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Alkaline phosphatase (ALP) is a native raw-milk enzyme used in many countries as the standard assay for rapidly validating the milk pasteurization process. Due to the increased restrictions on the production or import of cheeses produced from unpasteurized milk, ALP activity (<10 mU/g) in cheese was measured as a simple and reliable method to check proper milk pasteurization in cheese for both safety inspection and trading controls. In Sicily, the artisanal cheesemaking of the Protected Denomination of Origin (PDO) semi-hard cheeses made with raw sheep milk, includes the cooking of the curd, after whey separation, in a wooden vat under hot Scotta whey (≥80°C), for 3 to 4 h, and finally is left to cool at ambient temperature. Thus, the temperatures adopted during cheesemaking may inactivate the ALP enzyme. To this purpose, the aim of this study was to demonstrate how different temperatures of Scotta whey (35°C [T35], 60°C [T60], 70°C [T70], 80°C [T80], 90°C [T90], and 100°C [T100]) used during the second cooking of Pecorino cheeses after molding for 3 h, influence the ALP activity in fresh and 3-mo aged cheese, both at core and outside. The results highlight that the rate of reduction of ALP was greater with increasing temperature of the second cooking, in particular for T 80°C curd, indicating that the use of Scotta whey >80°C could be a breakpoint able to reduce the ALP activity to values <10 mU/g. Different effects between the core and the outside portions of the experimental cheeses were found, with a decrease in ALP activity more on the outside than in the core portions, in both fresh and 3-mo aged cheeses, for T80, T90, and T100 treatments. Care must be taken in using ALP to control the use of pasteurized milk in the production of PDO cheeses without considering the cheesemaking processes, such as the second cooking, which could be equal to pasteurization, and an adequate interaction of time and temperature can reduce the ALP activity to values comparable with cheeses produced with pasteurized milk., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

As part of a larger project aiming to develop management evaluation tools based on results from test-day (TD) models, the objective of this study was to examine the effect of physical composition of total mixed rations (TMR) tested quarterly from March 2006 through December 2008 on milk, fat, and protein yield curves for 25 herds in Ragusa, Sicily. A random regression sire-maternal grandsire model was used to estimate variance components for milk, fat, and protein yields fitted on a full data set, including 241,153 TD records from 9,809 animals in 42 herds recorded from 1995 through 2008. The model included parity, age at calving, year at calving, and stage of pregnancy as fixed effects. Random effects were herd × test date, sire and maternal grandsire additive genetic effect, and permanent environmental effect modeled using third-order Legendre polynomials. Model fitting was carried out using ASREML. Afterward, for the 25 herds involved in the study, 9 particle size classes were defined based on the proportions of TMR particles on the top (19-mm) and middle (8-mm) screen of the Penn State Particle Separator. Subsequently, the model with estimated variance components was used to examine the influence of TMR particle size class on milk, fat, and protein yield curves. An interaction was included with the particle size class and days in milk. The effect of the TMR particle size class was modeled using a ninth-order Legendre polynomial. Lactation curves were predicted from the model while controlling for TMR chemical composition (crude protein content of 15.5%, neutral detergent fiber of 40.7%, and starch of 19.7% for all classes), to have pure estimates of particle distribution not confounded by nutrient content of TMR. We found little effect of class of particle proportions on milk yield and fat yield curves. Protein yield was greater for sieve classes with 10.4 to 17.4% of TMR particles retained on the top (19-mm) sieve. Optimal distributions different from those recommended may reflect regional differences based on climate and types and quality of forages fed., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to examine the effect of the chemical composition of a total mixed ration (TMR) tested quarterly from March 2006 through December 2008 for milk, fat, and protein yield curves for 27 herds in Ragusa, Sicily. Before this study, standard yield curves were generated on data from 241,153 test-day records of 9,809 animals from 42 herds in Ragusa province collected from 1995 to 2008. A random regression sire-maternal grandsire model was used to develop variance components for yields. The model included parity, age at calving, year at calving, and stage of pregnancy as fixed effects. Random effects were herd × test date, sire and maternal grandsire additive genetic effect, and permanent environmental effect modeled using third-order Legendre polynomials. Model fitting was carried out using ASReml. Subsequently, the model with estimated variance components was used to examine the influence of TMR crude protein, soluble N, acid detergent lignin, neutral detergent fiber, acid detergent fiber, starch, and ash on milk, fat, and protein yield