Agro-Pedo-Climatological Zoning of Italy

Agro-Pedo-Climatological Zoning of Italy 

Application to grain maize, durum wheat, soft wheat, 

spring barley, sugar beet, rapeseed, sunflower, soybean, 

tomato 

S Bagli, JM Terres, J Gallego, A. Annoni, J.F Dallemand 

Monograph EUR 20550 EN 

EUROPEAN COMMISSION 

DIRECTORATE GENERAL 

JOINT RESEARCH CENTRE – ISPRA 

Institute for Environment & Sustainability 

Land Management unit

Agro-Pedo-Climatological Zoning of Italy 

Definition of homogeneous suitable agro-pedoclimatic 

zones 

Application to grain maize, durum wheat, soft wheat, 

spring barley, sugar beet, rapeseed, sunflower, soybean, 

tomato 

S Bagli, JM Terres, J Gallego, A. Annoni, J.F Dallemand 

Monograph EUR 20550 EN 

January 2003

Agro Pedo climatological zoning of Italy 

Foreword 

JRC (ANNONI) In progress 

IV


V


Table of Contents 

List of Figures and Tables..........................................................................................................XI 

Acknowledgments ......................................................................................................................... 1 

Introduction................................................................................................................................... 2 

1 Methodology .......................................................................................................................... 3 

1.1 Data Sets ....................................................................................................................4 

1.2 Crop Calendar in Italy................................................................................................4 

1.3 Climatological Index..................................................................................................5 

1.3.1 Temperature Satisfaction index -TSI.................................................................7 

1.3.2 Frost Risk Index FRI..........................................................................................7 

1.3.3 Heat Stress Risk Index - HSRI...........................................................................7 

1.3.4 Thermal Satisfaction Index -ThSI......................................................................7 

1.3.5 Vernalization Satisfaction Index - VSI..............................................................8 

1.3.6 Flowering Temperature Satisfaction Index - FTSI ............................................8 

1.4 Pedological data.........................................................................................................8 

1.4.1 AWC – Available Water Capacity...................................................................10 

1.4.2 Soil Depth ........................................................................................................11 

1.4.3 Texture .............................................................................................................12 

1.5 Morphology parameters...........................................................................................14 

1.5.1 Mean elevation.................................................................................................14 

1.5.2 Slope ................................................................................................................15 

1.6 Overlay analysis of Climatic zones and pedological units ......................................16 

1.7 Crop Water Satisfaction Index.................................................................................17 

1.8 Suitability Criteria....................................................................................................19 

1.9 Spatial Homogeneous Zoning..................................................................................19 

2 Discussion............................................................................................................................. 21 

3 Analysis of yield data from the AGRIT point survey..................................................... 23 

3.1 Data used for the analysis ........................................................................................23 

AGRIT observations and ISTAT data by province ........................................................23 

3.2 Grain maize analysis................................................................................................24 

Basic description of the Grain maize yield data. .............................................................26 

Analysis of Variance (Analysis) – ANOVA -of the Grain maize yield data...................28 

3.3 Common wheat analysis. .........................................................................................30 

ANOVA for common wheat............................................................................................30 

3.4 Barley analysis.........................................................................................................32 

3.5 Conclusions..............................................................................................................32 

4 Concluding Remarks .......................................................................................................... 33 

List of Acronyms......................................................................................................................... 35 

Bibliography ................................................................................................................................ 36 

Annex I: Crop Zoning Application ........................................................................................... 40 

Grain maize in Italy.................................................................................................................... 41 

Growing Period in Italy .......................................................................................................41 

Climatic Suitability Map......................................................................................................42 

Temperature Satisfaction Index -TSI...............................................................................42 

Frost Risk Index -FRI ......................................................................................................43 

Heat Stress Risk Index -HSRI..........................................................................................44 

Thermal Satisfaction Index - ThSI...................................................................................45 

VI


Pedological Suitability Criteria............................................................................................47 

Soil Depth ........................................................................................................................47 

Texture .............................................................................................................................48 

Morphology Suitability Criteria...........................................................................................49 

Mean elevation.................................................................................................................49 

Slope ................................................................................................................................50 

Crop Water Satisfaction Index Suitability ...........................................................................51 

Suitably Index Score............................................................................................................52 

Non Irrigated Scenario.....................................................................................................52 

Irrigated Scenario.............................................................................................................53 

Agro-Pedo-Climatological Zoning ......................................................................................54 


Irrigated Scenario.............................................................................................................56 

Soft Wheat - Barley in Italy ....................................................................................................... 58 





Heat Stress Risk Index -HSRI.........................................................................................61 

Thermal Satisfaction Index - ThSI..................................................................................62 

Vernalization Satisfaction Index - VSI...........................................................................64 


Soil Depth ........................................................................................................................65 

Texture .............................................................................................................................66 



Slope ................................................................................................................................68 






Durum Wheat in Italy ................................................................................................................ 73 







Flowering Temperature Satisfaction Index - FTSI .........................................................79 


Soil Depth ........................................................................................................................80 

Texture .............................................................................................................................81 



Slope ................................................................................................................................83 


Index Score ..........................................................................................................................85 



VII



Tomato in Italy............................................................................................................................ 88 








Soil Depth ........................................................................................................................94 

Texture .............................................................................................................................95 

Morphology Suitability Criteria for Tomato........................................................................96 


Slope ................................................................................................................................97 




Irrigated Scenario...........................................................................................................100 

Agro-Pedo-Climatological Zoning for Tomato .................................................................101 

Non Irrigated Scenario...................................................................................................101 


Sugar beet in Italy..................................................................................................................... 105 

Growing Period in Italy .....................................................................................................105 

Climatic Suitability Map....................................................................................................106 

Temperature Satisfaction Index -TSI.............................................................................106 

Frost Risk Index -FRI ....................................................................................................107 

Heat Stress Risk Index -HSRI.......................................................................................108 

Thermal Satisfaction Index - ThSI................................................................................109 

Flowering Temperature Satisfaction Index - FTSI .......................................................111 

Pedological Suitability Criteria..........................................................................................112 

Soil Depth ......................................................................................................................112 

Texture ...........................................................................................................................113 

Morphology Suitability Criteria.........................................................................................114 

Mean elevation...............................................................................................................114 

Slope ..............................................................................................................................115 

Crop Water Satisfaction Index Suitability .........................................................................116 

Suitably Index Score..........................................................................................................117 



Agro-Pedo-Climatological Zoning for Sugar beet.............................................................119 



Sunflower in Italy...................................................................................................................... 123 








VIII



Soil Depth ......................................................................................................................130 

Texture ...........................................................................................................................131 



Slope ..............................................................................................................................133 


Suitably Index Score Zoning .............................................................................................135 



Agro-Pedo-Climatological Zoning for Sunflower.............................................................137 



Soy Bean in Italy ....................................................................................................................... 141 









Soil Depth ......................................................................................................................148 

Texture ...........................................................................................................................149 



Slope ..............................................................................................................................151 


Soy Bean Suitably Index Score..........................................................................................153 



Agro-Pedo-Climatological Zoning for Soy Bean ..............................................................155 



Rapeseed in Italy....................................................................................................................... 159 








Vernalization Satisfaction Index - VSI.........................................................................166 


Soil Depth ......................................................................................................................167 

Texture ...........................................................................................................................168 



Slope ..............................................................................................................................170 

Crop Water Satisfaction Index Suitability for Rapeseed ...................................................171 

IX


Rapeseed Suitably Index Score..........................................................................................172 


Agro-Pedo-Climatological Zoning for Rapeseed ..............................................................173 


Annex II: Index Equations....................................................................................................... 175 

Temperature Satisfaction index -TSI.................................................................................176 

Frost Risk Index FRI..........................................................................................................176 

Heat Stress Risk Index - HSRI...........................................................................................176 

Thermal Satisfaction Index -ThSI......................................................................................176 

Vernalization Satisfaction Index - VSI..............................................................................177 

Flowering Temperature Satisfaction index -FTSI .............................................................177 

Crop Water Satisfaction Index (CWSI) .............................................................................178 

Soil Water Content.............................................................................................................178 

Annex III: Agro-pedo-climatological zones............................................................................ 179 

Grain maize Agro-pedo-climatological zones ...................................................................180 


Non-Irrigated Scenario...................................................................................................183 

Soft Wheat – Barley Agro-pedo-climatological zones ......................................................184 


Durum Wheat Agro-pedo-climatological zones ................................................................188 


Tomato Agro-pedo-climatological zones ..........................................................................193 



Sugar beet Agro-pedo-climatological zones......................................................................201 



Sunflower Agro-pedo-climatological zones ......................................................................205 



Soy bean Agro-pedo-climatological zones ........................................................................211 



Rapeseed Agro-pedo-climatological zones........................................................................216 


X


Figure 56– Durum Wheat Suitability Index Score Zoning in Non-Irrigated Scenario ..........................................85 

Figure 57 – Durum Wheat Suitability Class in Non-Irrigated Scenario.................................................................86 

Figure 58–Durum Wheat Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario ................................87 

Figure 59 Tomato TSI Index Suitability Classification .........................................................................................89 

Figure 60– Tomato FRI Index Suitability Classification.......................................................................................90 

Figure 61– Tomato HSRI Index Suitability Classification....................................................................................91 

Figure 62– Tomato ThSI Index Suitability Classification until Flowering Period ...............................................92 

Figure 63– Tomato ThSI Index Suitability Classification until Ripening Period .................................................93 

Figure 64– Tomato Soil depth Suitability Classification........................................................................................94 

Figure 65- Tomato Texture Suitability Classification.............................................................................................95 

Figure 66– Tomato Mean Elevation Suitability Classification...............................................................................96 

Figure 67– Tomato Slope Suitability Classification ..............................................................................................97 

Figure 68– Tomato CSWI Suitability Classification .............................................................................................98 

Figure 69– Tomato Suitability Index Score Zoning in Non-Irrigated Scenario.....................................................99 

Figure 70– Tomato Suitability Index Score Zoning in Irrigated Scenario ...........................................................100 

Figure 71 – Tomato Suitability Class in Non-Irrigated Scenario .........................................................................101 

Figure 72–Tomato Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario.........................................102 

Figure 73 – Tomato Suitability Class in Irrigated Scenario..................................................................................103 

Figure 74–Tomato Agro-Pedo-Climatological Zoning ID in Irrigated Scenario.................................................104 

Figure 75 Sugar beet TSI Index Suitability Classification...................................................................................106 

Figure 76– Sugar beet FRI Index Suitability Classification ................................................................................107 

Figure 77– Sugar beet HSRI Index Suitability Classification .............................................................................108 

Figure 78– Sugar beet ThSI Index Suitability Classification until Flowering Period.........................................109 

Figure 79– Sugar beet ThSI Index Suitability Classification until Ripening Period ..........................................110 

Figure 80– Sugar beet FTSI Index Suitability Classification during Flowering Period .....................................111 

Figure 81– Sugar beet Soil depth Suitability Classification .................................................................................112 

Figure 82- Sugar beet Texture Suitability Classification......................................................................................113 

Figure 83– Sugar beet Mean Elevation Suitability Classification ........................................................................114 

Figure 84– Sugar beet Slope Suitability Classification........................................................................................115 

Figure 85– Sugar beet CSWI Suitability Classification.......................................................................................116 

Figure 86– Sugar beet Suitability Index Score Zoning in Non-Irrigated Scenario ..............................................117 

Figure 87– Sugar beet Suitability Index Score Zoning in Irrigated Scenario.......................................................118 

Figure 88 – Sugar beet Suitability Class in Non-Irrigated Scenario.....................................................................119 

Figure 89–Sugar beet Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario ....................................120 

Figure 90 – Sugar beet Suitability Class in Irrigated Scenario.............................................................................121 

Figure 91–Sugar beet Agro-Pedo-Climatological Zoning ID in Irrigated Scenario ............................................122 

Figure 92 Sunflower TSI Index Suitability Classification...................................................................................124 

Figure 93– Sunflower FRI Index Suitability Classification.................................................................................125 

Figure 94– Sunflower HSRI Index Suitability Classification..............................................................................126 

Figure 95– Sunflower ThSI Index Suitability Classification until Flowering Period.........................................127 

Figure 96– Sunflower ThSI Index Suitability Classification until Ripening Period...........................................128 

Figure 97– Sunflower FTSI Index Suitability Classification during Flowering Period......................................129 

Figure 98– Sunflower Soil depth Suitability Classification..................................................................................130 

Figure 99– Sunflower Texture Suitability Classification......................................................................................131 

Figure 100– Sunflower Mean Elevation Suitability Classification ......................................................................132 

Figure 101– Sunflower Slope Suitability Classification......................................................................................133 

Figure 102– Sunflower CSWI Suitability Classification .....................................................................................134 

Figure 103– Sunflower Suitability Index Score Zoning in Non-Irrigated Scenario.............................................135 

Figure 104– Sunflower Suitability Index Score Zoning in Irrigated Scenario.....................................................136 

Figure 105– Sunflower Suitability Class in Non-Irrigated Scenario....................................................................137 

Figure 106–Sunflower Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario ..................................138 

Figure 107– Sunflower Suitability Class in Irrigated Scenario ............................................................................139 

Figure 108–Sunflower Agro-Pedo-Climatological Zoning ID in Irrigated Scenario...........................................140 

Figure 109 Soy Bean TSI Index Suitability Classification ..................................................................................142 

Figure 110– Soy Bean FRI Index Suitability Classification................................................................................143 

Figure 111– Soy Bean HSRI Index Suitability Classification.............................................................................144 

Figure 112– Soy Bean ThSI Index Suitability Classification until Flowering Period ........................................145 

Figure 113– Soy Bean ThSI Index Suitability Classification until Ripening Period...........................................146 

Figure 114– Soy Bean FTSI Index Suitability Classificationduring Flowering Period......................................147 

Figure 115– Soy Bean Soil depth Suitability Classification.................................................................................148 

XII


Figure 116– Soy Bean Texture Suitability Classification.....................................................................................149 

Figure 117– Soy Bean Mean Elevation Suitability Classification........................................................................150 

Figure 118– Soy Bean Slope Suitability Classification ........................................................................................151 

Figure 119– Soy Bean CSWI Suitability Classification ......................................................................................152 

Figure 120– Soy Bean Suitability Index Score Zoning in Non-Irrigated Scenario..............................................153 

Figure 121– Soy Bean Suitability Index Score Zoning in Irrigated Scenario ......................................................154 

Figure 122– Soy Bean Suitability Class in Non-Irrigated Scenario .....................................................................155 

Figure 123–Soy Bean Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario....................................156 

Figure 124– Soy Bean Suitability Class in Irrigated Scenario..............................................................................157 

Figure 125–Soy Bean Agro-Pedo-Climatological Zoning ID in Irrigated Scenario............................................158 

Figure 126 Rapeseed TSI Index Suitability Classification ..................................................................................160 

Figure 127– Rapeseed FRI Index Suitability Classification................................................................................161 

Figure 128– Rapeseed HSRI Index Suitability Classification.............................................................................162 

Figure 129– Rapeseed ThSI Index Suitability Classification until Flowering Period ........................................163 

Figure 130– Rapeseed ThSI Index Suitability Classification until Ripening Period ...........................................164 

Figure 131– Rapeseed FTSI Index Suitability Classification during Flowering Period.....................................165 

Figure 132– Rapeseed VSI Index Suitability Classification until Ripening Period.............................................166 

Figure 133– Rapeseed Soil depth Suitability Classification.................................................................................167 

