Application of ARIMAX model to forecast weekly cocoa black pod disease incidence

Abstract

The losses caused by cocoa black pod disease around the world exceeded $400 million due to inaccurate forecasting of cocoa black pod disease incidence which leads to inappropriate spraying timing. The weekly cocoa black pod disease incidence is affected by external factors, such as climatic variables. In order to overcome this inaccuracy of spraying timing, the forecasting disease incidence should consider the influencing external factors such as temperature, rainfall and relative humidity. The objective of this study is to develop a Autoregressive Integrated Moving Average with external variables (ARIMAX) model which tries to account the effects due to the climatic influencing factors, to forecast the weekly cocoa black pod disease incidence. With respect to performance measures, it is found that the proposed ARIMAX model improves the traditional Autoregressive Integrated Moving Average (ARIMA) model. The results of this forecasting can provide benefits especially for the development of decision support system in determine the right timing of action to be taken in controlling the cocoa black pod disease.