Further characterizing the within-field variability of watermark soil water sensor data over multiple site-years

Authors: VENISHETTY, VIVEK - Mississippi State University; LO, TSZ HIM - Mississippi State University; CONGER, STACIA - Louisiana State University; RIX, JACOB - University Of Missouri; YANES BUEZO, ROBERT - Mississippi State University; GHOLSON, DREW - Mississippi State University

Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/14/2024
Publication Date: 1/13/2025
Citation: Venishetty, V., Lo, T., Conger, S., Rix, J., Yanes Buezo, R., Gholson, D. 2025. Further characterizing the within-field variability of watermark soil water sensor data over multiple site-years. Applied Engineering in Agriculture. 41(1):23-36. https://doi.org/10.13031/aea.15924.
DOI: https://doi.org/10.13031/aea.15924

Interpretive Summary: The use of soil moisture sensors to trigger irrigation has been shown to improve agricultural water management and granular matrix sensors are some of the most common and affordable soil moisture sensors. However, due to soil moisture variability, the use of a single set of sensors per irrigation system might be detrimental to precise scheduling. The goal of this study was to assess soil water tension (soil moisture) variation within fields in order to characterize the level of uncertainty of sensor-based irrigation scheduling which may be due to differences in soil type, irrigation practices, and sensor variation. This study examined data from multiple Watermark 200SS sensor sets installed in four soybean fields that differed in soil texture and irrigation practices (rainfed or furrow irrigated). Across depths and years, the results revealed that, earlier in the growing season, variability increased during drying cycles and decreased during wetting events. However, later in the season the opposite tendency emerged, with variability decreasing during drying cycles and increasing during wetting events. The same time-related patterns remained when variability was analyzed in terms of volumetric water content instead of soil water tension. However, the use of volumetric water content showed more pronounced peaks in variability, and maximum variability was observed at intermediate volumetric water content. These findings will inform ongoing efforts to refine sensor based irrigation scheduling in various soil types of the Lower Mississippi River Basin. The findings and implications of this study may also inform the interpretation of soil water sensor data in similar soils beyond this region, supporting improved water management globally.

Technical Abstract: The adoption of granular matrix sensors to trigger irrigation has been shown to improve agricultural water management. Yet because soil moisture is variable, the use of a single sensor set per irrigation system might be detrimental to precise scheduling. The goal of this study was to assess soil water tension variation within fields in order to characterize the uncertainty of sensor-based irrigation scheduling. The study examined data from multiple Watermark 200SS sensor sets in four soybean fields that differed in soil texture and irrigation practices (rainfed or furrow irrigated). Across depths and years, the results revealed that, earlier in the growing season, variability increased during drying cycles and decreased during wetting events. The opposite tendency emerged later, where variability decreased during drying cycles and increased during wetting events. Such temporal patterns remained similar when variability was analyzed in terms of volumetric water content instead. However, peaks in variability were more pronounced, and maximum variability was observed at intermediate volumetric water content. These nuanced insights into variability dynamics will inform ongoing efforts to refine sensor based irrigation scheduling in loams and vertisols of the Lower Mississippi River Basin. The findings and implications of this study may also inform the interpretation of soil water sensor data in similar soils beyond this region, supporting sustainable agricultural water management globally.