1

AdagioAdagio

Agricultural Research CouncilResearch Unit for cropping systems in

dry environments (CRA-SCA) Bari, Italydomenico.ventrella@entecra.it

Climate change, vulnerability, and adaptation in agriculture –

the situation in Italy

Domenico Ventrella

2

Land use on Italy

3Water0.2Wet land41Forest51Agricultural area5Built up area

%Land

3

Agricultural land use in Emilia Romagna

Agricultural area 40%

The most important crops: winter wheat (soft and durum, in the last years), tomato, mais, forage crops (alfalfa)Tree crops: peach, apple, pear

The most important problems: Reduction of water resources for irrigationIrrigation as ordinary practice (corn, alfalfa..)Early flowering and late frost (for tree crops)No yield for alfalfa in summer

4

Agricultural land use in Puglia-Basilicata

Agricultural area 43%

The most important crops: durum winter wheat, tomato, winter vegetable, water melon, cabbage

Tree crops: vineyard, olive, almond, citrus

The most important problems: Reduction of water resources for irrigationSalinization in the costal areasIncrease of irrigation practiceSupplemental irrigation for winter cropsEarly flowering and late frost (for tree crops)

5

The Temperature variability in Italy

Alpine Region: AL

Po Plain: PP

Peninsular Plain: PI

ALPP

PI

Three Regions on Temperature Basis

From:TEMPERATURE AND PRECIPITATION VARIABILITY IN ITALY IN THE LAST TWO CENTURIES FROM HOMOGENISED INSTRUMENTAL TIME SERIESM. BRUNETTI, M.MAUGERI, F. MONTI and T. NANNIInt. J. Climatol. 26: 345–381 (2006)

6

The Temperature Variability in ItalyT mean T max T min

Yearly series

Winter series

Spring series

Summer series

Autumn series

Adapted from Brunetti et al. 2006

7

The Temperature Variability in Italy

Quite a uniform temperature trend was observed in the different regions, with an increment of 1 K per century all over Italy on a yearly basis.

Also on a seasonal basis the situation is quite uniform and no significant differences are evident, either for the different regions or for the different seasons.

Adapted from Brunetti et al. 2006

8

The Precipitation Variability in Italy

NWNEN

NES

CE SE

SO

Six Regions on Precipitation Basis

Adapted from Brunetti et al. 2006

9

The Precipitation Variability in Italy

Precipitation trend analysis showed a decreasing tendency. But the decreases are very low and rarely significant. Considering the average all over Italy, there is a 5% decrease per century in the annual precipitation amount, mainly due to the spring season (−9% per century)

Yearly series

Winter series

Spring series

Summer series

Autumn series

Adapted from Brunetti et al. 2006

10

What is changing in this century?

• Increase of global mean temperature: from 2 to 6° C and consequently increase of soil evaporation

• Increase of emission and concentration of CO2

• About the rainfall:– Increase or decreasing annual rainfall. – Increase rainfall intensity. – Changing of rainfall distribution.

11

Temperature change (C, 2071Temperature change (C, 2071--2100 minus 19612100 minus 1961--1990), 1990), MGME ensemble average, A1B scenarioMGME ensemble average, A1B scenario

DJFDJF

SONSON

MAMMAM

JJAJJA

from Giorgi and Lionello, submitted

12

Precipitation change (%, 2071Precipitation change (%, 2071--2100 minus 19612100 minus 1961--1990), 1990), MGME ensemble average, A1B scenarioMGME ensemble average, A1B scenario

DJFDJF MAMMAM

SONSONJJAJJA

from Giorgi and Lionello, submitted

The Authors conclude, for the end of the century, The Authors conclude, for the end of the century, forecasting:forecasting:

1)1) a reduction of summer precipitations in Southern Italy a reduction of summer precipitations in Southern Italy 2)2) little changes for the Northern Italy with a little increase little changes for the Northern Italy with a little increase

for the winter rainsfor the winter rains

Concerning the temperature, they forecast an increase quite Concerning the temperature, they forecast an increase quite uniform among the regions.uniform among the regions.

