regional impact assessment agmip ssa kickoff workshop john antle agmip regional econ team leader 1...
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Regional Impact Assessment
AgMIP SSA Kickoff Workshop
John AntleAgMIP Regional Econ Team Leader
1
Accra, GhanaSept 10-14 2012
2
AgMIP Economics Protocols
3
Crop Models
Aggregation
Global Econ Models
Climate Models RCPs & SSPs
Aggregate OutputsEquilibrium Prices
Regional and Global Model
Intercomparisonsand Impact
Assessments
RAPs
Regional Econ Models
AgMIP IA Framework
RCPs, SSPs and RAPs
Representative Ag Pathways• economic & social development storylines• agricultural technology trends• prices and costs of production• ag, conservation, other policy
TOA-MD: multi-dimensional assessment of CC impacts & adaptation
Systems are being used in
heterogeneous populations
A system is defined in terms of household, crop, livestock and
pond sub-systemsEconomic,
environmental and social indicators
Why use TOA? What about
other regional models?
l
mk
w
l
k
1000 0
l
2
1
mk(1)
r(2,0)
100r(2)
lmk(2)
mk(0)
Population mean
Adopter mean
Non-adopter mean
• There are two parts of TOA-MD simulations: • First, the model simulates the proportion of farms that would adopt a new system (system 2), and the proportion that would continue to use the “base” system (system 1)
• In CC impact assessment, “adopters” are those who gain from CC, “non-adopters” as those who lose from CC
• Second, based on the adoption rate of the system 2, the TOA-MD model simulates selected economic, environmental and social impact indicators for adopters, non-adopters and the entire population.
• Farm income; poverty; soil nutrients and SOM; food security; nutrition; health.
• This is a complex challenge! (Climate) x (crops + livestock) x (socio-econ factors)
• To make IA manageable, we carry out different types of simulation experiments
– reference scenarios for model evaluation, validation, intercomparison– sensitivity analysis, varying some parameters while holding others constant– “pathway” analysis to explore the range of possible future states of the world
• IA simulations involve multiple dimensions:– climate (base, future)– production system (current systems, adapted systems)– policy (mitigation, other)– socio-economic conditions (prices, costs of production, farm size, nutrition, etc.)
• Let us now fix policy and socio-economic parameters, and consider IA accounting for climate and production system changes
– then we can re-introduce those dimensions, i.e., replicate the analysis with those factors changed
The Climate Impact Assessment Challenge
We can simulate various “experiments” for climate impact assessment, depending on the type of modeling approach and objectives of the analysis: • Climate change impact without adaptation
– System 1 = base climate, base technology– System 2 = changed climate, base technology
• Climate change impact with adaptation (“standard” analysis)– System 1 = base climate, base technology– System 2 = changed climate, adapted technology
• Adoption of adapted technology with climate change:– System 1 = changed climate, base technology– System 2 = changed climate, adapted technology
Simulation Experiments for Impact Assessment
• Consider the case of CC without adaptation:– system 1 = base climate, base technology – system 2 = changed climate, base technology
• w = v1 – v2 measures the difference in income with the base and changed climates– w > 0 CC causes a loss– w < 0 CC causes a gain
• So we need to know the spatial distribution of w:
• mw = m1 - m2
• w2 = 1
2 + 22 - 21212
• We observe m1 and 12 , but not m2, 2
2 or 12 , so we use climate data + crop models or statistical models to estimate them
Example: Using TOA-MD to Quantify Economic Impacts of Climate Change
Define: Y1 has mean m1 and variance 12
Assume: Y2 = b Y1, b = Y2/Y1 = mb + b , i.i.d.(0,1) (true?)
Two cases: matched vs un-matched data (observed & simulated)
Matched: Y2 = b Y1
Un-matched: mean of Y2 is m2 = mb m1
22 = mb
2 12 + b
2 (12 + m1
2)
12 = mb 1/2
A Random Proportional Yield Model to Construct System 2
• Goal: use observed data from system 1 plus crop simulations to project yield distributions for system 2 • A = actual crop yield, B = simulated crop yield with current climate, C = simulated crop yield with changed climate, R = C/B, mR = mean of R etc.• Sources of variation: soils, weather management
– how to incorporate management variation?
