Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.

Introduction.For dairy producers who want to transform their milk, the ability of milk to coagulate is an important parameter. It makes it possible to transform milk into cheese. Therefore, it is necessary to understand the coagulation process and the techniques to measure it in order to achieve the best transformation performance. The objective of this review is to describe the milk coagulation process, the factors influencing it and the methods for measuring the coagulation of milk at lab level. Literature. The processing of milk into cheese involves three steps: coagulation, dewatering and refining. Coagulation is a key step which involves the use of rennet and depends on several parameters (pH, calcium content, temperature, etc.). Some milks never coagulate. To measure the coagulation ability of milk and identify different parameters in milk coagulation properties, the Formagraph, the computerized renneting meter and the Optigraph have been developed (reference methods). Equations have been developed using infrared spectrometry to predict the parameters obtained by the reference methods. Conclusions. The milk coagulation mechanism is known. However, the issue of non-coagulating milk persists and represents a real challenge in terms of yield. The use of infrared is a faster alternative to reference methods that measure the coagulation properties of milk, but still requires an improvement in prediction equations.

Description of the subject. Given the current low price of milk, a lot of producers have decided to process their milk into products with a higher added-value, including butter. However, all milks are not suitable to be transformed into butter. It would thus be useful to be able to predict milk processing properties. Objectives. The aim of this paper was to study the ability of milk to be processed into butter using infrared spectrophotometry. Method. A normalized protocol for the production of butter was developed. Milk samples (n = 110) collected between 2013 and 2016 were analyzed by near and medium infrared spectrometry (315 spectra). Butter samples were also analyzed by visible-near infrared spectrometry (220 spectra). Composition of the products was subsequently assessed using validated prediction equations. Principal components analyses were performed to discriminate samples. Results. Butter properties seemed to be influenced by seasons and feedings. Water content and color parameters could be predicted on the basis of butter infrared spectra. Conclusions. It was possible to correlate butter characteristics with milk properties. However, it was not possible to predict butter characteristics on the basis of milk near infrared spectra. It could be interesting to try predictions from milk medium infrared spectra.