Journal Article

Application of discriminant function analysis for forecasting wheat yield in Jaunpur district, Uttar Pradesh

VISHVA DEEPAK CHATURVEDI, PRATIBHA SINGH, SINGH, PIYUSH KUMAR

2024

Overview

Past research has extensively analyzed the impact of individual weather factors on wheat yields, leading to the use of discriminant function and principal component analysis to harness weekly weather data to develop robust forecast models (Agrawal et al., 2012; Sisodia et al., 2014; Kumar et al., 2019). Model 1 used five un-weighted weather indices Model 2 used five weighted weather indices Model 3 used 30 indices (both weighted and un-weighted, including interaction indices) Model 4 used five weighted and five un-weighted weather indices Model 5 used five un-weighted and 10 un-weighted interaction weather indices and Model 6 used five weighted and 10 weighted weather indices The weather data of five weather parameters (maximum and minimum temperatures, rainfall, wind velocity, and sunshine hours), from the 44th standard meteorological week (SMW) to the 7th SMW of the following year, for 15 years (2000-01 to 2014-15) were used for developing models. Authors contribution: P. K. Singh: Conceptualized, data analysis, and drafted the manuscript; P. Singh: Data collection and interpretation of the analysis; V. D. Chaturvedi: Reviewing, editing and final manuscript.

• Publisher: Association of Agrometeorologist,Association of agrometeorologists
• Subject: Accuracy / Agricultural production / Crop yield / Crop yield forecasting / Data analysis / Data collection / Discriminant analysis / Discriminant function analysis / Forecasting / Forecasting models / Function analysis / Impact analysis / Meteorological data / Minimum temperatures / Principal components analysis / Rain / Rainfall / Time series / Variables / Weather / Weather forecasting / Weather index / Weather indices / Weighted weather variables / Wheat / Wheat yield / Wheat yield prediction / Wind speed / Wind velocities