[Soil amelioration of different vegetation types in saline-alkali land of the Yellow River Delta, China].
Identifieur interne : 000147 ( Main/Corpus ); précédent : 000146; suivant : 000148[Soil amelioration of different vegetation types in saline-alkali land of the Yellow River Delta, China].
Auteurs : Jia Sun ; Jiang-Bao Xia ; Li Su ; Xi-Mei Zhao ; Yin-Ping Chen ; Xi-Yuan Yue ; Chuan-Rong LiSource :
- Ying yong sheng tai xue bao = The journal of applied ecology [ 1001-9332 ] ; 2020.
English descriptors
- KwdEn :
- MESH :
- chemical : Alkalies, Soil.
- geographic : China.
- Rivers, Tamaricaceae.
Abstract
Yellow River Delta is an important distribution area of coastal saline-alkali land in China. Revegetation is the main technology for ecological restoration during saline-alkali land amelioration. To explore the effects of different vegetation types on soil improvement in saline-alkali land and get the suitable model in the Yellow River Delta, four tree-grass compound models, Salix americana+Distichlis spicata, S. matsudana+D. spicata, Tamarix chinensis+Medicago sativa, and Fraxinus chinensis+T. chinensis+M. sativa, were set up, with pure S. americana forest as the control. Twenty indicators, including soil moisture physical parameters, saline-alkali content, soil nutrient contents, and microorganism quantity etc. were measured. Principal component analysis, cluster analysis and fuzzy mathematics were used to evaluate soil modification effect of different vegetation combinations. The results showed that all compound models significantly improved soil physical and che-mical properties in coastal saline-alkali land by increasing soil porosity, soil water storage, soil organic matter content, available nutrient content and soil microorganism quantity and reducing soil density. Among all the models, the tree-shrub-grass mixed model of F. chinensis+T. chinensis+M. sativa was the most effective in inhibiting salt and alkali stress and increasing soil nutrients and microorganism abundance, whereas the tree-grass mixed model of S. matsudana+D. spicata was the most effective in improving soil water physical properties. The combined effects of different vegetation patterns on soil amelioration in coastal saline-alkali land of the Yellow River Delta were arranged in order of F. chinensis+T. chinensis+M. sativa> S. matsudana+D. spicata> S. americana+D. spicata> T. chinensis+M. sativa.
DOI: 10.13287/j.1001-9332.202004.032
PubMed: 32530208
Links to Exploration step
pubmed:32530208Le document en format XML
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Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Su, Li" sort="Su, Li" uniqKey="Su L" first="Li" last="Su">Li Su</name><affiliation><nlm:affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Xi Mei" sort="Zhao, Xi Mei" uniqKey="Zhao X" first="Xi-Mei" last="Zhao">Xi-Mei Zhao</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Chen, Yin Ping" sort="Chen, Yin Ping" uniqKey="Chen Y" first="Yin-Ping" last="Chen">Yin-Ping Chen</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Yue, Xi Yuan" sort="Yue, Xi Yuan" uniqKey="Yue X" first="Xi-Yuan" last="Yue">Xi-Yuan Yue</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Chuan Rong" sort="Li, Chuan Rong" uniqKey="Li C" first="Chuan-Rong" last="Li">Chuan-Rong Li</name><affiliation><nlm:affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32530208</idno><idno type="pmid">32530208</idno><idno type="doi">10.13287/j.1001-9332.202004.032</idno><idno type="wicri:Area/Main/Corpus">000147</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000147</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">[Soil amelioration of different vegetation types in saline-alkali land of the Yellow River Delta, China].