Decision support system for developing sustainable and climate resilient irrigation landscapes

Agriculture is highly sensitive to climatic conditions, and climate change exacerbates this vulnerability by increasing temperatures and rainfall variability, leading to more frequent and intense weather events that cause significant agricultural production losses. Developing sustainable and resilient irrigation landscapes is therefore critical for ensuring food and livelihood security. In the Global South, watershed development projects have invested substantially in soil and water conservation to enhance irrigation water availability. However, maximizing the benefits of these investments requires linking them to sustainable agricultural practices through an agroecological approach. Such a systems perspective integrates irrigation supply, demand management, production, and value chains, thereby amplifying the benefits. To support integrated approaches, there is a need for decision support system (DSS) to characterize landscape suitability and prioritize interventions effectively.

Each landscape is unique, with distinct geographic, climatic, cultural, and ecological characteristics. As a result, a “one size fits all” approach cannot address the diverse needs of agroecological situations. Tailored interventions for land, water, and vegetation management are essential. This paper presents the development and application of a DSS designed to create sustainable, climate-resilient irrigation landscapes based on agroecological principles. The DSS outputs recommend optimal combinations of land use, water management, and farming practices—such as crop diversification, natural farming, and soil and moisture conservation—based on the natural resource base, agroecological context, and climate conditions.

The DSS is built on five key components: a) A knowledge base of agroecology-specific best practices for land, water, and crop management; b) Integration and visualization of spatial, non-spatial, and temporal data from public sources and field surveys; c) Robust decision rules for developing spatially explicit suitability matrices to support natural farming; d) Integrated crop and hydrological models to assess the impacts of interventions on resource sustainability, food security, and income under current and future climate scenarios; and e) An intuitive, interactive geospatial interface for landscape and watershed planning. The DSS was applied in two contrasting agricultural watersheds in India. Results demonstrate its effectiveness in simulating water balance and crop yields for rainfed and irrigated areas. Scenario simulations highlight that structural supply- or demand-side interventions alone are insufficient for building climate resilience. The DSS addresses these gaps, replacing ad hoc approaches with data-driven, evidence-based planning. The developed framework and tool are globally applicable, offering a systematic approach to planning water management interventions for sustainable agriculture.