Organic agriculture is often regarded as less damaging to the environment than conventional agriculture, though at the expense of lower yields. Field-specific precision agriculture may benefit organic production practices given the inherent need of organic farmers to understand spatiotemporal variation on large-scale fields. Here the primary research question is whether on-farm precision experimentation (OFPE) can be used as an adaptive management methodology to efficiently maximize farmer net returns using variable cover crop and cash crop seeding rates. Inputs of cash crop seed and previous-year green manure cover crop seed were experimentally varied on five different farms across the Northern Great Plains from 2019 to 2022. Experiments provided data to model the crop yield response, and subsequently net return, in response to input (seeding) rates plus a suite of other spatially explicit data from satellite sources. New, field-specific spatially explicit optimum input rates were generated to maximize net return including temporal variation in economic variables. Inputs were spatially optimized and using simulations it was found that the optimization strategies consistently out-performed other strategies by reducing inputs and increasing yields, particularly for non-tillering crops. By adopting site specific management, the average increase in net return for all fields was $50 ha−1. These results showed that precision agriculture technologies and remote sensing can be utilized to provide organic farmers powerful adaptive management tools with a focus on within-field spatial variability in response to primary input drivers of economic return. Continued OFPE for seeding rate optimization will allow quantification of temporal variability and subsequent probabilistic recommendations.

中文翻译：

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使用农场精确实验优化有机谷物农场的作物播种率


有机农业通常被认为比传统农业对环境的破坏更小，但代价是产量较低。鉴于有机农民了解大面积田地的时空变化的内在需求，特定领域的精准农业可能有利于有机生产实践。这里的主要研究问题是农场精确实验 （OFPE） 是否可以用作一种适应性管理方法，以使用可变覆盖作物和经济作物播种率有效地最大限度地提高农民的净回报。2019 年至 2022 年，在北部大平原的五个不同农场，经济作物种子和前一年绿肥覆盖作物种子的投入进行了实验性变化。实验提供了数据来模拟作物产量响应，以及随后的净回报，以响应输入（播种）率以及来自卫星来源的一组其他空间显式数据。生成了新的、特定于字段的空间显式最佳输入率，以最大化净回报，包括经济变量的时间变化。对投入进行了空间优化，并且使用模拟发现，优化策略通过减少投入和提高产量，特别是对于非分蘖作物，始终优于其他策略。通过采用特定地点的管理，所有田地的平均净回报增加为 50 美元 ha-1。这些结果表明，精准农业技术和遥感可用于为有机农民提供强大的适应性管理工具，重点关注田间空间变异性，以响应经济回报的主要输入驱动因素。 用于播种率优化的持续 OFPE 将允许量化时间变异性和随后的概率建议。