Coordinating AgMIP data and
models across global and
rsta.royalsocietypublishing.org regional scales for 1.5°C and
2.0°C assessments
Research Cynthia Rosenzweig1,2, Alex C. Ruane1,2, John Antle3,
Cite this article: Rosenzweig C et al. 2018 Joshua Elliott4, Muhammad Ashfaq5, Ashfaq Ahmad
Coordinating AgMIP data and models across Chatta5, Frank Ewert6,7, Christian Folberth8,
global and regional scales for 1.5°C and 2.0°C
assessments. Phil. Trans. R. Soc. A 376: Ibrahima Hathie9, Petr Havlik8, Gerrit
20160455.
http://dx.doi.org/10.1098/rsta.2016.0455 Hoogenboom
10, Hermann Lotze-Campen11,12, Dilys
S. MacCarthy13, Daniel Mason-D’Croz14,15, Erik
Accepted: 29 November 2017
Mencos Contreras1,2, Christoph Müller11, Ignacio
One contribution of 20 to a theme issue ‘The Perez-Dominguez16, Meridel Phillips1,2, Cheryl
Paris Agreement: understanding the physical 10 10 17
and social challenges for a warming world of Porter , Rubi M. Raymundo , Ronald D. Sands ,
1.5°C above pre-industrial levels’. Carl-Friedrich Schleussner11,18, Roberto O. Valdivia3,
Subject Areas: Hugo Valin8 and Keith Wiebe14
climatology
1Goddard Institute for Space Studies, National Aeronautics and
Keywords: Space Administration, 2880 Broadway, New York, NY 10025, USA
Agricultural Model Intercomparison and 2Center for Climate Systems Research, Columbia University, 2880
Improvement Project (AgMIP), climate Broadway, New York, NY 10025, USA
change, 1.5°C agricultural impacts, 3Department of Applied Economics, Oregon State University, 213
interdisciplinary, scales Ballard Hall, Corvallis, OR 97331, USA
4Computation Institute, University of Chicago, 5735 South Ellis
Author for correspondence: Avenue, Chicago, IL 60637, USA
Cynthia Rosenzweig 5University of Agriculture Faisalabad, University Main Road,
e-mail: cynthia.rosenzweig@nasa.gov,
Faisalabad, Pakistan
crr2@columbia.edu
6INRES-Crop Science, University of Bonn, Katzenburgweg 5, 53115
Bonn, Germany
7Leibniz Center for Agricultural Landscape Research (ZALF),
Eberswalder Strasse 84, 15374 Müncheberg, Germany
Electronic supplementary material is available 8Ecosystems Services and Management Program, International
online at https://dx.doi.org/10.6084/m9. Institute for Applied Systems Analysis, Schlossplatz 1, 2361
figshare.c.4033753. Laxenburg, Austria
2018 The Author(s) Published by the Royal Society. All rights reserved.
9Initiative Prospective Agricole et Rurale, 67 Rond-Point VDN–Ouest Foire, BP 16788, Dakar-Fann, Senegal 2
10Department of Agricultural and Biological Engineering, University of Florida, Frazier Rogers Hall, Gainesville,
FL 32611, USA
11Potsdam-Institut fur Klimafolgenforschung eV, PO Box 601203, 14412 Potsdam, Germany
12Humboldt-Universität zu Berlin, 10099 Berlin, Germany
13Soil and Irrigation Research Centre, University of Ghana, PO Box LG 68, Kpong, Ghana
14International Food Policy Research Institute, 1201 I Street NW, Washington, DC 20005, USA
15Commonwealth Science and Industrial Research Organisation, 306 Carmody Road, St Lucia, QLD 4067, Australia
16European Commission Joint Research Centre, Calle Inca Garcilaso 3, 41092 Sevilla, Spain
17Economic Research Service, United States Department of Agriculture, 355 E Street SW,Washington, DC 20024, USA
18Climate Analytics, Ritterstrasse 3, 10969 Berlin, Germany
CR, 0000-0002-7885-671X; DM-D’C, 0000-0003-0673-2301
The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed
novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture
and food security in a changing world. The present study aims to perform a proof of
concept of the CGRA to demonstrate advantages and challenges of the proposed framework.
This effort responds to the request by the UN Framework Convention on Climate Change
(UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C
above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit
and testable linkages across disciplines and scales, connecting outputs and inputs from the
Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half
a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model
Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models,
global agricultural economics models, site-based crop models and within-country regional
economics models. The CGRA consistently links disciplines, models and scales in order to
track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and
uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global
scale, there are mixed areas of positive and negative simulated wheat and maize yield changes,
with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially
evident in simulations that do not take into account direct CO2 effects on crops. These projected
global yield changes mostly resulted in increases in prices and areas of wheat and maize in
two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop
models had mixed results depending on the region and the crop. In conjunction with price
changes from the global economics models, productivity declines in the Punjab, Pakistan,
resulted in an increase in vulnerable households and the poverty rate.
This article is part of the theme issue ‘The Paris Agreement: understanding the physical and
social challenges for a warming world of 1.5°C above pre-industrial levels’.
1. Introduction
Agricultural practices influence global climate change through emissions of greenhouse gases
by land clearing, land-use change, animal husbandry, fertilizer application, tillage practices,
fuel use and rice cultivation. At the same time, agriculture is affected by climate change at
the within-country local-to-regional scale through effects of heatwaves, droughts and floods.
Reduction of greenhouse gases through the agricultural sector is included in the Nationally
Determined Contributions (NDCs) of many countries, and agricultural impacts are a major
source of vulnerability and also a prime focus of many National Adaptation Plans (NAPs).
How will global climate change processes and policies affect regional agriculture, and how
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
does regional agriculture in turn affect the global scale? In this paper, we describe the goals, 3
methodology and status of the Agricultural Model Intercomparison and Improvement Project
(AgMIP) [1] Coordinated Global and Regional Assessments (CGRA) [2,3] proof of concept
and its initial methodology using Half a degree Additional warming, Prognosis and Projected
Impacts (HAPPI) [4] climate scenarios and the Coupled Model Intercomparison Project Phase 5
(CMIP5) [5] transient simulations to respond to the request by the UN Framework Convention on
Climate Change (UNFCCC) for information about the implications of limiting global temperature
increases to 1.5°C and 2.0°C [6].
AgMIP began in 2010 and involves approximately 1000 climate scientists, crop and livestock
experts, economists and information technology specialists who conduct protocol-based activities
to significantly improve projections of agricultural responses to major stresses [1]. The motivation
for the CGRA comes from the multi-model ensembles of biophysical and socio-economic
simulations done by AgMIP scientists, which have enabled extensive model intercomparison at,
but generally not across, a range of scales based on farm-site networks and global grids (see
also [7]). AgMIP continues to focus on model improvements through protocol-based research on
agricultural system components, including individual crops (e.g. wheat, maize, rice, sugarcane,
canola and potatoes), soils, and pests and diseases. The complementary goal is to advance
learning on the interactions of agricultural mitigation and adaptation by focusing on explicit
and testable linkages across disciplines and scales. This is in contrast to the embedding of
reduced forms of these linkages in integrated assessment models (IAMs) [8,9]. To date, most
agricultural modelling has addressed research questions within particular disciplines and scales
(for exceptions related to policy assessment, see [10–12]).
At the large scale, the AgMIP Gridded (AgGrid) crop modelling initiative, largely through its
flagship Global Gridded Crop Model Intercomparison (GGCMI) project, has evaluated more than
a dozen global crop models in historical validation tests, coordinated climate impact assessments,
and conducted sensitivity and uncertainty studies [13–15]. Individual crop intercomparisons (e.g.
wheat, maize, rice, sugarcane, canola and potatoes) have tested many models, with projects
sometimes attracting as many as 30–40 different researchers, at multiple sites around the world
[16–19]. In close collaboration with the climate and crop-growth modellers, the AgMIP Global
Economics Team has analysed and compared the economic consequences of different climate
change impact and mitigation scenarios by means of more than 10 global economics models
[20–22]. Interdisciplinary agricultural research at the global scale has been mainly constrained to
the monodirectional information flow from biophysical crop modelling to economic modelling
[20,23]. This approach has enabled changes in crop yields at the global scale to be evaluated
in terms of price and welfare effects. Coupled exploration for IAMs has been proposed using
simplified climate and crop emulators informed by AgMIP results [7].
At the regional scale (defined as within-country agricultural areas), the AgMIP Regional
Integrated Assessment (RIA) project in sub-Saharan Africa and South Asia has developed new
methods to conduct stakeholder-driven research linking climate, crop, livestock and economics
models on impacts and adaptation in current and future climates [24–27]. In this approach,
multiple site-based crop models simulate the impacts of representative future climate scenarios,
and regional economics models are employed to test farmer responses to climate-driven crop
yield changes and to assess vulnerabilities of farm households.
In AgMIP CGRA, we build on this body of work by coordinating major modelling initiatives
across disciplines and scales through the use of unified climate, socio-economic and agronomic
assumptions [28–30]. Such explicit disciplinary and scale interactions enable improvements in
simulated processes, such as improved crop yield projections for embedding in economics
models, and validation for results from aggregated global models that may be interpreted
meaningfully for the regions where agriculture represents a significant share of the economy.
