for the three geographical macro areas North, Centre and south Italy.

soilDatabase storing, for every agricultural areas of the Italian municipalities, the soil parameter: soil depth, wilting point and field capacity.

FarmDatabase of the farms information useful for running the models extracted from the CQ database provided by Istat.

land useDatabase storing, for each farm, the characteristics of the irrigated land use generated by module 1. the data (e.g. irrigation system, crop type, irrigated surface, geographical localization) are used by module 2.

Irrigation water consumptionDatabase storing, for each farm irrigated crop, the irrigation water consumption computed by the model along with additional information such as crop irrigated surface, irrigation system and geographical localization.

The Database Management System (RDBMS) used is the open-source software MySQL version 5.1, the client-server connection and communication is ensured by a MySQLconnector.Theclientapplicationhasthreemodules:Module1,Sub-module1.1and Module 2.

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figure 5.1 - MArsAla software application: structure of the client-server architecture.

5.2 functions of the modules and sub-modules

Module 1 rebuilds the farm irrigated land use by processing the information col-lected 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 ir-rigation 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 aggre-gation 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 defini-tion, 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)

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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.

Module1performsanother important task: thedistributionof the farmirrigatedland 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 lo-cated. The territorial distribution is feasible since the CQ has a section reporting the extension and location, at municipality level (SezioneIV-Ubicazionedeiterreniede-gliallevamentiaziendali),forthefollowingfivecropgroups:Arableland(Seminativi), Vineyards (Vite), Other permanent crops (Coltivazionilegnoseagrarieesclusolavite), Kitchen gardens (Orti familiari) and Permanent grassland and pastures (Prati perma-nenti e pascoli). The territorial distribution is based on the proportional allocation of the irrigated surfaces indicated in the box no.22 to the different farm parcels by using the data reported for the five mentioned crop groups for each municipality where the parcels are located.

Sub-module 1.1 assigns the parameters RIS and f1 to each farm crop by using the two decision trees as required by Model C.

Module 2 performs the final computation by using the data stored in the databases listed in Table 2.1, irrigation water consumption is computed for each farm crop and stored along with the aggregated value of consumption for the farm in the Irrigation water consumption database, the graphical user interface is depicted in Figure 5.3.

figure 5.2 - graphical user interface (in italian language) of Module 1 for the allocation of irrigated surface of crop groups to the single crops.

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figure 5.3 - graphical user interface (in italian language) of Module 2. the great deal of controls allows the full management of the different parameters contributing to the esti-mation of the crops water consumption.

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conclusions

The methodology proposed allows to perform an estimation of the irrigation water con-sumption at farm level by using a models-based approach based on the integration of three modelsrelatedtothethreemainaspectsofirrigation:cropirrigationdemand(ModelA),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 col-lection. In particular, the acquisition of soil and climate data for the whole Italian agricul-tural 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 sam-pleofnearly300farmslocatedintofourItalianpilotregions: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 con-sumption in the Italian farms have been also considered. The simulation results, ob-tained 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 tak-ing 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 fac-tors (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 in-accuracy of the input data.

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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 impor-tance,bythevaluesofthefollowingparameters:

1. crop characteristics (in particular crop coefficient);

2. precipitation andETo;

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 char-acteristics,whoseparametersareextrapolatedatmunicipalitylevel:theminimumgeo-graphical 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.

• LackofsomeimportantfarmdetailsintheCensusquestionnaire.Resultsshowsthatbetterresultscouldbeachievedifthefollowinginformationwouldbecollected:

- 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 de-fining 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, con-struction materials and recent restoration of the network, dimensions, man-agement etc.) - in this case a better definition of the irrigation water distribu-tion efficiency could be realized.

• Lackofanharmonizedandcentralizeddatabaseofagrometeorologicaldatawitha 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 associ-ate more realistic values of precipitation andETo 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.

• Lowqualityofthesoilinformationandlackofanharmonizednationalmapwithenough spatial resolution. The national scenario is characterized by soil informa-tion produced and managed at regional level that are not harmonized and stand-ardized 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.

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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 question-naire 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.

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reFerences

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Maton L., Leenhardt D., Goulard M., Bergez J.-E., (2005), “Assessing the irrigation strategies over a wide geographical area from structural data about farming systems”, Agricul-turalsystems86,293-311,doi:10.1016/j.agsy.2004.09.010.

Nino P., Vanino S., (2009), UsodelsuoloestimadeifabbisogniirriguinelleareenonservitedareticollettivedeiConsorzidiBonificanelleRegioniMeridionali, INEA- Rapporto Irrigazione - ISBN 978-88-8145-174-6.

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ISTAT,(2008),Relazionitraagricolturaeambiente:dallestatisticheagliindicatori,Statis-ticainbreve,gennaio2008),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),AgrienvironmentalIndicators:Methodologies,DataNeedsandAvailability,Collana Essays, n. 16.

ISTAT, (2003), Principali fattori agricoli di pressione sull’ambiente, ISTAT, Roma, Argomen-ti, n. 27.

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Allen, R.G., (1995), Evaluation of procedures for estimating mean monthly solar radiation from air temperature, Report submitted to the United Nations Food and Agricultural Organization (FAO), Rome Italy.

Allen R.G., Pereira L.S, Raes D., Smith M., (1998), Crop evapotranspiration. Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56, Rome, Italy.

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Anyoji H., Wu I.P., (1994), “Normal distribution water application for drip irrigation sched-ules”,TransactionoftheASAE,37:159-164.

Baille A., (1994), Principle and methods for predicting crop water requirement in greenhouse environments,CahierOptionsMéditerranéennesvol.31:177-187

Bonciarelli F., Bonciarelli, U., (2003), Coltivazioni Erbacee, Edagricole Scolastico

Burt C.M., Clemmens A.J., Bliesner R., Merriam J.L., Hardy L., (2000), “Selection of irri-gation methods for agriculture”, On-farm Irrigation Committee, American Society of Civil Engineers, Reston, VI, US, pp 129.

Burt C.M., Clemmens A.J., Strelkoff T.S., Solomon K.H., Bliesner R.D., Hardy L.A., Howell T.A.,EisenhauerD.E.,(1997),“Irrigationperformancemeasures:efficiencyanduni-formity”,JournalofIrrigationandDrainageEngineering,123:423-442.

Christiansen J.E., (1942), Irrigation by sprinkling. University of California, Berkeley Agric Exp Sta Bull 670.

De Villele O., (1974), “Besoins en eau des cultures sous serre.. Essai de conduite de l’arrosage enfonctiondel’ensoleillement”,ActaHorticulturae,35:123-39.

Doorembos J., Pruitt W.O., (1977), Crop water requirements. FAO Irrigation and Drainage Paper No.24, Rome, Italy.

Doorenbos J., Pruitt W.O., (1977), Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper No. 24. Rome, Italy

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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 foodandfibreproduction”,in:M.E.Jensen,ed.,Designandoperationoffarmirrigationsystems, 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 transpirationofglasshousesplants”,Agric.Meteorology,3:167-196.

Lorite I.J., Mateos L., Fereres E., (2004), “Evaluating irrigation performance in a Mediterra-neanenvironment,variabilityamongcropsandfarmers”,IrrigationScience,23:85-92.

Lozano D., L. Mateos., (2008), “Usefulness and limitations of decision support systems for improvingirrigationschememanagement”,AgriculturalWaterManagement95:409-418.

MerriamJ.L.,KellerJ.,(1978),Farmirrigationsystemevaluation:Aguideformanagement,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 cropsanditsrelationshiptotheincomingradiation”,J.Agric.Eng.Research,2:111-22

Penman, H.L., (1948), “Natural evaporation from open water, bare soil and grass”, Proceed-ingsoftheRoyalSociety,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.AgronomiquesdeGembloux,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”,PlantPhysiology,2:71-78.

Warrick A.W., (1983), “Interrelationships of irrigation uniformity terms”, Journal of Irriga-tionandDrainageEngineering,109:317-332.

Wilcox J.C., Swailes G.E., (1947), “Uniformity of water distribution by some under tree or-chardsprinklers”,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-actionsoftheASAE,31:1743-1749.

Zucaro R., Tudini L., (2008), Rapporto Sullo Stato Dell’irrigazione in Toscana, INEA, Roma.

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chapter iii

APAT, (2007), Annuario dei dati ambientali, Roma.

Costantini E. A. C., Gardin L., Pagliai M., (2000), “Advances in soil survey, monitoring and applications in Italy”, in The European Soil Information System, (Eds. ESB and FAO), World Soil Resources Reports 91, FAO, Roma p. 97-101.

EUROPEAN SOIL BUREAU, (1998), The soil geographical data base of Europe at scale 1:1,000,000,JRC,Ispra,Varese,Italy.

FAO, (1978), Soil map of the world, FAO, Roma, Italy.

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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 meteoro-logicalfields:anapplicationinnorthernItaly”,SciencedelaTerre,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 cambia-mento climatico, CRA-UCEA & MIPAF, Roma.

Perini L., Salvati L., Ceccarelli T., Motisi A., Marra F.P., Caruso T., (2007), Atlante agrocli-matico – Scenari di cambiamento climatico, UCEA, Roma. Collana Climagri n. 52 (Atlante + CD), 72 pag. ISBN 88-901472-8-8.

SalvatiL.,LibertàA.,BrunettiA., (2005), “Bio-climatic evaluationofdrought severity: acomputational approach using dry spells”, Biota, 5, 67-77.

chapter iv

Rykiel E.J.Jr., (1996), “Testing ecological models: the meaning of validation”, EcologicalModeling,90:229-244.

glossary

BrouwerC.,GoffeauA.,HeibloemM.,(1985),IrrigationWaterManagement:TrainingManualNo. 1 - Introduction to Irrigation, FAO - Food and Agriculture Organization of the UnitedNations.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

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web sites

http://www.ISTAT.it/ambiente/

http://www.ISTAT.it/agricoltura/

http://www.netafim.com/glossary#i

http://www.irrigation.org/

http://www.scia.sinanet.apat.it/

http:/www.enterisi.it/

http://www.flow-aid.wur.nl/UK/

http://www.estsesia.it/

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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 evapo-transpiration (ETo), and its represents an integration of the effects of four primary characteristics that distinguish the crop from reference grass. These characteristics are:cropheight,albedo(reflectance)ofthecrop-soilsurface,canopyresistanceandevaporation 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 da-tum. Digital elevation models are typically used to represent terrain relief.

Depletion fraction (p) Average fraction of TotalAvailableSoilWater(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

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the plants or trees, a hole is made in the tube and equipped with an emitter. The wa-ter is supplied slowly, drop by drop, to the plants through these emitters.

Evapotranspiration or Crop Evapotranspiration The rate of water loss through transpira-tion 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 applica-tion - 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 distri-bution 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 in-cludes water applied for crop quality and temperature modification; i.e. frost control, cooling plant foliage and fruit. Application losses, such as evaporation, runoff, and

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deep percolation, are not included. It is expressed in millimetres per year or in m3/haperyear(1mm=10m3/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 cement-ed to moderately cemented rupture-resistance class. Cementation, bulk density, and the organization are such that roots cannot enter, except in cracks. Paralithic materi-als 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 de-fined as predictive functions of certain soil properties from other more available, eas-ily, 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 dif-ficult to extract. If the soil dries to the permanent wilting point, the plant can no longerremoveanywaterfromit:somewatermaystillbepresentbutiscompletelyunavailable.

Readily Evaporable Water (REW) The maximum total depth of water that can be evapo-rated 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 evapotran-spiration 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 affectingETo 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.

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RICA or Italian FADN The Italian network information system for gathering annually accountancy data from farms for the determination of incomes and business analy-sis 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 consid-ered commercial. The methodology applied aims to provide representative data along threedimensions:region,economicsizeandtypeoffarming.InItalytheFADNisbased 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 at-mosphere 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 watercontent(orsoilmoisture), 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 ten-sion), 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 (fieldcapacity) 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.