Figure 134– Rapeseed Texture Suitability Classification.....................................................................................168 

Figure 135– Rapeseed Mean Elevation Suitability Classification........................................................................169 

Figure 136– Rapeseed Slope Suitability Classification ........................................................................................170 

Figure 137– Rapeseed CSWI Suitability Classification .......................................................................................171 

Figure 138– Rapeseed Suitability Index Score Zoning in Non-Irrigated Scenario..............................................172 

Figure 139– Rapeseed Suitability Class in Non-Irrigated Scenario .....................................................................173 

Figure 140–Rapeseed Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario....................................174 

XIII


Table 1 –AWC Classification ..................................................................................................................................10 

Table 2 –Soil Depth Classification..........................................................................................................................11 

Table 3 –CEC Texture triangle................................................................................................................................12 

Table 4 –Texture Classification...............................................................................................................................13 

Table 5 –Elevation Classification............................................................................................................................14 

Table 6 –Slope Classification ..................................................................................................................................15 

Table 7 –Suitability Scores ......................................................................................................................................19 

Table 8 –Suitability Scores for CWSI index ...........................................................................................................20 

Table 9 –Crop Suitability Classification .................................................................................................................20 

Table 10 – Spatial consistency between ISTAT data and AGRIT observations...................................................23 

Table 11 – Time consistency between ISTAT data and AGRIT observations.......................................................23 

Table 12 –AGRIT Grain maize yield data...............................................................................................................26 

Table 13 –soil depth analysis...................................................................................................................................27 

Table 14 –elevation analysis....................................................................................................................................27 

Table 15 –ANOVA results for Grain maize............................................................................................................29 

Table 16 –Soil Depth test.........................................................................................................................................30 

Table 17 –ANOVA results for common wheat.......................................................................................................31 

Table 18 –ANOVA results for barley......................................................................................................................32 

Table 19 –Grain maize Calendar .............................................................................................................................41 

Table 20– Grain maize TSI Index Suitability Classification ..................................................................................42 

Table 21– Grain maize FRI Index Suitability Classification..................................................................................43 

Table 22– Grain maize HSRI Index Suitability Classification...............................................................................44 

Table 23– Grain maize ThSI Index Suitability Classification ................................................................................45 

Table 24– Grain maize Soil depth Suitability Classification..................................................................................47 

Table 25– Grain maize Texture Suitability Classification......................................................................................48 

Table 26– Grain maize Mean Elevation Suitability Classification.........................................................................49 

Table 27– Grain maize Slope Suitability Classification .........................................................................................50 

Table 28– Grain maize CSWI Suitability Classification ........................................................................................51 

Table 29– Grain maize Suitability Index Score Classification in Non-Irrigated Scenario ....................................52 

Table 30– Grain maize Suitability Index Score Classification in Irrigated Scenario.............................................53 

Table 31– Grain maize Suitability Classification in Non-Irrigated Scenario.........................................................54 

Table 32– Grain maize Suitability Classification in Irrigated Scenario .................................................................56 

Table 33 –Soft Wheat – Barley Calendar................................................................................................................58 

Table 34 – Soft Wheat – Barley TSI Index Suitability Classification....................................................................59 

Table 35– Soft Wheat – Barley FRI Index Suitability Classification.....................................................................60 

Table 36– Soft Wheat – Barley HSRI Index Suitability Classification..................................................................61 

Table 37– Soft Wheat – Barley ThSI Index Suitability Classification...................................................................62 

Table 38 – Soft Wheat – Barley VSI Index Suitability Classification ...................................................................64 

Table 39– Soft Wheat – Barley Soil depth Suitability Classification.....................................................................65 

Table 40– Soft Wheat – Barley Texture Suitability Classification.........................................................................66 

Table 41– Soft Wheat – Barley Mean Elevation Suitability Classification ...........................................................67 

Table 42– Soft Wheat – Barley Slope Suitability Classification............................................................................68 

Table 43– Soft Wheat – Barley CSWI Suitability Classification...........................................................................69 

Table 44– Soft Wheat – Barley Suitability Index Score Classification in Non-Irrigated Scenario.......................70 

Table 45– Soft Wheat - Barley Suitability Classification in Non-Irrigated Scenario ............................................71 

Table 46 –Durum Wheat Calendar..........................................................................................................................73 

Table 47 – Durum Wheat TSI Index Suitability Classification..............................................................................74 

Table 48– Durum Wheat FRI Index Suitability Classification...............................................................................75 

Table 49– Durum Wheat HSRI Index Suitability Classification............................................................................76 

Table 50– Durum Wheat ThSI Index Suitability Classification.............................................................................77 

Table 51– Durum Wheat ThSI Index Suitability Classification.............................................................................79 

Table 52– Durum Wheat Soil depth Suitability Classification...............................................................................80 

Table 53– Durum Wheat Texture Suitability Classification...................................................................................81 

Table 54– Durum Wheat Mean Elevation Suitability Classification......................................................................82 

Table 55– Durum Wheat Slope Suitability Classification ......................................................................................83 

Table 56– Durum Wheat CSWI Suitability Classification .....................................................................................84 

Table 57– Durum Wheat Suitability Index Score Classification in Non-Irrigated Scenario .................................85 

Table 58– Durum Wheat Suitability Classification in Non-Irrigated Scenario......................................................86 

Table 59 –Tomato Calendar.....................................................................................................................................88 

XIV


Table 60 – Tomato TSI Index Suitability Classification ........................................................................................89 

Table 61– Tomato FRI Index Suitability Classification .........................................................................................90 

Table 62– Tomato HSRI Index Suitability Classification ......................................................................................91 

Table 63– Tomato ThSI Index Suitability Classification .......................................................................................92 

Table 64– Tomato Soil depth Suitability Classification .........................................................................................94 

Table 65– Tomato Texture Suitability Classification .............................................................................................95 

Table 66– Tomato Mean Elevation Suitability Classification ................................................................................96 

Table 67– Tomato Slope Suitability Classification.................................................................................................97 

Table 68– Tomato CSWI Suitability Classification................................................................................................98 

Table 69– Tomato Suitability Index Score Classification in Non-Irrigated Scenario............................................99 

Table 70– Tomato Suitability Index Score Classification in Non-Irrigated Scenario..........................................100 

Table 71– Tomato Suitability Classification in Non-Irrigated Scenario ..............................................................101 

Table 72– Tomato Suitability Classification in Irrigated Scenario ......................................................................103 

Table 73 –Sugar beet Calendar..............................................................................................................................105 

Table 74 – Sugar beet TSI Index Suitability Classification..................................................................................106 

Table 75– Sugar beet FRI Index Suitability Classification...................................................................................107 

Table 76– Sugar beet HSRI Index Suitability Classification................................................................................108 

Table 77– Sugar beet ThSI Index Suitability Classification.................................................................................109 

Table 78– Sugar beet ThSI Index Suitability Classification.................................................................................111 

Table 79– Sugar beet Soil depth Suitability Classification...................................................................................112 

Table 80– Sugar beet Texture Suitability Classification.......................................................................................113 

Table 81– Sugar beet Mean Elevation Suitability Classification .........................................................................114 

Table 82– Sugar beet Slope Suitability Classification..........................................................................................115 

Table 83– Sugar beet CSWI Suitability Classification .........................................................................................116 

Table 84– Sugar beet Suitability Index Score Classification in Non-Irrigated Scenario.....................................117 

Table 85– Sugar beet Suitability Index Score Classification in Non-Irrigated Scenario.....................................118 

Table 86– Sugar beet Suitability Classification in Non-Irrigated Scenario..........................................................119 

Table 87– Sugar beet Suitability Classification in Irrigated Scenario..................................................................121 

Table 88 –Sunflower Phenological Calendar........................................................................................................123 

Table 89 – Sunflower TSI Index Suitability Classification ..................................................................................124 

Table 90– Sunflower FRI Index Suitability Classification...................................................................................125 

Table 91– Sunflower HSRI Index Suitability Classification ................................................................................126 

Table 92– Sunflower ThSI Index Suitability Classification .................................................................................127 

Table 93– Sunflower ThSI Index Suitability Classification .................................................................................129 

Table 94– Sunflower Soil depth Suitability Classification...................................................................................130 

Table 95– Sunflower Texture Suitability Classification.......................................................................................131 

Table 96– Sunflower Mean Elevation Suitability Classification..........................................................................132 

Table 97– Sunflower Slope Suitability Classification ..........................................................................................133 

Table 98– Sunflower CSWI Suitability Classification .........................................................................................134 

Table 99– Sunflower Suitability Index Score Classification in Non-Irrigated Scenario .....................................135 

Table 100– Sunflower Suitability Index Score Classification in Irrigated Scenario............................................136 

Table 101– Sunflower Suitability Classification in Non-Irrigated Scenario........................................................137 

Table 102– Sunflower Suitability Classification in Irrigated Scenario ................................................................139 

Table 103 –Soy Bean Calendar .............................................................................................................................141 

Table 104 – Soy Bean TSI Index Suitability Classification .................................................................................142 

Table 105– Soy Bean FRI Index Suitability Classification ..................................................................................143 

Table 106– Soy Bean HSRI Index Suitability Classification ...............................................................................144 

Table 107– Soy Bean ThSI Index Suitability Classification ................................................................................145 

Table 108– Soy Bean ThSI Index Suitability Classification ................................................................................147 

Table 109– Soy Bean Soil depth Suitability Classification ..................................................................................148 

Table 110– Soy Bean Texture Suitability Classification ......................................................................................149 

Table 111– Soy Bean Mean Elevation Suitability Classification.........................................................................150 

Table 112– Soy Bean Slope Suitability Classification..........................................................................................151 

Table 113– Soy Bean CSWI Suitability Classification.........................................................................................152 

Table 114– Soy Bean Suitability Index Score Classification in Non-Irrigated Scenario ....................................153 

Table 115– Soy Bean Suitability Index Score Classification in Irrigated Scenario.............................................154 

Table 116– Soy Bean Suitability Classification in Non-Irrigated Scenario .........................................................155 

Table 117– Soy Bean Suitability Classification in Irrigated Scenario .................................................................157 

Table 118 –RapeseedCalendar...............................................................................................................................159 

Table 119 – Rapeseed TSI Index Suitability Classification..................................................................................160 

XV


Table 120– Rapeseed FRI Index Suitability Classification ..................................................................................161 

Table 121– Rapeseed HSRI Index Suitability Classification ...............................................................................162 

Table 122– Rapeseed ThSI Index Suitability Classification.................................................................................163 

Table 123– Rapeseed ThSI Index Suitability Classification.................................................................................165 

Table 124 – Rapeseed VSI Index Suitability Classification .................................................................................166 

Table 125– Rapeseed Soil depth Suitability Classification ..................................................................................167 

Table 126– Rapeseed Texture Suitability Classification ......................................................................................168 

Table 127– Rapeseed Mean Elevation Suitability Classification .........................................................................169 

Table 128– Rapeseed Slope Suitability Classification..........................................................................................170 

Table 129– Rapeseed CSWI Suitability Classification.........................................................................................171 

Table 130– Rapeseed Suitability Index Score Classification in Non-Irrigated Scenario.....................................172 

Table 131– Rapeseed Suitability Classification in Non-Irrigated Scenario .........................................................173 

Table 132– Grain maize Agro-Pedo-Climatological Zoning ID in Irrigated Scenario........................................182 

Table 133– Grain maize Agro-Pedo-Climatological Zoning ID for Grain maize in Non-Irrigated Scenario.....183 

Table 134– Soft Wheat - Barley Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario...................187 

Table 135– Durum Wheat Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario ............................192 

Table 136– Tomato Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario.......................................195 

Table 137– Tomato Agro-Pedo-Climatological Zoning ID in Irrigated Scenario ...............................................200 

Table 138– Sugar beet Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario ..................................201 

Table 139– Sugar beet Agro-Pedo-Climatological Zoning ID in Irrigated Scenario...........................................204 

Table 140– Sunflower Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario...................................206 

Table 141– Sunflower Agro-Pedo-Climatological Zoning ID in Irrigated Scenario ...........................................210 

Table 142– Soy Bean Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario....................................212 

Table 143– Soy Bean Agro-Pedo-Climatological Zoning ID in Irrigated Scenario ............................................215 

Table 144– Rapeseed Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario ....................................218 

XVI


Acknowledgments 

We would like to thank the JRC European Soil Bureau for providing the soil and pedological 

data necessary for crop zoning and especially Mr. Ezio Rusco for his advice. 

We are also grateful to Miss. C. Le Bas from INRA Orleans (France) for providing 

information on crop requirements in term of agro-pedo conditions and the ITA consortium 

(Aldo Giovacchini and Paolo Ragni) for providing point observations of crop yields for 

validation. 

Last but not the least we would like to thank Mr. G. Libertà (JRC-IES- LMU) for developing 

the WEB GIS interface and A. De Jager (JRC-IES-LMU) for providing Data Base 

management. 

1


Introduction 

The agricultural productivity of a geographical area is dependant on many factors – inherent 

soil and terrain characteristics, climatic constrains, human behavior and management, and 

cultural norms (Waltman et al., 1999). 

Agro-ecozones have been proposed as a practical framework to model and simplify the 

complexity of agro ecosystems (Bailey, 1996; Omernik, 1993; Waltman et al., 1999). They 

are traditionally defined as major climate zones with a certain growing period suitable for 

crops. With its soil, landscape, and climatic characteristics, an agro-ecozone can represent the 

environmental constraints and potential for new cropping systems, economic viability, and 

sustainability (Follet, 1996). 

A large number of methodologies and approaches were developed, by different projects, in 

the last years to evaluate the land suitability for crop production and to identify spatially 

homogenous agro-ecological zones (AEZ) (Fischer et al., 2002). 

The land suitability method developed by FAO (1976) provides a widely used framework for 

assessing the suitability for a specific use, based on expert knowledge. Bydekerke 

(Bydekerke et al.1998) and Le Bas (Le Bas and King, 1997) defined a procedure to attribute 

suitability classes to individual land units matching land and climate characteristics with the 

crop requirements using decision trees. 

Fischer (Fischer et al., 2002) provided a standardized framework for the characterization of 

climate, soil and terrain conditions relevant to agricultural production, the AEZ matching 

procedure was used to identify crop-specific limitations of prevailing climate, soil and 

morphological parameters, under assumed levels of input and management conditions. This 

AEZ methodology uses land resources inventory to assess all feasible agricultural land-use 

options from specific management conditions and levels of inputs, and to quantify the 

expected production of relevant cropping activities. 

The GIS technology permitting to integrate, process, analyze and display digital spatial and 

non-spatial data, is widely used in the land suitability and zoning methodology applications. 

GIS can classify and display agro-pedo-climatic datasets and provide a valuable set of tools 

which allow the data to be transformed into information more useful for decision-making. 

This publication describes a Geographic Information System (GIS) -based agro-pedoclimatological 

zoning for Italy based on a matching methodology between climatic, 

pedologic and morphologic conditions and crop requirements. 

The different agro-pedo-climatological homogeneous zones were also classified in term of 

suitability criteria for the specific crop. 

2


1 Methodology 

This chapter describes the methodology for the derivation of agro-pedo-climatological zones 

and the definition of suitability homogeneous zones. The methodology has been tested and 

validated for the whole territory of Italy. 

