Wheat yield forecasting using statistical models

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dc.contributor.advisorAmrit Kaur
dc.contributor.authorLovepreet Kaur
dc.date.accessioned2019-11-11T04:24:09Z
dc.date.available2019-11-11T04:24:09Z
dc.date.issued2019
dc.description.abstractThe present study had been conducted to develop models based on weather variables for forecasting the wheat productivity of Amritsar, Ludhiana and Patiala districts of Punjab. The forty years data: (1970-71 to 2009-10) on wheat productivity and weather variables were used for model development and seven years data (2010-11 to 2016-17) for validation. The linear and non-linear models; simple linear, quadratic, cubic, fourth degree polynomial, monomolecular, logistic and gompertz were developed to remove the effect of technological factors over time. On the basis of goodness of fit statistics, the logistic model came best from linear and non-linear models. The detrended wheat productivity obtained after fitting the logistic model was used for forecasting the wheat productivity on the basis of weather variables; maximum temperature, minimum temperature, rainfall, relative humidity morning, relative humidity evening and bright sunshine hours. The fourteen weeks weather data of vegetative period of wheat crop had been utilized to forecast the productivity. Three weather indices based models; model I (un-weighted), model II (weighted) and model III (combined) were developed for each district using weather indices and detrended wheat productivity. The stepwise regression technique was applied and the results revealed that weighted model (model II) declared as best model for Amritsar, Ludhiana and Patiala districts explaining 60%, 67% and 52% variation in the detrended wheat productivity and had RMSPE 8.57%, 6.93% and 6.20% respectively. The weighted interaction of maximum and minimum temperature played crucial role in wheat productivity. The selected models followed the assumptions of residuals.en_US
dc.identifier.urihttp://krishikosh.egranth.ac.in/handle/1/5810134996
dc.keywordswheat, forecasting, linear, non-linear, weather, unweigted, weighted, variable, indices, R2, RMSPEen_US
dc.language.isoenen_US
dc.pages44en_US
dc.publisherPunjab Agricultural University, Ludhianaen_US
dc.research.problemWheat yield forecasting using statistical modelsen_US
dc.subStatisticsen_US
dc.subjectnullen_US
dc.themeWheat yield forecasting using statistical modelsen_US
dc.these.typeM.Scen_US
dc.titleWheat yield forecasting using statistical modelsen_US
dc.typeThesisen_US
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