Locusts, large gregarious and migratory grasshoppers, are pests of economic importance in several regions of the world because of the severe damage they can cause to crops. The Central American locust, Schistocerca piceifrons is the most important locust species in the Americas, and it is distributed in zones of Mexico, Central and South America. In Mexico, despite the efforts to survey and monitor S. piceifrons (Walker) populations, outbreaks are still difficult to predict and prevent, and high economic and ecological costs are incurred in controlling them. The purpose of this study was to build a dynamic model of locust growth and development as a function of environmental conditions in order to identify suitable conditions for the high reproduction rates of this insect. This information can be used to assist in locust management. A modular approach and numerical integration techniques were applied in model building. The main inputs of the model were daily rainfall and temperature data, and physical soil properties such as texture and depth. The model estimates the growth of non-cultivated grass in breeding zones and oviposition rates as a function of soil moisture. The development rates of the different locust stages are calculated as a function of temperature. The model satisfactorily represents S. piceifrons behaviour, and generates 2 generations per yr, the first in summer and the second in winter. In locations with suboptimal temperatures the second generation does not complete development until the next year. A good agreement was found between model outputs and field data from Yucatan, Mexico for 2008 to 2010. Based on these results the model is proposed for use as a tool to support S. piceifrons monitoring by the National Locust Control Program.