Using agricultural models to inform policy: Discussion points for researchers and policymakers

Abstract

Computer simulations are widely used to explore options and quantify outcomes in agriculture. For example, mathematical models have been used to estimate how much of the nitrogen (N) and phosphorus (P) delivered to the Gulf of Mexico comes from agricultural sources (Alexander et al. 2008); to calculate changes in grain yields as management practices are varied (Chen et al. 2014); and to investigate the effects of different precipitation regimes on soil erosion (Nearing et al. 2005). Physical models can be linked with economic and behavioral data to calculate potential costs, such as those of treating or replacing household well water contaminated by nitrates (NO₃) as grassland is converted to row crops (Keeler and Polasky 2014). Models can also estimate risk, such as that of crop failure in various climate change scenarios (Challinor et al. 2010). A wide variety of scales and systems can be simulated, from assessing farmers’ behavioral responses to climate change-related policies on a patchwork of single farms (Berger and Troost 2014), to predicting global losses of staple crops due to pest damage in a warmer world (Deutsch et al. 2018).