Mohammadi et al. (2025) Integrated Data Approaches in Crop Management: A Review on Advancing Productivity, Sustainability, and Climate Resilience

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: SVU-International Journal of Agricultural Sciences
• Year: 2025
• Date: 2025-11-24
• Authors: Nazir Khan Mohammadi, Mohammad Gul Arabzai
• DOI: 10.21608/svuijas.2025.439903.1514

Research Groups

This is an integrative literature review synthesizing studies from various research groups globally.

Short Summary

This review synthesizes 115 studies to evaluate how multi-source agricultural data improve productivity, climate resilience, and resource efficiency, finding that integrated data and machine learning enhance yield forecasting and stress detection, despite challenges in data interoperability and access.

Objective

• To evaluate how multi-source agricultural data improve productivity, climate resilience, and resource efficiency in global agricultural systems.

Study Configuration

• Spatial Scale: Global agricultural systems.
• Temporal Scale: Studies published between 2010–2025.

Methodology and Data

• Models used: Machine-learning models (as applied in reviewed studies).
• Data sources: Multi-source agricultural data, including soil health indicators, climatic trends, pest dynamics, irrigation efficiency, remote-sensing, and Internet of Things (IoT)-based monitoring systems.

Main Results

• Machine-learning models improve yield-forecasting accuracy by up to 30%.
• Integrated remote-sensing and IoT-based monitoring systems substantially enhance early detection of crop stress and pest outbreaks.
• Identified persistent limitations include data interoperability, uneven data access in low-resource regions, and privacy challenges associated with digital agriculture.
• Decision-support systems, precision-agriculture practices, and stakeholder engagement are pivotal for strengthening food security, reducing environmental impacts, and enhancing climate resilience.

Contributions

• A comparative synthesis of enabling technologies for agricultural data integration.
• An evaluation of the scalability and limitations of these technologies.
• A framework outlining priority areas for future data-integration research.
• Recommendations for robust data-integration architectures, standardized data protocols, and targeted capacity-building initiatives for farmers and extension systems.

Funding

• Not specified in the provided text.

Citation

@article{Mohammadi2025Integrated,
  author = {Mohammadi, Nazir Khan and Arabzai, Mohammad Gul},
  title = {Integrated Data Approaches in Crop Management: A Review on Advancing Productivity, Sustainability, and Climate Resilience},
  journal = {SVU-International Journal of Agricultural Sciences},
  year = {2025},
  doi = {10.21608/svuijas.2025.439903.1514},
  url = {https://doi.org/10.21608/svuijas.2025.439903.1514}
}