Statistical Modeling for Forecasting Fertilizer Consumption in India
DOI:
https://doi.org/10.14719/pst.1982Keywords:
ACF, ARIMA, Fertilizer consumption, Forecasting, Model selection criteria, PACF, Residual AnalysisAbstract
Fertilizers have contributed significantly to increased agricultural yields, particularly for cereal crops and they will still be an important part of the science-based farming that is needed to feed the world's growing population. Fertilizers replenish the soil nutrients lost by the harvested crops, promote the use of high-yielding cultivars and boost biomass in tropical soils that are deficient in nutrients. In this study, data on fertilizer consumption in India was gathered from Agricultural Statistics at a Glance from 1950-51 to 2020-21 and utilized to fit the ARIMA model and forecast future usage. Forecasting has been done using the Box-Jenkins ARIMA approach. The ARIMA model is the most popular and widely applied forecasting model for time series data. The data was calculated using autocorrelation and partial autocorrelation functions. R programming software was used to estimate model parameters. The performance of the fitted model was evaluated using various goodness of fit criteria, such as AIC, BIC and MAPE. Empirical results revealed that the ARIMA (1,2,1) model was best suited to forecasting India's future total fertilizer use. Similarly, the ARIMA model was fitted for nitrogen, phosphorus, and potassium consumption in India independently. Forecasts from 2021-22 to 2030-31 are calculated using the chosen model. By 2030-31, total fertilizer use is predicted to reach 32,058.55 thousand tonnes. Policymakers should preferably base their judgments on reliable forecasts in order to tighten policies and achieve outcomes. Predicting future events using an appropriate time series model will assist policymakers, marketing strategies in making decisions related to export/ import and developing appropriate fertilizer consumption strategies.
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