Author(s): Marina Martinez-Miguel; Marcelo Xavier Ruiz-Coello; Francisco Javier Sanz-Ronda; Juan Francisco Fuentes-Perez
Linked Author(s): Juan Francisco Fuentes Perez, Marcelo Xavier Ruiz Coello
Keywords: No Keywords
Abstract: Water scarcity and rising energy costs in Mediterranean agriculture demand precision irrigation tools. Conventional scheduling often relies on static, tabulated FAO-56 crop coefficients (Kc), which fail to capture the significant intra-annual variability of multi-cut crops like alfalfa. This can lead to water over-application, particularly during the lower-demand spring and autumn cycles. This paper presents an adaptive computational framework within the OASIS IoT network to model this dynamic Kc. We first demonstrate that static FAO-56 values are only accurate during peak summer months, while observed Kc is significantly lower in early and late season. We then validate this seasonal pattern using two independent methods: (1) a satellite-based Kc derived from Sentinel-2 NDVI (Kc = -0.2190 + 1.6198·NDVI) and (2) an in-situ Kc derived from measured soil moisture depletion curves. Finally, we propose a novel and continuous model using harmonic functions to mathematically describe the seasonal evolution of the FAO-56 parameters (e.g., Kc_ini, Kc_max). This harmonic model provides a robust, low-effort pathway for significant water savings. It enables a truly adaptive management system that can be initialized with FAO standards and then progressively self-calibrates as local data is collected, optimizing the food-water-energy nexus for alfalfa and other multi-harvest crops.
Year: 2026