13

The Climatic data used for the Pilot Assessments

The Regional Circulation Models adopted in this work was HadRM3P, developed by the Hadley Centre, UKHadRM3P has a spatial resolution of 0.44° latitude by 0.44° longitude and is the result of a dynamical downscaling. It takes boundary conditions from a coarser resolution global model and provides a higher spatial resolution of local topography and more realistic simulations of fine-scale weather features. In particular, the outputs from HadCM3 experiments provide the boundary conditions to drive a high resolution (~120 Km) model of the global atmosphere (HadAM3P). In turn, the outputs from this model provide the boundary conditions to drive the HadRM3PIn order to simulate climate change, two emission scenarios (A2 and B2) were selected among those proposed by the Special Report on Emissions Scenarios (IPCC 2000), to have a wide and representative range of changes in temperature patternsThe climatic daily data of the scenarios A2 and B2 (from 2071 to 2100) and reference period (REF, from 1961 to 1990) were utilized for SWAP and DSSAT applications with unique inizializations

14

Temperatures (maximum) anomalies of A2 scenario for January-February-March

15

Temperatures (maximum) anomalies of A2 scenario for April-May-June

16

Precipitation anomalies of A2 scenario for April-May-June

17

Precipitation anomalies of A2 scenario for October-November December

18

Adagio

Regione Sicilia Servizio Informativo Agrometeorologico SicilianoAntonino Drago

Metapontum AgrobiosGiovanni Lacertosa

ARPA - Agenzia Regionale Prevenzione Ambiente dell’Emilia Romagna Direzione TecnicaFranco Zinoni

CER Consorzio di bonifica di secondo grado per il Canale Emiliano-RomagnoloPaolo Mannini

Regione Puglia - Assessorato Risorse Agroalimentari - Settore AgricolturaLuigi Trotta

Agenzia Lucana di Sviluppo e di Innovazione in AgricolturaEmanuele Scalcione

CRA-UR per i sistemi colturali degli ambienti caldoaridi – BariCRA-UR per lo studio dei sistemi colturali – Metaponto (MT)Ricercatori

Università degli Studi di BasilicataDipartimento di Produzione Vegetale

Michele PerniolaStella Lovelli

Università di BariDipartimento di Scienze delle Produzioni VegetaliPietro Santamaria

Università di MilanoIstituto di fisica generale applicata – Climatologia storicaMaurizio Maugeri

Università di FirenzeDipartimento di Scienze Agronomiche e Gestione del Territorio AgroforestaleMarco Bindi

Universities and Applicants for Italy

19

The workshop in BariThree Sections:

general aspects of climate change and effects on physiology and productivitycase studies in different regionsresults of Pilot Assessments

20

Adagio

The vulnerability analysis consisted:

1 Herbaceous crops:

Pilot assessment by using DSSAT, SWAP and Cropsyst for

Tomato

Horticultural species – winter and summer cultivations Sorghum, Durum/soft wheat

21

Adagio

The vulnerability analysis consisted :

2 Tree Crops and durum wheat: Statistical analysis to evaluate the impact of CC on phenological aspects (time of flowering) and productivity using data-set collected in Italy

From: Historical effects of temperature and precipitationon California crop yields D.B. Lobell, K. N. Cahill, C. B. Field, Climatic Change (2007) 81:187–203

22

Adagio

Durum wheat

collection

Data di spigatura (gg)

80

90

100

110

120

130

140

1975 1980 1985 1990 1995 2000 2005 2010

APPULOCAPEITICRESODUILIOKARELMESSAPIASIMETOTRINAKRIAVALNOVA

Resa (q ha-1)