The zoning methodology was based on matching crop requirement parameters with 

meteorological, morphological and pedological conditions in a GIS environment, leading to: 

• The delineation of areas of homogeneous agro-pedo-climatic conditions, 

• The identification of crop-specific limitations for the prevailing climate, soil and 

terrain conditions; 

• The estimation of the potential and agronomical yield suitability for crops, under 

assumed normal level of input and management conditions, in irrigated and nonirrigated 

scenario. 

The following data, indices and parameters have been used for the geographic agro-pedoclimatological 

zoning of Italy. Thematic maps were developed for each of them. 

· Pedological parameters 

o Texture, 

o Available Water Capacity, 

o Soil Depth 

· Climatic indices specifically applied to crops: 

o Temperature Satisfaction Index during Germination Period –TSI 

o Frost Risk Index during development Period -FRI 

o Heat Stress Risk Index during development Period -HSRI 

o Thermal Satisfaction Index until Flowering Period -ThSI_TF 

o Thermal Satisfaction Index until Ripening Period – ThSI_TR 

o Vernalization Satisfaction Index during winter months – VSI 

o Flowering Temperature Satisfaction Index inside the crop specific the 

Flowering Period – FTSI 

· Crop Water Satisfaction Index (CWSI) combining soil and climate information 

· Morphological parameters 

o Elevation 

o Slope 

All maps were geo-referenced and elaborated using Arc View GIS (ESRI) software, the maps 

in this report are in geographical coordinates (un-projected) except the output zoning maps 

which are projected in UTM 32. 

Parameters were classified based on relevant pedological and climatic criteria (described in 

the next paragraph). 

The approach used consists of defining homogeneous areas for different crops respectively 

based on agronomical, pedological, morphological and climatological suitability constraints. 

Then, geographic layers were combined to obtain a specific crop suitability map of 

homogeneous zones for Italy. 

3


1.1 Data Sets 

The basic source materials used for spatial analysis of homogeneous zones were: 

• Crop Calendar Parameters: average date of sowing, germination, flowering and 

ripening for each crop region in Italy, this data area extrapolated for each culture from 

literature reported in bibliography (G. Narciso; 1992, J. Bignon; 1990, INRA 1995, 

Le Bas 1997) 

• Climatological Indices: are calculated from the JRC meteorological database. This 

database contains an historical series (1975-2002) of meteorological observations 

from 130 weather stations distributed throughout the country and interpolated into a 

regular grid of 50x50 km. 

• Pedological parameters: are derived from the digital version of the soil database of 

Italy (scale, 1:250,000) elaborated by the JRC European Soil Bureau, 

• Morphological parameters: they consist of Slope and Elevation. Slope parameter is 

derived from the soil database of Italy, while Elevation is provided by the Digital 

Elevation Model (grid cell size resolution of 250m, height accuracy of 10m) coming 

from the Italian Geological Survey 

1.2 Crop Calendar in Italy 

The matching procedure between climatic conditions and crops requirements needs in the 

first step information on the annual phenological calendar for each plant. The crop calendar 

defines the temporal characteristic of the different phenological stages of crops over Italian’s 

regions. 

For each region in Italy the average crop sowing decade and the occurrence of 3 principal 

phenological phases, crop germination, flowering and ripening was identified. The entire 

Italian territory was subdivided into three crop homogeneous zones: North, Centre and South. 

Figure 1 – Geographical zoning for crop calendar in Italy 

4


From the crop calendar it was possible to define the length of 4 growth stages in which the 

following climatic indices were calculates. 

• TG= Length in days from Sowing to Germination 

• TF= Length in days from Sowing to Flowering 

• TR= Length in days from Flowering to Ripening 

• TTOT= Length in days from Sowing to Ripening 

• TA= Length in days of the Vernalization Period 

• TB= Length in days of the critical period inside the Flowering Period 

1.3 Climatological Index 

Weather is one of the key components controlling agricultural production. Agrometeorological 

indices are widely used in agriculture yield forecasting application, risk 

assessment of critical weather conditions (drought, heat stress, etc..) and in suitability 

evaluation; they have the ability to describe the meteorological conditions through a simple 

representative value. For example the risk of drought for a specific region can be described 

with the Palmer Drought Severity Index (Palmer, 1965), which is a soil moisture algorithm 

calibrated for relatively homogeneous regions or the Standardized Precipitation Index (SPI) 

(McKee 1995) based on the probability of precipitation for any time scale. 

A method for assessing the importance of the temperature variation for agriculture in an area 

was described by Houvila (Houvila, 1964) with a frost risk index based on the deviation of 

the individual temperature from the mean value of the observations. 

In this study, climatic indices were defined to match crop agro-climatological requirements 

with climatic condition of the area of interest. 

The climatic indices were elaborated from daily meteorological data contained in the JRC 

meteo database, averaged over 25 years (resulting in 365 daily averaged values for each 

parameter). 

The meteorological data loaded in the JRC Database are collected from the Global 

Telecommunication System (GTS) of the World Meteorological Organization (WMO). The 

meteorological database contains information on the meteorological stations, daily 

meteorological observations and interpolated data. Meteorological data are: minimum and 

maximum temperature, rainfall, evapotranspiration calculated with the Penman formula 

(Penman 1948, 1956) and global radiation. Only stations that report at least this set of 

variables on a daily basis are included in the database. 

Figure 2 presents the Italian part of the network of meteorological stations included in the 

meteorological database (about 130 stations). 

5


Figure 2– Meteorological Grid and Weather Stations for Italy 

Temperature and evapotranspiration are interpolated from the existing network of 

meteorological stations to the grid center consists of selecting an optimum set of stations and 

then averaging the value of observed data and attributing it to the grid. Rainfall is taken from 

the nearest station. Selection of the optimum set of stations is based on the following criteria: 

proximity to grid centre, similarity in altitude and distance to the coast, position in relation to 

climatic barriers (i.e. mountain ranges) and a regular configuration surrounding the grid 

center. 

6


1.3.1 Temperature Satisfaction index -TSI 

This index is obtained from the number of days with a daily average temperature equal to or 

greater than a crop specific satisfaction-temperature computed over the Crop Germination 

Period divided by the total number of day of the Germination Period. 

TSI is the percentage of the Crop Germination Period in which the daily average temperature 

is suitable for germination. It represents the Temperature Satisfaction Index for crop 

germination. 

1.3.2 Frost Risk Index FRI 

This index is obtained from the number of days with a daily minimum temperature equal to 

or greater than a crop specific frost-temperature computed over the total development period 

(TTOT from sowing to ripening) divided by the total number of days of the development 

period. 

FRI is the percentage of the total development period in which the daily average temperature 

is above crop specific frost-temperature and the risk of possible frost is low, it represents the 

Frost Index during the crop growing period. 

1.3.3 Heat Stress Risk Index - HSRI 

This index is obtained from the number of days with a daily average long term temperature 

equal to or less than a crop specific heat stress-temperature computed over the total 

development period (TTOT from sowing to ripening) divided by the total number of day of 

the development period. 

HSRI is the percentage of the total development period with daily temperatures below crop 

specific heat stress-temperature and the risk of possible heat stress is low, it represents a Heat 

Stress Risk Index during the crop development period. 

1.3.4 Thermal Satisfaction Index -ThSI 

ThSi_TF and ThSI_TR indices are the cumulative sum of the average daily temperatures 

above a crop specific base temperature (temperature greater than a specific level), calculated 

respectively from sowing until flowering and from flowering until ripening period. 

ThSi_TF and ThSI_TR represent the cumulative thermal requirements during the two 

phenological period and they are used to compute the Thermal Satisfaction Index. 

7


1.3.5 Vernalization Satisfaction Index - VSI 

This index is obtained from the number of days with a daily average temperature between a 

crop specific lower and upper vernalization temperature computed during the crop 

vernalization period (winter months) divided by the total number of days of the Vernalization 

Period. 

VSI is the percentage of the Crop Vernalization Period in which the daily average 

temperature is within the interval defined by the lower and upper vernalization temperature 

and the risk of not having floral initiation is low. 

1.3.6 Flowering Temperature Satisfaction Index - FTSI 

This index is obtained from the number of days with a daily average temperature equal or 

greater than a crop specific flowering temperature computed during a crop specific subflowering 

period (critical decades for flowering divided by the total number of days of the 

period. 

The different indices described above were applied and adapted to each different crop of 

interest and moreover were applied to crops only when appropriate (no VSI for spring and 

summer crops for example). 

1.4 Pedological data 

In order to compare soil and terrain conditions with specific crop requirements for optimum 

growth and production, soil and terrain characteristics were matched against specific crop 

requirements derived from agricultural experiments and literature review. 

The pedological and morphological data of Italy are extracted from the 1:250,000 

Georeferenced Soil database of Europe developed by the JRC European Soil Bureau (ESB). 

3 parameters were elaborated to build a map of homogeneous pedological zones: 

• Available Water Capacity (AWC): the amount of water available for the plant root 

zone 

• Soil Depth: the depth of the soil layer that permits the development of roots 

• Texture: particle size composition of the soil, based on the FAO classification 

triangle. 

The soil map of Italy presents information about AWC, Soil Depth and Texture for polygons 

with soilscapes suitable for agriculture. 

For the elaboration of the Italian Soil Database at 1:250,000, soils suitable for agriculture 

were surveyed (soil analysis) and information was attributed to polygons. 

Some polygons (white areas) of the soil map do not have pedological data due to insufficient 

number of sample points; they generally correspond to non agricultural areas. 

The number of polygons with available information on pedological parameters (AWC, Soil 

Depth and Texture) is 6422 and it represents the 67 % of total area of Italy. 

8


Figure 3 –Soil Map of Italy with pedological data 

9


1.4.3 Texture 

Texture refers to particle size or the relative amounts of sand, silt, and clay. These primary 

soil constituents play an important role in drainage, nutrient fertility, compactability, 

elasticity, freeze-thaw behavior, ground water recharge, adsorption of pollutants, and many 

other properties that are relevant to agriculture, suitability analysis and environmental 

purposes. 

The soil map of Italy has been classified in 6 different Texture classes, according to the Soil 

Map of European Communities Triangle (CEC, 1985). 

Table 3 –CEC Texture triangle 

12


TEXTURE CLASSES CLASSES Color Legend 

No Texture 

T0 

Coarse 

Medium 

Medium Fine 

Fine 

T1 

T2 

T3 

T4 

Very Fine 

T5 

Table 4 –Texture Classification 

Figure 6 – Soil Texture 

13


1.5 Morphology parameters 

1.5.1 Mean elevation 

For each polygon of the soil map, the mean elevation was calculated through a zonal statistic 

average by crossing the soil map with the Digital Elevation Model of Italy. 

The different values were sorted into 5 classes. 

MEAN ELEVATION CLASSES Color Legend 

0-150 m E1 

150-300 m E2 

300- 600 m E3 

600-1000 m E4 

>1000 m E5 

Table 5 –Elevation Classification 

Figure 7 –Elevation 

14


1.5.2 Slope 

The slope data were extracted from the Soil Map of Italy database, this parameter is 

elaborated from the Digital Elevation Model of Italy. 

SLOPE 

CLASS 

0-2 % SLOPE1 

Color 

Legend 

2-5 % SLOPE2 

5-8 % SLOPE3 

8-15 % SLOPE4 

15-30 % SLOPE5 

> 30 % SLOPE6 

Table 6 –Slope Classification 

Figure 8 –Slope 

15


1.6 Overlay analysis of Climatic zones and pedological units 

Since the soil information was the most accurate data layer in term of spatial resolution, the 

analysis was carried out on the spatial entity of the soil polygons. For this purpose, an overlay 

was made to cross the climate information to the soil information. For each polygon of the 

soil map, a climatic class was attributed, based on the majority area of the climate grid 

occurring in the soil polygon. This result in a soil map with each of its polygons characterized 

with unique grid of meteorological parameters. 

Figure 9 – Overlay of homogeneous climatic Zones with the Soil information 

16


1.7 Crop Water Satisfaction Index 

The Crop Water Satisfaction Index (CWSI) is an indicator of satisfaction of crop water 

requirements combining soil and meteo information. It is based on the availability of water to 

the crop during the growing season. FAO studies (Doorenbos and Pruitt, 1977) have shown 

that the CWSI is related to crop production. 

The CSWI summarizes the amount to which cumulative crop water requirements have been 

met during the growing period and it is evaluated through a simple mass balance equation 

(CSWB model, FAO 1986, Frere and Popov 1979) where the content of soil water is 

monitored in a bucket defined by the Available Water Content (AWC, defined by the Water 

Holding Capacity (WHC) of the soil and the rooting depth) and the crop specific 

evapotranspiration (Annex II). 

CWSI for a growing period is calculated as the percent ratio between the duration (number of 

months) of the period with soil water content positive and the total duration of the growing 

period. 

In our case, it is calculated only for polygons with available pedological information on 

AWC. 

The water balance, or budget, of the specific crop is calculated in time increments (monthly 

in this case). The use of monthly time steps is a compromise between a good level of 

accuracy in meteorological input and an excessive data processing for water balance 

computation. The equation used by CSWB model is derived from the standard mass balance 

equation and is simplified in: 

Si 

= Si 

−1 

+ Ra 

− ETm 

where Si=soil moisture reserve (defined by AWC) at the end of the i th time interval, Si-1 soil 

moisture reserve at the end of the previous i th time interval. 

The maximum crop evapotranspiration is defined as: 

ETm = Kc 

* PET 

Potential evapotranspiration (PET) is a climatic variable, i.e. it is the "water demand of the 

atmosphere", often referred to as water "requirement" of a conventional crop. Actual crops 

have different requirements, which are related to crop development - early stages require little 

water - and weather conditions (for example, dry and windy conditions increase water 

demand). 

PET for this study was calculated from daily meteorological data through the FAO-Penman 

equation. 

In order to estimate real crop water requirements, PET values must be corrected through the 

use of a crop coefficient (K c ). Values of K c higher than 1.0 (i.e. ET m >PET) mean well 

developed crops, while values of K c lower than 1.0 (i.e. ET m


Figure 10 – Kc Crop Coefficient for specific ET 

Figure 11 represents the evolution of soil water content for a specific polygon during the 

Grain maize growing period, in this case. We assumed initial soil moisture equal to the 

available water- capacity of the soil (AWC). 

Maize Water Balance for polygon n.1252 

160 

140 

120 

100 

Water balance in mm 

80 

60 

40 

20 

0 

1 2 3 4 5 6 7 8 9 

-20 

-40 

Month 

WS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9 

150 150 150 118.984 76.668 12.323 -26.897 -23.3265 12.2145 

Figure 11 – Grain maize Monthly Water Balance for polygon n. 1252 

18


1.8 Suitability Criteria 

The different pedo-climatic parameters and indices are classified in terms of suitability 

criteria specific for each crop. 

