Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

Abstract

One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.

Keywords

• crop modeling
• sugarcane
• Ivory Coast
• machine learning
• vegetation index
• yield forecast