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Acronyms And AbbreviAtions

AM The Italian Air ForceAP Autonomous ProvinceAPAT see ISPRAARPA Regional Agency for Environmental ProtectionARPAS Hydrometeoclimatic Department of the Regional Environmental Protection Agency of SardiniaAWC Available Water ContentBDAN Banca Dati Agrometeorologica NazionaleCAP Common Agricultural Policy CISIS Centro Interregionale per i Sistemi informatici, geografici e statisticiCLC CORINE Land CoverCNR National Research CouncilCQ Census QuestionnaireCRA Agricultural Research CouncilCRA-ABP Research Centre for Agrobiology and PedologyCRA-CMA (ex CRA-UCEA) Central Office for Crop EcologyCSIC see IAS-CSICCWR Crop Water Requirement DBMS Database Management SystemDOC Controlled Designation of OriginDOCG Controlled and Guaranteed Designation of OriginDU Distribution UniformityEAP (EU) Environmental Action PlanEAP European Action Programs in the Field of the EnvironmentEC European CommissionEDP Electronic Data Processing EEA European Environment AgencyENAV Italian Company for Air Navigation Services EU European UnionEUROSTAT Statistical Office of the European UnionFADN Farm Accountancy Data NetworkFAO Food and Agriculture Organization of the United NationsFSS Farm Structure SurveysGIS Geographic Information SystemsGIWR Gross Irrigation Water RequirementsIAS-CSIC Instituto de Agricoltura Sostenibile – Consejo Superior de Investigaciones CientificasIGT Typical Geographical IndicationILRC Irrigation and Land Reclamation Consortium

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IPCC International (or Intergovernmental) Panel on Climatic ChangeISPRA (ex APAT) National Institute for the Protection and Environmental Research ISTAT Italian National Statistics InstituteJRC Joint Research CentreLAU Local Administrative UnitMATTM Ministry of the Environment, Land and SeaMiPAAF Ministry of Agricultural, Food and Forestry PoliciesMS Member StatesNIWR Net Irrigation Water RequirementsNSSG National Statistic Service of GreeceNUTS Nomenclature of Territorial Units for StatisticsOECD Organization for Economic Cooperation and DevelopmentPDO Protection Designation of Origin PGI Protected Geographical IdentificationPQ Pilot QuestionnairePTF Pedotransfer FunctionRAW Readily Available WaterREW Readily Evaporable WaterRICA (Italian FADN) Rete d’Informazione Contabile AgricolaRIS Relative Irrigation SupplyRZWD Root Zone Water DeficitRZWHC Root Zone Water Holding CapacitySAPM Statistics on Agricultural Production MethodsSCIA National System for the collection, elaboration and diffusion of climatological data of environmental interestSIAN National Agricultural Information SystemSIGRIAN Sistema Informativo per la Gestione delle Risorse Idriche in Agricoltura SIMN National Service for Study of Waters and SeasSINA National Information System for Environmental MonitoringTAW Total Available WaterTEW Total Evaporable WaterUAA Utilised Agricultural Area or Agricultural Area (AA)UGM General Office for MeteorologyUTC Universal Coordinated TimeWBS Work Breakdown Structure WFD Water Framework DirectiveWMO World Meteorological OrganizationWP Work Package

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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 SezioneIV - Ubicazionedeiterreniedegliallevamentiaziendali.

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 larg-est 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 relativeirrigatedsurfaceandirrigationsystemtothefarmirrigatedlanduse:

• 22.4.b-Grainmaize(Maisdagranella);

• 22.4.e-Potato(Patata);

• 22.4.f-Sugarbeet(Barbabietoladazucchero);

• 22.4.g-Rapeandturniprape(Colzaeravizzone)

• 22.4.h-Sunflower(Girasole);

• 22.4.m-Greenmaize(Maisverde);

• 22.4.p-Permanentgrasslandandpastures(Prati permanenti e pascoli);

• 22.4.u-Otherpermanentcrops(Altrecoltivazionilegnoseagrarie).

Inadditionthefollowingconsiderationhavebeendone:

• theirrigatedsurfacereportedin22.4.m-Greenmaizeisthesumoftheirrigatedsurface of 8.10.b.47-Corn grass (Maisinerba) and 8.10.b.48-Corn for silage (Maisamaturazionecerosa) since the crops can be considered equivalent therefore, the disaggregation procedure is not required.

• theirrigatedsurfacereportedin22.4.p-Permanent grassland and pastures is the sum of the irrigated surface of 11.1.86-Permanent grassland (Prati permanenti),

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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.

• theirrigatedsurfacereportedin22.4.u-Otherpermanentcropscanbedifferentfrom 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 laproduzionedigranella) 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 ce-reali). Among these, only Sorghum has the highest chance to be irrigated in Italy, there-fore 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 proce-dure since the irrigation water consumption estimation is carried out by using the meth-odology 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 mensaincoltivazionidipienocampo), 8.7.a.32-Plum tomato in open field (Pomodoro da industriaincoltivazionidapienocampo), 8.7.a.33-Other fresh vegetables in open field (Altreortiveincoltivazionidapienocampo), 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.

• Theirrigatedsurfacein22.4.lissplitproportionallyamongtwosubgroupsmadeupofcropsconsideredequivalent:“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).

121

• Thesurfaceallocatedtothesubgroup“Otherhorticulturalcrops” is split propor-tionally 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 an-nually 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 avvicen-date) 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 (Altrierbaimonofitidicereali) 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 grass-land 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 thesumoftheirrigatedsurfaceofthefollowingcrops:

• 8.5-Fodderrootsandbrassicas(Piantesarchiatedaforaggio);

• 8.6.a.18-Tobacco(Tabacco);

• 8.6.a.19-Hops(Luppolo);

• 8.6.d.26-Soybean(Soia);

• 8.6.d.27-Linseed(Semidilino);

• 8.6.d.28-Otheroilseedcrops(Altrepiantedisemioleosi);

• 8.6.e.29-Aromaticplants,medicinalandculinaryplants(Piante aromatiche, me-dicinali,spezieedacondimento);

• 8.6.f.30-Otherindustrialcrops(Altre piante industriali);

• 8.8.a.39-Flowersandornamentalplantsinopenfields(Fiori e piante ornamen-tali in piena aria);

• 8.11-Seeds(Sementi);

• 8.12.a.52-Fallowlandwithoutanysubsidies(Terreniariposononsoggettiare-gime di aiuto)

• 8.12.a.53-Fallowlandsubjecttothepaymentofsubsidies,withnoeconomicuse(Terreniaripososoggettiaregimediaiuto)

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 or-namental 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 in9.1-Vineyards:

• 21.1.1999-Qualitywine(Uvaperlaproduzionediviniadenominazionediorig-inecontrollata(DOC)econtrollatagarantita(DOCG));

122

• 21.2.2999-Otherwines(Uvaperlaproduzionedialtrivini);

• 21.3.3999-Tablegrapes(Uvadatavola);

• 21.4.4001-Ungraftedwines(Vitinoninnestate).

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 sur-faceandthecropsurfaceinproduction):

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,theproceduretocreatetheirrigatedlanduseforVineyardsisthefollowing:

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 definedfordistributingtheirrigatedsurface:

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 irrigationisappliedaccordingtothefollowingpriorities:

1. The irrigated surface is allocated proportionally to new planting Fruit of subtropi-cal 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;

123

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 fol-lowingcropssurfaces:

• 8.7.a.36-Tabletomatounderglass(Pomodoro da mensa in serra), 8.7.a.37-Other fresh vegetables under glass (Altre ortive in serra), 8.7.a.38-Fresh vegetables un-der low (not-accessible) protective cover (Ortive protette in tunnel, campane, ecc.);

• 8.8.b.40-Flowersandornamentalplantsunderglass(Fiori e piante ornamentali protetti in serra), 8.8.b.41-Flowers and ornamental plants under low (not-acces-sible) protective cover (Fiori e piante ornamentali protetti in tunnel, campane, ecc.);

• 9.7-Permanentcropsunderglass(Coltivazionilegnoseagrarieinserra).

A dedicated routine has been implemented for the estimation of the water consump-tion (see paragraph 2.6).

aNNex 2

6th generAl AgriculturAl census questionnAire(in italian language)

127

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 neiriquadri 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

Censimento generale dell’agricoltura24 OTTOBRE 2010(art. 17 del decreto legge 25 settembre 2009, n. 135, convertito con modificazioni dalla legge 20 novembre 2009, n. 166)

QUESTIONARIO DI AZIENDA AGRICOLA

6°Numero identificativo Istat

Sistema statistico nazionaleIstituto nazionale di statistica

Mod. Istat CEAGR

Agricoltura_2Col_pp_1_7:Agricoltura_Tric_pp_1_7 6-05-2010 9:59 Pagina 1

128

2

B ESITO DELLA RILEVAZIONE

B.1 AZIENDA RILEVATA 1�(compilare sempre il presente questionario)

B.2 AZIENDA IN LISTA NON RILEVATA(compilare solo il riquadro in bianco a pagina 14 del questionario)

a. Irreperibilità del conduttore 2�b. Rifiuto 3�c. Altra motivazione 4�

(specificare……………………………………………………………………)

B.3 AZIENDA IN LISTA NON ESISTENTE O DOPPIONE(compilare solo il riquadro in bianco a pagina 14 del questionario; per i casi g ed h, riempire anche il riquadro D)

d. Terreni destinati a soli orti familiari 5�o allevamenti per autoconsumoo aziende esclusivamente forestali

e. Soggetto che non ha mai esercitato 6�attività agricola

f. Terreni agricoli definitivamente abbandonati 7�o destinati ad altro uso o aziendeesclusivamente zootecniche che hanno totalmente dismesso l’attività senza cessione ad altri

g. Azienda agricola interamente affittata, 8�ceduta, assorbita, fusa o smembrata

h. Unità da ricondurre ad aziendaesistente (doppione) 9�

C CESSIONI PARZIALI (in caso di risposta al quesito B.1)

D UNITÀ COLLEGATE ALLE AZIENDE IN LISTA (da compilare per i casi B.3g, B.3h e per risposta SI al riquadro C)

Cognome e nome della persona fisica o denominazione della società o ente

che conduce l’azienda

Indirizzo, Comune e Provincia CUAA o Codice fiscale della personafisica o della società

o ente che conduce l’azienda

In caso di risposta SI compilare il riquadro D indicando le notiziedell’azienda/e che ha/hanno acquisito parzialmente i terreni o gliallevamenti

L’azienda ha ceduto parzialmente terreni agricoli oallevamenti ad altra/e azienda/e nell’annata agraria2009/2010?

SI 1�NO 2�

D UNITÀ COLLEGATE ALLE AZIENDE IN LISTA (da compilare per i casi B.3g, B.3h e per risposta SI al riquadro C)

Il centro aziendale è localizzato a meno di 5 km dalla residenza o sede legale del conduttore? 1�SI 2�NO

E UBICAZIONE DEL CENTRO AZIENDALEQuesto riquadro deve essere compilato solo se l’ubicazione del centro aziendale è diversa dalla residenza o dalla sede legale del conduttorePer centro aziendale si intende il complesso dei fabbricati connessi all’attività aziendale situato entro il perimetro dei terreni aziendalioppure, in assenza di fabbricati, il luogo che identifica la maggior parte della superficie aziendale

Cognome e nome della persona fisica o denominazione della società o ente

che conduce l’azienda

Indirizzo, Comune e Provincia CUAA o Codice fiscale della personafisica o della società

o ente che conduce l’azienda

C CESSIONI PARZIALI (in caso di risposta al quesito B.1)

caso g: compilare il riquadro D indicando le notizie dell’azienda/eche ha/hanno acquisito i terreni o gli allevamenti

caso h: compilare il riquadro D indicando le notizie dell’aziendagià in lista o già intervistata

Indirizzo (Via/Piazza/Località e numero civico del centro aziendale) C.A.P.

Denominazione Comune Codice Istat

Sez. censuaria Foglio di mappa catastale

Denominazione Provincia Codice Istat Telefono fisso (prefisso e n.)

Per tutti i Comuni esclusi quelli di Trento e Bolzanoe quelli elencati nell’appendice B del libretto d’istruzioni

Per i Comuni con catasto tavolare elencati nell’appendice Bdel libretto d’istruzioni

Per i Comuni delle province di Trento e BolzanoPer i Comuni con catasto a foglio aperto elencati nell’appendice B

del libretto d’istruzioni

a a a

Sez. censuaria Foglio e Particella catastale

Sez. censuaria Particella catastale Tipo

a aComune catastale Particella catastale Tipo

/

a/


129

5

Sottoposti amanutenzione

durante gli ultimi tre anni

Di nuovarealizzazionenegli ultimi

tre anni

ELEMENTI DEL PAESAGGIO AGRARIO

5.1 Siepi 01 1� 2�5.2 Filari di alberi 02 1� 2�5.3 Muretti 03 1� 2�

Indicare la presenzadi elementi linearidel paesaggio

Cod.

