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