The classification consists of 4 classes: 

• S1 Optimal Condition 

• S2 Sub-Optimal Condition 

• S3 Acceptable Condition 

• NS Non Suitable Condition 

1.9 Spatial Homogeneous Zoning 

The spatial homogeneous zoning for a culture over Italy follows 3 criteria: 

1. Crop Suitability Index Score: The methodology consists in a scoring system in order 

to compute a Crop Suitability Index, which ranks all the previously described agropedo-climatological 

suitability indices. Despite the various indices used have not a 

linear response to crops yield the choice to apply a simple scoring system is justified 

on the basis that we are not deriving a physical value of crop suitability or yield but 

rather defining areas with homogeneous agro-pedo-climatological conditions. The 

scoring methodology assumes that all the different parameters, except the CWSI in 

Irrigated Scenario, have the same weight in the total suitability index. All the factors 

were reclassified according to the following tables: 

Crop Suitability Class for 

each parameters 

Crop Suitability Index Score 

S1 10 

S2 5 

S3 1 

NS -1000 

Table 7 –Suitability Scores 

19


For the CWSI were defined three Non-Suitable classes in irrigated scenario, to account the 

availability of meteorological water, the following suitability index scores were applied: 

Non Irrigated Scenario 

Crop Water Satisfaction 

Index –CWSI classification 


S1 10 

S2 5 

S3 1 

NS -1000 

Irrigated Scenario 

Crop Water Satisfaction 

Index –CWSI classification 


S1 20 

S2 15 

S3 10 

NS1 5 

NS2 3 

NS3 1 

Table 8 –Suitability Scores for CWSI index 

Finally, all agro-pedo-climatic scores were combined through a simple addition in order 

to determine the Crop Suitability Index (CSI). In particular two different scores were 

calculated, the first in Non-Irrigated Condition and the second in Irrigated condition: 

CSI_Non_Irrigated= Sum the suitability score of all above parameters (climate, 

pedology, morphology) 

CSI_Irrigated= The water constraint parameters were removed (not considered), so the 

Available Water Content and the CWSI Suitability Score were excluded from the final 

computation of the Crop Suitability Index 

The Crop Suitability Index was classified in four homogeneous suitability classes: 

Crop Suitability Index Classes 

S3 - Optimal Condition 

S2 - Sub-Optimal Condition 

S1 - Acceptable Condition 

NS - Non Suitable 

Table 9 –Crop Suitability Classification 

2. Definition of Agro-pedo-climatological homogeneous zones: This zoning is based 

on the determination of all possible pedological, climatological and morphological 

suitability classes in Irrigated and Non-Irrigated Scenario. All possible combinations 

that contain only one non suitable parameter class are classified as Non Suitable (NS) 

20


2 Discussion 

This paragraph discusses the results of the zoning methodology outputs for the crops of 

interest: Grain maize, durum wheat, winter wheat, barley, tomato, soy bean, rapeseed, sugar 

beet. 

A detailed step-by-step description of the zoning results for each crop is reported in the 

annexes. 

The outputs consist in different zoning maps for each crop: 

• A Suitability Map where each zone is characterized in terms of potential crop 

suitability in non-irrigated and irrigated scenarios. The suitability score index for each 

zone is classified in the following four classes: 

o S1 Optimal Suitability – land having no limitation for the cultivation of a 

given crop; 

o S2 Sub-Optimal Suitability: land having only minor limitations that in 

aggregate are moderate and may reduce productivity only marginally 

o S3 Acceptable Suitability: land with limitations that in aggregate can be severe 

and as such reduce productivity significantly but cultivation is still possible. 

o NS Non Suitable: land with a major constraint for crops cultivation which is 

impossible (physically or economically) to remove. 

• An Agro-Pedo-Climatological Map where each zone is characterized with uniform 

suitability classes for each pedological, climatological and morphological parameters 

in Irrigated and Non-Irrigated Scenario. In this zoning map each class of polygon is 

characterized with uniform suitability classification of the entire set of pedological, 

climatological and morphological parameters. These agroecozones are geographic 

areas which share similar biophysical characteristic for crop production, such as soil, 

landscape, and climate, which define the potential for agricultural productivity in 

terms of suitability. 

The Grain maize suitability index score zoning map in non-irrigated scenario (figure 29) 

points out that only in a few number of polygons, concentrated in the north of Italy, the water 

requirement and the pedological characteristics (AWC) are satisfactory for Grain maize 

production without irrigation. 

In irrigated scenario (figure 27) the high values of suitability score index are principally 

located in the north of Italy where the pedological and meteorological condition are optimal 

for high Grain maize yield. However, for some zones located in the north of Italy (north of 

the Udine Province), some polygons were found unsuitable for the soil depth condition (less 

than 50 cm) while the cultivation is present according to expert knowledge. Some 

verifications of the soil map for these polygons are underway. 

The map of agro-pedo-climatological zoning for Grain maize display 139 (figure 28) and 31 

(Figure 30) suitable classes respectively in the irrigated and non-irrigated scenarios. 

For soft wheat, barley, durum wheat and rapeseed the suitability index score zoning map was 

developed only in the non-irrigated scenario (figures 43, 57, 139). The resulting maps are 

similar and they have an high potential suitability class (S1) that cover a large area of the 

Italian territory. The slight differences between the zoning maps are explained by a different 

phenological calendar while the pedological requirements are the same for all the crops. 

Rapeseed has a suitability more restricted to the north of Italy (Po Valley). 

21


The number of agro-pedo-climatological classes defined in non-irrigated scenario are 121 

(figure 44) for soft-wheat and barley, 183 (figure 58) for durum wheat and 123 (figure 140) 

for rape. 

Sugar beet is a crop with high water requirement, the resulting suitability index score map 

(figure 86) in non-irrigated scenario displays only few suitable zones located in the north of 

Italy. For the remaining areas, the potential suitability is possible only with irrigation. 

In irrigated scenario the suitability index score for sugar beet has higher values in the north 

(Emilia-Romagna and Piemonte flat areas), centre-west (Maremma – Toscana) and some 

areas in the south (Campania and Puglia), while the east coast is characterized by a suboptimal 

suitability value. The unsuitable zones are located in Puglia and north of Udine 

province where, as for Grain maize, the constraint of soil depth is applied. 

The maps of agro-pedo-climatological zoning for sugar beet display 108 (figure 91) and 25 

(Figure 89) classes respectively in the irrigated and non-irrigated scenarios. 

Sunflower (figure 103) and Soy bean (figure 120) have a large number of suitability zones in 

non-irrigated scenario, 81 agro-pedo-climatological zones for sunflower (figure 106) and 65 

agro-pedo-climatological zones for soy bean, with high suitability index score in the north of 

Italy (Po valley). In irrigated (figure 107 for sunflower and figure 124 for soy bean) scenario, 

the non suitable zones are represented by steep and high land, while high potential suitability 

are located in Emilia Romagna, Torino Province, Toscana and Foggia Province. 

Sunflower has 189 agro-pedo-climatological zones (figure 108) while soy bean has 122 agropedo-climatological 

zones in irrigated scenario. 

A large part of north Italy Po Valley (with the exception of Piemonte) and some areas in 

Toscana, Campania, Lazio and Puglia are suitable for tomato (figure 69) production also in 

non-irrigated conditions and are characterized with high suitability index score. 

In irrigated scenario (figure 70) only few additional areas become suitable, in particular the 

entire Puglia Region; in fact the high thermal requirements cannot be satisfied in a vast area 

of the north-west and in some part of central Apennine. 

The number of agro-pedo-climatological zones defined in non-irrigated is 119 (figure 72) 

while 232in irrigated scenario (figure 74). 

22


3 Analysis of yield data from the AGRIT point survey 

3.1 Data used for the analysis 

The yield data come from the AGRIT ground surveys on a sample of points between 1997 

and 2001. They have been obtained by expert opinion on the field about 3 weeks before 

harvest. Therefore they do not take into account the meteorological events in the last 

phenological period. We cannot attribute a weight to each observation because we do not 

have sufficient information on the sampling method that has been used for the AGRIT yield 

survey. Yield values above 250 q/ha have been deleted, considered unreliable. There is a 

considerable number of very low yield values, including 21 zero values, but they have been 

kept as credible (diseases or other causes of total crop loss). Until now, it’s no possible to 

evaluate correctly the reliability of data provided from the ITA consortium. Actually these 

data are considered experimental and not used operationally for agricultural statistics. 

Pedological and climate information, as described above in this publication. 

A digital terrain model (DTM) with a resolution of 250m. 

Data of a specific yield survey for Grain maize in some areas of 5 provinces in northern Italy. 

AGRIT observations and ISTAT data by province 

Comparing the mean yield by province of the AGRIT observations with the ISTAT (Istituto 

Nazionale di Statistica) data (average for the available years 1997-2001) indicates that the 

correlation of both data sources is good, but not perfect, with some considerable 

disagreement for Grain maize. We can call this correlation “spatial consistency”. This means 

that in general terms AGRIT observations tend to be higher in the provinces in which ISTAT 

data give higher yields. All correlations are computed using the crop area of the province 

(ISTAT) as weight of each observation. 

Durum wheat 0.92 

Common wheat 0.89 

Grain maize 0.67 

Barley 0.86 

Table 10 – Spatial consistency between ISTAT data and AGRIT observations. 

To measure the consistency of the time evolution of the ISTAT data and the AGRIT 

observations, we have computed as a correlation of residuals: 

Time consistency = corr resid Istat 

resid 

where 

resid 

Istat, prov 

= yIstat,Pr 

ov, 

t 

− 

( ) 

, Pr ov, 

y 

Istat, 

Pr ov 

Agrit, 

Pr ov 

and 

resid 

Agrit, prov 

= y 

Agrit,Pr 

ov, 

t 

− 

Durum wheat 0.07 

Common wheat 0.30 

Grain maize 0.06 

Barley 0.30 

Table 11 – Time consistency between ISTAT data and AGRIT observations. 

y 

Agrit, 

Pr ov 

Time consistency is much lower than spatial consistency between both data sources, i.e. the 

increase or decrease of yield from one year to the next is nearly not correlated. This low level 

23


24 

of consistency is difficult to interpret, and indicates that some prudence is needed to interpret 

the results obtained with these data. Such results will need further confirmation or rejection 

with a specific data collection. However, even if we have some doubts on the reliability of the 

AGRIT yield observations on georeferenced points, they are at the moment the only data we 

can use to validate the homogeneity of pedo-climatic zones. 

3.2 Grain maize analysis 

The areas considered as suitable for Grain maize cultivation roughly correspond with the 

areas where most yield data are observed, but 9% approximately of the observations fall on 

areas considered as non suitable by the soil map. However this does not mean that 9% of the 

cultivated Grain maize falls on such “non-suitable” areas (remember that weights could not 

be computed). 

There may be several reasons for that. The most likely reasons are scale and boundary 

accuracy, i.e. maize is cultivated in suitable patches that are too small to be reported in the 

soil map or near the borders between polygons and are incorrectly attributed to a different 

polygon, for example many of these observations fall on polygons labeled as “urban”. 

Anyway a less restrictive definition of “suitable soil” might be recommended. 

About 11% of the observations lye in polygons for which pedological data is not specified in 

the pedological map because but soil scientists consider that the available information is 

insufficient to attribute a reliable label to the polygon; such points are considered for this 

analysis as missing data. 

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Q/ha 

Figure 12– Location of the AGRIT observations for Grain maize

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Figure 14– AGRIT observations with missing data in the soil map 

25


Basic description of the Grain maize yield data. 

We stress again that the absence of extrapolation weights for the sample units and the 

observation date prevent from considering the simple means as “valid statistical estimates”. 

However they provide interesting indications. 

The mean observed yield obviously changes from year to year and has a tendency to increase 

(technological trend). In spite of the significant inter-annual change, we can observe that the 

standard deviation of the yields in the same year is of the same order of the global standard 

deviation. This means that the spatial variability of yield is much larger than the time 

variability. The average of the yield data in polygons identified by the soil map as non 

suitable is much lower, as expected. The yield data in areas with missing data for suitability 

(generally missing soil data) is significantly higher. This suggests that an additional effort 

should be made to fill missing soil data. 

Year 1997 1998 1999 2000 2001 all 

all data N 2157 2073 1920 2665 2932 11741 

mean 82.4 82.4 88.1 92.8 90.5 87.7 

st. dev 32.2 30.7 26.7 31.2 37.8 32.7 

suitable N 1472 1435 1182 1779 1996 7864 

mean 85.1 85.5 90.9 95.9 95.7 91.2 

st. dev 29.9 28.6 25.8 28.7 33.3 30.1 

non suitable N 441 406 464 508 548 2367 

mean 70.3 68.7 77.6 76.7 61.9 70.9 

st. dev 35.6 33.4 27.8 36.7 41.4 36.0 

missing N 244 232 274 378 388 1516 

soil data mean 87.5 86.9 94.2 99.6 104.6 96.0 

st. dev 34.5 31.6 23.4 27.1 34.5 31.2 

Table 12 –AGRIT Grain maize yield data 

Four categories of soil depth have been defined in the zoning, but only 3 of them are 

represented: Very suitable (S1), Sufficient (S3), and non Suitable (NS). Means by soil depth 

class and region suggest that the link between soil depth and yield is rather irregular. This 

fact is confirmed by the results of the Analysis of Variance given below. 

26


Soil depth 

Region very suitable sufficient non suitable no data 

mean n mean n mean n mean n 

Abruzzo 60.4 232 56.7 180 55.0 1 

Basilicata 37.7 31 41.3 14 31.8 14 

Calabria 49.0 91 38.3 16 42.5 2 48.3 22 

Campania 43.9 855 31.4 137 41.3 13 41.6 118 

Emilia R. 90.4 807 84.9 68 72.6 22 74.5 2 

Friuli 126.3 166 125.0 369 117.2 178 117.5 88 

Lazio 84.9 302 67.2 58 78.1 119 85.3 60 

Liguria 68.3 3 65.0 1 40.0 1 55.0 1 

Lombardia 107.8 1013 112.3 881 109.3 90 111.9 525 

Marche 61.2 296 57.4 261 47.9 60 45.5 44 

Molise 40.1 318 51.3 17 26.8 18 

Piemonte 88.6 376 94.3 445 89.5 48 84.7 24 

Puglia 88.7 174 92.6 93 89.7 170 91.7 99 

Sardegna 83.3 37 73.0 5 75.9 5 

Sicilia 50.5 2 60.3 8 48.7 3 40.0 1 

Toscana 61.3 110 66.9 6 46.6 7 47.5 4 

Trentino 

A.A. 

58.0 11 55.0 2 

Umbria 71.5 483 70.9 18 74.2 6 62.5 33 

Val 72.5 1 67.7 5 

Ven 104.1 996 106.9 585 104.1 289 98.0 201 

Table 13 –soil depth analysis 

The link between yield and other soil characteristics is also irregular, but the link with 

elevation is more clear: 

elevation n obs mean yield 

0-100 m 6236 100.3 

100-200 m 2000 90.7 

200-300 m 1391 79.2 

300-500 m 1292 61.3 

>500 m 822 40.4 

Table 14 –elevation analysis 

27


Analysis of Variance (Analysis) – ANOVA -of the Grain maize yield data. 

The Analysis of Variance helps us to identify the degree of link between yield observations 

and categorical independent variables representing our information on soil, climate, altitude 

and administrative units. Many textbooks give good descriptions of ANOVA (see for 

example Arnold, 1981, Seber, 1984). Some care is needed when ANOVA is carried out with 

a computer package on an “unbalanced” data set, i.e. when the number of observations for 

different values of the independent variables is very irregular. The most usual algorithms for 

ANOVA are not applicable for data sets with a strongly unbalanced design. We have used the 

GLM procedure (General linear model) of the SAS (statistical software) package, that 

provides a suitable algorithm for unbalanced ANOVA. 

The GLM procedure has been applied for the yield of maize using the following independent 

variables: 

• Region 

• Year 

• Soil depth 

• Soil texture 

• “Irrigation”: number of months that the crop would need to be irrigated, according to 

an agromet model. 

• Elevation. 