3

1 FORMA GIURIDICA(è ammessa una sola risposta)

1.1 Azienda individuale 01�1.2 Società semplice 02 �1.3 Altra società di persone (S.n.c., S.a.s., ecc.) 03 �1.4 Società di capitali (S.p.a., S.r.l., ecc.) 04 �1.5 Società cooperativa 05 �1.6 Amministrazione o Ente pubblico 06 �

(Stato, Regioni, Province, Comuni, ecc.)1.7 Ente (Comunanze, Università, Regole, ecc.) 07 �

o Comune che gestisce proprietà collettive1.8 Ente privato senza fini di lucro 08 �1.9 Altra forma giuridica 09 �

(specificare……………...………………….............)

6

3 CORPI AZIENDALI DI TERRENO

3.1 Corpi che costituiscono l’azienda n.

2

Notizie generali sull’aziendasezione I

SISTEMA DI CONDUZIONE

2.1 Forma di conduzione (è ammessa una sola risposta)

a. Conduzione diretta del coltivatore 01�b. Conduzione con salariati (in economia) 02�c. Altra forma di conduzione 03�

(specificare…………...…………………............)

I TOTALI della Superficie Totale e della SAU devono essere ugualiai corrispondenti dati riportati ai punti 17 e 12, pagina 5

c

4 STATO DI ATTIVITÀ DELL’AZIENDA

4.1 Nell’annata agraria 2009/2010 l’unità agricola è stata:

a) Attiva 1�b) Temporaneamente inattiva 2�

(compilare solo il riquadro in bianco a pagina 14 del questionario)

INFORMATIZZAZIONE DELL’AZIENDA

6.1 L’azienda dispone di computere/o altre attrezzature informaticheper fini aziendali?Se SI rispondere al punto 6.1.1 e successivi, se NO passareal punto 6.2 e successivi

6.1.1L’azienda usa normalmente proprieattrezzature informatiche per:a. Servizi amministrativi

(contabilità, paghe, ecc.)b. Gestione informatizzata

di coltivazionic. Gestione informatizzata

degli allevamenti

6.2 L’azienda utilizza normalmente larete Internet per le proprie attività?

6.3 L’azienda ha un sito web oppureuna o più pagine su Internet?

6.4 L’azienda fa commercioelettronico per:a. La vendita di prodotti e servizi

aziendalib. L’acquisto di prodotti e servizi

1�SI 2�NO

1�SI 2�NO

1�SI 2�NO

1�SI 2�NO

1�SI 2�NO

1�SI 2�NO

1�SI 2�NO

1�SI 2�NO

7 SOSTEGNO ALLO SVILUPPO RURALE

7.1 Indicare se l’azienda ha beneficiato di una o più delleseguenti misure nel corso del 2008-2009-2010 a. Insediamento di giovani agricoltori (misura 112) 01�b. Utilizzo di servizi di consulenza (misura 114) 02�c. Ammodernamento 03�

delle aziende agricole (misura 121)

d. Accrescimento del valore aggiunto 04�dei prodotti agricoli e forestali (misura 123)

e. Cooperazione per lo sviluppo 05�di nuovi prodotti, processi e tecnologie nel settore agricolo e alimentare e in quelloforestale (misura 124)

f. Rispetto delle norme basate sulla 06�legislazione comunitaria (misura 131)

g. Partecipazioni degli agricoltori ai sistemi 07�di qualità alimentare (misura 132)

h. Indennità a favore degli agricoltori 08�delle zone montane (misura 211)

i. Indennità a favore degli agricoltori 09�delle zone caratterizzate da svantaggi naturali diverse da zone montane (misura 212)

l. Indennità Natura 2000 (misura 213) 10�m.Indennità connesse alla Direttiva Quadro 11�

2000/60/CE sulle acque (misura 213)

n. Pagamenti agro-ambientali (misura 214) 12�di cui nel quadro dell’agricoltura biologica 13�di cui nel quadro dell’agricoltura integrata 14�

o. Pagamenti per il benessere degli animali 15�(misura 215)

p. Sostegno agli investimenti non produttivi 16�(misura 216)

q. Diversificazione in attività non agricole 17�(misura 311)

r. Incentivazione di attività turistiche (misura 313) 18�

2.2 Titolo di possesso dei terreni

SUPERFICIETOTALE

SUPERFICIEAGRICOLA

UTILIZZATA (SAU)Ettari Are Ettari Are

a. Proprietà, usufrutto, ecc.

b. Affitto

c. Uso gratuito

2.3 TOTALE


130

4

sezione II

8 SEMINATIVI

Informazioni per aziende con terrenisezione IIA questa sezione (pagine 4, 5, 6 e 7) devono rispondere le aziende con terreniNOTA: Le aziende esclusivamente zootecniche che abbiano ricoveri per animali devono comunque indicare le superfici relativea questi fabbricati a pagina 5, al punto 16 “Altra superficie”

Utilizzazione dei terreni (annata agraria 2009 - 2010)

segue SEMINATIVI

8.1 Cereali per la produzione digranella (1)

Cod.

SUPERFICIECOLTIVAZIONEPRINCIPALE

Ettari Are

a. Frumento tenero e spelta 01

b. Frumento duro 02

c. Segale 03

d. Orzo 04

e. Avena 05

f. Mais (escluso mais in erbae a maturazione cerosa daindicare al punto 8.10b)

06

g. Riso 07

h. Sorgo 08

i. Altri cereali 09

8.2 Legumi secchi (1)

a. Pisello (proteico e secco) 10

b. Fagiolo secco 11

c. Fava 12

d. Lupino dolce 13

e. Altri legumi secchi 14

8.3 Patata (1) 15

8.4 Barbabietola da zucchero 16

8.5 Piante sarchiate da foraggio 17

8.6 Piante industriali

a. Tabacco 18

b. Luppolo 19

c. Piante tessili

- Cotone 20

- Lino 21

- Canapa 22

- Altre piante tessili 23

d. Piante da semi oleosi (1)

- Colza e ravizzone 24

- Girasole 25

- Soia 26

- Semi di lino 27

- Altre piante di semi oleosi 28

e. Piante aromatiche,medicinali, spezie e dacondimento

29

f. Altre piante industriali 30

8.7 Ortive

In piena aria

Cod.

SUPERFICIECOLTIVAZIONEPRINCIPALE

Ettari Are

a. In coltivazioni di pieno campo

- Pomodoro da mensa 31

- Pomodoro da industria 32

- Altre ortive 33

b. In orti stabili ed industriali


- Altre ortive 35

Protette

a. In serra


- Altre ortive 37

b. In tunnel, campane, ecc. 38

8.8 Fiori e piante ornamentali

a. In piena aria 39

b. Protetti

- In serra 40

- In tunnel, campane, ecc. 41

8.9 Piantine

a. Orticole 42

b. Floricole ed ornamentali 43

c. Altre piantine 44

8.10 Foraggere avvicendate (1)

a. Prati avvicendati

- Erba medica 45

- Altri prati avvicendati 46

b. Erbai

- Mais in erba 47

- Mais a maturazionecerosa 48

- Altri erbai monofiti dicereali 49

- Altri erbai 50

8.11 Sementi 51

8.12 Terreni a riposo

a. Non soggetti a regime diaiuto 52

b. Soggetti a regime di aiuto(buone condizioniagronomiche e ambientali)

53

8.13 TOTALE SEMINATIVI 54

(1) Comprese le superfici destinate alle produzioni di sementi


131

5

FUNGHI(coltivati in grotte,sotterranei o in appositiedifici)

Cod.SUPERFICIE

Ettari Are

ORTI FAMILIARIper autoconsumo

85

PRATI PERMANENTI E PASCOLI

11.1 Prati permanenti (utilizzati) 86

11.2 Pascoli (utilizzati)

a. Pascoli naturali 87

b. Pascoli magri 88

11.3 TOTALE PRATI PERMANENTI E PASCOLI UTILIZZATI 89

11.4 PRATI PERMANENTI E PASCOLINON PIÙ DESTINATI ALLA PRO-DUZIONE, AMMESSI A BENEFI-CIARE DI AIUTI FINANZIARI

90

SUPERFICIE AGRICOLAUTILIZZATA (SAU)Somma dei punti 8.13, 9.8,10, 11.3 e 11.4

91

ARBORICOLTURA DA LEGNO

13.1 Pioppeti 92

13.2 Altra arboricoltura da legno 93

13.3 TOTALE ARBORICOLTURADA LEGNO 94

BOSCHI14.1 Boschi a fustaia 95

14.2 Boschi cedui 96

14.3 Altra superficie boscata 97

14.4 TOTALE BOSCHI 98

SUPERFICIE AGRARIANON UTILIZZATAEsclusi i terreni a riposo indicati alpunto 8.12

99

ALTRA SUPERFICIEAree occupate da fabbricati, cortili,strade poderali, stalle, superfici afunghi, ecc.

100

SUPERFICIE TOTALEDELL’AZIENDASomma dei punti 12, 13.3, 14.4, 15 e 16

101

9

10

11

12

13

14

15

16

17

18

COLTIVAZIONI LEGNOSE AGRARIE

segue Utilizzazione dei terreni (annata agraria 2009 - 2010)

Cod.

SUPERFICIE

TotaleDi cui in

produzione

Ettari Are Ettari Are

9.1 Vite (2) 55

9.2 Olivo per la produzione di

a. Olive da tavola 56

b. Olive per olio 57

9.3 Agrumi

a. Arancio 58

b. Mandarino 59

c. Clementina e suoi ibridi 60

d. Limone 61

e. Altri agrumi 62

9.4 Fruttiferi

a. Frutta fresca di origine temperata

- Melo 63

- Pero 64

- Pesco 65

- Nettarina (pesca noce) 66

- Albicocco 67

- Ciliegio 68

- Susino 69

- Fico 70

- Altra frutta 71

b. Frutta fresca di origine sub-tropicale

- Actinidia (kiwi) 72

- Altra frutta 73

c. Frutta a guscio

- Mandorlo 74

- Nocciolo 75

- Castagno 76

- Noce 77

- Altra frutta 78

9.5 Vivai

a. Fruttiferi 79 XXX X

b. Piante ornamentali 80 XXX X

c. Altri 81 XXX X

9.6 Altre coltivazioni legnoseagrarie(compresi gli alberi di Natale)

82

9.7 Coltivazioni legnoseagrarie in serra 83

9.8 TOTALE COLTIVAZIONILEGNOSE AGRARIE 84

(2) La superficie totale deve coincidere con quella indicata al punto21.5 di pagina 6

Gli ORTI FAMILIARI sono piccole superfici utiliz-zate prevalentemente per la coltivazione di or-taggi e piante arboree (vite, olivo, fruttiferi) sparse,anche in consociazione tra loro, la cui produzioneè destinata esclusivamente al consumo del con-duttore e della sua famiglia (autoconsumo)

Cod. SUPERFICIE INVESTITA (m2)

102 ha

COLTIVAZIONI ENERGETICHE(colture utilizzate per la produzione di energia)

20.1 Soggette a contratto di coltivazione

20 Cod.SUPERFICIE

Ettari Are

104

SERRE19Cod. SUPERFICIE DI BASE (m2)

103 ha

sezione IIsezione II


132

6

Notizie particolari sulla vite

21 NATURA DELLA PRODUZIONE21.1 Uva per la produzione di vini

a denominazione di originecontrollata (vini DOC)e controllata e garantita(vini DOCG)

VITIGNI (denominazione)

Cod.

