Map Quality for Site‐Specific Fertility Management
The quality of soil fertility maps affects the efficacy of site‐specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using seve...
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| Veröffentlicht in: | Soil Science Society of America journal 2001-09, Vol.65 (5), p.1547-1558 |
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| container_title | Soil Science Society of America journal |
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| creator | Mueller, T. G. Pierce, F. J. Schabenberger, O. Warncke, D. D. |
| description | The quality of soil fertility maps affects the efficacy of site‐specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using several strategies including grid‐point (30‐ and 100‐m regular grids), grid cell (100‐m cells), and a simulated soil map unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation‐exchange capacity (CEC)] data were predicted using ordinary kriging, inverse distance weighted (IDW), and nearest neighbor (NN) interpolations for the various data sets. Each resulting map was validated against an independent data (n = 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal (prediction efficiencies ≤48%) at high sample densities and poor at lower densities (i.e., 61‐ and 100‐m grids; prediction efficiencies |
| doi_str_mv | 10.2136/sssaj2001.6551547x |
| format | Article |
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G. ; Pierce, F. J. ; Schabenberger, O. ; Warncke, D. D.</creator><creatorcontrib>Mueller, T. G. ; Pierce, F. J. ; Schabenberger, O. ; Warncke, D. D.</creatorcontrib><description>The quality of soil fertility maps affects the efficacy of site‐specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using several strategies including grid‐point (30‐ and 100‐m regular grids), grid cell (100‐m cells), and a simulated soil map unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation‐exchange capacity (CEC)] data were predicted using ordinary kriging, inverse distance weighted (IDW), and nearest neighbor (NN) interpolations for the various data sets. Each resulting map was validated against an independent data (n = 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal (prediction efficiencies ≤48%) at high sample densities and poor at lower densities (i.e., 61‐ and 100‐m grids; prediction efficiencies <21%). The average optimal distance exponent at each scale of measurement was 1.5. The performance of kriging relative to IDW methods (with a distance exponent of 1.5) improved with increasing sampling intensity (i.e., IDW was superior to kriging for 100% of cases with the 100‐m grid, 79% of the cases with the 61.5‐m grid scale, and 67% of the cases with the 30‐m grid). Practically, there was little difference between these interpolation methods. Grid sampling with a 100‐m grid, grid cell sampling, and simulated soil map unit sampling yielded similar prediction efficiencies to those for the field average approach, all of which were generally poor.</description><identifier>ISSN: 0361-5995</identifier><identifier>EISSN: 1435-0661</identifier><identifier>DOI: 10.2136/sssaj2001.6551547x</identifier><identifier>CODEN: SSSJD4</identifier><language>eng</language><publisher>Madison: Soil Science Society</publisher><subject>Agriculture ; Agronomy. Soil science and plant productions ; Biological and medical sciences ; Experiments ; Fundamental and applied biological sciences. Psychology ; General agronomy. Plant production ; Generalities. Analysis and diagnosis methods ; Soil fertility ; Soil management ; Soil quality ; Soil science ; Soil surveys, classification and mapping ; Soil surveys, classification and mapping, soil genesis ; Soil-plant relationships. Soil fertility. Fertilization. Amendments ; Soils</subject><ispartof>Soil Science Society of America journal, 2001-09, Vol.65 (5), p.1547-1558</ispartof><rights>Published in Soil Sci. Soc. Am. 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G.</creatorcontrib><creatorcontrib>Pierce, F. J.</creatorcontrib><creatorcontrib>Schabenberger, O.</creatorcontrib><creatorcontrib>Warncke, D. D.</creatorcontrib><title>Map Quality for Site‐Specific Fertility Management</title><title>Soil Science Society of America journal</title><description>The quality of soil fertility maps affects the efficacy of site‐specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using several strategies including grid‐point (30‐ and 100‐m regular grids), grid cell (100‐m cells), and a simulated soil map unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation‐exchange capacity (CEC)] data were predicted using ordinary kriging, inverse distance weighted (IDW), and nearest neighbor (NN) interpolations for the various data sets. Each resulting map was validated against an independent data (n = 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal (prediction efficiencies ≤48%) at high sample densities and poor at lower densities (i.e., 61‐ and 100‐m grids; prediction efficiencies <21%). The average optimal distance exponent at each scale of measurement was 1.5. The performance of kriging relative to IDW methods (with a distance exponent of 1.5) improved with increasing sampling intensity (i.e., IDW was superior to kriging for 100% of cases with the 100‐m grid, 79% of the cases with the 61.5‐m grid scale, and 67% of the cases with the 30‐m grid). Practically, there was little difference between these interpolation methods. Grid sampling with a 100‐m grid, grid cell sampling, and simulated soil map unit sampling yielded similar prediction efficiencies to those for the field average approach, all of which were generally poor.