Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m 2 . The mean biophysical yield effect with no incremental CO 2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.climate change adaptation | model intercomparison | integrated assessment | agricultural productivity C limate change alters weather conditions and thus has direct, biophysical effects on agricultural production. Assessing the ultimate consequences of these effects after producers and consumers respond requires detailed assessments at every step in the impact chain from climate through to crop and economic modeling.Comparisons of results from global studies that have attempted such model integration in the past show substantial differences in effects on key economic variables. Studies in the early 1990s found that climate change would have limited agricultural impacts globally, but with varying effects across regions (1-3). Adaptation and carbon dioxide (CO 2 ) fertilization effects were the two largest sources of variation in the results. New simulation approaches emerged in the mid-2000s, with gridded representation of yield impacts and more comprehensive coverage of variability in climate model projections (4, 5). However, these studies still relied on a single crop model and a single economic model. The number of economic models used for these types of analysis has remained relatively limited, and there has been no attempt to compare their behavior systematically. The Fourth Assessment Report of the Inte...

Feeding nine to ten billion people by 2050 and preventing dangerous climate change are two of the greatest challenges facing humanity. Both challenges must be met whilst reducing the impact of land management on ecosystem services that deliver vital goods and services, and support human health and well-being. Few studies to date have considered the interactions between these challenges. In this study we briefly, outline the challenges, review the supplyand demand-side climate mitigation potential available in the Agriculture, Forestry and Other Land Use (AFLOU) sector, and options for delivering food security. We briefly outline some of the synergies and trade-offs afforded by mitigation practices, before presenting an assessment of the mitigation potential possible in the AFOLU sector under possible future scenarios in which demand-side measures co-delivery to aid food security.We conclude that whilst supply-side mitigation measures, such as changes in land management, might either enhance or negatively impact food security, demand-side mitigation measures, such as reduced waste or demand for livestock products, should benefit both food security and greenhouse gas (GHG) mitigation. Demand-side measures offer a greater potential (1.5-15.6 Gt CO 2 -eq. yr -1 ) in meeting both challenges than do supply-side measures (1.5-4.3 Gt CO 2 -eq. yr -1 at carbon prices between 20 and 100 US$ tCO 2 -eq.given the enormity of challenges, all options need to be considered. Supply-side measures should be implemented immediately, focussing on those that allow the production of more agricultural product per unit of input. For demand-side measures, given the difficulties in their implementation and lag in their effectiveness, policy should be introduced quickly, and should aim to co-deliver to other policy agendas, such as improving environmental quality, or

Journal articleIFPRI3; ISI; CRP2EPTD; PIMPRCGIAR Research Program on Policies, Institutions, and Markets (PIM

Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty. JEL classifications: Q10, Q11, Q16, Q21, Q54, Q55

Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of "real world commodity prices" differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between −0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro-economic modelers aiming to inform the agricultural and development policy debate require