Factor analysis to investigate genotype and genotype × environment interaction effects on pro-vitamin A content and yield in maize synthetics

Mengesha, Wende; Menkir, Abebe; Meseka, Silvestro; Bossey, Bunmi; Agbona, Afolabi; Burgueño, Juan; Crossa, José

doi:10.1007/s10681-019-2505-3

Cited by 15 publications

(16 citation statements)

References 49 publications

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“…In these cases, the SH × environment interaction component was greater than the genetic variance component in most of the crop seasons. This is very frequent in most of the situations in which various hybrids are evaluated in the same crop season (Tonk et al, 2011;Nzuve et al, 2013;Ndhlela et al, 2014;Mengesha et al, 2019). In the conditions evaluated, the significant effect of the interaction is expected due to the expressive environmental variation of numerous factors, such as climate, soil fertility, and management practices that occurs in the different locations in which maize experiments are conducted (Noia Junior et al, 2019;Embrapa, 2020).…”

Section: Discussionmentioning

confidence: 99%

Mega-environment analysis of maize breeding data from Brazil

Pereira

Ramalho

Resende

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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The development and recommendation of single cross maize hybrids (SH) to be used in extensive land areas (mega-environments), and in different crop seasons requires many experiments under numerous environmental conditions. The question we asked is if the data from these multi-environment experiments are sufficient to identify the best hybrid combinations. The aim of this study was to critically analyze the phenotype data of experiments of yield, established by a large seed producing company, under a high level of imbalance. Data from evaluation of 2770 SH were used from experiments conducted over four years, involving the first and second crop seasons, in 50 locations of different years and regions of Brazil. Different types of analysis were carried out and genetic and non-genetic components were estimated, with emphasis on the different interactions of the SH with the environments. Results showed that the coincidence of common hybrids in these experiments is normally small. The estimates of the correlations between of the hybrids coinciding in the environments two by two is of low magnitude. The hybrid × crop season interaction was always expressive; however, the interactions of hybrids and other environmental variables were also important. Under these conditions, alternatives were discussed for making with the information obtained from the experiments, can be more efficient on the process to obtain new hybrids by companies.

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Section: Discussionmentioning

confidence: 99%

Mega-environment analysis of maize breeding data from Brazil

Pereira

Ramalho

Resende

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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“…Dawson et al (2013) made genomic predictions within groups of environments clustered using several methods, and of those reported, the FA model had superior accuracy across environments with differing levels of genetic correlation. Mengesha et al (2019) used the FA model to identify stability and adaptability of maize varieties as it related to both grain yield and provitamin A content. This study showed that the FA model can be applied to FN, a wheat end-use quality trait.…”

Section: Discussionmentioning

confidence: 99%

“…Mengesha et al. (2019) used the FA model to identify stability and adaptability of maize varieties as it related to both grain yield and provitamin A content. This study showed that the FA model can be applied to FN, a wheat end‐use quality trait.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have used the FA model to handle the complex G×E associated with unbalanced multienvironment trial datasets in a variety of crops including, but not limited to, wheat, barley (Hordeum vulgare L.), sorghum [Sorghum Crop Science bicolor (L.) Moench], lentil (Lens culinaris Medik. ), and maize (Zea mays L.) (Burgueño, Crossa, Cornelius, & Yang, 2008;Dawson et al, 2013;Kelly, Smith, Eccleston, & Cullis, 2007;Mengesha et al, 2019). Smith and Cullis (2018) developed a simple method of summarizing the results of the FA model to compare varieties, and it has been successfully adopted by the Australian government organization responsible for conducting small grain and legume field trials.…”

