Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats

María Gabriela Pizarro Inostroza, Francisco Javier Navas González, Vincenzo Landi, José Manuel León Jurado, Juan Vicente Delgado Bermejo, Javier Fernández Álvarez and María del Amparo Martínez Martínez
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María Gabriela Pizarro Inostroza: Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain
Francisco Javier Navas González: Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain
Vincenzo Landi: Department of Veterinary Medicine, University of Bari “Aldo Moro”, 70010 Valenzano, Italy
José Manuel León Jurado: Centro Agropecuario Provincial de Córdoba, Diputación Provincial de Córdoba, 14071 Córdoba, Spain
Juan Vicente Delgado Bermejo: Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain
Javier Fernández Álvarez: National Association of Breeders of Murciano-Granadina Goat Breed, Fuente Vaqueros, 18340 Granada, Spain
María del Amparo Martínez Martínez: Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain

Mathematics, 2020, vol. 8, issue 9, 1-21

Abstract: SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the lactation curve of 159 Murciano-Granadina goats selected for genotyping analyses. Lactation curve shape, peak and persistence were evaluated for each model using 3107 milk yield controls with an average of 3.78 ± 2.05 lactations per goat. Best fit (Adjusted R 2 ) values (0.47) were reached by the five-parameter logarithmic model of Ali and Schaeffer. Three main possibilities were detected: non-fitting (did not converge), standard (Adjusted R 2 over 75%) and atypical curves (Adjusted R 2 below 75%). All the goats fitted for 38 models. The ability to fit different possible functional forms for each goat, which progressively increased with the number of parameters comprised in each model, translated into a higher sensitivity to explaining curve shape individual variability. However, for models for which all goats fitted, only moderate increases in explanatory and predictive potential (AIC, AICc or BIC) were found. The Ali and Schaeffer model reported the best fitting results to study the genetic variability behind goat milk yield and perhaps enhance the evaluation of curve parameters as trustable future selection criteria to face the future challenges offered by the goat dairy industry.

Keywords: goodness of fit; linear and nonlinear regression; mathematical modeling; parametric models; shape of lactation curve (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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