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

Authors: Mauget, Steven; MCCALLISTER-MITCHELL, DONNA - Texas Tech University

Submitted to: Q Open
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/11/2021
Publication Date: 7/9/2021
Citation: Mauget, S.A., McCallister-Mitchell, D. 2021. Managing to climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network. Q Open. 1(2). https://doi.org/10.1093/qopen/qoab013.
DOI: https://doi.org/10.1093/qopen/qoab013

Interpretive Summary: Although the Southern High Plains (SHP) is a leading U.S. upland cotton production region it’s dry and short summer growing seasons can reduce yields. During 2012-2018 an average of 64% of SHP planted cotton acres were un-irrigated and that fraction may increase as the Ogallala aquifer depletes. Given the region’s risky agricultural production conditions and increasing reliance on rainfall to maintain profitability, its producers need to know which rainfed management practices are best for the SHP summer climate and environment. To determine best practices for the region’s two leading rainfed crops – cotton and sorghum – an ARS scientist from Lubbock, Texas used crop simulation models and Texas Tech University mesonet weather data to determine the crop’s best planting dates and compare their profitability under rainfed conditions. Although sorghum is normally a secondary crop for SHP rainfed producers, these model simulations suggest that it may be more profitable and less risky than cotton under certain sorghum price conditions. As a result, SHP cotton farmers might consider planting sorghum, or combining cotton with sorghum production, to stay profitable without irrigation.

Technical Abstract: A scheme for simulating, quantifying, and managing climate-related risk was used to identify best management practices for U.S. Southern High Plains (SHP) rainfed agriculture under current climate conditions. The method is based on the use of a Regional Frequency Analysis approach and crop models to convert large samples of recent growing season weather outcomes into dense cotton and sorghum yield distributions. Yield simulations were repeated over a range of planting dates for both crops. Optimal planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Climate-representative yield distributions were converted into corresponding profit distributions reflecting 2005-2019 Texas commodity prices and fixed production costs. Both crop’s profitability under current SHP rainfed production conditions were then compared via comparison of median profits and loss probability, and through a stochastic dominance analysis that assumed a slightly risk-averse producer. Under the more variable commodity price conditions of 2005-2016, July 1-planted sorghum tended to produce higher median profits and lower loss probabilities than April 24-planted cotton. Under the relatively stable 2017-2019 price regime both crops produced similar median profits and loss probabilities. Stochastic dominance analysis suggests that cotton would be the preferred crop under most 2017-2019 price conditions, but that under 2005-2016 prices sorghum would almost always be preferred by a slightly risk-averse producer. When cotton is late-planted on June 5, which occurs frequently because of re-planting caused by hail and wind events, July 1-planted sorghum is preferred over all 2005-2019 price conditions.