The error in agricultural systems model prediction depends on the variable being predicted ?
Statistical theory allows us to better understand model prediction errors. Average squared error is always larger than squared error of the average (over time or space). Agricultural systems models are often used to predict average quantities. We propose a method for estimating squared error of the average, based on mixed models. In two case studies, the squared error of the average was 57% and 10% of the average squared error.
error apsim ghg emissions irrigation maize nitrous oxide uncertainty