01020304050607080

1975 1980 1985 1990 1995 2000 2005 2010

APPULOCAPEITICRESODUILIOKARELMESSAPIASIMETOTRINAKRIAVALNOVA

Yield

Flowering date

23

Adagio

Almond Collection

Produzione kg pianta-1

05

10152025

1976

1978

1980

1982

1984

1986

1988

1990

1992

Piena fioritura (gg)

20

40

60

80

1976 1978 1980 1982 1984 1986 1988 1990 1992

Yield

Flowering date

24

Adagio

Peach tree Collection

Inizio Fioritura

08-Feb

18-Feb

28-Feb

10-Mar

20-Mar

30-Mar

1985 1990 1995 2000 2005 2010

Spring CrestMay CrestSun Crest

Produzione (kg pianta-1)

0

10

20

30

40

50

1985 1990 1995 2000 2005 2010

Spring CrestMay CrestSun Crest

Yield

Flowering date

25

Statistical analysis to evaluate the impact of CC on phenological and productivity aspects

of durum wheatDomenico Vitale, Domenico Ventrella

1) Preliminary analysis on meteo data: quality control, Homogenization and gap filling, test to evaluate presence of trend

2) Relationship between flowering time and: (i) cumulated daily temperatures with three different threshold; (ii) precipitation

3) Relationship of flowering time and yield with monthly data of temperature (minimum and maximum) and precipitation

26

1) Correlation between temperature and precipitation

-1

-0.5

0

0.5

1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec-1

-0.5

0

0.5

1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

-1

-0.5

0

0.5

1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

-1

-0.5

0

0.5

1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

T min – T Max

-1

-0.5

0

0.5

1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

T min – Prec

T max – Prec

27

2) Relationship between flowering time and:(i) cumulated daily temperatures with three different threshold 0-10-15°C; (ii) precipitation

tYtYCultivarMessapia 0.90Karel 0.89Capeiti 0.66Simeto 0.64Appulo 0.59Valnova 0.58Trinakria 0.57Creso 0.48Duilio 0.32

2R CultivarSimeto 0.93Karel 0.89Trinakria 0.83Capeiti 0.82Valnova 0.78Messapia 0.68Duilio 0.66Appulo 0.61Creso 0.52

2R

Flowering Time Yield

Vegetative period=f (+ Cumulated temperatures above 0°C)

Vegetative period=f (- Cumulated temperatures above 5 and 10°C)

28

3) Relationship of flowering time and yield with monthly data of temperature (minimum and maximum) and

precipitation3.1 Explorative analysis based on independent regressions between flowering

time/yield and monthly means of meteo data: 18 for flowering time ( 6 months x 3 meteo data); 36 for yield (12 months x 3 meteo data)

2

,, tJbXaXY tJt +=

CAPEITI MESSAPIA

SIMETO

TRINAKRIA

VALNOVA

CRESO

APPULO

DUILIO

KAREL

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

0

0.2

0.4

0.6

Nov Dec Jan Feb Mar Apr

CAPEITI MESSAPIA VALNOVA

CRESO DUILIO SIMETO

APPULO KAREL TRINAKRIA

0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jan Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dec

Flowering time Yield

R2

Problem: autocorrelation between meteo data (Tmax vs Tmin, Tmin vs Rainfall ..)

29

3) Relationship of flowering time and yield with monthly data of temperature (minimum and

