Transcript Two questions:  What can we do with the TOA-MD model? What comes out?  What data do we need to implement the.

Two questions:
 What can we do with the TOA-MD model?
What comes out?
 What data do we need to implement the TOAMD model?
What goes in?
Understanding and Using
TOA-MD 5.0 Model Software:
Basic Learning Module
John Antle
&
Roberto Valdivia
Oregon State University
tradeoffs.oregonstate.edu
© John Antle and Roberto Valdivia 2011 – all rights reserved
Last Modified: 05/10/12
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 comes out?


Economic impact and vulnerability:
◦ % of gainers and loser in the population
◦ economic gains and losses, and net gain (or loss)
Economic, environmental and social indicators
◦ two types:
 population means
 % of population falling below (or above) a threshold
◦ Economic: mean farm income, income per capita (per household
member), poverty rate in the population
◦ Environmental: e.g., mean loss in soil nutrients; % of population
losing more than X% per season; mean rate of change in soil C, etc.
◦ Social: e.g., mean change in women’s control of assets; % of
population with adequate calorie consumptions (% of population at
risk of “hunger”)
What comes out?
The model simulates these impacts in a heterogeneous
farm population
 The population can be stratified to differentiate subpopulations with distinct characteristics, e.g.,
◦ Geography
◦ System type: “semi-subsistence” versus “cash crop”
◦ Socio-economic characteristics: farm size; household
wealth; gender of decision maker


The type of stratification is a key design decision
made by the Research Team!
Example: Impact of CC on Subsistence, Dairy and Irrigated
Farms in Vihiga and Machakos Districts, Kenya
Note the stratification!
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 110 : 17-29.
6
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
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

Note the key role of
the mean and
variance of ω!
% gainers
Losses
100
Gains
% losers
r(2)
What goes in? Modeling the Opportunity Cost
Distribution
• Adoption (or CC impact) is based on the distribution of
opportunity cost.
• The distribution is defined by its mean and variance
• The model contains 3 sub-systems: crop (C), livestock (L) and
aquaculture (P)
• each subsystem is assumed to be statistically
independent
• mean returns to each subsystem can be measured
directly, or can be constructed as a weighted sum of the
returns to the activities in each sub-system.
Calculating Mean Returns and Opportunity Cost
Farms are heterogeneous so  = v1 – v2 varies across farms.
We assume  is normally distributed and thus use the mean and variance of .
Mean: E() = E(v1) – E(v2) = NR1 – NR2 ($/farm)
Suppose system 1 has one crop activity, then:
NR1 = PC11 YC11 – CC11 – FC11/S
PC11 = price of output for activity ($/Y/time)
YC11 = yield of activity 1 (Y/farm/time)
CC11 = variable cost of activity 1 ($/farm /time)
FC11 = fixed cost of activity 1 ($/farm/time)
S = annuity factor based on T1, T2 and R to convert fixed cost into an
annuity value (see discussion of discounting in the Advanced Learning
Module, and in the User Guide Appendix).
Note: subscripts are system, activity
System 2 with 1 crop activity:
NR2 = PC21 YC21 – CC21 – FC21/S
PC21 = price of output for activity ($/Y/time)
YC21 = yield of activity 1 (Y/farm/time)
CC21 = variable cost of activity 1 ($/farm/time)
FC21 = fixed cost of activity 1 ($/farm/time)
S = annuity factor based on T1, T2 and R
Note: subscripts are system, activity
Constructing the Mean of Opportunity Cost
Bold variables are calculated in the model
-Move mouse over the variable names to see their description-
System 1
Crops
PC1g,YC1g,
CC1g, FC1g,
WC1g
NRC1
Livestock
PL1g,YL1g,
CL1g, FL1g,
WL1g
NRL1
System 2
Ponds
PP1g,YP1g,
CP1g, FP1g,
WP1g
NRP1
Crops
PC2g,YC2g,
CC2g, FC2g,
WC2g
NRC2
Livestock
PL2g,YL2g,
CL2g, FL2g,
WL2g
NRL2
Ponds
PC2g,YP2g,
CP2g, FP2g,
WP2g
NRP2
NR2 = NRC2 + NRL2+ NRP2 - FCOST /S
NR1 = NRC1 + NRL1 + NRP1
OPPCOST = NR1 – NR2
Constructing the Variance of Opportunity Cost
Bold variables are calculated in the model
-Move mouse over the variable names to see their description-
System 1
Crops
SC1g, RHOC1
 SC1
Livestock
SL1g, RHOL1
 SL1
System 2
Ponds
Crops
SP1g, RHOP1
 SP1
S12 = SC12 + SL12 + SP12
SC2g, RHOC2
 SC2
RHO12
Livestock
SL2g, RHOL2
 SL2
Ponds
SP2g, RHOP2
 SP2
S22 = SC22 + SL22 + SP22 + SFCOST2
SO12 2 = S12 + S22 - 2 S1 S2 RHO12
SO12 = standard deviation of opportunity cost
Proof-of-Concept Exercise: Pacific NW Wheat
System, USA



Goal: test methods & process for linking climate data to
CropSyst and TOA-MD
Use simplest case: WW-F system in REACCH region
2007 ag census data:
◦ 978 “winter wheat” farms with 2.26 million cropped acres,
average farm size 2308 ac
◦ 860,000 acres in WW wheat-fallow
◦ average yield 51 bu/ac, average 38% in fallow
Proof-of-Concept Exercise
Experiments, Surveys
& Expert data
Ag Census & other
secondary data
Climate data
RCPs
CropSyst
RAPs
Relative yield
distributions
TOA-MD Model
Economic,
Environmental and
Social Indicators
SSPs
Global & Regional
Econ Models
Prices and Costs
TOA-MD Model
Opportunity cost, system choice,
gains & losses from CC
Opportunity cost  = v1 – v2
follows distribution ()
()
In CC impact assessment
 = gains or losses
ω>0
ω<0
(adopters or
gainers)
(non-adopters
or losers)

“every farm has its ”
CropSyst Simulations (Claudio Stockle et al)
Land suitability
(WW = dark blue)
Simulated WWF yield differences
(future minus historical)
Using the “Random Proportional 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
WWF Farm Statistics and Relative Yield Distribution
Ryield = 2020-2035 yield/1995-2010 yield
WWF Farm Statistics
2007 Ag Census
Ryield distribution
978 farms
2.26 x 106 cropped acres + fallow
860,000 ww acres
Farm size (ac)
Yield (bu/ac)
Fallow (%)
Ryield
Mean
2308
51
38
1.25
Std
782
13
8
0.73
Why is mean Ryield > 1?
Climate Impact and Adaptation




Simluate impact of CC on WWF without adaptation
Simulate WW variety adapted to future climate
Assumptions:
◦ mean yield + 10%
◦ more resilient variety increases yields in marginal areas,
but lowers yields in favorable areas (spatial variance
decreased 25%)
◦ no change in cost of production
Only WWF impacted by climate in this exercise
TOA-MD Climate Impact and Adaptation Analysis
1000000
Impact
Adapt-low var
Adapt-high var
800000
Gain
50.25989611
70.88453839
Loss
16.38116467
13.45840206
Net Loss
-33.87873145
-57.42613633
100.4529197
42.78395737
-57.66896228
Predicted adoption rates:
Low-variance = 63%
High-variance = 57%
600000
400000
Net Loss
200000
0
0
10
20
30
40
50
60
70
80
90
-200000
CC impact
-400000
Adapt-Low variance
Adapt-high variance
-600000
-800000
-1000000
Key point: using an average value for the
region wouldPercent
not provide
information about
of Population
the distributional effects or adoption rates!
100