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Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks
There is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands acr...
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Published in: | European journal of soil science 2021-07, Vol.72 (4), p.1575-1589 |
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creator | Vrebos, Dirk Jones, Arwyn Lugato, Emanuele O'Sullivan, Lilian Schulte, Rogier Staes, Jan Meire, Patrick |
description | There is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long‐term simulation with the DayCent model were used to build crop‐specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade‐offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade‐offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management.
Highlights
When increasing a soil function, how do trade‐offs affect the other functions under different conditions?
Bayesian networks evaluate trade‐offs between soil functions and estimate their maximal delivery.
Maximizing a soil function has varied effects on other functions depending on soil, climate and management.
Differences in trade‐offs make some locations more suitable for increasing a soil function then others. |
doi_str_mv | 10.1111/ejss.13039 |
format | article |
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Highlights
When increasing a soil function, how do trade‐offs affect the other functions under different conditions?
Bayesian networks evaluate trade‐offs between soil functions and estimate their maximal delivery.
Maximizing a soil function has varied effects on other functions depending on soil, climate and management.
Differences in trade‐offs make some locations more suitable for increasing a soil function then others.</description><identifier>ISSN: 1351-0754</identifier><identifier>EISSN: 1365-2389</identifier><identifier>DOI: 10.1111/ejss.13039</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Agricultural land ; Bayesian analysis ; Bayesian modelling ; Climate ; DayCent ; European Union ; Indicators ; mapping ; maximization ; Networks ; Performance evaluation ; Probability theory ; Soil ; Soil analysis ; Soil conditions ; soil function ; Soil management ; Soils ; trade‐offs</subject><ispartof>European journal of soil science, 2021-07, Vol.72 (4), p.1575-1589</ispartof><rights>2020 British Society of Soil Science</rights><rights>2021 British Society of Soil Science</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3379-b60d4350126000c7820909952f63652b1844678726a0cd7ea61e1446b2ceee343</citedby><cites>FETCH-LOGICAL-c3379-b60d4350126000c7820909952f63652b1844678726a0cd7ea61e1446b2ceee343</cites><orcidid>0000-0002-8115-0304</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Vrebos, Dirk</creatorcontrib><creatorcontrib>Jones, Arwyn</creatorcontrib><creatorcontrib>Lugato, Emanuele</creatorcontrib><creatorcontrib>O'Sullivan, Lilian</creatorcontrib><creatorcontrib>Schulte, Rogier</creatorcontrib><creatorcontrib>Staes, Jan</creatorcontrib><creatorcontrib>Meire, Patrick</creatorcontrib><title>Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks</title><title>European journal of soil science</title><description>There is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long‐term simulation with the DayCent model were used to build crop‐specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade‐offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade‐offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management.
Highlights
When increasing a soil function, how do trade‐offs affect the other functions under different conditions?
Bayesian networks evaluate trade‐offs between soil functions and estimate their maximal delivery.
Maximizing a soil function has varied effects on other functions depending on soil, climate and management.
