An evaluation of the statistical methods for testing the performance of crop models with observed data

By J. Yang, J. Yang, S. Liu, Gerrit Hoogenboom. 2014

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Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry (2017, methodology): “…(a) graphically using 95% confidence intervals of prediction (SigmaPlot©) and R2 values (while recognising that assumptions of its use may have been violated, i.e. autocorrelation, normality, and heteroskedasticity), and (b) using a combination of statistics as recommended by Yang et al. (2014).”
Development of a nitrogen recommendation tool for corn considering static and dynamic variables (2019, methodology): “Compared to complex simulation models (Liu et al., 2013; Puntel et al., 2016; Sela et al., 2017; Yang et al., 2014), the prediction error of the full EONR model is similar (MAE ̃ 30–40 kg N ha−1).”