Assessment of Climate Change Impact on Agriculture Giacomo Trombi, Roberto Ferrise, Marco Moriondo & Marco Bindi DiSAT – University of Florence Rome – IFAD – July, 24 th 2008 Case studies

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

Objectives Summary Database Simulation Models Impact Assessment A/M Strategies

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.) Objectives Summary Database Simulation Models Impact Assessment A/M Strategies

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

Geographic databaseSpatial climatic database Climatic limits Impact of future scenarios Analysis of climatic factors Estimate potato potential cultivation area General Framework

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

2070 suitable area Climate Change impact on suitable potato cultivation area Winter Avg. Temp. < 24°C Annual Precip. > 350 mm Changes in climatic variables (Temp., Rad., Precip.) General Circulation Models Environmental constraints for growth Change in area of cultivation from Moriondo et al., 2008 (work in progress)

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 areasto have lower reduction of suitable cultivation areas to maintain good yieldsto maintain good yields from Moriondo et al., 2008 (work in progress)

Suitable area and development cycle Distribution of cultivation: –Shifting of suitable areas (  Temperatures) * –Expansion to higher altitudes ** * * from Downing et al., 2000 (report of EU Clivara project) ** ** from Moriondo et al., 2008 (work in progress) Lenght of development cycle*: Northern Europe :  Central Europe :  2-3 weeks Southern Europe :  up to 5 weeks Potential suitable area 2030 Lost areas Stable areas Gain areas

Climate change impact assessment in N-E Argentina

Simulating the possible impact of climate change on yield of Soybean Wheat Scope of the work Climate change impact assessment in N-E Argentina

Observed data (Tmin, Tmax, rainfall and solar radiation) from a net of stations for period Meteorological available data Climate change impact assessment in N-E Argentina

Projected Data from A2 and B2 scenarios of GCM HadCM3 (Tmin, Tmax, rainfall and solar radiation) Meteorological available data Climate change impact assessment in N-E Argentina Calculation of difference between observed data for present ( ) and projected data for future periods ( ).

Climate Change: variation of mean annual temperature respect to present period A2A2 B2B2 B2B2 A2A Climate change impact assessment in N-E Argentina Meteorological available data

Soil type (soil depth and granulometry) Geographical available data Climate change impact assessment in N-E Argentina Land use (crop distribution)

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

Current meteorological data ( ) 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 Impacts on potato Late Blight

Impact of climate change on soil erosion in North Argentina

Area studied North Argentina (east and west) Time periods considered Present ( ) A22 ( ) A23 ( ) B22 ( ) B23 ( )

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 useincreased erosion  Soil cultivated with graminae and legumes (high production) less erosion  Soil cultivated with graminae and legumes (moderate production) increased erosion  Pasturesincreased erosion  Natural forestsless 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 CO 2 -Emission scenarios General Circulation Models (GCMs) Downscaling (Statistical) Stochastic Weather Generator Local observed climate (Temp, Precip, flow river) Stochastic Scenarios – Base/Climate change Scenarios (Temp, Precip, Evap) Hydrologic model (Precipitation/runoff) Changes in runoff Hydrologic model perfomace Changes in Temp, Precip, Evap Climate local characteristics Methodological schematic Observed records Simulated river flow Precipitation / Evaporation runoff

GCM and local observations Location of the rain gauges (51 stations with daily precipitation available). rain gauge Grid point of CGCM2 SUPPLIERS: ITAIPU BINACIONAL DINAC/DMH SIMEPAR IAPAR ANA 11 DATASETS COMPLETED BASIC CONSISTENCY a) P>0 b) P<Lim max (Used: 150 or 200 and 250 mm) c) Alert: Raining day when P>Lim for to asses missing value By Visual Basic applications

Impact of CC on precipitations Variation in rainfall for the scenario GCM2 A2 (2010 – 2040) vs Observed

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

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