The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies

Abstract

Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; and Winter, J. M., "The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies" (2013 a b s t r a c tThe Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to [CO 2 ] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with midcentury climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations' resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.

Estimating sowing and harvest dates based on the Asian summer monsoon

“…Recent climate impact studies such as AgMIP (Rosenzweig et al, 2013(Rosenzweig et al, , 2014 and ISIMIP (Warszawski et al, 2013, 2014) have highlighted the importance of reliable input data for models. Section 1 highlights the scale of the uncertainties present when solely using a global sowing and harvest dataset to simulate region-specific cropping patterns.…”

Climate change impacts on sugarcane attainable yield in southern Brazil

“…There has been a call for such an approach in studies on climate change impacts on agriculture (Rotter et al 2011;Rosenzweig et al 2012) to further address uncertainties.…”

Soil water extraction patterns of lucerne grown on stony soils

“…The crop is often grown under water-limited production conditions due to its capacity to extract water from deep soil layers through an extensive taproot system (Brown et al 2005;Sheaffer et al 1988). The ability to estimate lucerne yield and water use under these conditions is important to further support current efforts to enhance methods for assessing regional and global agricultural productivity and environmental impacts (Rosenzweig et al 2013;Rötter et al 2011;Verburg et al 2007). In particular, the accurate estimation of time and intensity of drought stress is an important determinant of lucerne yield under rainfed environments (Robertson et al 2002;Sheaffer et alfrom the soil (Brown et al 2009) and demand (Brown et al 2012) during the period of crop growth.…”