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INTEGRATING GEOSTATISTICS FOR OPTIMISING AGRICULTURAL SUPPLY CHAIN: A STATISTICAL EVALUATION FOR AGRI-WAREHOUSE ACCESSIBILITY
Keywords:
agriculture supply chain management, geostatistics, distance estimation and optimisation, minimum distance, post-harvest loss reduction, agriculture warehouse accessibilityDOI:
https://doi.org/10.17654/0972361725076Abstract
This study applies a statistics-driven solution to optimise agricultural warehouse accessibility in India to reduce post-harvest losses and improve supply chain efficiency. This work synthesises existing literature to examine current scenarios and solution approaches in the agricultural supply chain (ASC), focusing on digital integration and innovation. The review is structured into four thematic sections and complemented by a quantitative and applied statistics research methodology, employing a dataset of 6,422 agricultural warehouses for geospatial analysis with statistical modelling to evaluate and compare distance computation methods. Minimum distances are calculated using the Haversine algorithm, enabling real-time, customisable search results to support evidence-based storage decisions. Descriptive statistics, regression modelling, and Bland-Altman agreement analysis reveal the Haversine method’s systematic and predictable underestimation of road distances, with an average bias of approximately 59 km and a mean absolute percentage error of 23%. These results highlight that while Haversine is computationally faster, adjustment factors are necessary for accurate logistics planning. This work operationalises geolocation-based agri-warehouse accessibility and selection in the Indian agricultural supply chain by analysing geospatial data to reduce post-harvest losses, which offers direct benefits for farmers, warehouse managers, logistics providers, and policymakers through improved storage allocation, optimised routing, and data-driven infrastructure planning. The research allows stakeholders to make faster and better-informed storage and logistics choices, leading to shorter transport distances, lower operational costs, and reduced food spoilage.
Received: August 19, 2025
Accepted: October 1, 2025
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