curves. The data set contained 46,531 test-day milk yield records from 3,554 cows in the 27 herds recorded during the study period. Initially, an analysis was performed using one dietary component (one-component analysis) within each model as a fixed effect associated with the test-day record closest to the months the TMR was sampled within each herd. An interaction was included with the nutrient component and days in milk. The effect of the TMR chemical component(s) was modeled using a ninth-order Legendre polynomial. The conditional Wald F-statistic for the fixed effects revealed significant effects for acid detergent fiber, neutral detergent fiber, crude protein, starch, and their interactions with days in milk on milk, fat, and protein yield. On the basis of these results, a multicomponent analysis was performed in which crude protein, neutral detergent fiber, and starch were simultaneously included in the model with days in milk interactions. Although both analyses revealed that diet composition influenced production responses depending on lactation stage, the multiple-component analysis showed more pronounced effects of starch and neutral detergent fiber relative to crude protein for all traits throughout lactation., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Not only feed but also respective bolus particle size could alter diet efficiency and cow performance. The objective of this project was to characterize particle size of selected feeds and respective swallowed boli. Feed samples included 6 different particle length rye grass hay samples, 1 grass silage, 1 corn silage, and 1 total mixed ration (TMR). Rye grass hay samples consisted of long hay and chopped hay particles retained on the 19- (19&#95;PSPS hay), 8- (8&#95;PSPS hay), and 1.18-mm (1.18&#95;PSPS hay) Penn State Particle Separator (PSPS) screens and those collected on the pan (PSPS&#95;pan hay). A sixth hay treatment was rye grass forage cut at 50-mm lengths and dried to hay (50-mm hay). Treatments were offered to 4 nonlactating and 4 lactating cows following rumen evacuation. Swallowed boli were collected and the number of chews per gram of ingested feed dry matter was determined. Feed and bolus particles of lengths ≥5mm were collected on a 1.6-mm screen using a horizontal wet sieving technique. This cut point was chosen, as the literature suggests that most fecal particles are shorter than 5mm. Dry matter proportions on this screen (PROP&#95;1.6) were determined and particle lengths of retained particles were measured by image analysis. Mean particle lengths (ML) were calculated considering particles ≥5mm in length. Boli of long hay, of 19&#95;PSPS hay, of 8&#95;PSPS hay, and of 50-mm hay had similar ML of 10 to 11mm. Bolus PROP&#95;1.6 were also similar between these treatments, ranging from 0.54 to 0.69. Bolus particle lengths and distributions of these treatments were not related to respective hay particles. Bolus of 1.18&#95;PSPS hay had PROP&#95;1.6 of 0.51 and a smaller ML of 8mm. The PSPS&#95;pan hay had PROP&#95;1.6 of only 0.33, but was still chewed intensely. Apparently, little particle size reduction occurred when cows ate the TMR or the silages. Feed and respective bolus PROP&#95;1.6 were as follows: 0.66 and 0.59 in grass silage, 0.52 and 0.55 in corn silage, and 0.44 and 0.38 in the TMR. Feed and respective bolus ML were as follows: 13.8 and 11.6mm in grass silage, 12.0 and 11.2mm in corn silage, and 13.1 and 12.5mm in the TMR. Rye grass hay particles retained on PSPS screens ≥8mm, with ML of at least 25mm were longer compared with TMR particles, but respective bolus particles were shorter. Bolus particle size is not associated with the size of large feed particles chewed to a constant size that is appropriate for deglutition. This size may be related to feed chemical composition., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Body condition score (BCS) is considered an important tool for management of dairy cattle. The feasibility of estimating the BCS from digital images has been demonstrated in recent work. Regression machines have been successfully employed for automatic BCS estimation, taking into account information of the overall shape or information extracted on anatomical points of the shape. Despite the progress in this research area, such studies have not addressed the problem of modeling the shape of cows to build a robust descriptor for automatic BCS estimation. Moreover, a benchmark data set of images meant as a point of reference for quantitative evaluation and comparison of different automatic estimation methods for BCS is lacking. The main objective of this study was to develop a technique that was able to describe the body shape of cows in a reconstructive way. Images, used to build a benchmark data set for developing an automatic system for BCS, were taken using a camera placed above an exit gate from the milking robot. The camera was positioned at 3 m from the ground and in such a position to capture images of the rear, dorsal pelvic, and loin area of cows. The BCS of each cow was estimated on site by 2 technicians and associated to the cow images. The benchmark data set contained 286 images with associated BCS, anatomical points, and shapes. It was used for quantitative evaluation. A set of example cow body shapes was created. Linear