A multi-year process has created a conceptual framework for assessing global- and regional-
scale modelling of crops, livestock and economics across major agricultural regions worldwide
(figure 1) [2,3]. This CGRA framework is designed to facilitate the production of consistent
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
tion | stak
4
e
c holde
tera rn ii ntr e
lde adaptati
r
on acto io
eh
n
k
resource genetics
management
   |   glo
ilie
nce bal t
land and s core
o 
scenario sets
water NAPsextreme events
use and shocks
current and future
climate
diet sustainable NDCs
pathways agn development taiostics e
r
and unc
biofuels soil carbon
livestock nitrogen
m itig a tio n
Figure 1. CGRA climate and food research framework to provide guidance for agricultural adaptation, mitigation, food security
and agricultural policy. (Online version in colour.)
multi-model, multidiscipline and multiscale assessments of agricultural and food security
impacts, including robust characterizations of uncertainty and risk.
In the CGRA framework, there are four dimensions: adaptation, mitigation, food security
and agricultural policy. The context for these dimensions involves extreme events and shocks,
current and future climate, and sustainable development. The overall research question is:
How can the world’s food system respond to climate change, as well as other shocks, such as
economic crises and conflicts, in the context of sustainable development? Different initiatives
within AgMIP address this and related questions by leveraging data and models to forecast
regional and global food systems across time scales ranging from in-season monitoring to multi-
decadal impacts [13,16,24]. The AgMIP consortium is also working with nutrition scientists to
incorporate metrics into assessments that go well beyond just calories, including dietary intake
and nutrients [3]. Potential topics for CGRA assessments are: evaluations of adaptations such
as genetics and resource management [31]; mitigation options including biofuels, soil carbon,
nitrogen management and livestock system transitions [32]; food security issues such as diet
standards and land use [33]; and agricultural policy questions related to NDCs and NAPs as
well as import/export controls and public subsidies [34].
Different degrees of coordination can be identified across the CGRA assessment activities (see
the first table in appendix A) from loosely coordinated to directly linked. ‘Loosely coordinated’
studies share a common set of climate, socio-economic and agronomic scenarios and assumptions.
This enables comparative analysis of results across a wide range of studies. ‘Directly linked’
studies explicitly provide model outputs from one discipline and/or scale to be applied as inputs
to other models from another discipline and/or scale.
The present study aims to perform a proof of concept of the CGRA for 1.5°C and 2.0°C of
global warming above pre-industrial conditions, demonstrating advantages and challenges of
holder interaction
stake
| fo
od security |
sta
|     risk and re
nty
| stakeh
n ol
ctio
der int
nte
ra era
i ct
er
ion
ld
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
lder inter
action
eho |
stak
agricultural policy s| takeho
onal s
cale
i |
reg
di
the proposed framework. Here, we describe the key scenario assumptions, model components 5
and major uncertainties in the CGRA framework prototype application. Ruane et al. (see row A in
the first table in appendix A, and ref. [2]) provide details of the full AgMIP CGRA for 1.5°C and
2.0°C warming.
2. Scenarios and models
(a) Climate scenarios
AgMIP CGRA research is being conducted using a wide range of climate and socio-economic
scenario approaches [2]. One scenario approach embedded in AgMIP CGRA 1.5°C/2.0°C
research is based on CMIP5 transient simulations of twenty-first century climate changes for
Representative Concentration Pathway RCP4.5 [5]. This RCP4.5 transient approach allows a more
explicit coupling of time frame and climate projections in the CGRA analyses. Another approach
is the use of bias-corrected climate scenarios from 20-member ensembles of three HAPPI global
climate models (GCMs), which enables an extensive assessment of impacts of extreme weather
events in the agricultural sector at 1.5°C and 2.0°C [4,35].
The HAPPI simulations aim to reproduce the stabilized climate at 1.5°C or 2.0°C above pre-
industrial conditions, which stands in contrast to the RCP4.5 transient simulations that represent
an emissions pathway developed for CMIP5. RCP4.5 was not designed to reach a specific climate
stabilization and, therefore, application requires the identification of time periods within each
GCM where global temperature is approximately 1.5°C or 2.0°C above pre-industrial conditions.
Not all such GCM simulations achieve 2.0°C global warming, and those that do reveal an evolving
climate trajectory in which a steady climatology is hard to characterize.
RCP2.6 transient runs help to explore one potential pathway for mitigation, underscoring the
likelihood of a peak and decline in global temperatures for high-mitigation futures. The AgMIP
Climate Team is comparing RCP2.6 and HAPPI [2], and some related CGRA projects are using
RCP2.6 (see row B in the first table in appendix A). AGCLIM50 Phase 2 is employing RCP1.9 for
the 1.5°C climate realization and RCP2.6 for the 2.0°C climate realization [22].
(b) Socio-economic scenarios: Shared Socio-economic Pathways and Representative
Agricultural Pathways
Shared Socio-economic Pathways (SSPs) and Representative Agricultural Pathways (RAPs)
provide global and regional trajectories of non-climate factors for the analyses (see the second
table in appendix A). The SSPs set the macro- and socio-economic global context with some
agricultural information, but agricultural trends differ in the different realizations of the
marker scenarios [29,36]. RAPs are agriculture-specific extensions of the SSPs envisaging future
agricultural policy and development for global and regional analyses [30]. At the regional level,
the development of RAPs enables direct linkages to stakeholders, representation of different
farming systems, and inclusion of analysis of many more variables, such as vulnerable farm
households, livelihoods and poverty [30,37].
(c) Global and regional crop models
Global-scale evaluation of crop productivity is a major challenge for climate impact and
adaptation assessment. Rigorous global assessments that are able to inform planning and policy-
making benefit from consistent input data and assumptions across regions and time that use
mutually agreed protocols designed by the modelling community [38]. The AgMIP GGCMI has
built a community of researchers that collaborate to perform coordinated global and regional
high-resolution impact assessments and model intercomparison studies. In turn, these improve
GGCMI applications and understanding of climate impacts on global food production, as well
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
as regional and temporal variations in these responses. For a list of global crop modelling being 6
done for the CGRA study, see the first table in appendix A.
For crops at the regional scale in CGRA, AgMIP works with the Decision Support System
for Agrotechnology Transfer (DSSAT) and the Scientific Impact assessment and Modelling
Platform for Advanced Crop and Ecosystem management (SIMPLACE). DSSAT, which comprises
crop simulation models for over 42 crops (as of v.4.6), is supported by database management
programs for soil, weather, and crop management and experimental data, and by utilities
and application programs [39,40]. The crop models in DSSAT simulate growth, development
and yield as a function of soil–plant–atmosphere dynamics, and they have been adopted for
many applications ranging from on-farm management to regional assessments of the impacts
of climate variability and climate change. DSSAT has been in operation for more than 20 years
by researchers, educators, consultants, extension agents, growers, and policy- and decision-
makers in over 100 countries worldwide. SIMPLACE uses modular software architecture within
standard technologies, which reduces the effort in model development and customization. The
multithreaded high-performance architecture enables calibration and simulations at different
spatial scales [41].
GGCMI simulations were conducted using stabilization scenarios with bias-corrected daily
input of HAPPI GCMs [4]. The trend-preserving bias-correction method [42] applied is adopted
from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) protocol [43]. These
simulations calculated crop productivity for 600 years (3 GCMs× 20 ensemble members× 10
years each) for the present and future (1.5°C and 2.0°C) with GGCMI models and protocols. The
large sample of simulation years allows for analysis of extreme yield losses and changes in the
distribution of crop productivity in the baseline and the 1.5°C and 2.0°C scenarios (see row I in
the first table in appendix A).
(d) Global and regional economics models
The role of global economics models in this study is to anticipate economic responses to changes
in crop productivity, including changes in cropland area, crop mix, imports, exports, production,
consumption and prices [20]. The current global system generally shows little flexibility in total
crop consumption patterns compared with a future world without climate changes, adjusting
all other economic variables to motivate production that satisfies demand and substituting
abundant crops where similar crops had lower production totals. Changes in economic variables,
specifically world prices of crops, are made available to the regional economics models. The
CGRA proof-of-concept study utilizes the International Model for Policy Analysis of Agricultural
Commodities and Trade (IMPACT) partial equilibrium model [44] and the Future Agricultural
Resources Model (FARM) computable general equilibrium model [45].
AgMIP applies the Trade-Off Analysis model for Multi-Dimensional impact assessment (TOA-
MD) [46,47] to implement the regional economic analysis for CGRA. The TOA-MD model is a
parsimonious, generic model for analysis of technology adoption and impact assessment, and
ecosystem services analysis. The TOA-MD model was designed to simulate technology adoption
and impact in a population of heterogeneous farms. In the TOA-MD model, farmers are presented
with a binary choice: they can operate with a current or base production system 1, or they can
switch to an alternative system 2.
In a technology adoption and impact analysis, the model simulates the proportion of farms
that would adopt the new or alternative system, as well as the impacts of the new system by
simulating impact indicators defined by the user. The choice of system is based on the distribution
of expected economic returns in the farm household population, so the predicted adoption rate
can be interpreted as the rate that is economically feasible. The model can simulate the full
range of adoption rates from zero to 100%, and thus can be helpful in studying impacts if other
factors (e.g. financial or behavioural) constrain adoption. Impacts are estimated based on the
statistical relationship between expected returns to the alternative system and outcome variables
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
PPIA .0 °
C DT Dcrop area
H nd 
2 DP
 a Dyield Dconsumption
1.5 CO2 Dcarbon
global: climate crops/livestock economics DyieldDtrade
monthly
climate CO Dprices2
statistics daily
climate Dyield
series Dcarbon
regional: climate crops/livestock economics
Dpoverty
Dnet returns
% vulnerable
Figure 2. CGRA framework for HAPPI 1.5°C and 2.0°C proof-of-concept assessment (= change). (Online version in colour.)
Table 1. Regions analysed for each discipline in the CGRA proof-of-concept study (see the first table in appendix A for further
analyses).
