Consumption of wheat is widespread and increasing in South Africa. However, global wheat production is
projected to decline. Wheat yield forecasting is therefore crucial for ensuring food security for the country.
The objective of this study was to investigate whether the anthesis wheat growth stage is suitable for
forecasting dryland wheat yields in the Central Free State region using satellite imagery and linear
predictive modelling. A period of 10 years of Normalized Difference Vegetation Index data smoothed with
a Savitzky–Golay filter and 10 years of wheat yield data were used for model calibration. Diagnostic plots
and statistical procedures were used for model validation and assessment of model adequacy. The period
30 days before harvest during the anthesis stage was established to be the best period during which to
use the linear regression model. The calibrated model had a coefficient of determination of 0.73, a p-value
of 0.00161 and a root mean squared error of 0.41 tons/ha. Residual plots confirmed that a linear model
had a good fit for the data. The quantile-quantile plot provided evidence that the residuals were normally
distributed, which means that assumptions of linear regression were fulfilled and the model can be used
as a forecasting tool. Model validation showed high levels of accuracy. The evidence indicates that use
of Moderate Resolution Imaging Spectroradiometer data during the anthesis growth stage is a reliable,
cost-effective and potentially time-saving alternative to ground-based surveys when forecasting dryland
wheat yields in the Central Free State.
Developing a cost-effective technique based on satellite imagery for wheat yield forecasting is vital for
food security planning in South Africa.