Milk urea content is receiving growing interest from science and industry as a tool to infer the protein adequacy of dairy cows' diets, nitrogen excretion and its environmental impact, and efficiency ...of animals' protein metabolism. Fourier-transform infrared (FTIR) prediction is a high-throughput method for rapidly and cheaply evaluating milk urea content at the population level. Existing knowledge of the major sources of variation (e.g., year, season, farming system, individual herd, and the cow's breed, parity, stage of lactation, and productive potential) is fragmentary. The objective of this work was to study at the population level the simultaneous effects of all the major sources of variation and their most important interactions. Milk urea content in 1,759,706 test day milk samples collected from 291,129 lactations of 115,819 cows from 6,430 herds over 8 yr was predicted by FTIR. The milk urea content data (and also milk protein percentage, for comparison) were analyzed using a linear model that included the effects of parity, days in milk (DIM) class, year, month, herd intensiveness level, cow productivity level, breed, and herd intensiveness and cow productivity levels within breed. All sources of variation of milk urea content proved highly significant, the most important in terms of F-value being breed > year > herd intensiveness level > parity. The ranking for milk protein was very different (DIM class > herd intensiveness level > parity > breed). The patterns of the least squares means for urea and protein contents of milk were also very different and sometimes contrasting. The seasonal variation in urea was sinusoidal. Urea content increased during the first 4 mo of lactation and then remained almost stable before decreasing after 11 mo. Specialized dairy breeds had lower average milk urea content than dual-purpose breeds; in the former case it was lower in Holsteins than in Brown Swiss, and in the latter it was lower in Simmentals than in Alpine Greys. The effect of herd intensiveness level was much stronger than the effect of cow productivity level; the increase in milk urea with increasing herd average daily milk yield was almost linear in the case of dairy breeds but curvilinear in dual-purpose breeds. The large differences in breed and the modest relationships with the cow's productive potential require further analysis at the genetic level to obtain information of potential use in genetic improvement of the dairy cow populations.
Preservation of biodiversity and genetic improvement of livestock populations are often considered to be antagonistic. Biodiversity affects meat quality (MQ) on different levels: among species (only ...three species yield 88% of the world’s meat); among breeds within species (there are more than 3000 cattle breeds worldwide, but about half are at an unknown risk of extinction, and only one-fourth of the others are not endangered); among animals within breed (in cattle populations with millions of individuals the median effective size is equivalent to only about 100 unrelated animals); and between alleles within animal genes (the inbreeding coefficients of individual animals are increasing with selection, and particularly genomic selection GS). Beef quality traits are very particular because they cannot be directly measured on living animals. The genetic improvement of beef quality can be pursued by different techniques. In chronological order, they are: phenotypic selection, which has created breed differentiation (not useful for MQ traits); selective breeding (heritability of beef quality traits varies considerably according to breed, trait and conditions; indirect selection through NIRS predictions, etc., (could be useful); the fixation of major gene mutations (the myostatin gene for double muscling, calpain (CAPN)-calpastatin (CAST) genes for beef tenderness; diacylglycerol O-Acyltransferase 1 for beef marbling, etc.); genomic and other omic approaches (strong increase in scientific studies, genome-wide association studies (GWAS), GS, gene network identification, etc.); the cloning of animals (not useful); and the cloning of tissues (cultivated meat).HIGHLIGHTSThe biodiversity of beef cattle populations is threatened by globalisation, intensification, and (genomic) selection, which increase inbreeding;Indirect phenotyping (NIRS) and genomic selection (GS) will allow the genetic improvement of beef quality traits.Animal cloning has no practical use, and cell cloning (cultured meat) needs further research and ethical debate.
Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. ...Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9–30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum—the visible, infrared-A, or infrared-B range—may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.
Abstract
Spectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This ...research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet–visible and near-infrared region (UV–Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV–Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV–Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.