and polynomial kernel principal component analysis was used to reconstruct shapes of cows using a linear combination of basic shapes constructed from the example database. In this manner, a cow's body shape was described by considering her variability from the average shape. The method produced a compact description of the shape to be used for automatic estimation of BCS. Model validation showed that the polynomial model proposed in this study performs better (error=0.31) than other state-of-the-art methods in estimating BCS even at the extreme values of BCS scale., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Earlier studies identified large between-herd variation in estimated lactation curve parameters from test-day milk yield and milk composition records collected in Ragusa province, Italy. The objective of this study was to identify sources of variation able to explain these between-herd differences in milk production curves, by estimating associations of animal breed (Holstein Friesian vs. Brown Swiss), feeding system [separate feeding (SF) vs. total mixed ration (TMR)], and TMR chemical composition on milk and milk components herd curves. Data recorded from 1992 through 2007 for test-day (TD) milk, fat, and protein yields from 1,287,019 records of 148,951 lactations of 51,489 cows in 427 herds were processed using a random regression TD model. Random herd curves (HCUR) for milk, fat, and protein yields were estimated from the model per herd, year, and parity (1, 2, and 3+) using 4-order Legendre polynomials. From March 2006 through December 2007, samples of TMR were collected every 3 mo from 37 farms in Ragusa province. Samples were analyzed for dry matter, ash, crude protein, soluble nitrogen, acid detergent lignin, neutral detergent fiber, acid detergent fiber, and starch. Traits used to describe milk production curves were peak, days in milk at peak, persistency, and mean. Association of feeding system and animal breed with HCUR traits was investigated using a general mixed model procedure. Association of TMR chemical composition with HCUR traits was investigated using multivariate analysis with regression and stepwise model selection. Results were consistent for all traits and parities. Feeding system was significantly associated with HCUR peak and mean, with higher values for TMR. Animal breed was significantly associated with HCUR persistency, with higher values for Brown Swiss herds. Furthermore, animal breed influenced HCUR peak and mean, with higher values for Holstein Friesian herds. Crude protein had the largest effect on HCUR peak and mean, whereas the interaction between crude protein and dry matter mainly affected persistency. When provided by a national evaluation system, HCUR can be used as an indicator of herd feeding management., (Copyright © 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Test-day (TD) models are used in most countries to perform national genetic evaluations for dairy cattle. The TD models estimate lactation curves and their changes as well as variation in populations. Although potentially useful, little attention has been given to the application of TD models for management purposes. The potential of the TD model for management use depends on its ability to describe within- or between-herd variation that can be linked to specific management practices. The aim of this study was to estimate variance components for milk yield, milk component yields, and somatic cell score (SCS) of dairy cows in the Ragusa and Vicenza areas of Italy, such that the most relevant sources of variation can be identified for the development of management parameters. The available data set contained 1,080,637 TD records of 42,817 cows in 471 herds. Variance components were estimated with a multilactation, random-regression, TD animal model by using the software adopted by NRS for the Dutch national genetic evaluation. The model comprised 5 fixed effects [region x parity x days in milk (DIM), parity x year of calving x season of calving x DIM, parity x age at calving x year of calving, parity x calving interval x stage of pregnancy, and year of test x calendar week of test] and random herd x test date, regressions for herd lactation curve (HCUR), the animal additive genetic effect, and the permanent environmental effect by using fourth-order Legendre polynomials. The HCUR variances for milk and protein yields were highest around the time of peak yield (DIM 50 to 150), whereas for fat yield the HCUR variance was relatively constant throughout first lactation and decreased following the peak around 40 to 90 DIM for lactations 2 and 3. For SCS, the HCUR variances were relatively small compared with the genetic, permanent environmental, and residual variances. For all the traits except SCS, the variance explained by random herd x test date was much smaller than the HCUR variance, which indicates that the development of management parameters should focus on between-herd parameters during peak lactation for milk and milk components. For SCS, the within-herd variance was greater than the between-herd variance, suggesting that the focus should be on management parameters explaining variances at the cow level. The present study showed clear evidence for the benefits of using a random regression TD model for management decisions.