Gnanasekaran et al. (2025) Agronomic and environmental dimensions of large-scale irrigation projects for sustainable agriculture

Identification

• Journal: Plant Science Today
• Year: 2025
• Date: 2025-11-20
• Authors: M. Gnanasekaran, A P Sivamurugan, S Selvakumar, V Vasumathi
• DOI: 10.14719/pst.10266

Research Groups

• Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
• Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
• Department of Rice, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
• Department of Remote Sensing & GIS, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

Short Summary

This review synthesizes the agronomic and environmental dimensions of large-scale irrigation projects (LSIPs) globally, highlighting their crucial role in food security and water management while addressing challenges posed by climate change and proposing integrated, climate-resilient strategies. It emphasizes the necessity of combining adaptive management, community engagement, modern technologies, and hydrological models for sustainable agricultural productivity and water security.

Objective

• To comprehensively review the agronomic and environmental dimensions of large-scale irrigation projects (LSIPs), assessing the major factors, opportunities, and challenges in their planning, implementation, and management, with a specific focus on modern technologies, sustainability, and stakeholder involvement in the context of climate change.

Study Configuration

• Spatial Scale: Global, with detailed case studies from China (Yangtze River Valley/Three Gorges Dam), Egypt (Nile River/Aswan High Dam), Australia (Murrumbidgee Irrigation Area), and the USA/Mexico (Colorado River Basin).
• Temporal Scale: Literature review spanning from 1968 to 2025.

Methodology and Data

• Models used: Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), Mann-Kendall test, Sen’s slope estimator, Drought Indices Calculator (DrinC), Penman equation, Artificial Neural Network (ANN), Principal Component Analysis (PCA), Adaptive-Network-based Fuzzy Inference Systems (ANFIS), Co-active Neuro-Fuzzy Inference System (CANFIS), Hybrid Multiple Linear Regression, Statistical Downscaling Model (SDSM), Multivariate analysis (PCA, hierarchical clustering), CASA model, Generalized Likelihood Uncertainty Estimation (GLUE), Two-parameter monthly water balance model, Large-scale coupled hydrological-hydrodynamic models, SPARE: WATER model, Watershed Modeling System (WMS), ARC-GIS, Digital Elevation Model (DEM), HEC-RAS, Standardised Flow Index (SFI), Gamma Probability Distribution Function (GPDF), Generalized Additive Models (GAM), Integrated Flow and Flood Modeling (IFFM), Stylized, quasi-distributed, parsimonious coupled socio-hydrologic system model, SIMHYD model, ParFlow-CLM, CROPWAT 8.0, Random Forest algorithms.
• Data sources: Academic databases (Google Scholar, ResearchGate, TNAU e-Library, Scopus, MDPI, Elsevier, Springer, Taylor & Francis, John Wiley, CeRA, Indian Journals, DOAJ, Web of Science), Satellite imagery (Sentinel-1A SAR, Sentinel-2 optical data, MODIS datasets), Reanalysis products (AgERA5, Climate Hazards Group Infrared Precipitation with Station data - CHIRPS), Global Climate Models (GCMs), In situ gauge data, Ground truth data, Historical hydrological and climate data.

Main Results

• Large-scale irrigation projects (LSIPs) are indispensable for global food security and water resource management, but their sustainability is severely threatened by climate change, leading to altered rainfall patterns and increased extreme weather events.
• Effective LSIP management necessitates sustainable water strategies, including water pricing, participatory irrigation management, and advanced irrigation scheduling methods like regulated deficit irrigation and conjunctive use of multi-quality water.
• Modern technologies such as Artificial Intelligence (AI), remote sensing, Internet of Things (IoT), and machine learning, alongside hydrological models (e.g., HEC-HMS, SWAT, ANN), are crucial for precise water use, monitoring, forecasting, and enhancing water use efficiency.
• Case studies (Three Gorges Dam, Aswan High Dam, Murrumbidgee Irrigation Area, Colorado River Basin) illustrate LSIPs' significant contributions to flood control, hydropower generation, and agricultural development, but also reveal substantial environmental and social concerns, including population displacement, ecological degradation, salinization, waterlogging, and reduced streamflows.
• Integrated approaches that balance economic growth, social equity, and environmental conservation are vital. This includes adopting climate-resilient cropping patterns (e.g., switching from water-intensive crops), diversified farming, and utilizing soil amendments (e.g., zeolites, biochar) to improve water retention.
• Geospatial and remote sensing technologies (e.g., Sentinel-1A, Sentinel-2, MODIS) provide continuous monitoring capabilities for water availability, irrigated areas, crop health, and disaster risk assessment, enabling site-specific policy frameworks.

Contributions

• Provides a comprehensive synthesis of the agronomic and environmental dimensions of large-scale irrigation projects (LSIPs), integrating global perspectives with specific regional case studies.
• Identifies and elaborates on the critical factors, opportunities, and challenges in the planning, implementation, and sustainable management of LSIPs under changing climate conditions.
• Reviews a wide array of modern technologies (AI, remote sensing, IoT) and hydrological models used for assessing, monitoring, and optimizing LSIP performance.
• Highlights the dual nature of LSIPs, showcasing their benefits in food security and development alongside their environmental and socio-economic drawbacks, drawing lessons for future projects.
• Proposes a holistic framework for climate-resilient LSIP management, emphasizing technological integration, policy decisions, community engagement, and sustainable agricultural practices.

Funding

• Tamil Nadu Agricultural University (TNAU), Coimbatore, Tamil Nadu, India (for library and web source facilities, and providing opportunity and resources).
• Department of Agronomy, TNAU, Coimbatore, Tamil Nadu, India.
• Centre for Water and Geospatial Studies, TNAU, Coimbatore, Tamil Nadu, India.
• V. O. Chidhambaranar Agricultural College and Research Institute, Killikulam, India (for support in the publication process).

Citation

@article{Gnanasekaran2025Agronomic,
  author = {Gnanasekaran, M. and Sellaperumal, P and Sivamurugan, A P and Dhanaraju, M and Selvakumar, S and Kaliaperumal, R and Vasumathi, V},
  title = {Agronomic and environmental dimensions of large-scale irrigation projects for sustainable agriculture},
  journal = {Plant Science Today},
  year = {2025},
  doi = {10.14719/pst.10266},
  url = {https://doi.org/10.14719/pst.10266}
}