Punjab, Pakistan Nioro, Senegal US Pacific Northwest Global
climate   
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
crops   
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
economics   
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(e.g. economic, environmental or social outcomes). Regional prices are taken from the AgMIP
global economics models for the different SSPs and/or mitigation scenarios.
The TOA-MD model assesses climate impacts by using an analogy to technology adoption.
Farms cannot choose whether to have climate change or not, but if farms had such a choice, those
that would choose to ‘adopt’ climate change are those who would gain from it; farms that would
prefer not to ‘adopt’ climate change are those who would lose from it. An important implication of
this model, when predicting a technology adoption rate, is that the rate is typically above zero and
below 100%—it is rare for all farms to adopt a technology because in a heterogeneous population
not all farms perceive it to be beneficial. The analogy to climate impact assessment is that there
are typically both losers and gainers from climate change. The phenomenon of losers and gainers
from climate change can be explained (at least in part) by the heterogeneity in the conditions in
which the farms operate, such as soils, water resources, topography, climate, the farm household’s
socio-economic characteristics, and the broader economic, institutional and policy setting [48].
3. Proof-of-concept assessment
Figure 2 and table 1 show the explicit linkages for the elements of the CGRA 1.5°C and 2.0°C
HAPPI proof-of-concept assessment.
(a) Global scale
The global-scale simulations tested future 1.5°C and 2.0°C climate scenarios with and without
mitigation policies (i.e. a carbon tax).
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 2. Climate scenarios and assumptions of the AgMIP HAPPI proof-of-concept study.
8
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HAPPI pre-industrial period 1861–1880
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Baseline observed climate
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
GGCMI current period 1980–2009
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HAPPI current period 2006–2015
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CO2 concentration levels
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HAPPI current period 390 ppm
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HAPPI 1.5°C 423 ppm
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HAPPI 2.0°C 487 ppm
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HAPPI future time frame 2106–2115
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AgMIP CGRA current time frame 2010
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AgMIP CGRA future time frame 2050
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
GCMs CAM4-2°, CanAM4, HadAM3P, MIROC5, NorESM1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83–500 ensemble members, each running 10 years for each period
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(i) Global climate scenarios
HAPPI climate simulations are the anchor of the proof-of-concept assessment, with results
compiled from large (83–500 members) ensembles from five global climate modelling groups (see
row B in the first table in appendix A, and refs [2] and [4]). HAPPI provides a framework for
the generation of climate data describing how the climate, and in particular extreme weather,
might differ from the present day in worlds that are 1.5°C and 2.0°C warmer than pre-
industrial conditions [4]. AgMIP CGRA has selected 2050 as the timing to be associated with
the HAPPI climate projections [2], in order to enable associated economic model simulations.
This falls among the more rapid stabilization curves projected to achieve 1.5°C or 2.0°C climate
realizations [49].
The HAPPI results were processed to provide ensemble mean monthly change patterns
for 1.5°C and 2.0°C for maximum and minimum temperature (and their standard deviations),
precipitation and number of rainy days. HAPPI climate projections provide the basis for regional
climate scenarios generated in combination with local observations following the AgMIP Climate
Scenario Protocols [25], as well as global scenarios for growing-season average temperature and
precipitation changes for each of the four major crops (wheat, maize, rice and soya bean) for global
crop model sensitivity analyses [2]. Figure 3 presents climate scenarios for wheat under 2.0°C
across the ensemble median for all five HAPPI GCMs, revealing substantial regional differences
(particularly for precipitation change projections) (see electronic supplementary material, figure
S1 for climate scenarios for 2.0°C rainfed maize). Comparisons between HAPPI and transient
simulations from CMIP5 [5] show that overall projections are consistent with a subset of the larger
CMIP5 model ensemble [2].
Climate scenarios and assumptions within this CGRA proof-of-concept study are consistent
with the HAPPI guidelines to the extent possible (table 2). Bias-corrected HAPPI climate scenarios
provide additional realism due to substantial differences in extreme events between the 1.5°C
and 2.0°C climate realizations [4,35]. Climate scenarios in the proof-of-concept CGRA global crop
simulations do not include explicit changes in extreme events, and therefore the effects of changes
in extremes are not reflected in the economics model results.
(ii) Global crops
To project changes in crop yields at the global scale, multi-model ensembles were developed
from HAPPI climate realizations and AgMIP GGCMI results. The HAPPI mean growing-season
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.0°C rainfed wheat 9
(a) CAM4-2° DT ( f ) CAM4-2° DP
(b) CanAM4 DT (g) CanAM4 DP
(c) HadAM3P DT (h) HadAM3P DP
(d ) MIROC5 DT (i) MIROC5 DP
(e) NorESM1 DT ( j) NorESM1 DP
0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 –20 –15 –10 –5 0 5 10 15 20
temperature change (°C) precipitation change (%)
Figure 3. Climate scenarios for rainfed wheat growing season under the 2.0°C climate realizations across five HAPPI GCMs.
(a–e) Changes in temperature and (f–j) changes in precipitation. Grid cells with less than 10 ha of wheat not shown.
temperature and precipitation changes were located on yield response surfaces developed from
AgMIP GGCMI sensitivity tests conducted under combinations of changes to CO2, temperature,
water and nitrogen [2,7,13]. Results for the four major crops were projected for five GCMs
under rainfed and irrigated growing conditions for 1.5°C and 2.0°C climate realizations, holding
nitrogen levels at present conditions. Figure 4 shows rainfed wheat changes for the 2.0°C HAPPI
scenario (see electronic supplementary material, figure S2 for the same results for rainfed maize).
Regional differences are evident across the GCMs, but large-scale patterns are strongly influenced
by the global gridded crop model (GGCM). In general, in the 2.0°C climate realization with CO2
effects, wheat increases in the Pacific Northwest, southern Europe and northern China, while
reductions are seen in the North American interior, northern Eurasia, South Asia and most
Southern Hemisphere production regions. The Lund–Potsdam–Jena managed Land (LPJmL)
model is optimistic given its beneficial representation of CO2 processes.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.0°C rainfed wheat with CO2 effects 10
(a) CAM4-2° pDSSAT ( f ) CAM4-2° GEPIC (k) CAM4-2° LPJmL
(b) CanAM4 pDSSAT (g) CanAM4 GEPIC (l) CanAM4 LPJmL
(c) HadAM3P pDSSAT (h) HadAM3P GEPIC (m)HadAM3P LPJmL
(d ) MIROC5 pDSSAT (i) MIROC5 GEPIC (n) MIROC5 LPJmL
(e) NorESM1 pDSSAT ( j) NorESM1 GEPIC (o) NorESM1 LPJmL
–30 –20 –10 0 10 20 30
wheat yield change (%)
Figure 4. Projected rainfed wheat yield change (compared with HAPPI 2006–2015 current period). Columns show different
global gridded crop models ((a–e) parallel Decision Support System for Agrotechnology Transfer, pDSSAT; (f–j) Geographic
Information System-based Environmental Policy Integrated Climate, GEPIC; and (k–o) Lund–Potsdam–Jena managed Land,
LPJmL), and rows show five HAPPI GCMs that provided driving climate projections. Grid cells with less than 10 ha of wheat not
shown.
Figure 5 compares differences between the 1.5°C and 2.0°C climate realizations and the three
GGCM projections (for the CanAM4 climate scenario) for rainfed wheat with and without direct
effects of CO2 (see electronic supplementary material, figure S3 for results for rainfed maize).
When direct effects of CO2 are taken into account, yield is higher in 2.0°C compared to 1.5°C
in many locations due to the effect of higher CO2 concentrations. In simulations without CO2
effects, crop yields associated with 2.0°C are lower in many regions of the world than with 1.5°C.
The range between these projections represents the limits of uncertain CO2 effects across different
crop species and farming systems, which is a persistent challenge for crop models given that
experimental results are limited to a relatively small set of regions and conditions [50–53].
(iii) Global economics
The GGCMI crop yield changes derived from the AgMIP HAPPI 1.5°C and 2.0°C climate scenarios
were applied as inputs to the IMPACT and FARM models under future scenarios with and
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rainfed wheat with and without CO2 effects 2.0°C–1.5°C 11
(a) pDSSAT with CO2 effects (d) pDSSAT no CO2 effects
(b) GEPIC with CO2 effects (e) GEPIC no CO2 effects
(c) LPJmL with CO2 effects ( f ) LPJmL no CO2 effects
–15 –10 –5 0 5 10 15
wheat yield change (%)
Figure 5. Difference between 2.0°C and 1.5°C rainfed wheat yield with (a–c) and without (d–f ) CO2 effects for three global
gridded crop models (pDSSAT, GEPIC and LPJmL) and the CanAM4 HAPPI scenario.
without mitigation policies (the latter imposed through a carbon tax). Figure 6 is an example
of these results, showing global price and area changes for maize and wheat under 2.0°C. Results
show uncertainty in world commodity price changes without a carbon tax simulated by the two
global economics models associated with the exogenous climate shocks produced by the five
HAPPI GCMs, each simulated by three GGCMs (GEPIC [54], LPJmL [55] and pDSSAT [56]). Maize
prices increase in all three GGCMs in IMPACT, while in the FARM economics model for pDSSAT
and GEPIC prices increase and decrease slightly for LPJmL. Wheat prices increase for the pDSSAT
and GEPIC simulations, while simulations driven by LPJmL have higher CO2 effects leading to
higher production and a reduction in prices. Maize area increases across all GGCMs and both
global economic models. The FARM model has lower wheat price changes than IMPACT, but
differs in the associated pressure on land use (small changes except for reduction in wheat area
for LPJmL). Results in the FARM model with mitigation show much greater increases in prices
(+20% for maize and wheat) and large decreases in crop areas (−16% for maize and −14% for
wheat).
