Yaqoob et al. (2026) Variable Rate Irrigation Through Digital Agriculture for Sustainable Water Management: A Meta Review on Current Challenges and Future Directions

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-04-01
• Authors: Nauman Yaqoob, Aitazaz A. Farooque, Syed Hamid Hussain Shah, Farhat Abbas, Muhammad Hassan
• DOI: 10.1007/s11269-026-04616-0

Research Groups

• Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, Canada
• Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peter’s Bay, PE, Canada
• Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
• College of Engineering and Technology, University of Doha for Science and Technology, Doha, Qatar

Short Summary

This meta-review synthesizes advancements, challenges, and future directions in Variable Rate Irrigation (VRI) systems, integrating digital agriculture technologies like AI, ML, and smart sensing for sustainable water management. It highlights VRI's potential to optimize water use, increase crop yield, and reduce greenhouse gas emissions by addressing spatial variability in agricultural fields.

Objective

• To conduct a comprehensive meta-review of Variable Rate Irrigation (VRI) systems, focusing on current advancements, challenges, and future directions in data-driven irrigation for sustainable water management.
• To specifically examine the development of prescription maps, innovative control strategies (especially AI/ML-based), and the practical implementation of VRI systems.

Study Configuration

• Spatial Scale: Global (meta-review of studies from all world regions); Case study in Charlottetown, Prince Edward Island (PEI), Canada.
• Temporal Scale: Publications from 2000 to 2023.

Methodology and Data

• Models used: Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL), Artificial Neural Networks (ANNs), Fuzzy Logic, Model Predictive Control (MPC), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), SWAT (Soil, Water, and Topography) model, Penman-Monteith method, Linear Mixed Models.
• Data sources: Peer-reviewed studies from Dimensions AI, Web of Science, Scopus. For VRI systems and case study: Soil moisture sensors (Time Domain Reflectometers - TDR, Sentek probes), weather stations (temperature, humidity, wind speed, rainfall, solar radiation), satellite imagery, drone/UAV-acquired multispectral and thermal imagery, electrical conductivity (EC) sensors (Veris 3100, DUALEM), topographic data, organic matter content, LiDAR imagery, GHG emissions data (non-steady-state closed chamber method, LI-COR Smart Chamber system).

Main Results

• VRI, especially when supported by AI and ML, significantly improves water productivity and enables sustainable irrigation strategies.
• VRI systems can reduce water use by 25% to 29% and improve yields compared to uniform irrigation.
• VRI contributes to reducing greenhouse gas (GHG) emissions (e.g., N2O) by limiting nutrient leaching, with irrigated fields showing lower GHG intensity than rainfed systems.
• The integration of terrain data with EC measurements, organic matter, and LiDAR imagery allows for the creation of specialized "SWAT maps" for highly precise irrigation.
• AI-based control strategies (ANN, Fuzzy Logic, MPC) are effective for managing pivot irrigation, improving water efficiency, reducing energy consumption, and increasing crop yield.
• Despite advancements, VRI adoption is limited due to high initial costs, increased maintenance, system complexity, and the need for improved data integration frameworks and IoT infrastructure.
• Economic assessments suggest payback periods for VRI can range from 3 to 7 years, with long-term benefits outweighing initial costs through water savings, yield improvements, and reduced energy use.

Contributions

• Provides a comprehensive meta-review specifically addressing AI- and ML-based applications in Variable Rate Irrigation (VRI), filling a gap in existing literature.
• Identifies emerging knowledge gaps and proposes innovative pathways to improve the efficiency and flexibility of modern irrigation systems.
• Highlights the critical role of prescription maps, innovative control strategies, and realistic implementation of VRI systems.
• Introduces a new methodology for developing irrigation prescription maps by integrating terrain data with Electrical Conductivity (EC) measurements, organic matter, and LiDAR imagery to create "SWAT maps."
• Demonstrates VRI's direct contribution to United Nations Sustainable Development Goals (SDGs), particularly "Zero Hunger," "Clean Water and Sanitation," and "Climate Action," through optimized water use, reduced GHG emissions, and enhanced food security.

Funding

• Croptimistic Technology Inc.
• Mitacs
• Natural Sciences and Engineering Research Council of Canada (NSERC)

Citation

@article{Yaqoob2026Variable,
  author = {Yaqoob, Nauman and Farooque, Aitazaz A. and Shah, Syed Hamid Hussain and Abbas, Farhat and Hassan, Muhammad},
  title = {Variable Rate Irrigation Through Digital Agriculture for Sustainable Water Management: A Meta Review on Current Challenges and Future Directions},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-026-04616-0},
  url = {https://doi.org/10.1007/s11269-026-04616-0}
}