Managing to climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network

Abstract

Abstract Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for US Southern High Plains (SHP) unirrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005–19 commodity prices and fixed production costs. Both crops’ profitability under variable price conditions and current SHP climate conditions were then compared based on median profit and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.