In this research two-dimensional GC was used to analyse, for the first time, the detailed fatty acid (FA) profiles of 11 dairy matrices: raw milk (evening whole, evening partially skimmed, morning ...whole, and vat milk), cream, fresh cheese, whey, ricotta, scotta, 6- and 12-month-ripened cheeses, obtained across artisanal cheese- and ricotta-making trials carried out during the summer period while cows were on highland pastures. Samples were collected during 7 cheese- and ricotta-making procedures carried out at 2-week intervals from bulk milk to study possible differences in the transfer and modification of FA. Compared with morning milk, evening milk had fewer de novo synthetised FA. The detailed FA profile of partially skimmed milk differed little from that of evening whole milk before skimming, but the cream obtained differed from partially skimmed milk and from fresh cheese in about half the FA, due mainly to higher contents of all de novo FA, and lower contents of n-3 and n-6 FA. Fresh cheese and whey had similar FA profiles. The ricotta manufacturing process affected the partition of FA between ricotta and scotta, the FA profile of the latter differing in terms of groups and individual FA from the former, whereas ricotta and fresh cheese had similar composition of FA. In general, there was an increase in medium-chain saturated FA, and a decrease in many polyunsaturated FA during the first 6 months of ripening, but not during the second 6 months. Two-dimensional GC yielded a very detailed and informative FA profile on all the 11 dairy products and by-products analysed.
Cheese presents extensive variability in physical, chemical, and sensory characteristics according to the variety of processing methods and conditions used to create it. Relationships between the ...many characteristics of cheeses are known for single cheese types or by comparing a few of them, but not for a large number of cheese types. This case study used the properties recorded on 1,050 different cheeses from 107 producers grouped into 37 categories to analyze and quantify the interrelationships among the chemical and physical properties of many cheese types. The 15 cheese traits considered were ripening length, weight, firmness, adhesiveness, 6 different chemical characteristics, and 5 different color traits. As the 105 correlations between the 15 cheese traits were highly variable, a multivariate analysis was carried out. Four latent explanatory factors were extracted, representing 86% of the covariance matrix: the first factor (38% of covariance) was named Solids because it is mainly linked positively to fat, protein, water-soluble nitrogen, ash, firmness, adhesiveness, and ripening length, and negatively to moisture and lightness; the second factor (24%) was named Hue because it is linked positively to redness/blueness, yellowness/greenness, and chroma, and negatively to hue; the third factor (17%) was named Size because it is linked positively to weight, ripening length, firmness, and protein; and the fourth factor (7%) was named Basicity because it is linked positively to pH. The 37 cheese categories were grouped into 8 clusters and described using the latent factors: the Grana Padano cluster (characterized mainly by high Size scores); hard mountain cheeses (mainly high Solids scores); very soft cheeses (low Solids scores); blue cheeses (high Basicity scores), yellowish cheeses (high Hue scores), and 3 other clusters (soft cheeses, pasta filata and treated rind, and firm mountain cheeses) according to specific combinations of intermediate latent factors and cheese traits. In this case study, the high variability and interdependence of 15 major cheese traits can be substantially explained by only 4 latent factors, allowing us to identify and characterize 8 cheese type clusters.
The present study aims to validate the Gompertz model to predict the growth performance of chicken crosses according to growth curve parameters of the parental lines and the estimated heterosis for ...each curve parameter. A total of 252 one-day-old chicks of both sexes belonging to 6 genotypes, including Ross 308, Sassò (SA), Bionda Piemontese (BP), and Robusta Maculata (RM), and the crosses between these local breeds and SA (BP × SA and RM × SA) were randomly allocated in 18 pens (3 pens/genotype) in mixed-sex groups (14 animals/pen; 7 females and 7 males). The individual body weight (BW) of all birds was recorded once a week from hatching until slaughtering (81 d for Ross 308; 112 d for SA, 140 d for the other genotypes). We drew up our final dataset with 240 birds (40 birds/genotype; 20 females and 20 males). The growth curve of each genotype was described using the Gompertz model, and the heterosis for each growth curve parameter was calculated as the difference between F1 crosses and the average of parental breeds. The predicted growth curve parameters were evaluated by cross-validation. The Gompertz model accurately estimated the growth curves of all the genotypes (R2 > 0.90). Heterosis was significant for almost all growth curve parameters in both crosses (P < 0.05). Heterosis ranged from −13.0 to +11.5%, depending on parameters, but varied slightly between the crossbreeds (BP × SA and RM × SA). The predicted values of adult BW, weight at the inflection point, and maximum growth rate were overestimated for BP × SA and underestimated for RM × SA, with a mean error between observed and predicted values <│2.7│% for all the curve parameters. In conclusion, the growth performance of chicken crosses between local breeds and commercial strains can be accurately predicted with Gompertz parameters of the parental lines adjusting for heterosis.
