Optimizing wheat supplementary irrigation: Integrating soil stress and crop water stress index for smart scheduling

A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat ( Triticum aestivum L .) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling. • Accuracy enhanced by integrating CWSI with soil moisture stress; irrigate at CWSI ≥0.35 for optimal wheat yields. • The indigenous sensing device enables efficient and intelligent irrigation scheduling. • Yield and water productivity can be predicted using the CWSI approach across different irrigation regimes.

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Publication Details

Journal
Agricultural Water Management
Published
2024-10-22
DOI
https://doi.org/10.1016/j.agwat.2024.109104
Citations
8
Primary Topic
Rice Cultivation and Yield Improvement
Type
article
Field-Weighted Citation Impact
4.80
Controls
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article

Optimizing wheat supplementary irrigation: Integrating soil stress and crop water stress index for smart scheduling

Vinay Kumar Sehgal, D. K. Singh, A. Sarangi, Murtaza Hasan et al.
8 citations
Agricultural Water Management
Rice Cultivation and Yield Improvement
4.80
article

Optimizing wheat supplementary irrigation: Integrating soil stress and crop water stress index for smart scheduling

Vinay Kumar Sehgal, D. K. Singh, A. Sarangi, Murtaza Hasan, Arti Kumari
article en
8 citations

Abstract

A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat ( Triticum aestivum L .) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling. • Accuracy enhanced by integrating CWSI with soil moisture stress; irrigate at CWSI ≥0.35 for optimal wheat yields. • The indigenous sensing device enables efficient and intelligent irrigation scheduling. • Yield and water productivity can be predicted using the CWSI approach across different irrigation regimes.

Agricultural Water ManagementVol. 305
Indian Council of Agricultural Research (IN), ICAR Research Complex for NEH Region (IN), Indian Institute of Water Management (IN), Indian Agricultural Research Institute (IN)
Zero hunger
Openalex Percentile: Top 5%
Rice Cultivation and Yield Improvement
4.80
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