Transcript land use

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
Objectives
Simulation Models
Database
A/M Strategies
Impact Assessment
Assess the impacts of present
and future climate change on
agriculture
Summary
Objectives
Simulation Models
Database
A/M Strategies
Impact Assessment
Summary
Objectives
Simulation Models
Database
A/M Strategies
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
future
scenarios
Climatic
limits
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
Lost areas
• Lenght of development cycle*:
• 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
2
B
2
20302059
20302059
A
2
B
2
20702099
20702099
Climate Change:
variation of mean
annual temperature
respect to present
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
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
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
Simulated
river flow
Hydrologic model
(Precipitation/runoff)
Hydrologic model perfomace
Methodological schematic
runoff
Changes in runoff
GCM and local observations
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
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"
Scenario CGCM2 A2 - 2010/2040
PRECIPITATION CHANGE FOR REGION "PR"
Scenario CGCM2 A2 - 2010/2040
180,0
200,0
160,0
140,0
150,0
mm/m
mm/m
120,0
100,0
100,0
80,0
60,0
50,0
40,0
20,0
0,0
0,0
Jan
Feb
Mar
Apr
May
Observed
Jun
Jul
Aug
2010/2040
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Observed
Jun
Jul
Aug
2010/2040
Sep
Oct
Nov
Dec
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