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.

中文翻译：

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优化小麦补充灌溉：整合土壤胁迫和作物水分胁迫指数以实现智能调度


进行了一项为期两年的田间试验，将土壤水分胁迫与作物水分胁迫指数 （CWSI） 相结合，以优化不同灌溉条件下冬小麦 （Triticum aestivum L.） 的灌溉。该研究在新德里 ICAR-IARI 的水技术中心 （WTC-02） 进行，那里的气候显示受季风影响的潮湿亚热带和半干旱条件混合在一起。使用随机区组设计 （RBD），应用了五种灌溉处理：15 %、30 %、45 % 和 60 % 水平的完全灌溉和亏缺灌溉 （DI）。使用开发的集成传感设备记录冠层和环境空气温度数据以及蒸气压亏缺 （VPD），以经验确定抽穗前和抽穗后阶段 CWSI 计算的基线下限方程和上限阈值。发现前头和后头的斜率 （m）、基线下限方程的截距 （c） 和上限阈值 （UL）：m：-1.94，c：-1.33，UL：1.92°C 和 m：-1.30，c：-2.37，UL：2.0°C。结果表明，缺水水平的增加导致粮食产量、生物量生产和收获指数显着下降。平均季节性 CWSI 和产量属性之间的强负相关 （R² = 0.95 和 0.93） 突出了 CWSI 在不同灌溉条件下产量预测中的效用。当 CWSI ≥ 0.35 时，建议根据 CWSI 方法安排灌溉，以获得最佳小麦产量。将 CWSI 与土壤水分胁迫相结合，可以实时了解作物水分状况，从而实现更精确、更智能的灌溉调度。