Stability analysis of maize cultivars adapted to tropical environments using AMMI analysis

Oyekunle, M.; Menkir, Abebe; Mani, H.; Olaoye, G.; Usman, I. S.; Ado, S. G.; Abdullahi, Umma; Ahmed, H. O.; ., L. Hassan; Abdulmalik, R. O.; Abubakar, Hussaini

doi:10.1556/0806.44.2016.054

Cited by 26 publications

(25 citation statements)

References 23 publications

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“…These results highlight the necessity of multi-year and multi-location testing, before hybrid could be recommended for a certain region. This is in accordance with OYEKUNLE et al (2017b).…”

Section: Resultssupporting

confidence: 92%

“…AMMI model found its application in testing of interactions genotype x environment (GxE), genotype x year (GxY), or genotype x environment x year (GxExY) in different crops: sugar beet (ĆIRIĆ et al, 2017), onion (PAVLOVIĆ et al, 2017, spring barley (PRŢULJ et al, 2015), barley (MIROSAVLJEVIĆ et al, 2014, triticale (KAYA and OZER, 2014), soybean (SOUSA et al, 2015), perennial ryegrass (LAKIĆ et al, 2015), rice (BOSE KUMAR et al, 2014), and of course maize (MITROVIĆ et al, 2012;NZUVE et al, 2013;SHIRI 2013;KRIŢMANIĆ et al, 2014;ОYEKUNLE et al, 2017a;2017b;BADU-APRAKU et al, 2011).…”

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confidence: 99%

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Evaluation of maize grain yield and yield stability by AMMI analysis

et al. 2018

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Significant genotype x environment interaction for quantitative traits, such is grain yield, reduces the usefulness of genotype means, over all environments, for selecting superior genotypes. AMMI model is a valuable statistical tool in identifying systemic variation contained in the interaction effect. Obtained data could be applied in maximizing yield potential in every environment based on both narrow and wide genotype adaptability, without the necessity of developing breeding programs for smaller targeted environments. Precise assortment of superior genotypes, with the assistance of AMMI model, leads to the better recommendation of newly bred hybrids, and thus increasing maize grain yield in a targeted environment. In this research genotype x environment interaction and yield stability of 36 maize hybrids of FAO 300-700 maturity group was investigating. The trial was set according to Randomized Complete Block Design (RCBD). Data were processed in order to obtain average estimates of grain yield, and yield stability was assessed by the method of AMMI analysis. The highest average grain yield was achieved in 2011 (11.62 t/ha), and the lowest in the most stressful and dry 2012 (6.90 t/ha). In the region Loznica L2 the highest average yield was noticed (13.81 t/ha), while at L7 (Sremska Mitrovica) average grain yield was the lowest (6.97 t/ha). Results of AMMI analysis gave precise recommendation for production of maize hybrids in certain environments, by determining winning areas of hybrids H20, H11 and H36. Medium early maturing and high yielding hybrids (H11 and H20) are therefore considered more favorable for production in environments with lower precipitation, while high yielding and more stable hybrids H21 and H35 are suitable for a wider range of environments. Hybrid H36 (FAO 700) showed its full potential at L2, and L3 which did not suffer from a lack of moisture. This hybrid also expressed its best potential in environments with favorable conditions.

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“…These results highlight the necessity of multi-year and multi-location testing, before hybrid could be recommended for a certain region. This is in accordance with OYEKUNLE et al (2017b).…”

Section: Resultssupporting

confidence: 92%

mentioning

confidence: 99%

Evaluation of maize grain yield and yield stability by AMMI analysis

et al. 2018

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“…For the early and medium groups, this approach provided three of the more stable clusters and for the late-maturity genotypes, four stable environmental strata were produced (Table 4). These results agree with those obtained by other authors, in environmental stratification studies for sorghum grain yield (Rakshit et al, 2012), barley (Mohammadi et al, 2009;Mortazavian et al, 2014), and maize (Oyekunle et al, 2017). The AMMI1 model offers greater repeatability of the clusters, concerning more parameterized AMMI models, which incorporate more axes of the GxE interaction (Gauch, 1990(Gauch, , 1992Ebdon and Gauch, 2002).…”

Section: Discussionsupporting

confidence: 93%

Research Article Environmental stratifications for soybean cultivar recommendation and its consistency over time.

et al. 2017

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ABSTRACT. Stratification of environments is a strategy to capitalize genotype x environment (GxE) interaction, which can optimize the process of assessment and cultivar recommendation, increasing productivity in a target environmental population. The objective of this study was to assess environmental stratification methods based on the analysis of GxE interaction, to identify consistent agronomic zones across time for soybean. Grain yield data of inbred lines from three maturity groups (early, medium, and late) were used. Lines and cultivars were tested in regional variety trials during three growing seasons at eighteen locations in the tropics of Central Brazil. Three methods were applied to stratify the environments. The first was based on joint analyses of variance for all the pairs of locations within each growing year. The second was based on a distance measure between each pair of locations, which was related to the GxE interaction estimated via additive main effects and multiplicative interaction analysis. The third was based on the approach of winning genotypes. The stratification results from the first two methods were not consistent across the growing seasons. However, the winning genotype approach provided consistent environmental stratification across years. From locations used in the genotypic assessment, three environmental clusters were identified for the early and medium maturity groups of soybean, and four clusters for the late maturity group. The use of different genotypic sets across years reinforces the predictive value of the environmental stratification established.

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“…Studies with a relatively low percentage of explanation in IPCA1 were observed in trials with Brassica spp. (38.6–54.6% [Bocianowski et al, 2019]); sugarcane (32.9‐35% [Ramburan et al, 2011]); maize (26.4% [Balestre et al, 2009)] and 40.8% [Oyekunle et al, 2017]); and wheat (43.5% [Bornhofen et al, 2017]; 32.1% [Tigabu et al, 2017]; 33.1‐38.2% [Veenstra et al, 2019]). In this sense, biplots with low GEI pattern recovery must be interpreted cautiously in trial networks with prevalence of crossover interaction, since only the simple part of the GEI can be represented in the first main components and the complex part of the GEI may be discarded.…”

Section: Discussionmentioning

confidence: 99%

Mean Performance and Stability in Multi‐Environment Trials I: Combining Features of AMMI and BLUP Techniques

et al. 2019

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Abbreviations: AMMI, additive main effects and multiplicative interaction; ASV, additive main effects and multiplicative interaction stability value; AVRC, index and the ranks of the mean yields; BLUP, best linear unbiased prediction; EV, averages of the squared eigenvector values; GEI, genotype × environment interaction; HMGV, harmonic mean of genotypic values; HMRPGV, harmonic mean of relative performance of genotypic values; IPCA, interaction principal component axis; LMM, linear mixed-effect model; MET, multi-environment trials; NF, no fungicide; RCBD, randomized complete block design; RMSPD, root mean square prediction difference; SPIC, sums of the absolute value of the IPCA scores; SVD, singular value decomposition; WAASB, weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixedeffect model; WAASBY, weighted average of weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixed-effect model and response variable; WF, with fungicide; Za, absolute value of the relative contribution of interaction principal component axes to the interaction. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

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Stability analysis of maize cultivars adapted to tropical environments using AMMI analysis

Cited by 26 publications

References 23 publications

Evaluation of maize grain yield and yield stability by AMMI analysis

Evaluation of maize grain yield and yield stability by AMMI analysis

Research Article Environmental stratifications for soybean cultivar recommendation and its consistency over time.

Mean Performance and Stability in Multi‐Environment Trials I: Combining Features of AMMI and BLUP Techniques

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