In silico design of crop ideotypes under a wide range of water availability

Abstract

Given the changing climate and increasing impact of agriculture on global resources, it is important to identify phenotypes which are global and sustainable optima. Here, an in silico framework is constructed by coupling evolutionary optimization with thermodynamically sound crop physiology, and its ability to rationally design phenotypes with maximum productivity is demonstrated, within well-defined limits on water availability. Results reveal that in mesic environments, such as the North American Midwest, and semi-arid environments, such as Colorado, phenotypes optimized for maximum productivity and survival under drought are similar to those with maximum productivity under irrigated conditions. In hot and dry environments like California, phenotypes adapted to drought produce 40% lower yields when irrigated compared to those optimized for irrigation. In all three representative environments, the trade-off between productivity under drought versus that under irrigation was shallow, justifying a successful strategy of breeding crops combining best productivity under irrigation and close to best productivity under drought.