Automated Author Profile

F. J. Navas González

Current S-Index

1.3

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.7

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

13.5%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Non-parametric analysis of the effects of αS1-casein genotype and parturition non-genetic factors on milk yield and composition in Murciano-Granadina goats

Limited sample sizes imply parametric assumptions could be violated, even if traits have been reported to fulfil parametric assumptions. Parametric studies have addressed a non-significant influence of CSN1S1 genes on Murciano-Granadina milk yield, fat, protein and dry extract. We used non-parametric categorical tests to find alternative statistical methods to analyse the power to explain the variability found in the population regarding milk yield and its components. We analysed 2090 records for milk yield, and its components from 710 Murciano-Granadina CSN1S1-genotyped goats. Categorical regression equations were issued to predict which and at what level these factors may determine milk yield (kg), fat (kg), protein (kg) and dry extract (kg). All environmental (farm and parturition year) and animal-inherent factors (genotype, birth type and age) resulted statistically significant (p Non-parametric tests are crucial if normality and heteroskedasticity analyses fail.Murciano-Granadina milk traits compared with highly selected international breeds’.E allele combinations and BB reported highest effects on milk components and yield. Non-parametric tests are crucial if normality and heteroskedasticity analyses fail. Murciano-Granadina milk traits compared with highly selected international breeds’. E allele combinations and BB reported highest effects on milk components and yield.

Authors

  • M. G. Pizarro ;
  • V. Landi ;
  • F. J. Navas González ;
  • J. M. León ;
  • J. V. Delgado
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.8168033January 2019

Non-parametric analysis of the effects of αS1-casein genotype and parturition non-genetic factors on milk yield and composition in Murciano-Granadina goats

Limited sample sizes imply parametric assumptions could be violated, even if traits have been reported to fulfil parametric assumptions. Parametric studies have addressed a non-significant influence of CSN1S1 genes on Murciano-Granadina milk yield, fat, protein and dry extract. We used non-parametric categorical tests to find alternative statistical methods to analyse the power to explain the variability found in the population regarding milk yield and its components. We analysed 2090 records for milk yield, and its components from 710 Murciano-Granadina CSN1S1-genotyped goats. Categorical regression equations were issued to predict which and at what level these factors may determine milk yield (kg), fat (kg), protein (kg) and dry extract (kg). All environmental (farm and parturition year) and animal-inherent factors (genotype, birth type and age) resulted statistically significant (p Non-parametric tests are crucial if normality and heteroskedasticity analyses fail.Murciano-Granadina milk traits compared with highly selected international breeds’.E allele combinations and BB reported highest effects on milk components and yield. Non-parametric tests are crucial if normality and heteroskedasticity analyses fail. Murciano-Granadina milk traits compared with highly selected international breeds’. E allele combinations and BB reported highest effects on milk components and yield.

Authors

  • M. G. Pizarro ;
  • V. Landi ;
  • F. J. Navas González ;
  • J. M. León ;
  • J. V. Delgado
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.8168033.v1January 2019