Development of a Mathematical Model for Corn Production: A Case Study in the Democratic Republic of Congo.

Current efforts to refine agriculture and thereby to maximize food production require a good understanding of environmental forces affecting crop yield. Plant growth depends on the interactions among climate, plant, soil, and the agronomic conditions faced during the growing season. Variations in these parameters are critical in the output of a given crop cultivar. If parameters are not optimal then crop productivity is restricted. Simulation in silico of complex systems such as the crop production ecosystem is a route to understanding and predicting crop yields. In the domain of selection and crop yield two issues recur - whether the variety is adapted to a particular ecotype, and which agricultural technique is best suited for a particular cultivar. Researchers and agronomists may observe varietal degeneration but not be concerned by the forces amplifying this outcome. Reported here is an attempt to develop a mathematical model reflecting the behaviour of three corn varieties growing in areas of the Democratic Republic of Congo (DRC). The model of interannual variability in performance allows agronomists to select varieties for target areas based on climate, soil, and agronomic conditions. The main variables affecting corn production in the DRC are discussed. In the present study, attempts were made to develop a mathematical equation which fits the behavior of three corn varieties growing in particular areas in the Democratic Republic of Congo (DRC). This equation models the interannual variability of the agronomic performance of each variety. This will allow agronomists to predict crop production and to select corn varieties in targeted areas based on climate, soil, and agronomic conditions. The main variables that affect corn production within the context of DRC will be discussed.