Joshua Mott

Methods for soil test phosphorus (STP) differ globally and even within countries. While soil P tests often correlate, the relationship is often specific to a region or study, making any conversion equation difficult to transfer between contexts. Agronomic recommendations, P transport models, syntheses, and other works lack a strong basis for converting between STP. We fulfil this need for converting between two common STP measurements – Olsen P and Mehlich-3 P – through models which account explicitly for soil properties. Hypothesizing that soil properties at the sample level govern how STP values relate, we built models using a combined dataset of ca. 900 soils across the conterminous US and Canada, spanning 10 soil orders (USDA), 3 to 89 % clay, and pH 4 to 9. Model complexity ranged from conventional ‘region-level’ regressions (no soil data) to models adapting to many facets of soil P chemistry, presenting users several viable options suitable for their data context. Depending on data availability, the user could convert between Olsen P and Mehlich-3 P with half the error or less of conventional ‘region-level’ regressions. This reduction in conversion error impacts agronomic recommendations, environmental risk assessments (e.g., P index), and calibration of P transport models (e.g., SWAT+). While it remains best to simply measure the STP of interest, the conversion models here should prove useful in many contexts where that is not feasible. We provide the models in ready-to-use formats, depending on the covariates available to the user and whether the user wants to apply the equations in a spreadsheet or within model code.

Methods for soil test phosphorus (STP) differ globally and even within countries. While soil P tests often correlate, the relationship is often specific to a region or study, making any conversion equation difficult to transfer between contexts. Agronomic recommendations, P transport models, syntheses, and other works lack a strong basis for converting between STP. We fulfil this need for converting between two common STP measurements – Olsen P and Mehlich-3 P – through models which account explicitly for soil properties. Hypothesizing that soil properties at the sample level govern how STP values relate, we built models using a combined dataset of ca. 900 soils across the conterminous US and Canada, spanning 10 soil orders (USDA), 3 to 89 % clay, and pH 4 to 9. Model complexity ranged from conventional ‘region-level’ regressions (no soil data) to models adapting to many facets of soil P chemistry, presenting users several viable options suitable for their data context. Depending on data availability, the user could convert between Olsen P and Mehlich-3 P with half the error or less of conventional ‘region-level’ regressions. This reduction in conversion error impacts agronomic recommendations, environmental risk assessments (e.g., P index), and calibration of P transport models (e.g., SWAT+). While it remains best to simply measure the STP of interest, the conversion models here should prove useful in many contexts where that is not feasible. We provide the models in ready-to-use formats, depending on the covariates available to the user and whether the user wants to apply the equations in a spreadsheet or within model code.

Methods for soil test phosphorus (STP) differ globally and even within countries. While soil P tests often correlate, the relationship is often specific to a region or study, making any conversion equation difficult to transfer between contexts. Agronomic recommendations, P transport models, syntheses, and other works lack a strong basis for converting between STP. We fulfil this need for converting between two common STP measurements – Olsen P and Mehlich-3 P – through models which account explicitly for soil properties. Hypothesizing that soil properties at the sample level govern how STP values relate, we built models using a combined dataset of ca. 900 soils across the conterminous US and Canada, spanning 10 soil orders (USDA), 3 to 89 % clay, and pH 4 to 9. Model complexity ranged from conventional ‘region-level’ regressions (no soil data) to models adapting to many facets of soil P chemistry, presenting users several viable options suitable for their data context. Depending on data availability, the user could convert between Olsen P and Mehlich-3 P with half the error or less of conventional ‘region-level’ regressions. This reduction in conversion error impacts agronomic recommendations, environmental risk assessments (e.g., P index), and calibration of P transport models (e.g., SWAT+). While it remains best to simply measure the STP of interest, the conversion models here should prove useful in many contexts where that is not feasible. We provide the models in ready-to-use formats, depending on the covariates available to the user and whether the user wants to apply the equations in a spreadsheet or within model code.

Methods for soil test phosphorus (STP) differ globally and even within countries. While soil P tests often correlate, the relationship is often specific to a region or study, making any conversion equation difficult to transfer between contexts. Agronomic recommendations, P transport models, syntheses, and other works lack a strong basis for converting between STP. We fulfil this need for converting between two common STP measurements – Olsen P and Mehlich-3 P – through models which account explicitly for soil properties. Hypothesizing that soil properties at the sample level govern how STP values relate, we built models using a combined dataset of ca. 900 soils across the conterminous US and Canada, spanning 10 soil orders (USDA), 3 to 89 % clay, and pH 4 to 9. Model complexity ranged from conventional ‘region-level’ regressions (no soil data) to models adapting to many facets of soil P chemistry, presenting users several viable options suitable for their data context. Depending on data availability, the user could convert between Olsen P and Mehlich-3 P with half the error or less of conventional ‘region-level’ regressions. This reduction in conversion error impacts agronomic recommendations, environmental risk assessments (e.g., P index), and calibration of P transport models (e.g., SWAT+). While it remains best to simply measure the STP of interest, the conversion models here should prove useful in many contexts where that is not feasible. We provide the models in ready-to-use formats, depending on the covariates available to the user and whether the user wants to apply the equations in a spreadsheet or within model code.

The natural background contribution from grasslands and forest lands is important to consider in research and management to address the contribution of agricultural, industrial, and urban lands to water quality degradation. To our knowledge, no study has compiled and analyzed reference water quality from small reference grasslands and forests even though land use export coefficients for background water quality are assigned at that scale in decision support tools and models, total maximum daily load projects, and comparative analysis. Thus, our major objective was to summarize nitrogen (N), phosphorus (P), and sediment loads in runoff from grassland and forested reference watersheds. Measured annual nutrient loads were available from 13 grassland and nine forest reference sites in 12 North American Level II ecoregions. The grassland reference sites were relatively arid with annual runoff 300 kg/ha), while forest reference watersheds tended to have higher dissolved N loads. This research provides valuable summary results and initial comparisons related to reference water quality across the United States that can serve as a benchmark to compare how anthropogenic activities affect this vital ecosystem service.

The pursuit of sustainable phosphorus (P) management represents a long‐standing challenge in agricultural arenas, with far‐reaching implications for the environment and societal development. Few scholars are as synonymous with P science as Andrew Sharpley. Renowned over his 44‐year professional career for providing foundational insights into the fate and management of agricultural P, Sharpley also became the central figure in organizing responses to concerns over the contribution of non‐point source P pollution to eutrophication. As a global authority, Sharpley led teams that crafted scientific consensus for use by managers and policymakers in the face of complexity and uncertainty. His leadership offers lessons to those continuing in his footsteps and a model for all who desire to achieve scientific impact. This special collection of papers celebrates the perspectives, contributions, and legacy of Andrew Sharpley.