Climate Vulnerability of Farm Households:
New Methods and Evidence
John M. Antle
Professor of Applied Economics
Oregon State University
Presented at Resources for the Future January 28 2015
1
Acknowledgements
 AgMIP & collaborators (agmip.org)
 USDA Ag Research Service
 UKAID (DFID)
 USAID
 REACCH-PNA & collaborators
2
Themes
 A new “community of science” approach to
agricultural systems research (AgMIP)
 New approaches & methods to regional and global
climate change assessment
 Results from two new regional assessment studies
 Looking ahead … US assessment, AR6, and beyond
3
Agricultural Model Inter-comparison and
Improvement Project (AgMIP)
• A new global community of science: climate,
water, soils, crops & livestock, economics,
pests & diseases…led by:
• Cynthia Rosenzweig, GISS and Columbia U
• Jim Jones, U Florida
• Jerry Hatfield, Ag Research Service, USDA
• John Antle, Oregon State U
• More than 600 participating scientists
• Crop, livestock and economic model
inter-comparisons
• Protocol-based regional and global
integrated assessments
• Next Generation Ag System Models
• Sustainable Ag Systems
AgMIP.org
4
Ag, Food and Climate Change
 The Goal: sustainable food & nutritional security under
future bio-physical and socio-economic conditions
 National, local and household relevance (global?)
 Beyond commodity production, to the entire food system
 Assessment not yet feasible: major data and methodological
challenges remain
 Vulnerability: who is at risk of loss, and who can gain?
 Urban consumers: primarily price effects?
 Rural ag households: production and price changes affect income,
availability, stability
 Mitigation and adaptation: what can we do, sustainably?
5
REACCH - Regional Approaches to Climate
Change in Pacific Northwest Agriculture
5-year project funded by USDA-NIFA
University of Idaho
Oregon State University
Washington State University
USDA-ARS
+ 100 scientists & students
Large-scale wheat-fallow and annual
cropped systems typical of
“industrial commodity agriculture”
6
AgMIP Regional Assessment Teams
5-year project, DFID funded
8 regional teams, 18 countries, ≈ 200 scientists
Data, models, scenarios designed &
implemented by multi-disciplinary teams &
stakeholders
Small-scale, mixed crop and crop-livestock
systems; principal crops vary by region (maize,
millet/peanut, rice, wheat) typical of “semisubsistence agriculture”
7
For the full AgMIP story:
8
AgMIP regional integrated assessment method:
beyond average impact to vulnerability
E. Linkages from subnational regions to
national and global
A. Global & national prices,
productivity and representative ag
pathways and scenarios (RAPS)
System 2:  < 0
(gainers)
(ω)
D. Technology adoption
and distribution of
economic,
environmental and
social impacts
System 1:  > 0
(losers)
 (losses)
B. Complex farm household systems
C. Heterogeneous regions
9
AgMIP regional integrated assessment method:
beyond average impact to vulnerability
TOA-MD model simulates gains and losses
E. Linkages fromtradeoffs.oregonstate.edu
subnational regions to
national and global
national prices,
prices,
A. Global & national
productivity and representative
representative ag
ag
scenarios (RAPS)
(RAPS)
pathways and scenarios
(ω)
(ω)
System 2:  < 0
(gainers)
D. Technology adoption
and distribution of
economic,
environmental and
social impacts
Loss
System 1:  > 0
(losers)
Average 0
impact
household systems
systems
B. Complex farm household

(losses)
 (losses)
C.Heterogeneous
Heterogeneousregions
regions
C.
10
TOA-MD Model: vulnerability before
and after adaptation
Distribution
of losses after
adaptation
Distribution of losses
before adaptation
11
Relative yield distributions: linking biophysical and economic models to represent
heterogeneity and vulnerability
Relative Yields of Spring Pea Projected in 2050 at RCP 8.5
(Using Conventional Tillage)
GCM 1
GCM 9
8
GCM 9
4
0
1.5 - 1.6
1.4 - 1.5
1.3 - 1.4
1.2 - 1.3
1.1 - 1.2
1 - 1.1
.9 - 1
.8 - .9
.7 - .8
2
Density
10
5
1.5 - 1.6
1.4 - 1.5
1.3 - 1.4
1.2 - 1.3
1.1 - 1.2
1 - 1.1
.9 - 1
.8 - .9
.7 - .8
0
Density
6
15
GCM 1
.9
1
1.1
1.2
1.3 1.4
.6
.8
GCM 2
1
1.2
1.4
1.2
1.4
GCM 10
8
GCM 10
4
2
Density
1.5 - 1.6
1.4 - 1.5
1.3 - 1.4
1.2 - 1.3
1.1 - 1.2
1 - 1.1
.9 - 1
.8 - .9
.7 - .8
0
2
4
6
1.5 - 1.6
1.4 - 1.5
1.3 - 1.4
1.2 - 1.3
1.1 - 1.2
1 - 1.1
.9 - 1
.8 - .9
.7 - .8
0
Density
8
6
10
GCM 2
.6
.8
1
1.2
1.4
.6
.8
1
Source: Author and collaborators, REACCH-PNA Project
12
Representative Ag Pathways: assessing CC
impacts under plausible future bio-physical and
socio-economic conditions
 Many regional assessments simulate
impacts of future climate under
current socio-economic conditions
 AgMIP RIA methods create plausible
future scenarios
 Global “Shared Socio-Economic Pathways”
 Agriculture-specific bio-physical and socioeconomic pathways (RAPs)