maximum) and precipitation

2

,,3

2

,,2

2

,,1 ,,3,,2,,1 tJtJtJfXeXdXcXbXaXY tJtJtJt +++++=

3.2 Choice of regression model with the three most significant climatic variables

30

APPULO CAPEITICRESO

120

130

140

150

160

170

12 13 14 15 16 17 18 19 20tmax nov

120

130

140

150

160

170

10 12 14 16 18 20 22tmax mar

120

130

140

150

160

170

0 20 40 60 80 100prec mar

110

120

130

140

150

160

170

10 12 14 16 18 20tmax mar

110

120

130

140

150

160

170

12 13 14 15 16 17 18 19tmax nov

120

130

140

150

160

170

0 2 4 6 8tmin gen

110

120

130

140

150

160

170

12 14 16 18 20tmax nov

120

130

140

150

160

170

10 12 14 16 18 20tmax mar

120

130

140

150

160

170

0 2 4 6 8tmin mar

DUILIO KAREL MESSAPIA

120

130

140

150

160

170

13 14 15 16 17 18 19 20tmax nov

120

130

140

150

160

170

0 10 20 30 40 50 60 70 80 90prec mar

120

130

140

150

160

170

10 12 14 16 18 20 22tmax mar

115

125

135

145

155

10 11 12 13 14 15 16 17 18 19tmax mar

115

125

135

145

155

0 1 2 3 4 5 6 7tmin gen

115

125

135

145

155

12 13 14 15 16 17 18 19tmax nov

110

120

130

140

150

160

170

13 14 15 16 17 18 19 20tmax nov

110

120

130

140

150

160

170

2 3 4 5 6 7 8 9 10tmin apr

110

120

130

140

150

160

170

10 11 12 13 14 15 16 17 18 19tmax mar

Examples of scatter plots for vegetative period lenght

31

Examples of scatter plots for YieldAPPULO CAPEITICRESO

10

20

30

40

50

60

70

2 3 4 5 6 7 8 9 10 11tmin nov

10

20

30

40

50

60

70

0 25 50 75 100 125 150prec nov

10

20

30

40

50

60

70

0 20 40 60 80 100prec apr

20

30

40

50

60

0 1 2 3 4 5 6 7 8tmin mar

20

30

40

50

60

2 3 4 5 6 7 8 9 10 11tmin nov

10

20

30

40

50

60

70

10 11 12 13 14 15 16tmax dic

20

30

40

50

60

9 10 11 12 13 14 15 16tmax dic

20

30

40

50

60

24 26 28 30 32 34tmax giu

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8tmin mar

DUILIO KAREL MESSAPIA

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140prec nov

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140prec apr

10

20

30

40

50

60

70

2 4 6 8 10tmin apr

20

30

40

50

60

0 20 40 60 80 100 120prec gen

20

30

40

50

60

24 26 28 30 32 34tmax giu

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90prec set

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100prec apr

20

30

40

50

60

70

12 13 14 15 16 17 18 19 20tmin set

20

30

40

50

60

70

5 6 7 8 9 10tmin nov

32

Minimum Temperature and Precipitationin November

Precipitation in April

Minimum Temperature in March

Yield

Maximum temperature of November (+)

Maximum Temperature of March (-)

Rainfall in March (for Creso, Duilio and Simeto)

Vegetative periodlenght

3) Relationship of flowering time and yield with monthly data of temperature (minimum and

maximum) and precipitation:

Results

33

Case studies in Basilicata: winter scarola (lettuce), grain sorghum and water melon

Domenico Ventrella, Luisa Giglio

Agricultural Research CouncilResearch Unit for cropping systems in dry

environments (CRA-SCA) Bari, Italy

34

The area of reference:

35

The Maximum Temperature

05

1015202530354045

Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic

T m

ax (°

C)

RIF B2 A2

36

The Minimum Temperature

0

10

20

30

40

50

GenFebMarAprMagGiu Lug AgoSetOttNov Dic

T m

in (°

C)

RIF B2 A2

37

The Precipitations

0102030405060708090

Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic

Piog

gia

(mm

)RIF B2 A2

38

SWAP (Soil-Water-Atmosphere-Plant)

A physical based model that simulates water and solute in saturated an unsaturated soils

The model is particularly useful for field scale

39

The daily evapotranspiration = the evapotranspirative demandof the atmosphere

past

pastfutureETp ETp

ETpETp −=Δ 100

+3 +4

+11+14

+16

+20

0

5

10

15

20

25

B2 A2 B2 A2 B2 A2

scarola scarola melon melon sorghum sorghum

Autumn Winter Spring Summer

40Minimum canopy resistancercPartitioning factors of dry

matter into root, leaves, stems and storage organs.

