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Utilizing reflectance spectroscopy to generate the necessary soil data to drive innovations in precision agriculture and soil management is an increasing focus of agronomic research. One of the key limitations for widespread practical adoption of reflectance spectroscopy is hardware cost, and lower cost hardware is actively being developed. This study evaluated two inexpensive nano Fourier‐transform near infrared spectrometers in the laboratory. One was a laboratory‐based analyzer (LabFlow) and the second was a field portable analyzer (Field Probe). Soil spectra were collected in the shortwave infrared range and processed using wavelet transforms and machine learning models. The optimal wavelet transforms and machine learning model were selected using cross validation on the training dataset, and performance of the optimal model was evaluated using an independent testing dataset. The Field Probe configuration total nitrogen model had the best performance when compared to the LabFlow laboratory analyzer with an R2 of 0.91, a concordance correlation coefficient of 0.95, and an root mean square error of 0.03. Soil inorganic carbon did not perform as well with an R2 of 0.65. However, performance was likely limited by a large number of low values and a limited range in the training dataset. Overall, these results highlight the potential for lower cost spectrometers to provide useful soil data for soil management applications. Core Ideas: There is a potential for low‐cost spectrometers to provide useful soil data for soil management applications.Optimal signal processing, model type, and model hyperparameters are dataset specific.Error in reference values is important to consider regarding model performance. [ABSTRACT FROM AUTHOR]

There is increasing demand for more detailed soil maps to support fine-scale land use planning, soil carbon management, and precision agriculture in Saskatchewan. Predictive soil mapping that incorporates a combination of environmental covariates provides a cost-effective tool for generating finer resolution soil maps. This study focused on mapping soil properties for multiple soil horizons in Saskatchewan using historical legacy soil data in combination with remote sensing band indices, bare soil composite imagery, climate data, and terrain attributes. Mapped soil properties included soil organic carbon content (SOC), total nitrogen, cation exchange capacity (CEC), electrical conductivity (EC), inorganic carbon (IOC), sand and clay content, and total profile soil organic carbon stocks. For each of these soil properties, a recursive feature elimination was undertaken to reduce the number of features in the overall model. This process involved iteratively removing features such that random forest out-of-bag error was minimized. Final random forest models were built for each property and evaluated using an independent test dataset. Overall, predictive models were successful for SOC (R2 = 0.71), total nitrogen (R2 = 0.65), CEC (R2 = 0.46), sand content (R2 = 0.44) and clay content (R2 = 0.55). The methods used in this study enable mapping of a greater geographic region of Saskatchewan compared to those previously established that relied solely on bare soil composite imagery. [ABSTRACT FROM AUTHOR]