(b) Regional scale
The motivation for the coordinated simulations at the regional scale are to explore the effects of
global mitigation policies (e.g. carbon tax) and local ones (e.g. introduction of biofuel crops and
adoption of no-till cultivation), and the impacts of climate change on regional farming systems
and farmer livelihoods. Regional approaches also incorporate more detailed information about
local climate, cultivars, management and household economics [57].
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
SSP2 + 2.0°C with and without mitigation 12
(a) price changes
35 CAM4-2°
CanAM4
30 HadAM3P
MIROC5
25 NorESM1
(%) with mitigation
20 +20% +20%
15
10
5
0
–5
maize wheat
–10
AT IC mL AT IC mL AT IC mL AT IC mL
SS EP PJ SS EP PJ S P J S P J
pD G L D G L
S E P S E P
p pD G L pD G L
IMPACT FARM IMPACT FARM
(b) area changes
15
10
5
0
–16% –14%
maize wheat
–5
SA
T
PI
C
Jm
L AT C LS PI Jm SA
T IC mL AT ICE m
L
DS G LP
E
DS G LP S
EP PJ SSG L GE
P J
p p pD pD L
P
IMPACT FARM IMPACT FARM
Figure 6. Global economic impacts of 2.0°C climate realization on maize and wheat (a) price and (b) area under an SSP2 2050
scenario with no mitigation, compared with an equivalent scenario without climate impacts. Results presented for IMPACT
and FARM global economics models driven by agricultural production changes from three global gridded crop models and five
HAPPI GCMs. For comparison, price and area changes are presented from a FARM simulation with no direct climate impacts on
agriculture but with a carbon tax implemented to create a 2.0°C pathway.
(i) Regional climate scenarios
HAPPI seasonal changes were imposed on historical observations to create statistically
downscaled local climate scenarios for crop model simulations at regional scales utilizing
area change (%) price change (%)
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(a) mean monthly temperature (°C) (c) mean monthly temperature (°C) 13
36
observed (1980–2009)
35 Bahawalnagar, Punjab, +1.5°C
34 Nioro, SenegalPakistan +2.0°C
30
32
25 30
20 28
15 26
10 24
J F M A M J J A S O N D J F M A M J J A S O N D
(b) mean monthly precipitation (mm) (d) mean monthly precipitation (mm)
50 300
40 250
200
30
150
20
100
10 50
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
Figure 7. Local climate projections for Bahawalnagar, Punjab, Pakistan (a,b) and Nioro, Senegal (c,d) from five HAPPI GCMs
representing 1.5°C and 2.0°C climate realizations. (Note differences in scales.)
the AgMIP mean-and-variability approach developed for the RIAs in sub-Saharan Africa and
South Asia [25]. Historical observations, from either local weather stations or AgMERRA (the
Agricultural applications version of the Modern-Era Retrospective analysis for Research and
Applications) [58], were adjusted to match the new temperature distributions; the method
maintains the shape of the precipitation distribution, while changing the number of rainy days.
The result is a 31-year weather series for each GCM scenario (see figure 7 for mean monthly
temperature (°C) and mean monthly precipitation (mm) for observed and projected 1.5°C and
2.0°C assessments for Bahawalnagar, Punjab, Pakistan and Nioro, Senegal).
(ii) Regional crops
The AgMIP HAPPI 1.5°C and 2.0°C downscaled climate scenarios were employed in DSSAT
simulations for the agricultural regions of the Punjab, Pakistan at five sites (Bahawalnagar,
Bahawalpur, Lodhran, Multan and Rahim Yar Khan) and Nioro, Senegal. (The SIMPLACE
model was also operated for Senegal.) An additional analysis of farming systems in the US
Pacific Northwest used the DeNitrification DeComposition (DNDC) model [59]. Methods (inputs,
calibration and validation) for the regional crop and economics model simulations in Pakistan,
Senegal and the US Pacific Northwest are described in Ahmad et al. [60], Adiku et al. [61] and
Antle et al. [62], respectively.
The CGRA framework allows exploration of uncertainty in projections of the biophysical
impacts of HAPPI climate scenarios as simulated by a locally calibrated crop model (DSSAT)
and global gridded crop models from GGCMI. Figure 8 compares results from the five
GCMs for Punjab wheat and Nioro maize. The global gridded version of DSSAT (pDSSAT)
agrees most strongly with the local projections for both regional crops. GEPIC and LPJmL
projections are much less pessimistic about maize production, while LPJmL is optimistic
about wheat.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
site-based and gridded crop model yield 14
changes with and without CO2 effects
20
DSSAT
15 pDSSAT
GEPIC
10 LPJmL
median of wo
5 CO2 projections
0
–5
–10
–15
–20
–25
–30
Pakistan wheat Senegal maize
+1.5°C +2.0°C +1.5°C +2.0°C
Figure 8. Regional and global yield projections for wheat in Pakistan (mean of five sites) and maize in Senegal. Results
presentedwithCO2 effects for locally configuredDSSAT simulations and correspondinggrid cells fromglobal gridded cropmodels
(pDSSAT, GEPIC and LPJmL). Box-and-whiskers represent five HAPPI GCMs and dots represent the median of corresponding
simulations without (wo) CO2 effects.
(iii) Regional economics
The TOA-MD tested the following hypotheses:
H1: Global mitigation efforts to achieve 1.5°C will have quantifiable impacts on
farm households in low- and high-income countries and on achieving sustainable
development goals, through effects of mitigation policies and prices (MPP), cost of
production, land use and trade (H1 was tested in Punjab, Pakistan).
H2: In countries with mitigation policies, farm households will benefit from participation
in mitigation activities and will contribute to net greenhouse gas mitigation and other
sustainable development goals (H2 was tested in the US Pacific Northwest).
The AgMIP RIA approach [27] quantified the impacts of 1.5°C and 2.0°C global warming and
global mitigation policies and associated price changes on regional farming systems by answering
the following RIA questions:
Question 1: What are the impacts of global warming (of 1.5°C and 2.0°C) and global
mitigation policies and prices on production systems in the near term (i.e. 2030)?
Question 2: What are the impacts of global warming (of 1.5°C and 2.0°C) and global
mitigation policies and prices on future (i.e. 2050) production systems?
Different assumptions in the global economic modelling implemented in IMPACT were used
to design a set of scenarios as a sensitivity test for different global price changes on key crops of
the farming system. IMPACT results included price and productivity projections to 2030 and 2050
under five GCMs and three global gridded crop models (pDSSAT, GEPIC and LPJmL). The core
questions and scenarios design for Punjab, Pakistan are described in table 3.
change (%)
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 3. Scenario design for the 1.5°C and 2.0°C assessments for Punjab, Pakistan implemented with TOA-MD.
15
Question 1: 2030a/Question 2: 2050b
scenario system 1 system 2
scenario 1 no climate change, no major climate change, no major agricultural
agricultural global mitigation global mitigation policies
policies
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
scenario 2 no climate change, no major climate change, agricultural global
agricultural global mitigation mitigation policies based on SSP1
policies
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
scenario 3 no climate change, agricultural global climate change, agricultural global
mitigation policies based on SSP1 mitigation policies based on SSP1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
aAssuming no significant change in crop yields due to climate change. Trends to 2030 based on a sustainable development RAP.
bRegional crop modelling estimated impacts of climate change on crop yields. Trends to 2050 based on a sustainable development RAP.
TOA-MD estimates the changes in key economic indicators such as the average impact on farm
income, vulnerability of farm income to loss (e.g. the percentage of the population expected to lose
farm income and the magnitude of the losses) and changes in poverty indicators (e.g. headcount
poverty rate and poverty gap). Other environmental and social indicators can be included given
available data, including changes in food security indicators (e.g. proportion of the population
consuming a nutritionally adequate diet) and greenhouse gas emissions.
Punjab, Pakistan. In the Punjab region of Pakistan, output data from a global economics model
(i.e. IMPACT) represented the likely impacts of global mitigation policies on wheat and cotton
prices under 1.5°C and 2.0°C scenarios by 2030 and 2050 (table 4). The analysis shows that, in
the near term, the overall impact of changes in crop yields, prices and costs of production on
farm income is small with the 2.0°C, resulting in a slightly negative change in net farm income
(between −1% and −3% on average across the different districts in the Punjab region). The
longer-term analysis shows a more negative picture for the Punjab region in Pakistan. Between
63 and 73% of households are vulnerable to losses; net farm income might be reduced between
10 and 21%, with net economic losses up to 41% of farm income. These conditions can increase
poverty rates by 12–38%, demonstrating that the severity of impacts differs across the five districts
tested in the Punjab region. The impacts in the near term (2030) are much smaller than the
longer term (2050) when the impacts of climate change on crop yields are larger; price increases
also tend to be larger but may not offset yield losses. See figure 8 for regional crop results for
Punjab, Pakistan.
US Pacific Northwest. Table 5 presents results addressing the two hypotheses H1 and H2 in
the context of question 1, based on an analysis of the wheat-based production system in the US
Pacific Northwest [63]. It evaluated the introduction of the biofuel oilseed crop Camelina sativa
into the rainfed winter wheat–fallow system and the adoption of no-till cultivation for wheat.
