Abstract

MASHABA, Zinhle et al. Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region. S. Afr. j. sci. [online]. 2017, vol.113, n.11-12, pp.1-6. ISSN 1996-7489.  http://dx.doi.org/10.17159/sajs.2017/20160201.

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. SIGNIFICANCE: •Developing a cost-effective technique based on satellite imagery for wheat yield forecasting is vital for food security planning in South Africa.

Keywords : dryland wheat; wheat yield; NDVI; MODIS; food security.

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