Watershed-scale spatial prediction of agricultural land phosphorus mass balance and soil phosphorus metrics: A bottom-up approach

Analysis of nutrient balance at the watershed scale, including for phosphorus (P), is typically accomplished using aggregate input datasets, resulting in an inability to capture the variability of P status across the study region. This study presents a set of methods to predict and visualize partial P mass balance, soil P saturation ratio (PSR), and soil test P for agricultural parcels across a watershed in the Lake Champlain Basin (Vermont, USA) using granular, field-level data. K-means cluster analyses were used to group agricultural parcels by soil texture, average slope, and crop type. Using a set of parcels accounting for similar to 21% of the watershed's agricultural land and having known soil test and nutrient management parameters, predictions of partial P mass balance, PSR, and soil test P for agricultural land across the watershed were made by cluster, incorporating uncertainty. This resulted in an average partial P balance of 5.5 +/- 0.2 kg P ha-1 year-1 and an average PSR of 0.0399 +/- 0.0002. Furthermore, approximately 30% of agricultural land had predicted soil test P values above optimum levels. Results were used to visualize areas with high P loss potential. Such data and visualizations can inform watershed P modeling and assist practitioners in nutrient management decision making. These techniques can also serve as a framework for bottom-up modeling of nutrient mass balance and soil metrics in other regions. Watershed-scale nutrient status predictions are typically made using aggregated datasets. Watershed-scale nutrient status predictions can be informed by field-level data. Spatial heterogeneity is preserved when using field-level data to predict nutrient status at the watershed scale. Granular predictions can inform regional nutrient management policy. Excessive inputs of phosphorus (P) to aquatic ecosystems can result in undesirable effects, including harmful algal blooms. Efforts are underway to improve management of P, an essential nutrient for plant growth, in watersheds across the United States and elsewhere. As part of these efforts, it is important to assess soil P levels and P mass balance (inputs to soils minus outputs from soils). Here, we present methods to predict and visualize soil P metrics and partial P mass balance for agricultural parcels across a watershed using field-level data. Our study is based in a watershed within the Lake Champlain Basin (Vermont, USA). We predict an average partial P balance of 5.5 kg P ha-1 year-1 for agricultural land, with 30% of agricultural land area characterized by soil test P values greater than optimum levels. We visualize these results using maps and discuss how these techniques can be useful in other regions.