SUPERFICIETOTALEA VITE

SUPERFICIE INVESTITA A VITE SECONDO L’ANNO DI IMPIANTO

Posterioread agosto

2007

Da settembre2004 ad

agosto 2007

Da settembre2000 ad

agosto 2004

Da settembre1990 ad

agosto 2000

Da settembre1980 ad

agosto 1990

Anterioreal settembre

1980

Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are Ettari Are

1……

1……

1……

1……

1……

1……

1……

1……

1……

1……

1……

1……

1……

TOTALE………………………………. 1999

2……

2……

2……

2……

2……

2……

2……

2……

2……

2……

2……

2……

2……

TOTALE………………………………. 2999

21.2 Uva per la produzione di altri vini

VITIGNI (denominazione)

21.3 Uva da tavola 3999

21.4 Viti non innestate 4001

21.5 TOTALE PARZIALE (1)(somma dei dati ai punti 21.1,21.2, 21.3 e 21.4)

4002

21.6 Viti madri da portinnesto 4003

21.7 Barbatelle 4004

21.8 TOTALE SUPERFICIE A VITE(somma dei dati ai punti 21.5,21.6 e 21.7)

4999

21.9 TOTALE UVA DA VINO RACCOLTA Cod. QUINTALI

21.9.1 Per la produzione di viniDOC e DOCG 5001

21.9.2 Per la produzione di altri vini 5002

sezione II

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


133

23.1 ColtivazioniCod.

SUPERFICIEBIOLOGICA

SUPERFICIEDOP E IGP

Ettari Are Ettari Are

a. Cereali 01

b. Legumi secchi 02

c. Patata 03

d. Barbabietola da zucchero 04 XXX XX

e. Piante da semi oleosi 05 XXX XX

f. Ortive 06

g. Foraggere avvicendate 07 XXX XX

h. Prati permanenti e Pascoli(esclusi pascoli magri)

08 XXX XX

i. Vite 09 XXX XX

l. Olivo 10

m. Agrumi 11

n. Fruttiferi 12

o. Altre coltivazioni(tabacco, fiori, piantearomatiche, ecc)

13

23.2 TOTALE 14

di cui Superficie agricola utilizzata infase di conversione al biologico 15 XXX XX

7

22 23IRRIGAZIONE (esclusa l’irrigazione di soccorso)AGRICOLTURA BIOLOGICA EPRODUZIONI DI QUALITÀ DOP E IGPColtivazioni (Annata agraria 2009-2010)

Metodi di produzione agricola (annata agraria 2009 - 2010)

22.1 Superficie irrigabile

Cod. Ettari Are

01

22.2 Superficie effettivamente irrigata 02

22.3 Superficie media irrigata nelleultime 3 annate agrarie 03

SUPERFICIE BIOLOGICA: Superficie agricola utilizzata in cui si applicano me-todi di produzione biologica certificati o in fase di conversione secondo lenorme comunitarie o nazionaliSUPERFICIE DOP E IGP: Superficie principale o secondaria per la qualel’azienda è controllata e certificata dal competente organismo di controllo

22.4 Coltivazioni irrigate almenouna volta nell’annataagraria 2009-2010

Cod.SUPERFICIE

IRRIGATACodice

Sistema diirrigazione (1)Ettari Are

a. Cereali per la produzionedi granella(escluso mais e riso)

01

b. Mais da granella 02

c. Riso 03

d. Legumi secchi 04

e. Patata 05

f. Barbabietola da zucchero 06

g. Colza e ravizzone 07

h. Girasole 08

i. Piante tessili 09

l. Ortive in piena aria 10

m. Mais verde (in erba ed amaturazione cerosa)

11

n. Altre foraggereavvicendate 12

o. Altri seminativi(tabacco, fiori, ecc.)

13

p. Prati permanenti e pascoli 14

q. Vite 15

r. Olivo 16

s. Agrumi 17

t. Fruttiferi 18

u. Altre coltivazioni legno seagrarie 19

v. Arboricoltura da legno 20

22.5 TOTALE SUPERFICIE IRRIGATA(deve corrispondere al punto 22.2)

21 XXXXX

22.6 Fonte di approvvigionamento dell’acqua irrigua(è ammessa una sola risposta)

- Acque sotterranee all’interno o nelle vicinanze dell’azienda 01�- Acque superficiali all’interno dell’azienda

(bacini naturali ed artificiali) 02�- Acque superficiali al di fuori dell’azienda

(laghi, fiumi o corsi d’acqua) 03�Acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo

- con consegna a turno 04�- con consegna a domanda 05�- Altra fonte 06�

22.7 Barrare la casella se l’azienda utilizza servizidi consulenza irrigua e/o sistemi dideterminazione del fabbisogno irriguo 01�

Indicare le lavorazioni effettuatesui SEMINATIVI Cod.

SUPERFICIE

Ettari Are

24.1 Lavorazione convenzionale(aratura)

01

24.2 Lavorazione di conservazione (a strisce, verticale, a porche permanenti)

02

24.3 Nessuna lavorazione 03

La somma dei codici 01, 02 e 03 deve essere minore o uguale aquanto riportato al punto 8.13 di pagina 4

LAVORAZIONE DEL TERRENO

25.1 Copertura invernale del suolo aSEMINATIVI

Cod.SUPERFICIE

Ettari Are

a. Colture invernali(ad esempio frumento autunno-vernino) 01

b. Colture di copertura o intermedie 02

c. Residui colturali(ad esempio stoppie, paglia, pacciame) 03

d. Nessuna copertura 04

25.2 Avvicendamento dei SEMINATIVI

a. Monosuccessione 05

b. Avvicendamento libero 06

c. Piano di rotazione 07

La somma dei codici da 01 a 04 e dei codici da 05 a 07 deve essereminore o uguale a quanto riportato al punto 8.13 di pag. 4

25.3 Inerbimento controllato dellesuperfici a COLTIVAZIONILEGNOSE AGRARIE

08

CONSERVAZIONE DEL SUOLO(1) Indicare il codice del sistema di irrigazione unico o prevalente.

1 Scorrimento superficiale edinfiltrazione laterale

2 Sommersione

3 Aspersione (a pioggia)4 Microirrigazione5 Altro sistema

sezione II

24

25


134

8

26 BOVINI

27 BUFALINI

28 EQUINI

Informazioni per aziende con allevamentisezione III

BIOLOGICICapi

DOP e IGPCapi

Cod.

CAPICod.

L’azienda possiede allevamenti 29 per autoconsumo?

L’azienda possiede allevamenti 30 destinati alla vendita?se SI indicare i soli capi destinati alla vendita ai punti da 31 a 37 se NO passare al punto 38

1�SI 2�NO

1�SI 2�NO

A questa sezione (pagine 8 e 9) devono rispondere solo le aziende con allevamenti o quelle con terreni che applicano effluentidi 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 completamentela 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

31 OVINI

32 CAPRINI

31.1 Pecore

a. Da latte 18

b. Altre 19

31.2 Altri ovini 20

31.3 TOTALE OVINI 21

32.1 Capre 22

32.2 Altri caprini 23

32.3 TOTALE CAPRINI 24

SE L’AZIENDA POSSIEDE ALLEVAMENTI DIVERSI DA BOVINI, BUFALINI O EQUINI INDICARE

CAPI

CAPI

Cod.

33 SUINI

34 AVICOLI

35 CONIGLI

CAPI

33.1 Di peso inferiore a 20 kg 25

33.2 Da 20 kg a meno di 50 kg 26

33.3 Da ingrasso di 50 kg e più

a. Da 50 kg a meno di 80 kg 27

b. Da 80 kg a meno di 110 kg 28

c. Da 110 kg e più 29

33.4 Da riproduzione di 50 kg e più

a. Verri 30

b. Scrofe montate 31

c. Altre scrofe 32

33.5 TOTALE SUINI 33

34.1 Polli da carne 34

34.2 Galline da uova 35

34.3 Tacchini 36

34.4 Faraone 37

34.5 Oche 38

34.6 Altri allevamenti avicoli 39

34.7 TOTALE AVICOLI 40

35.1 Fattrici 41

35.2 Altri conigli 42

35.3 TOTALE CONIGLI 43

36.1 TOTALE STRUZZI 44

37.1 Api 45 �37.2 Altri allevamenti 46�

Cod.

CAPICod.

CAPICod.

36 STRUZZI CAPI

NUMERO ALVEARI

Cod.

37 ALTRI ALLEVAMENTI Cod.

XXX

Cod.

Cod.

Cod.

CAPI

CAPI

38

26.1 Di età inferiore a 1 anno

a. Maschi 01

b. Femmine 02

26.2 Da 1 anno a meno di 2 anni

a. Maschi 03

b. Femmine 04

26.3 Di 2 anni e più

a. Maschi 05

b. Femmine

- Giovenche (manze) da allevamento 06

- Giovenche (manze) da macello 07

- Vacche da latte 08

- Altre vacche (da carne o da lavoro) 09

26.4 TOTALE BOVINI 10

27.1 Annutoli (vitelli bufalini) 11

27.2 Bufale 12

27.3 Altri bufalini 13

27.4 TOTALE BUFALINI 14

28.1 Cavalli 15

28.2 Altri equini (asini, muli, bardotti, ecc.) 16

28.3 TOTALE EQUINI 17

ALLEVAMENTIBIOLOGICI: Capi di bestiameallevati con metodi diproduzione biologica ecertificati secondo lenorme comunitarieo nazionali esclusi quelli in fase di conversione al biologico

ALLEVAMENTIDOP E IGP: Capi per i qualil’azienda è controllatae certificatadal competenteorganismo di controllo

AGRICOLTURA BIOLOGICA E PRODUZIONIDI QUALITÀ DOP E IGP - ALLEVAMENTI

38.1 Allevamenti

a. Bovini 01

b. Bufalini 02

c. Equini 03 XXX

d. Ovini 04

e. Caprini 05

f. Suini 06

g. Avicoli 07

h. Conigli 08 XXX

i. Api 09 � 10�l. Altri allevamenti

(incl. Struzzi) 11 �


135

PASCOLO39

TIPOLOGIA DEI TERRENI A PASCOLO Cod.NUMERO TOTALE

DI ANIMALI AL PASCOLO

SUPERFICIE UTILIZZATA(prati permanenti, pascolie foraggere avvicendate) NUMERO MESI

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……………………………………………………………………………………………………………………………………………………………………………………………………………………………

39.1 L’azienda ha avuto animali al pascolo? 1�SI 2�NO In caso di risposta negativa passare al punto 40

TIPOLOGIA DI STABULAZIONE DEL BESTIAME40

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

41

40.1 Vacche da latte e Bufale Cod. Numero mediodi animali (1)

a. In stabulazione fissa con uso dilettiera (produzione di letame)

01

b. In stabulazione fissa senza usodi lettiera (produzione di liquame)

02

c. In stabulazione libera con uso dilettiera (produzione di letame)

03

d. In stabulazione libera senza usodi lettiera (produzione di liquame)

04

40.2 Altri Bovini e Bufalini

a. In stabulazione con uso dilettiera (produzione di letame)

05

b. In stabulazione senza uso di lettiera (produzione di liquame)

06

40.3 Suini

a. Su fessurato (o grigliato) parziale 07

b. Su fessurato (o grigliato) totale 08

c. Su lettiera permanente 09

Cod. Numero mediodi animali (1)

d. Su pavimento pieno 10

e. All’aperto 11

40.4 Galline ovaiole

a. A terra con accesso all’esterno 12

b. A terra al chiuso 13

c. In gabbia (tutti i tipi) 14

c1. In gabbia con nastro di asportazione delle deiezioni

15

c2. In gabbia con fossa di stoccaggiodi deiezioni liquide

16

c3. In gabbia con fossa di stoccaggiodi deiezioni solide

17

40.5 Polli da carne

a. A terra con accesso all’esterno 18

b. A terra al chiuso 19

9

EFFLUENTI ZOOTECNICI Cod. ACCUMULOIN CAMPO

PLATEA VASCA LAGUNA

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 �

APPLICAZIONE DEGLI EFFLUENTI ZOOTECNICI DI ORIGINE ANIMALE42

EFFLUENTI ZOOTECNICI (Indicare la superficie trattata secondo le seguenti applicazioni): Cod.SAU TRATTATA CON

EFFLUENTI ZOOTECNICIEttari 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

Metodi di gestione degli allevamenti (nell’annata agraria 2009 - 2010)sezione III

(1) Il numero medio di animali può non coincidere con il numero di capidichiarati a pagina 8.


