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Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J., Büchner, M., et al. (2015). The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev., 8(2), 261–277.
Abstract: We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12-15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.
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Bennett, E., Carpenter, S. R., Gordon, L. J., Ramankutty, N., Balvanera, P., Campbell, B., et al. (2014). Toward a more resilient agriculture. The Solutions Journal, 5(5), 65–75.
Abstract: Agriculture is a key driver of change in the Anthropocene. It is both a critical factor for human well-being and development and a major driver of environmental decline. As the human population expands to more than 9 billion by 2050, we will be compelled to find ways to adequately feed this population while simultaneously decreasing the environmental impact of agriculture, even as global change is creating new circumstances to which agriculture must respond. Many proposals to accomplish this dual goal of increasing agricultural production while reducing its environmental impact are based on increasing the efficiency of agricultural production relative to resource use and relative to unintended outcomes such as water pollution, biodiversity loss, and greenhouse gas emissions. While increasing production efficiency is almost certainly necessary, it is unlikely to be sufficient and may in some instances reduce long-term agricultural resilience, for example, by degrading soil and increasing the fragility of agriculture to pest and disease outbreaks and climate shocks. To encourage an agriculture that is both resilient and sustainable, radically new approaches to agricultural development are needed. These approaches must build on a diversity of solutions operating at nested scales, and they must maintain and enhance the adaptive and transformative capacity needed to respond to disturbances and avoid critical thresholds. Finding such approaches will require that we encourage experimentation, innovation, and learning, even if they sometimes reduce short-term production efficiency in some parts of the world.
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