Integrating geospatial analysis and the suitability-resources quality index to optimize crop suitability for sustainable agriculture

This study develops a framework for sustainable agriculture in an arid Egyptian region, enhancing food security through advanced assessments of crop suitability. By integrating freely accessible Google Earth Engine (GEE) data with field observations and laboratory analyses, the framework employs GEE, Microsoft Excel, and ArcGIS for geospatial analysis and visualization. In resource-scarce environments impacted by climate change, selecting crops with high suitability and minimal Crop Water Requirements (CWR) and Crop Fertilizer Requirements (CFR) is vital for sustainable practices. Hence, this study proposes the Suitability-Resources Quality Index (SRQI), a decision-support metric designed to optimize crop selection. Results indicated that the SRQI index-guided optimized selection of the top two highly suitable crops reduced CWR by 14–34% and CFR by 34–88% across various crop types and seasons. The unoptimized crop suitability analysis suggested that sunflower and cotton are optimal summer crops, while alfalfa and faba bean are prioritized for winter cropping. For year-round vegetable production, cabbage, watermelon, and pepper are top choices. For fruit trees, deciduous options such as apple and fig, alongside evergreen banana and olive trees, are suggested. However, the SRQI endorses sunflower and soybean for summer cropping systems, while prioritizing faba bean and sugar beet for winter. Across seasons, watermelon, cabbage, and pea are the top vegetable choices, and for fruit trees, deciduous grape and fig, alongside evergreen olive and date palm, are recommended. This integrated monitoring and assessment framework prioritizes crops with lower CWR and CFR, supporting water- and land-related Sustainable Development Goals (SDGs) and promoting resilient agricultural systems in arid environments to mitigate climate change.