Performance of tropical early-maturing maize cultivars in multiple stress environments

Badu-AprakuB.,; MenkirA.,; AjalaS.,; Akinwale, R.O.; Oyekunle, M.; Obeng-antwiK.,

doi:10.4141/cjps10059

Cited by 64 publications

(39 citation statements)

References 23 publications

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“…To our knowledge, the only available studies employing such methodology are those conducted by Cirilo et al (2009);Badu-Apraku et al (2010) and Badu-Apraku et al (2011), in which genotypes with potentially aggregator alleles favorable for the efficient use of nitrogen were identified.…”

Section: Introductionmentioning

confidence: 99%

GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen

et al. 2017

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Nitrogen is essential for sustaining life on the planet, and it is the most important nutrient for obtaining high agricultural production. However, their use leads to the release of nitrous oxide with a global warming potential 296 times higher than the CO 2 molecule, making it a challenge to reduce their use in agriculture. The objective of this research was to identify efficient popcorn inbred lines and responsive nitrogen use and exhibit a good expansion volume. For this, 29 inbred lines from the Germplasm Collection of Darcy Ribeiro North Fluminense State University (UENF) were evaluated at two contrasting levels of nitrogen availability (low and ideal) at two representative locations in the north and northwest of the state of Rio de Janeiro, Brazil, arranged in a randomized block design with three replicates. These inbred lines were discriminated against efficient use of nitrogen by multivariate GGE Biplot. Selective accuracy was close to 1, showing that the genotypes were enough to provide contrasting success in selection procedures. The first two main components (PC) retained 93.82% of the total variation, and PC1 furnished an information ratio (IR) that was unaffected by noise. L77 was the most unstable line, while P7, P2, P6, P3, P5, P4, P9, P10, P8, P9, L70, L74, and L55 were efficient and responsive. The GGE biplot method is recommended for the reliable identification of popcorn lines that are efficient and responsive to the use of nitrogen.Index terms: Zea mays; multivariate analysis; abiotic stress; G x E interaction. RESUMOO nitrogênio é indispensável à manutenção da vida no planeta, sendo o mais importante nutriente para a obtenção de elevadas produções agrícolas. No entanto, a sua utilização ocasiona a liberação de óxido nitroso, com potencial de aquecimento global 296 vezes maior que a molécula de CO 2 , tornando-se um grande desafio a redução de seu uso na agricultura. O objetivo desta pesquisa foi identificar linhagens de milho-pipoca eficientes e responsivas no uso de nitrogênio e que apresentem um elevada capacidade de expansão. Para isto, avaliaram-se 29 linhagens da Coleção de Germoplasma da Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF) em dois níveis contrastantes de nitrogênio (baixo e ideal) em dois locais representativos do Norte e Noroeste do Estado do Rio de Janeiro, Brasil, arranjadas no delineamento em blocos casualizados com três repetições. Estas linhagens foram discriminadas em relação a eficiência no uso do nitrogênio pela técnica multivariada GGE Biplot. A acurácia seletiva foi próxima de 1, revelando que os genótipos foram suficientes contrastantes para proporcionar sucesso em procedimentos seletivos. Os dois primeiros componentes principais (CP) retiveram 93,82% da variação total, sendo que o CP1 expressou relação de informação (IR) não impactada por ruídos. L77 foi a linhagem mais instável, enquanto P7, P2, P6, P3, P5, P4, P9, P10, P8, P9, L70, L74 e L55 foram eficientes e responsivas. Recomenda-se o método GGE Biplot na identificação fidedigna de l...

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Section: Introductionmentioning

confidence: 99%

GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen

et al. 2017

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“…Improvement for drought tolerance has most often resulted in specific adaptation and improved performance under low-N conditions, indicating that tolerance to either stress involves common adaptive mechanism (Bänziger et al, 1999;Badu-Apraku et al, 2011, 2015a. It is therefore becoming increasingly important to adopt a holistic approach to identify genotypes with tolerance to a range of stresses expected in the target environment in WCA instead of compartmentalizing different stresses (Badu-Apraku et al, 2010). During the past decade, the International Institute of Tropical Agriculture Maize Improvement Program (IITA-MIP) has therefore paid increasing attention and devoted scarce resources to develop new products endowed with high yield potential and stability across a broad range of moisture availability and growing conditions.…”

mentioning

confidence: 99%

Yield Gains in Extra‐Early Maize Cultivars of Three Breeding Eras under Multiple Environments

