Pre-harvest forecast of pigeon-pea yield using discriminant function analysis of weather variables (MAUSAM, 2016)
In the present paper, an application of discriminant function analysis of weather variables (minimum & maximum temperature, Rainfall, Rainy days, Relative humidity 7 hr & 14 hr, Sunshine hour and Wind velocity )for developing suitable statistical models to forecast pigeon-pea yield in Faizabad district of Eastern Uttar Pradesh has been demonstrated. Time series data on pigeon-pea yield for 22 years (1990-91 to 2011-12) have been divided into three groups, viz., congenial, normal, and adverse based on de-trended yield distribution. Considering these groups as three populations, discriminant function analysis using weekly data on eight weather variables in different forms has been carried out. The sets of discriminant scores obtained from such analysis have been used as regressor variables along with time trend variable and pigeon-pea yield as regressand in development of statistical models. In all nine models have been developed. The forecast yield of pigeon-pea have been obtained from these models for the year 2009-10, 2010-11 and 2011-12, which were not included in the development of the models. The model 4 and 9 have been found to be most appropriate on the basis of R2adj, percent deviation of forecast, percent root mean square error (%RMSE) and percent standard error (PSE) for the reliable forecast of pigeon-pea yield about two and half months before the crop harvest.