1Great Zimbabwe University, Gary Magadzire School of Agriculture, Department of Livestock, Wildlife and Fisheries, P.O. Box 1235, Masvingo
*Corresponding Author: W ashaya Soul, Great Zimbabwe University, Gary Magadzire School of Agriculture, Department of Livestock, Wildlife and Fisheries, P.O. Box 1235, Masvingo. Email: jabulaniwashaya@yahoo.com
Zimbabwe has been experiencing a tremendous shrinkage in the dairy industry for the past two decades; hence, the future is uncertain. The single-variable time series analysis is a useful tool for forecasting supply and demand, particularly for small and medium businesses. As a result, models based on historical data patterns can easily proffer plausible forecasts that are critical for improved planning. The study aimed to investigate the trends of actual milk production and forecast the volume of milk at the Nharira-Lancashire dairy scheme.
The present study forecasts milk supply for up to 2 years using historical data from 1995-2020. The ARIMA time-series (p, d, q) model was applied to predict monthly milk yield. Twenty-five years of data on milk yield collected from the milk collection centre (MCC) were used for modelling. The presence of a trend was checked through time series plot, stationarity through autocorrelation (ACF) and partial auto-correlation functions (PACF).
The ARIMA (1, 1, 1) model was found to be the best-fitted model for the prediction of monthly milk yield. The results showed that milk yield data is seasonal and follows a particular trend. The milk production is critically low during the months of August to September, while 2009 had the lowest volumes of milk. The forecasts showed that milk yields would increase by 27.5% in 2022.
ARIMA, Milk output, Seasonality, Trend analysis