136

10

Ubicazione dei terreni e degli allevamenti aziendalisezione IVTutti i terreni aziendali e/o gli allevamenti sono localizzati nel Comune del centro aziendale? 1�SI 2�NOSe SI passare alla sezione successiva, se NO compilare ciascun riquadro sottostante per ogni Comune in cui sono localizzatele coltivazioni e/o gli allevamenti (se i Comuni sono più di 8 utilizzare fogli aggiuntivi)

Riquadro N° (Riferito al comune del centro aziendale)

PROVINCIA

COMUNE

Cod.SUPERFICIEEttari Are

a. Seminativi (punto 8.13) 01

b. Vite (punto 9.1) 02

c. Coltivazioni legnose agrarie, escluso vite(punto 9.8 meno punto 9.1)

03

d. Orti familiari (punto 10) 04

e. Prati permanenti e pascoli (punto 11.3 + 11.4) 05

1.1 SAU (punto 12) 06

f. Arboricoltura da legno (punto 13.3) 07

g. Totale boschi (punto 14.4) 08

h. Super. non utiliz. e altra super. (punto 15 + 16) 09

1.2 SUPERFICIE TOTALE (punto 17) 10

Codice ISTAT Denominazione


COLTIVAZIONI (SEZ. II)1

Cod. CAPI

a. Bovini e Bufalini (punto 26.4 + 27.4) 01

b. Suini (punto 33.5) 02

c. Ovi-caprini (punto 31.3 + 32.3) 03

d. Avicoli (punto 34.7) 04

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

05 �

ALLEVAMENTI (SEZ. III)2

Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �


Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �


Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �



137

Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �


Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �


Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �


Riquadro N°

PROVINCIA

COMUNE





03



1.1 SAU (punto 12) 06








Cod. CAPI






05 �


11

Ubicazione dei terreni e degli allevamenti aziendalisezione IV

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


138

ALTRA MANODOPERA AZIENDALE IN FORMA CONTINUATIVAIn forma continuativa: persone che nell’annata agraria di riferimento hanno lavorato continuativamente nell’azienda, indipendentemente dalla durata settimanale dellavoro. 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.

12

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

Cod. SESSOANNO DINASCITA

CITT

ADIN

ANZA

(1)

COND

IZIO

NEPR

OFES

IONA

LE (2

) LAVORO SVOLTO IN AZIENDA(attività agricole e connesse)

ALTRE ATTIVITÀREMUNERATIVE

EXTRA-AZIENDALI

Numerogiorni

Media oregiornaliera

% del tempodedicato ad attivitàconnesse elencate

al quesito 48di pagina 13

Tem

pode

dica

to(3

)

Setto

re d

iat

tività

prev

alen

te(4

)

Posi

zion

e(5

)

43.1 Conduttore (16 anni e più - responsabilegiuridico ed economico dell’azienda)

101 1 2 M F 19 b a a c b c 321 a a

43.2 Coniuge 201 1 2 FM 19 b a a c b c 321 a a43.3 Altri componenti della famiglia

(16 anni e più) che lavorano in aziendaxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 301 1 2 FM 19 b a a c b c 321 a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 302 1 2 FM 19 b a a c b c 321 a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 303 1 2 FM 19 b a a c b c 321 a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 304 1 2 FM 19 b a a c b c 321 a a43.4 Altri componenti della famiglia che

non lavorano in azienda(compresi i minori di 16 anni)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 401 1 2 FM a a a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 402 1 2 FM a a a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 403 1 2 FM a a a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 404 1 2 FM a a a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 405 1 2 FM a a a a43.5 Parenti del conduttore che lavorano

in azienda (16 anni e più)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 501 1 2 FM 19 b a a c b c 321 a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 502 1 2 FM 19 b a a c b c 321 a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 503 1 2 FM 19 b a a c b c 321 a axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 504 1 2 FM 19 b a a c b c 321 a a43.6 TOTALE GIORNATE DI LAVORO DELLA MANODOPERA FAMILIARE 601 g

44.1 TOTALE GIORNATE DI LAVORO IN FORMA CONTINUATIVA .................................................cod. 602 g

Cod.

CONT

RATT

O(1

)

SESSO

ANNO

DI N

ASCI

TA

CITT

ADIN

ANZA

(2)

LAVORO SVOLTO IN AZIENDA(attività agricole e connesse)

Numerogiorni

Mediaore

giornaliera

% del tempodedicato ad

attività connesseelencate


701 a 1 2 FM 19 b a c b c702 a 1 2 FM 19 b a c b c703 a 1 2 FM 19 b a c b c704 a 1 2 FM 19 b a c b c705 a 1 2 FM 19 b a c b c706 a 1 2 FM 19 b a c b c707 a 1 2 FM 19 b a c b c708 a 1 2 FM 19 b a c b c709 a 1 2 FM 19 b a c b c710 a 1 2 M F 19 b a c b c

Cod.

CONT

RATT

O(1

)

SESSO

ANNO

DI N

ASCI

TA

CITT

ADIN

ANZA

(2)

LAVORO SVOLTO IN AZIENDA(attività agricole e connesse)

Numerogiorni

Mediaore

giornaliera


attività connesseelencate


711 a 1 2 FM 19 b a c b c712 a 1 2 FM 19 b a c b c713 a 1 2 FM 19 b a c b c714 a 1 2 FM 19 b a c b c715 a 1 2 FM 19 b a c b c716 a 1 2 FM 19 b a c b c717 a 1 2 FM 19 b a c b c718 a 1 2 FM 19 b a c b c719 a 1 2 FM 19 b a c b c720 a 1 2 M F 19 b a c b c

43FAMIGLIA DELCONDUTTORE E PARENTI

Compilare sempre se è stata data risposta a pagina 3 -Forma giuridica, al punto 1.1 od al punto 1.2 (solo incaso di società semplice costituita esclusivamente o inparte da familiari o parenti che svolgono lavoro inazienda) o per altre forme giuridiche comprendenti per-sone legate da vincoli di parentela.

ddddd

(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

(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


139

13

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

ALTRA MANODOPERA AZIENDALEIN FORMA SALTUARIA

Persone che non hanno lavorato continuativamente nell’annata agraria 2009-2010,es: assunte per singole fasi lavorative, per lavori di breve durata, stagionali o saltuari

Cod.

NUMERO PERSONE Lavoro svolto in azienda(attività agricole

e connesse)CITTADINANZA

TOTALEitaliana

AltroPaeseU.E.

PaeseextraU. E.

N. giornateconvertite ingg. di 8 ore


attivitàconnesse

a. Maschi 11

b. Femmine 21

TOTALE 31

45 LAVORATORI NON ASSUNTIDIRETTAMENTE DALL’AZIENDA46

NOTIZIE SUL CAPO AZIENDA(da compilare sempre)

47.1 Quale dei soggetti già dichiarati ai punti 43 o 44 dipagina 12 svolge anche la funzione di capo azienda(indicare il codice)? c

47.2 Titolo di studio (il più elevato)

a. Nessuno 01 �b. Licenza di scuola elementare 02 �c. Licenza di scuola media inferiore 03 �

Indirizzo agrario Altro tipo

d. Diploma di qualifica che non 04 � 05 �permette accesso universitario(2-3 anni)

e. Diploma di scuola media superiore 06 � 07 �f. Laurea o diploma universitario 08 � 09 �

47.3 Il capo azienda ha frequentato negliultimi 12 mesi corsi di formazioneprofessionale? 1�SI 2�NO

47

48 ATTIVITÀ REMUNERATIVE CONNESSEALL’AZIENDA

48.1 Se nell’azienda sono state svolte attività remunerativediverse da quelle agricole, ma ad essa connesse, pre-cisare se trattasi di: Cod.

a. Agriturismo 01�b. Attività ricreative e sociali 02�c. Fattorie didattiche 03�d. Artigianato 04�e. Prima lavorazione dei prodotti agricoli 05�f. Trasformazione di prodotti vegetali 06�g. Trasformazione di prodotti animali 07�h. Produzione di energia rinnovabile 08�i. Lavorazione del legno (taglio, ecc.) 09�l. Acquacoltura 10�m. Lavoro per conto terzi utilizzando mezzi di

produzione dell’azienda- attività agricole 11�- attività non agricole 12�

n. Servizi per l’allevamento 13�o. Sistemazione di parchi e giardini 14�p. Silvicoltura 15�q. Produzione di mangimi completi e complementari 16�r. Altre attività (specificare……………………………) 17�

48.2 Indicare quale delle attività sopra elencate è la più remunerativa in termini economici (indicare il codice) b

48.3 Indicare il peso percentuale dell’attività sopraindicata (punto 48.2) rispetto al totale delle attività %elencate al punto 48.1 (indicare un valore percentuale) c

49 CONTOTERZISMO(giornate di lavoro convertite in giornate di 8 ore)

CONTOTERZISMO ATTIVO49.1 Indicare le giornate di lavoro svolte con mezzi meccanici

propri presso altre aziende agricole eCONTOTERZISMO PASSIVO

49.2 Indicare se l’azienda ha usufruito di lavoro effettuatocon persone e mezzi extra-aziendali 1�SI 2�NO

Se SI indicare:49.2.1 Giornate di lavoro effettuate in azienda e49.2.2 - di cui da altre aziende agricole e

50 PRODUZIONE DI MANGIMIPER IL REIMPIEGO IN AZIENDA

50.1 Nell’azienda sono stati prodottimangimi completi e complementariper il reimpiego in azienda? 1�SI 2�NO

49.3 Tipo di operazioni effettuatein azienda


AFFIDAMENTO COMPLETO(di una o più coltivazioni)

01

AFFIDAMENTO PARZIALEa. Aratura 02

b. Fertilizzazione 03

c. Semina 04

d. Raccolta meccanica e primalavorazione di vegetali

05

e. Altre operazioni per le coltivazioni 06

f. Altre operazioni non sulle superfici(specificare………………………………) 07 �

Cod.

NUMERO PERSONE Lavoro svolto in azienda(attività agricole

e connesse)CITTADINANZA

TOTALEItaliana

AltroPaeseU.E.

PaeseextraU. E.

N. giornateconvertite ingg. di 8 ore


attivitàconnesse

TOTALE 41

51 IMPIANTI PER LA PRODUZIONE DI ENERGIA RINNOVABILE(sia per la vendita che per il reimpiego in azienda)

51.1 L’azienda possiede impianti per la produzione di energia rinnovabile? 1�SI 2�NO In caso di risposta NO passare al punto 52

51.2 In caso di risposta SI indicare la tipologia di impianto per tipo di fonte energetica

a. Eolica 01�b. Biomassa 02�

- tra cui biogas 03�

c. Solare 04�d. Idroenergia 05�e. Altre fonti di energia rinnovabile

(specificare…………………………………………) 06�

sezione V


140

14

52 CONTABILITÀ

Indicare se l’azienda ha:a. Contabilità forfettaria 01�b. Contabilità ordinaria 02�c. Nessuna contabilità 03�

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 �

54 AUTOCONSUMO

54.1 La famiglia del conduttore consuma i prodotti aziendali?

1�SI 2�NO

Se SI

54.1.1 Indicare se l’azienda autoconsuma

a. Tutto il valore della produzione finale 01 �b. Oltre il 50% del valore della produzione

finale 02 �c. Il 50% o meno del valore della produzione

finale 03 �

53

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

RICAVI

Indicare la percentuale di ricavi lordi provenienti da %

a. Vendita di prodotti aziendali 01 cb. Altre attività remunerative connesse all’azienda 02 cc. Pagamenti diretti 03 cTOTALE PERCENTUALE 1 0 0

55.1 Prodotti vegetaliCod.

VENDITA DIRETTAAL CONSUMATORE

In azienda Fuori azienda

VENDITA ADALTRE AZIENDE

AGRICOLE

VENDITA ADIMPRESE

INDUSTRIALI

VENDITE ADIMPRESE

COMMERCIALI

VENDITA OCONFERIMENTO AD

ORGANISMIASSOCIATIVI

TOTALE %

% % % % % %

a. Cereali 01 100b. Piante industriali e proteiche 02 100c. Ortive e patate 03 100d. Frutta compresi agrumi 04 100e. Uva da vino 05 100f. Uva da tavola 06 100g. Olive 07 100h. Florovivaismo 08 100i. Foraggi 09 100

55.2 Prodotti animalil. Animali vivi 10 100m. Latte 11 100n. Altri 12 100

55.3 Prodotti trasformatio. Vino e mosto 13 100p. Olio 14 100q. Formaggi e altri prodotti lattierocaseari 15 100r. Altri prodotti di origine animale 16 100s. Altri prodotti di origine vegetale 17 100

55.4 Prodotti forestali 18 100

Dichiaro di essere stato intervistatodal rilevatore:

L’INTERVISTATO

……………………………………………………………………

(Firma)

Dichiaro che i dati sono stati rilasciati inconformità alle istruzioni ricevute

IL RILEVATORE

………………………………………………………………………

(Firma)

h Data ……………………

Dichiaro di aver revisionato il questionario

IL REVISORE

……………………………………………………………………

(Firma)

