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Modeling and simulation have become fundamental tools for the microclimatic analysis of greenhouses under various climatic conditions. These models allow precise control of the climate inside the structures and the optimization of their performance under any situation. In Colombia, the availability of energy balance models adapted to local greenhouses and their climate is limited, which affects the decision-making of both technical advisors and growers. This study focused on calibrating and evaluating a dynamic energy balance model to predict the thermal behavior of an innovative type of plastic-covered greenhouse designed for the Bogotá savanna. The selected model considers fundamental heat and mass transfer processes, incorporating parameters that depend on the architecture of the structure and local climatic conditions, making it suitable for protected agriculture in Colombia. The results of the post-calibration evaluation showed that the model is highly accurate, with a temperature prediction efficiency close to 86%. This ensures that the model can accurately predict the thermal behavior of the greenhouse being evaluated. It is important to note that the model can also anticipate phenomena characteristics of Colombian greenhouses, such as thermal inversion. This advance has become a valuable tool for decision-making in protected agriculture in the region.

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. This study aimed to carry out an uncertainty analysis (UA) and a global spatiotemporal sensitivity analysis (SA) for the parameters of the SIMPLE model, which uses 13 parameters, has two state variables and uses daily weather data to simulate crop growth and development. A Monte Carlo simulation was performed for the UA, and Sobol’s method was used for the SA. Four automatic weather stations representing the climatic conditions of the different bean-producing areas in Zacatecas, Mexico, and a four-year historical series of each station for irrigated and rainfed common bean crops were analyzed. From the UA the coefficients of variation (CV) for thermal time were 11.49% and 11.47%, for biomass the CV were 47.94% and 37.80% and for yield the CV were 49.52% and 39.70% for irrigated and rainfed beans, respectively. From the SA, the most influential parameters for irrigated beans were Tsum > Swater > Tbase > I50A > Topt and for rainfed beans, Tsum > Tbase > I50A > Topt > Swater, according to indices calculated on biomass and thermal time. In conclusion, UA was able to accurately quantify the uncertainty of the biomass, and SA allowed the identification of the most influential of the parameters of the SIMPLE model applied to a common bean crop.