• Slope 

( reg) + f ( year) + f ( sdepth) + f ( stext + f ( irrig) + f ( elev) + f ( ) + ε 

Y i 1 2 

3 

4 

) 5 

6 

7 

= f 

slope 

The ANOVA results indicate which is the part of the yield variability that can be explained 

by each of the variables. The results presented here correspond to 10479 yield observations 

on points where soil data are available. We simplify the results here, focusing on two types of 

statistics: the so-called type I and type III sum of squares (SS). The type I sum of squares 

assumes that the independent variables are introduced stepwise in the model; it indicates the 

part of variance that is explained by each variable excluding the variance that has been 

explained by variables introduced before; therefore it is order-dependent. The type III sum of 

squares indicates the part of variance that is explained by each variable and is not explained 

by any of the other variables, thus it coincides with the type I sum of squares for the last 

variable introduced. 

We have introduced the Region as first variable and we can observe a big difference between 

the type I SS and the type III SS. For example the region can explain 60% of the total 

variance. 13.6% can be explained only by the region. The difference 60-13.6=46.4% can be 

explained by the region or by the other variables. 

The interpretation of the difference between type I and type III SS depends on the a priori 

assumptions of the zoning criteria: if we assume that the homogeneous zones must be inside 

administrative regions anyway, type I SS must be considered introducing the region as first 

independent variable in the ANOVA, but if we accept that homogeneous zones can straddle 

regional borders, different options can be considered. 

28


Region 

Degrees 

of 

freedom 

type I SS % 

Type III SS 

% 

19 6712429 60.0 1523686 13.6 

Year 4 144598 1.3 121317 1.1 

Soil depth 2 10384 0.1 4135 0.0 

texture 2 859 0.0 4668 0.0 

Irrigation needed 7 128521 1.1 18332 0.2 

Elevation (5 

4 323791 2.9 149580 1.3 

classes) 

Slope 3 119113 1.1 119113 1.1 

total model 7439695 66.5 

Total SS 11178659 

Table 15 –ANOVA results for Grain maize 

Results suggest that after removing the strong variability by region, only the topography 

(elevation and slope) is able to explain a significant part of the yield variability. The interannual 

variability is also moderate. 

Several additional questions can be put, among which: 

How much the model is improved introducing provinces instead of regions? 

Which is the improvement with a finer partition of altitudes (12 intervals of 100 m, for 

example)? 

Introducing the province increases the % of variability explained from 66.5 to 69.1, hence 

there is still a noticeable, but not huge, variability between provinces in the same region. 

Introducing 12 intervals of altitude leaves the correlation index (r 2 ) practically unchanged 

(66.6% or 69.1% with regions or provinces). This suggests that estimating yield by provinces 

is reasonable, as well as making the difference among 4 intervals of elevation, while a much 

finer partition of altitudes is unnecessary. The best results were obtained with 5 elevation 

intervals with thresholds 100, 200, 300 and 500 meters (the threshold 500 m works slightly 

better than 600 m). 

The results are very similar excluding 729 values lower than 30 q/ha, that can be interpreted 

as “catastrophic event”, and too far from the concept of potential yield. 

29


3.3 Common wheat analysis. 

Yield observations of common wheat have been analysed, in order to assess if the 

conclusions reached for maize are valid for other crops. There are 8816 AGRIT observations 

linked to a polygon. 

The next parameters are considered: 

• Temperature Satisfaction Index - TSI, 

• Thermal Satisfaction Index at flowering-ThSI 

• Thermal Satisfaction Index at ripening - ThSI 

• Frost Risk Index - FRI, 

• Heat Stress Risk Index - HSRI 

• Soil depth 

• Soil texture 

• Elevation 

• Slope 

• Irrigation needed (water deficit). 

More than 96% of the observations are suitable for all meteorological variables, none of them 

fall in areas considered as non suitable for heat risk, and only 1 out pf 8816 falls in a polygon 

labelled as non suitable for frost risk. This shows that the choice of meteorological thresholds 

is consistent with the crop location (little crop area in “non suitable zones”), but the 

suitability might need to be subdivided into a larger number of values to improve the yield 

variability reduction. The behaviour of yield for different types of soil is consistent in general 

terms. For example for soil depth we get: 

ANOVA for common wheat 

soil depth n obs mean yield 

very good 7275 49.2 

sufficient 796 45.5 

Unsuitable 5 32.8 

No data 740 46.6 

Table 16 –Soil Depth test 

Some meteorological variables for which all or nearly all observations are in the same class 

are excluded from the analysis: Temperature satisfaction (1 observation non suitable), frost 

risk (1 observation non suitable) and heat risk (all observations suitable). Two observations 

with likely wrong yield values 177 and 278 q/ha have been removed. The rest of the values 

are below 100 q/ha. 

For common wheat the percentage of variance explained by the model is lower than for 

maize, but the main characteristics remain similar: 

Moderate inter-annual variation. 

A significant part of the variance corresponds to the difference of yield means by region. 

Pedo-climatological information gives little additional contribution after the region has been 

introduced as independent variable. 

Elevation is the physical parameter with the highest capacity of variance reduction. 

30


Region 

Degrees of 

freedom 

Type I SS % 

Type III 

SS 

16 1107611 56.5 422894 21.6 

Year 4 16719 0.9 15067 0.8 

Soil depth 3 18826 1.0 2980 0.2 

texture 3 1344 0.1 2793 0.1 

Irrigation needed 3 4404 0.2 6344 0.3 

Thermal 

satisfaction 1 5242 0.3 881 0.0 

flowering 

Thermal satisf. 

ripen. 

1 24 0.0 327 0.0 

Elevation 4 62912 3.2 41294 2.1 

Slope 4 9763 0.5 9763 0.5 

total model 1226845 62.6 

total SS 1961174 

Table 17 –ANOVA results for common wheat 

For this table the elevation thresholds used were 100, 200, 300 and 500 m. The elevation is 

computed for the single point rather than an average for the polygon. 

Introducing the province instead of the region, the % of variance explained increases up to 

66.5% 

If we keep only region, year and elevation (5 classes), the proportion of variance explained is 

61.4% (65.6% using the province instead of the region). If we drop observations with a yield 

below 15 q/ha to be closer to the concept of potential yield, the shares of variance are very 

similar. 

% 

31


3.4 Barley analysis 

The zoning proposed for barley and the definition of the suitability variables is identical to 

the ones for common wheat and the conclusions are quite similar, although the part of the 

variance explained by the elevation is smaller, as illustrated in the table below, where the 

altitude is again classified into 5 classes with thresholds 100, 200, 300 and 500 m. Four 

observations with yield > 100 q/ha (between 168 and 320) are considered wrong and 

removed. 

Degrees 

of 

freedom 

Type I 

SS 

% 

Type 

III SS 

Region 16 816392 48.5 489734 29.1 

Year 4 11865 0.7 11678 0.7 

Soil depth 3 12205 0.7 4297 0.3 

Soil texture 3 1600 0.1 1820 0.1 

Irrigation 3 1320 0.1 687 0.0 

Thermal 

satisfaction 

flowering 

Thermal 

satisfaction 

ripening 

% 

1 2023 0.1 7 0.0 

1 325 0.0 336 0.0 

Elevation 4 21557 1.3 21557 1.3 

867287 51.6 

total 1682315 

Table 18 –ANOVA results for barley 

3.5 Conclusions. 

This analysis was carried out with the only available set of georeferenced point yield 

observations covering most agricultural areas in Italy. These data have a limited reliability, 

mainly because the observations were made about three weeks before harvest. Therefore the 

validity of the conclusions drawn from this analysis needs to be confirmed (or not) with more 

reliable yield observations on points with known co-ordinates. However, even with this 

caution warning, several suggestions can be made: 

• Most of the yield variability explained by the available soil map seems to coincide 

with the variability explained by administrative units. 

• A number of yield observations fall in polygons for which soil data have not been 

reported because the information available is considered insufficient. 

• The altitude classified into 5 intervals with 100, 200, 300 and 500 meters has 

reasonable, although not extremely strong power to explain yield variability. 

If the results obtained so far are confirmed with ad-hoc data acquisitions, a first simple way 

of defining areas of homogeneous potential would be a partition of provinces subdivided into 

5 altitude classes. Further aggregations can be made to avoid zones that are too small or 

which contain a small agricultural area. 

32


4 Concluding Remarks 

In the course of this project, we have developed a methodology to derive potential 

homogeneous suitable zones for different annual crops by combining in a GIS environment 

pedological, morphological, meteorological and information on crop phenology. 

The zoning methodology has been applied to different crops over Italian territory and is based 

on matching the crop requirement parameters with meteorological, morphological and 

pedological conditions. This procedure identifies crop-specific limitations of prevailing 

climate, soil and terrain resources under normal farming practice and management conditions 

in irrigated and non-irrigated scenario. 

The methodology developed uses pedological soil database from the European Soil Bureau, 

climatological and morphological data available from JRC databases. The procedure was 

completely implemented inside a GIS-based modeling framework; the results are estimated 

by pedological polygon unit. 

The first output of agro-pedo-climatological zoning methodology consists of a delineation of 

homogeneous areas characterized by the same environmental (agro, meteo and pedological 

characteristics) conditions for each crop of interest. Under the hypothesis of uniform crop 

management conditions, one can assume that each zone is characterized by a potential 

homogenous yield. 

The second output of the methodology, defines a suitability index that group several agropedo-climatological 

homogeneous zones in four classes of potential suitability: NS-Non 

Suitable, S1 High Suitable, S2 Suitable and S3- Acceptable. This suitability characterization 

is an indicator for clustering the zones in areas with a theoretical similar yield potential. 

During the development and the application of the zoning methodology several limitations of 

the data were identified, we also suggest some improvements for the future: 

• Climate data were represented in a grid of 50 km, this resolution of meteo information 

is too low for a good representation of the spatial distribution of meteorological 

conditions. It is desirable to obtain more detailed meteorological data. A possible 

source could be from UCEA (Ufficio Centrale di Ecologia Agraria) which collects 

and manages data from a large number of weather stations and interpolates data in a 

grid of 30 km. An alternative approach could be the use of local area weather models 

that provide meteorological conditions on a grid from 5 to 30 km. 

• The climatic crop requirements were matched with several meteo climatic indices 

computed using long term averaged data over 25 years. In this way it was not possible 

to consider the annual variability of climatic events, like frost, heat stress or drought. 

Future developments of the zoning methodology should consider the use of daily data 

(not averaged) to take into account the inter annual variability of the weather during 

the crop development. Furthermore to improve the assessment of the inter-annual 

variability of the climatic conditions, long series (typically 100-500 years) of weather 

parameters could be simulated through the use of a stochastic weather generator, 

statistically trained on the 25 years of available observation. The advantage of such 

method lies in the quantification of probability of occurrence of a particular 

meteorological situation based on much longer climatic time series. 

• The polygon of pedological soil database is the basic unit of the zoning methodology. 

The spatial resolution (1:250,000) was considered accurate for the crops zoning 

activity, but for some pedological parameters, such as Soil Depth, some problems 

occurred and a verification is necessary using regional pedological information 

(Province of Udine, Puglia Region). 

33


The statistical validation highlighted some data deficiency in the statistical data sets, various 

sources of crop yields statistics were composed and crop yield are not consistent amongst 

themselves. 

An attempt at validation of the agro-pedo-climatological zones was done from AGRIT field 

measurements data but could not confirm the spatial homogeneous zones defined in this 

report. 

From the statistical analysis, most of the variance would be explained by the province and the 

elevation parameter. However, these findings should not be used before a proper validation is 

done using yield field measurements done by ISMEA in 2002 for Grain maize in 5 provinces 

of Italy (Ferrara, Mantova, Udine, Torino, Padova). 

Only after reliable field data are available for validation, definitive conclusions on the zoning 

methodology may be drawn. 

34


List of Acronyms 

ESB: European Soil Bureau 

ESRI: Environmental Systems Research Institute 

FAO: Food And Agriculture Organization Of The United Nations 

GIS: Geographic Information System 

GTS: Global Telecommunication System 

ISTAT: Istituto Nazionale di Statistica 

JRC: Joint Research Centre 

UCEA Ufficio Centrale di Ecologia Agraria 

UTM: Universal Transverse Mercator 

WMO: World Meteorological Organization 

35


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Communities. Report 53/3. The Winand Staring Centre, Wageningen, and the Joint Research 

Centre Ispra, Italy 

Vossen P., Rijks D., 1995. Early crop yield assessment of the EU Countries: the system 

implemented by the Joint Research Centre., Joint Research Centre, Ispra, Italy 

Waltman W.J., Mortensen D.A., Cassam K.G., et al., 1999. Agroecozones of Nebraska:. 

Agronomy Abstracts, Annual Meetings, ASA-CSAA-SSSA, Salt Lake City, UT 

39

Annexes 

Annex I: Crop Zoning Application 

40

Annex I - Grain maize Zoning 

Grain maize in Italy 

Grain maize (Zea mays L.) is one of the most spread crop, thanks to the easy way to cultivate, 

to the fast growth (only two months and a half) and to its adaptability: it can grow both in 

equatorial temperature and polar one, both at sea level and at 3.000-3.500 meters of heights. 

Italy is the second Eu producer after France, with more than 10 millions of tonnes, and a total 

area over 1 million of hectares, which is the 25% of the total Eu area. At regional level, 90% 

of grain maize area, is concentrated in 5 regions (Veneto, Lombardia, Piemonte, Friuli 

Venezia Giulia ed Emilia Romagna). 

In spite of the two varieties of maize, one white and one yellow, only the second one is 

cultivated and consumed in our Country. 

Growing Period in Italy 

Spatial Zone Sowing Germination Flowering Ripening 

North II decade/April I decade /May III decade /July II decade /September 

Centre III decade/April I decade /May II decade /July I decade /September 

South-Islands I decade/May II decade /May II decade /July III decade /August 

Table 19 –Grain maize Calendar 

41


Climatic Suitability Map 

Temperature Satisfaction Index -TSI 

The Grain maize Specific Satisfaction Temperature was fixed at 10 Cº. 

The Temperature Satisfaction Index (TSI) for Grain maize was classified in term of 

suitability in the following classes: 

Suitability Classes TSI Color Legend 

S Suitable >=80 % 

NS Non Suitable


Frost Risk Index -FRI 

The Grain maize specific Frost Temperature was assumed equal to 4 Cº. 

The Frost Risk Index (FRI) for Grain maize was classified in term of suitability in the 

following classes: 

Suitability Classes FRI_TG Color Legend 

S Suitable 100 % 

NS Non Suitable


Heat Stress Risk Index -HSRI 

The Grain maize specific Heat Stress Temperature was assumed equal to 32 Cº. 

The Heat Stress Risk Index (HSRI) for Grain maize was classified in term of suitability in the 


Suitability Classes 

HSRI_TG 

Color Legend 

S 100 % 

NS


Thermal Satisfaction Index - ThSI 

The Grain maize specific Base Temperature for this index was assumed equal to 6 Cº. 

