Predicting milk mid-infrared spectra from first-parity Holstein cows using a test-day mixed model with the perspective of herd management.
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[1] G. Corrieu,et al. Monitoring of fermentation by infrared spectrometry: Alcoholic and lactic fermentations , 1993 .
[2] D. Kelton,et al. Use of test day milk fat and milk protein to detect subclinical ketosis in dairy cattle in Ontario. , 1997, The Canadian veterinary journal = La revue veterinaire canadienne.
[3] J. Hamann,et al. Potential of specific milk composition variables for cow health management , 1997 .
[4] G. Wiggans,et al. A computationally feasible test day model for genetic evaluation of yield traits in the United States. , 1997, Journal of dairy science.
[5] R. Palm. L'analyse en composantes principales : principes et applications , 1998 .
[6] I. Misztal,et al. Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications. , 2001, Journal of animal science.
[7] S. Sivakesava,et al. Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy. , 2002, Journal of dairy science.
[8] Alternative modeling of fixed effects in test day models to increase their usefulness for management decisions , 2002 .
[9] N. Gengler,et al. Prediction of daily milk, fat, and protein production by a random regression test-day model. , 2004, Journal of dairy science.
[10] S. Garrigues,et al. Nutritional parameters of commercially available milk samples by FTIR and chemometric techniques , 2004 .
[11] A. D. de Roos,et al. Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in The Netherlands. , 2004, Journal of dairy science.
[12] J. Nousiainen,et al. Use of herd solutions from a random regression test-day model for diagnostic dairy herd management. , 2007, Journal of dairy science.
[13] R. Veerkamp,et al. Variance components for test-day milk, fat, and protein yield, and somatic cell score for analyzing management information. , 2008, Journal of dairy science.
[14] H. Soyeurt,et al. Accessing genotype by environment interaction using within- and across-country test-day random regression sire models. , 2009, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.
[15] P. Dardenne,et al. Potential estimation of major mineral contents in cow milk using mid-infrared spectrometry. , 2009, Journal of dairy science.
[16] L. Laloux,et al. Modeling milk urea of Walloon dairy cows in management perspectives. , 2009, Journal of dairy science.
[17] H. Soyeurt,et al. Genetic variability of milk components based on mid-infrared spectral data. , 2010, Journal of dairy science.
[18] Nicolas Gengler,et al. Adding value to test-day data by using modified best prediction method , 2010 .
[19] P. Dardenne,et al. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. , 2011, Journal of dairy science.
[20] M. Mele,et al. Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk. , 2011, Animal : an international journal of animal bioscience.
[21] P. Carnier,et al. Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows. , 2011, Journal of dairy science.
[22] R. Veerkamp,et al. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. , 2011, Journal of dairy science.
[23] Peter Christen,et al. Data Pre-Processing , 2012 .
[24] G. Bittante,et al. The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from unprocessed bovine milk samples. , 2013, Journal of dairy science.
[25] T. Meuwissen,et al. Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats. , 2013, Journal of dairy science.
[26] Hae-Young Kim. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis , 2013, Restorative dentistry & endodontics.
[27] G. Bittante,et al. Genetic analysis of the Fourier-transform infrared spectra of bovine milk with emphasis on individual wavelengths related to specific chemical bonds. , 2013, Journal of dairy science.
[28] Genetic and environmental information in goat milk Fourier transform infrared spectra. , 2013, Journal of dairy science.
[29] H. Soyeurt,et al. Genetic variability of the mid-infrared prediction of lactoferrin content in milk for Walloon Holstein first-parity cows , 2013 .
[30] Jan LW Rademaker,et al. Verification of fresh grass feeding, pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk , 2014 .
[31] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[32] D. Ballabio,et al. Multi-method Approach to Trace the Geographical Origin of Alpine Milk: a Case Study of Tyrol Region , 2016, Food Analytical Methods.
[33] A. Brodkorb,et al. Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows. , 2015, Journal of dairy science.
[34] P. Dardenne,et al. Capitalizing on fine milk composition for breeding and management of dairy cows. , 2016, Journal of dairy science.
[35] H. Bovenhuis,et al. Genetic and environmental variation in bovine milk infrared spectra. , 2016, Journal of dairy science.
[36] J. A. Fernández Pierna,et al. Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network. , 2016, Journal of dairy science.
[37] P. Carnier,et al. Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk. , 2017, Journal of dairy science.
[38] L. Dale,et al. Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra. , 2017, Journal of dairy science.
[39] B. Dagnachew,et al. An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle. , 2017, Journal of dairy science.
[40] M. Gibbert,et al. How Does Material Resource Adequacy Affect Innovation Project Performance? A Meta-Analysis , 2017 .
[41] R. Leardi,et al. Building of prediction models by using Mid-Infrared spectroscopy and fatty acid profile to discriminate the geographical origin of sheep milk , 2017 .
[42] J. Sölkner,et al. First results in the use of milk mid-infrared spectra in the detection of lameness in Austrian dairy cows , 2017 .
[43] B. Kuhla,et al. Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers. , 2018, Journal of dairy science.
[44] G. de los Campos,et al. A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows. , 2019, Journal of dairy science.
[45] Jascha Sohl-Dickstein,et al. Measuring the Effects of Data Parallelism on Neural Network Training , 2018, J. Mach. Learn. Res..
[46] L. Janss,et al. Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey. , 2019, Journal of dairy science.
[47] Zhiquan Wang,et al. Contribution of milk mid-infrared spectrum to improve the accuracy of test-day body weight predicted from stage, lactation number, month of test and milk yield , 2019, Livestock Science.