2023, Vol. 8, Special Issue 5, Part J
Meteorological parameters-based pre-harvest forecasting of wheat crop yield by using discriminant function analysisAuthor(s):
Neeraj Singh, Boya Venkatesu, Anushree Shukla and Ume KulsumAbstract:
In the present paper, an application of discriminant function analysis on meteorological parameters for developing suitable statistical models to forecast pre-harvest wheat yield in Azamagrh district of Eastern Uttar Pradesh has been demonstrated. Time series data on wheat yield for 18 years (2000-01 to 2017-18) 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 of crop season on five meteorological parameters has been carried out. The discriminant scores obtained from this have been used as regressor variables along with time trend in development of statistical models. In all six procedures using weekly weather data have been proposed. The models developed have been used to forecast the wheat yield for the year 2015-16 and 2017-18 which were not included in the development of the models. It has been found that most of the models provide reliable forecast of the wheat yield about two months before the harvest. However, the model -D5 has been found to be the most suitable among all the models developed.Pages: 654-664 | Views: 71 | Downloads: 7Download Full Article: Click Here
How to cite this article:
Neeraj Singh, Boya Venkatesu, Anushree Shukla, Ume Kulsum. Meteorological parameters-based pre-harvest forecasting of wheat crop yield by using discriminant function analysis. Int J Stat Appl Math 2023;8(5S):654-664.