Integrated Data Approaches in Crop Management: A Review on Advancing Productivity, Sustainability, and Climate Resilience | ||
| SVU-International Journal of Agricultural Sciences | ||
| Articles in Press, Accepted Manuscript, Available Online from 24 November 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/svuijas.2025.439903.1514 | ||
| Authors | ||
| N. K. Mohammadi* 1; M. G. Arabzai2 | ||
| 1State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, 2College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China | ||
| 2College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China | ||
| Abstract | ||
| The growing complexity of global agricultural systems necessitates a data-driven paradigm to optimize crop management and enhance sustainability. This review followed an integrative literature analysis, synthesizing 115 peer-reviewed studies published between 2010–2025 to evaluate how multi-source agricultural data improve productivity, climate resilience, and resource efficiency. The study examines the integration of diverse data sources including soil health indicators, climatic trends, pest dynamics, and irrigation efficiency and evaluates their collective contribution to efficient and resilient agricultural production. Key findings reveal that machine-learning models improve yield-forecasting accuracy by up to 30%, while integrated remote-sensing and IoT-based monitoring systems substantially enhance early detection of crop stress and pest outbreaks. The review identifies persistent limitations related to data interoperability, uneven data access in low-resource regions, and privacy challenges associated with digital agriculture. Novel contributions of this review include a comparative synthesis of enabling technologies, an evaluation of their scalability and limitations, and a framework outlining priority areas for future data-integration research. The study underscores the pivotal role of decision-support systems, precision-agriculture practices, and stakeholder engagement in strengthening food security, reducing environmental impacts, and enhancing climate resilience. Recommendations emphasize the need for robust data-integration architectures, standardized data protocols, and targeted capacity-building initiatives for farmers and extension systems. Integrating digital technologies with modern agronomic practices holds transformative potential for achieving sustainable intensification of agriculture on a global scale. | ||
| Keywords | ||
| Precision Agriculture; Data Integration; Crop Management; Machine Learning; Climate Resilience | ||
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