Section: Core Ideasmentioning

confidence: 99%

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Application of the factor analytic model to assess wheat falling number performance and stability in multienvironment trials

et al. 2020

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A factor analytic model was used to characterize data generated with the Hagberg–Perten falling number (FN) method, a measure of wheat (Triticum aestivum L.) quality influenced by genotype‐by‐environment interactions. The FN method detects starch degradation due to the presence of the enzyme α‐amylase in wheat grain such that a low FN indicates high α‐amylase activity and high risk of poor end‐product quality. Because farmers receive severe discounts for low FN, FN data have been collected over multiple years for the Washington State University multilocation variety trials to help farmers and breeders identify lower risk varieties. Analysis of these data to objectively rank varieties is challenging because the dataset is unbalanced and because FN is subject to complex genotype‐by‐environment interactions. Low FN can result from environmental differences at multiple stages in grain development because there are two major causes of α‐amylase accumulation in grain, late‐maturity α‐amylase (LMA) and preharvest sprouting (PHS). A five‐factor analytic model extracted explicit measures of overall performance and of stability in variable environments from historical FN data from the multilocation trial, providing a basis for breeding and planting decisions. Whereas a linear model explained 70.3% of the variation, the five‐factor analytic model accounted for 92.5% of variation in the data. Examination of factor loadings enabled us to separate environments and genotype response to either PHS or LMA, specifically. This is the first application of a factor analytic model to evaluate the end‐use quality trait FN, providing a method to rank varieties for grower decisions and breeder selections.

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“…The biosynthesis and accumulation of carotenoids in crops are modulated both by the crop genotype and environmental factors [ 27 , 28 ]. Studies have demonstrated that many factors, including soil type, temperature, moisture, light intensity, occurrence of biotic and abiotic stresses, altitude, maturity cycle, and cropping practices significantly affect the synthesis and accumulation of individual carotenoids in maize and other crops [ 23 , 27 , 29 , 30 , 31 , 32 , 33 ]. Consequently, evaluating provitamin A biofortified maize hybrids involving diverse parents with medium to high provitamin A content, across variable climatic and other field growing conditions, is critical to elucidate the nature of the relationship between provitamin A accumulation and agronomic performance.…”

Section: Introductionmentioning

confidence: 99%

Unravelling the Effect of Provitamin A Enrichment on Agronomic Performance of Tropical Maize Hybrids

Menkir

Dieng

Mengesha

et al. 2021

Plants

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Maize is consumed in different traditional diets as a source of macro- and micro-nutrients across Africa. Significant investment has thus been made to develop maize with high provitamin A content to complement other interventions for alleviating vitamin A deficiencies. The current breeding focus on increasing β-carotene levels to develop biofortified maize may affect the synthesis of other beneficial carotenoids. The changes in carotenoid profiles, which are commonly affected by environmental factors, may also lead to a trade-off with agronomic performance. The present study was therefore conducted to evaluate provitamin A biofortified maize hybrids across diverse field environments. The results showed that the difference in accumulating provitamin A and other beneficial carotenoids across variable growing environments was mainly regulated by the genetic backgrounds of the hybrids. Many hybrids, accumulating more than 10 µg/g of provitamin A, produced higher grain yields (>3600 kg/ha) than the orange commercial maize hybrid (3051 kg/ha). These hybrids were also competitive, compared to the orange commercial maize hybrid, in accumulating lutein and zeaxanthins. Our study showed that breeding for enhanced provitamin A content had no adverse effect on grain yield in the biofortified hybrids evaluated in the regional trials. Furthermore, the results highlighted the possibility of developing broadly adapted hybrids containing high levels of beneficial carotenoids for commercialization in areas with variable maize growing conditions in Africa.

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Factor analysis to investigate genotype and genotype × environment interaction effects on pro-vitamin A content and yield in maize synthetics

Cited by 15 publications

References 49 publications

Mega-environment analysis of maize breeding data from Brazil

Mega-environment analysis of maize breeding data from Brazil

Application of the factor analytic model to assess wheat falling number performance and stability in multienvironment trials

Unravelling the Effect of Provitamin A Enrichment on Agronomic Performance of Tropical Maize Hybrids

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