Abstract
Knowledge of interrelationships between grain yield and its contributing components will improve the efficiency of breeding programs through the use of appropriate selection indices. Previous path analyses studies in maize ( Zea mays L.) treated yield components as first‐order variables. The present study, based on evaluation of 90 experimental maize hybrids (comprising one diallel and one line × tester set) at two locations in India, utilizes a sequential path model for analysis of genetic associations among grain yield and its related traits by ordering the various variables in first‐, second‐, and third‐order paths on the basis of their maximum direct effects and minimal collinearity. The sequential path model showed distinct advantages over the conventional path model in discerning the actual effects of different predictor variables. Two first‐order variables, namely 100‐grain weight and total number of kernels per ear, revealed highest direct effects on total grain weight ( p = 0.74 and p = 0.78, respectively), while ear length, ear diameter, number of kernel rows, and number of kernels per row were found to fit as second‐order variables. All direct effects were found to be significant, as indicated by bootstrap analysis. Test for the goodness‐of‐fit revealed that the sequential path model provided better fit to various datasets analyzed in the study. Correlations between the predicted values of various response variables in the second season dataset based on the path coefficients of the first season were high, except for ear length and number of kernels per row. The applicability of the model has been confirmed through analysis of two additional datasets during 2000. The results indicated the utility of the sequential path model for determining the interrelationships among grain yield and related traits in maize.
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Claims 8
Claim · model-written summary
The sequential path model better discerned predictor effects than the conventional path model.
Verbatim
e0“sequential path model showed distinct advantages over the conventional path model in discerning the actual effects of different predictor variables”
Claim · model-written summary
100-grain weight had a high direct effect on total grain weight, p = 0.74.
- Organ / stage
- 100‐grain weighte0
- Outcome
- total grain weighte0
- Direction
- highest direct effectse0
- Value
- p = 0.74e0
Not reportedTaxon
Verbatim
e0“100‐grain weight and total number of kernels per ear, revealed highest direct effects on total grain weight ( p = 0.74 and p = 0.78, respectively)”
Claim · model-written summary
Total kernels per ear had a high direct effect on total grain weight, p = 0.78.
- Organ / stage
- total number of kernels per eare0
- Outcome
- total grain weighte0
- Direction
- highest direct effectse0
- Value
- p = 0.78e0
Not reportedTaxon
Verbatim
e0“100‐grain weight and total number of kernels per ear, revealed highest direct effects on total grain weight ( p = 0.74 and p = 0.78, respectively)”
Claim · model-written summary
Ear length, ear diameter, kernel row number and kernels per row fit as second-order variables.
- Outcome
- second‐order variablese0
Not reportedComparatorValue
Verbatim
e0“ear length, ear diameter, number of kernel rows, and number of kernels per row were found to fit as second‐order variables”
Claim · model-written summary
All direct effects were significant in bootstrap analysis.
- Outcome
- direct effectse0
Not reportedTaxonDirection
Verbatim
e0“All direct effects were found to be significant, as indicated by bootstrap analysis”
Claim · model-written summary
Sequential path model fit was better across the analyzed datasets.
Verbatim
e0“sequential path model provided better fit to various datasets analyzed in the study”
Claim · model-written summary
Predicted-response correlations between seasons were high except for ear length and kernels per row.
- Outcome
- Correlations between the predicted values of various response variablese0
- Direction
- highe0
- Qualifier
- except for ear length and number of kernels per rowe0
Not reportedTaxon
Verbatim
e0“Correlations between the predicted values of various response variables in the second season dataset based on the path coefficients of the first season were high, except for ear length and number of kernels per row”
Claim · model-written summary
The model was applicable to two additional datasets analyzed in 2000.
Verbatim
e0“applicability of the model has been confirmed through analysis of two additional datasets during 2000”