New Methods to Assess
Climate Change Impacts and Adaptation
for Poor Agricultural Households
John M. Antle
Roberto Valdivia
Agricultural and Resource Economics
Oregon State University
Seminar presented at the UK Department for
International Development, London, May 10 2012.
http://tradeoffs.oregonstate.edu
Motivation




Research -- and common sense! -- suggest that poor agricultural
households are among the most vulnerable to climate change and
face some of the greatest adaptation challenges
Rural households and agricultural systems are heterogeneous,
implying CC impacts – and value of adaptation strategies -- will vary
within and between these populations
Farmers’ choice among adaptation options involves self-selection
that must be taken into account for accurate representation of
adaptation options
Impacts of climate change and adaptation depend critically on future
socio-economic conditions
Modeling impact and adaptation to climate change: policy analysis
of the “third kind”
The challenge: model the “counterfactual” of future technology, climate and socioeconomic conditions.
Much more challenging than ex post impact assessment or extrapolation!
Indicator
System 2
System 1
time
Example: Impact of CC on Subsistence, Dairy and Irrigated
Farms in Vihiga and Machakos Districts, Kenya
Vihiga
Machakos
Poverty Rate (% of farm population living on <$1 per day)
Scenario
No Dairy
Dairy
Total
No Dairy
Dairy
Irrigated
Total
base
RAP1 base
RAP2 base
CC
RAP1 CC
RAP2 CC
85
65
89
89
71
91
38
17
48
49
18
50
62
41
68
69
44
71
85
72
91
89
77
93
43
30
50
51
33
53
54
46
57
57
47
57
73
60
79
78
64
81
Net Loss (percentage of mean agricultural income in base system)
CC
26
27
27
32
RAP1 CC
30
5
8
35
RAP2 CC
26
7
10
25
31
11
14
33
12
8
32
19
16
RAP1 = positive development pathway, low challenges to adaptation
RAP2 = adverse development pathway, high challenges to adaptation
Claessens, Antle, Stoorvogel, Valdivia, Thornton & Herrero. 2012. A
method for evaluating climate change adaptation strategies for smallscale farmers using survey, experimental and modeled data.
Agricultural Systems (in press).
4
Towards improved methods…

The methods used to assess CC impact and adaptation to date are not
well suited to assess CC impacts and adaptation potential
◦ Averaged (aggregated) climate, technical and socio-economic data -- and corresponding
“representative farm” or aggregate models -- fail to represent heterogeneity and
technological detail essential to analysis of adaptation
◦ Analysis of impacts of future climate done with current socio-economic system and
technology
◦ Limited measures of economic impact (land values, gross returns), lack of distributional
impacts.

Tradeoff Analysis for Multi-Dimensional Impact Assessment (TOA-MD): a
micro-simulation approach to multi-dimensional impact assessment
◦ A parsimonious, generic framework to analyze impacts of CC, adaptation in
heterogeneous populations of farm households

Representative Agricultural Pathways (RAPs): A systematic approach to
scenario design (under development via AgMIP)
◦ Ag-specific scenarios building on and linked to RCPs and SSPs
What is the TOA-MD Model?
TOA-MD is a unique simulation tool for multi-dimensional
impact assessment

◦ based on a statistical description of a heterogeneous farm population
◦ simulates impacts of changes in:
 technology and socio-economic conditions
 environmental conditions such as climate
 policy interventions such as Payments for Ecosystem Services
Global registered users
What is the TOA-MD Model?
 TOA-MD is designed to simulate
experiments for a population of farms
using a “base” production system (System 1), and an alternative System 2
 TOA-MD is designed to utilize the available data to attain the best
possible approximation, given the available time and other resources
available to conduct the analysis
◦ can be used for ex post and ex ante analysis
◦ an alternative to econometric models that require large panel datasets
 TOA-MD is designed to facilitate
analysis of the inevitable uncertainties
associated with impact assessment through sensitivity analysis.
◦ Can use preliminary or “minimum data,” provide guidance for efficient collection of
additional data when needed

Software with documentation in SAS and Excel, available to registered
users at tradeoffs.oregonstate.edu
◦ Self-guided course and training workshops
Using TOA-MD to Assess Climate
Impacts and Adaptation
 Step 1: Design RAPs and scenarios
◦ technical, economic, social, policy pathways linked to global SSPs
 Step 2: Identify and characterize base system,
adapted system(s)
 Step 3: Quantify impacts of CC on base and adapted system(s)
 Step 4: Simulate impacts without adaptation
◦ impacts on farm net returns (“losers” and “gainers” from climate change)
◦ impacts on other economic (e.g., poverty) or non-economic (e.g., health, environment)
indicators
 Step 5: Simulate impacts with adaptation
◦ gains from adaptation
◦ economic and non-economic indicators
Steps 1-3: RAPs, climate and systems:
Vihiga District, Kenya
 RAPs storylines provide a framework in which qualitative information can
be translated into model parameters
◦ how to make this process more systematic and transparent?
 Climate data & models simulate productivity impacts of climate change
 Farmers & scientists
evaluate adaptation options
Step 4: Using TOA-MD to Simulate CC
Impacts without adaptation
TOA-MD can simulate various “experiments” for climate impact
assessment. To evaluate adaptation investments we consider:
 Costs of CC: impacts of climate change without adaptation
◦ System 1 = base climate, base technology
◦ System 2 = changed climate, base technology
 Benefits of Adaptation: adapted technology with climate change
◦ System 1 = changed climate, base technology
◦ System 2 = changed climate, adapted technology
 These can be done for alternative RAPs
 First we consider impacts without adaptation, then adaptation
Using TOA-MD to Quantify Economic Impacts of CC

