Comparative analysis of weather-driven models for sorghum yield prediction in Bundelkhand

Bundelkhand region of Uttar Pradesh and Madhya Pradesh states faces various problems such as continuous drought, poor management of rainfall, soil loss, single crop focus, poverty and continuous migration of people out of the area.Agriculture in this area is based on rain fed crops such as pulses and oilseeds (Kalia et al., 2021).With its ability to tolerate dry spells and prosper in harsh soil conditions, sorghum is most suited cereal crop for Bundelkhand's semi-arid land where rainfall is uncertain, and water access is scarce.Sorghum is a vital basic food for rural families.It is extremely sensitive to temperature and photoperiod; it is important to study the role of meteorological variables to improve sorghum production and its quality.India is the third-largest producer of sorghum in the world with 6.0 million metric tons (9% of global production) after United States (14% of global production) and Nigeria (10% of global production) (IPAD 2024(IPAD -2025)).Agrometeorological indicators are vital for adapting agricultural planning, which enhances crop management and promotes food security under threat due to climate change.In the past, researchers have employed important statistical methods for crop harvesting challenges due to weather condition and its prediction on pre harvest forecasting (Garde et al., 2015;Maurya et al., 2025).Machine learning models employ algorithms to assess data, spot trends, and develop connects within the dataset, as compared to statistical models that link weather data and agricultural output using preestablished mathematical equations.Machine learning models have been successfully used to predict the yields of a number of crops including cashew, pigeon pea, rice, sorghum, coconut, wheat and soybean (Satpathi et al.,