Applying provincially derived pedotransfer function and digital soil map predictions of nitrogen indices at the field scale

There is a pressing need to provide crop producers with tools for estimating soil nitrogen (N) mineralization at the local level and to support variable N rate technologies. With provincial-scale datasets being used to train predictive models, there is an opportunity to observe how these predictions perform at the field scale, especially in fields below provincial soil quality benchmarks. This study examined how provincially derived decision support tools (DSTs) for estimating growing season N mineralization performed at the field scale for informing N fertilizer recommendations. DSTs included a 30 m resolution digital soil map (DSM) and novel pedotransfer functions (PTFs) developed using machine learning and predictor variables from a provincial database. Six agricultural fields were sampled for comparison with DST predictions, as well as to create novel infield maps (5 m resolution) of N indices. DSTs were successful at placing field N mineralization potential in context to provincial averages, achieving PTF predictions with a concordance (CCC) as high as 0.91 in comparison to direct soil measures, and 71%–100% of these direct measures within the 90% confidence interval of DSM predictions. Uncertainty decreased substantially using infield data, and infield maps of N indices achieved a CCC as high as 0.83. Field-scale predictors for N mineralization were different from DSM results, with relief variables having high importance. This study demonstrated how provincially derived PTF and DSMs of N indices could be used to inform N fertilizer recommendations. Development of field-scale N maps also introduces a powerful tool for guiding variable rate N applications.