Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping

Abstract

Phosphorus (P) is a macronutrient that often limits the productivity and growth of terrestrial ecosystems, but it is also one of the main causes of eutrophication in aquatic systems at both local and global levels. P content in soils can vary largely, but usually, only a small fraction is plant-available or in an organic form for biological utilization because it is bound in incompletely weathered mineral particles or adsorbed on mineral surfaces. Furthermore, in agricultural ecosystems, plant-available P content in topsoil is mainly controlled by fertilization and land management. To understand, model, and predict P dynamics at the landscape level, the availability of detailed observation-based P data is extremely valuable. We used more than 388,000 topsoil plant-available P samples from the period 2005 to 2021 to study spatial and temporal variability and land-use effect on soil P. We developed a mapping approach based on existing databases of soil, land-use, and fragmentary soil P measurements by land-use classes to provide spatially explicit high-resolution estimates of topsoil P at the national level. The modeled spatially detailed (1:10,000 scale) GIS dataset of topsoil P is useful for precision farming to optimize nutrient application and to increase productivity; it can also be used as input for biogeochemical models and to assess P load in inland waters and sea.

Keywords

• agricultural land
• geographical information system
• interpolation
• land use
• machine learning bagging model
• soil phosphorus mapping