Statistical Modeling of Weather-based Yield Forecasting for Young Mature Oil Palm

Reliable and timely yield forecasts provide important means for various management decisions in oil palm plantation relating to budgeting, storage, distribution and marketing. A stepwise multiple linear regression (MLR) model is established with monthly oil palm yield as the dependent variable employing agrometeorological variables in cumulated time-lag period prior to harvest as the independent variables. This study showed that the percentage available water holding capacity (%AWHC) have significant implications on monthly fresh fruit bunch yield per hectare attained. The MLR model displayed acceptably performance as multiple coefficient of determination (R2) reached 68% with palm age and %AWHC as the predictor variables, this implying that 68% of the variability in yield achieved was explained by the model. Therefore, it is possible to predict monthly oil palm yield for nine months ahead with reasonably accuracy. In addition, the relevance between agroclimatic variability and flowering phenology of oil palm was briefly reviewed. The key physiological stages of inflorescence abortion and sex determination affected by the soil moisture were at lag period of 9-11 and 22-23 months before harvest respectively.