[PDF][PDF] Modeling sugarcane yields in the Kenya sugar industry: A SARIMA model forecasting approach
The purpose of this study was to fit a model that forecasts quarterly sugarcane yields in
Kenya. Seasonal ARIMA models are explored and tested. Seasonal ARIMA (2, 1, 2)(2, 0, 3)
4 is found to be the best model that fits quarterly sugarcane yields from 1973-2015.
Sugarcane yields data collected quarterly from 1973-2014 is used for modeling and
SARIMA (2, 1, 2)(2, 0, 3) 4 model is fit and 2015 quarterly forecasts are compared against
the actual quarterly yields in 2015. If all factors are held constant, the model predicted a drop …
Kenya. Seasonal ARIMA models are explored and tested. Seasonal ARIMA (2, 1, 2)(2, 0, 3)
4 is found to be the best model that fits quarterly sugarcane yields from 1973-2015.
Sugarcane yields data collected quarterly from 1973-2014 is used for modeling and
SARIMA (2, 1, 2)(2, 0, 3) 4 model is fit and 2015 quarterly forecasts are compared against
the actual quarterly yields in 2015. If all factors are held constant, the model predicted a drop …
Abstract
The purpose of this study was to fit a model that forecasts quarterly sugarcane yields in Kenya. Seasonal ARIMA models are explored and tested. Seasonal ARIMA (2, 1, 2)(2, 0, 3) 4 is found to be the best model that fits quarterly sugarcane yields from 1973-2015. Sugarcane yields data collected quarterly from 1973-2014 is used for modeling and SARIMA (2, 1, 2)(2, 0, 3) 4 model is fit and 2015 quarterly forecasts are compared against the actual quarterly yields in 2015. If all factors are held constant, the model predicted a drop in sugarcane yields in 2016 to 60 (95% CI: 34.58, 84.69) tonnes of cane per hectare (tch) in 2016, 54 (95% CI: 26.24, 82.43) tch in 2017 and 51.48 (95% CI: 21.51, 81.45) tch in 2018. A steady increase would be observed again from 2020-2024.
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