Data …………………..Codice rilevatore

Altre informazioni (annata agraria 2009 - 2010)sezione VI


141

15

PROMEMORIA PER IL REVISOREPrincipali controlli di compatibilità del questionarioSegnare i riquadri per ogni regola di revisione verificata in caso contrario indicare nelle annotazioni i problemiriscontrati

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 quesiti1 (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 attivitàconnesse” 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 LIBRETTOD’ISTRUZIONE PER LA RILEVAZIONE

ANNOTAZIONI


142

Principiali riferimenti normativi

- Decreto legislativo 6 settembre 1989, e successive modificazioni e integrazioni - ”Norme sul Sistemastatistico nazionale e sulla riorganizzazione dell’Istituto nazionale di statistica”;

- Decreto legislativo 30 giugno 2003, n. 196, e successive modificazioni e integrazioni - “Codice in ma-teria di protezione dei dati personali”;

- Codice di deontologia e di buona condotta per i trattamenti di dati personali a scopi statistici e di ri-cerca scientifica effettuati nell’ambito del Sistema statistico nazionale (allegato A.3 del d.lgs. 30 giugno2003, n. 196);

- Decreto del Presidente del Consiglio dei Ministri 3 agosto 2009 - “Approvazione del Programma stati-stico 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 comportanol’obbligo di risposta da parte dei soggetti privati, a norma dell’art. 7 del decreto legislativo 6 settembre1989 n. 322 (G.U. 14 dicembre 2009 - serie gen.- n. 290);

- Decreto del Presidente della Repubblica 31 dicembre 2009 - Elenco delle rilevazioni statistiche, com-prese nel Programma statistico nazionale per il triennio 2008-2010, aggiornamento 2009-2010, per lequali per l’anno 2010 la mancata fornitura dei dati configura violazione dell’obbligo di risposta, ai sensidell’art. 7 del decreto legislativo 6 settembre 1989, n. 322 (G.U. 17 marzo 2010 - serie gen. - n. 63).

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 rilevazionestabiliti dal Regolamento (CE) n. 1166/2008 del Consiglio e del Parlamento europeo, del 19 novembre2008, 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 rilevazionedi 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 obbligodi risposta per i soggetti privati, a norma dell’art. 7 del d.lgs. 6 settembre 1989, n. 322, approvato con DPR15 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 deld.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 materiadi protezione dei dati personali (d.lgs. 30 giugno 2003, n. 196 e Codice di deontologia e di buona condottaper i trattamenti di dati personali a scopi statistici e di ricerca scientifica effettuati nell’ambito del Sistemastatistico 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 sta-tistici 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 lemodalità previste dall’art. 7 del Codice di deontologia per i trattamenti di dati personali effettuati nell’am-bito del Sistema statistico nazionale. La diffusione dei dati potrà avvenire anche in forma disaggregata inconformità 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 dellaDirezione dei censimenti generali (DCCG) dell’Istat e i responsabili degli Uffici di censimento, ai quali è pos-sibile rivolgersi anche per quanto riguarda l’esercizio dei diritti dell’interessato.

SEGRETO STATISTICO, OBBLIGO DI RISPOSTA,TUTELA DELLA RISERVATEZZA E DIRITTI DEGLI INTERESSATI

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pilot questionnAire And compilAtion guidelines (in italian language)

145

speciFiche tecniche

questionArio Aziende Agricole

1. descriZione QuestionArio

1.1 frontespizio

Riportarenegliappositispazi:

- 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 automaticamen-te dal database.

Selezionarelatipologiadiaziendaintervistatasecondolecaratteristichedi:

- 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.

Riportareinoltre:

- 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

Perconduttoresiintendelapersonachedifattogestiscel’aziendainlocoecioèlapersona fisica che assicura la gestione corrente e quotidiana.

Ilrilevatoredovràindicareperilconduttoreleseguentiinformazioni:

- sesso;

- anno di nascita;

- titolo di studio ultimato. Il rilevatore dovrà indicare al punto 1.3 il più elevato tito-lo di studio conseguito dal conduttore distinguendo, per la laurea ed il diploma di scuola media superiore, tra indirizzo agrario e indirizzo di altro tipo.

146

- 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.

Inparticolareverificareche:

- 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’annataagrariadiriferimentoèl’annataagraria2007-2008.

1.2.4 Fonte di approvvigionamento

Specificarequaleèlafonteolefontidiapprovvigionamentodell’acquairriguaeperciascuna di esse indicare la percentuale di utilizzo durante la stagione.

è distinto l’autoapprovviggionamento per derivazione diretta da corpi d’acqua super-ficiali o sotterranei, senza vincoli per quanto riguarda le modalità di presa e di utilizzazio-ne 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 solleva-mentodell’acquadallafonte.Inparticolare:

147

- 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 (approvvi-gionamento, 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 me-dia, 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 regi-me di agricoltura biologica o di produzione integrata.

2.11)Indicaresel’olivetoèsottopostoastressidricocontrollato;nelcasopositivoin-dicare 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.

148

1.4 sezione 3 Uso del suolo (annata agraria 2007-2008)

Lasezioneèdivisain3sottosezioniperseminativi,coltivazionilegnoseedaltrecolture.

Riportare esclusivamente sole le colture irrigate.

In generale per le coltivazioni riportare le caratteristiche prevalenti. Ad esempio, se unacolturaèirrigataconduesistemidiirrigazione,riportareilsistemadiirrigazioneap-plicato sulla maggioranza della superficie.

1.4.1 Seminativi

Perognicolturapresenteilrilevatoredovràinserireleseguentiinformazioni:

- il comune in cui si trova la coltura;

-ilnomedellacoltura.L’elencodellecolturedefiniteperquestasottosezioneèripor-tato 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

corsodell’annataagrariadiriferimento.Questadomandaèpresentesolonelcasodei 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 agricol-tura biologica o di produzione integrata;

- Nella casella dei dettagli,riportarealcuneinformazionispecificheriguardo:

~perlespecieortive:indicareilnumerodiciclicolturalichevengonoeffettuati;

~perilmaisdagranellaodainsilato:indicarelaclasseFAOutilizzata;

~perl’erbamedica:indicareilnumeroditagli.

- 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 sistemadiirrigazioneècodificatonelboxallafinedellasezione.

- 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.

149

1.4.2 Coltivazioni legnose agrarie




- Indicare la superficie in produzione e la superficie in produzione irrigata.

- Indicare la superficie di nuovo impianto e la superficie di nuovo impianto irrigata.

- barrare la casella nel caso la coltura sia destinata a produzioni di qualità (DOC, DOCG, DOP, IGP).

- barrare la casella nel caso la coltivazione della coltura avvenga in regime di agricol-tura biologica o di produzione integrata.

- Inserire la data di inizio e fine irrigazione. Verificare la congruenza delle date inserite.

- Indicare il sistema di irrigazione unico o prevalente per quella specifica coltura.

-Ilsistemadiirrigazioneècodificatonelboxallafinedellasezione.





1.4.3 Altre coltivazioni




- Indicare la superficie totale e la superficie irrigata.

- barrare la casella nel caso la coltivazione della coltura avvenga in regime di agricol-tura biologica o di produzione integrata;

- Inserire la data di inizio e fine irrigazione. Verificare la congruenza delle date inserite.

- Indicare il sistema di irrigazione unico o prevalente per quella specifica coltura.

Ilsistemadiirrigazioneècodificatonelboxallafinedellasezione.





150

2. inseriMento dAti

Perunacorrettavisualizzazionedeldatabaseènecessarial’installazionediAccess2007 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’inse-rimento 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).

151

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 percentualidell’acqua in modo da raggiungere obbligatoriamente il 100% distribuendo i valori nei vari campi.

152

5. “Gestionedell’acqua”ècostituitadavaricampiedaduecampichesiattivanosolo se viene spuntata la casella “L’azienda utilizza servizi di consulenza irrigua?” e “In caso di mancanza di acqua, irriga soltanto certe colture?”.

6. UsodelSuolo:questaèlamascherageneraleperl’inserimentodellecoltivazioni.Perogni tipodicolturac’èunbottonechepermette l’inserimentodiunnuovorecord (nuova coltura) e un bottone per visualizzare ed eventualmente modificare le coltivazioni inserite.

153

7. UsodelSuolo:Seminativi.Premendoiltastoinseriscisiapriràunanuovamasche-ra 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.

Bastacompilareseguendoquestepriorità:1)comune,2)coltura,3)superficieto-tale, 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. UsodelSuolo:ColtivazioniLegnose.Laproceduraèsimileaquellaperiseminati-vi,èimportantecompilaretuttiicampidellatabellainbassoetuttiicampinellaparte alta.

9. UsodelSuolo:AltreColture.Laproceduraèsimileaquellaper iseminativi,èimportante compilare tutti i campi della tabella in basso e tutti i campi nella parte alta.

154

10.RiepilogoAzienda.Daquestamascheraèpossibilemodificare lamaggiorpartedei dati inseriti durante la procedura guidata. Per attivare le modifiche bisogna premere il tasto “modifica” altrimenti non sarà possibile editare i campi.

155

3. docuMentAZione fotogrAficA

Sarebbe utile scattare delle fotografie degli elementi più significativi sulla pratica irriguaaziendalecome:

• contatori/misuratoridivolumidiacquadell’azienda(qualorapresenti)

• sorgenteirrigua(pozzo/canale/presadirete)

• impiantidisollevamento(pompe/...)

• associazionecoltura-sistemadiirrigazione(seattualmenteincoltivazionenell’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.

156

QuestionArio AZiende Agricole

rilevatore

Data rilevazione

Codice rICa

Codice Progressivo regionale

tipologiA di AZiendA

Ordinamento prevalente irriguo

barbabietola, mais, coltivazioni foraggere

patata, girasole, soia

agrumi, frutteti, ortive

frumento, vite, olivo

Fonte di approvvigionamento prevalenteacqua pubblica

autoapprovvigionamento

superficie (saU)Grande (> 20 ha)

Piccola (< 20 ha)

sistema di irrigazione prevalente

microirrigazione

Pioggia

Infiltrazione - scorrimento

notiZie del conduttore

______________________________________________________________________________________________________________________________________________

Cognome e nome della persona fisica o denominazione della società o ente che gestisce l’azienda

ubicAZione del centro AZiendAle

Luogo dove viene svolta la maggior parte o l’intera attività agricola (località dove sono pre-senti fabbricati rurali o la maggior parte delle particelle aziendali)

regione

Provincia

Comune

Indirizzo

157

seZione 1 Notizie generali sull’azienda

1. notiZie sul conduttore

1.1 sesso m F

1.2 anno di nascita

1.3 titolo di studio (il più elevato) Indirizzo agrario altro tipo

a) laurea o diploma universitario

b) Diploma di scuola media superiore

c) Diploma di qualifica che non permette l’accesso universitario (2-3 anni)

d) licenza di scuola media inferiore

e) licenza di scuola elementare

f) Nessuno

1.4 Il conduttore ha frequentato negli ultimi 12 mesi corsi di formazione professionale sì NO

2. inforMAtiZZAZione dell’AZiendA

2.1 l’azienda dispone di personal computer e/o altre attrezzature informatiche per fini aziendali? sì NO

2.2 se sI Gestione informatizzata di coltivazioni sì NO

3. superfici

ha

3.1 superficie totale dell’azienda

3.2 superficie agricola utilizzata (saU)

3.3 superficie irrigabile

3.4 superficie effettivamente irrigata nell’annata

3.5 superficie media irrigata negli ultimi 3 anni

3.6 Corpi che costituiscono l’azienda

4. fonte di ApprovvigionAMento dell’AcQuA irriguA

(sono ammesse risposte multiple)

(sì/no) % utilizzo

- acque sotterranee all’interno o nelle vicinanze dell’azienda

- acque superficiali all’interno dell’azienda (bacini naturali e artificiali)

- acque superficiali al di fuori dell’azienda (laghi, fiumi o corsi d’acqua)

- acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo con consegna a turno

- acquedotto, consorzio di irrigazione e bonifica o altro ente irriguo con consegna a domanda

- altra fonte (specificare) ________________________________________________________

158

Se l’azienda si approvvigiona da un Consorzio di Bonifica indicare il nome del Consorzio.

