Predicting soybean yield in a dry and wet year using a soil productivity index
A soil-based productivity index (PI) has been developed and is being tested as a means of quantitatively assessing potential soil productivity and predicting crop yield. Validation of the PI requires the PI-yield calibration for various soil-crop-climate systems. It is hypothesized that PI predictab...
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Veröffentlicht in: | Plant and soil 2003-03, Vol.250 (2), p.175-182 |
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description | A soil-based productivity index (PI) has been developed and is being tested as a means of quantitatively assessing potential soil productivity and predicting crop yield. Validation of the PI requires the PI-yield calibration for various soil-crop-climate systems. It is hypothesized that PI predictability and accuracy would be enhanced by inclusion of a soil water balance component. This study aims at developing a sufficiency factor that accounts for dynamics of soil water influenced by weather to improve the PI predictability. Soybeans (Glycine max [L.] Merr.) were grown in 1992 and 1993 on Mexico soil (fine, montmorillonitic, mesic Mollic Endoaqualfs). Test plots had altered A-horizon thicknesses of 0, 12.5, 25, and 37.5 cm over Bt horizons. A range of PI values in the plots resulted due to A-horizon treatment. The PI increased with increasing A-horizon thicknesses or depth to the Bt horizons. The PI was highly correlated with plot yield in 1992, a relatively dry year, in comparison with 1993, a relatively wet year. Inclusion of a factor assessed by daily balance of soil water significantly enhanced PI predictive power by 20% in both years. The factor best improved the PI predictability when based on the number of soil dry-wet cycles for given depth during the growing season. This study illustrates that yearly variation of soil water induced by weather should be considered for assessing crop performance based on soil properties. |
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Validation of the PI requires the PI-yield calibration for various soil-crop-climate systems. It is hypothesized that PI predictability and accuracy would be enhanced by inclusion of a soil water balance component. This study aims at developing a sufficiency factor that accounts for dynamics of soil water influenced by weather to improve the PI predictability. Soybeans (Glycine max [L.] Merr.) were grown in 1992 and 1993 on Mexico soil (fine, montmorillonitic, mesic Mollic Endoaqualfs). Test plots had altered A-horizon thicknesses of 0, 12.5, 25, and 37.5 cm over Bt horizons. A range of PI values in the plots resulted due to A-horizon treatment. The PI increased with increasing A-horizon thicknesses or depth to the Bt horizons. The PI was highly correlated with plot yield in 1992, a relatively dry year, in comparison with 1993, a relatively wet year. Inclusion of a factor assessed by daily balance of soil water significantly enhanced PI predictive power by 20% in both years. The factor best improved the PI predictability when based on the number of soil dry-wet cycles for given depth during the growing season. This study illustrates that yearly variation of soil water induced by weather should be considered for assessing crop performance based on soil properties.</description><identifier>ISSN: 0032-079X</identifier><identifier>EISSN: 1573-5036</identifier><identifier>DOI: 10.1023/A:1022801322245</identifier><identifier>CODEN: PLSOA2</identifier><language>eng</language><publisher>Dordrecht: Kluwer Academic Publishers</publisher><subject>Biological and medical sciences ; Clay soils ; Climate system ; Crop yield ; Fundamental and applied biological sciences. Psychology ; Growing season ; Moisture content ; Rain ; Soil depth ; Soil dynamics ; Soil horizons ; Soil productivity ; Soil properties ; Soil water ; Soil water balance ; Soils ; Soybeans ; Water balance ; Weather</subject><ispartof>Plant and soil, 2003-03, Vol.250 (2), p.175-182</ispartof><rights>2003 Kluwer Academic Publishers</rights><rights>2003 INIST-CNRS</rights><rights>Kluwer Academic Publishers 2003</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c278t-86a74ca30a6a141067289ecb8b1ae15a39c6f7331412322bda2d384e1719ed713</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/24129944$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/24129944$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,27924,27925,58017,58250</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14659732$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, J.</creatorcontrib><creatorcontrib>Hammer, R.D.</creatorcontrib><creatorcontrib>Thompson, A.L.</creatorcontrib><creatorcontrib>Blanchar, R.W.</creatorcontrib><title>Predicting soybean yield in a dry and wet year using a soil productivity index</title><title>Plant and soil</title><description>A soil-based productivity index (PI) has been developed and is being tested as a means of quantitatively assessing potential soil productivity and predicting crop yield. Validation of the PI requires the PI-yield calibration for various soil-crop-climate systems. It is hypothesized that PI predictability and accuracy would be enhanced by inclusion of a soil water balance component. This study aims at developing a sufficiency factor that accounts for dynamics of soil water influenced by weather to improve the PI predictability. Soybeans (Glycine max [L.] Merr.) were grown in 1992 and 1993 on Mexico soil (fine, montmorillonitic, mesic Mollic Endoaqualfs). Test plots had altered A-horizon thicknesses of 0, 12.5, 25, and 37.5 cm over Bt horizons. A range of PI values in the plots resulted due to A-horizon treatment. The PI increased with increasing A-horizon thicknesses or depth to the Bt horizons. The PI was highly correlated with plot yield in 1992, a relatively dry year, in comparison with 1993, a relatively wet year. Inclusion of a factor assessed by daily balance of soil water significantly enhanced PI predictive power by 20% in both years. The factor best improved the PI predictability when based on the number of soil dry-wet cycles for given depth during the growing season. This study illustrates that yearly variation of soil water induced by weather should be considered for assessing crop performance based on soil properties.