 Created by regional teams with stakeholder
input
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Extent of Vulnerability (% households vulnerable to loss)
Extent of Vulnerability without adaptation:
AgMIP Sub-Saharan Africa & South Asia Teams
-40.0
120.0
100.0
Each point a regional scenario:
Blue = CC + current socio-econ conditions
Red = CC + future socio-econ conditions
(higher prices, incomes)
80.0
60.0
Compare to IPCC
40.0
projected range of
average yield
changes (-40 % to 0) 20.0
-30.0
-20.0
0.0
-10.0
0.0
10.0
20.0
Net Economic Impact (% of farm income)
Q1
30.0
40.0
50.0
Q2
14
Extent of Vulnerability without adaptation:
Extent of Vulnerability (% households vulnerable to loss)
REACCH-PNA winter wheat – fallow
-40
80
70
60
Maroon = large farms: CC + current conditions & prices
Blue = large farms: CC + future cond + high prices
Black = large farms: CC + future conditions + low prices
Green = small farms: CC + current conditions & prices
Red = small farms: CC + future conditions + high prices
Tan = small farms: CC + future conditions + low prices
50
40
Future scenarios:
• Dysfunctional world
• Business as usual
• Sustainable world
30
20
10
0
-30
Q1
-20
Q2 - HH
-10
0
10
20
Net Economic Impact (% of farm income)
Q2 - LL
Q1 - Small
Q2 - Small - High
30
40
50
Q2 - Small - Low
15
Extent of Vulnerability without adaptation:
AgMIP Regions + REACCH
Extent of Vulnerability (% households vulnerable to loss)
120.0
-40.0
100.0
Blue = AgMIP CC + current socio-econ conditions
Red = AgMIP CC + future socio-econ conditions
Green = REACCH all scenarios
80.0
60.0
40.0
20.0
0.0
-30.0
-20.0
-10.0
0.0
10.0
20.0
Net Economic Impact (% of farm income)
Q1
Q2
30.0
40.0
50.0
Q2 PNW
16
Magnitude of Vulnerability without adaptation:
AgMIP Sub-Saharan Africa & South Asia Teams
50.0
Level of Vulnerability (% loss in farm income)
45.0
40.0
Blue = CC + current socio-econ conditions
Red = CC + future socio-econ conditions
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
-40.0
-30.0
-20.0
-10.0
0.0
10.0
Net Impact (% of farm income)
Q1
20.0
30.0
40.0
Q2
17
Magnitude of Vulnerability without adaptation:
AgMIP Teams + REACCH
60.0
50.0
Level of Vulnerability (% loss in farm income)
Level of Vulnerability (% loss in farm income)
50.0Blue = AgMIP CC + current socio-econ conditions
40.0
40.0
-30.0
-40.0
35.0
30.0
30.0
25.0
20.0
20.0
15.0
10.0
10.0
5.0
0.0
-40.0
Red = AgMIP CC + future socio-econ conditions
Green = REACCH all scenarios
45.0
-20.0
-30.0
0.0
-10.0 -10.0 0.0 0.0 10.0 10.0 20.020.0
-20.0
NetNet
Impact
farmincome)
income)
Impact(%
(% of
of farm
Q1
Q2
Q1
30.0
30.0
40.0
40.0
50.0
Q2Q2 PNW
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Taking Action: Designing & Testing Meaningful
Mitigation and Adaptation Strategies
• Large-scale models lack sufficient detail for this purpose!
• Systems approach essential to design & test sustainable
mitigation and adaptation options
• Agronomic adaptation: variety choice, timing of operations, etc
• Economic adaptation: intensive margin (within system crop choice, land
allocation) and extensive margin (between system)
• Sustainability: genetic, soil, water resources, health & nutrition, …
• Future society: industry structure, infrastructure, policy, institutions
• AgMIP Regional Teams, REACCH
• strategies developed with local stakeholders
• use plausible future scenarios…”Representative Ag Pathways”
19
Looking ahead…the AgMIP agenda
• Translating impacts on income into food & nutritional
security, financial, environmental outcomes
• Expand coordinated regional-global integrated assessments
•
•
•
•
Protocol-based for transparency, comparability
New generation of modular, open-source, inter-operable models & data
Evaluation of climate, model, scenario uncertainty
Relevance: stakeholder-designed adaptation mitigation strategies
• Working with many partners towards next US assessment,
AR6 and beyond!
20
Impact of CC on yields?
Source: IPCC AR-5, WGII, Ch 7.
Median yield changes (%) for RCP8.5 (2070–2099 in comparison to 1980–2010 baseline) with CO2 effects over five GCMs x
seven GGCMs for rainfed maize (35 ensemble members). Hatching indicates areas where more than 70% of the ensemble
members agree on the directionality of the yield change. Gray areas indicate historical areas with little to no yield capacity
(Rosenzweig et al., PNAS 2013).
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Are we at a turning point for agricultural prices?
Source: IPCC AR-5, WGII, Ch 7
• AgMIP global econ model inter-comparison (Nelson et al. PNAS 2014):
•
•
•
Without climate change, trend to 2050 highly uncertain (+/- 50%)
Effect of climate change likely positive, but also highly uncertain (0 to + 50%)
Results strongly determined by assumed productivity trends
22