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ARS Home » Southeast Area » Stoneville, Mississippi » Sustainable Water Management Research » Research » Publications at this Location » Publication #413798
Research Project: Development of Best Management Practices, Tools, and Technologies to Optimize Water Use Efficiency and Improve Water Distribution in the Lower Mississippi River Basin

Location: Sustainable Water Management Research

Title: Metrics for Evaluating Interreplicate Variability of Irrigation Scheduling Sensors

Author
item LO, TSZ HIM - Mississippi State University
item RIX, JACOB - Mississippi State University
item PRINGLE, H.C. - Mississippi State University
item RUDNICK, DARAN - University Of Nebraska
item GHOLSON, DREW - Mississippi State University
item NAKABUYE, HOPE - University Of Nebraska
item KATIMBO, ABIA - University Of Nebraska

Submitted to: American Society of Agricultural and Biological Engineers
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/23/2023
Publication Date: 2/28/2024
Citation: Lo, T., Rix, J.P., Pringle, H., Rudnick, D.R., Gholson, D.M., Nakabuye, H.N., Katimbo, A. 2024. Metrics for Evaluating Interreplicate Variability of Irrigation Scheduling Sensors. American Society of Agricultural and Biological Engineers. 67(1):115-126. https://doi.org/10.13031/ja.15513.
DOI: https://doi.org/10.13031/ja.15513

Interpretive Summary: For irrigation scheduling sensors, measurement variability among replicates is undesirable because it leads to imprecise recommendations on irrigation timing. However, there is a scarcity of appropriate yet practical methods to compare sensors in terms of this variability. The present study presented eight metrics for such comparisons and applied these metrics to two datasets with different soil water sensors. Among the evaluated sensors, the neutron probe and the granular matrix sensor were found to exhibit less variability. The present study contributes to solving the problem by providing useful methods for comparing variability regardless of sensor type and calibration accuracy. This accomplishment will help future research on developing and assessing ways to decrease the uncertainty of sensor-based irrigation scheduling.

Technical Abstract: Much of the research on irrigation scheduling sensors, especially soil water sensors, assesses and refines the accuracy of sensor calibrations. However, a sensor with an accurate calibration but high variability among replicates may require a larger-than-acceptable number of replicates for informing recommendations of optimal irrigation timing. To compare the interreplicate variability of sensors across types and calibration accuracy levels, this study presented eight metrics: (1) absolute spread-to-change ratio, (2) shifted spread-to-change ratio, (3) coefficient of change variation, (4) standard deviation of relative value, (5) standard deviation of relative change, (6) standard deviation of absolute triggering date, (7) standard deviation of shifted triggering date, and (8) standard deviation of relative triggering date. These metrics enabled comparisons either by nondimensionalizing sensor measurements or by expressing interreplicate variability in terms of time. For demonstrating their usage and their particularities, the metrics were applied to two datasets that included soil water sensor types such as neutron probe (503DR), dielectric sensor (TDR-315, CS616, CS655, HydraProbe II, 5TE, TEROS 12, Drill & Drop), and granular matrix sensor (Watermark 200SS). The neutron probe in the single-depth dataset and the granular matrix sensor in the multi-depth dataset generally displayed less interreplicate variability than other evaluated sensor types over multiple drying cycles. Future research is suggested to calculate and improve the eight metrics for identifying combinations of sensor types, deployment methods, and data interpretation techniques that minimize interreplicate variability and maximize irrigation scheduling precision.