Identifying fine climatic parameters for high maize yield using pattern mining: case study from Benin (West Africa)

Climate change is significantly affecting crop yields in sub-Saharan Africa. This impact is associated with the inability of farmers to control climatic conditions. Therefore, an accurate prediction of crop yields is necessary to help farmers make good decisions. This paper highlights links between climatic parameters and maize yield in Benin to ensure early yield prediction using pattern mining. The datasets used contain climate and maize yield data over the last 26 years in 5 districts with synoptic stations located in two agro-climatic zones (Sudanian and Sudano-Guinean) in Benin. To find association rules, climate variables were aggregated with yield using “year” and “districts” variables and through supervised machine learning models: Support vector machine, K Nearest Neighbour, Artificial Neural Network, Decision Tree, and Recurrent Neural Network. The decision tree technique provided good accuracy (R2 = 0.998, MSE = 0.021, MAE = 0.0008). However, the model obtained is not easily interpretable. We then used it to augment the dataset to apply an association rules algorithm: the frequent pattern growth algorithm. This allows us to build relationships easily interpretable by the general public. Results showed that most of the rules obtained in both agro-climatic zones are associations between the minimum and maximum temperature, humidity, sunstroke, rainfall, and evapotranspiration. Moreover, the highest maize yield is obtained by combining the medium values of these parameters. The best trends are observed in the Sudano- Guinean zone for medium values of minimum temperature, rainfall, evapotranspiration, maximum temperatures, and humidity. In the Sudanian zone, the high maize yield is observed for medium values of minimum temperature, maximum temperature, and maximum humidity. The identified association rules demonstrated a reliable and promising approach to optimize maize yield.