In the scenario presented here representing the near term (2020–2030), crop yields are assumed
to be increased by higher CO2 in the atmosphere and a warmer, wetter winter wheat growing
season; and wheat prices and costs of production are assumed to increase due to mitigation
policies. The analysis considers two domestic mitigation policy options. One is to support the
development of a market for camelina as a biofuel with a relatively favourable price; the other
is to pay farmers for net reductions in soil-based greenhouse gas emissions. The analysis was
implemented with simulations of an agroecosystem model (DNDC) to estimate crop yield and soil
emissions, combined with a life cycle analysis to evaluate overall impacts on the global warming
potential (GWP) of the change in system. The economic analysis was implemented using
the TOA-MD economic impact assessment model parametrized with farm-level agricultural
census data.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
Table 4. Impacts of 1.5°C and 2.0°C global carbon tax on five villages in the Punjab cotton–wheat system in Pakistan simulated by the TOA-MD model. Global economic output data included five GCMs and
three global gridded crop models. Results shown are the average across GCMs, crop models and scenarios. Aggregated results for the Punjab region are weighted averages based on the area of each district. Hhs,
households.
near term (2030) longer term (2050)
average average
extent of economic economic
vulnerability average % losses (% of change in extent of average % losses (% of change in
(% vulnerable change in farm farm net poverty rate vulnerability (% change in farm farm net poverty rate
climate region hhs) net returns returns) (%) vulnerable hhs) net returns returns) (%)
1.5°C Bahawalnagar 50.0 0.0 −23.6 2.9 65.1 −11.0 −27.1 23.0
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bahawalpur 50.6 −0.5 −24.3 4.1 68.9 −14.2 −28.6 18.4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lodhran 45.5 3.8 −25.5 −13.4 67.5 −14.4 −31.3 12.7
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multan 48.9 1.2 −35.4 −1.8 65.5 −16.0 −38.6 14.4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rahim Yar Khan 50.0 0.0 −24.8 −1.0 63.8 −10.7 −28.2 26.7
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Punjab region 49.43 0.54 −25.86 −1.14 65.80 −12.63 −29.82 18.12
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.0°C Bahawalnagar 53.1 −2.3 −24.2 5.7 71.0 −15.7 −29.2 32.4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bahawalpur 53.6 −2.7 −24.8 7.2 73.2 −18.0 −30.6 27.6
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lodhran 48.3 1.5 −26.0 −11.3 73.1 −19.8 −33.9 19.1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multan 51.0 −1.1 −35.8 0.4 70.5 −21.8 −41.4 23.5
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rahim Yar Khan 53.0 −2.3 −25.4 1.8 69.3 −15.3 −30.3 38.3
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Punjab region 52.27 −1.74 −26.41 1.35 71.11 −17.36 −32.04 27.53
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 5. Projected adoption rates and impacts of a winter wheat–fallow–camelina cropping system by farmers currently
17
following a wheat–fallow rotation in the US Pacific Northwest. Note: Scenarios represent prices of wheat, camelina and carbon
offsets [63]. GWP, global warming potential.
adopter impact on change in soil change in
scenario farm size adopters (%) farm income (%) emissions (%) GWP (%)
low prices large 41.623 208.976 −73.739 −17.647
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
small 45.671 387.433 −81.114 −19.439
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
high prices large 72.302 303.789 −131.171 −34.721
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
small 74.266 577.479 −134.888 −35.816
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 5 presents results from two scenarios, one with low wheat, camelina and carbon offset
prices, and the other with high prices [63]. These two scenarios span plausible ranges of crop
prices projected by global IAMs, and plausible values for biofuel crop and carbon offset prices
in a policy environment supporting low greenhouse gas emissions. The analysis shows that
between 40 and 75% of farms could benefit from changing from the winter wheat–fallow system
to the alternative system, depending on their size, location and the productivity of the alternative
practices, and the price and policy regime. For those farms that would adopt, a favourable policy
environment could generate a win–win outcome, with farm incomes substantially increased and
with greenhouse gas emissions substantially reduced. In the high-price scenario, the farming
system transitions from a net source of soil emissions to a net sink, and the GWP of all activities
associated with the farming system is reduced by 35%.
(c) Synthesis of Coordinated Global and Regional Assessments proof-of-concept results
At the global scale with direct effects of CO2 taken into account, there were mixed areas of positive
and negative simulated wheat and maize yield changes, with declines in some breadbasket
regions. Without CO2 effects, production declined in all model combinations for both climate
realizations. Overall, higher CO2 concentrations have a beneficial effect that leads to higher
yields for 2.0°C, but substantial uncertainty in CO2 response underscores the potential for
lower total production given slightly higher temperature and more pronounced precipitation
changes. Results for maize, which as a C4 crop has a lower response to CO2 concentrations, are
predominantly negative in pDSSAT, with mixed to positive yield changes in GEPIC and LPJmL.
With 2.0°C maize yield changes are more negative than those of 1.5°C in many places of the
world, both with and without CO2 effects. These global wheat and maize yield changes resulted
primarily in increases in prices, with greater increases at 1.5°C compared with 2.0°C, and increases
in land area for these crops, especially maize. The regional crop simulations, with CO2 effects
taken into account, showed that maize yield declines for the most part were greater at 2.0°C than
at 1.5°C in Nioro, Senegal. In Pakistan, wheat yield declines were similar in both 2.0°C and 1.5°C.
In conjunction with price changes from the global economics models, productivity declines in the
Punjab, Pakistan, resulted in an increase in vulnerable households and in the poverty rate. In the
US Pacific Northwest, the introduction of a biofuel oilseed crop generated a win–win outcome,
with farm incomes increased and greenhouse gas emissions reduced. Further analysis is needed
to test results at the two scales at multiple locations.
4. Cross-disciplinary and scale issues
A number of cross-disciplinary and scale issues arose during the CGRA proof-of-concept study.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(a) Timing: climate, crops and economics 18
Coordinated timing is a critical need for cross-disciplinary assessment. A key issue is the setting
of the timing for the agricultural sector simulations. HAPPI climate scenarios are based on a
current period and future climate equilibrium, with both 1.5°C and 2.0°C placed in a 2106–
2115 time horizon constrained by sea surface temperatures and greenhouse gas concentrations;
however, these simulations are not strongly connected to this decade and can potentially be used
as a timeless representation of these new equilibrium worlds [4]. One exception appears to be
a reduction in aerosol pollution assumed for South and East Asia (see row B in the first table
in appendix A, and ref. [4]). Likewise, crop yield projections in CGRA are similarly timeless in
their comparison of the current climate with a scenario of future climate while holding farm
management and technology steady.
There are also difficulties in estimating economic and technological trends affecting
agricultural markets past the middle of the century. Pathways of socio-economic development,
policy priorities and technological advances are inextricably linked to their transient evolution. It
follows that a 1.5°C equilibrium in 2030 would force the agricultural sector to face a different set
of challenges than would be present if the same equilibrium were to arrive in 2050, 2070 or 2100.
The bulk of AgMIP economic assessments do not extend beyond 2050 because the estimation of
RAPs [30] encompasses assumptions on scales comparable to the overall differences with today,
making longer-term projections more uncertain [20,21,27]. In AGCLIM50 Phase 2 [22] and the
Energy Modeling Forum [64], some global economics models will provide results to 2070 and
2100, respectively.
The CGRA proof-of-concept assessment places the 1.5°C and 2.0°C equilibria in 2050, allowing
the climate signal to be contrasted on the same time horizon; however, it is important that this
time horizon be contextualized given mitigation pathways, potential overshoots in mid-century
temperatures and studies that adopt alternative assumptions. A further step is to compare results
driven by the newer versions of the HAPPI GCMs to crop and economics model results driven
by previous GCM versions in earlier AgMIP assessments.
(b) CO2 levels: climate and crops
The HAPPI CO2 level for 2.0°C (487 ppm; compared with 390 ppm in 2010) is high relative to 2050
RCP2.6 levels (443 ppm) and therefore results in yield differences due to crop model responses
to carbon dioxide through photosynthetic and crop water use efficiency mechanisms [65–68].
Just as each climate model has a distinguishing climate sensitivity (relationship between CO2
and global temperature) [69], the HAPPI CO2 effects on crop model responses merit further
investigation.
For crop models, there is active research on how well CO2 physiological effects are captured
in the current parametrizations (e.g. [50,66]). Some crop models simulate the effects of nitrogen
deficiencies on realization of CO2 responses (e.g. pDSSAT, EPIC, GEPIC and PEGASUS) and
these tend to project much more severe impacts from climate change [15]. It is well known that
the strengths of CO2 physiological effects vary by crop type (C3 and C4), but there is variation
among the crops in each of these types [51,52]. A continuing CGRA research issue involves
how to represent CO2 fertilization effects in the wider range of crops included in the economics
models.
(c) Extreme events: climate, crops and economics
Changes in the distribution of extreme events can be analysed in the HAPPI and CMIP5 climate
scenarios. However, the method that created the climate scenarios for the GGCMI crop modelling
does not shift inter-annual distributions. At the local level, stretched distributions allow for
changes in the frequency and magnitude of extreme temperatures or the number of rainy days
[25]. All disciplinary groups are working on advancing in this area. Some of the global economics
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
models are beginning to put in year-to-year variability [70], but these efforts have not yet come to 19
full fruition. Some IAMs are starting to add crop responsiveness to shifts in the number of degree
days. GGCMI efforts employing bias-corrected daily HAPPI inputs allow for a more thorough
assessment of changes in the distribution of extremes [35].
(d) Interactions of global and regional economics
In the course of the CGRA proof-of-concept assessment, the need for national-scale analyses
emerged as crucial. Global models provide contextual boundaries and summarize broad trends
for regional economics modelling; often, they are not able to represent subnational behaviour that
is not always generalizable. Regional TOA-MD simulations were driven by broad regional prices
(e.g. African maize prices), which can differ from global prices according to supply, demand and
import/export costs, but are at a broader scale than is desired for many local analyses. Country-
level modelling is needed to bridge the gap between global economics models and the subnational
models in the CGRA. To respond to this need, global economics models are becoming more
disaggregated than in the past, presenting more results at the national level.
