Spatial variability in soil, crop, and topographic features, combined with temporal variability between seasons can result in variable annual yield patterns within a paddock. The complexity of interactions between yield-limiting factors such as soil nutrients and soil water require specialist statistical processing to be able to quantify variability, and thus inform crop management practices. This study uses multiple linear regression models, Cubist regression and feed-forward neural networks to predict spatial maize-grain (Zea mays) yield at two sites in the Waikato Region, New Zealand. The variables considered were: crop reflectance data from satellite imagery, soil electrical conductivity, soil organic matter, elevation, rainfall, temperature, solar radiation, and seeding density. This exercise explores methods which may be useful in predicting yield from proximal and remote sensed data with higher resolution than traditional low spatial resolution point sampling using soil testing and yield response curves.

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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. Keywords: GIS, Machine learning, Precision farming, Spatiotemporal.

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