Linear relations among phenological, morphological, productive and protein-nutritional traits in early maturing and super-early maturing maize genotypes

Alves, Bruna Mendonça; Filho, Alberto Cargnelutti; Toebe, Marcos; Burin, Cláudia

doi:10.1016/j.jcs.2016.06.013

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…The significance of this correlation indicates that Group I, formed by the characters related to the chemical quality of grains, and Group II, which includes the characters related to agronomic performance, are not independent. As previously observed in a study with corn (Alves et al 2016), a canonical correlation analysis pointed out associations between traits related to agronomic performance and nutritional traits.…”

Section: Resultssupporting

confidence: 70%

Associations between agronomic performance and grain chemical traits in oat

Silveira¹,

Oliveira²,

Maltzhan³

et al. 2020

Commun. Plant Sci.

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Great progress has already been achieved in breeding white oats (Avena sativa L.). However, breeders of this species need to make available to the market higher yielding and higher grain quality cultivars. Therefore, it is necessary to optimize the strategies of selection of superior genotypes. The present work aimed to estimate the correlation between traits related to agronomic performance and traits related to chemical composition of white oat grains, seeking to add understanding for the selection of higher yielding genotypes with high nutritional quality. Thirty families from the cross between the cultivars Albasul and UPF 15 were used, and 31 families from the cross between the cultivars IAC 7 and UFRGS 19. Six traits related to agronomic performance and five traits corresponding to the chemical composition of the grains were evaluated. To assess the relationship between the different traits evaluated, simple correlation and canonical correlation analyzes were performed. The results demonstrate the existence of simple correlations between grain chemical constituents and traits related to agronomic performance. The pair-by-pair relationship between traits of these two groups was also observed, but the environmental action strongly interfered with these correlations. The canonical correlation analysis allowed the verification of dependence between the evaluated trait groups where genotypes with higher number of grains per panicle, high mass of one thousand grains, with lower stature and lower number of spikelets, will have lower lipid and total fiber content, but with higher protein contents. Highlighted Conclusions 1. There is a relationship of dependence between the agronomic performance trait group and the oat grain chemical constituent group. 2. Indirect selection of grains with higher nutritional quality and high agronomic performance will be feasible providing there is a strict control of environmental conditions. 3. Selections that seek superiority in all traits studied will not be possible, given the presence of negative correlations.

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Section: Resultssupporting

confidence: 70%

Associations between agronomic performance and grain chemical traits in oat

Silveira¹,

Oliveira²,

Maltzhan³

et al. 2020

Commun. Plant Sci.

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“…These results may provide a rationale for indirect selection for growth traits. At the same time, the correlation between growth traits and nutritional traits is weak, which is similar to the findings of Alves [39]. However, the canonical correlation analysis of growth traits and nutritional traits showed that the overall correlation between the two was relatively high.…”

Section: Correlation Of Phenotypic Variationsupporting

confidence: 83%

Geographical Variation in the Growth and Nutritional Traits of Leaf Powder from Broussonetia papyrifera (L.) L’Hér. ex Vent. from Different Provenances

Zhou

Que

et al. 2022

Forests

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Broussonetia papyrifera (L.) L’Hér. ex Vent, a perennial deciduous tree, is used in feed, medicine, papermaking, environmental protection, and ecological restoration. This paper ttook 33 provenances from the natural distribution as the research objects, observes their growth and nutritional traits, and analyzes the laws of geographical variation. The repeatability was 0.80–0.88 and 0.48–0.91, respectively. The correlation was significant (the correlation coefficient was 0.764). The variation is greatly affected by the latitudinal direction. Through clustering, the 33 provenances were clustered into 4 groups and most provenances with relatively close geographical origins were clustered together, showing differences between different geographical regions. Using leaf biomass (0.4662 g) and crude protein content (14.39%) as the selection index, the Sichuan Mianyang (SCMY), Chongqing Kaizhou (CQKZ), Shanghai (SH), and Fujian Nanping (FJNP) provenances were selected as fast-growing, high-yield, and high-quality paper mulberry provenances. This study provides the basis for the selection of excellent paper mulberry trees.

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“…Numerous bi-and multivariate techniques, such as linear correlation coefficients (Toebe et al, 2015), canonical correlation (Alves et al, 2016), and path analysis (Toebe et al, 2017), have been applied to identify the direction and magnitude of the associations between corn traits. Multiple linear regression has also been used to predict the behavior of one principal variable as a function of two or more explanatory variables in corn.…”

Section: Introductionmentioning

confidence: 99%

Reference sample size for multiple regression in corn

Filho

Toebe

2020

Pesq. agropec. bras.

Self Cite

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The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y = β0 + β1X1 + β2X2. The Y, X1, and X2 traits were measured in 361, 373, and 416 plants, respectively, of single-, three-way, and double-cross hybrids in the 2008/2009 crop year; and in 1,777, 1,693, and 1,720 plants, respectively, of single-, three-way, and double-cross hybrids in the 2009/2010 crop year, totaling 6,340 plants. Descriptive statistics were calculated, and frequency histograms and scatterplots were created. The sample size (number of plants) for the estimate of the β0, β1, and β2 parameters, of the residual standard error, the coefficient of determination, the variance inflation factor, and the condition number between the explanatory traits of the model (X1 and X2) were determined by resampling with replacement. Measuring 260 plants is sufficient to adjust precise multiple regression models of corn grain yield as a function of ear length and ear diameter. The Y = -229.76 + 0.54X1 + 6.16X2 model is a reference for estimating corn grain yield.

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Linear relations among phenological, morphological, productive and protein-nutritional traits in early maturing and super-early maturing maize genotypes

Cited by 6 publications

References 12 publications

Associations between agronomic performance and grain chemical traits in oat

Associations between agronomic performance and grain chemical traits in oat

Geographical Variation in the Growth and Nutritional Traits of Leaf Powder from Broussonetia papyrifera (L.) L’Hér. ex Vent. from Different Provenances

Reference sample size for multiple regression in corn

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