Estimation of Wheat Crop Evapotranspiration from Meteorological Dataset for Anand, India

The goal of evapotranspiration estimation research is to measure water loss through transpiration and evaporation in order to enable effective management of water resources, enhance irrigation techniques, evaluate the effects of climate change, and support sustainable ecological and agricultural planning. The study was conducted at Regional Research Station (RRS) is located in Anand and part of Anand Agricultural University (AAU) positioned in Middle Gujarat, India. The Penman-Monteith (FAO-PM) method of the Food and Agriculture Organization was used to estimate long-term trends in wheat evapotranspiration in Anand. Monthly and seasonal evapotranspiration for the years 2011 to 2020 was calculated using CROPWAT software. When estimating reference evapotranspiration (ETo) using meteorological parameters, the FAO-PM approach was used as it is known for its reliability. Temperature, humidity, wind speed, solar radiation, and other ground-based data were gathered from the meteorological observatory and analyzed to calculate ETo and, in turn, crop evapotranspiration (ETc) for wheat while taking crop-specific coefficients (Kc) into account. The monthly evapotranspiration for wheat, as calculated using the FAO-PM method from 2011 to 2020, ranged from 34 to 40 mm in December and 253.7 to 285.2 mm in February. The seasonal ET for the same period varied between 501.2 mm and 561.3 mm. Study concludes that crop has higher water demands during their peak growth stage. Wheat evapotranspiration showed upward trend from 2011 to 2020. The findings provide insight into the relationship between wheat water demand and climatic parameters, temporal fluctuations in ETc, and effective irrigation planning techniques in the face of shifting weather patterns. Recommending the estimation of wheat crop evapotranspiration from a meteorological dataset for Anand using the CROPWAT model is justified because the model efficiently utilizes localized weather data (such as temperature, humidity, wind speed, and solar radiation) to provide accurate and region-specific irrigation planning and water management insights, optimizing agricultural productivity.