Agronomy, Год журнала: 2025, Номер 15(4), С. 942 - 942

Опубликована: Апрель 12, 2025

Soil–water management is fundamental to plant ecophysiology, directly affecting resilience under both anthropogenic and natural stresses. Understanding Agricultural Soil–Water Management Properties (ASWMPs) therefore essential for optimizing water availability, enhancing harvest resilience, enabling informed decision-making in intelligent irrigation systems, particularly the face of climate variability soil degradation. In this regard, present research develops predictive models ASWMPs based on grain size distribution dry bulk density soils, integrating traditional mathematical approaches advanced computational techniques. By examining 900 samples from NaneSoil database, spanning diverse crop species (Avena sativa L., Daucus carota Hordeum vulgare Medicago Phaseolus vulgaris Sorghum Pers., Triticum aestivum Zea mays L.), several are proposed three key ASWMPs: soil-saturated hydraulic conductivity, field capacity, permanent wilting point. Mathematical demonstrate high accuracy (71.7–96.4%) serve as practical agronomic tools but limited capturing complex soil–plant-water interactions. Meanwhile, a Deep Neural Network (DNN)-based model significantly enhances performance (91.4–99.7% accuracy) by uncovering nonlinear relationships that govern moisture retention availability. These findings contribute precision agriculture providing robust soil–water management, ultimately supporting against environmental challenges such drought, salinization, compaction.

Язык: Английский