The ThSI_TF (Flowering) and ThSI_TR (Ripening) Thermal Satisfaction Index (ThSI) for 

Maize until flowering and ripening period were classified in term of suitability in the 


Suitability Classes ThSI_TF Color Legend 

S1 Optimal 

>=1500 C°d 

S2 Sub-Optimal 1000-1500 

NS Non Suitable 

=1000 C°d 

S2 Sub-Optimal 800-1000 



Figure 19– Grain maize ThSI Index Suitability Classification until Ripening Period 

46


Pedological Suitability Criteria 

Soil Depth 

Suitability Classes Soil Depth Color Legend 

D1 


D2 

D3 

S3 Acceptable 

D4 

S1 Optimal 

D5 

Table 24– Grain maize Soil depth Suitability Classification 

Figure 20– Grain maize Soil depth Suitability Classification 

47


Texture 

Suitability Classes TEXTURE Color Legend 

S3 Acceptable 

T1 

S2 Sub-Optimal 

S1 Optimal 


T2 

T3 

T4 


T5 

Table 25– Grain maize Texture Suitability Classification 

Figure 21– Grain maize Texture Suitability Classification 

48


Morphology Suitability Criteria 

Mean elevation 

Suitability Classes Mean Elevation Color Legend 

S1 Optimal 

E1 

S2 Sub Optimal 

E2 

S3 Acceptable 

E3 


E4 

E5 

Table 26– Grain maize Mean Elevation Suitability Classification 

Figure 22– Grain maize Mean Elevation Suitability Classification 

49


Slope 

Suitability Classes Slope Color Legend 

S1 Optimal 

SLOPE1 


SLOPE2 

S3 Acceptable 

SLOPE3 

SLOPE4 


SLOPE5 

SLOPE6 

Table 27– Grain maize Slope Suitability Classification 

Figure 23– Grain maize Slope Suitability Classification 

50


Crop Water Satisfaction Index Suitability 

The Crop Satisfaction Index for Grain maize was calculated following the methodology 

described in the paragraph 1.7, on the basis of value of Grain maize water requirements 

(Grain maize Kc equal to 0.4 from January until March, 1.10 from April until July, and 0.6 

from August until September), the CWSI Suitability classes were defined: 

CWSI Suitability Classes CWSI Color Legend 

S1 Optimal 100% 

S2 Sub-Optimal 86% 

S3 Acceptable 71% 

NS Non Suitable


Suitably Index Score 



Suitability Index 

Score 

S3 0-40 0.42 

S2 40-50 1.6 

% Area Color Legend 

S1 50-70 6.8 

NS




Suitability Index 

Score 

Area % 

S3 0-40 10.6 

Color Legend 

S2 40-60 34.2 

S1 60-80 7.47 

NS


Agro-Pedo-Climatological Zoning 


Suitability Calsses Area % Color Legend 

S – Suitable 8.9 

NS – Non Suitable 91.1 

Table 31– Grain maize Suitability Classification in Non-Irrigated Scenario 

Figure 27– Grain maize Suitability Class in Non-Irrigated Scenario 

54


The number of agro-pedo-climatologic zones identified for Grain maize in non-irrigated 

scenario is 31. 

Figure 28–Grain maize Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

55



Suitability Classes Area % Color Legend 



Table 32– Grain maize Suitability Classification in Irrigated Scenario 

Figure 29–Grain maize Suitability Class in Irrigated Scenario 

56


The number of agro-pedo-climatologic zones identified for Grain maize in irrigated scenario 

is 139. 

Figure 30– Grain maize Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

57

Annex I - Soft Wheat - Barley 

Soft Wheat - Barley in Italy 

Soft wheat (Triticum aestivum L.) is the most cultivated cereal in the world and it represents 

about the 40% of the world crop production, with 220 millions of hectares cultivated. It is a 

continental crop, and in Italy it spreads mainly in the Northern-Central regions. In the 90’s, 

the total area and production decreased both for the less specialization and for the decreasing 

of the Community aid, foreseen in the revision of the OMC. Today, for the production, the 

incidence of the sector on the Eu one is about the 3%. At regional level, Emilia Romagna has 

one third of the total Italian area, followed by Piemonte and Umbria, which has recorded the 

most decreasing. Concerning the varieties Serio is the most cultivated, and then Centauro and 

Mieti. 

Barely (Hordeum vulgare L.) is one of the most ancient cereals in the world. It grows also in 

little rich soils and it adapts to every climate, on the plain or on the mountain, thanks to the 

short vegetative cycle, which in some varieties is less then three months. Italy is not 

specialized for barely and the total area is 333 thousands of hectares, which is the 3% of Eu 

total area. The crop is spread in all the regions but mainly in the Southern ones. 

The most important varieties are: barely esastic, tetrastic and distic and, as the wheat, these 

varieties are divided in durum and soft barely. The first one is for human consumption while 

the second one, (the distic barely) is for the malt production directed to the beer industry and 

to the whisky production. 



North II deca./October I deca. /November II deca. /May III deca /June 

Centre I deca./November II deca. / November II deca /May III deca /June 

South-Islands II deca./November III deca. / November III deca /April II deca /June 

Table 33 –Soft Wheat – Barley Calendar 

58




The Soft Wheat - Barley Specific Satisfaction Temperature was fixed at 2 Cº. 

The Temperature Satisfaction Index (TSI) for Soft Wheat - Barley was classified in term of 



TSI 


Color 

Legend 

NS Non Suitable



The Soft Wheat - Barley specific Frost Temperature was assumed equal to -10 Cº. 

The Frost Risk Index (FRI) Soft Wheat -Barley was classified in term of suitability in the 



FRI_TG 


Color 

Legend 

NS Non Suitable



The Soft Wheat - Barley specific Heat Stress Temperature was assumed equal to 32 Cº. 

The Heat Stress Risk Index (HSRI) Soft Wheat -Barley was classified in term of suitability in 

the following classes: 


HSRI_TG 

S 100 % 

Color 

Legend 

NS



The Soft Wheat - Barley specific Base Temperature for this index was assumed equal to 0 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Soft Wheat - Barley until 

flowering and ripening period were classified in term of suitability in the following classes: 


S Suitable 



S Suitable 

ThSI_TF 

>=1250 C°d 

=900 C°d 

Color 

Legend 

Color 

Legend 



Figure 35– Soft Wheat – Barley ThSI Index Suitability Classification until Ripening Period 

63


Vernalization Satisfaction Index - VSI 

The Soft Wheat - Barley specific Vernalization Satisfaction Temperature is between -1 and 

12 Cº. 

The Vernalization period for Soft Wheat – Barley is fixed from germination to the end of first 

decade in February. 

The Vernalization Satisfaction Index (VSI) for Soft Wheat - Barley was classified in term of 



TSI 

Color Legend 


NS Non Suitable



Soil Depth 



D1 

D2 

S3 Acceptable 

D3 

D4 

S1 Optimal 

D5 

Table 39– Soft Wheat – Barley Soil depth Suitability Classification 

Figure 37– Soft Wheat – Barley Soil depth Suitability Classification 

65


Texture 


S3 Acceptable 

T1 


S1 Optimal 

S1 Optimal 

T2 

T3 

T4 


T5 

Table 40– Soft Wheat – Barley Texture Suitability Classification 

Figure 38– Soft Wheat – Barley Texture Suitability Classification 

66





S1 Optimal 

E1 

E2 

E3 


E4 


E5 

Table 41– Soft Wheat – Barley Mean Elevation Suitability Classification 

Figure 39– Soft Wheat – Barley Mean Elevation Suitability Classification 

67


Slope 


S1 Optimal 

SLOPE1 

SLOPE2 

SLOPE3 


SLOPE4 

S3 Acceptable 

SLOPE5 


SLOPE6 

Table 42– Soft Wheat – Barley Slope Suitability Classification 

Figure 40– Soft Wheat - Barley Slope Suitability Classification 

68



The Crop Satisfaction Index for Soft Wheat - Barley was calculated following the 

methodology described in the paragraph 1.7, on the basis of value of Soft Wheat - Barley 

water requirements (Soft Wheat - Barley Kc equal to 0.4 from January until March, 1.10 from 

April until May, and 0.4 until June ), the CWSI Suitability classes were defined: 

Suitability Classes CWSI Color Legend 

S1 Optimal 100-83% 

S2 Sub-Optimal 83-67% 

S3 Acceptable 67-50% 

NS Non Suitable




Suitability Classes Suitability Index Score % Area Color Legend 

S3 0-30 6 

S2 30-40 32 

S1 40-50 35 

NS





S – Suitable 73 

NS – Non Suitable 27 

Table 45– Soft Wheat - Barley Suitability Classification in Non-Irrigated Scenario 

Figure 43– Soft Wheat - Barley Suitability Class in Non-Irrigated Scenario 

71


The number of agro-pedo-climatologic zones identified for Soft Wheat - Barley in nonirrigated 


Figure 44–Soft Wheat - Barley Suitable Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

72

Annex I - Durum Wheat 

Durum Wheat in Italy 

Durum wheat (Triticum durum) is a crop developed till 60’s mainly in the South of Italy, 

because of its drought and hot - resistant. In the last 20 years, durum wheat has spread also in 

the Centre-North regions, since the introduction of new varieties cold –resistant. Today, Italy 

is the main Eu producer, with the 44% of the total Eu area. Coming into force of the CAP 

reform, - MacSharry ’92 and Agenda 2000, ’99 – it helped to increase this position, because 

against the decreasing of the total area cultivated to wheat in complex in all Eu, Italy has 

become more specialized towards durum wheat. So the area to durum wheat was stable for all 

the 90’s while the area to soft wheat decreased. At regional level, Puglia is the more 

specialized region, followed by Sicilia and Basilicata. Among the Centre-North regions, 

Toscana, Marche and Lazio are the most important. Concerning the varieties Simeto is the 

most cultivated, and then Duilio, Arcangelo and Creso. 



North III deca./October II deca. /November I deca. /May III deca /June 

Centre III deca./November I deca. / December I deca /May II deca /June 

South-Islands I deca./December III deca. / December II deca /April I deca /June 

Table 46 –Durum Wheat Calendar 

73




The Durum Wheat Specific Satisfaction Temperature was fixed at 3 Cº. 

The Temperature Satisfaction Index (TSI) for Durum Wheat was classified in term of 




NS Non Suitable



The Durum Wheat specific Frost Temperature was assumed equal to -8 Cº. 

The Frost Risk Index (FRI) for Durum Wheat was classified in term of suitability in the 




NS Non Suitable



The Durum Wheat specific Heat Stress Temperature was assumed equal to 32 Cº. 

The Heat Stress Risk Index (HSRI) for Durum Wheat was classified in term of suitability in 


Suitability Classes HSRI_TG Color Legend 

S 100 % 

NS



The Durum Wheat specific Base Temperature for this indices were assumed equal to 0 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Durum Wheat until 



S Suitable 


>=1250 C°d 

=700 C°d 


Figure 49– Durum Wheat ThSI Index Suitability Classification until Ripening Period 

78


Flowering Temperature Satisfaction Index - FTSI 

The Durum Wheat Specific Flowering Satisfaction Temperature was fixed at 8 Cº. 

The Flowering Temperature Satisfaction Index (FTSI) for Durum Wheat was classified in 

term of suitability in the following classes: 


FTSI_TB 

Color Legend 

S1 Optimal >90% 


NS Non Suitable



Soil Depth 



D1 

D2 

S3 Acceptable 

D3 

D4 

S1 Optimal 

D5 

Table 52– Durum Wheat Soil depth Suitability Classification 

Figure 51– Durum Wheat Soil depth Suitability Classification 

80


Texture 


S3 Acceptable 

T1 


S1 Optimal 

S1 Optimal 

T2 

T3 

T4 


T5 

Table 53– Durum Wheat Texture Suitability Classification 

Figure 52- Durum Wheat Texture Suitability Classification 

81





S1 Optimal 

E1 

E2 

E3 


E4 


E5 

Table 54– Durum Wheat Mean Elevation Suitability Classification 

Figure 53– Durum Wheat Mean Elevation Suitability Classification 

82


Slope 


S1 Optimal 

SLOPE1 

SLOPE2 

SLOPE3 


SLOPE4 

S3 Acceptable 

SLOPE5 


SLOPE6 

Table 55– Durum Wheat Slope Suitability Classification 

Figure 54– Durum Wheat Slope Suitability Classification 

83



The Crop Satisfaction Index for Durum Wheat was calculated following the methodology 

described in the paragraph 1.7, on the basis of value of Durum Wheat water requirements 

(Durum Wheat Kc equal to 0.4 from January until March, 1.10 from April until May, and 0.4 

until June ), the CWSI Suitability classes were defined: 





NS Non Suitable


Index Score 



S3 0-40 5 

S2 40-50 25 

S1 50-60 22 

NS







Table 58– Durum Wheat Suitability Classification in Non-Irrigated Scenario 

Figure 57 – Durum Wheat Suitability Class in Non-Irrigated Scenario 

86


The number of agro-pedo-climatologic zones identified for Soft Wheat - Barley in nonirrigated 


Figure 58–Durum Wheat Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

87

Annex I - Tomato 

Tomato in Italy 

Tomato (Lycopersicon lycopersicum) is a crop coming from South America and it was 

cultivated in Europe for a long time as an ornamental plant, because the fruits were 

considered inedible. At the beginning of XIX century the intensive cultivation the tomato 

processing started. 

It’s a crop for temperate-hot climate and it adapts to all soils but it needs water during the hot 

season. Tomato is spread in all over our country but it is mainly cultivated in Puglia, Emilia 

Romagna, Sicilia, Calabria and Campania. Tomato is distinguished according to the 

destination: tomato for human consumption and tomato for processing into peeled, puree, 

juices. The varieties for human consumption have round-globular fruits, of different size, and 

the most spread varieties are: Marmande, Money Marker, Early Pack and some hybrid as 

Montecarlo and Fandango.The varieties direct to peeled tomato production have cylindrical 

fruits and the most famous is San Marzano.The varieties direct to puree tomato production 

have square- long fruits and the most famous are: Petomech, Indo, Missouri, Roma, Rio 

Grande. 

In Italy, the cultivation has a long tradition and Italy is the third producer in the world for 

production and exportation and the first producer in Eu. It produces over the 40% of the 

European production (about 6.400 tonnes) with area of 123 thousands hectares, that is the 

50% of the total European Union area. 



North II deca./May III deca. /May III deca. /June III deca /July 

Centre III deca./April I deca. / May II deca /June III deca /July 

South-Islands I deca./April II deca. / April I deca /June III deca /July 

Table 59 –Tomato Calendar 

88




The Tomato Specific Satisfaction Temperature was fixed at 12 Cº. 

The Temperature Satisfaction Index (TSI) for Tomato was classified in term of suitability in 




NS Non Suitable



The Tomato specific Frost Temperature was assumed equal to 7 Cº. 

The Frost Risk Index (FRI) for Tomato was classified in term of suitability in the following 

classes: 



NS Non Suitable



The Tomato specific Heat Stress Temperature was assumed equal to 30 Cº. 