FRTB, FLTB, FSTB, FOTB

Maximum rooting depth of crop

RDCCrop heightCH

Efficiency of conversion of assimilates into leaves, storage organs, roots and stems

CVL, CVO, CVR, CVS

Initial total crop dry weightTDWI

Relative maintenance respiration of leaves, storage organs, roots and stems

RML, RMO, RMR, RMS

Specific leaf areaSLATB

Instantaneous gross assimilation rate at light saturation

AMAXMaximum relative increase in LAI

RGRLAI

Light use efficiency single leaf

EFFTemperature sum from anthesis to maturity

TSUMAM

Life span of leaves under optimum condition

SPANTemperature sum from emergence to anthesis

TSUMEA

Calibrated parameters Measured parameters

Detailed approach (WOFOST) to simulate the sorghum growth

Cycle: spring - summer

Date of sowing : 01 may

Date of harvesting: 7 september

Type of soil: clay

41

The calibration for sorghum in 2000

0

2

4

6

8LAI

R2= 0.96a= -0.50 nsb= 1.13 nsRMSE= 14.14%

R2= 0.98a= -5.95 kg ha-1 nsb= 0.90 nsRMSE= 15.24%

0

10000

20000

30000

4-mag 23-giu 12-ago 1-ott

Biomass (kg ha-1)

42

The validation for sorghum in 1993

0

2

4

6

8

0

10000

20000

30000

4-mag 23-giu 12-ago 1-ott

LAI

Biomass (kg ha-1)

R2= 0.91a= 0.35 nsb= 0.98 nsRMSE= 16.39%

R2= 0.97a= 363.68 kg ha-1 nsb= 0.75 nsRMSE= 31.04%

43

0

10000

20000

30000

4-mag 23-giu 12-ago 1-ott

0

2

4

6

8

LAI

R2= 0.89a= -0.28 nsb= 1.32 nsRMSE= 45.64%

R2= 0.94a= 292.20 kg ha-1 nsb= 0.84 nsRMSE= 25.71%

Biomass (kg ha-1)

The validation for sorghum in 1994

44

0

10000

20000

30000

4-mag 23-giu 12-ago 1-ott

0

2

4

6

8

LAIR2= 0.92a= -0.81 nsb= 1.10 nsRMSE= 18.88%

R2= 0.94a= -177.15 kg ha-1 nsb= 0.68 **RMSE= 42.75%

Biomass (kg ha-1)

The validation for sorghum in 1995

45

Water Balance for Sorghum cultivation

-80

-70

-60

-50

-40

-30

-20

-10

0

Lc P I Teff Eeff

B2 A2

Lc=cycle lenghtP=precipitation

I=Irrigation

Teff=Actual TranspitrationEeff=Actual Evaporation

past

pastfuture

YYY −

=Δ 100

46

The Yield of Sorghum

-70

-60

-50

-40

-30

-20

-10

0

s.s. Granella

B2A2

Biomass Grain

47

The cycle of Sorghum

0

20

40

60

80

100

RIF B2 A2

Days

from

tran

spla

ntig

0

5

10

15

20

25

30

Gra

in y

ield

(q/h

a)

Vegetative period Reproductive period

Yield

48

Scarola (Cichorium endiviavar. latifolium Hegicv Grovers Giant)

• Cycle: winter• Date of transplantig: 15 October• Date of harvest: 3 March• Soil: sand