_______________________________________________________________________

_______________________________________________________________________

5. iMpiAnti di sollevAMento utiliZZAti per l’ApprovvigionAMento

Potenza totale (kW) Consumo elettrico medio annuo totale (kWh)

Ore di funzionamento totali

seZione 2 Notizie generali sull’azienda

2.1 L’azienda utilizza servizi di consulenza irrigua? Sì No

Se Sì, quali __________________________________________________________

_______________________________________________________________________

2.2 Su cosa basa il momento di intervento irriguo? (ammessa una sola risposta)

Disponibilità idrica sì NO

andamento climatico sì NO

esperienza, metodi empirici sì NO

modelli telematici sì NO

2.3 Ci sono stati ammodernamenti della rete idrica aziendale negli ultimi 10 anni?

Sì No

2.4 L’azienda aderisce alle indennità connesse alla Direttiva Quadro 2000/60/CE sulle ac-que (Misura 213 del PSR)

Sì No

2.5 L’azienda dispone di tutta l’acqua necessaria per soddisfare il fabbisogno idrico coltu-rale?

Sì No

2.6 Se piove ed ha il turno di irrigazione, irriga ugualmente?

Sì No

159

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 pro-duzione 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.

_______________________________________________________________________

_______________________________________________________________________

160

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sistema di irrigazione (vedi codici)

n. adacquate

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163

AllegAti

tipologiA di AZiende

regione emilia – romagna | Ordinamento prevalente irriguo: barbabietola, mais, coltivazioni foraggere

fonte prevalente dimensione sistema irrigazione prevalente n° aziende


Grande

(> 20 ha)

microirrigazione 3Infiltrazione - scorrimento 2aspersione 11

Piccole

(<20 ha)

microirrigazione 1Infiltrazione – scorrimento 1aspersione 7

Pubblica

Grande

(> 20 ha)


Piccole

(<20 ha)


tOtale 66

regione campania | Ordinamento prevalente irriguo: patata, girasole, soia



Grande

(> 20 ha)

microirrigazione 1Infiltrazione - s corrimento 1aspersione 1

Piccole

(<20 ha)


Pubblica

Grande

(> 20 ha)


Piccole

(<20 ha)


tOtale 12

regione campania | Ordinamento prevalente irriguo: agrumi, frutteti, ortive



Grande

(> 20 ha)


Piccole

(<20 ha)


Pubblica

Grande

(> 20 ha)


Piccole

(<20 ha)


tOtale 50

164

regione puglia (classe A>50%) | Ordinamento prevalente irriguo: vite, olivo



Grande

(> 20 ha)


Piccole

(<20 ha)


Pubblica

Grande

(> 20 ha)


Piccole

(<20 ha)


tOtale 97

regione sardegna | Ordinamento prevalente irriguo: barbabietola, mais, coltivazioni foraggere



Grande

(> 20 ha)

microirrigazione 1

Infiltrazione - scorrimento 1

aspersione 8

Piccole

(<20 ha)

microirrigazione 1


aspersione 1

Pubblica

Grande

(> 20 ha)

microirrigazione 2


aspersione 30

Piccole

(<20 ha)

microirrigazione 1


aspersione 6

tOtale 54

elenco delle colture – seMinAtivi

id descrizione1 Frumento tenero2 Frumento duro3 segale4 Orzo5 avena6 mais7 riso8 sorgo9 Cereali minori10 Pisello (proteico e secco) o fresco11 Fagiolo fresco o secco12 Fava fresca o secca13 lupini14 Ceci15 lenticchie

165

16 Patata17 Barbabietola da zucchero18 Piante sarchiate da foraggio19 tabacco20 luppolo21 Cotone22 lino23 Canapa24 altre piante tessili25 Colza e ravizzone26 Girasole27 soia28 semi di lino29 altre piante di semi oleosi30 erbe Officinali31 altre piante industriali 32 Pomodoro da mensa33 Pomodoro da industria34 melanzana35 Peperone36 Insalate (indivia riccia e scarola, lattuga)37 Insalate (indivia riccia e scarola, lattuga)38 radicchio o Cicoria39 melone40 Cocomero41 Cetriolo da mensa42 Cetriolo da sottaceti43 Zucchina44 Finocchio45 Carota46 Broccoletto di rapa47 Cavolo cappuccio48 Cavolo verza49 Cavolo di Bruxelles50 altri cavoli51 Cavolfiore e cavolo broccolo52 Fava fresca o secca53 Fagiolino54 Fagiolo fresco o secco55 Pisello (proteico e secco) o fresco56 asparago57 aglio e scalogno58 Cipolla59 Carciofo60 Fragola61 Prezzemolo62 Basilico63 sedano64 spinacio65 Zucca66 Barbabietola da orto67 altre ortive da pieno campo68 serre colture ortive69 Fiori e piante ornamentali in pieno campo70 serre per fiori e piante ornamentali71 Fiori e piante ornamentali in tunnel o campane72 Piantine orticole73 Floricole ed ornamentali74 altre piantine

166

75 Prati di leguminose76 altri prati avvicendati77 mais in erba78 mais a maturazione cerosa79 altri erbai monifiti di cereali80 altri erbai81 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 descrizione84 Vite da tavola irrigua85 Vite da vino da tavola86 Vite da vino DOC87 Olive da tavola88 Olive da olio89 arancio90 mandarino91 Clementina92 limone93 Bergamotto94 melo95 Pero96 Pesco97 Nettarina98 albicocca99 Ciliegio100 susino101 Fico102 Diospiro103 Frutteto misto104 altra frutta temperata105 actinidia106 altra frutta di origine sub tropicale107 mandorlo108 Nocciolo109 Castagno110 Noce111 altra frutta a guscio112 Vivai fruttiferi113 Vivai piante ornamentali114 altri vivai115 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

167

aNNex 4

dAtAbAse oF meAn irrigAtion wAter consumption used For rice cultivAtion

region province Municipality irrigation water volume (m3/ha)

source

Piemonte Biella Brusnengo 13000 6Piemonte Biella Castelletto Cervo 13000 6Piemonte Biella Cavaglia’ 13000 6Piemonte Biella Dorzano 13000 6Piemonte Biella Gifflenga 13000 6Piemonte Biella massazza 13000 6Piemonte Biella masserano 13000 6Piemonte Biella mottalciata 13000 6Piemonte Biella salussola 13000 6Piemonte Biella Villanova Biellese 13000 6Piemonte Cuneo Barge 13000 6Piemonte Cuneo Bra 13000 6Piemonte Cuneo Cherasco 13000 6Piemonte Cuneo Costigliole saluzzo 13000 6Piemonte Cuneo envie 13000 6Piemonte Cuneo moretta 13000 6Piemonte Cuneo morozzo 13000 6Piemonte Cuneo saluzzo 13000 6Piemonte Cuneo savigliano 13000 6Piemonte Novara Barengo 11000 1Piemonte Novara Bellinzago Novarese 11000 1Piemonte Novara Biandrate 11000 1Piemonte Novara Borgolavezzaro 11000 1Piemonte Novara Briona 11000 1Piemonte Novara Caltignaga 11000 1Piemonte Novara Cameri 11000 1Piemonte Novara Casalbeltrame 11000 1Piemonte Novara Casaleggio Novara 11000 1Piemonte Novara Casalino 11000 1Piemonte Novara Casalvolone 11000 1Piemonte Novara Castellazzo Novarese 11000 1Piemonte Novara Cerano 11000 1Piemonte Novara Galliate 11000 1Piemonte Novara Garbagna Novarese 11000 1Piemonte Novara Granozzo con monticello 11000 1Piemonte Novara landiona 11000 1Piemonte Novara mandello Vitta 11000 1Piemonte Novara momo 11000 1Piemonte Novara Nibbiola 11000 1Piemonte Novara Novara 11000 1Piemonte Novara recetto 11000 1Piemonte Novara romentino 11000 1Piemonte Novara san Nazzaro sesia 11000 1Piemonte Novara san Pietro mosezzo 11000 1Piemonte Novara sillavengo 11000 1Piemonte Novara sozzago 11000 1

168

Piemonte Novara terdobbiate 11000 1Piemonte Novara tornaco 11000 1Piemonte Novara trecate 11000 1Piemonte Novara Vespolate 11000 1Piemonte Novara Vicolungo 11000 1Piemonte Novara Vinzaglio 11000 1Piemonte torino Borgaro torinese 13000 6Piemonte torino Calluso Cavour 13000 6Piemonte torino Chivasso 13000 6Piemonte torino rivarolo Canavese 13000 6Piemonte torino san Benigno Canavese 13000 6Piemonte torino san raffaele Cimena 13000 6Piemonte torino scalenghe 13000 6Piemonte torino settimo torinese 13000 6Piemonte torino Verolengo 13000 6Piemonte Vercelli albano Vercellese 15000 1Piemonte Vercelli arborio 15000 1Piemonte Vercelli asigliano Vercellese 15000 1Piemonte Vercelli Balocco 15000 1Piemonte Vercelli Bianze’ 15000 1Piemonte Vercelli Borgovercelli 15000 1Piemonte Vercelli Buronzo 15000 1Piemonte Vercelli Caresana 15000 1Piemonte Vercelli Caresana Blot 15000 1Piemonte Vercelli Carisio 15000 1Piemonte Vercelli Casanova elvo 15000 1Piemonte Vercelli Cigliano 15000 1Piemonte Vercelli Collobiano 15000 1Piemonte Vercelli Costanzana 15000 1Piemonte Vercelli Crova 15000 1Piemonte Vercelli Desana 15000 1Piemonte Vercelli Fontanetto Po 15000 1Piemonte Vercelli Formigliana 15000 1Piemonte Vercelli Gattinara 15000 1Piemonte Vercelli Ghislarengo 15000 1Piemonte Vercelli Greggio 15000 1Piemonte Vercelli lamporo 15000 1Piemonte Vercelli lenta 15000 1Piemonte Vercelli lignana 15000 1Piemonte Vercelli livorno Ferraris 15000 1Piemonte Vercelli motta dei Conti 15000 1Piemonte Vercelli Olcenengo 15000 1Piemonte Vercelli Oldenico 15000 1Piemonte Vercelli Palazzolo Vercellese 15000 1Piemonte Vercelli Pertengo 15000 1Piemonte Vercelli Pezzana 15000 1Piemonte Vercelli Prarolo 15000 1Piemonte Vercelli Quinto Vercellese 15000 1Piemonte Vercelli rive 15000 1Piemonte Vercelli roasio 15000 1Piemonte Vercelli ronsecco 15000 1Piemonte Vercelli rovasenda 15000 1Piemonte Vercelli salasco 15000 1Piemonte Vercelli sali Vercellese 15000 1Piemonte Vercelli san Germano Vercellese 15000 1Piemonte Vercelli san Giacomo Vercellese 15000 1Piemonte Vercelli santhia’ 15000 1Piemonte Vercelli stroppiana 15000 1

169

Piemonte Vercelli tricerro 15000 1Piemonte Vercelli trino Vercellese 15000 1Piemonte Vercelli tronzano 15000 1Piemonte Vercelli Villarboit 15000 1Piemonte Vercelli Villata 15000 1Piemonte alessandria Balzona 15000 1Piemonte alessandria Borgo san martino 15000 1Piemonte alessandria Casale monferrato 15000 1Piemonte alessandria Castellazzo Bormida 15000 1Piemonte alessandria Frassineto sul Po 15000 1Piemonte alessandria Giarole 15000 1Piemonte alessandria Isola san antonio 15000 1Piemonte alessandria masio 15000 1Piemonte alessandria morano sul Po 15000 1Piemonte alessandria Occimiano 15000 1Piemonte alessandria Oviglio 15000 1Piemonte alessandria Pomano monferrato 15000 1Piemonte alessandria sezzadio 15000 1Piemonte alessandria ticinetto 15000 1Piemonte alessandria Valmaccca 15000 1Piemonte alessandria Villanova 15000 1lombardia milano abbiategrasso 40200 1lombardia milano albairate 40200 1lombardia milano assago 40200 1lombardia milano Basiglio 40200 1lombardia milano Besate 40200 1lombardia milano Binasco 40200 1lombardia milano Boffalora sopra ticino 40200 1lombardia milano Buccinasco 40200 1lombardia milano Busto Garolfo 40200 1lombardia milano Calvignasco 40200 1lombardia milano Carpiano 40200 1lombardia milano Casarile 40200 1lombardia milano Casorezzo 40200 1lombardia milano Cassinetta di lugagnano 40200 1lombardia milano Cernusco sul naviglio 40200 1lombardia milano Cisliano 40200 1lombardia milano Colturano 40200 1lombardia milano Corbetta 40200 1lombardia milano Cusago 40200 1lombardia milano Gaggiano 40200 1lombardia milano Gudo Visconti 40200 1lombardia milano lacchiarella 40200 1lombardia milano locate triulzi 40200 1lombardia milano magenta 40200 1lombardia milano mediglia 40200 1lombardia milano melegnano 40200 1lombardia milano milano 40200 1lombardia milano morimondo 40200 1lombardia milano motta Visconti 40200 1lombardia milano Noviglio 40200 1lombardia milano Opera 40200 1lombardia milano Ozzero 40200 1lombardia milano Pieve emanuele 40200 1lombardia milano robecchetto con Induno 40200 1lombardia milano robecco sul Naviglio 40200 1lombardia milano rosate 40200 1lombardia milano rozzano 40200 1