The Heat Stress Risk Index (HSRI) for Tomato was classified in term of suitability in the 



S 100 % 

NS



The Tomato specific Base Temperature for this indices were assumed equal to 12 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Tomato until flowering 

and ripening period were classified in term of suitability in the following classes: 


S1 Optimal Suitable 

>=1100 C°d 

S2 Sub-Optimal Suitable 960-1100 


=1350 C°d 

S2 Sub-Optimal Suitable 940-1350 



Figure 63– Tomato ThSI Index Suitability Classification until Ripening Period 

93



Soil Depth 



D1 

D2 

S3 Acceptable 

D3 

D4 

S1 Optimal 

D5 

Table 64– Tomato Soil depth Suitability Classification 

Figure 64– Tomato Soil depth Suitability Classification 

94


Texture 



S1 Optimal 


T1 

T2 

T3 

S3 Acceptable 

T4 

S3 Acceptable 

T5 

Table 65– Tomato Texture Suitability Classification 

Figure 65- Tomato Texture Suitability Classification 

95


Morphology Suitability Criteria for Tomato 



E1 

S1 Optimal 

E2 

S3 Acceptable 

E3 

E4 


E5 

Table 66– Tomato Mean Elevation Suitability Classification 

Figure 66– Tomato Mean Elevation Suitability Classification 

96


Slope 


S1 Optimal 

SLOPE1 

SLOPE2 


SLOPE3 

S3 Acceptable 

SLOPE4 

SLOPE5 


SLOPE6 

Table 67– Tomato Slope Suitability Classification 

Figure 67– Tomato Slope Suitability Classification 

97



The Crop Satisfaction Index for Tomato was calculated following the methodology described 

in the paragraph 1.7@, on the basis of value of Tomato water requirements (tomato Kc equal 

to 0.4 in April, 1.10 from May until June, and 0.8 until July ), the CWSI Suitability classes 

were defined: 





NS Non Suitable





S3 0-40 6.5 

S2 40-50 8.8 

S1 50-65 20.39 

NS




S3 0-40 5.3 

S2 40-55 20.1 

S1 55-75 28.3 

NS


Agro-Pedo-Climatological Zoning for Tomato 





Table 71– Tomato Suitability Classification in Non-Irrigated Scenario 

Figure 71 – Tomato Suitability Class in Non-Irrigated Scenario 

101


The number of agro-pedo-climatologic zones identified for tomato in non-irrigated scenario is 

119. 

Figure 72–Tomato Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

102






Table 72– Tomato Suitability Classification in Irrigated Scenario 

Figure 73 – Tomato Suitability Class in Irrigated Scenario 

103


The number of agro-pedo-climatologic zones identified for tomato in irrigated scenario is 

232. 

Figure 74–Tomato Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

104

Annex I - Sugar beet 

Sugar beet in Italy 

Sugarbeet (Beta vulgaris L.) is a variety of beetroot, from which roots two third of the sugar 

world production is obtained. This crop is sowed in autumn and it adapts in deep and 

permeable soils, which keep water in the summer because the roots penetrate deeply in the 

soils. It’s a crop which adapts to temperature about 15-20° C, in wet summer regions. Italy is 

the third Eu producer, after France and Germany, with a production over 11 thousands tonnes 

and with a total area of 223 thousands hectares, which is the 12% of the total Eu area. Over 

60% of the area is in three regions: Emilia Romagna, Veneto and Marche. 

Sugar beet is a biennial plant. In the first year epigeal germination leads to the development 

of a rosette, @, glossy leaves. Leaf production continues through the first season while the 

root swells and accumulates sucrose. Roots crops are harvested before the onset of winter 

frost. In Italy, cultivation takes place mainly in Emilia Romagna and to a less extend in Nord- 

Est and Centre. There, the production is mainly for sugar and spring sowing varieties are 

used. In the south, only a marginal cultivations (less of 15 % of national production) are used 

for sugar and autumn sowing for seeds. 

In this report we only evaluate the sugar production varieties characterized with a spring 

sowing. 



North III deca./March I deca. /April II deca. /June II deca /September 

Centre III deca./March I deca. / April I deca /June I deca /September 

South-Islands I deca./March III deca. / March III deca /May III deca /August 

Table 73 –Sugar beet Calendar 

105




The Sugar beet Specific Satisfaction Temperature was fixed at 6 Cº. 

The Temperature Satisfaction Index (TSI) for Sugar beet was classified in term of suitability 

in the following classes: 



NS Non Suitable



The Sugar beet specific Frost Temperature was assumed equal to 4 Cº. 

The Frost Risk Index (FRI) for Sugar beet was classified in term of suitability in the 




NS Non Suitable



The Sugar beet specific Heat Stress Temperature was assumed equal to 30 Cº. 

The Heat Stress Risk Index (HSRI) for Sugar beet was classified in term of suitability in the 



S 100 % 

NS



The Sugar beet specific Base Temperature for this indices were assumed equal to 5 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Sugar beet until 



S1 Suitable 


>=450 C°d 

=1600 C°d 


Figure 79– Sugar beet ThSI Index Suitability Classification until Ripening Period 

110



The Sugar beet Specific Flowering Satisfaction Temperature was fixed at 6 Cº. 

The Flowering Temperature Satisfaction Index (FTSI) for Sugar beet was classified in term 

of suitability in the following classes: 

Suitability Classes FTSI_TB Color Legend 



NS Non Suitable



Soil Depth 



D1 

D2 

D3 

S3 Acceptable 

D4 

S1 Optimal 

D5 

Table 79– Sugar beet Soil depth Suitability Classification 

Figure 81– Sugar beet Soil depth Suitability Classification 

112


Texture 



T1 

S1 Optimal 

S1 Optimal 


T2 

T3 

T4 

S3 Acceptable 

T5 

Table 80– Sugar beet Texture Suitability Classification 

Figure 82- Sugar beet Texture Suitability Classification 

113





S1 Optimal 

E1 

E2 

E3 


E4 


E5 

Table 81– Sugar beet Mean Elevation Suitability Classification 

Figure 83– Sugar beet Mean Elevation Suitability Classification 

114


Slope 


S1 Optimal 

SLOPE1 

SLOPE2 


SLOPE3 

S3 Acceptable 

SLOPE4 

SLOPE5 


SLOPE6 

Table 82– Sugar beet Slope Suitability Classification 

Figure 84– Sugar beet Slope Suitability Classification 

115



The Crop Satisfaction Index for Sugar beet was calculated following the methodology 

described in the paragraph 1.7, on the basis of value of Sugar beet water requirements (Sugar 

beet Kc equal to 0.5 from March until April, 1.10 from May until June, and 0.7 until 

September ), the CWSI Suitability classes were defined: 





NS Non Suitable





S3 0-40 0.36 

S2 40-50 1.4 

S1 50-60 5.5 

NS




S3 0-40 14.1 

S2 40-50 20.4 

S1 50-70 23.59 

NS


Agro-Pedo-Climatological Zoning for Sugar beet 





Table 86– Sugar beet Suitability Classification in Non-Irrigated Scenario 

Figure 88 – Sugar beet Suitability Class in Non-Irrigated Scenario 

119


The number of agro-pedo-climatologic zones identified for sugar beet in non-irrigated 


Figure 89–Sugar beet Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

120






Table 87– Sugar beet Suitability Classification in Irrigated Scenario 

Figure 90 – Sugar beet Suitability Class in Irrigated Scenario 

121


The number of agro-pedo-climatologic zones identified for sugar beet in irrigated scenario is 

108. 

Figure 91–Sugar beet Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

122

Annex I - Sunflower 

Sunflower in Italy 

Sunflower (Helianthus annuus L.) comes from North-West America and it arrived in Europe 

in the XVI th century, first as ornamental plant and then, in the first part of 800’s, as oil plant, 

when a method to extract oil was defined. 

It’s an annual crop, with spring-summer cycle, with a good adaptability because it can be 

cultivated both in heavy soils and in sandy soils, but, coming from temperate climate, it 

adapts better to high temperature. Italy is the third producer in the Eu, after France and Spain, 

with a production of 411 thousands tonnes and a total area of 210 thousands of hectares and, 

as the rape, the sunflower was spread from the half of 80’s and the half of 90’s, following the 

community aid. This crop is mainly spread in the Central area of the country: Toscana, 

Umbria and Marche have over the 60% of the total area. 



North II deca./April I deca. /May I deca. /July II deca /September 

Centre II deca./April III deca. / April I deca /July II deca /September 

South-Islands I deca./April II deca. / April III deca /June I deca /August 

Table 88 –Sunflower Phenological Calendar 

123




The Sunflower Specific Satisfaction Temperature was fixed at 6 Cº. 

The Temperature Satisfaction Index (TSI) for Sunflower was classified in term of suitability 




NS Non Suitable



The Sunflower specific Frost Temperature was assumed equal to 4 Cº. 

The Frost Risk Index (FRI) Sunflower was classified in term of suitability in the following 

classes: 



NS Non Suitable



The Sunflower specific Heat Stress Temperature was assumed equal to 30 Cº. 

The Heat Stress Risk Index (HSRI) Sunflower was classified in term of suitability in the 



S 100 % 

NS



The Sunflower specific Base Temperature for this indices were assumed equal to 5 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Sunflower until 



S1 Optimal Suitable 


Suitable 


>=1500 C°d 

1200-1500 

=1100 C°d 


Figure 96– Sunflower ThSI Index Suitability Classification until Ripening Period 

128



The Sunflower Specific Flowering Satisfaction Temperature was fixed in 15 Cº. 

The Flowering Temperature Satisfaction Index (FTSI) for Sunflower was classified in term of 





NS Non Suitable



Soil Depth 



D1 

D2 

S3 Acceptable 

D3 

D4 

S1 Optimal 

D5 

Table 94– Sunflower Soil depth Suitability Classification 

Figure 98– Sunflower Soil depth Suitability Classification 

130


Texture 



T1 

S1 Optimal 

T2 

S1 Optimal 

T3 

S3 Acceptable 

T4 

S3 Acceptable 

T5 

Table 95– Sunflower Texture Suitability Classification 

Figure 99– Sunflower Texture Suitability Classification 

131





E1 

S1 Optimal 

E2 


S3 Acceptable 

E3 

E4 


E5 

Table 96– Sunflower Mean Elevation Suitability Classification 

Figure 100– Sunflower Mean Elevation Suitability Classification 

132


Slope 


S1 Optimal 

SLOPE1 

SLOPE2 

S3 Acceptable 

SLOPE3 

SLOPE4 


SLOPE5 

SLOPE6 

Table 97– Sunflower Slope Suitability Classification 

Figure 101– Sunflower Slope Suitability Classification 

133



The Crop Satisfaction Index for Sunflower was calculated following the methodology 

described in the paragraph 1.7, on the basis of value of Sunflower water requirements 

(Sunflower Kc equal to 0.4 from April until May, 1.10 from June until July, and 0.4 until 





NS Non Suitable


Suitably Index Score Zoning 



S3 0-50 4.6 

S2 50-60 11.4 

S1 60-70 13.12 

NS




S3 0-50 32.15 

S2 50-65 34.8 

S1 65-80 18.66 

NS


Agro-Pedo-Climatological Zoning for Sunflower 





Table 101– Sunflower Suitability Classification in Non-Irrigated Scenario 

Figure 105– Sunflower Suitability Class in Non-Irrigated Scenario 

137


The number of agro-pedo-climatologic zones identified for sunflower in non-irrigated 


Figure 106–Sunflower Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

138






Table 102– Sunflower Suitability Classification in Irrigated Scenario 

Figure 107– Sunflower Suitability Class in Irrigated Scenario 

139


The number of agro-pedo-climatologic zones identified for sunflower in irrigated scenario is 

189. 

Figure 108–Sunflower Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

140

Annex I - Soy Bean 

Soy Bean in Italy 

Soya beans (Glycine max L.) is a leguminous crop coming from Asia and imported in Europe 

since XVIIIth century and in America since first years of 800’s, where large scale 

cultivations started. Today, United States and Brazil are the main producers. 

It’s an annual crop with spring-summer cycle, with the same climate needs as Grain maize. It 

is light sensitive (it needs of dark to flower), so the flowering depends also from the latitude, 

and it is little drought - resistant. Its ideal habitat is that one with subtropical condition, with 

hot and wet summer. Italy is the first European producer wit a production of 888 thousands 

tonnes and a total area of about 230 thousands of hectares, which are decreasing, as for the 

entire oil seed crop. 

As the leguminous plants, also the Soya beans is a crop which is used to improve the soils, 

because it fixes the air nitrogen and it produces a big quantity of organic substances which 

help the soils. 

It is rotate with the maize and, as the maize, it is mainly spread in the Northern regions and 

particularly in: Veneto, Lombardia, Piemonte, Friuli Venezia Giulia and Emilia Romagna. 



North I deca./May II deca. /May III deca. /June III deca /September 

Centre III deca./April I deca. / May II deca /June III deca /September 

South-Islands III deca./April I deca. / May II deca /June III deca / September 

Table 103 –Soy Bean Calendar 

141




The Soy Bean Specific Satisfaction Temperature was fixed at 10 Cº. 

The Temperature Satisfaction Index (TSI) for Soy Bean was classified in term of suitability 




NS Non Suitable



The Soy Bean specific Frost Temperature was assumed equal to 5 Cº. 

The Frost Risk Index (FRI) Soy Bean was classified in term of suitability in the following 

classes: 



NS Non Suitable



The Soy Bean specific Heat Stress Temperature was assumed equal to 32 Cº. 

The Heat Stress Risk Index (HSRI) Soy Bean was classified in term of suitability in the 



S 100 % 

NS



The Soy Bean specific Base Temperature for this indices were assumed equal to 4 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Soy Bean until flowering 



S Suitable 


>=800 C°d 

=2000 C°d 


Figure 113– Soy Bean ThSI Index Suitability Classification until Ripening Period 

146



The Soy Bean Specific Flowering Satisfaction Temperature was fixed in 12 Cº. 

The Flowering Temperature Satisfaction Index (FTSI) for Soy Bean was classified in term of 





NS Non Suitable



Soil Depth 



D1 

D2 

S3 Acceptable 

D3 

D4 

S1 Optimal 

D5 

Table 109– Soy Bean Soil depth Suitability Classification 

Figure 115– Soy Bean Soil depth Suitability Classification 

148


Texture 



T1 


S1 Optimal 

T2 

T3 

T4 


T5 

Table 110– Soy Bean Texture Suitability Classification 

Figure 116– Soy Bean Texture Suitability Classification 

149





S1 Optimal 

E1 


S3 Acceptable 

E2 

E3 

E4 


E5 

Table 111– Soy Bean Mean Elevation Suitability Classification 

Figure 117– Soy Bean Mean Elevation Suitability Classification 

150


Slope 


S1 Optimal 


SLOPE1 

SLOPE2 

S3 Acceptable 

SLOPE3 

SLOPE4 


SLOPE5 

SLOPE6 

Table 112– Soy Bean Slope Suitability Classification 

Figure 118– Soy Bean Slope Suitability Classification 

151



The Crop Satisfaction Index for Soy Bean was calculated following the methodology 

described in the paragraph 1.7, on the basis of value of Soy Bean water requirements (Soy 

Bean Kc equal to 0.4 from April until May, 1.10 from June until July, and 0.4 until 






NS Non Suitable


Soy Bean Suitably Index Score 



S3 0-40 4.7 

S2 40-50 15 

S1 50-60 6.9 

NS




S3 0-40 12 

S2 40-55 38 

S1 55-70 10 

NS


Agro-Pedo-Climatological Zoning for Soy Bean 





Table 116– Soy Bean Suitability Classification in Non-Irrigated Scenario 

Figure 122– Soy Bean Suitability Class in Non-Irrigated Scenario 

155


The number of agro-pedo-climatologic zones identified for soy bean in non-irrigated scenario 

is 65. 

Figure 123–Soy Bean Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

156






Table 117– Soy Bean Suitability Classification in Irrigated Scenario 

Figure 124– Soy Bean Suitability Class in Irrigated Scenario 

157


The number of agro-pedo-climatologic zones identified for soy bean in irrigated scenario is 

122. 

Figure 125–Soy Bean Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

158

Annex I - Rapeseed 

Rapeseed in Italy 

Rape (Brassica napus L.) is a crop coming from Mediterranean area and it was utilized since 

the Middle Age for the oil extracted from the seeds, used for the lighting. In Europe, this crop 

has mainly spread in the North: Germany, France, Poland, Great Britain, Denmark and 

Sweden are the main producers. In Italy, the crop spread from the half of 80’s, thanks to the 

incentives as “deficiency payment”, come into force between 1981 and 1991 and then thanks 

to the introduction of community aid such as the cereals aid. This situation has produced an 

exponential increasing of the area cultivated to rape, stopped only after Agenda 2000 came 

into force. 

