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CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria

dc.contributor.authorAdnan, A.A.
dc.contributor.authorDiels, J.
dc.contributor.authorJibrin, J.M.
dc.contributor.authorKamara, A.Y.
dc.contributor.authorShaibu, A.S.
dc.contributor.authorCraufurd, P.
dc.contributor.authorMenkir, A.
dc.date.accessioned2022-04-22T09:58:47Z
dc.date.available2022-04-22T09:58:47Z
dc.date.issued2020-08-15
dc.identifier.citationAdnan, A.A., Diels, J., Jibrin, J.M., Kamara, A.Y., Shaibu, A.S., Craufurd, P. & Menkir, A. (2020). CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria. Field Crops Research, 253: 107826, 1-11.
dc.identifier.issn0378-4290
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7428
dc.description.abstractWhen properly calibrated and evaluated, dynamic crop simulation models can provide insights into the different components of genotype by environment interactions (GEIs). Modelled outputs could be used to complement data from multi-environment trials. Field experiments were conducted in the rainy and dry seasons of 2015 and 2016 across four locations in maize growing regions of Northern Nigeria using 16 maize varieties planted under near-optimal conditions of moisture and soil nitrogen. The CERES-Maize model was calibrated using data from three locations and two seasons (rainy and dry) and evaluated using data from one location and two seasons all in 2015. Observed data from the four locations and two seasons in 2016 was used to create eight different environments. Two profile pits were dug in each location and were used separately in the simulations for each environment to provide replicated data for stability analysis in a combined ANOVA. The effects of the environment, genotype and GEI were highly significant (p = 0.001) for both observed and simulated grain yields. The environment explained 67 % and 64 % of the variations in observed and simulated grain yields respectively. The variance component of GEI (13 % for observed and 15 % for simulated) were lower but still considerable when compared to that of genotypes (19 % for observed and 21 % for simulated). From the stability analysis of the observed and simulated grain yields using six different stability models, three models (ASV, Ecovalence, and Sigma) ranked Ife Hybrid as the most stable variety. The slope of the regression (bi) model ranked Sammaz 11 as the most stable variety, while the Shukla model ranked Sammaz 28 as the most stable variety. Long-term seasonal analysis with the CERES-Maize model revealed that early and intermediate maturing varieties produce high yields in both wet and dry savannas, early and extra-early varieties produce high yields only in the dry savannas, while late maturing varieties produce high yields only in the wet savannas. When properly calibrated and evaluated, the CERES-Maize model can be used to generate data for GEI and stability studies of maize genotype in the absence of observed field data.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipAfrica Center of Excellence in Dryland Agriculture
dc.format.extent1-11
dc.language.isoen
dc.subjectMaize
dc.subjectGenotypes
dc.subjectEnvironment
dc.subjectStability
dc.subjectAnalysis
dc.subjectNigeria
dc.titleCERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria
dc.typeJournal Article
cg.contributor.crpGrain Legumes
cg.contributor.crpMaize
cg.contributor.affiliationBayero University Kano
cg.contributor.affiliationKatholieke Universiteit, Leuven
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.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiotech and Plant Breeding
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidADNAN:2020
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectGenetic Improvement
cg.iitasubjectMaize
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Diseases
cg.iitasubjectPlant Genetic Resources
cg.iitasubjectPlant Health
cg.iitasubjectPlant Production
cg.journalField Crops Research
cg.notesOpen Access Article; Published online: 11 May 2020
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1016/j.fcr.2020.107826
cg.iitaauthor.identifierAlpha Kamara: 0000-0002-1844-2574
cg.iitaauthor.identifierAbebe Menkir: 0000-0002-5907-9177
cg.futureupdate.requiredNo
cg.identifier.issue107826
cg.identifier.volume253


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