Differences in trade‐offs make some locations more suitable for increasing a soil function then others.</description><subject>Agricultural land</subject><subject>Bayesian analysis</subject><subject>Bayesian modelling</subject><subject>Climate</subject><subject>DayCent</subject><subject>European Union</subject><subject>Indicators</subject><subject>mapping</subject><subject>maximization</subject><subject>Networks</subject><subject>Performance evaluation</subject><subject>Probability theory</subject><subject>Soil</subject><subject>Soil analysis</subject><subject>Soil conditions</subject><subject>soil function</subject><subject>Soil management</subject><subject>Soils</subject><subject>trade‐offs</subject><issn>1351-0754</issn><issn>1365-2389</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kE1OwzAQhS0EEqWw4QSW2CGljO38eQlV-VMlFgGxtNzEoSkhLp6EKjuOwBk5CQ5hzWzmzeib0dMj5JTBjPm6MBvEGRMg5B6ZMBFHARep3B90xAJIovCQHCFuAJhgUk7Ic7bVbaVraj503XlpG6qbgrZOF-b788uWpZ913WOF1JYUbVXTsmvygUTarp3tXtb0SvcGK93QxrQ7617xmByUukZz8ten5Ol68Ti_DZYPN3fzy2WQC5HIYBVDEYoIGI8BIE9SDhKkjHgZe_N8xdIwjJM04bGGvEiMjplhfrXiuTFGhGJKzsa_W2ffO4Ot2tjOecOoeBQmTAoGkafORyp3FtGZUm1d9aZdrxioITg1BKd-g_MwG-FdVZv-H1It7rNsvPkBhvVwqw</recordid><startdate>202107</startdate><enddate>202107</enddate><creator>Vrebos, Dirk</creator><creator>Jones, Arwyn</creator><creator>Lugato, Emanuele</creator><creator>O'Sullivan, Lilian</creator><creator>Schulte, Rogier</creator><creator>Staes, Jan</creator><creator>Meire, Patrick</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>L.G</scope><scope>P64</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-8115-0304</orcidid></search><sort><creationdate>202107</creationdate><title>Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks</title><author>Vrebos, Dirk ; Jones, Arwyn ; Lugato, Emanuele ; O'Sullivan, Lilian ; Schulte, Rogier ; Staes, Jan ; Meire, Patrick</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3379-b60d4350126000c7820909952f63652b1844678726a0cd7ea61e1446b2ceee343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Agricultural land</topic><topic>Bayesian analysis</topic><topic>Bayesian modelling</topic><topic>Climate</topic><topic>DayCent</topic><topic>European Union</topic><topic>Indicators</topic><topic>mapping</topic><topic>maximization</topic><topic>Networks</topic><topic>Performance evaluation</topic><topic>Probability theory</topic><topic>Soil</topic><topic>Soil analysis</topic><topic>Soil conditions</topic><topic>soil function</topic><topic>Soil management</topic><topic>Soils</topic><topic>trade‐offs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vrebos, Dirk</creatorcontrib><creatorcontrib>Jones, Arwyn</creatorcontrib><creatorcontrib>Lugato, Emanuele</creatorcontrib><creatorcontrib>O'Sullivan, Lilian</creatorcontrib><creatorcontrib>Schulte, Rogier</creatorcontrib><creatorcontrib>Staes, Jan</creatorcontrib><creatorcontrib>Meire, Patrick</creatorcontrib><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>European journal of soil science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vrebos, Dirk</au><au>Jones, Arwyn</au><au>Lugato, Emanuele</au><au>O'Sullivan, Lilian</au><au>Schulte, Rogier</au><au>Staes, Jan</au><au>Meire, Patrick</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks</atitle><jtitle>European journal of soil science</jtitle><date>2021-07</date><risdate>2021</risdate><volume>72</volume><issue>4</issue><spage>1575</spage><epage>1589</epage><pages>1575-1589</pages><issn>1351-0754</issn><eissn>1365-2389</eissn><abstract>There is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long‐term simulation with the DayCent model were used to build crop‐specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade‐offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade‐offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management.
Highlights
When increasing a soil function, how do trade‐offs affect the other functions under different conditions?
Bayesian networks evaluate trade‐offs between soil functions and estimate their maximal delivery.
Maximizing a soil function has varied effects on other functions depending on soil, climate and management.
Differences in trade‐offs make some locations more suitable for increasing a soil function then others.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/ejss.13039</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-8115-0304</orcidid><oa>free_for_read</oa></addata></record> |
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source | Wiley-Blackwell Read & Publish Collection |
subjects | Agricultural land Bayesian analysis Bayesian modelling Climate DayCent European Union Indicators mapping maximization Networks Performance evaluation Probability theory Soil Soil analysis Soil conditions soil function Soil management Soils trade‐offs |
title | Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks |
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