  Pre-season prediction of crop production outcomes such as grain yields and N
losses can provide insights to stakeholders when making decisions. Simulation
models can assist in scenario planning, but their use is limited because of
data requirements and long run times. Thus, there is a need for more
computationally expedient approaches to scale up predictions. We evaluated the
potential of five machine learning (ML) algorithms as meta-models for a
cropping systems simulator (APSIM) to inform future decision-support tool
development. We asked: 1) How well do ML meta-models predict maize yield and N
losses using pre-season information? 2) How many data are needed to train ML
algorithms to achieve acceptable predictions?; 3) Which input data variables
are most important for accurate prediction?; and 4) Do ensembles of ML
meta-models improve prediction? The simulated dataset included more than 3
million genotype, environment and management scenarios. Random forests most
accurately predicted maize yield and N loss at planting time, with a RRMSE of
14% and 55%, respectively. ML meta-models reasonably reproduced simulated maize
yields but not N loss. They also differed in their sensitivities to the size of
the training dataset. Across all ML models, yield prediction error decreased by
10-40% as the training dataset increased from 0.5 to 1.8 million data points,
whereas N loss prediction error showed no consistent pattern. ML models also
differed in their sensitivities to input variables. Averaged across all ML
models, weather conditions, soil properties, management information and initial
conditions were roughly equally important when predicting yields. Modest
prediction improvements resulted from ML ensembles. These results can help
accelerate progress in coupling simulation models and ML toward developing
dynamic decision support tools for pre-season management.
