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					  Assessment of
  Climate Change Impact on
  Agriculture
  Case studies

Giacomo Trombi, Roberto Ferrise, Marco Moriondo & Marco Bindi
                DiSAT – University of Florence
                Rome – IFAD – July, 24th 2008
             Summary         Simulation Models     A/M Strategies


Objectives             Database        Impact Assessment




Assess the impacts of present
and future climate change on
         agriculture
             Summary         Simulation Models     A/M Strategies


Objectives             Database        Impact Assessment
             Summary         Simulation Models       A/M Strategies


Objectives             Database         Impact Assessment


 Workflow:
 1. Database structuring (meteorological & geographical)
    –   Data retrieving & processing  inputs for simulation model
 2. Simulation Models
    –   Model choice
    –   Model calibration & validation
    –   Model run (input data from MDB & GDB)
 3. Impact assessment (model output)
 4. Adaptation/Mitigation strategies
 5. Simulation of A/M strategies (steps 2.,3.,4.)
Assessments done
•   Land Use – Potato Cultivation in S.A.
•   Crop – Crops in N.E. Argentina
•   Pest & Disease - World
•   Erosion – Soil erosion in north Argentina
•   Hydrology – Itaipu Hydropower Basin
Impacts of climate change on
 potato cultivation in South
          America
Estimate potato potential cultivation area
General Framework
Geographic database                      Spatial climatic database



                         Analysis of
                      climatic factors




   Impact of                                      Climatic
     future                                        limits
   scenarios
  Estimate Climatic limits
• Several climatic indexes were analyzed to define their influence
  in determining potato cultivated area
• Relevance of each parameter was estimated according to the
  methodology adopted by Arundel (2005)
• Major climatic indexes cause major
  deviations of potential cultivation area
  from the actual


    Winter Avg. Temp. <24°C

    Annual Prec. >350mm
     Climate Change impact on
     suitable potato cultivation area
Environmental constraints for growth               Change in area of cultivation


     Winter Avg. Temp. < 24°C
      Annual Precip. > 350 mm



   from Moriondo et al., 2008 (work in progress)


     General Circulation Models


    Changes in climatic variables
            (Temp., Rad., Precip.)
                                                       2070 suitable area
Adaptation strategies:
heat stress tolerant cv. vs suitable area

Adaptations (hybrids that
perform better in warmer
environment, e.g. with
spp. Phureja in their
pedigree) may allow:
  • to have lower
    reduction of
    suitable cultivation
    areas
  • to maintain good
    yields



     from Moriondo et al., 2008
     (work in progress)
       Suitable area and development cycle
                                                                Potential suitable area 2030

• Distribution of
  cultivation:
   – Shifting of suitable areas
     ( Temperatures) *
   – Expansion to higher
     altitudes **

                                                                   Gain areas
                                                                   Stable areas

• Lenght of development cycle*:        Lost areas


     • Northern Europe :        
     • Central Europe :          2-3 weeks
     • Southern Europe :         up to 5 weeks
   * from Downing et al., 2000 (report of EU Clivara project)
   ** from Moriondo et al., 2008 (work in progress)
   Climate change impact
assessment in N-E Argentina
   Climate change impact
assessment in N-E Argentina
Scope of the work
Simulating the possible impact
of climate change on yield of
•Soybean
•Wheat
   Climate change impact
assessment in N-E Argentina
Meteorological available data
Observed data (Tmin, Tmax,
rainfall and solar radiation)
from a net of stations for
period 1960-2006
   Climate change impact
assessment in N-E Argentina
Meteorological available data
Projected Data from A2 and B2
scenarios of GCM HadCM3 (Tmin,
Tmax, rainfall and solar radiation)

Calculation of difference
between observed data for
present (1970-2000) and
projected data for future
periods (2001-2100).
   Climate change impact
assessment in N-E Argentina
Meteorological available data
 A           A
     2030-       2070-
 2   2059    2   2099    Climate Change:
                         variation of mean
                         annual temperature
B            B           respect to present
     2030-       2070-
2    2059    2   2099    period
   Climate change impact
assessment in N-E Argentina
Geographical available data
Soil type
(soil depth and
granulometry)

Land use
(crop distribution)
   Climate change impact
assessment in N-E Argentina
Crop growth model
CropSYST growth
model calibrated and
validated for wheat
and soybean
   Climate change impact
assessment in N-E Argentina
              Wheat
              Yield
              Assessment
              General decrease of
              wheat yield over the
              region
   Climate change impact
assessment in N-E Argentina
              Soybean
              Yield
              Assessment
              General decrease of
              soybean yield over the
              region
       Pest and diseases
Impacts on potato Late Blight
       Quiroz et al., 2004
  Impacts on potato Late
          Blight
• Current meteorological data (1961-1990)
  were used to estimate the number of
  pesticide sprays needed to protect
  potatoes from LB across the world
• Potential potato cultivation area was
  assessed by using only climatic variables
  Impacts on potato Late
          Blight
• Climate was assumed to change with an
  average increase of temperature of
  +2°C over the whole planet
• A forecast model (Simcast) was then run
  to assess the impact of such a change on
  LB
             Impacts on potato Late
                     Blight
                  Risk of Late blight expressed as number of pesticide sprays




