Biplot Analysis of Forage Combining Ability in Maize Landraces

Abstract

In Argentina, maize (Zea mays L.) hybrids selected for silage production and local adaptation are not available for most temperate regions where silage maize can be produced. This study sought maize landraces that could be used as germplasm sources to enhance forage yield and quality in warm temperate areas and emphasized eight landraces not previously selected for grain yield. These were crossed in a diallel mating design and the 28 F1 population hybrids, the eight parental populations, and four commercial hybrids were evaluated in four environments in Argentina for combining abilities and to determine heterotic patterns among germplasm sources, using a biplot diallel analysis. Significant midparent heterosis (MPH) was observed for ear yield (EY), stover yield (SY), and whole plant yield (WY). General combining ability (GCA) effects were significant for EY, SY, and WY. Specific combining ability (SCA) effects were not significant for any trait. The graphical representation offered by biplot analysis allowed a rapid and effective overview of GCA and SCA effects of the populations, their performance in crosses, as well as grouping patterns of similar genotypes. Significant variation among checks was observed for EY and SY. On average, commercial hybrids had greater EY, but lower SY, than landraces and population hybrids. Some landrace crosses showed superior or similar WY than commercial checks, indicating the breeding potential of the evaluated germplasm.Abbreviations: ATC, average tester coordinate; ED, in vitro digestibility of ear dry matter; EY, ear dry matter yield; GCA, general combining ability; GGE, genotype main effect plus genotype 3 environment interaction; INTA, Instituto Nacional de Tecnología Agropecuaria; LAMP, Latin American Maize Project; MPH, midparent heterosis; NIRS, near infrared reflectance spectroscopy; PC, principal component; PC1, first principal component; PC2, second principal component; SCA, specific combining ability; SD, in vitro digestibility of stover dry matter; SY, stover dry matter yield; WD, in vitro digestibility of whole-plant dry matter; WY, whole-plant dry matter yield.

Nitrogen Use Efficiency of Three Maize (Zea Mays L.) Cultivars

“…Table (3) shows that cultivars differ significantly in grains yield in both seasons. Differences among maize cultivars with respect to yield have been reported by some researcher which they found the same result [23,24]. The variation in grain yield among cultivars could be attributed to differences in genetic makeup, environment and interaction between these aspects.…”

Cell Wall Composition and Ruminant Digestibility of Various Maize Tissues Across Development

“…Therefore, stover quality must be determined at harvest time to accurately identify high ADF acid detergent fiber, NDFD neutral detergent fiber digestibility quality genotypes. The lack of correlations with early developmental stages, primarily before grain filling, could be explained by the presence of different efficiencies of remobilizing sugars to the ear in different genotypes [2,9], as well as the ability of different tissue types within the same genotype to have different efficiencies of remobilizing sugars to the ear and altered physiology throughout development [4,20,21,28]. In addition to a lack of correlation between early plant part digestibility and whole plant digestibility at R6, there is also less variation for this trait at these early developmental stages.…”

Identifying superior spring wheat genotypes through diallel approaches

“…This methodology was developed originally to analyze multi-environment trial data (Yan, 2001). It allows extracting visual information to assess the performance of the GCA and SCA of each genotype, classify groups of parents with a similar genetic background (Yan and Hunt, 2002;Bertoia et al, 2006;Darvishzadeh et al, 2009). GGE biplot has the advantage of graphically displays the effects of genotype (G) and genotype by environment (GE) interaction, and enhancing the ability to understand the patterns of the data in comparison with conventional methods of diallel analysis (Badu-Apraku et al, 2011;Dehghani et al, 2012).…”