</title><author><name sortKey="Sun, Jia" sort="Sun, Jia" uniqKey="Sun J" first="Jia" last="Sun">Jia Sun</name><affiliation><nlm:affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Xia, Jiang Bao" sort="Xia, Jiang Bao" uniqKey="Xia J" first="Jiang-Bao" last="Xia">Jiang-Bao Xia</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Su, Li" sort="Su, Li" uniqKey="Su L" first="Li" last="Su">Li Su</name><affiliation><nlm:affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Xi Mei" sort="Zhao, Xi Mei" uniqKey="Zhao X" first="Xi-Mei" last="Zhao">Xi-Mei Zhao</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Chen, Yin Ping" sort="Chen, Yin Ping" uniqKey="Chen Y" first="Yin-Ping" last="Chen">Yin-Ping Chen</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Yue, Xi Yuan" sort="Yue, Xi Yuan" uniqKey="Yue X" first="Xi-Yuan" last="Yue">Xi-Yuan Yue</name><affiliation><nlm:affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Chuan Rong" sort="Li, Chuan Rong" uniqKey="Li C" first="Chuan-Rong" last="Li">Chuan-Rong Li</name><affiliation><nlm:affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Ying yong sheng tai xue bao = The journal of applied ecology</title><idno type="ISSN">1001-9332</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Alkalies (MeSH)</term><term>China (MeSH)</term><term>Rivers (MeSH)</term><term>Soil (MeSH)</term><term>Tamaricaceae (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Alkalies</term><term>Soil</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Rivers</term><term>Tamaricaceae</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Yellow River Delta is an important distribution area of coastal saline-alkali land in China. Revegetation is the main technology for ecological restoration during saline-alkali land amelioration. To explore the effects of different vegetation types on soil improvement in saline-alkali land and get the suitable model in the Yellow River Delta, four tree-grass compound models, Salix americana+Distichlis spicata, S. matsudana+D. spicata, Tamarix chinensis+Medicago sativa, and Fraxinus chinensis+T. chinensis+M. sativa, were set up, with pure S. americana forest as the control. Twenty indicators, including soil moisture physical parameters, saline-alkali content, soil nutrient contents, and microorganism quantity etc. were measured. Principal component analysis, cluster analysis and fuzzy mathematics were used to evaluate soil modification effect of different vegetation combinations. The results showed that all compound models significantly improved soil physical and che-mical properties in coastal saline-alkali land by increasing soil porosity, soil water storage, soil organic matter content, available nutrient content and soil microorganism quantity and reducing soil density. Among all the models, the tree-shrub-grass mixed model of F. chinensis+T. chinensis+M. sativa was the most effective in inhibiting salt and alkali stress and increasing soil nutrients and microorganism abundance, whereas the tree-grass mixed model of S. matsudana+D. spicata was the most effective in improving soil water physical properties. The combined effects of different vegetation patterns on soil amelioration in coastal saline-alkali land of the Yellow River Delta were arranged in order of F. chinensis+T. chinensis+M. sativa> S. matsudana+D. spicata> S. americana+D. spicata> T. chinensis+M. sativa.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">32530208</PMID><DateCompleted><Year>2020</Year><Month>06</Month><Day>15</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>15</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1001-9332</ISSN><JournalIssue CitedMedium="Print"><Volume>31</Volume><Issue>4</Issue><PubDate><Year>2020</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Ying yong sheng tai xue bao = The journal of applied ecology</Title><ISOAbbreviation>Ying Yong Sheng Tai Xue Bao</ISOAbbreviation></Journal><ArticleTitle>[Soil amelioration of different vegetation types in saline-alkali land of the Yellow River Delta, China].</ArticleTitle><Pagination><MedlinePgn>1323-1332</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.13287/j.1001-9332.202004.032</ELocationID><Abstract><AbstractText>Yellow River Delta is an important distribution area of coastal saline-alkali land in China. Revegetation is the main technology for ecological restoration during saline-alkali land amelioration. To explore the effects of different vegetation types on soil improvement in saline-alkali land and get the suitable model in the Yellow River Delta, four tree-grass compound models, Salix americana+Distichlis spicata, S. matsudana+D. spicata, Tamarix chinensis+Medicago sativa, and