Published 2026-09-22
Keywords
- Soil-moisture drydown,
- Irrigation events,
- Positive-state model,
- Log-ratio error,
- Model comparison
- Reproducible research ...More
Copyright (c) 2026 Gustavo A. Maldonado-Batres, Marlon M. López-Flores, William Campillay-Llanos (Autor/a)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
Purpose. We develop a reference-based positive-state law to describe sensor-reported soil-moisture recovery after irrigation and evaluate its conditional empirical performance without claiming a hydraulic mechanism or universal superiority. Methods. The ratio θ/θref was modeled in shifted dimensionless time by proportional relaxation. An open dataset from a single strawberry farm was processed through a single archived analysis pipeline. Flow increments greater than 5 L defined candidate irrigation records, and recovery fitting used the first contiguous sequence strictly above the event-specific reference. Proportional, additive-power, additive-exponential, and stretched-exponential curves were fitted to identical observations using log-ratio loss and evaluated by full-window diagnostics and a 70/30 elapsed-time hold-out. Results. Of 101 detected events, 36 met the restrictive criteria. Median full-window RMSElog was 0.00351 for the proportional curve, 0.00360 for additive power, 0.00597 for the exponential, and 0.00298 for the stretched exponential. The proportional curve outperformed the exponential and additive power in 32/36 events, but the stretched exponential had lower full-window error in 33/36. In ordered-time hold-out, proportional and stretched-exponential performance was evenly divided (18/36). Conclusions. The contribution is a positive, interpretable parameterization and a transparent, reproducible workflow rather than a uniquely best empirical curve. Calibrated sensors, uncertainty propagation, weather covariates, and multi-site external validation are required before operational use.
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