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1
AdagioAdagio
Agricultural Research CouncilResearch Unit for cropping systems in
dry environments (CRA-SCA) Bari, [email protected]
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