Badu‐Apraku¹,

Yallou

Ofori

et al. 2017

Agronomy Journal

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Core Ideas The study determined genetic improvement in grain yield of the cultivars during the breeding eras, investigated trait associations, and identified high‐yielding and stable cultivars across multiple‐stress and non‐stress environments. The study revealed an annual genetic gain of 2.72 and 2.28% for the cultivars under multiple‐stress and non‐stress environments. Cultivars 2004 TZEE‐Y Pop STR C4, TZEE‐W Pop STR QPM C0, TZEE‐W Pop STR BC2 C0 of era 2 and TZEE‐W STR 107 BC1, TZEE‐W Pop STR C5, and 2012 TZEE‐Y DT STR C5 of era 3 were the highest yielding and stable across multiple‐stress environments while 98 Syn EE‐W from era 1, FERKE TZEE‐W STR, TZEE‐W Pop STR C3, TZEE‐Y Pop STR QPM C0 from era 2, and TZEE‐W Pop STR C5, 2009 TZEE‐OR2 STR QPM, 2009 TZEE‐W STR, TZEE‐Y STR 106, TZEE‐W DT C0 STR C5 from era 3 were the most outstanding across non‐stress environments. We conclude that substantial progress has been made in breeding for multiple‐stress tolerant extra‐early maize cultivars in West and central Africa. Availability of extra‐early maize cultivars has facilitated the expansion of maize production into savannas of West and Central Africa (WCA). Fifty‐six extra‐early maize cultivars of three breeding eras;1995 to 2000, 2001 to 2006, and 2007 to 2012 were evaluated for 2 yr under 24 multiple‐stress and 28 non‐stress environments in WCA. Objectives of the study were to determine genetic improvement in grain yield of cultivars developed during the breeding eras, and identify high‐yielding and s multiple‐stress and non‐stress environments. Yield gains from era 1 to era 3 under multiple stresses was associated with increased days to anthesis, reduced stalk lodging, and improved husk cover. Cultivars 2004 TZEE‐Y Pop STR C4, TZEE‐W Pop STR QPM C0, and TZEE‐W Pop STR BC2 C0 of era 2; and TZEE‐W STR 107 BC1, TZEE‐W Pop STR C5, and 2012 TZEE‐Y DT STR C5 of era 3 were high‐yielding and stable across multiple‐stress environments while 98 Syn EE‐W from era 1, FERKE TZEE‐W STR, TZEE‐W Pop STR C3, and TZEE‐Y Pop STR QPM C0 from era 2, and TZEE‐W Pop STR C5, 2009 TZEE‐OR2 STR QPM, 2009 TZEE‐W STR, TZEE‐Y STR 106, and TZEE‐W DT C0 STR C5 from era 3 were outstanding across non‐stress environments and should be tested extensively and commercialized. Considerable improvement has been made in breeding for multiple‐stress tolerant extra‐early maize cultivars.

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“…It complements the results of the ANOVA in that, after the ANOVA had shown significant mean squares for GEI, GGE biplot graphically analyzes the nature of the interactions. The investigation into GEI becomes more interesting with the introduction of the concept of crossover interaction and GGE biplot has increasingly been used in GEI data analysis in agriculture (Yan et al 2000;Crossa et al 2002;Badu-Apraku et al 2010). For this type of analysis, Gauch (1992) reported that GGE biplot is superior to other multivariate statistical tools especially the Additive Main Effect and Multiplicative Interaction (AMMI) in that GGE biplot has many visual interpretations and it particularly allows visualization of crossover GEI.…”

Section: Multi-environment Analysismentioning

confidence: 99%

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Akinwale¹,

Fakorede²,

Badu‐Apraku³

et al. 2014

Cereal Research Communications

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Genotype main effect plus genotype-by-environment interaction (GGE) biplot produces a graphical display of results that facilitates a better understanding of complex genotype-by-environment interaction in multi-environment trials of breeding and agronomic experiments. However, the full potential and weaknesses of this powerful tool are not fully understood by breeders, agronomists, entomologists and pathologists. The objective of this paper was to review the usefulness of this statistical tool and enumerate some of its weaknesses. Its main application has so far been in the analysis of multi-environment data. It has been used to analyze the performance of crop cultivars under multiple stress environments, from which ideal cultivars, mega-environments, and core testing sites were identified. More recently, GGE biplot has been employed in genetic analysis of diallel data to estimate the combining abilities and identify heterotic groups among inbred parents. Genotype-by-trait biplot has also been utilized in trait profile analysis, and in identification of traits that are reliable for indirect selection of a target primary trait. Two major shortcomings of this tool are (i) failure to identify more than two distinct, contrasting groups in diallel studies and (ii) lack of statistical tests for most of its graphical displays. Other aspects of GGE biplot that need further study and development are (i) estimation of genetic variances, covariances, and heritability, including the analysis of data generated from North Carolina Designs I, II, and III as well as other genetic designs, considering their importance in plant breeding programs; (ii) analysis of Quantitative Trait Loci (QTL) data for proper understanding of the genetic constitution of each individual plant or line; and (iii) analysis of Genotype-by-pathogen or insect strain interaction data. Nevertheless, GGE biplot has helped greatly in the accurate analysis and interpretation of data from breeding and agronomic field evaluation experiments.

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Performance of tropical early-maturing maize cultivars in multiple stress environments

Cited by 64 publications

References 23 publications

GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen

GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen

Yield Gains in Extra‐Early Maize Cultivars of Three Breeding Eras under Multiple Environments

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

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