article · Sustainability
This research evaluates agricultural land suitability in Attur Taluk, India, using an integrated framework combining geographic information systems, a fuzzy analytic hierarchy process, and geostatistical modelling. By analysing ten topographical, climatic, and soil parameters, the study mapped spatial variability across the region to guide land-use decisions. The fuzzy analytic hierarchy approach outperformed standard equal weighting schemes, achieving an area under the curve of 0.71 compared to 0.602. The results designated 17.31 per cent of the area as highly suitable for farming, 41.32 per cent as moderately suitable, and 7.82 per cent as permanently unsuitable. The study also quantified synergies and trade-offs with global sustainability goals, identifying positive alignments with zero hunger, climate action, and clean water, alongside conflicts caused by reliance on rain-fed agriculture and soil degradation.
Rising food demand, environmental degradation, and resource scarcity require precise spatial planning for farming. By pinpointing suitable agricultural zones and mapping soil variability, this approach helps policymakers allocate land effectively. It highlights how agricultural expansion can be balanced with wider sustainability goals, helping communities maintain food production without worsening land degradation or water vulnerability.
This work represents an applied and tested methodology that could form the basis of spatial decision-support tools for agricultural planners, regional authorities, and land-use consultants. While demonstrated as academic research on a regional case study, the framework could be integrated into commercial geographic information system software or agricultural advisory platforms. Further software development and validation across other geographies would be required to bring it to market.
AI-generated from the published abstract. Always read the original work before citing.
The precise selection of agricultural land is essential for guaranteeing global food security and sustainable development. Additionally, agricultural land suitability (AgLS) analysis is crucial for tackling issues including resource scarcity, environmental degradation, and rising food demands. This research examines the synergies and trade-offs among the sustainable development goals (SDGs) using a hybrid geographic information system (GIS)–fuzzy analytic hierarchy process (FAHP)–geostatistical framework for AgLS analysis in Attur Taluk, India. The area was chosen for its varied agro-climatic conditions, riverine habitats, and agricultural importance. Accordingly, data from ten topographical, climatic, and soil physiochemical variables, such as slope, temperature, and soil texture, were obtained and analyzed to carry out the study. The geostatistical analysis demonstrated the spatial variability of soil parameters, providing essential insights into key factors in the study area. Based on the receiver operating characteristic curve analysis, the results showed that the FAHP method (AUC = 0.71) outperformed the equal-weighting scheme (AUC = 0.602). Moreover, suitability mapping designated 17.31% of the study area as highly suitable (S1), 41.32% as moderately suitable (S2), and 7.82% as permanently unsuitable (N2). The research identified reinforcing and conflicting correlations with SDGs, emphasizing the need for policies to address trade-offs. The findings showed 40% alignment to climate action (SDG 13) via improved resilience, 33% to clean water (SDG 6) by identifying low-salinity zones, and 50% to zero hunger (SDG 2) through sustainable food systems. Conflicts arose with SDG 13 (20%) due to reliance on rain-fed agriculture, SDG 15 (11%) from soil degradation, and SDG 2 (13%) due to inefficiencies in low-productivity zones. A sustainable action plan (SAP) can tackle these issues by promoting drought-resistant crops, nutrient management, and participatory land-use planning. This study can provide a replicable framework for integrating agriculture with global sustainability objectives worldwide.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/su17030809
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
