Gast ::
| Hauptseite > Publikationsdatenbank > How well do crop modeling groups predict wheat phenology, given calibration data from the target population? > print |
| Hauptseite > Publikationsdatenbank > How well do crop modeling groups predict wheat phenology, given calibration data from the target population? > print |
| 001 | 889706 | ||
| 005 | 20211025141311.0 | ||
| 024 | 7 | _ | |a 10.1016/j.eja.2020.126195 |2 doi |
| 024 | 7 | _ | |a 1161-0301 |2 ISSN |
| 024 | 7 | _ | |a 1873-7331 |2 ISSN |
| 024 | 7 | _ | |a 2128/26800 |2 Handle |
| 024 | 7 | _ | |a WOS:000620810400008 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-00328 |
| 082 | _ | _ | |a 640 |
| 100 | 1 | _ | |a Wallach, Daniel |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a How well do crop modeling groups predict wheat phenology, given calibration data from the target population? |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2021 |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 1635160040_2772 |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 Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population. |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Palosuo, Taru |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Thorburn, Peter |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Gourdain, Emmanuelle |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Asseng, Senthold |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Basso, Bruno |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Buis, Samuel |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Crout, Neil |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Dibari, Camilla |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Dumont, Benjamin |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Ferrise, Roberto |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Gaiser, Thomas |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Garcia, Cécile |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Gayler, Sebastian |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Ghahramani, Afshin |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Hochman, Zvi |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Hoek, Steven |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Hoogenboom, Gerrit |0 P:(DE-HGF)0 |b 17 |
| 700 | 1 | _ | |a Horan, Heidi |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Huang, Mingxia |0 P:(DE-HGF)0 |b 19 |
| 700 | 1 | _ | |a Jabloun, Mohamed |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Jing, Qi |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Justes, Eric |0 P:(DE-HGF)0 |b 22 |
| 700 | 1 | _ | |a Kersebaum, Kurt Christian |0 P:(DE-HGF)0 |b 23 |
| 700 | 1 | _ | |a Klosterhalfen, Anne |0 P:(DE-Juel1)159313 |b 24 |
| 700 | 1 | _ | |a Launay, Marie |0 P:(DE-HGF)0 |b 25 |
| 700 | 1 | _ | |a Luo, Qunying |0 P:(DE-HGF)0 |b 26 |
| 700 | 1 | _ | |a Maestrini, Bernardo |0 P:(DE-HGF)0 |b 27 |
| 700 | 1 | _ | |a Mielenz, Henrike |0 P:(DE-HGF)0 |b 28 |
| 700 | 1 | _ | |a Moriondo, Marco |0 P:(DE-HGF)0 |b 29 |
| 700 | 1 | _ | |a Nariman Zadeh, Hasti |0 P:(DE-HGF)0 |b 30 |
| 700 | 1 | _ | |a Olesen, Jørgen Eivind |0 P:(DE-HGF)0 |b 31 |
| 700 | 1 | _ | |a Poyda, Arne |0 P:(DE-HGF)0 |b 32 |
| 700 | 1 | _ | |a Priesack, Eckart |0 P:(DE-HGF)0 |b 33 |
| 700 | 1 | _ | |a Pullens, Johannes Wilhelmus Maria |0 P:(DE-HGF)0 |b 34 |
| 700 | 1 | _ | |a Qian, Budong |0 P:(DE-HGF)0 |b 35 |
| 700 | 1 | _ | |a Schütze, Niels |0 P:(DE-HGF)0 |b 36 |
| 700 | 1 | _ | |a Shelia, Vakhtang |0 P:(DE-HGF)0 |b 37 |
| 700 | 1 | _ | |a Souissi, Amir |0 P:(DE-HGF)0 |b 38 |
| 700 | 1 | _ | |a Specka, Xenia |0 P:(DE-HGF)0 |b 39 |
| 700 | 1 | _ | |a Srivastava, Amit Kumar |0 P:(DE-HGF)0 |b 40 |
| 700 | 1 | _ | |a Stella, Tommaso |0 P:(DE-HGF)0 |b 41 |
| 700 | 1 | _ | |a Streck, Thilo |0 P:(DE-HGF)0 |b 42 |
| 700 | 1 | _ | |a Trombi, Giacomo |0 P:(DE-HGF)0 |b 43 |
| 700 | 1 | _ | |a Wallor, Evelyn |0 P:(DE-HGF)0 |b 44 |
| 700 | 1 | _ | |a Wang, Jing |0 P:(DE-HGF)0 |b 45 |
| 700 | 1 | _ | |a Weber, Tobias K. D. |0 P:(DE-HGF)0 |b 46 |
| 700 | 1 | _ | |a Weihermüller, Lutz |0 P:(DE-Juel1)129553 |b 47 |
| 700 | 1 | _ | |a de Wit, Allard |0 P:(DE-HGF)0 |b 48 |
| 700 | 1 | _ | |a Wöhling, Thomas |0 P:(DE-HGF)0 |b 49 |
| 700 | 1 | _ | |a Xiao, Liujun |0 P:(DE-HGF)0 |b 50 |
| 700 | 1 | _ | |a Zhao, Chuang |0 P:(DE-HGF)0 |b 51 |
| 700 | 1 | _ | |a Zhu, Yan |0 P:(DE-Juel1)156394 |b 52 |
| 700 | 1 | _ | |a Seidel, Sabine J. |0 P:(DE-HGF)0 |b 53 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.eja.2020.126195 |g Vol. 124, p. 126195 - |0 PERI:(DE-600)2016158-X |p 126195 - |t European journal of agronomy |v 124 |y 2021 |x 1161-0301 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/889706/files/accepted.pdf |y Published on 2021-01-14. Available in OpenAccess from 2022-07-14. |
| 909 | C | O | |o oai:juser.fz-juelich.de:889706 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 47 |6 P:(DE-Juel1)129553 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l Erde im Wandel – Unsere Zukunft nachhaltig gestalten |1 G:(DE-HGF)POF4-210 |0 G:(DE-HGF)POF4-217 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten |9 G:(DE-HGF)POF4-2173 |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-08-31 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-08-31 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-08-31 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-08-31 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2020-08-31 |
| 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 DBCoverage |0 StatID:(DE-HGF)1060 |2 StatID |b Current Contents - Agriculture, Biology and Environmental Sciences |d 2020-08-31 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2020-08-31 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2020-08-31 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2020-08-31 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b EUR J AGRON : 2018 |d 2020-08-31 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-08-31 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-08-31 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2020-08-31 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-08-31 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IBG-3-20101118 |k IBG-3 |l Agrosphäre |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IBG-3-20101118 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|