Projecting future crop productivity for global economic modeling

By Christoph Müller, R. Robertson. 2014

259 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/f2841b5db9239447df8a6fc84623952c93ec6328

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Citations in the corpus

The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3 (2015, methodology): “This follows the general approach for incorporating projected yield changes from biophysical models into economics models as outlined in Müller and Robertson (2014).”
Large-scale bioenergy production: how to resolve sustainability trade-offs? (2018, background): “The optimization process is subject to various spatially explicit biophysical conditions, which are derived by the global crop growth, vegetation, and hydrology model LPJmL [32, 33].”
Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century (2017, unknown): “…inputs (0.5 (cid:1) resolution) for MAgPIE, such as agricultural yields, carbon densities and water availability, are derived from a dynamic global vegetation, hydrology and crop growth model, the Lund-Potsdam-Jena model for managed Land (LPJmL) (Bondeau et al., 2007; Müller and Robertson, 2014).”
Agriculture and climate change in global scenarios: why don't the models agree (2014, methodology): “See Müller and Robertson (2014) for a discussion of why the LPJmL sugar results in S3 and S4 are likely more appropriate than the DSSAT-derived results.”
Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios (2015, methodology): “These exogenous climate impacts are represented as multiplicative shocks, and are derived using a delta approach as in previous AgMIP work (Müller and Robertson 2014, Nelson et al 2014a) to estimate relative yield changes per crop and region, allowing for more consistent application of climate…”
Land‐use change trajectories up to 2050: insights from a global agro‐economic model comparison (2014, background): “S4 and S6 represent pessimistic scenarios with the climate change effect on yield based on a high growth rate in GHG concentrations and no additional CO2 fertilization effects (see Müller and Robertson (2013) for a discussion).”
The role of international trade in managing food security risks from climate change (2015, methodology): “This paper improves on the previous work by capitalizing on a newly available library of climate impact results based on the latest Global Gridded Crop Model (GGCM) inter-comparison project (Elliott et al. 2014; Müller and Robertson 2014; Nelson et al. 2014; Rosenzweig et al. 2014).”