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...

Bending the curve of terrestrial biodiversity needs an integrated strategy Summary paragraph Increased efforts are required to prevent further losses of terrestrial biodiversity and the ecosystem services it provides 1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity 3-yet, just feeding the growing human population will make this a challenge 4. We use an ensemble of land-use and biodiversity models to assess whether (and if so, how) humanity can reverse terrestrial biodiversity declines due to habitat conversion, a major threat to biodiversity 5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, may allow to feed the growing human population while reversing global terrestrial biodiversity trends from habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land, and generalize landscapelevel conservation planning, biodiversity trends from habitat conversion could become positive by mid-century on average across models (confidence interval: 2042-2061), but not for all models. Food prices could increase and, on average across models, almost half (confidence interval: 34-50%) of future biodiversity losses could not be avoided. However, additionally tackling the drivers of landuse change may avoid conflict with affordable food provision and reduces the food system's environmental impacts. Through further sustainable intensification and trade, reduced food waste, and healthier human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats, such as climate change, must be addressed to truly reverse biodiversity declines, our results show that bold conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy. Reversing biodiversity trends by 2050 Without further efforts to counteract habitat loss and degradation, we projected that global biodiversity will continue to decline (BASE scenario; Fig. 1). Rates of loss over time for all nine BDIs in 2010-2050 were close to or greater than those estimated for 1970-2010 (Extended data Extended Data Table 1). For various biodiversity aspects, on average across IAM and BDI combinations, peak losses over the 2010-2100 period were: 13% (range: 1-26%) for the extent of suitable habitat, 54% (range: 45-63%) for wildlife population density, 5% (range: 2-9%) for local compositional intactness , 4% (range: 1-12%) for global extinctions, and 4% (range: 2-8%) for regional extinctions (Extended Data Table 1). Percentage losses were greatest in biodiversity-rich regions (Sub-Saharan Africa, South Asia, South East Asia, the Caribbean and Latin America; Extended Data Fig. 2). The projected future trends for habitat loss and degradation and its driv...

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

A B S T R A C TProjected increases in population, income and consumption rates are expected to lead to rising pressure on the land system. Ambitions to limit global warming to 2°C or even 1.5°C could also lead to additional pressures from land-based mitigation measures such as bioenergy production and afforestation. To investigate these dynamics, this paper describes five elaborations of the Shared Socio-economic Pathways (SSP) using the IMAGE 3.0 integrated assessment model framework to produce regional and gridded scenarios up to the year 2100. Additionally, land-based climate change mitigation is modelled aiming for long-term mitigation targets including 1.5°C. Results show diverging global trends in agricultural land in the baseline scenarios ranging from an expansion of nearly 826 Mha in SSP3 to a decrease of more than 305 Mha in SSP1 for the period 2010-2050. Key drivers are population growth, changes in food consumption, and agricultural efficiency. The largest changes take place in Sub-Saharan Africa in SSP3 and SSP4, predominantly due to high population growth. With low increases in agricultural efficiency this leads to expansion of agricultural land and reduced food security. Land use also plays a crucial role in ambitious mitigation scenarios. First, agricultural emissions could form a substantial component of emissions that cannot be fully mitigated. Second, bioenergy and reforestation are crucial to create net negative emissions reducing emissions in SSP2 in 2050 by 8.7 Gt CO 2 /yr and 1.9 Gt CO 2 /yr, respectively (1.5°C scenario compared to baseline). This is achieved by expansion of bioenergy area (516 Mha in 2050) and reforestation. Expansion of agriculture for food production is reduced due to REDD policy (290 Mha in 2050) affecting food security especially in Sub-Saharan Africa indicating an important trade-off of land-based mitigation. This set of SSP land-use scenarios provides a comprehensive quantification of interacting trends in the land system, both socio-economic and biophysical. By providing high resolution data, the scenario output can improve interactions between climate research and impact studies.