2025, Vol. 10, Issue 1, Part A
Forecasting of area production and productivity of mustard by hybrid model
Author(s): Priyanka Sahu, Neelam Chouksey and Pushpendra Kumar
Abstract: This study examines the trends in the area, production, and productivity of mustard in the Surguja district of Chhattisgarh from 1966-67 to 2021-22. Data were sourced from the Economic and Political Weekly Research Foundation (EPWRF), ICRISAT, and the Directorate of Agriculture, Chhattisgarh. The ARIMA (Autoregressive Integrated Moving Average) model was used to forecast future trends, complemented by a hybrid ARIMA-ANN (Artificial Neural Network) model to address complex data patterns. For mustard area production and productivity, the ARIMA model alone could not fully capture the nonlinear trends in the data. By adding ANN to create a hybrid ARIMA-ANN model, the forecasts became more accurate, with lower error rates. This hybrid model effectively captured both simple and complex patterns, making it more reliable for predicting mustard trends. These findings underscore the importance of selecting appropriate models based on data characteristics to enhance forecasting accuracy in agriculture.
Pages: 49-58 | Views: 24 | Downloads: 7Download Full Article: Click Here
How to cite this article:
Priyanka Sahu, Neelam Chouksey, Pushpendra Kumar. Forecasting of area production and productivity of mustard by hybrid model. Int J Stat Appl Math 2025;10(1):49-58.