Supporting Data and Code for \"Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network\"

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 U.S. Southern High Plains (SHP) un-irrigated 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 2005-2019 commodity prices and fixed production costs. Both crop’s profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and th...

若缺乏可靠的季节气候预报，农民及其他对天气敏感行业的管理者可能会采用仅适配近期气候条件的耕作管理方案。为验证这一原理，本研究采用由密集气象网络驱动的作物模拟模型，确定了美国南部高平原（U.S. Southern High Plains, SHP）地区非灌溉农业的气候最优播种期。该方法将大量南高平原生长季天气样本转化为不同播种期下具有气候代表性的棉花与高粱产量分布。最优播种期定义为使棉花皮棉（4月24日）与高粱籽粒（7月1日）产量中位数达到最大的播种日期。随后将这些最优产量分布转化为对应的利润分布，该分布反映了2005-2019年的大宗商品价格与固定生产成本。随后基于利润中位数与亏损概率，对比了两种作物在可变价格条件与当前南高平原气候条件下的盈利能力，并...