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Options for calibrating ceres-maize genotype specific parameters under data-scarce environments

dc.contributor.authorAdnan, A.A.
dc.contributor.authorDiels, J.
dc.contributor.authorJibrin, J.M.
dc.contributor.authorKamara, A.
dc.contributor.authorCraufurd, Peter Q.
dc.contributor.authorShaibu, A.S.
dc.contributor.authorMohammed, B.
dc.contributor.authorTonnang, Z.E.H.
dc.date.accessioned2019-12-04T11:33:52Z
dc.date.available2019-12-04T11:33:52Z
dc.date.issued2019-02-19
dc.identifier.citationAdnan, A.A., Diels, J., Jibrin, J.M., Kamara, A., Craufurd, P., Shaibu, A.S., ... & Tonnang, Z.E.H. (2019). Options for calibrating ceres-maize genotype specific parameters under data-scarce environments. PLOS ONE, 14(2), 1-20.
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/20.500.12478/5833
dc.descriptionOpen Access Journal
dc.description.abstractMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.
dc.format.extent1-20
dc.language.isoen
dc.rightsCC-BY-4.0
dc.subjectMaize
dc.subjectLeaves
dc.subjectStems
dc.subjectCalibration
dc.subjectExperiments
dc.subjectAgricultural Research
dc.titleOptions for calibrating ceres-maize genotype specific parameters under data-scarce environments
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.crpMaize
cg.contributor.affiliationBayero University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.creator.identifierAlpha Kamara: 0000-0002-1844-2574
cg.creator.identifierIbrahim Mohammed: 0000-0001-5199-5528
cg.researchthemeNATURAL RESOURCE MANAGEMENT
cg.researchthemePLANT PRODUCTION & HEALTH
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectMaize
cg.iitasubjectNatural Resource Management
cg.iitasubjectPlant Health
cg.iitasubjectPlant Production
cg.journalPLOS ONE
cg.howpublishedFormally Published
cg.accessibilitystatusOpen Access
local.dspaceid105361
cg.identifier.doihttps://dx.doi.org/10.1371/journal.pone.0200118


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