Accounting for the spatial variation of phosphorus available explained by environmental covariates

The demand for maps of soil attributes in agriculture has increased during the past years, especially aiming the rational use of phosphorus fertilizer which can lead to serious environmental damage, like eutrophication of water bodies. The development of strategies and protocols for the accurate spatial modeling of P is necessary to a sustainable soil fertility management and food production. The objective of this study was to account for the spatial variation of available phosphorus explained by environmental covariates in southern Brazilian farm. The study was conducted in an agricultural area of 162 ha, located in the county of Tupancireta similar to, Rio Grande do Sul State. A set of 162 soil samples was collected in the 0-10 cm layer. Based on magnetic susceptibility and chemical and physical analysis, 9 soil maps were produced by Ordinary Kriging to be used as covariates. A Digital Elevation Model with 12 m of resolution was used to derive 13 topographic covariates. To predict the spatial distribution of the available soil phosphorus content, a set of six models was formulated based on different combination of the covariates. The models were fitted using a Random forest algorithm. Independent probability samples (n = 50) were used to evaluate the maps. Available phosphorus content from samples varied from 4.79 to 220.45 mg dm-3, with an average of 48.80 mg dm-3. Among predictive models, the one fitted using only topographic covariates (model 1) presented the highest predictive error (RMSE = 30.65 mg dm-3). When all available covariates were included in the model formulation, the predictive error decreased (RMSE = 28.05 mg dm-3). In general, including soil covariates result in better prediction than using only topographic covariates. The lack of soil covariates related to clay and iron fractions could be replaced for magnetic susceptibility data.

Automated summary

The study aimed to account for the spatial variation of available phosphorus in southern Brazilian farm. It was found that models incorporating soil covariates could better predict soil phosphorus content.