170

lombardia milano san donato milanese 40200 1lombardia milano san giuliano milanese 40200 1lombardia milano san zenone al lambro 40200 1lombardia milano sesto san Giovanni 40200 1lombardia milano settimo milanese 40200 1lombardia milano trezzano sul Naviglio 40200 1lombardia milano tribiano 40200 1lombardia milano truccazzano 40200 1lombardia milano Vermezzo 40200 1lombardia milano Vernate 40200 1lombardia milano Villa Cortese 40200 1lombardia milano Vizzolo Predabissi 40200 1lombardia milano Zelo surrigone 40200 1lombardia milano Zibido san Giacomo 40200 1lombardia lodi Casaletto lodigiano 40200 1lombardia lodi Caselle lurani 40200 1lombardia lodi Cavenago Dádda 40200 1lombardia lodi Codogno 40200 1lombardia lodi Cornegliano laudense 40200 1lombardia lodi Galgagnano 40200 1lombardia lodi Graffignana 40200 1lombardia lodi lodi 40200 1lombardia lodi lodivecchio 40200 1lombardia lodi mulazzano 40200 1lombardia lodi Orio litta 40200 1lombardia lodi Ospedaletto lodigiano 40200 1lombardia lodi Ossago 40200 1lombardia lodi Pieve Fissiraga 40200 1lombardia lodi santángelo lodigiano 40200 1lombardia lodi secugnago 40200 1lombardia lodi senna lodigiana 40200 1lombardia lodi tavazzano con Villavesco 40200 1lombardia lodi Valera Fratta 40200 1lombardia lodi Villanova sillaro 40200 1lombardia lodi Zelo Buon Persico 40200 1lombardia mantova Bigarello 30000 1lombardia mantova Castel Dário 30000 1lombardia mantova Castelbelforte 30000 1lombardia mantova Guidizzolo 30000 1lombardia mantova mantova 30000 1lombardia mantova Ostiglia 30000 1lombardia mantova Porto mantovano 30000 1lombardia mantova roncoferraro 30000 1lombardia mantova roverbella 30000 1lombardia mantova san Giorgio di mantova 30000 1lombardia mantova sustinente 30000 1lombardia mantova Villimpenta 30000 1lombardia Pavia alagna 40200 2lombardia Pavia albonese 40200 2lombardia Pavia albuzzano 40200 2lombardia Pavia Badia Pavese 40200 2lombardia Pavia Bascape´ 40200 2lombardia Pavia Bastida Pancarana 40200 2lombardia Pavia Battuda 40200 2lombardia Pavia Belgioioso 40200 2lombardia Pavia Bereguardo 40200 2lombardia Pavia Borgarello 40200 2lombardia Pavia Borgo san siro 40200 2

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lombardia Pavia Bornasco 40200 2lombardia Pavia Breme 40200 2lombardia Pavia Bressana Bottarone 40200 2lombardia Pavia Candia lomellina 40200 2lombardia Pavia Carbonara al ticino 40200 2lombardia Pavia Casorate Primo 40200 2lombardia Pavia Cassolnovo 40200 2lombardia Pavia Castello Dágogna 40200 2lombardia Pavia Castelnovetto 40200 2lombardia Pavia Cava manara 40200 2lombardia Pavia Ceranova 40200 2lombardia Pavia Ceretto lomellina 40200 2lombardia Pavia Cergnago 40200 2lombardia Pavia Certosa di Pavia 40200 2lombardia Pavia Chignolo Po 40200 2lombardia Pavia Cilavegna 40200 2lombardia Pavia Confienza 40200 2lombardia Pavia Copiano 40200 2lombardia Pavia Corteolona 40200 2lombardia Pavia Costa De´ Nobili 40200 2lombardia Pavia Cozzo lomellina 40200 2lombardia Pavia Cura Carpignano 40200 2lombardia Pavia Dorno 40200 2lombardia Pavia Ferrera erbognone 40200 2lombardia Pavia Filighera 40200 2lombardia Pavia Frascarolo 40200 2lombardia Pavia Galliavola 40200 2lombardia Pavia Gambarana 40200 2lombardia Pavia Gambolo´ 40200 2lombardia Pavia Garlasco 40200 2lombardia Pavia Genzone 40200 2lombardia Pavia Gerenzago 40200 2lombardia Pavia Giussago 40200 2lombardia Pavia Gravellona lomellina 40200 2lombardia Pavia Gropello Cairoli 40200 2lombardia Pavia Inverno e monteleone 40200 2lombardia Pavia landriano 40200 2lombardia Pavia langosco 40200 2lombardia Pavia lardirago 40200 2lombardia Pavia linarolo 40200 2lombardia Pavia lomello 40200 2lombardia Pavia magherno 40200 2lombardia Pavia marcignago 40200 2lombardia Pavia marzano 40200 2lombardia Pavia mede 40200 2lombardia Pavia mezzana Bigli 40200 2lombardia Pavia mezzana rabattone 40200 2lombardia Pavia mezzanino 40200 2lombardia Pavia miradolo terme 40200 2lombardia Pavia mortara 40200 2lombardia Pavia Nicorvo 40200 2lombardia Pavia Olevano di lomellina 40200 2lombardia Pavia Ottobiano 40200 2lombardia Pavia Palestro 40200 2lombardia Pavia Parona 40200 2lombardia Pavia Pavia 40200 2lombardia Pavia Pieve albignola 40200 2lombardia Pavia Pieve del Cairo 40200 2

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lombardia Pavia Pieve porto morone 40200 2lombardia Pavia Pizzarrosto Pezzana 40200 2lombardia Pavia robbio 40200 2lombardia Pavia rognano 40200 2lombardia Pavia roncaro 40200 2lombardia Pavia rosasco 40200 2lombardia Pavia san Genesio ed Uniti 40200 2lombardia Pavia san Giorgio di lomellina 40200 2lombardia Pavia san martino siccomario 40200 2lombardia Pavia san Zenone Po 40200 2lombardia Pavia sannazzaro de´ Burgondi 40200 2lombardia Pavia santa Cristina e Bissone 40200 2lombardia Pavia santálessio con Vialone 40200 2lombardia Pavia santángelo lomellina 40200 2lombardia Pavia sartirana lomellina 40200 2lombardia Pavia scaldasole 40200 2lombardia Pavia semiana 40200 2lombardia Pavia siziano 40200 2lombardia Pavia sommo 40200 2lombardia Pavia spessa 40200 2lombardia Pavia suardi 40200 2lombardia Pavia torre Beretti e Castellaro 40200 2lombardia Pavia torre dei Negri 40200 2lombardia Pavia torre dárese 40200 2lombardia Pavia torre dÍsola 40200 2lombardia Pavia torrevecchia Pia 40200 2lombardia Pavia travaco´ siccomario 40200 2lombardia Pavia trivolzio 40200 2lombardia Pavia tromello 40200 2lombardia Pavia trovo 40200 2lombardia Pavia Valeggio lomellina 40200 2lombardia Pavia Valle lomellina 40200 2lombardia Pavia Valle salimbene 40200 2lombardia Pavia Velezzo lomellina 40200 2lombardia Pavia Vellezzo Bellini 40200 2lombardia Pavia Vidigulfo 40200 2lombardia Pavia Vigevano 40200 2lombardia Pavia Villa Biscossi 40200 2lombardia Pavia Villanova dárdenghi 40200 2lombardia Pavia Villanterio 40200 2lombardia Pavia Vistarino 40200 2lombardia Pavia Voghera 40200 2lombardia Pavia Zeccone 40200 2lombardia Pavia Zeme 40200 2lombardia Pavia Zerbo 40200 2lombardia Pavia Zerbolo´ 40200 2lombardia Pavia Zinasco 40200 2lombardia Bergamo antegnate 30000 1Veneto Padova Bagnoli di sopra 15000 1Veneto Padova Codevigo 15000 1Veneto rovigo Porto tolle 10500 2Veneto rovigo Porto Viro 10500 2Veneto rovigo salara 10500 2Veneto rovigo taglio di Po 10500 2Veneto Venezia eraclea 10500 2Veneto Venezia mira 10500 2Veneto Vicenza arzignano 12750 2Veneto Vicenza Grumolo delle abbadesse 12750 6

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Veneto Vicenza lonigo 12750 6Veneto Verona Bovolone 15000 1Veneto Verona Casaleone 15000 1Veneto Verona Cerea 15000 1Veneto Verona erbe´ 15000 1Veneto Verona Gazzo Veronese 15000 1Veneto Verona Isola della scala 15000 1Veneto Verona mozzecane 15000 1Veneto Verona Nogara 15000 1Veneto Verona Nogarole rocca 15000 1Veneto Verona Oppeano 15000 1Veneto Verona Palu´ 15000 1Veneto Verona salizzole 15000 1Veneto Verona sorga´ 15000 1Veneto Verona trevenzuolo 15000 1Veneto Verona Vigasio 15000 1emilia-romagna Bologna malalbergo 9033.3 6emilia Bologna medicina 9033.3 6emilia Bologna molinella 9033.3 6emilia Bologna san Pietro in Casale 9033.3 6emilia Ferrara argenta 15000 2emilia Ferrara Berra 15000 2emilia Ferrara Bondeno 15000 2emilia Ferrara Codigoro 15000 2emilia Ferrara Comacchio 15000 2emilia Ferrara Copparo 15000 2emilia Ferrara Ferrara 15000 2emilia Ferrara Goro 15000 2emilia Ferrara Jolanda di savoia 15000 2emilia Ferrara lagosanto 15000 2emilia Ferrara massa Fiscaglia 15000 2emilia Ferrara mesola 15000 2emilia Ferrara mezzogoro (Codigoro) 15000 2emilia Ferrara Ostellato 15000 2emilia Ferrara tresigallo 15000 2emilia modena Carpi 8500 2emilia modena Novi di modena 8500 2emilia Piacenza Castelvetro Piacentino 9033.3 6emilia reggio emilia Gualtieri 3600 2emilia reggio emilia Guastalla 3600 2toscana Grosseto Grosseto 8000 2toscana siena murlo 1500 4sardegna Cagliari muravera 12000 1sardegna Cagliari san Gavino monreale 12000 1sardegna Oristano Cabras 14000 2sardegna Oristano Nurachi 14000 2sardegna Oristano Oristano 14000 2sardegna Oristano Palmas arborea 14000 2sardegna Oristano san Vero milis 14000 2sardegna Oristano santa Giusta 14000 2sardegna Oristano siamaggiore 14000 2sardegna Oristano simaxis 14000 2sardegna Oristano tramatza 14000 2sardegna Oristano Zeddiani 14000 2Calabria Cosenza Cassano allo Ionio 8750 2Calabria Cosenza Corigliano Calabro 8750 2Calabria Cosenza sibari (Cassano allo Ionio) 8750 2Calabria Cosenza Villapiana 8750 2

Printing

CSR Centro Stampa e Riproduzione s.r.l.

Via di Pietralata, 157 - 00158 Roma

Printing completed in June 2013

isbn 978-88-8145-289-7

Series environMentAL And AgricuLturAL poLicy water resource Management

research is now able to analyze and evaluate, within an integrated and multidisciplinary ap-proach, 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 re-sources 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 fu-ture 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 cli-mate 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.

Documents

A ModeL-bAsed irrigAtion wAter consuMption …dspace.crea.gov.it/bitstream/inea/601/1/MARSALa_Lupia.pdfcomputational model, the irrigation water consumption at farm level in Italy