As many decisions and investments are made on a national scale, the lack of availability
of national-scale data and outputs continues to be a bottleneck for both national and regional
assessments. Country-level modelling would help to downscale global model results, and such
national-level modelling is more readily usable by the subnational-level models. Conversely,
CGRA also provides the opportunity to aggregate and summarize subnational modelling to a
country scale. These national aggregations can then inform global economic models. Across all
scales, policy assumptions need to be clarified and SSPs need to be matched to regional RAPs
developed with stakeholders.
(e) Mitigation and adaptation
For global agricultural mitigation, the main mechanisms studied in the IAMs are reduction of
direct non-CO2 emissions from agriculture, reduction of CO2 emissions from land-use change
and forest sink enhancement, and biomass for energy production. At regional scales, increasing
soil organic carbon is of active interest. There needs to be much more interaction between
the global and regional scales to determine how farming systems could and would actually
adopt mitigation practices, and conversely, how mitigation in agriculture could conflict with or
benefit local adaptation needs. The 1.5°C and 2.0°C worlds include effects of both mitigation
and direct climate impacts, and thus associated adaptation interventions need to be taken
into account.
(f) Interaction of global crops and economics
While AgMIP continues to focus on the intercomparison of agricultural models and the
development of subsequent improvements, the CGRA aims to facilitate the best possible
interchange of information between disciplines and scales. The GGCMI has so far focused on the
four major crops, as these can be simulated by a broad range of models [13,14]. The translation
of climate change impacts on crop yields, as simulated by GGCMs, to climate change impacts on
agricultural commodities, as simulated in economics models, requires mapping between entities
that is increasingly complicated if information is only available for a limited number of crops [23].
Within the GGCMI ensemble, some crop models can provide simulations for a broader set of
crops (see also [15]) and managed grasslands [71], so that economics models could be much better
informed in a coordinated simulation of climate change impacts on an as-broad-as-possible set of
crops and grasslands. The evaluation of less-prominent crops and livestock remains a challenge,
and if certain crops can only be simulated by a single or a small number of models, the crop
model-specific uncertainty cannot be assessed. In general there are fewer livestock models in both
global and regional scales.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Conclusion 20
Along with the continuing undiminished focus on model improvement through multi-model
ensembles for individual crops, livestock, soils and their embedded processes, regional farming
systems and global economics, a new complementary focus of AgMIP is the creation of protocols
for CGRAs. CGRA results will have direct implications for international climate policy, national
mitigation and adaptation planning, and development aid with a focus on food security. The
extension of AgMIP to encompass improvement of linkages across scales and disciplines will help
to make its research relevant to a wider range of agricultural stakeholders, in both developing and
developed countries.
The multiple-model approach at each stage of the CGRA is important because it demonstrates
that there remains considerable uncertainty in climate impact results, both in direction and
magnitude of change and in geographical patterns. Characteristics of these uncertainties can be
traced back to their origins in climate models (which often dictate regional patterns), crop models
(which determine large-scale responses to temperature, precipitation and CO2) and economics
models (which translate production changes into price and area changes). Furthermore, the
wider perspective that cross-scale intercomparison studies provide also helps to illustrate and
communicate to users of climate impacts information that the results of individual models
or studies are uncertain, while ensemble approaches provide more robust information. This
is an important contribution of the AgMIP intercomparison programme. Furthermore, the
CGRA approach enables multiple spin-off analyses to test the sensitivity of results to key
assumptions and to consider interactions of processes and realism of projections across disciplines
and scales.
Results from the HAPPI 1.5°C and 2.0°C climate scenarios show the potential for higher
prices globally and substantial regional disruption to production, prices and land use. Future
impacts are also affected by the costs of mitigation policies on the agricultural sector, which
may overwhelm the direct biophysical impacts of these relatively small climate changes. CGRA
findings will also inform and improve integrated assessment modelling, nitrogen and carbon
cycle modelling, and projections of mitigation and adaptation impacts on land, water resources,
ecosystems and food security.
Data accessibility. All data will be made available in the AgMIP Data Interchange (https://data.agmip.org/).
Electronic supplementary material is available online at rs.figshare.com.
Authors’ contributions. C.R., A.C.R., J.A. and J.E. led the development of the overall methodology. A.C.R., M.P. and
C.-F.S. (HAPPI team) created climate projections. J.E. and C.M. led and conducted global gridded crop model
simulations and C.F. conducted GEPIC global gridded crop model simulations. K.W., H.L.-C., D.M.-D’C.,
R.S., P.H., H.V. and I.P.-D. conducted global economics model simulations. G.H., C.P., R.M.R., D.S.M. and F.E.
contributed to site-based crop modelling. J.A., R.O.V., M.A. and I.H. did regional economics modelling. A.C.
and D.S.M. led RIAs in Pakistan and West Africa, respectively. E.M.C. provided analysis of CGRA results. All
the authors contributed to the development of CGRA protocols.
Competing interests. We declare we have no competing interests.
Funding. We received funding from the USDA Office of The Chief Economist through USDA award 58-0111-
16-010 to Columbia University.
Acknowledgements. We thank the USDA Office of The Chief Economist for supporting the 1.5°C and 2.0°C
Assessment through USDA award 58-0111-16-010 to Columbia University (C.Z. Mutter, Managing PI), the
Agricultural Research Service for their contribution to AgMIP activities at ARS facilities and IIASA for co-
hosting the AgMIP Workshop on the Impacts of 1.5°C and 2°C Warming on Agriculture. IFPRI’s contribution
is also supported by the CGIAR Research Program on Policies, Institutions, and Markets (PIM). We thank
Shari Lifson and Greg Reppucci for graphics assistance.
Disclaimer. The views expressed are those of the authors and should not be attributed to USDA or any of its
agencies, including the Office of the Chief Economist, the Economic Research Service, or the Agricultural
Research Service.
Appendix A
See tables 6 and 7.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
Table 6. Activities and projects supporting AgMIP CGRA. Projects participating in the proof-of-concept study are in italics. n.a., not applicable.
project name disciplines scale sites/grids climate models crop models economic models
A. Full AgMIP CGRA for 1.5°C and 2°C climate–crop–economics all five HAPPI GCMs three global gridded models IMPACT, FARM, TOA-MD
(pDSSAT, GEPIC and LPJmL)
and DSSAT
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B. AgMIP Climate climate global five HAPPI GCMs; comparison n.a. n.a.
with 29 CMIP5 GCMs’
transient simulations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C. GGCMI Phase 2 crops (maize, soya, rice, global grids n.a., for sensitivity/emulators; three complete sets (GEPIC, n.a.
spring wheat and winter yield projections estimated LPJmL and pDSSAT),+ nine
wheat) from five HAPPI GCMs and partial sets (GEPIC, CARAIB,
compared with 29 CMIP5 PROMET, PEPIC,
GCMs’ transient simulations ORCHIDEE-crop, PRYSBI2,
EPIC-IIASA, APSIM-UGOE
and LPJ-GUESS)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
D. AgMIP Global Economics Team economics global five HAPPI GCMs GEPIC, LPJmL, pDSSAT IMPACT, FARM
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
E. AgMIP Global Economics Team economics global n.a. n.a. MAgPIE, GLOBIOM, AIM,
Phase 2 IMPACT, IMAGE, CAPRI,
MAGNET, GCAM, FARM,
ENVISAGE
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F. AgMIP CGRA 1.5°C and 2°C economics regionalafarming systems five HAPPI GCMs DSSAT TOA-MD
. . . . . . . .R. .e. .g. .i.o. .n. .a. .l. .E. .c.o. .n. .o. .m. . .i.c. .s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(Continued.)
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
Table 6. (Continued.)
project name disciplines scale sites/grids climate models crop models economic models
G. AgMIP-Wheat crops (wheat) global network of (60) sites five HAPPI GCMs 34 wheat models n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
H. EU-MACSUR Integrated Assessments crops-economics continental five GCMs n.a. n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
I. HAPPI GGCMI crops global three to four HAPPI GCMs GEPIC, PEPIC, PEGASUS, n.a.
(downscaled) LPJ-GUESS, LPJmL,
ORCHIDEE-crop
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
J. Project funded by the IAM/economics global/17 regions n.a. n.a. AIM, CAPRI, GCAM,
Ministry of GLOBIOM, IMAGE,
Environment, Japan, IMPACT, MAgPIE
. . . . . . . .p. . r. .o. .j.e. .c.t. . (. .2. .–. .1. .7.0. .2. .). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
K. SSP Decomposition economics global/10 regions n.a. n.a. GCAM, MAgPIE, IMAGE,
MAGNET, GLOBIOM,
IMPACT, FARM
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
L. AGCLIM50 Phase 1 and 2 economics global comparison of RCP2.6 and n.a. GLOBIOM, CAPRI, IMAGE,
(1.5°C and 2°C) RCP1.9 MAGNET
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
M. WASCAL/CIWARA crops regional farming systems five HAPPI GCMs DSSAT, SIMPLACE n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
N. Rapid Assessment of Agriculture crops regional farming systems five HAPPI GCMs DSSAT (Senegal, Pakistan, USA), n.a.
in a 1.5°C Scenario: an AgMIP CropSyst (US Pacific
CGRA (UFL) Northwest)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
aAgMIP regions are defined as subnational or within-country areas practising a similar farming system.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table7. SSPs andRAPs in theAgMIPCGRA.Projects participating in theproof-of-concept studyare in italics. n.a., not applicable.
