FORECASTING OF PINEAPPLE YIELD IN TRIPURA AND BIHAR USING NON LINEAR MODELS AND IT'S TREND ANALYSIS
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Date
2021
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DRPCAU, PUSA
Abstract
The present study is based on time series data collected for 32 years (1987-88 to 2018-19) to study the trend of area, production and productivity of pineapple in Tripura and Bihar and also to forecast the pineapple yield by using different non-linear models namely Logistic model, Gompertz model and Monomolecular model. Data from 1987-88 to 2017-18 are used for analysis purpose and 2018-19 data is used for model validation purpose. Socio economic condition of the farmers of Gomati district of Tripura, also taken as a objective of these experiment.
Though the actual values of area, production and productivity were fluctuating over the period but overall trend is showing an linearly increasing pattern. Kendall, Spearman and Pearson test is done for testing the validity and result shows a highly significant positive correlated value for all the test.
After comparing these three non-linear models with eleven statistics it is clear that Logistic model is most appropriate model for forecasting the pineapple yield in both the sate as it shows highest value of R2, adj. R2, R27 and R28 and lowest value of RSS, MAPE, MAE,
MSE, RMSE, RSE and MSE. nn. Lowest Chi-square value of was found in Logistic model for both the state. In case of forecasting for productivity of pineapple in Tripura, the Logistic model shows closest value to actual productivity with a forecast error of 19.79% in OSAF method. In case of Tripura, percentage forecast error is some extent to high and that is due to highly variation in yield during that period of time. Whereas in case of forecasting the pineapple yield for Bihar, all the models shows almost similar result in OSAF method. Percentage forecast error for Bihar is 2.45 % for selected Logistic model. Forecasted Pineapple yield in Tripura for the year 2018 to 2022 are almost equal i.e.17.44 q/hac, and for Bihar for the year 2018-2022 are almost equal to 26.71 q/ha. Selected Logistic model for forecasting of yield of Pineapple for Tripura and Bihar are as below:
Ŷ =17.4488/ (1 +3.4532 *exp(-0.2696*t) ) for Tripura
Ŷ =26.72232/ (1 +10.50813*exp(-0.3022*t) ) for Bihar
Annual income of the majority of the farmers of the study area are in between 1 lakh to 5 lakh and age, occupation, education, size of land holding, farming experience, farm mechanization and type of land shows a significant correlation with annual farmers income. The regression analysis shows that size of operational land shows highly significant with the farmer’s annual income.