Assessment of Climate Change Impact on Agriculture

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

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

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

4. Summary Simulation Models A/M Strategies Database Impact Assessment Objectives Workflow: • Database structuring (meteorological & geographical) • Data retrieving & processing  inputs for simulation model • Simulation Models • Model choice • Model calibration & validation • Model run (input data from MDB & GDB) • Impact assessment (model output) • Adaptation/Mitigation strategies • Simulation of A/M strategies (steps 2.,3.,4.)

5. 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

6. Impacts of climate change on potato cultivation in South America

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

8. Estimate Climatic limits • Winter Avg. Temp. <24°C • Annual Prec. >350mm 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

9. Climate Change impact on suitable potato cultivation area 2070 suitable 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.)

10. Adaptation strategies: • 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) heat stress tolerant cv. vs suitable area

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

12. Climate change impact assessment in N-E Argentina

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

14. 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

15. 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).

16. Climate change impact assessment in N-E Argentina Meteorological available data A2 A2 2030-2059 2070-2099 Climate Change: variation of mean annual temperature respect to present period B2 B2 2070-2099 2030-2059

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

18. Climate change impact assessment in N-E Argentina Crop growth model CropSYST growth model calibrated and validated for wheat and soybean

19. Climate change impact assessment in N-E Argentina Wheat Yield Assessment General decrease of wheat yield over the region

20. Climate change impact assessment in N-E Argentina Soybean Yield Assessment General decrease of soybean yield over the region

21. Pest and diseasesImpacts on potato Late Blight Quiroz et al., 2004

22. 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

23. 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

24. 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

25. 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

26. Impact of climate change on soil erosion in North Argentina

27. Area studied Time periods considered • Present (1971- 2000) • A22 (2030-2059) A23 (2070-2099) • B22 (2030-2059) B23 (2070-2099) • North Argentina (east and west)

28. Parameters (I) • Factor L (lot length)  Giordani & Zanchi, 1995 • Factor S (slope)  Giordani & Zanchi, 1995 on data from DEM Factor R (Erosion Index)  interpolation of data from meteorological stations Factor K (Soil Erodibility)  from CIOMTA soil map

29. 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.

30. 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)

31. Results (II) Average variation of soil erosion keeping current land use (scenario A23).

32. Results (III) Average variation of soil erosion changing land use [intensive cultivation]

33. Results (IV) Average variation of soil erosion changing land use [undisturbed forest]

34. Conclusions

35. Impact of climate change on the hydrology of the Itaipu hydropower basin

36. Itaipu Basin

37. Methodology Local observed climate (Temp, Precip, flow river) CO2-Emission scenarios 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 (Precipitation/runoff) Simulated river flow runoff Hydrologic model perfomace Changes in runoff Methodological schematic

38. GCM and local observations rain gauge Grid point of CGCM2 Location of the rain gauges (51 stations with daily precipitation available). 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 SUPPLIERS: ITAIPU BINACIONAL DINAC/DMH SIMEPAR IAPAR ANA 11 DATASETS COMPLETED

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

40. 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.

41. 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

42. Thank you for your attention