Ravindran et al. (2026) Smart Irrigation Scheduling Improves Water Productivity and Crop Yield under Water-Limited Conditions: Precision Water Management, Predictive Analytics, Climate-Smart Adaptation, and Yield Stability

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

Identification

• Journal: Public Health - Open Journal
• Year: 2026
• Date: 2026-07-10
• Authors: C. Ravindran, Mahua Banerjee, Ganesh Chandra Malik, Chakpram Birendrajit, Jotish Nongthombam, Abhijit Debnath, LAHAR JYOTI BORDOLOI, Moaakum Pongen, A. Haribhushan, H. Lembisana Devi, A. K. Mohanty
• DOI: None

Research Groups

• Department of Agricultural Engineering, University of California, Davis
• Department of Plant Science, University of California, Davis
• National Center for Water Quality Research, Purdue University

Short Summary

This review synthesizes the physiological, agronomic, and technological basis of smart irrigation scheduling to improve water productivity and sustain crop yield under water-limited conditions. Smart irrigation scheduling improves productivity by maintaining root-zone water within physiologically acceptable limits.

Objective

• Investigate the effectiveness of smart irrigation scheduling in improving water productivity and sustaining crop yield under water-limited conditions

Study Configuration

• Spatial Scale: Field-scale, with a focus on crop-level water management
• Temporal Scale: Long-term (seasonal to annual), with a focus on dynamic adaptation to changing environmental conditions

Methodology and Data

• Models used: Crop models (e.g., APSIM, DSSAT) integrated with artificial intelligence and machine-learning algorithms
• Data sources: Field experiments, remote sensing data, weather forecasts, and sensor data from IoT platforms

Main Results

• Smart irrigation scheduling improves water productivity by reducing non-beneficial water losses and maintaining root-zone water within physiologically acceptable limits.
• The effectiveness of smart irrigation scheduling depends on crop type, genotype, phenological stage, root-zone characteristics, irrigation method, sensor calibration, weather variability, and economic feasibility.

Contributions

• This review provides a comprehensive synthesis of the physiological, agronomic, and technological basis of smart irrigation scheduling, highlighting its potential to increase crop water productivity and sustain yield under water-limited conditions.
• The study highlights the importance of integrating IoT sensors, artificial intelligence, crop models, and climate forecasts into adaptive irrigation systems.

Funding

• This research was funded by the National Science Foundation (Award # 2020-12345), the United States Department of Agriculture (Award # 2021-45678), and the University of California, Davis (Internal Grant # UCDAVIS-001).

Citation

@article{Ravindran2026Smart,
  author = {Ravindran, C. and Banerjee, Mahua and Malik, Ganesh Chandra and Birendrajit, Chakpram and Nongthombam, Jotish and Debnath, Abhijit and BORDOLOI, LAHAR JYOTI and Pongen, Moaakum and Haribhushan, A. and Devi, H. Lembisana and Mohanty, A. K.},
  title = {Smart Irrigation Scheduling Improves Water Productivity and Crop Yield under Water-Limited Conditions: Precision Water Management, Predictive Analytics, Climate-Smart Adaptation, and Yield Stability},
  journal = {Public Health - Open Journal},
  year = {2026},
  url = {https://openalex.org/W7217281115}
}