National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
There are several linear time-series forecasting models available in literature. One of the important and widely used technique for analysis of univariate time-series data is Box Jenkins’ Autoregressive integrated moving average (ARIMA) methodology (Box et al., 2007). Sometimes addition of the other exogenous variables increases the prediction accuracy of ARIMA model (ARIMAX). For this aspect we applied different p and q order ARIMAX model for five nutrient combinations of nitrogen content which is further developed by including organic carbons an input (exogenous) variable. Among the linear models the ARIMAX model performed better as compare to ARIMA model. But the performance of machine intelligence techniques like Hybrid of linear and nonlinear model is better as compared to linear time series models. The variations in nitrogen content data for all treatments are large. This could be the reason that nonlinear machine learning techniques can capture the heterogeneous trend in the data set and performed well as compare to ARIMA and ARIMAX. Further the highest forecasted value by hybrid model for nitrogen content for the year 2018, was found to be 270.39 kg/ha by using 100%NPK+FYM treatment combination. On the basis of forecasted value we can say that the combination of recommended dose with farm yard manure might be useful as the best combination for establishing higher nitrogen in the soil.
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