SWAP WITH SIMPLE METHOD TO SIMULATE CROP GROWTH

49

Water Balance

-40-35-30-25-20-15-10

-505

10

Lc P I ETeff D

B2 A2

Lc=cycle lenghtP=precipitation

I=Irrigation

ETeff=Actual EvapotranspirationD=Drainage

Scarola: WINTER LETTUCE

50

• Transplanting time: end of march –April

• Inter-row of 2.5-3 m – plant every 1-1.5 m

• Black Plastic Film of 0.50 m• Drip irrigation• Tunnel with transparent and plastic

film in the first month of cultivation to bring forward the crop growth

The Water melon cultivation in SouthernItaly

51

-80

-70

-60

-50

-40

-30

-20

-10

0

10

Lc P I Tp Te Ep Ee

B2A2

Lc=cycle lenghtP=precipitation

I=Irrigation

T = TranspirationE=Evaporation

D=Drainage

52

Clim

ate

chan

ge Increase of Temperature

Increase of evaporative demand of

atmosphere

Variation on Rainfall

Shortening of the crop cycle

Irrig

atio

n re

quire

men

t at

sea

sona

l sca

le

+

-

-/+

53

VULNERABILITY OF TOMATO TO CLIMATE CHANGE IN PUGLIA

Michele Rinaldi and Domenico VentrellaMichele Rinaldi and Domenico VentrellaConsiglio per la Ricerca e Sperimentazione in AgricolturaConsiglio per la Ricerca e Sperimentazione in Agricoltura

UnitUnitàà di Ricerca per i sistemi colturali degli ambienti caldodi Ricerca per i sistemi colturali degli ambienti caldo--aridi,aridi,exex--Istituto Sperimentale Agronomico di BariIstituto Sperimentale Agronomico di Bari

54

The area of reference:

55

DSSAT 4.0.2.0

CROPGRO CROPGRO modelmodel

56

COCO22 and CROPGROand CROPGRO

Photosynthesis at leaf scale

Photosynthesis at canopy scale

UpscalingUpscalingSubSub--modelmodel

Model that simulates the relationships between

Photosynthesis Water balance Canopy energy balance

Relationship between Co2 and water balance

Water stress

Stomata closure

Reduction of flux of CO2 in the canopy

Reduction ofPhotosynthes

is rate

High [CO2 ]Stomata closure

Decrease of plant transpiration

57

TDM 2004

0

4

8

12

16

0 20 40 60 80 100 120days after transplanting

t ha-1

TDM 2003

0

4

8

12

16

0 20 40 60 80 100 120

days after transplanting

t ha-1

The calibration of CROPGRO

5 - 15 cm

0

0.2

0.4

0.6

0 20 40 60 80 100 120

m3 m

-3

15 - 30 cm

0

0.2

0.4

0.6

0 20 40 60 80 100 120

m3 m

-3

30 - 45 cm

0

0.2

0.4

0.6

0 20 40 60 80 100 120

days after transplanting

m3 m

-3

5 - 15 cm

0

0.2

0.4

0.6

0 20 40 60 80 100 120

15 - 30 cm

0

0.2

0.4

0.6

0 20 40 60 80 100 120

30 - 45 cm

0

0.2

0.4

0.6

0 20 40 60 80 100 120

days after transplanting

For biomass

For soil water content

58

Tomato simulated with CROPGRO model

Genotype: Genotype: ibrid PS 1296

SoilSoil: : siltsilt--clayclay soilsoilTransplantigTransplantig time: first of time: first of MayMayHarvesHarves tymetyme: : halfhalf of of AugustAugustTypicalTypical irrigationirrigation: : localizedlocalized methodmethod

59

0

1

2

3

4

5

6

7

8G

enna

io

Febb

raio

Mar

zo

Apr

ile

Mag

gio

Giu

gno

Lugl

io

Ago

sto

Set

tem

bre

Otto

bre

Nov

embr

e

Dic

embr

e

Futuro B2Futuro A2

Anomalies of T MAX

MAY - AUGUST

Anomalies of T MIN

012

3456

78

Gen

naio

Febb

raio

Mar

zo

Apr

ile

Mag

gio

Giu

gno

Lugl

io

Ago

sto

Set

tem

bre

Otto

bre

Nov

embr

e

Dic

embr

e

Futuro B2Futuro A2

MAY - AUGUST

60

Anomalies of precipitation (mm)

