Garg et al. (2025) Estimation of wheat yields and water savings with deficit irrigation in water-stressed NW India

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-12-05
• Authors: Divyam Garg, Hemant Kumar
• DOI: 10.1016/j.agwat.2025.110036

Research Groups

• Civil Engineering Department, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India

Short Summary

This study evaluates efficient irrigation scheduling strategies for spring wheat in water-stressed NW India using the AquaCrop-OSPy model calibrated with Particle Swarm Optimization. It demonstrates that 1.7–38.1% irrigation water savings can be achieved with less than 5% yield loss, significantly improving irrigation water productivity and aiding groundwater conservation.

Objective

• To test the performance of a novel swarm-intelligence based calibration technique (Particle Swarm Optimization) in calibrating the multi-parameter crop model AquaCrop-OSPy.
• To estimate the improvements in water savings and irrigation water productivity for wheat using novel low-tech irrigation strategies (Historical Rainfall Based Fixed Interval - HRFI, and Modified Predefined Calendar-based - MPC) in comparison to soil moisture threshold-based irrigation (SMT).
• To determine the most suitable irrigation approaches for dry, normal, and wet years.

Study Configuration

• Spatial Scale: Mahendragarh district, Haryana state, North-Western India (district-scale).
• Temporal Scale: 26 seasons from 1997–98 to 2022–23.

Methodology and Data

• Models used:
  • AquaCrop-OSPy (ACOSP) crop growth model (Python-based open-source version of FAO's AquaCrop model).
  • Particle Swarm Optimization (PSO) algorithm for model calibration.
  • Hargreaves-Samani method for calculating daily reference evapotranspiration (ET0).
• Data sources:
  • Daily gridded maximum and minimum temperature data (1° x 1° spatial resolution) from India Meteorological Department (IMD).
  • Daily gridded rainfall data (0.25° x 0.25° spatial resolution) from IMD.
  • Soil texture information from Central Ground Water Board (CGWB) district level groundwater information booklets (average soil texture: loamy sand).
  • Seasonal district-level crop yield data from the Directorate of Economics and Statistics (DES), Department of Agriculture and Farmers Welfare, Ministry of Agriculture and Farmers’ Welfare, Government of India.

Main Results

• The AquaCrop-OSPy model was successfully calibrated for spring wheat using Particle Swarm Optimization (PSO) with 9 parameters, achieving an RMSE of 0.28 tonnes per hectare, an R² of 0.44, and an NRMSE of 6.6%.
• A total of 47 irrigation strategies were tested across three scenarios (Soil Moisture Threshold - SMT, Historical Rainfall Based Fixed Interval - HRFI, and Modified Predefined Calendar-based - MPC).
• For strategies maintaining yield loss under 5% compared to full irrigation:
  • SMT strategies achieved 18.1–35% water savings.
  • HRFI strategies achieved 12.3–38.1% water savings.
  • MPC strategies achieved 1.7–24.9% water savings.
• Irrigation water productivity (IWP) could be improved by 0.18–0.35 kg/m³ using SMT and HRFI strategies.
• The most suitable irrigation approach varied with climatic conditions: SMT was best for wet years (higher water savings and IWP), while HRFI performed better for dry years (higher water savings and IWP).

Contributions

• Quantifies the potential water savings achievable through low-tech efficient irrigation scheduling for surface flood irrigated spring wheat in a groundwater-depleted region of North-Western India.
• Demonstrates the feasibility of auto-calibrating the AquaCrop-OSPy model for local conditions and spring wheat cultivar using Particle Swarm Optimization (PSO), enabling extension to other data-limited regions.
• Proposes and evaluates simpler, low-tech alternative irrigation strategies (HRFI and MPC) for resource-poor farmers who lack access to soil moisture sensors, while considering inter-annual climatic variability.
• Highlights the significant role of crop-water models like ACOSP in optimizing irrigation decision-making for limited resource farmers.
• Provides evidence for water-conservative approaches to address groundwater depletion and reduce food-energy-water vulnerability in India and other South-East Asian countries.

Funding

• National Supercomputing Mission (NSM) for computing resources of ‘PARAM Ganga’ at the Indian Institute of Technology Roorkee (implemented by C-DAC, supported by the Ministry of Electronics and Information Technology (MeitY) and Department of Science and Technology (DST), Government of India).
• Anusandhan National Research Foundation’s grant on “Characterizing the Role of Groundwater in Buffering the Food-Energy-Water Nexus against Interannual Climatic Shocks through Hydroeconomic Modelling” (award number ANRF/ECRG/2024/003742/ENS).

Citation

@article{Garg2025Estimation,
  author = {Garg, Divyam and Kumar, Hemant},
  title = {Estimation of wheat yields and water savings with deficit irrigation in water-stressed NW India},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.110036},
  url = {https://doi.org/10.1016/j.agwat.2025.110036}
}