Mastitis is one of the most prevalent and costly diseases in dairy cattle. It results in changes in milk composition and quality which are indicators of udder inflammation in absence of clinical ...signs. We applied structural equation modeling (SEM) - GWAS aiming to explore interrelated dependency relationships among phenotypes related to udder health, including milk yield (MY), somatic cell score (SCS), lactose (%, LACT), pH and non-casein N (NCN, % of total milk N), in a cohort of 1,158 Brown Swiss cows. The phenotypic network inferred via the Hill-Climbing algorithm was used to estimate SEM parameters. Integration of multi-trait models-GWAS and SEM-GWAS identified six significant SNPs for SCS, and quantified the contribution of MY and LACT acting as mediator traits to total SNP effects. Functional analyses revealed that overrepresented pathways were often shared among traits and were consistent with biological knowledge (e.g., membrane transport activity for pH and MY or Wnt signaling for SCS and NCN). In summary, SEM-GWAS offered new insights on the relationships among udder health phenotypes and on the path of SNP effects, providing useful information for genetic improvement and management strategies in dairy cattle.
Milk from cows and buffaloes represents the major source of dairy products worldwide. Cheese is consumed fresh, ripened, cooked, or as recipe ingredient in many different types. Using Paneer and ...Tosella/Schiz model cheeses as a case study of cooked cheeses, in this short communication we evaluated the cheese yield and quality of fresh cheese before and after cooking comparing the two major dairy species (cows and buffaloes). A total of 75 model cheeses (37 Paneer and 38 Tosella/Schiz) were made. Slices of all the cheeses were cooked on a griddle at 130 °C. Milk, whey, and cheese composition traits, cheese-making efficiency traits, and cooked cheese quality traits were evaluated using a mixed model that included the fixed effects of cheese type and animal species, and their interaction, and the random effects of session, animal within species, and the residual. Higher cheese yields are obtained from the buffalo milk not only because more concentrated in nutrients, but also for the greater recovery of nutrients in cheese. There was a greater recovery of protein with the acid/heat Paneer procedure than the rennet Tosella/Schiz procedure, but we found a species × cheese type interaction for fresh cheese yield. The interaction was related mainly to the higher water retained in the Tosella/Schiz curd rather than to total solids. This led also to greater cooking weight-loss compared with Paneer. The qualitative traits of cheese were highly affected by griddling. Future research on the sensorial perception of cooked cheeses from the two major dairy species is needed to predict consumer response.
This study aimed to provide a comprehensive evaluation of the greenhouse gases (GHG) emission between 1991 and 2021 of both the entire Italian dairy cattle sector (ITA_POP) and the registered ...Holstein Friesian population only (HF). An attributional cradle-to-farm gate Life Cycle Assessment was performed, including emissions (methane–CH4, nitrous oxide–N2O, carbon dioxide–CO2) due to animal and manure management, feed production, and farm materials. The functional units were 1 year and 1 kg of fat- and protein-corrected milk. We applied 100-year global warming potential (GWP) metric. For 2011–2021 period, we also applied GWP star (GWP*). Data originated from Italian GHG accounting and official milk recording systems. From 1991 to 2021, annual GHG emission of ITA_POP decreased from 20.4 ± 2.7 to 15.5 ± 2.1 Mt CO2e and emission intensity (EI) from 1.90 ± 0.25 to 1.17 ± 0.16 kg CO2e/kg corrected milk. The share of officially registered HF cows in ITA_POP increased from 36 to 70%. The annual emission of HF increased by 32% to 12.2 ± 1.8 Mt CO2e, but its EI decreased by 34% to 1.09 ± 0.16 kg CO2e/kg corrected milk. The accumulation in GHGs in 2011–2021 period due to ITA_POP (100-year GWP) continuously increased. When considering the short lifetime of CH4 (GWP*), the rate of increase in the same years was quite lower, with a flat pattern in the last 5 years. The decrease in CH4 emission is compensating the accumulation of N2O and CO2 from fossil fuels, highlighting the need of considering GHGs lifetime in the estimation of dairy cow sector emissions.
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