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dc.contributor.authorLu, Yang
dc.contributor.authorChibarabada, Tendai P.
dc.contributor.authorZiliani, Matteo G.
dc.contributor.authorOnema, Jean Marie Kileshye
dc.contributor.authorMcCabe, Matthew
dc.contributor.authorSheffield, Justin
dc.date.accessioned2021-04-13T06:27:35Z
dc.date.available2021-04-13T06:27:35Z
dc.date.issued2021-04-02
dc.date.submitted2020-12-04
dc.identifier.citationLu, Y., Chibarabada, T. P., Ziliani, M. G., Onema, J.-M. K., McCabe, M. F., & Sheffield, J. (2021). Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management, 252, 106884. doi:10.1016/j.agwat.2021.106884
dc.identifier.issn1873-2283
dc.identifier.issn0378-3774
dc.identifier.doi10.1016/j.agwat.2021.106884
dc.identifier.urihttp://hdl.handle.net/10754/668709
dc.description.abstractParameter calibration is normally required prior to crop model simulation, which can be a time-consuming and data-intensive task. Meanwhile, the growth stages of different hybrids/cultivars of the same crop often show some similarities, which implies that phenological parameters calibrated for one hybrid/cultivar may be useful for the simulation of another. In this study, a data assimilation framework is proposed to reduce the requirement for parameter calibration for maize simulation using AquaCrop. The phenological parameters were uniformly scaled from previous research performed in a different location for a different maize hybrid, and other parameters were taken from default settings in the model documentation. To constrain simulation uncertainties, soil moisture and canopy cover observations were assimilated both separately and jointly in order to update model states. The methodology was tested across a rain-fed field in Nebraska for 6 growing seasons. The results suggested that the under-calibrated model with uniformly scaled phenological parameters captured the temporal dynamics of crop growth, but may lead to large estimation bias. Data assimilation effectively improved model performance, and the joint assimilation outperformed single-variable assimilation. When soil moisture and canopy cover were jointly assimilated, the overall yield estimates (RMSE = 1.24 t/ha, nRMSE = 11.48%, R2 = 0.695) were improved over the no-assimilation case (RMSE = 2.01 t/ha, nRMSE = 18.61%, R2 = 0.338). Sensitivity analyses suggested that the improvement was still evident with temporally sparse soil moisture observations and a small ensemble size. Further testing using observations within 90 days after planting demonstrated that the method was able to predict yield around 3 months before harvest (RMSE = 1.7 t/ha, nRMSE = 15.74%). This study indicated that maize yield can be estimated and predicted accurately by monitoring the soil moisture and canopy status, which has potential for regional applications using remote sensing data.
dc.description.sponsorshipThis work was partly funded through the ‘A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model’ project, supported by the King Abdullah University of Science and Technology, Grant number OSR-2017-CRG6, and through the ‘Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BRECcIA)’ project, which is supported by UK Research and Innovation as part of the Global Challenges Research Fund, Grant number NE/P021093/1. The authors thank David Scoby from University of Nebraska-Lincoln for providing data.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0378377421001499
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Agricultural Water Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Agricultural Water Management, [252, , (2021-04-02)] DOI: 10.1016/j.agwat.2021.106884 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleAssimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentDivision of Biological and Environmental Science and Engineering (BESE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
dc.contributor.departmentEarth System Observation and Modelling
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.identifier.journalAgricultural Water Management
dc.rights.embargodate2023-04-02
dc.eprint.versionPost-print
dc.contributor.institutionGeography and Environment, University of Southampton, Southampton, United Kingdom
dc.contributor.institutionWaternet, PO Box MP600, Mount Pleasant, Harare, Zimbabwe
dc.identifier.volume252
dc.identifier.pages106884
kaust.personZiliani, Matteo G.
kaust.personMcCabe, Matthew
kaust.grant.numberOSR-2017-CRG6
dc.date.accepted2021-03-22
dc.identifier.eid2-s2.0-85103636416
kaust.acknowledged.supportUnitOSR
dc.date.published-online2021-04-02
dc.date.published-print2021-06


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