-20-15-10-505

1015202530

Gen

naio

Febb

raio

Mar

zo

Apr

ile

Mag

gio

Giu

gno

Lugl

io

Ago

sto

Set

tem

bre

Otto

bre

Nov

embr

e

Dic

embr

e

Futuro B2Futuro A2

MAY-AUGUST

61

Simulations with CROPGRO

Simulations:- Potential

- With automatic irrigation

Climatic series:- Past

- Future B2- Future A2

1) Without CO2 effect(Simulation 1Simulation 1)

2) With effect of CO2

(Simulation 2Simulation 2)

62

Results

63

Simulation 2 (with [CO2])Biomassa epigea (t ha-1)

0

5

10

15

20

25

30

Passato Futuro -B2

Futuro -A2

Passato Futuro -B2

Futuro -A2

IRR AUTO 45 POTENZIALE

Resa in bacche fresche (t ha-1)

0

50

100

150

200

250

Passato Futuro -B2

Futuro -A2

Passato Futuro -B2

Futuro -A2

IRR AUTO 45 POTENZIALE

Biomass (t ha-1)

Yield (t ha-1)

Biomassa epigea (t ha-1)

0

5

10

15

20

25

30

Passato Futuro -B2

Futuro -A2

Passato Futuro -B2

Futuro -A2

IRR AUTO 45 POTENZIALE

Simulation 1 (without CO2)

Resa in bacche fresche (t ha-1)

0

50

100

150

200

250

Passato Futuro -B2

Futuro -A2

Passato Futuro -B2

Futuro -A2

IRR AUTO 45 POTENZIALE

Yield (t ha-1)

Biomass (t ha-1)

64

Simulazione 2 (with [CO2])Harvest Index

0.00.10.20.30.40.50.60.7

Passato Futuro -B2

Futuro -A2

Passato Futuro -B2

Futuro -A2

IRR AUTO 45 POTENZIALE

Durata del ciclo colturale (giorni)

707580859095

100105110115

Passato Futuro - B2 Futuro - A2

Numero di irrigazioni

02468

1012141618

Passato Futuro - B2 Futuro - A2

IRR AUTO 45

Volume di acqua irrigua (mm)

0

100

200

300

400

500

600

700

Passato Futuro - B2 Futuro - A2

IRR AUTO 45

Number of Irrigations

Cycle lenght

Irrigation depth (mm)

65

CONCLUSIONS

The shortening of cycle, because of warming, determines very significant effects for Sorghum, Scarola and Water melon causing reduction of rainfall, ET and yield (Sorghum). The variations ofirrigation requirements are less consistent ranging from -15 to +5%.

Tomato, a C3 plant with a spring-summer cycle, has been simulated according taking and no-taking in account the CO2 effect No taking in account the CO2 the climate change for A2 and B2 determined shortening of cycle, increasing of water use, and yield, WUE and WUEIRR

Taking in account the CO2 overcomes the negative effect of Climate Change on tomato yield

66

Outlook to WP3

1) Carry out the regional pilot assessment in order to define agronomic adaptation strategies by means of climate scenario analysis and modelling approach. The candidate agronomic practices will be:

Early sowing (or transplanting) timesAdoption of varieties with longer cycle Irrigation strategies to save water and increase the Wtaer Use

Efficiency Supplemental Irrigation for rainfed-crop (winter wheat)Possibility of extending the crop season to include other

crops (if the water irrigation is not a limiting factor)

67

2) Above all for tree crops, to analyze available studies in national and international literature regarding the ongoing and feasible potential adaptation measures on selected and representative agroecosystems in Italy (Puglia/Basilicata regions and Emilia-Romagna region)

3) Analysis of uncertainties, cost/benefits, risks, opportunities for co-benefits of adaptation measures

Outlook to WP3