Data-Driven Irrigation and Water Productivity: Smart Systems, Modeling, and Decision Support for Water‑Smart Cropping

BackgroundIrrigation decision-making at farm level is shaped by a heterogeneous and interacting set of biophysical, economic, and institutional drivers, among which water scarcity, energy costs, and climate variability are prominent, while technological and informational options expand the range of feasible choices available to farmers. In this context, water-smart food production depends on decision-relevant tools, models, and delivery systems that enable farmers to evaluate trade-offs, manage risk, and optimize outcomes under constrained and uncertain conditions.The four selected papers share a common analytical spine: data- and model-informed irrigation management combined with practical, field-ready technologies. Two studies focus on OPSIS-based irrigation decision support in sugarcane, linking irrigation scheduling to soil moisture dynamics, crop growth, and yield outcomes, and assessing applicability under real production conditions. A third paper applies the HYDRUS-1D model to evaluate drip irrigation efficiency, providing a mechanistic representation of soil–water processes that can inform irrigation design and scheduling decisions. The fourth paper evaluates an indigenous, low-cost smart fertigation system, connecting precision delivery to physiological responses, root development, and yield performance.Together, these contributions illustrate a coherent pathway from modeling and decision support to on-farm implementation, with an emphasis on measurable outcomes related to water use, soil moisture dynamics, productivity, and crop performance.GoalsThis Research Topic aims to consolidate evidence and methods that enhance water productivity, understood in a broad and decision-oriented sense, including biophysical efficiency (e.g., yield or biomass per unit of water), economic performance per unit of water accounting for input and energy costs, and nutritional or functional outcomes per unit of water where relevant.Specifically, the Topic seeks contributions that advance decision support and scheduling; modeling and validation using mechanistic and empirical models such as HYDRUS-1D; scalable and affordable low-cost smart irrigation and fertigation systems; and outcome-oriented evaluation using quantitative performance metrics including irrigation water use efficiency, yield stability, soil moisture dynamics, energy implications, and sustainability co-benefits.ScopeWe welcome Original Research, Methods, and Review articles that address irrigation improvement from field to farm scale, including precision irrigation scheduling, soil moisture sensing and decision rules, drip and micro-irrigation performance, fertigation control, crop–soil–climate model coupling, and comparative evaluations across agro-ecological and socio-economic contexts.Studies may focus on a wide range of cropping systems and production environments, provided they clearly link irrigation interventions to measurable water-use, productivity, and performance outcomes. Contributions that bridge the gap between model outputs and farmer-facing decisions, document constraints to adoption, or compare low-cost and high-technology approaches are especially encouraged. Topic editors: Damodhara Rao Mailapalli, Gajanan Laxmikant Sawargaonkar Source: https://www.frontiersin.org/research-topics/77714/data-driven-irrigation-and-water-productivity-smart-systems-modeling-and-decision-support-for-water-smart-cropping