guest ::
| Home > Publications database > Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison > print |
| Home > Publications database > Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison > print |
| 001 | 878466 | ||
| 005 | 20210304134018.0 | ||
| 024 | 7 | _ | |a 10.1002/vzj2.20058 |2 doi |
| 024 | 7 | _ | |a 2128/25603 |2 Handle |
| 024 | 7 | _ | |a altmetric:88110544 |2 altmetric |
| 024 | 7 | _ | |a WOS:000618773300053 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-02871 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Groh, Jannis |0 P:(DE-Juel1)158034 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison |
| 260 | _ | _ | |a Alexandria, Va. |c 2020 |b GeoScienceWorld |
| 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 1599584813_31238 |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 Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models. |
| 536 | _ | _ | |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) |0 G:(DE-HGF)POF3-255 |c POF3-255 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Diamantopoulos, Efstathios |0 0000-0001-7870-0291 |b 1 |
| 700 | 1 | _ | |a Duan, Xiaohong |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Ewert, Frank |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Herbst, Michael |0 P:(DE-Juel1)129469 |b 4 |
| 700 | 1 | _ | |a Holbak, Maja |0 0000-0001-5164-2327 |b 5 |
| 700 | 1 | _ | |a Kamali, Bahareh |0 0000-0002-8070-0175 |b 6 |
| 700 | 1 | _ | |a Kersebaum, Kurt‐Christian |0 0000-0002-3679-8427 |b 7 |
| 700 | 1 | _ | |a Kuhnert, Matthias |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Lischeid, Gunnar |0 0000-0003-3700-6062 |b 9 |
| 700 | 1 | _ | |a Nendel, Claas |0 0000-0001-7608-9097 |b 10 |
| 700 | 1 | _ | |a Priesack, Eckart |0 0000-0002-5088-9528 |b 11 |
| 700 | 1 | _ | |a Steidl, Jörg |0 0000-0002-6599-0450 |b 12 |
| 700 | 1 | _ | |a Sommer, Michael |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Pütz, Thomas |0 P:(DE-Juel1)129523 |b 14 |
| 700 | 1 | _ | |a Vereecken, Harry |0 P:(DE-Juel1)129549 |b 15 |
| 700 | 1 | _ | |a Wallor, Evelyn |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Weber, Tobias K. D. |0 0000-0002-3448-5208 |b 17 |
| 700 | 1 | _ | |a Wegehenkel, Martin |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Weihermüller, Lutz |0 P:(DE-Juel1)129553 |b 19 |
| 700 | 1 | _ | |a Gerke, Horst H. |0 0000-0002-6232-7688 |b 20 |
| 773 | _ | _ | |a 10.1002/vzj2.20058 |g Vol. 19, no. 1 |0 PERI:(DE-600)2088189-7 |n 1 |p e20058 |t Vadose zone journal |v 19 |y 2020 |x 1539-1663 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/878466/files/vzj2.20058.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/878466/files/vzj2.20058.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:878466 |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 0 |6 P:(DE-Juel1)158034 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)129469 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 14 |6 P:(DE-Juel1)129523 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 15 |6 P:(DE-Juel1)129549 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 19 |6 P:(DE-Juel1)129553 |
| 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 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-01-16 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b VADOSE ZONE J : 2018 |d 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2020-01-16 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |d 2020-01-16 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-01-16 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2020-01-16 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Blind peer review |d 2020-01-16 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |f 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1060 |2 StatID |b Current Contents - Agriculture, Biology and Environmental Sciences |d 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-01-16 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-01-16 |
| 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 |
|---|