</description><subject>Biological and medical sciences</subject><subject>Clay soils</subject><subject>Climate system</subject><subject>Crop yield</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Growing season</subject><subject>Moisture content</subject><subject>Rain</subject><subject>Soil depth</subject><subject>Soil dynamics</subject><subject>Soil horizons</subject><subject>Soil productivity</subject><subject>Soil properties</subject><subject>Soil water</subject><subject>Soil water balance</subject><subject>Soils</subject><subject>Soybeans</subject><subject>Water balance</subject><subject>Weather</subject><issn>0032-079X</issn><issn>1573-5036</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNo9z81LwzAUAPAgCs7p2ZMQBI_VvKTNh7cx5gcM9aDgrbw2mWTUdiat2v_ejA1Pj8f7vS9CzoFdA-PiZnabAtcMBOc8Lw7IBAolsoIJeUgmjAmeMWXej8lJjGu2zUFOyNNLcNbXvW8_aOzGymFLR-8aS31LkdowUmwt_XE9HR0GOsStxGR9Qzehs0Pq_fb9mLx1v6fkaIVNdGf7OCVvd4vX-UO2fL5_nM-WWc2V7jMtUeU1CoYSIQcmFdfG1ZWuAB0UKEwtV0qIVOPpncoit0LnDhQYZxWIKbnczU0nfA0u9uW6G0KbVpaqADBaFiahqz3CWGOzCtjWPpab4D8xjCXkCSnBk7vYuXXsu_Bf52m5MXku_gBspWVa</recordid><startdate>20030301</startdate><enddate>20030301</enddate><creator>Yang, J.</creator><creator>Hammer, R.D.</creator><creator>Thompson, A.L.</creator><creator>Blanchar, R.W.</creator><general>Kluwer Academic Publishers</general><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>3V.</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7X2</scope><scope>88A</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M0K</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>RC3</scope><scope>SOI</scope></search><sort><creationdate>20030301</creationdate><title>Predicting soybean yield in a dry and wet year using a soil productivity index</title><author>Yang, J. ; Hammer, R.D. ; Thompson, A.L. ; Blanchar, R.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c278t-86a74ca30a6a141067289ecb8b1ae15a39c6f7331412322bda2d384e1719ed713</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Biological and medical sciences</topic><topic>Clay soils</topic><topic>Climate system</topic><topic>Crop yield</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Growing season</topic><topic>Moisture content</topic><topic>Rain</topic><topic>Soil depth</topic><topic>Soil dynamics</topic><topic>Soil horizons</topic><topic>Soil productivity</topic><topic>Soil properties</topic><topic>Soil water</topic><topic>Soil water balance</topic><topic>Soils</topic><topic>Soybeans</topic><topic>Water balance</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, J.</creatorcontrib><creatorcontrib>Hammer, R.D.</creatorcontrib><creatorcontrib>Thompson, A.L.</creatorcontrib><creatorcontrib>Blanchar, R.W.</creatorcontrib><collection>Pascal-Francis</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Agricultural Science Collection</collection><collection>Biology Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Plant and soil</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, J.</au><au>Hammer, R.D.</au><au>Thompson, A.L.</au><au>Blanchar, R.W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting soybean yield in a dry and wet year using a soil productivity index</atitle><jtitle>Plant and soil</jtitle><date>2003-03-01</date><risdate>2003</risdate><volume>250</volume><issue>2</issue><spage>175</spage><epage>182</epage><pages>175-182</pages><issn>0032-079X</issn><eissn>1573-5036</eissn><coden>PLSOA2</coden><abstract>A soil-based productivity index (PI) has been developed and is being tested as a means of quantitatively assessing potential soil productivity and predicting crop yield. Validation of the PI requires the PI-yield calibration for various soil-crop-climate systems. It is hypothesized that PI predictability and accuracy would be enhanced by inclusion of a soil water balance component. This study aims at developing a sufficiency factor that accounts for dynamics of soil water influenced by weather to improve the PI predictability. Soybeans (Glycine max [L.] Merr.) were grown in 1992 and 1993 on Mexico soil (fine, montmorillonitic, mesic Mollic Endoaqualfs). Test plots had altered A-horizon thicknesses of 0, 12.5, 25, and 37.5 cm over Bt horizons. A range of PI values in the plots resulted due to A-horizon treatment. The PI increased with increasing A-horizon thicknesses or depth to the Bt horizons. The PI was highly correlated with plot yield in 1992, a relatively dry year, in comparison with 1993, a relatively wet year. Inclusion of a factor assessed by daily balance of soil water significantly enhanced PI predictive power by 20% in both years. The factor best improved the PI predictability when based on the number of soil dry-wet cycles for given depth during the growing season. This study illustrates that yearly variation of soil water induced by weather should be considered for assessing crop performance based on soil properties.</abstract><cop>Dordrecht</cop><pub>Kluwer Academic Publishers</pub><doi>10.1023/A:1022801322245</doi><tpages>8</tpages></addata></record> |
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subjects | Biological and medical sciences Clay soils Climate system Crop yield Fundamental and applied biological sciences. Psychology Growing season Moisture content Rain Soil depth Soil dynamics Soil horizons Soil productivity Soil properties Soil water Soil water balance Soils Soybeans Water balance Weather |
title | Predicting soybean yield in a dry and wet year using a soil productivity index |
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