Delibere ANAFIBJ: riflessioni e considerazioni

Lo scorso 16 Novembre 2022, presso la sede dell’ANAFIBJ, si sono tenute le riunioni della Commissione Tecnica Centrale (CTC) della razza Frisona e della razza Jersey. In questo articolo vogliamo riportarvi alcune delle riflessioni e dei commenti condivisi con i tecnici dell’Associazione e gli altri membri delle CTC

Predictive ability of mid-infrared spectroscopy for major mineral composition and coagulation traits of bovine milk by using the uninformative variable selection algorithm

Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n = 923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000 cm 121 was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale

Application of a handheld near-infrared spectrometer to predict gelatinized starch, fiber fractions, and mineral content of ground and intact extruded dry dog food

The aim of the present study was to investigate the ability of a handheld near-infrared spectrometer to predict total and gelatinized starch, insoluble fibrous fractions, and mineral content inextruded dry dog food. Intact and ground samples were compared to determine if the homogenization could improve the prediction performance of the instrument. Reference analyses were performed on 81 samples for starch and 99 for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergentlignin (ADL), and minerals, and reflectance infrared spectra (740 to 1070 nm) were recorded with aSCiO™near-infrared (NIR) spectrometer. Prediction models were developed using modified partial least squares regression and both internal (leave-one-out cross-validation) and external validation.The best prediction models in cross-validation using ground samples were obtained for gelatinized starch (residual predictive deviation, RPD = 2.54) and total starch (RPD = 2.33), and S (RPD = 1.92), while the best using intact samples were obtained for gelatinized starch (RPD = 2.45), total starch (RPD = 2.08), and K (RPD = 1.98). Through external validation, the best statistics were obtained for gelatinized starch, with an RPD of 2.55 and 2.03 in ground and intact samples, respectively. Overall, there was no difference in prediction models accuracy using ground or intact samples. In conclusion, the miniaturized NIR instrument offers the potential for screening purposes only for total and gelatinized starch, S, and K, whereas the results do not support its applicability for the other traits

Effect of morphological characteristics and productive traits on the survival of dual-purpose Simmental cows

In recent years, dairy farmers have observed a substantial decrease in cow survival, with a direct negative consequence on the profitability. Shorter lifespan raises questions about animal welfare and farming conditions at which cows are exposed to. Traditionally, the cows’ productive life length is affected by voluntary and involuntary culling, e.g. sale, slaughter, salvage, or death. Culling risks are affected by animal-related features such as calving events, lactation stage, energy balance, reproduction, and aging, but also external factors, namely management and season. The present study aimed to investigate the morphological and productive traits affecting the survival of Italian Simmental dual-purpose cattle. Data available belonged to 2656 Italian Simmental dairy cows from 324 dairy herds (Emilia Romagna region, Italy). Cows involved in the study were linear classified once, as primiparous, between the 2002/2003 to 2019/2020 dairy seasons. Kaplan-Meier survival analyses were performed with the LIFETEST procedure of SAS software v 9.4 using milk yield, muscularity, and body condition score (in classes) as independent variable and survival at the subsequent lactation up to the 6th as dependent variable. As expected, the culling risk increased with parity, i.e. as the age of cows progressed. In general, animals with a low production level and medium body conditions have the highest probability of survival compared to high-producing cows. This trend was more evident in later parities: in fact, cows in parity 5 with low milk production and medium muscularity were more likely (+20.11% of probability) to survive at the subsequent lactation compared to others (13.99 vs 10.49, for medium and high production levels, respectively). Moreover, high-producing cows with medium body conditions were those with the lowest probability to survive at the subsequent lactation. The reasons that could explain these findings may be linked to the metabolic stress experienced by the cows during lactation. Indeed, especially during the peripartum period, lactating cows undergo a state of negative energy balance and a reduction of immune competence. Results from this study indicated that other than productivity level, morphological traits are important for making culling decisions in the Italian Simmental cattle

Phenotypic characterisation of major mineral composition predicted by mid-infrared spectroscopy in cow milk

Population-level phenotyping is, for some traits, hardly achievable due to limitation of reference analyses. Mid-infrared spectroscopy (MIRS) is a quick and cost-effective laboratory technique allowing the prediction of innovative milk quality features on a large scale. The present study aimed at investigating factors associated with milk Ca, K, Mg, Na and P contents predicted by MIRS models on a large multi-breed spectral data set of 123,240 test-day records. Two dairy (Holstein–Friesian and Brown Swiss) and two dual-purpose (Simmental and Alpine Grey) cattle breeds were considered. Sources of variation of predicted milk mineral contents were studied using linear mixed models, including the fixed effects of breed, month of sampling, stage of lactation, parity and interactions between the main effects. Random factors were herd nested within breed, cow nested within breed and the residual. Calcium content was greater in milk of dual-purpose than dairy cattle breeds. Simmental cows produced milk with the greatest content of Ca and Na, and milk of Holstein-Friesian had the lowest P content. Variation of content of Ca, Mg, Na and P throughout lactations exhibited an opposite trend to that of milk yield, with the lowest values around the peak of lactation. On the other hand, K content mirrored the trend of milk yield. Multiparous cows had lower content of milk minerals, with the exception of Na, compared with first parity animals. Environmental factors identified in the present study can be considered for within-breed genetic evaluation to adjust records of milk mineral contents for these effects

