This research investigated the spatial and temporal distribution of volumetric soil water content (VWC) and total soil water across 64 sampling locations (8 × 8 grid) in a production-scale field, reported as a unitextural soil unit by SSURGO. Consequently, the required number and placement of soil moisture monitoring locations were calculated to represent soil water status with varying degrees of variability. Each soil layer in the root zone was subject to 5–9% of mean spatial VWC variability and maximum variability as high as 21% during the 2014 and 2015 growing seasons. Total soil water showed spatial behavior, which was as high as 13%, with mean spatial variability of 5%. In 2014, the field minimum and maximum temporal CVs in VWC were 9 and 27%, respectively, which increased to 14 and 33% in 2015, respectively. Representative soil moisture and its variability were accounted for using 1–45 monitoring locations, depending on observed variability, acceptable measurement error, and confidence interval (CI). When the commonly used marginal error of 2% was considered, the numbers of sensor deployment locations were 8 (90% CI) and 11 (95% CI), which reduced to 5 (90% CI) and 4 (95% CI) sensors with 3% marginal error. We demonstrate that although SSURGO data can be valuable to make preliminary assessments, relying on it to spatially characterize soil units and their properties for effective within-soil unit management, especially for variable rate water and nutrient applications, can create significant challenges.

中文翻译：

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单层土壤均匀管理田间土壤水分异质性及传感器部署

本研究调查了生产规模田地中 64 个采样位置（8 × 8 网格）的体积土壤含水量 (VWC) 和总土壤水的空间和时间分布，SSURGO 将其报告为单一结构土壤单元。因此，需要计算土壤水分监测位置的数量和位置，以代表具有不同程度变异性的土壤水分状况。在 2014 年和 2015 年的生长季节，根区的每个土壤层均受到 5-9% 的平均空间 VWC 变异性和高达 21% 的最大变异性。土壤总水分表现出空间行为，高达13%，平均空间变异性为5%。2014 年，VWC 的场最小和最大时间 CV 分别为 9% 和 27%，2015 年分别增加到 14% 和 33%。根据观察到的变异性、可接受的测量误差和置信区间 (CI)，使用 1-45 个监测位置计算了代表性土壤水分及其变异性。当考虑常用的 2% 边际误差时，传感器部署位置的数量为 8 个（90% CI）和 11 个（95% CI），减少到 5 个（90% CI）和 4 个（95% CI）传感器边际误差为 3%。我们证明，尽管 SSURGO 数据对于进行初步评估可能很有价值，但依靠它在空间上表征土壤单元及其特性以进行有效的土壤单元内管理，特别是对于可变速率的水和养分应用，可能会带来重大挑战。和置信区间 (CI)。当考虑常用的 2% 边际误差时，传感器部署位置的数量为 8 个（90% CI）和 11 个（95% CI），减少到 5 个（90% CI）和 4 个（95% CI）传感器边际误差为 3%。我们证明，尽管 SSURGO 数据对于进行初步评估可能很有价值，但依靠它在空间上表征土壤单元及其特性以进行有效的土壤单元内管理，特别是对于可变速率的水和养分应用，可能会带来重大挑战。和置信区间 (CI)。当考虑常用的 2% 边际误差时，传感器部署位置的数量为 8 个（90% CI）和 11 个（95% CI），减少到 5 个（90% CI）和 4 个（95% CI）传感器边际误差为 3%。我们证明，尽管 SSURGO 数据对于进行初步评估可能很有价值，但依靠它在空间上表征土壤单元及其特性以进行有效的土壤单元内管理，特别是对于可变速率的水和养分应用，可能会带来重大挑战。