Fraxinus chinensis+T. chinensis+M. sativa, were set up, with pure S. americana forest as the control. Twenty indicators, including soil moisture physical parameters, saline-alkali content, soil nutrient contents, and microorganism quantity etc. were measured. Principal component analysis, cluster analysis and fuzzy mathematics were used to evaluate soil modification effect of different vegetation combinations. The results showed that all compound models significantly improved soil physical and che-mical properties in coastal saline-alkali land by increasing soil porosity, soil water storage, soil organic matter content, available nutrient content and soil microorganism quantity and reducing soil density. Among all the models, the tree-shrub-grass mixed model of F. chinensis+T. chinensis+M. sativa was the most effective in inhibiting salt and alkali stress and increasing soil nutrients and microorganism abundance, whereas the tree-grass mixed model of S. matsudana+D. spicata was the most effective in improving soil water physical properties. The combined effects of different vegetation patterns on soil amelioration in coastal saline-alkali land of the Yellow River Delta were arranged in order of F. chinensis+T. chinensis+M. sativa> S. matsudana+D. spicata> S. americana+D. spicata> T. chinensis+M. sativa.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Jia</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xia</LastName><ForeName>Jiang-Bao</ForeName><Initials>JB</Initials><AffiliationInfo><Affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Su</LastName><ForeName>Li</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Xi-Mei</ForeName><Initials>XM</Initials><AffiliationInfo><Affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Yin-Ping</ForeName><Initials>YP</Initials><AffiliationInfo><Affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yue</LastName><ForeName>Xi-Yuan</ForeName><Initials>XY</Initials><AffiliationInfo><Affiliation>Binzhou University/Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou 256603, Shandong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Chuan-Rong</ForeName><Initials>CR</Initials><AffiliationInfo><Affiliation>College of Forestry, Shandong Agricultural University, Taishan Forest Ecosystem Research Station/Key Laboratory of National Forestry and Grassland Administration for Silviculture of the Lower Yellow River, Tai'an 271018, Shandong, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><VernacularTitle>黄河三角洲盐碱地不同植被模式的土壤改良效应.</VernacularTitle></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Ying Yong Sheng Tai Xue Bao</MedlineTA><NlmUniqueID>9425159</NlmUniqueID><ISSNLinking>1001-9332</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000468">Alkalies</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012987">Soil</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000468" MajorTopicYN="N">Alkalies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045483" MajorTopicYN="N">Rivers</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012987" MajorTopicYN="Y">Soil</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032363" MajorTopicYN="Y">Tamaricaceae</DescriptorName></MeshHeading></MeshHeadingList><OtherAbstract Type="Publisher" Language="chi"><AbstractText>黄河三角洲是我国滨海盐碱地的重要分布区,种植植被是盐碱地绿色改良的主要生态修复措施。为探讨滨海盐碱地不同植被模式的土壤改良效应,探索适宜植被模式,选取黄河三角洲盐碱地竹柳+NyPa草、旱柳+NyPa草、柽柳+紫花苜蓿、白蜡+柽柳+紫花苜蓿4种林草措施为研究对象,以纯竹柳为对照,测定土壤水分物理参数、盐碱含量、土壤养分及微生物数量等20个指标,并利用主成分分析、聚类分析和模糊数学隶属函数等统计方法评价了不同植被模式的土壤改良效应。结果表明: 林草复合模式可显著改善滨海盐碱地的土壤理化性能,增加土壤孔隙度和贮水量,降低土壤密度,提高土壤有机质、速效养分含量和土壤微生物数量。其中,白蜡+柽柳+紫花苜蓿的乔灌草混交模式在压碱抑盐、增加土壤养分和微生物数量的效果最好,而旱柳+NyPa草的乔草混交模式改良土壤水分物理性能的效果最好。不同植被模式对黄河三角洲滨海盐碱地的综合改良效应表现为白蜡+柽柳+紫花苜蓿>旱柳+NyPa草>竹柳+NyPa草>柽柳+紫花苜蓿。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">forest and grass measure</Keyword><Keyword MajorTopicYN="N">soil microorganism</Keyword><Keyword MajorTopicYN="N">soil nutrient</Keyword><Keyword MajorTopicYN="N">soil porosity</Keyword><Keyword MajorTopicYN="N">soil salt content</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32530208</ArticleId><ArticleId IdType="doi">10.13287/j.1001-9332.202004.032</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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