Today the sector, with about 30.000 hectares, has an incidence of 1% on the Eu one. In 5 

regions (Sardegna, Puglia, Lazio Toscana and Piemonte), there is the 80% of the total area. 

The most spread varieties in Italy, are the autumn ones, which have a autumn- spring cycle. 



North I deca./October I deca. /November III deca. /April III deca /June 

Centre II deca./October I deca. / November II deca /April II deca /June 

South-Islands III deca./October II deca. / November II deca /April I deca / June 

Table 118 –RapeseedCalendar 

159




The Rapeseed Specific Satisfaction Temperature was fixed at 5 Cº. 

The Temperature Satisfaction Index (TSI) for Rapeseed was classified in term of suitability 




NS Non Suitable



The Rapeseed specific Frost Temperature was assumed equal to -8 Cº. 

The Frost Risk Index (FRI) Rapeseed was classified in term of suitability in the following 

classes: 



NS Non Suitable



The Rapeseed specific Heat Stress Temperature was assumed equal to 30 Cº. 

The Heat Stress Risk Index (HSRI) Rapeseed was classified in term of suitability in the 



S 100 % 

NS



The Rapeseed specific Base Temperature for this indices were assumed equal to 0 Cº. 

The ThSI_TF and ThSI_TR Thermal Satisfaction Index (ThSI) for Rapeseed until flowering 



S Suitable 



S Suitable 

ThSI_TF 

>=900 C°d 

=600 C°d 

Color 

Legend 

Color 

Legend 



Figure 130– Rapeseed ThSI Index Suitability Classification until Ripening Period 

164



The Rapeseed Specific Flowering Satisfaction Temperature was fixed at 5 Cº. 

The Flowering Temperature Satisfaction Index (FTSI) for Rapeseed was classified in term of 





NS Non Suitable



The Rapeseed specific Vernalization Satisfaction Temperature is between -2 and 10 Cº. 

The Vernalization period for Rapeseed is fixed from germination to the end of first decade in 

February. 

The Vernalization Satisfaction Index (VSI) for Rapeseed was classified in term of suitability 




NS Non Suitable



Soil Depth 



D1 

D2 

S3 Acceptable 

S1 Optimal 

D3 

D4 

D5 

Table 125– Rapeseed Soil depth Suitability Classification 

Figure 133– Rapeseed Soil depth Suitability Classification 

167


Texture 



T1 

S3 Acceptable 

S1 Optimal 

S1 Optimal 

T2 

T3 

T4 

S3 Acceptable 

T5 

Table 126– Rapeseed Texture Suitability Classification 

Figure 134– Rapeseed Texture Suitability Classification 

168





E1 

S1 Optimal 

E2 


S3 Acceptable 

E3 

E4 


E5 

Table 127– Rapeseed Mean Elevation Suitability Classification 

Figure 135– Rapeseed Mean Elevation Suitability Classification 

169


Slope 


S1 Optimal 

SLOPE1 

SLOPE2 


SLOPE3 

S3 Acceptable 

SLOPE4 

SLOPE5 


SLOPE6 

Table 128– Rapeseed Slope Suitability Classification 

Figure 136– Rapeseed Slope Suitability Classification 

170


Crop Water Satisfaction Index Suitability for Rapeseed 

The Crop Satisfaction Index for Rapeseed was calculated following the methodology 

described in the paragraph 1.7, on the basis of value of Rapeseed water requirements 

(Rapeseed Kc equal to 0.5 from January until March, 1.10 from April until May, and 0.6 until 

June ), the CWSI Suitability classes were defined: 





NS Non Suitable


Rapeseed Suitably Index Score 



S3 0-40 19.4 

S2 40-50 31.2 

S1 50-60 15.4 

NS


Agro-Pedo-Climatological Zoning for Rapeseed 





Table 131– Rapeseed Suitability Classification in Non-Irrigated Scenario 

Figure 139– Rapeseed Suitability Class in Non-Irrigated Scenario 

173


The number of agro-pedo-climatologic zones identified for rapeseed in non-irrigated scenario 

is 123. 

Figure 140–Rapeseed Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

174

Annex II – Index Equation 

Annex II: Index Equations 

175


Temperature Satisfaction index -TSI 

TSI is equal to: 

∑Ti 

TG 

TSI _ TG = • 100 

TG 

mean 

⎪⎧ 

1 ⇔ Ti 

≥ Crop _ Specific _ Temp 

Ti 

= ⎨ 

mean 

⎪⎩ 0 ⇔ Ti 

< Crop _ Specific _ Temp 

where: 

TG= Length of Germination Period in days 

T = Daily average long term temperature of i-day during the germination period 

mean 

i 

Frost Risk Index FRI 

FRI is equal to: 

∑ 

Ti 

TTOT 

FRI _ TTOT = • 100 

TTOT 

min 

⎪⎧ 

1 ⇔ Ti 

≥ Crop _ Specific _ FrostTemp 

Ti 

= ⎨ 

min 

⎪⎩ 0 ⇔ Ti 

< Crop _ Specific _ FrostTemp 

where: 

TTOT= Length of Growing Period in days calculated from sowing to end of ripening 

T = Daily average long term minimum temperature of i-day during the growing period 

min 

i 

Heat Stress Risk Index - HSRI 

HSRI is equal to: 

∑Ti 

TTOT 

HSRI _ TTOT = • 100 

TTOT 

mean 

⎪⎧ 

1 ⇔ Ti 

≤ Crop _ Specific _ HeatTemp 

Ti 

= ⎨ 

mean 

⎪⎩ 0 ⇔ Ti 

> Crop _ Specific _ HeatTemp 

where: 

TTOT= Length of Growing Period in days calculated from sowing to end of ripening 

T = Daily average long term temperature of i-day during the growing period 

mean 

i 

Thermal Satisfaction Index -ThSI 

LGP32_TTOT is equal to: 

176


ThSI _ TR = ∑Ti 

TR 

mean mean 

⎪⎧ 

Ti 

= Ti 

⇔ Ti 


Ti 

= ⎨ 

mean 

⎪⎩ Ti 

= 0 ⇔ Ti 


ThSI _ TF = ∑Ti 

T 

i 

⎪⎧ 

Ti 

= ⎨ 

⎪⎩ Ti 

TF 

mean mean 

= Ti 

⇔ Ti 


mean 

= 0 ⇔ T < Crop _ Specific _ Temp 

i 

where: 

TF= Length of Flowering Period in days calculated from sowing to end of flowering 

TR= Length of Ripening Period in days calculated from flowering to ripening 

T 

mean 

i 

= Daily average long term temperature of i-day during the growing period 


VSI is equal to: 

∑Ti 

TTOT 

VSI _ TA = • 100 

TTOT 

mean 

⎧1 

⇔ Min _ Crop _ Specific _ Temp ≤ Ti 

≤ 

⎪ 

mean 

Ti 

= ⎨0 

⇔ Ti 

> Max _ Crop _ Specific _ Temp 

⎪ mean 

0 ⇔ T < Min _ Crop _ Specific _ Temp 

Max _ Crop _ Specific _ Temp 

⎩ i 

where: 

TA= Length of Crop Vernalization Period in days calculated from flowering to a crop 

specific decade 

T 

mean 

i 

= Daily average long term temperature of i-day during the Vernalization Period 

Flowering Temperature Satisfaction index -FTSI 

TSI is equal to: 

∑Ti 

TB 

FTSI _ TB = • 100 

TB 

mean 

⎪⎧ 

1 ⇔ Ti 


Ti 

= ⎨ 

mean 

⎪⎩ 0 ⇔ Ti 


where: 

TB= Length of Critical Flowering Period in days 

T 

mean 

i 

= Daily average long term temperature of i-day during the critical flowering period 

177


Crop Water Satisfaction Index (CWSI) 

CWSI: 

CWSI 

NSWC 

p 

GP 

p 

i 

= 

∑ 

GP 

NSWC 

GP 

p 

i 

⎪⎧ 

0 ⇔ SWC 

= ⎨ 

⎪⎩ 1 ⇔ SWCi 

• 100 

p 

i 

p 

< 0 

≥ 0 

GP= Number of month of the growing period 

i= i-month of the growing period 

Soil Water Content 

SWC 

p 

i 

= 

− 

p 

min( SWCi 

1 

+ ( PPTi 

p 

− ETC 

where: 

p 

SWC 

i 

is soil water content at time step i for the polygon p, 

p 

AWC is the Available Water Content of the soil for the polygon p 

PPT is the cumulated rainfall at time step i for the polygon p, 

p 

i 

p 

i 

), AWC 

p 

ETC 

i 

is the cumulated Crop-Specific Evapotranspiration at time step i for the polygon p 

i is the time step index. 

p 

ETCi 

is evaluated multiplying the potential Evapotranspiration ( ET 

0 

) by the 

crop specific water requirements parameter over the entire growth cycle. 

p 

ETC = K • ET 0 

i 

c 

p 

) 

K 

c 

average 

178


Annex III: Agro-pedo-climatological zones 

179

Annex II – Grain maize 

Grain maize Agro-pedo-climatological zones 


AGRO-PEDO-CLIMATOLOGICAL SUITABLE ZONES 

CL_MZ_ZONING_IRR 

FREQ 

COUNT ID_Cl_MZ_IRR 

S/M1_S1/M2_S1/M3_S/M4_S/M5_IRR/NS1_E1/S1_SL1/S1_D4/S3_T1/S3 22 1 














































180












































S/M1_S1/M2_S1/M3_S/M4_S/M5_IRR/S1_E1/S1_SL1/S1_D4/S3_T2/S2 9 89 












181









































Table 132– Grain maize Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

182


Non-Irrigated Scenario 

AGRO-PEDO-CLIMATOLOGICAL SUITABLE ZONES 

CL_MZ_ZONING_NIRR 

FREQ 

COUNT 

ID_Cl_MZ_NIRR 

S/M1_S/M2_S/M3_S/M4_S/M5_IRR/S1_E/S1_SL/S1_D/S1_T2/S2 116 1 































Table 133– Grain maize Agro-Pedo-Climatological Zoning ID for Grain maize in Non-Irrigated Scenario 

183


Soft Wheat – Barley Agro-pedo-climatological zones 


CL_WB_SW_Zoning_NIRR COUNT ZONE_ID 

S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E1/S1_SL1/S1_D3/s3_T2/S2 41 1 


S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E1/S1_SL1/S1_D4/S1_T1/S3 26 3 
















S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E1/S1_SL5/s3_D3/s3_T2/S2 2 19 

S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E1/S1_SL5/s3_D4/S1_T2/S2 3 20 



























184















S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E4/s2_SL1/S1_D4/S1_T2/S2 1 60 




S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E4/s2_SL4/S2_D3/s3_T2/S2 10 64 


S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E4/s2_SL5/s3_D3/s3_T2/S2 4 66 

S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S1_E4/s2_SL5/s3_D4/S1_T2/S2 4 67 


































185











































S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/S2_E4/s2_SL5/s3_D3/s3_T2/S2 11 142 





S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/s3_E1/S1_SL1/S1_D3/s3_T2/S2 2 147 

S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/s3_E1/S1_SL3/S1_D4/S1_T4/S1 1 148 







186


S/M1_S/M2_S/M3_S/M4_S/M5_S/M6_IRR/s3_E4/s2_SL1/S1_D4/S1_T2/S2 9 155 

Table 134– Soft Wheat - Barley Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

187


Durum Wheat Agro-pedo-climatological zones 


CL_WB_HW_ZONING_NIRR COUNT ID_HW_ZONING 

S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_IRR/S1_E1/S1_SL1/S1_D3/s3_T2/S2 41 1 


S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_IRR/S1_E1/S1_SL1/S1_D4/S1_T1/S3 26 3 












































188
























































189
























































190
























































191
























Table 135– Durum Wheat Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

192


Tomato Agro-pedo-climatological zones 


CL_WB_PD_ZONING_NIRR COUNT ID_ZON_NIR 





























S/M1_S1/M2_S1/M3_S/M4_S/M5_IRR/S3_E4/s3_SL1/S1_D4/S1_T2/S1 3 29 


















193
























































194





















Table 136– Tomato Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

195



CL_WB_PD_IRR COUNT ID_ZON_IRR 






































S/M1_S1/M2_S1/M3_S/M4_S/M5_IRR/NS1_E4/s3_SL1/S1_D3/S3_T2/S1 1 38 












196
























































197
























































198
























































199























Table 137– Tomato Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

200


Sugar beet Agro-pedo-climatological zones 


CL_WB_SB_ZONING_NIRR COUNT ID_NIRR 


























Table 138– Sugar beet Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

201



CL_WB_SB_ZONING_IRR COUNT ID_IRR 

S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_IRR/NS1_E1/S1_SL1/S1_D4/S3_T2/S1 112 1 



































S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_IRR/NS1_E4/s2_SL1/S1_D4/S3_T2/S1 6 36 













202
























































203








Table 139– Sugar beet Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

204


Sunflower Agro-pedo-climatological zones 


CL_WB_SF_ZONING_NIRR COUNT ID_NIRR 

S/M1_S1/M2_S/M3_S/M4_S/M5_S1/M6_IRR/S1_E1/S1_SL1/S1_D3/S3_T2/S1 17 1 















































205












S/M1_S1/M2_S/M3_S/M4_S/M5_S1/M6_IRR/S2_E4/s3_SL1/S1_D4/S1_T2/S1 1 58 
























Table 140– Sunflower Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

206



CL_WB_SF_ZONING_IRR COUNT ID_IRR 

S/M1_S1/M2_S/M3_S/M4_S/M5_S1/M6_IRR/NS1_E1/S1_SL1/S1_D3/S3_T2/S1 140 1 













































S/M1_S1/M2_S/M3_S/M4_S/M5_S1/M6_IRR/NS1_E4/s3_SL1/S1_D3/S3_T2/S1 1 46 



207
























































208
























































209



































Table 141– Sunflower Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

210


Soy bean Agro-pedo-climatological zones 


CL_WB_SY_ZONING_NIRR COUNT ID_NIRR 















































211





















Table 142– Soy Bean Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

212



CL_WB_SY_ZONING_IRR COUNT ID_IRR 

















































213
























































214






















Table 143– Soy Bean Agro-Pedo-Climatological Zoning ID in Irrigated Scenario 

215


Rapeseed Agro-pedo-climatological zones 


CL_WB_RP_ZONING_NIRR COUNT ID_NIRR 

S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/S1_E1/S1_SL1/S1_D3/s3_T2/S3 41 1 


S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/S1_E1/S1_SL1/S1_D4/S2_T2/S3 53 3 









































S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/S1_E4/s3_SL1/S1_D4/S2_T2/S3 1 44 



216



S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/S1_E4/s3_SL4/S3_D3/s3_T2/S3 13 48 





















































217









S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/s3_E1/S1_SL1/S1_D3/s3_T2/S3 2 108 

S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/s3_E1/S1_SL1/S1_D5/S1_T2/S3 4 109 












S/M1_S/M2_S/M3_S/M4_S/M5_S1/M6_S/M7_IRR/s3_E4/s3_SL1/S1_D4/S2_T2/S3 9 121 



Table 144– Rapeseed Agro-Pedo-Climatological Zoning ID in Non-Irrigated Scenario 

218

Agro-Pedo-Climatological Zoning of Italy

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