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Abstract. Precision farming is about managing within-field spatial variability by applying appropriate inputs with the right amount, at the right place, and at the right time. This typically leads to the delineation of site-specific management zones that represent different yield potentials. However, a focus on spatiotemporal interactions is generally lacking. This technical note explores the viability of predicting spatial yields within fields, in the small-field arable production commonly practiced in New Zealand, using supervised machine learning algorithms. The methods used are multiple linear regression (MLR), feed-forward backpropagation neural network (FFNN), classification and regression tree (CART), random forest (RF), XGBoost, and Cubist, using predictors compiled from a range of spatiotemporal factors, including soil electrical conductivity (EC), soil organic matter (OM), elevation, rainfall, growing degree days, and solar radiation. Despite poor results (R2 = 0.06 to 0.50) for predicting the spatial yield of each year, the RF, XGBoost, and Cubist models demonstrated greater capability for modeling spatiotemporal interactions than the previously tested FFNN and MLR. The inclusion of consistently calibrated yield data for additional years and more related variables (e.g., soil moisture and canopy cover) could improve the models. The results of these modeling analyses could lead to the delineation of dynamic yield management zones for improving the precision of mid-season fertilizer prescriptions to improve yield.