Assessing spatiotemporal variations in soil water is critical to many decisions in precision agriculture. In dryland crop production regions with marginal precipitation, growers use crop rotations and surface preparation practices to build soil water natural capital over preceding seasons for cash crop production. However, significant gaps exist in quantifying linkages between topography, soil properties, and site-specific agronomic practices to crop water use, and to predict temporal changes in soil water storage. In this study, we used time-lapse electromagnetic induction to measure apparent electrical conductivity (EC_a) to infer spatiotemporal variability of soil water while comparing crop (winter wheat [Triticum aestivum L.], spring peas [Pisum sativum L.], and spring barley [Hordeum vulgare L.]) as well as tillage (no till and chisel plow) on a split-plot design in the Palouse region of northern Idaho. Weekly measurements of EC_a were converted to soil water content using multiple linear regression with the help of principal component analysis. Separating factors of temporal stability and variability allowed us to derive crop-specific calibrated relationships between soil water content and the additional variables growing degree days, elevation, clay content, and silt content. Results suggest that spring peas retained the highest water content, followed by spring barley and winter wheat. Resultant maps show highly structured and consistent patterns in EC_a being driven primarily by crop type that are apparent even in uncalibrated imagery. Although soil EC_a tended to be greater in no-tillage compared with chisel plow treatments, we found no significant differences in soil water content between the two. This may be partially due to the limited number of years of no-till practice at this site.

中文翻译：

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使用电磁感应技术绘制作物和耕作与土壤水相互作用的延时绘图

评估土壤水的时空变化对于精准农业的许多决策至关重要。在降水稀少的旱地作物生产区，种植者使用轮作和表面整备措施，在前一季为经济作物生产建立土壤水分自然资本。然而，在量化地形、土壤特性和特定地点的农艺实践与作物用水之间的联系以及预测土壤蓄水量的时间变化方面存在重大差距。在这项研究中，我们使用延时电磁感应测量表观电导率 (EC _a ) 来推断土壤水分的时空变异性，同时比较作物（冬小麦 [ Triticum aestivum L.]、春豌豆 [ Pisum sativum L.]L.] 和春大麦 [ Hordeum vulgare L.]）以及在爱达荷州北部帕卢斯地区的裂地设计中耕作（免耕和凿犁）。在主成分分析的帮助下，使用多元线性回归将每周测量的 EC _a转换为土壤含水量。时间稳定性和可变性的分离因素使我们能够推导出土壤含水量与附加变量生长期天数、海拔、粘土含量和淤泥含量之间的作物特定校准关系。结果表明，春豌豆的含水量最高，其次是春大麦和冬小麦。结果图显示了 EC _{a 中}高度结构化和一致的模式主要由即使在未经校准的图像中也很明显的裁剪类型驱动。尽管与凿犁处理相比，免耕处理中的土壤 EC _a往往更大，但我们发现两者之间的土壤含水量没有显着差异。这可能部分是由于该站点免耕实践的年数有限。