Gast ::
| Hauptseite > Publikationsdatenbank > Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil > print |
| Hauptseite > Publikationsdatenbank > Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil > print |
| 001 | 888036 | ||
| 005 | 20230815122833.0 | ||
| 024 | 7 | _ | |a 10.1016/j.still.2020.104611 |2 doi |
| 024 | 7 | _ | |a 0167-1987 |2 ISSN |
| 024 | 7 | _ | |a 1879-3444 |2 ISSN |
| 024 | 7 | _ | |a 2128/26246 |2 Handle |
| 024 | 7 | _ | |a WOS:000528029900008 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-04614 |
| 082 | _ | _ | |a 640 |
| 100 | 1 | _ | |a Moraes, Moacir Tuzzin de |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2020 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1605809084_29122 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Soil 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. |
| 536 | _ | _ | |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) |0 G:(DE-HGF)POF3-255 |c POF3-255 |x 0 |f POF III |
| 536 | _ | _ | |a DFG project 390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion |0 G:(GEPRIS)390732324 |c 390732324 |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Debiasi, Henrique |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Franchini, Julio Cezar |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Mastroberti, Alexandra Antunes |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Levien, Renato |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Leitner, Daniel |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Schnepf, Andrea |0 P:(DE-Juel1)157922 |b 6 |
| 773 | _ | _ | |a 10.1016/j.still.2020.104611 |g Vol. 200, p. 104611 - |0 PERI:(DE-600)1498737-5 |p 104611 - |t Soil & tillage research |v 200 |y 2020 |x 0167-1987 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/888036/files/Moraes%20et%20al%202020%20Manuscript%20%28postprint%29.pdf |y Published on 2020-03-02. Available in OpenAccess from 2022-03-02. |
| 909 | C | O | |o oai:juser.fz-juelich.de:888036 |p openaire |p open_access |p driver |p VDB:Earth_Environment |p VDB |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)157922 |
| 913 | 1 | _ | |a DE-HGF |l Terrestrische Umwelt |1 G:(DE-HGF)POF3-250 |0 G:(DE-HGF)POF3-255 |2 G:(DE-HGF)POF3-200 |v Terrestrial Systems: From Observation to Prediction |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |b Erde und Umwelt |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2020-09-03 |
| 915 | _ | _ | |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 |0 LIC:(DE-HGF)CCBYNCND4 |2 HGFVOC |
| 915 | _ | _ | |a Embargoed OpenAccess |0 StatID:(DE-HGF)0530 |2 StatID |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b SOIL TILL RES : 2018 |d 2020-09-03 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-09-03 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2020-09-03 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1060 |2 StatID |b Current Contents - Agriculture, Biology and Environmental Sciences |d 2020-09-03 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-09-03 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2020-09-03 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-09-03 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IBG-3-20101118 |k IBG-3 |l Agrosphäre |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IBG-3-20101118 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|