Improving the quality of coffee yield forecasting in Dak Lak Province, Vietnam, through the utilization of remote sensing data

Abstract

This research aimed to identify sensitive areas for Robusta coffee trees in Dak Lak province, Vietnam, where frequent droughts caused fluctuations in productivity. To improve yield forecasting, a mask was developed to extract potential predictive variables from satellite-derived vegetation indices (VIs). Correlation coefficients between VIs and coffee yield were analyzed to determine sensitive areas, and grid cells with high multiple correlation coefficients and a variable over time were used to build the mask for extracting VIs as predictor variables. The study found that sensitive areas had more challenging farming conditions than long-term crops, and the Vegetation Health Index was the most appropriate index for predicting coffee yield. The forecast quality for 6-8 months in advance was relatively high, with a ‘Willmott’s index of agreement’ ranging from 0.85 to 0.97 and the Mean Absolute Percentage Error ranging from 4.9% to 7.5%. Compared to previous research, the forecast quality has significantly improved. This study provides valuable insights for predicting coffee yield in Dak Lak and highlights the importance of considering sensitive areas and VIs for accurate forecasting.

Keywords

• coffee yield
• sensitive area
• satellite-derived
• vegetation indices