• Mean bias: mA > mY
– biases in R = C/B causes a bias in estimate of mR
– note mR mC /mB
• Variance bias: B and C A causes bias in R (var of a ratio)
Role of Crop Models in CC Impact Assessment
We will use the TOA-MD model setup from Claessens et al. (2012 Ag Systems) to simulate impacts of CC without adaptation on the Machakos farming system:• maize: using the Crop Model Team estimates of climate impacts on yields• beans, mixed subsistence, dairy: 20% average reduction in productivity, no change in variance of net returns•irrigated vegetables: no change in mean or variance
Example: Maize Yields in Machakos, Kenya
Results: Observed and DSSAT Crop Yields
Claessens et al. 2012 use data for Machakos DSSAT simulations from Thornton et al. 2010 Ag Systems which predicted R = 0.74.
Machakos Base System Alternative Systems*
System 1 System 2 System 3
Activities Area Crop Yield Net Returns CC 2030 imz dpsplw dpsp dpsp100 dpsp120
Ha/season/farm Kg/ha/season KSh/ha ---------------------------% of base yield-----------------------------------------
Mixed 0.95 (1.39) 1187 (1631) 7085 (13313) 80 80 80 80 80 80
Maize 0.78 (0.79) 1597 (1624) 12704 (16996) 79 95 74 74 74 74
Beans 0.44 (0.59) 1390 (1374) 24658 (17942) 74 74 74 74 74 74
Vegetables 0.75 (1.00) 4121 (3369) 40718 (139490) 100 100 100 100 100 100
Napier Grass 1.49 (3.10) 12318 (14435) 11310 (18146) 80 80 80 80 80 80
DPSP roots - 7100 (4501) 24475 (16204) - - 42 100 100 100
DPSP vines - 12600 (9013) 18900 (13520) - - 83 100 100 100
Liters/season/farm
Milk - 1784 (1992) 39238 (48208) 80 80 80 80 100 120 *CC = climate change, imz = improved maize, dpsp = dual purpose sweet potato, dpsplw = low yielding dpsp, dpsp100 = dpsp with 100% of base milk yield under CC, dpsp120 = dpsp with 120% of base milk yield under CC.
Machakos production activities and system characterization under climate change
(Claessens et al. 2012 Ag Systems)
CC without adaptation
Sensitivity Analysis: Mean Relative Maize Yield
0
10
20
30
40
50
60
70
0.7 0.79 0.8 0.9 1 1.1
% Losers
% Loss Farm Income
% Loss Per Capita Income
% Increase Poverty
Sensitivity Analysis: Between-System Correlation
0
10
20
30
40
50
60
70
0.8 0.85 0.9 0.95
RHO12 Correlation between returns in system 1 and system 2
% Losers
% Loss in Farm Returns
% Loss Per Capita Income
% Increase in Poverty
Impacts by Strata and Aggregated
Note: mean relative maize yield = 0.79, between system correlation = 0.9
Stratum 1 = subsistence farms, no dairy or irrigationStratum 2 = mixed crop-livestock with dairyStratum 3 = irrigated veges and mixed crop-livestock
Stratum % Losers % Net Loss % Loss PC Inc Base Poverty (%) Poverty Increase1 60.1 30.3 20.0 85.4 3.42 69.1 30.3 26.9 42.9 8.23 56.3 40.4 35.0 53.1 3.5
All Farms 60.9 33.7 24.8 73.1 4.6
CC Impacts for Socio-Economic Scenarios (RAPs) with Low (1) and High (2) Challenges to Adaptation
RAP1 = low challenges to adaptation; more commercially-oriented farms with 50% more land allocated to maize, mean relative maize yield = 1, net returns SD reduced 20%, higher maize and dairy prices, 20% increase in farm size, 50% increase in off-farm income
RAP2 = high challenges to adaptation; farms maintain subsistence orientation with minimal adaptation to CC, higher maize and dairy prices, 20% increase in production cost, 20% reduction in farm size
Scenario % Losers PC Inc (%) Poverty (%)Base n.a. 100 73.1RAP1 22.5 150 63.4RAP2 71.5 56 81
Goals for the Workshop
– Identify & describe regional systems– Identify regional data & issues– Implement climate-crop-TOA-MD applications for
Machakos case study– Economists: review TOA-MD BLM, prepare regional
case studies– Plan for
• RAPs workshops/design• Implement IA for regional case studies• Prepare strategy for full regional implementation