Assessment of Climate Change Impact on Agriculture

Giacomo Trombi, Roberto Ferrise, Marco Moriondo & Marco Bindi

DiSAT – University of Florence

Rome – IFAD – July, 24th 2008

Case studies

Assess the impacts of present and future climate

change on agriculture

ObjectivesObjectives

SummarySummary

DatabaseDatabase

Simulation ModelsSimulation Models

Impact AssessmentImpact Assessment

A/M StrategiesA/M Strategies

ObjectivesObjectives

SummarySummary

DatabaseDatabase

Simulation ModelsSimulation Models

Impact AssessmentImpact Assessment

A/M StrategiesA/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 database Spatial climatic database

Climatic limits

Impact of future

scenarios

Analysis of climatic factors

Estimate potato potential cultivation areaGeneral 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 area2070 suitable area

Climate Change impact on suitable potato cultivation area

Winter Avg. Temp. < 24°CAnnual 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 Adaptations (hybrids that perform better in warmer perform better in warmer environment, e.g. with environment, e.g. with spp. Phureja in their spp. Phureja in their pedigree) may allow:pedigree) may allow:

• to have lower to have lower reduction of reduction of suitable cultivation suitable cultivation areasareas

• to maintain good to maintain good yieldsyields

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 1960-2006

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 (1970-2000) and projected data for future periods (2001-2100).

Climate Change:

variation of mean annual temperature respect to present period

A2

B2

B2

A2

2070-2099

2070-2099

2030-2059

2030-2059

Climate change impact assessment in N-E

ArgentinaMeteorological available data

Soil type

(soil depth and granulometry)

Geographical available dataGeographical 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

Climate change impact assessment in N-E

ArgentinaWheatYieldAssessment

General decrease of wheat yield over the region

Climate change impact assessment in N-E

ArgentinaSoybeanYieldAssessment

General decrease of soybean yield over the region

Pest and diseases

Impacts on potato Late Blight

Quiroz et al., 2004

• 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

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 (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 production)

less erosion

Soil cultivated with graminae and legumes (moderate production)

increased erosion

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

MethodologyCO2-Emission scenariosGeneral 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 runoffHydrologic model perfomace

Changes in Temp, Precip, EvapClimate local characteristics

Methodological schematic

Observed records

Simulatedriver 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 BINACIONALDINAC/DMHSIMEPARIAPARANA

11 DATASETSCOMPLETED

BASIC CONSISTENCYa) P>0b) 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

PRECIPITATION CHANGE FOR REGION "PR"Scenario CGCM2 A2 - 2010/2040

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

160,0

180,0

200,0

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

mm

/m

Observed 2010/2040

PRECIPITATION CHANGE FOR REGION "MG"Scenario CGCM2 A2 - 2010/2040

0,0

50,0

100,0

150,0

200,0

250,0

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

mm

/m

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