000888036 001__ 888036
000888036 005__ 20230815122833.0
000888036 0247_ $$2doi$$a10.1016/j.still.2020.104611
000888036 0247_ $$2ISSN$$a0167-1987
000888036 0247_ $$2ISSN$$a1879-3444
000888036 0247_ $$2Handle$$a2128/26246
000888036 0247_ $$2WOS$$aWOS:000528029900008
000888036 037__ $$aFZJ-2020-04614
000888036 082__ $$a640
000888036 1001_ $$0P:(DE-HGF)0$$aMoraes, Moacir Tuzzin de$$b0$$eCorresponding author
000888036 245__ $$aSoil compaction impacts soybean root growth in an Oxisol from subtropical Brazil
000888036 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2020
000888036 3367_ $$2DRIVER$$aarticle
000888036 3367_ $$2DataCite$$aOutput Types/Journal article
000888036 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1605809084_29122
000888036 3367_ $$2BibTeX$$aARTICLE
000888036 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000888036 3367_ $$00$$2EndNote$$aJournal Article
000888036 520__ $$aSoil mechanical impedance, hypoxia and water stress are the main soil physical causes of reduced root growth, but they are rarely included in root growth models. The aim of this work was to study the impact of soil compaction on soybean root growth in an Oxisol using extensive field data as well as a mechanistic model that is sensitive to soil physical conditions. Soybean was cultivated under field conditions in a Rhodic Eutrodox in four treatments. The treatments consisted of three soil compaction levels (no-tillage system, areas trafficked by a tractor, and trafficked by a harvester) and soil chiselling management (performed in an area previously cultivated under no-tillage). Soil structural properties (soil penetration resistance, bulk density, total porosity, macroporosity and microporosity), root system parameters (root length density, root dry mass and root anatomy) and crop production components (grain yield, shoot dry biomass) were determined for the four treatments down to 50 cm soil depth. A mechanistic model, sensitive to mechanical and hydric stresses, was applied to simulate soybean root growth. The model was able to simulate the interaction between the soil physical conditions and soybean root growth. Soil compaction differentiated vertical root distribution according to a stress reduction function impeding root elongation. Consequently, root growth was influenced by soil physical conditions during the cropping season, and simulated root length density showed strong agreement to measured data. Soybean grain yield was reduced due to both compaction (caused by harvester traffic) and excessive loosening (promoted by chiselling) relative to the no-tillage system. Soil physical attributes (i.e., soil bulk density, penetration resistance, macroporosity and microporosity) were only weakly correlated with grain yield and root growth. This may be due to the fact that those soil physical attributes are static properties that do not represent the dynamics of mechanical and hydric stresses during the growing season. Soil compaction changed the anatomy, shape and size of roots. Moreover, cortex cells were deformed in the secondary root growth stage. In the compacted soil, mechanical impedance had a major effect on root growth, while in the loose soil, the matric potential (water stress) represented the major soil physical limitation to root growth. Soil chiselling increased the root length density, but it reduced the grain yields due water stress. The study showed that soybean root growth was successfully modelled with respect to soil physical conditions (mechanical impedance, hypoxia and water stress) for different compaction levels of a Rhodic Eutrudox.
000888036 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000888036 536__ $$0G:(GEPRIS)390732324$$aDFG project 390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion $$c390732324$$x1
000888036 588__ $$aDataset connected to CrossRef
000888036 7001_ $$0P:(DE-HGF)0$$aDebiasi, Henrique$$b1
000888036 7001_ $$0P:(DE-HGF)0$$aFranchini, Julio Cezar$$b2
000888036 7001_ $$0P:(DE-HGF)0$$aMastroberti, Alexandra Antunes$$b3
000888036 7001_ $$0P:(DE-HGF)0$$aLevien, Renato$$b4
000888036 7001_ $$0P:(DE-HGF)0$$aLeitner, Daniel$$b5
000888036 7001_ $$0P:(DE-Juel1)157922$$aSchnepf, Andrea$$b6
000888036 773__ $$0PERI:(DE-600)1498737-5$$a10.1016/j.still.2020.104611$$gVol. 200, p. 104611 -$$p104611 -$$tSoil & tillage research$$v200$$x0167-1987$$y2020
000888036 8564_ $$uhttps://juser.fz-juelich.de/record/888036/files/Moraes%20et%20al%202020%20Manuscript%20%28postprint%29.pdf$$yPublished on 2020-03-02. Available in OpenAccess from 2022-03-02.
000888036 909CO $$ooai:juser.fz-juelich.de:888036$$pdnbdelivery$$pVDB$$pVDB:Earth_Environment$$pdriver$$popen_access$$popenaire
000888036 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)157922$$aForschungszentrum Jülich$$b6$$kFZJ
000888036 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0
000888036 9141_ $$y2020
000888036 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-09-03
000888036 915__ $$0LIC:(DE-HGF)CCBYNCND4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
000888036 915__ $$0StatID:(DE-HGF)0530$$2StatID$$aEmbargoed OpenAccess
000888036 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bSOIL TILL RES : 2018$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-09-03
000888036 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2020-09-03$$wger
000888036 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-09-03
000888036 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000888036 980__ $$ajournal
000888036 980__ $$aVDB
000888036 980__ $$aUNRESTRICTED
000888036 980__ $$aI:(DE-Juel1)IBG-3-20101118
000888036 9801_ $$aFullTexts