In the vast, sun-kissed coastal expanse of India, where the land meets the sea, a groundbreaking study has emerged, offering a beacon of hope for farmers and policymakers alike. Led by Nishtha Sawant of the ICAR-Central Coastal Agricultural Research Institute, this innovative research combines the power of remote sensing, geospatial analysis, and multi-criteria decision-making to pinpoint the most suitable areas for rice and coconut cultivation. The findings, published in ‘Scientific Reports’ (formerly known as ‘Nature Scientific Reports’), promise to revolutionize agricultural practices and bolster food security in the region.
The study, which marks the first of its kind for the entire coastal region of India, employs the Analytic Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS) and remote sensing. By analyzing nine critical parameters—including elevation, slope, soil depth, drainage, texture, pH, soil organic carbon, rainfall, and temperature—alongside a land use land cover (LULC) constraint map, the researchers have created detailed suitability maps for rice and coconut cultivation.
“Our approach not only identifies the most suitable areas for these crops but also ensures that natural resources are preserved,” Sawant explains. “By strategically cultivating rice and coconut in highly and moderately suitable locations, we can increase crop yield and achieve financial viability without causing harm to the environment.”
The results are striking: approximately 13.68% of the study area is deemed highly suitable for rice cultivation, with around 19.26% and 18.35% being moderately and marginally suitable, respectively. For coconut cultivation, about 11% of the area is highly suitable, with 27.40% and 18.34% being moderately and marginally suitable. However, the study also reveals that about 35% of the total study region is permanently unsuitable for any type of cultivation.
The suitability maps were validated using the area under the receiver operating characteristic curve (AUROC), with values of 0.764 for rice and 0.740 for coconut, indicating high accuracy. These findings offer a roadmap for farmers and policymakers to optimize crop production and resource management.
The implications of this research extend far beyond the agricultural sector. As the global demand for sustainable food production grows, so does the need for precise, data-driven decision-making. This study serves as a blueprint for other regions facing similar challenges, demonstrating the power of integrating advanced technologies with traditional agricultural practices.
“By leveraging remote sensing, GIS, and AHP, we can create a more resilient and productive agricultural landscape,” Sawant adds. “This approach not only benefits farmers but also contributes to the broader goals of food security and environmental sustainability.”
As we look to the future, the fusion of remote sensing, geospatial analysis, and multi-criteria decision-making holds immense potential. This research paves the way for more sophisticated and nuanced approaches to crop suitability analysis, enabling us to adapt to changing climatic conditions and optimize resource use. By embracing these technologies, we can ensure that our agricultural practices are not only productive but also sustainable, securing a brighter future for generations to come.