23
project name SSPs RAPsa
A. Full AgMIP CGRA for 1.5°C and 2°C SSP1, SSP2 all RAPs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B. AgMIP Climate n.a. n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C. GGCMI Phase 2 n.a., do consider increased n.a., do consider increased
fertilizer and adaptation in fertilizer and adaptation in
CTWN-A CTWN-A
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
D. AgMIP Global Economics Team all SSPs n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
E. AgMIP Global Economics Team Phase 2 SSP1 n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F. AgMIP CGRA 1.5°C and 2°C Regional Economics SSP1 RAP4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G. AgMIP-Wheat n.a. n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
H. EU-MACSUR Integrated Assessment SSP2 (perhaps SSP1 and SSP3) n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
I. HAPPI GGCMI n.a. n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
J. Project funded by the Ministry of SSP1,2,3 n.a.
. . . . . . . . .E. n. . .v. i. r. .o. .n. .m. . .e. .n. .t.,. .J. a. .p. . .a.n. . .,.p. . .r.o. .j.e. .c.t. . (. .2. .–. .1. .7.0. .2. .). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
K. SSP Decomposition SSP1,2,3,4,5 n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
L. AGCLIM50 Phase 1 SSP 1–3 n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
L. AGCLIM50 Phase 2 (1.5°C and 2°C) SSP 2 European Union
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
M. WASCAL/CIWARA n.a n.a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
N. Rapid Assessment of Agriculture in a n.a. green road, with and without C
1.5°C Scenario: an AgMIP CGRA (UFL) prices
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
aRAPs are agriculture-specific extensions of the SSPs for global and regional analyses; see [30].
References
1. Rosenzweig C et al. 2013 The Agricultural Model Intercomparison and Improvement Project
(AgMIP): protocols and pilot studies. Agric. Forest Meteorol. 170, 166–182. (doi:10.1016/
j.agrformet.2012.09.011)
2. Ruane AC et al. 2018 AgMIP Coordinated Global and Regional Assessment: +1.5°C protocols.
AgMIP. See http://www.agmip.org/wp-content/uploads/2018/02/AgMIP-CGRA-1.5-
Protocol-Feb-15-2018.pdf.
3. Rosenzweig C, Antle J, Elliott J. 2015 Assessing impacts of climate change on food security
worldwide. Meeting report. EOS Earth & Space Science News. See https://eos.org/meeting-
reports/assessing-impacts-of-climate-change-on-food-security-worldwide.
4. Mitchell D et al. 2017 Half a degree Additional warming, Prognosis and Projected
Impacts (HAPPI): background and experimental design. Geosci. Model Dev. 10, 571–583.
(doi:10.5194/gmd-10-571-2017)
5. Taylor KE, Stouffer RJ, Meehl GA. 2012 An overview of CMIP5 and the experiment design.
Bull. Am. Meteorol. Soc. 93, 485–498. (doi:10.1175/BAMS-D-11-00094.1)
6. UNFCCC. 2015 Paris Agreement. 21st Conference of the Parties. Paris, France: United Nations.
7. Ruane AC et al. 2017 An AgMIP framework for improved agricultural representation in IAMs.
Environ. Res. Lett. 12, 125003. (doi:10.1088/1748-9326/aa8da6)
8. Füssel HM. 2010 Modeling impacts and adaptation in global IAMs. Wiley Interdisc. Rev. Clim.
Change 1, 288–303. (doi:10.1002/wcc.40)
9. van Vuuren DP, Lowe J, Stehfest E, Gohar L, Hof AF, Hope C, Warren R, Meinshausen M,
Plattner GK. 2011 How well do integrated assessment models simulate climate change? Clim.
Change 104, 255–285. (doi:10.1007/s10584-009-9764-2)
10. van Ittersum MK et al. 2008 Integrated assessment of agricultural systems—a component-
based framework for the European Union (SEAMLESS). Agric. Syst. 96, 150–165.
(doi:10.1016/j.agsy.2007.07.009)
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11. Ewert F et al. 2009 A methodology for enhanced flexibility of integrated assessment in 24
agriculture. Environ. Sci. Policy 12, 546–561. (doi:10.1016/j.envsci.2009.02.005)
12. Ewert F, van Ittersum MK, Heckelei T, Therond O, Bezlepkina I, Andersen E. 2011 Scale
changes and model linking methods for integrated assessment of agri-environmental systems.
Agric. Ecosyst. Environ. 142, 6–17. (doi:10.1016/j.agee.2011.05.016)
13. Elliott J et al. 2015 The Global Gridded Crop Model Intercomparison: data and modeling
protocols for Phase 1 (v1. 0). Geosci. Model Dev. 8, 261–277. (doi:10.5194/gmd-8-261-2015)
14. Müller C et al. 2017 Global gridded crop model evaluation: benchmarking, skills, deficiencies
and implications. Geosci. Model Dev. 10, 1403. (doi:10.5194/gmd-10-1403-2017)
15. Rosenzweig C et al. 2014 Assessing agricultural risks of climate change in the 21st century
in a Global Gridded Crop Model Intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273.
(doi:10.1073/pnas.1222463110)
16. Bassu S et al. 2014 How do various maize crop models vary in their responses to climate
change factors? Glob. Change Biol. 20, 2301–2320. (doi:10.1111/gcb.12520)
17. Asseng S et al. 2013 Uncertainty in simulating wheat yields under climate change. Nat. Clim.
Change 3, 827–832. (doi:10.1038/nclimate1916)
18. Li T et al. 2015 Uncertainties in predicting rice yield by current crop models under a wide
range of climatic conditions. Glob. Change Biol. 21, 1328–1341. (doi:10.1111/gcb.12758)
19. Fleisher DH et al. 2017 A potato model inter-comparison across varying climates and
productivity levels. Glob. Change Biol. 23, 1258–1281. (doi:10.1111/gcb.13411)
20. Nelson GC et al. 2014 Climate change effects on agriculture: economic responses to biophysical
shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279. (doi:10.1073/pnas.1222465110)
21. Wiebe K et al. 2015 Climate change impacts on agriculture in 2050 under a range of
plausible socioeconomic and emissions scenarios. Environ. Res. Lett. 10, 085010. (doi:10.1088/
1748-9326/10/8/085010)
22. van Meijl H et al. 2017 Challenges of global agriculture in a climate change context by 2050
(AgCLIM50). JRC Science for Policy Report, EUR 28649 EN. (doi:10.2760/772445)
23. Müller C, Robertson RD. 2014 Projecting future crop productivity for global economic
modeling. Agric. Econ. 45, 37–50. (doi:10.1111/agec.12088)
24. Rosenzweig C, Hillel D (eds). 2015 Handbook of climate change and agroecosystems: the
Agricultural Model Intercomparison and Improvement Project, parts 1 and 2. London, UK: Imperial
College Press.
25. Ruane AC, Winter JM, McDermid SP, Hudson NI. 2015 AgMIP climate data and scenarios
for integrated assessment. In Handbook of climate change and agroecosystems: the Agricultural
Model Intercomparison and Improvement Project, part 1 (eds C Rosenzweig, D Hillel), pp. 45–78.
London, UK: Imperial College Press. (doi:10.1142/9781783265640_0003)
26. Thorburn PJ, Boote KJ, Hargreaves JNG, Poulton PL, Jones JW. 2015 Cropping systems
modeling in AgMIP: a new protocol-driven approach for regional integrated assessments.
In Handbook of climate change and agroecosystems: the Agricultural Model Intercomparison and
Improvement Project, part 1 (eds C Rosenzweig, D Hillel), pp. 79–100. London, UK: Imperial
College Press. (doi:10.1142/9781783265640_0004)
27. Antle J et al. 2015 AgMIP’s transdisciplinary agricultural systems approach to regional
integrated assessment of climate impacts, vulnerability, and adaptation. In Handbook of
climate change and agroecosystems: the Agricultural Model Intercomparison and Improvement
Project, part 1 (eds C Rosenzweig, D Hillel), pp. 27–44. London, UK: Imperial College Press.
(doi:10.1142/9781783265640_0002)
28. Moss RH et al. 2010 The next generation of scenarios for climate change research and
assessment. Nature 463, 747–756. (doi:10.1038/nature08823)
29. O’Neill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, Mathur R, van Vuuren
DP. 2014 A new scenario framework for climate change research: the concept of Shared
Socioeconomic Pathways. Clim. Change 122, 387–400. (doi:10.1007/s10584-013-0905-2)
30. Valdivia RO et al. 2015 Representative agricultural pathways and scenarios for regional
integrated assessment of climate change impacts, vulnerability, and adaptation. In Handbook
of climate change and agroecosystems: the Agricultural Model Intercomparison and Improvement
Project, part 1 (eds C Rosenzweig, D Hillel), pp. 101–145. London, UK: Imperial College
Press. (doi:10.1142/9781783265640_0005)
31. Rosegrant MW et al. 2014 Food security in a world of natural resource scarcity: the role of agricultural
technologies. Washington, DC: International Food Policy Research Institute. See http://www.
ifpri.org/cdmref/p15738coll2/id/128022/filename/128233.pdf.
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
32. Havlík P et al. 2014 Climate change mitigation through livestock system transitions. Proc. Natl 25
Acad. Sci. USA 111, 3709–3714. (doi:10.1073/pnas.1308044111)
33. Valin H et al. 2014 The future of food demand: understanding differences in global economic
models. Agric. Econ. 45, 51–67. (doi:10.1111/agec.12089)
34. Springmann M, Mason-D’Croz D, Robinson S, Wiebe K, Godfray HC, Rayner M, Scarborough
P. 2016 Mitigation potential and global health impacts from emissions pricing of food
commodities. Nat. Clim. Change 7, 69–74. (doi:10.1038/nclimate3155)
35. Schleussner CF et al. 2016 Differential climate impacts for policy-relevant limits to global
warming: the case of 1.5°C and 2°C. Earth Syst. Dyn. 7, 327–351. (doi:10.5194/esd-7-327-2016)
36. Popp A et al. 2017 Land-use futures in the Shared Socio-economic Pathways. Glob. Environ.
Change 42, 331–345. (doi:10.1016/j.gloenvcha.2016.10.002)
37. Palazzo A et al. 2017 Linking regional stakeholder scenarios and shared socioeconomic
pathways: quantified West African food and climate futures in a global context. Glob. Environ.
