International Journal Of Horticulture, Agriculture And Food Science(IJHAF)

Accurate and comprehensive agricultural data is essential for research, planning, and policymaking, forming the foundation for evidence-based decisions at both state and national levels. This study presents the construction of a binary state–crop availability matrix (A) for India, representing data coverage for 28 states and Union Territories over a five-year period from 2020 to 2024. In this matrix, each row corresponds to a year, each column corresponds to a specific crop, and each element is encoded as 1 if reliable data for that crop in the corresponding year and state exists, and 0 if data is missing or incomplete. By capturing the presence or absence of crop production data in a structured binary format, the A matrix provides a systematic overview of data availability across the country. The matrix reveals significant heterogeneity in reporting patterns across states, reflecting differences in the scale of agricultural activity, crop diversity, and administrative capacity. Larger states with diversified cropping systems, such as Karnataka, Tamil Nadu, and Madhya Pradesh, tend to exhibit a higher proportion of 1’s, indicating comprehensive data coverage and robust reporting infrastructure. Conversely, smaller Union Territories such as Lakshadweep, Chandigarh, and Daman and Diu display larger proportions of 0’s, highlighting gaps due to limited cultivation, fewer resources, or lower prioritization of statistical reporting. These systematic differences underscore the structural nature of data disparities and emphasize the need for targeted interventions to improve data collection in under-represented regions. Beyond identifying missing records, the A matrix provides a versatile foundation for a wide range of data-driven agricultural analyses. It enables quantitative assessment of regional reporting completeness, informs prioritization of capacity-building initiatives, and supports resource allocation to states or crops where data gaps are most pronounced. Furthermore, the matrix serves as a replicable framework for other countries or sectors seeking to evaluate the quality and coverage of their datasets. By combining this binary representation with analytical methods such as matrix algebra, similarity analyses, and multivariate techniques, researchers and policymakers can derive insights into inter-state crop overlaps, co-occurrence patterns, and regional specialization, ultimately contributing to more efficient planning, equitable resource distribution, and strategic interventions in agricultural development.