</description><subject>Agriculture</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>Experiments</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Generalities. Analysis and diagnosis methods</subject><subject>Soil fertility</subject><subject>Soil management</subject><subject>Soil quality</subject><subject>Soil science</subject><subject>Soil surveys, classification and mapping</subject><subject>Soil surveys, classification and mapping, soil genesis</subject><subject>Soil-plant relationships. Soil fertility. Fertilization. Amendments</subject><subject>Soils</subject><issn>0361-5995</issn><issn>1435-0661</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqNkM1Kw0AUhQdRsFZfwFUQxFXqTOYnmWUprT9URKLQ3XA7zsiUNIkzCdqdj-Az-iSmtiq4cnMv3Pudw-EgdEzwICFUnIcQYJFgTAaCc8JZ-rqDeoRRHmMhyC7qYSpIzKXk--gghEVHcolxD7EbqKO7FgrXrCJb-Sh3jfl4e89ro511OpoY37iv7w2U8GSWpmwO0Z6FIpij7e6jh8n4fnQZT28vrkbDaaypFLOYsUxSOefSJJrNqeDWAmDMWDpnqUks5daAYJSkloPIiDRUAqGPEhMtM25oH51tfGtfPbcmNGrpgjZFAaWp2qBSRilmQrKOPPlDLqrWl104lRCBecok6aBkA2lfheCNVbV3S_ArRbBa16h-alTfNXai060zBA2F9VBqF36VjGRd1rTjxhvuxRVm9Q9nlQ-vkzxfz-68vc7oJ24Ph3E</recordid><startdate>200109</startdate><enddate>200109</enddate><creator>Mueller, T. G.</creator><creator>Pierce, F. J.</creator><creator>Schabenberger, O.</creator><creator>Warncke, D. D.</creator><general>Soil Science Society</general><general>Soil Science Society of America</general><general>American Society of Agronomy</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>SOI</scope><scope>KR7</scope></search><sort><creationdate>200109</creationdate><title>Map Quality for Site‐Specific Fertility Management</title><author>Mueller, T. G. ; Pierce, F. J. ; Schabenberger, O. ; Warncke, D. D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396X-448939b59e2c4b365ffaa00447b47e2f35fea64317f5a6819e39a13d901c985e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Agriculture</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>Experiments</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agronomy. Plant production</topic><topic>Generalities. Analysis and diagnosis methods</topic><topic>Soil fertility</topic><topic>Soil management</topic><topic>Soil quality</topic><topic>Soil science</topic><topic>Soil surveys, classification and mapping</topic><topic>Soil surveys, classification and mapping, soil genesis</topic><topic>Soil-plant relationships. Soil fertility. Fertilization. Amendments</topic><topic>Soils</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mueller, T. G.</creatorcontrib><creatorcontrib>Pierce, F. J.</creatorcontrib><creatorcontrib>Schabenberger, O.</creatorcontrib><creatorcontrib>Warncke, D. 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D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Map Quality for Site‐Specific Fertility Management</atitle><jtitle>Soil Science Society of America journal</jtitle><date>2001-09</date><risdate>2001</risdate><volume>65</volume><issue>5</issue><spage>1547</spage><epage>1558</epage><pages>1547-1558</pages><issn>0361-5995</issn><eissn>1435-0661</eissn><coden>SSSJD4</coden><abstract>The quality of soil fertility maps affects the efficacy of site‐specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using several strategies including grid‐point (30‐ and 100‐m regular grids), grid cell (100‐m cells), and a simulated soil map unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation‐exchange capacity (CEC)] data were predicted using ordinary kriging, inverse distance weighted (IDW), and nearest neighbor (NN) interpolations for the various data sets. Each resulting map was validated against an independent data (n = 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal (prediction efficiencies ≤48%) at high sample densities and poor at lower densities (i.e., 61‐ and 100‐m grids; prediction efficiencies <21%). The average optimal distance exponent at each scale of measurement was 1.5. The performance of kriging relative to IDW methods (with a distance exponent of 1.5) improved with increasing sampling intensity (i.e., IDW was superior to kriging for 100% of cases with the 100‐m grid, 79% of the cases with the 61.5‐m grid scale, and 67% of the cases with the 30‐m grid). Practically, there was little difference between these interpolation methods. Grid sampling with a 100‐m grid, grid cell sampling, and simulated soil map unit sampling yielded similar prediction efficiencies to those for the field average approach, all of which were generally poor.</abstract><cop>Madison</cop><pub>Soil Science Society</pub><doi>10.2136/sssaj2001.6551547x</doi><tpages>12</tpages></addata></record> |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Agriculture Agronomy. Soil science and plant productions Biological and medical sciences Experiments Fundamental and applied biological sciences. Psychology General agronomy. Plant production Generalities. Analysis and diagnosis methods Soil fertility Soil management Soil quality Soil science Soil surveys, classification and mapping Soil surveys, classification and mapping, soil genesis Soil-plant relationships. Soil fertility. Fertilization. Amendments Soils |
| title | Map Quality for Site‐Specific Fertility Management |
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