Analysis of statistical methods to evaluate the performance of simulation models in horticultural crops - Soto-Bravo, Freddy; González-Lutz, María Isabel (Agronomía Mesoamericana, 2019)
Introduction. Every simulation model must be calibrated and validated, in order to avoid speculative and inaccurate conclusions. The methods to evaluate simulation models are usually applied “by habit”, without specifying basic methodological details which leads to the use of terminology and symbology that could cause confusion Objective. The objective in the present study was to analyze the different statistical methods employed to evaluate the performance of simulation models in agriculture, and thus propose which is the most suitable from the practical point of view. Materials and Methods. Statistical methods based on difference and regression analysis, between measured and simulated values were analyzed. Regarding the difference analysis group, the used methods were root mean square error (RMSE), mean absolute error (MAE), relative error (RE), adjustment index (d), me bianas error (MBE) and the model efficiency (E). In the case of the regression analysis the intercept, linear regression (b) and determination (R2) coefficients, and the estimation confidence limits were scrutinize. Results. The ER, d and E, are measures which objective is the comparison between different models to simulate a given variable, instead of evaluating the performance of the model as such. The root square mean error usually used to evaluate differences between observed and simulated values is different from the RMSE regression. The different cases illustrated with the “Eurotate_N” model demonstrated the apropriate practical application of the regression analysis as statistical tool to evaluate its capacity to simulate fruit yield, volumetric soil moisture, evapotranspiration and dry matter in tomato crop under greenhouse. Conclusion. The most appropriate statistical method proposed to evaluate a simulation model in tomato was the regression analysis.