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

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-avai...

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Main Authors: Anne Kull, Tambet Kikas, Priit Penu, Ain Kull
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Agronomy
Subjects:
Online Access:https://www.mdpi.com/2073-4395/13/5/1183
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author Anne Kull
Tambet Kikas
Priit Penu
Ain Kull
author_facet Anne Kull
Tambet Kikas
Priit Penu
Ain Kull
author_sort Anne Kull
collection DOAJ
description 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.
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spelling doaj.art-1eab0d1015174e2da236142988bcf1be2023-11-18T00:04:16ZengMDPI AGAgronomy2073-43952023-04-01135118310.3390/agronomy13051183Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution MappingAnne Kull0Tambet Kikas1Priit Penu2Ain Kull3Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51006 Tartu, EstoniaCentre of Estonian Rural Research and Knowledge, Teaduse 4, 75501 Saku, EstoniaCentre of Estonian Rural Research and Knowledge, Teaduse 4, 75501 Saku, EstoniaInstitute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51003 Tartu, EstoniaPhosphorus (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.https://www.mdpi.com/2073-4395/13/5/1183agricultural landgeographical information systeminterpolationland usemachine learning bagging modelsoil phosphorus mapping
spellingShingle Anne Kull
Tambet Kikas
Priit Penu
Ain Kull
Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
Agronomy
agricultural land
geographical information system
interpolation
land use
machine learning bagging model
soil phosphorus mapping
title Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
title_full Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
title_fullStr Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
title_full_unstemmed Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
title_short Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping
title_sort modeling topsoil phosphorus from observation based statistical approach to land use and soil based high resolution mapping
topic agricultural land
geographical information system
interpolation
land use
machine learning bagging model
soil phosphorus mapping
url https://www.mdpi.com/2073-4395/13/5/1183
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