Scenario and Statistical Modeling of Oilseed Production for Jammu and Kashmir

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dc.contributor.advisorRizvi, S.E.H.
dc.contributor.authorSingh, ChibPrerna Ran
dc.date.accessioned2023-11-13T10:44:57Z
dc.date.available2023-11-13T10:44:57Z
dc.date.issued2023-10-25
dc.description.abstractIt is well known that India has 20.8% of the total area globally under oilseeds and accounts for 10% of global production having fourth largest position and hence oilseeds occupy an important position in the country’s economyin terms of area and production. As a result, it is critical to have an idea of future production and productivity. Thus, the present investigation entitled “Scenario and Modeling of Oilseeds Production for Jammu and Kashmir” has beenconducted with the objectives to study the aspects relating to behaviour of time series data on production and productivity of oilseeds crops, selection of an appropriate model, it’s fitting, testing and forecasting future values using time series modelling and Artificial Neural Network (ANN) models. For this purpose, secondary data were obtained for UT of Jammu & Kashmir as well at national level for the period of 1984 to 2022. Then the data were analysedfor obtaining the trend for the period using models like linear, quadratic, cubic, exponential, compound, power etc.Descriptive statistics wereobtained on entire data as well as on decadal basis to have information scenario of oilseeds production.The best models were chosen based on minimum Root Mean Square Error (RMSE).Time series models were obtained for oilseedsproduction and productivity at national as well as Jammu and Kashmir levels. Based on minimum RMSE, at national level the Logarithmic model for area, Linear model for production as well as productivity were found to be of best fit. Similarly, at Jammu and Kashmir level for area, productionand productivity,Cubic, Cubic and Quadratic models, respectively, were found to be of best fit.Instability index for area, production, and productivity of oilseeds at national level and at Jammu & Kashmir level indicated that instability in area, production, and productivity of oilseeds in India were of low level whereas in UT of Jammu and Kashmir it was of medium level. Growthmodels such asGompertz model, Monomolecular and Sigmoid were fitted for National and Jammu & Kashmir levels so as to observe growth in oilseeds production. Based on exponential model, overall percentage compound growth rates for area in Jammu and Kashmir during study period was negative (-0.12%) wheras as at national level it was 7.02%. For production atJammu and Kashmir levelit showedgradual growth with 0.08% and somewhat satisfactory in case of productivity with 1.01% CGR value.For production at all India level it showed remarkable growth with 2.53% and better in case of productivity with 1.81% CGR value.Using RStudio Estimated parameters for National of Gompertz, S-curve.This indicates increasing growth pattern over the timeseries ARIMAmodel best suited for production are ARIMA (2,0,1) forUT of Jammu and Kashmir level. For Artificial Neural network (ANN), the data were normalised and then the normalised data were further divided into two parts train data and test data. For the accuracy, data here were divided into (80:20) where 80% is train data sand 20% is test data, its MSE value is 0.16. Comparative to ANN, time series model is found to be the best fit.
dc.identifier.citationSingh, C,P,R .2023. Scenario and Statistical Modeling of Oilseeds Production for Jammu and Kashmir. M. Sc. Thesis, Sher-e-Kashmir University of Agricultural Sciences and Technology, Jammu
dc.identifier.other37083
dc.identifier.urihttps://krishikosh.egranth.ac.in/handle/1/5810201068
dc.language.isoEnglish
dc.pages104
dc.publisherSher-e-Kashmir University of Agricultural Sciences & Technology of Jammu
dc.relation.ispartofseriesM.Sc. 733
dc.subStatistics
dc.themeScenario and Statistical Modeling of Oilseed Production for Jammu and Kashmir
dc.these.typeM.Sc
dc.titleScenario and Statistical Modeling of Oilseed Production for Jammu and Kashmir
dc.typeThesis
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