Change 45, 227–242. (doi:10.1016/j.gloenvcha.2016.12.002)
38. Elliott J, Müller C. 2015 The AgMIP GRIDded crop modeling initiative (AgGRID) and
the Global Gridded Crop Model Intercomparison (GGCMI). In Handbook of climate change
and agroecosystems: the Agricultural Model Intercomparison and Improvement Project, part
1 (eds C Rosenzweig, D Hillel), pp. 175–190. London, UK: Imperial College Press.
(doi:10.1142/9781783265640_0007)
39. Hoogenboom G et al. 2014 Decision Support System for Agrotechnology Transfer (DSSAT), Version
4.6. Prosser, WA: DSSAT Foundation. www.DSSAT.net.
40. Jones JW et al. 2003 The DSSAT cropping system model. Eur. J. Agron. 18, 235–265.
(doi:10.1016/S1161-0301(02)00107-7)
41. SIMPLACE. 2017 Scientific Impact assessment and Modelling Platform for Advanced Crop and
Ecosystem management. Crop Science Bonn. See http://www.simplace.net/Joomla/index.php.
42. Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F. 2013 A trend-preserving bias
correction—the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236. (doi:10.5194/esd-4-219-2013)
43. Frieler K et al. 2016 Assessing the impacts of 1.5°C global warming—simulation protocol of
the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10,
4321–4345. (doi:10.5194/gmd-10-4321-2017)
44. Robinson S, Mason-D’Croz D, Islam S, Sulser TB, Robertson R, Zhu T, Gueneau A, Pitois G,
Rosegrant MW. 2015 The International Model for Policy Analysis of Agricultural Commodities and
Trade (IMPACT); Model description for version 3. IFPRI Discussion Paper No. 1483. International
Food Policy Research Institute, Washington, DC.
45. Sands RD, Jones CA, Marshall E. 2014 Global drivers of agricultural demand and supply.
Economic Research Report No. ERR-174, 56pp. Washington, DC: US Department of
Agriculture, Economic Research Service. See https://www.ers.usda.gov/publications/pub-
details/?pubid=45275.
46. Antle JM, Valdivia RO. 2011 TOA-MD 5.0: Trade-off Analysis Model for Multi-Dimensional
Impact Assessment. See http://tradeoffs.oregonstate.edu.
47. Antle JM, Stoorvogel JJ, Valdivia RO. 2014 New parsimonious simulation methods and tools
to assess future food and environmental security of farm populations. Phil. Trans. R. Soc. B
369, 20120280. (doi:10.1098/rstb.2012.0280)
48. Antle J, Valdivia RO. 2016 Assessing climate change impact, adaptation, vulnerability, and food
security of farm households: the AgMIP Regional Integrated Assessment approach. World Economic
and Social Survey 2016: Climate Change resilience—an opportunity for reducing inequalities.
Background Paper for the WESS 2016. United Nations. See http://bit.ly/2a8si5H.
49. Sanderson BM et al. 2017 Community climate simulations to assess avoided impacts in 1.5 and
2 °C futures. Earth Syst. Dyn. 8, 827–847. (doi:10.5194/esd-8-827-2017)
50. Boote KJ, Allen LH Jr, Vara Prasad PV, Jones JW. 2010 Testing effects of climate change
in crop models. In Handbook of climate change and agroecosystems: impacts, adaptation, and
mitigation (eds D Hillel, C Rosenzweig), pp. 109–129. London, UK: Imperial College Press.
(doi:10.1142/9781848166561_0007)
51. O’Leary GJ et al. 2015 Response of wheat growth, grain yield and water use to elevated
CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid
environment. Glob. Change Biol. 21 (7), 2670–2686. (doi:10.1111/gcb.12830)
52. Kimball BA. 2016 Crop responses to elevated CO2 and interactions with H2O, N, and
temperature. Curr. Opin. Plant Biol. 31, 36–43. (doi:10.1016/j.pbi.2016.03.006)
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53. Leakey AD, Bishop KA, Ainsworth EA. 2012 A multi-biome gap in understanding of 26
crop and ecosystem responses to elevated CO2. Curr. Opin. Plant Biol. 15 (3), 228–236.
(doi:10.1016/j.pbi.2012.01.009)
54. Folberth C, Gaiser T, Abbaspour KC, Schulin R, Yang H. 2012 Regionalization of a large-scale
crop growth model for sub-Saharan Africa: model setup, evaluation, and estimation of maize
yields. Agric. Ecosyst. Environ. 151, 21–33. (doi:10.1016/j.agee.2012.01.026)
55. von Bloh W, Schaphoff S, Müller C, Rolinski S, Waha K, Zaehle S. 2017 Implementing the
nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL
(version 5). Geosci. Model Dev. Discuss. 2017, 35pp. (doi:10.5194/gmd-2017-228)
56. Elliott J, Kelly D, Chryssanthacopoulos J, Glotter M, Jhunjhnuwala K, Best N, Wilde M, Foster
I. 2014 The parallel System for Integrating Impact Models and Sectors (pSIMS). Environ. Model.
Softw. 62, 509–516. (doi:10.1016/j.envsoft.2014.04.008)
57. Rosenzweig C et al. 2018 Protocols for AgMIP Regional Integrated Assessments, version 7.0.
See http://www.agmip.org/wp-content/uploads/2018/02/AgMIP-Protocols-for-Regional-
Integrated-Assessment-v7-0-20180218sm.pdf.
58. Ruane AC, Goldberg R, Chryssanthacopoulos J. 2015 Climate forcing datasets for agricultural
modeling: merged products for gap-filling and historical climate series estimation. Agric.
Forest Meteorol. 200, 233–248. (doi:10.1016/j.agrformet.2014.09.016)
59. Gilhespi SL et al. 2014 First 20 years of DNDC (DeNitrification DeComposition): model
evolution. Ecol. Model. 292, 51–62. (doi:10.1016/j.ecolmodel.2014.09.004)
60. Ahmad A et al. 2015 Impact of climate change on the rice–wheat cropping system of Pakistan.
In Handbook of climate change and agroecosystems: the Agricultural Model Intercomparison and
Improvement Project, part 2 (eds C Rosenzweig, D Hillel), pp. 219–258. London, UK: Imperial
College Press. (doi:10.1142/9781783265640_0019)
61. Adiku S et al. 2015 Climate change impacts on west African agriculture: an integrated regional
assessment (CIWARA). In Handbook of climate change and agroecosystems: the Agricultural Model
Intercomparison and Improvement Project, part 2 (eds C Rosenzweig, D Hillel), pp. 25–73.
London, UK: Imperial College Press. (doi:10.1142/9781783265640_0014)
62. Antle JM, Zhang H, Mu J, Abatzoglou J, Stöckle CO. 2017 Methods to assess between-system
adaptations to climate change: dryland wheat systems in the Pacific Northwest United States.
Agric. Ecosyst. Environ. 253, 195–207. (doi:10.1016/j.agee.2017.03.017)
63. Antle JM, Cho S, Tabatatie SMH, Valdivia RO. 2018 Economic and environmental performance
of dryland wheat-based farming systems in a 1.5°C world. Mitigation and adaptation strategies
for global change. (doi:10.1007/s11027-018-9804-1)
64. Stanford University. 2018 Energy Modeling Forum [online]. See https://emf.stanford.edu/
about [accessed 21 February 2018].
65. Kimball BA. 2010 Lessons from FACE: CO2 effects and interactions with water, nitrogen,
and temperature. In Handbook of climate change and agroecosystems: impacts, adaptation, and
mitigation (eds D Hillel, C Rosenzweig), pp. 87–107. London, UK: Imperial College Press.
(doi:10.1142/9781848166561_0006)
66. Deryng D et al. 2016 Regional disparities in the beneficial effects of rising CO2 concentrations
on crop water productivity. Nat. Clim. Change 6, 786–790. (doi:10.1038/nclimate2995)
67. Cammarano D et al. 2016 Uncertainty of wheat water use: simulated patterns and sensitivity
to temperature and CO2. Field Crops Res. 198, 80–92. (doi:10.1016/j.fcr.2016.08.015)
68. Durand JL et al. 2017 How accurately do maize crop models simulate the interactions of
atmospheric CO2 concentration levels with limited water supply on water use and yield?
Eur. J. Agron. (doi:10.1016/j.eja.2017.01.002)
69. Flato G et al. 2013 Evaluation of climate models. In Climate change 2013: the physical science
basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change (eds TF Stocker et al.), vol. 5, pp. 741–866. Cambridge, UK: Cambridge
University Press.
70. Ermolieva T, Havlík P, Ermoliev Y, Mosnier A, Obersteiner M, Leclère D, Khabarov N, Valin
H, Reuter W. 2016 Integrated management of land use systems under systemic risks and
security targets: a stochastic global biosphere management model. J. Agric. Econ. 67, 584–601.
(doi:10.1111/1477-9552.12173)
71. Rolinski S et al. 2018 Modeling vegetation and carbon dynamics of managed grasslands at the
global scale with LPJmL 3.6. Geosci. Model Dev. 11, 429–451. (doi:10.5194/gmd-11-429-2018)
rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 376: 20160455
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .