The purpose of the paper is to propose and test a new approach to simulating farmers' agronomic adaptation to climate change based on the pattern of adoption of technological innovation/substitution over time widely described as a S-shaped (or logistic) curve, i.e., slow growth at the beginning followed by accelerating and then decelerating growth, ultimately leading to saturation. The approach we developed is tested using the Erosion Productivity Impact Calculator crop model applied to corn production systems in the southeastern U.S. using a high-resolution climate change scenario. Corn is the most extensively grown crop in the southeastern U.S. The RegCM limited area model nested within the CSIRO general circulation model generated the scenario. We compare corn yield outcomes using this new form of adaptation (logistic) with climatically optimized (clairvoyant) adaptation. The results show logistic adaptation to be less effective than clairvoyant adaptation in ameliorating climate change impacts on yields, although the differences between the two sets of yields are statistically significant in one case only. These results are limited by the reliance on a single scenario of climate change. We conclude that the logistic technique should be tested widely across climate change scenarios, crop species, and geographic areas before a full evaluation of its effect on outcomes is possible.
All Science Journal Classification (ASJC) codes
- Global and Planetary Change
- Atmospheric Science