Metrics for Evaluating Interreplicate Variability of Irrigation Scheduling Sensors

Abstract

Highlights Lower variability among replicates is associated with greater reliability for recommending irrigation timing. Eight metrics were presented for variability comparisons independent of sensor types and calibration accuracy. One neutron thermalization type tended to be less variable than six dielectric types in a comparison at 0.3 m depth. One granular matrix type tended to be less variable than one dielectric type in a comparison across the top 1 m of soil. 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. Keywords: Assessment, Comparison, Index, Nondimensionalization, Precision, Reliability, Soil water, Standardization, Uncertainty, Variation.