Integrating multi-year crop inventories as a proxy for soil management within a digital soil mapping framework for predicting nitrogen indices

Abstract

For the international digital soil mapping (DSM) community, adequate spatial estimates of nitrogen (N) mineralization have yet to be generated. This is due, in part, to an inability to capture critical N controls at the regional and provincial scales. While the influence of climate, vegetation, and relief are accessible predictors in DSM, the effect of soil management is known for its important influence on N dynamics, but has hitherto been elusive for soil mappers. For the purpose of producing N maps to inform N fertilizer management, the intention of this study was to determine the importance of novel crop frequency layers, as a proxy for soil management, through the development of provincial scale DSMs of total nitrogen (TN), biological nitrogen availability (BNA) and the estimate of N mineralization over a growing season (GSN) as calculated from TN and BNA results. Crop frequency covariates were developed that estimated the frequency a particular crop type was planted over a 10-year period, thus capturing cropping system and tillage intensity. TN results were 27% higher using crop frequency layers and the support vector machine learner, with a Lin’s concordance correlation coefficient (concordance) of 0.45. BNA predictions increased by 24% using crop frequency layers and the stochastic gradient boosting learner for a final concordance of 0.45. GSN showed the least improvement using the crop frequency layers (6%) but resulted in the highest concordance (0.47) using crop frequency layers with the stochastic gradient boosting learner. The stable N pool, represented by TN, showed climate covariates with the highest importance; whereas, the labile pool, based on BNA measures, was best predicted and controlled by organism covariates. The successful inclusion of crop frequency layers into N maps indicated that the number of times in forages and potatoes over a 10-year period was of the greatest importance. As tillage intensity is most pronounced in potatoes, and as forages contribute to increased biomass and building soil organic matter levels, the results showed that increasing the number of years in forages had a positive correlation with GSN and the stable and labile N pools.

Keywords

• Digital soil mapping
• Soil nitrogen mineralization
• Machine learning
• Pedotransfer functions