000889347 001__ 889347
000889347 005__ 20211025141311.0
000889347 0247_ $$2doi$$a10.1016/j.agrformet.2020.108289
000889347 0247_ $$2ISSN$$a0168-1923
000889347 0247_ $$2ISSN$$a1873-2240
000889347 0247_ $$2Handle$$a2128/26785
000889347 0247_ $$2altmetric$$aaltmetric:97518502
000889347 0247_ $$2WOS$$aWOS:000610797100011
000889347 037__ $$aFZJ-2021-00236
000889347 082__ $$a550
000889347 1001_ $$0P:(DE-HGF)0$$aWallach, Daniel$$b0
000889347 245__ $$aMulti-model evaluation of phenology prediction for wheat in Australia
000889347 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2021
000889347 3367_ $$2DRIVER$$aarticle
000889347 3367_ $$2DataCite$$aOutput Types/Journal article
000889347 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1635151257_28701
000889347 3367_ $$2BibTeX$$aARTICLE
000889347 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000889347 3367_ $$00$$2EndNote$$aJournal Article
000889347 520__ $$aPredicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
000889347 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000889347 588__ $$aDataset connected to CrossRef
000889347 7001_ $$0P:(DE-HGF)0$$aPalosuo, Taru$$b1$$eCorresponding author
000889347 7001_ $$0P:(DE-HGF)0$$aThorburn, Peter$$b2
000889347 7001_ $$0P:(DE-HGF)0$$aHochman, Zvi$$b3
000889347 7001_ $$0P:(DE-HGF)0$$aAndrianasolo, Fety$$b4
000889347 7001_ $$0P:(DE-HGF)0$$aAsseng, Senthold$$b5
000889347 7001_ $$0P:(DE-HGF)0$$aBasso, Bruno$$b6
000889347 7001_ $$0P:(DE-HGF)0$$aBuis, Samuel$$b7
000889347 7001_ $$0P:(DE-HGF)0$$aCrout, Neil$$b8
000889347 7001_ $$0P:(DE-HGF)0$$aDumont, Benjamin$$b9
000889347 7001_ $$0P:(DE-HGF)0$$aFerrise, Roberto$$b10
000889347 7001_ $$0P:(DE-HGF)0$$aGaiser, Thomas$$b11
000889347 7001_ $$0P:(DE-HGF)0$$aGayler, Sebastian$$b12
000889347 7001_ $$0P:(DE-HGF)0$$aHiremath, Santosh$$b13
000889347 7001_ $$0P:(DE-HGF)0$$aHoek, Steven$$b14
000889347 7001_ $$0P:(DE-HGF)0$$aHoran, Heidi$$b15
000889347 7001_ $$0P:(DE-HGF)0$$aHoogenboom, Gerrit$$b16
000889347 7001_ $$0P:(DE-HGF)0$$aHuang, Mingxia$$b17
000889347 7001_ $$0P:(DE-HGF)0$$aJabloun, Mohamed$$b18
000889347 7001_ $$0P:(DE-HGF)0$$aJansson, Per-Erik$$b19
000889347 7001_ $$0P:(DE-HGF)0$$aJing, Qi$$b20
000889347 7001_ $$0P:(DE-HGF)0$$aJustes, Eric$$b21
000889347 7001_ $$0P:(DE-HGF)0$$aKersebaum, Kurt Christian$$b22
000889347 7001_ $$0P:(DE-HGF)0$$aLaunay, Marie$$b23
000889347 7001_ $$0P:(DE-HGF)0$$aLewan, Elisabet$$b24
000889347 7001_ $$0P:(DE-HGF)0$$aLuo, Qunying$$b25
000889347 7001_ $$0P:(DE-HGF)0$$aMaestrini, Bernardo$$b26
000889347 7001_ $$0P:(DE-HGF)0$$aMoriondo, Marco$$b27
000889347 7001_ $$0P:(DE-HGF)0$$aOlesen, Jørgen Eivind$$b28
000889347 7001_ $$0P:(DE-HGF)0$$aPadovan, Gloria$$b29
000889347 7001_ $$0P:(DE-HGF)0$$aPoyda, Arne$$b30
000889347 7001_ $$0P:(DE-HGF)0$$aPriesack, Eckart$$b31
000889347 7001_ $$0P:(DE-HGF)0$$aPullens, Johannes Wilhelmus Maria$$b32
000889347 7001_ $$0P:(DE-HGF)0$$aQian, Budong$$b33
000889347 7001_ $$0P:(DE-HGF)0$$aSchütze, Niels$$b34
000889347 7001_ $$0P:(DE-HGF)0$$aShelia, Vakhtang$$b35
000889347 7001_ $$0P:(DE-HGF)0$$aSouissi, Amir$$b36
000889347 7001_ $$0P:(DE-HGF)0$$aSpecka, Xenia$$b37
000889347 7001_ $$0P:(DE-HGF)0$$aKumar Srivastava, Amit$$b38
000889347 7001_ $$0P:(DE-HGF)0$$aStella, Tommaso$$b39
000889347 7001_ $$0P:(DE-HGF)0$$aStreck, Thilo$$b40
000889347 7001_ $$0P:(DE-HGF)0$$aTrombi, Giacomo$$b41
000889347 7001_ $$0P:(DE-HGF)0$$aWallor, Evelyn$$b42
000889347 7001_ $$0P:(DE-HGF)0$$aWang, Jing$$b43
000889347 7001_ $$0P:(DE-HGF)0$$aWeber, Tobias K. D.$$b44
000889347 7001_ $$0P:(DE-Juel1)129553$$aWeihermüller, Lutz$$b45
000889347 7001_ $$0P:(DE-HGF)0$$ade Wit, Allard$$b46
000889347 7001_ $$0P:(DE-HGF)0$$aWöhling, Thomas$$b47
000889347 7001_ $$0P:(DE-HGF)0$$aXiao, Liujun$$b48
000889347 7001_ $$0P:(DE-HGF)0$$aZhao, Chuang$$b49
000889347 7001_ $$0P:(DE-Juel1)156394$$aZhu, Yan$$b50
000889347 7001_ $$0P:(DE-HGF)0$$aSeidel, Sabine J$$b51
000889347 773__ $$0PERI:(DE-600)2012165-9$$a10.1016/j.agrformet.2020.108289$$gVol. 298-299, p. 108289 -$$p108289 -$$tAgricultural and forest meteorology$$v298-299$$x0168-1923$$y2021
000889347 8564_ $$uhttps://juser.fz-juelich.de/record/889347/files/BioRXiv_2020_708578v3.full.pdf$$yOpenAccess
000889347 909CO $$ooai:juser.fz-juelich.de:889347$$popenaire$$popen_access$$pVDB$$pdriver$$pdnbdelivery
000889347 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129553$$aForschungszentrum Jülich$$b45$$kFZJ
000889347 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
000889347 9141_ $$y2021
000889347 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bAGR FOREST METEOROL : 2018$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000889347 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-09-04
000889347 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2020-09-04$$wger
000889347 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-09-04
000889347 920__ $$lyes
000889347 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000889347 980__ $$ajournal
000889347 980__ $$aVDB
000889347 980__ $$aI:(DE-Juel1)IBG-3-20101118
000889347 980__ $$aUNRESTRICTED
000889347 9801_ $$aFullTexts