CC without adaptation case:
◦ system 1 = base climate, base technology
◦ system 2 = changed climate, base technology

 = v1 – v2 measures the difference in income with the base
and changed climates
◦  > 0  CC causes a loss
◦  < 0  CC causes a gain

There is a distribution of  in the farm population
◦
◦
◦
◦
“Every farm has its ”
 = 1 - 2
2 = 12 + 22 - 21212
We observe 1 and 12 , but not 2, 22 or 12 , so we use climate data
+ crop models or statistical models to estimate them
TOA-MD approach: modeling systems used by
heterogeneous populations
A system is defined in terms of
household, crop, livestock and
aquaculture sub-systems
Systems are
being used in
heterogeneous
populations
(ω)
Distribution of gains and
losses due to CC
= v1 – v2 = losses from CC
v1 = present income
v2 = future income
Losses  > 0
Gains  < 0
0
 = losses
Map of a
heterogeneous
region
The areas under the
adoption curve measure
economic gains and
losses from climate
change
Losses  > 0
()
Gains  < 0

% gainers
Losses
100
Gains
% losers
r(2)
A “Proportional Relative Yield” Model to Link Crop
Model Simulations to TOA-MD
Define: A = actual crop yield
B = simulated crop yield with current climate
C = simulated crop yield with changed climate
R = C/B
R = mean of R, R = std dev of R
CC = climate perturbed yields = R x A
Assume: R = R + R ,   i.i.d.(0,1)
Then:
2 = R 1
22 = R 2 12 + R2 (12 + 1 2)
12 = R 1/2
Note: most econ
models just use the
mean, not the variance
or correlation!
Representing heterogeneous productivity
impacts of CC


Relative yield concept: R = future yield/present yield
Future yield = R x current expected yield
DSSAT maize yield
simulations for 45 farms
in Machakos, present
climate vs 2030s,
current management
Climate change impacts in Vihiga and
Machakos Districts, Kenya
Climate change impacts in Vihiga and
Machakos Districts, Kenya
Vihiga
Scenario
Machakos
No Dairy
Dairy
Total
No Dairy
Net Loss (percentage of mean agricultural income in base system)
CC
26
27
27
32
RAP1 CC
30
5
8
35
RAP2 CC
26
7
10
25
Dairy
Irrigated
Total
31
11
14
33
12
8
32
19
16
43
51
30
33
50
53
54
57
46
47
57
57
73
78
60
64
79
81
Poverty Rate (% of farm population living on <$1 per day)
base
CC
RAP1 base
RAP1 CC
RAP2 base
RAP2 CC
85
89
65
71
89
91
38
49
17
18
48
50
62
69
41
44
68
71
85
89
72
77
91
93
Step 5: Impacts with Adaptation
Improved
maize
Dual-purpose
sweet potato
(ω)
Adoption of adapted
technology
= v1 – v2 = opportunity cost
v1 = base tech, future climate
v2 = adapted tech, future climate
Non-adopters  > 0
Adopters  < 0

0
Map of a
heterogeneous
region
Derivation of adoption
rate from spatial
distribution of opportunity
cost with adoption
threshold a = 0

ω>0
()
r(2)
100
ω<0
Predicted adoption
rate
Adoption rates of adapted technologies
Vihiga
Scenario
Machakos
No Dairy
Dairy
Adoption Rate (percentage of farm population)
imz
62
52
dpsplw
52
51
dpsp
74
57
dpsp1
74
77
dpsp12
74
90
RAP1 imz
71
56
RAP1 dpsp
73
58
Total
No Dairy
Dairy
Irrigated
Total
56
51
64
77
84
62
64
54
58
61
61
61
57
60
51
53
55
65
74
54
55
51
50
51
55
59
52
51
53
56
59
61
63
56
58
CC impacts with adaptation: improved maize
and dual-purpose sweet potato, net returns
50000
Dual purpose
sweet potato
45000
NET RETURNS PER FARM
40000
Improved maize
Current climate
35000
30000
25000
CC no adaptation
20000
0
10
20
30
40
50
60
ADOPT_A
IMZA
DPSPA
70
80
90
100
Gains from adoption of improved maize
60000
Adopters
Non-adopters
55000
NET RETURNS PER FARM
50000
45000
Current climate
40000
35000
30000
Adoption rate of
adapted technology
25000
CC no adaptation
20000
0
10
20
30
40
50
60
ADOPT_A
IMZA
IMZ_2
IMZ_1
70
80
90
100
Gains from adoption of dpsp
60000
Adopters
55000
Non-adopters
NET RETURNS PER FARM
50000
Current climate
45000
40000
35000
30000
Adoption rate of
adapted technology
25000
CC no adaptation
20000
0
10
20
30
40
50
60
ADOPT_A
DPSPA
DPSP_2
DPSP_1
70
80
90
100
CC impacts with adaptation: improved maize
and dual-purpose sweet potato, poverty
79
CC no adaptation
78
77
POVERTY
76
Improved maize
75
74
73
Current climate
DPSP
72
71
0
10
20
30
40
50
ADOPT_A
IMZA
DPSPA
60
70
80
90
100
Conclusions
 TOA-MD is a unique simulation tool for multi-dimensional
impact assessment of agricultural systems designed which
incorporates:
◦ heterogeneity of agricultural systems
◦ effects of self-selection on impacts
◦ socio-economic scenarios (qualitative pathway and quantitative scenarios)
 TOA-MD is designed for
◦ climate impact assessment and adaptation analysis
◦ technology adoption and impact assessment
◦ payments for ecosystem services and other policy interventions
 TOA-MD provides a generic modeling framework designed to be used by
multi-disciplinary research teams
◦ software, documetation and training available from the TOA Team
More info is available at : http://tradeoffs.oregonstate.edu