Lower risk in warmer
areas (< 22 C)




                                                          Higher risk in cooler
                                                          areas (> 13 C)
                  from Quiroz et al., 2004
   Impacts on potato Late
           Blight
A result
• Climate warming up may cause a
  reduction in the risk of infection in a
  significant part of the potential area of
  cultivation
Impact of climate change on
   soil erosion in North
         Argentina
Area studied
• North Argentina
  (east and west)

Time periods considered
• Present (1971- 2000)
• A22 (2030-2059) A23 (2070-2099)
• B22 (2030-2059) B23 (2070-2099)
Parameters (I)
• Factor R (Erosion Index) 
  interpolation of data from
  meteorological stations
• Factor K (Soil Erodibility)  from
  CIOMTA soil map
• Factor L (lot length)  Giordani &
  Zanchi, 1995
• Factor S (slope)  Giordani &
  Zanchi, 1995 on data from DEM
Parameters (II)
• Factor C
  – Effect of vegetation on soil erosion
  – Vegetation cover type
  – Crop rotations
  – Cultivation techniques
  – Residue management
  – Data from CIOMTA soil map reclassified as
    in Giordani & Zanchi, 1995.
Results (I)
• Annual Erosion (average) for
  department (present period)

• Mean variation of annual erosion (%)
  of future periods in comparison to
  present (both w/ and w/o applying
  different land use hypothesis)
Results (II)
Average variation
of soil erosion
keeping current
land use (scenario
A23).
Results (III)
Average variation
of soil erosion
changing land use
[intensive
cultivation]
Results (IV)
Average variation
of soil erosion
changing land use
[undisturbed
forest]
 Conclusions

Land Use changes
Current land use                                   increased erosion   
Soil cultivated with graminae and legumes (high    less erosion        
production)
Soil cultivated with graminae and legumes          increased erosion   
(moderate production)
Pastures                                           increased erosion   
Natural forests                                    less erosion        
Altitude and slope cause West zone to have higher erosion values
Impact of climate change on
 the hydrology of the Itaipu
     hydropower basin
Itaipu Basin
Methodology
   Local observed climate                                 CO2-Emission scenarios
   (Temp, Precip, flow river)                             General Circulation Models
                                                          (GCMs)
             Climate local characteristics
                                                                        Changes in Temp, Precip, Evap

                            Downscaling (Statistical)
                            Stochastic Weather Generator


                Stochastic Scenarios – Base/Climate change
                Scenarios (Temp, Precip, Evap)
                                       Precipitation / Evaporation
     Observed records

                             Hydrologic model
               Simulated     (Precipitation/runoff)
               river flow                                                           runoff


  Hydrologic model perfomace                                         Changes in runoff

  Methodological schematic
GCM and local observations
  BASIC CONSISTENCY                                         SUPPLIERS:
                                                            ITAIPU BINACIONAL
  a) P>0                                                    DINAC/DMH
  b) P<Lim max (Used:                                       SIMEPAR
                                                            IAPAR
     150 or 200 and 250 mm)                                 ANA
  c) Alert:
     Raining day when P>Lim for
     to asses missing value
                                                       11 DATASETS
  By Visual Basic applications                         COMPLETED



     rain gauge
     Grid point of CGCM2
 Location of the rain gauges (51 stations with daily precipitation available).
          Impact of CC on precipitations
        Variation in rainfall for the scenario
        GCM2 A2 (2010 – 2040) vs Observed
       250,0
                                                                                                      200,0

                           PRECIPITATION CHANGE FOR REGION "MG"                                                            PRECIPITATION CHANGE FOR REGION "PR"
                                                                                                      180,0
                                Scenario CGCM2 A2 - 2010/2040                                                                   Scenario CGCM2 A2 - 2010/2040
       200,0                                                                                          160,0


                                                                                                      140,0

       150,0                                                                                          120,0
mm/m




                                                                                               mm/m
                                                                                                      100,0

       100,0                                                                                           80,0


                                                                                                       60,0


        50,0                                                                                           40,0


                                                                                                       20,0


         0,0                                                                                            0,0
               Jan   Feb    Mar   Apr   May        Jun   Jul     Aug   Sep   Oct   Nov   Dec                  Jan   Feb   Mar   Apr   May        Jun   Jul     Aug   Sep   Oct   Nov   Dec

                                        Observed         2010/2040                                                                    Observed         2010/2040
  Changes in runoff
• Runoff is expected to increase over west side of the
  basin, while decreasing on the opposite side




  Changes in mean annual runoff in scenario CGCM2-A2 2010/2040 by sub-basin.
Impacts on agriculture
• Increased runoff:
  – Higher soil erosion
  – Decrease in soil water content
  – Decrease of soil fertility
• Decreased runoff:
  – Higher soil fertility
  – Higher soil water content
  – Less soil erosion
Thank you for your attention

				
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