ARIMA Modeling for Maize Production and Productivity in Jammu Division

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dc.contributor.advisorRizvi, S.E.H.
dc.contributor.authorTahir, Mohd
dc.date.accessioned2022-09-30T07:55:51Z
dc.date.available2022-09-30T07:55:51Z
dc.date.issued2022-09
dc.description.abstractAn investigation was conducted with the objectives to assess the production and productivity of maize on decadal basis along with instability index as well as to fit ARIMA model (s) for production and productivity of maize in Jammu division. It is known that maize is one of the world's most important cereal crops which it helps to ensure food security in the majority of developing countries. Maize is emerging as India's third most important crop after rice and wheat. As a result, it is critical to have an idea of future production and productivity. For this purpose, secondary data were obtained from the Directorate of Agriculture, Jammu for the period 1990 to 2019. The data were analyzed for obtaining the trend on a decadal basis and for entire period using ten known models such as the linear, quadratic, cubic, exponential etc. were fitted. The statistically best models were chosen based on adjusted R2 and co-efficient of determination (R2) and an instability index has been calculated. On the basis of time series data set of the study period, the overall average production under maize in Jammu division was 403.327 MT and overall average productivity was 18.72 q/ha. Different time series models were obtained for production as well as productivity of maize for Jammu division. On the basis of R2 and adj. R2, the cubic model for production as Ŷ=299.496+28.993t-2.178t2+0.047t3 was found to be best fitted (p-value<.023) and for productivity also cubic model obtained as Ŷ=12.634+1.829t-1.44t2+0.003t3 was found to be best fitted (p-value < .001) for overall Jammu division. The instability index for production was obtained as 11.371 and for productivity it was 8.744. Further using R-Software the data has been analyzed for the ARIMA model. An autoregressive integrated moving average (ARIMA) is a type of regression analysis that shows how strong a dependent variable is in comparison to other changing variables. The model's ultimate goal is to predict future time series movement by examining differences between values in the series rather than actual values. ARIMA models are used when there is evidence of non-stationarity in the data. Non-stationary data are always transformed into stationary data in time series analysis. Different ARIMA model combinations were created, and appropriate ARIMA models were fitted after the data was judged for stationarity. The statistically appropriate model was chosen based on goodness of fit criteria, including Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), RMSE, MAE assumptions of normality and residual independence. The best fitted model ARIMA (1, 3, 2) for the production and ARIMA (2, 2, 1) for productivity of maize crop in Jammu division. Based on these models, maize production and productivity have been forecasted for the five year from 2020-2024 with the values for production are 532.86, 573.41, 601.12, 642.52, 687.70 MT and for productivity the forecasted values are 27.05, 29.19, 31.72, 32.88 and 34.71q/ha, respectively.en_US
dc.identifier.citationPreferred for your work. Tahir.M. 2022. ARIMA Modeling for Maize Production and Productivity in Jammu Division M.Sc Thesis, SKUAST-Jammu, Indiaen_US
dc.identifier.other36895
dc.identifier.urihttps://krishikosh.egranth.ac.in/handle/1/5810188336
dc.keywordsARIMA MODEL, MAIZE PRODUCTION, INSTABILITY, ADJUSTED R2, TREND, FORECASTING.en_US
dc.language.isoEnglishen_US
dc.pages91en_US
dc.publisherSher-e-Kashmir University of Agricultural Sciences and Technology of Jammuen_US
dc.subStatisticsen_US
dc.themeARIMA Modeling for Maize Production and Productivity in Jammu Divisionen_US
dc.these.typeM.Scen_US
dc.titleARIMA Modeling for Maize Production and Productivity in Jammu Divisionen_US
dc.typeThesisen_US
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