Gains in Grain Yield of Early Maize Cultivars Developed During Three Breeding Eras under Multiple Environments

Badu‐Apraku, B.; Fakorede, M. A. B.; Oyekunle, M.; Yallou, G.C.; Ofori, Kwadwo; Haruna, Akiko; Usman, I. S.; Akinwale, R.O.

doi:10.2135/cropsci2013.11.0783

Cited by 46 publications

(61 citation statements)

References 32 publications

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“…The regression analysis, including the parameters and the graphical display of the regression line and the distinction between the different eras were done using the Excel software in the Microsoft Office suite 2007. A multiple trait base index (MI) that integrated superior grain yield, EPP, anthesis-silking interval, plant and ear aspects, stay-green characteristic, Striga damage rating and number of emerged Striga plants under multiple stress and outstanding grain yield under non-stress environments was used to select the top 15 and worst 10 hybrids (Badu-Apraku et al, 2015b). Each trait was standardized to minimize the effect of the different scales.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is desirable to incorporate drought tolerance into cultivars that have resistance to Striga in the Sudan and northern Guinea savannas where intermittent drought is prevalent, as the two stresses occur together. Presently, farmers in Striga endemic agro-ecologies of WCA are demanding extra-early and early (90-95 d to maturity) cultivars with combined resistance or tolerance to Striga and drought and are unwilling to adopt maize cultivars that do not possess both adaptation to droughtprone environments and Striga resistance (Badu-Apraku et al, 2013a, 2013b, 2015b. 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.…”

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confidence: 99%

“…However, only limited studies have been performed to assess progress made in the extra-early maturing cultivars developed over years in WCA. Information on the genetic gains in extra-early cultivars is crucial for determining whether or not the investments in research in the subregion are justified and to gain a better understanding of how selection has influenced important traits such as grain yield in maize (Kamara et al, 2012;Badu-Apraku et al, 2013b, 2015a, 2015b. The limited information on genetic gains from selection make it difficult to ascertain completely the genetic gain that has been made for grain yield in relationship to N fertility, drought tolerance and Striga resistance in the numerous cultivars that have been released in WCA during the past two decades.…”

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confidence: 99%

“…However, despite the tremendous advances in the improvement of the extraearly maize, information is completely lacking on the genetic gains in grain yield and other agronomic traits of the extraearly cultivars developed during the three breeding eras. The identification of traits of potential value and modifications in breeding methodologies and strategies are crucial for increased progress in future breeding of the extra-early maize cultivars (Badu-Apraku et al, 2015b).…”

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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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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

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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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“…In several published literature over the years, reviews highlighting weaknesses, strengths and best practices of the stability and/or GEI models have been undertaken (Crossa, 1990;Piepho, 1994;Ye et al, 2013). Additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by environment (GGE) biplot methodology have recently gained popularity and are most preferred (Yan & Kang, 2003;Badu-Apraku et al, 2013, 2015. AMMI allows exhaustive data analysis by performing regular analysis of variance (ANOVA) and estimating interaction effects through principal component analysis (PCA) which somewhat increases precision in trait estimates and enables reliable selections (Gauch et al, 2008;Hongyu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Grain Yield Performance and Stability of Quality Protein Maize Single Cross Hybrids in Mid-altitude Environment in Uganda

Ayiga-Aluba¹,

Asea²,

Kwemoi³

et al. 2018

JAS

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Stability in performance is important for determining adaptation and recommendation of pre-commercial crop varieties. This study was conducted with the following objectives: i) to determine stability of grain yield for 55 quality protein maize (QPM) single cross hybrids generated from 14 inbred lines ii) to determine the pattern of grouping of QPM hybrids and test environments based on grain yield response. The test hybrids were generated during the second season of 2015 and evaluated in three agro-ecological zones during the first season of 2016. Two checks were used: Longe 5D, a popular QPM hybrid and a top cross of Longe 5D with CML511. Additive main effects and multiplicative interaction (AMMI) and genotype and genotype by environment interaction (GGE) analyses were used to assess the stability of the hybrids. Results showed highly significant differences between genotypes, environments and GEI. The first principal component axis (IPCAI) was significant (p < 0.01) and accounted for 61.5% of the interaction effect. Both (IPCAI) and IPCAII) cumulatively contributed to entire degrees of freedom available for interaction component. Hybrid QPMSC-29 had the highest grain yield across environments. The AMMI biplot clearly depicted the genotypes on the bases of their adaptation patterns. Hybrids QPMSC-43, QPMSC-12, QPMSC-18 and QPMSC-29 were found to be more stable and responsive to favorable environments. Among them QPMSC-18 was more stable across locations. The AMMI biplot successfully identified 2 mega-environments as Namulonge and Bulindi in the first mega-environment with QPMSC-29 as the winning genotype and Masaka as the second mega-environment with QPMSC-10 as the winning genotypes. Hybrid, QPMSC-46 was an ideal genotype with above average score for grain yield. The single cross hybrids QPMSC-29, QPMSC-18 and QPMSC-10 were identified as stable yielder across environments in addition to higher yield. These hybrids can be recommended for all the three locations, for cultivation.

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Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria

Shehu

Garba

Jibrin

et al. 2022

Soil Science Soc of Amer J

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Developing optimal strategies for nutrient management of soils and crops at a larger scale requires an understanding of nutrient limitations and imbalances. The availability of extensive data (n = 1,781) from 2‐yr nutrient omission trials in the most suitable agroecological zone for maize (Zea mays L.) in Nigeria (i.e., the northern Guinea savanna) provides an opportunity to assess nutrient limitations and imbalances using the concept of multi‐ratio compositional nutrient diagnosis (CND). We also compared and contrasted the use of linear regression models and bootstrap forest machine learning to predict maize yield based on nutrient concentration in ear leaves. The results showed that 35% of the experimental plots had low yields due to nutrient imbalances (hereafter referred to as low yield imbalanced [LYI]). These experimental plots were dominated by control plots (without any nutrients applied), plots without N fertilization, and plots without P fertilization. Using the control plot as the ultimate indicator of nutrient imbalance, the significantly limiting nutrients in order of decreasing frequency of deficiency were N, P, S, Ca > Cu, and B. Both linear regression and bootstrap forest machine learning models fairly predicted maize grain yield based on nutrient concentration in ear leaves only in the LYI group and when examining all data with an independent validation dataset. These results suggest that nutrient management strategies, especially through the site‐specific management approach, should consider S, Ca, Cu, and B in addition to the existing nutrients N, P, and K to improve nutrient balance and maize yield in the study area.

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Gains in Grain Yield of Early Maize Cultivars Developed During Three Breeding Eras under Multiple Environments

Cited by 46 publications

References 32 publications

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

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

Grain Yield Performance and Stability of Quality Protein Maize Single Cross Hybrids in Mid-altitude Environment in Uganda

Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria

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