phenotypic variation of milk fatty acid composition of pinzgauer cattle breed

This paper investigated sources of variation of milk fatty acid (FA, g/100 g total FA) profile routinely predicted by mid-infrared spectroscopy in Pinzgauer dual-purpose cattle breed. A total of 19,578 individual milk samples collected from 1232 cows and 77 herds between 2011 and 2014 were available for phenotypic characterisation. Data were analysed using a linear mixed model which included stage of lactation, parity and their interaction as fixed effects, and cow and herd-test-day as random effects. Milk yield averaged 20.87 kg/d, and means for fat, protein and casein were 4.01, 3.44 and 2.72%, respectively. Saturated FA (SFA), monounsaturated FA (MUFA) and polyunsaturated FA (PUFA) averaged 69.20, 25.43 and 3.19 g/100 g total FA, respectively. About half of the SFA were provided by C16:0 (31.24 g/100 g total FA), and almost all MUFA by C18:1 (22.18 g/100 g total FA). Fixed effects were significant in explaining the variation of the studied traits. Milk of first-parity cows had the lowest percentage of SFA, medium-chain FA and C14:0, and the greatest percentage of MUFA, PUFA, trans FA, short- and long-chain FA, and C18:1. Saturated FA, C14:0 and C16:0 increased from calving until 120 days in milk, whereas unsaturated FA, MUFA, PUFA, long-chain FA, C18:0 and C18:1 decreased. Although both parity and stage of lactation affected milk FA, greater variation was observed during lactation than among parities

Grazing affects metabolic pattern of individual cow milk

Effective traceability tools able to characterize milk from pasture are important to safeguard low-input farming systems, niche dairy products, and local traditions. The aims of the present study were to investigate the ability of proton nuclear magnetic resonance (1H NMR) spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season, and to assess the effects of grazing on milk metabolites. The research trial involved a single alpine holding with 72 lactating cows. Individual milks were repeatedly sampled from the same animals before (i.e., d −3 and −1) and after (i.e., d 2, 3, 7, 10, and 14) the onset of the grazing period. One-dimensional 1H NMR spectra of milk extracts were collected through a Bruker spectrometer. Random forest discriminant analysis was applied to 1H NMR spectra to predict the period of collection for each sample. Data concerning the relative abundance of milk metabolites were analyzed through a linear mixed model, which included the fixed effects of period of sampling, cow breed, stage of lactation, and parity, and the random effect of cow nested within breed. The random forest model exhibited great accuracy (93.1%) in discriminating between samples collected on d −3, −1, 2, and 3 and those collected on d 7, 10, and 14. Univariate analysis performed on the 40 detected metabolites highlighted that milk samples from pasture had lower levels of 14 compounds (with fumarate being the most depressed metabolite) and greater levels of 15 compounds (with methanol and hippurate being the most elevated metabolites). Results indicate that milk 1H NMR spectra are promising to identify milk produced in different conditions. Also, our study highlights that grazing is associated with significant changes of milk metabolic profile, suggesting the potential use of several metabolites as indicators of farm management

Phenotypic characterisation of milk technological traits,protein fractions,and major mineral and fatty acid composition of Burlina cattle breed

The aim of the present study was to characterise milk of Burlina local cattle breed for traits of technological and nutritional relevance, such as milk coagulation properties (MCP), and protein, major mineral and fatty acid (FA) composition. Burlina is mainly reared in mountain areas of Veneto Region (Italy) and it has been inserted in conservation plans aiming to avoid biodiversity loss and marginal pasture areas abandonment. Eighty-one individual milk samples were collected in four farms. Milk coagulation properties were determined using Formagraph, and protein, mineral and FA composition were analysed in high performance liquid chromatography, inductively coupled plasma optical emission spectrometry and gas chromatography, respectively. Results evidenced good protein percentage (3.38%) and considerable casein content (28.89 mg/mL), as well as a desirable FA profile, with ω-6 to ω-3 ratio of 4.04. Somatic cell score, averaging 3.13, is a trait that should be enhanced through the improvement of farm management. This would have positive effects on MCP. Among milk minerals, the most and less abundant were K (1493.53 mg/kg) and Mg (110.07 mg/kg), respectively. Overall, herd, parity and lactation stage explained moderate to low variation of the studied traits. Results of the present study could be useful to valorise Burlina local breed and preserve biodiversity in marginal areas

Study of fertility traits in Italian Jersey Cattle

Selection Index of Italian Jersey cattle is composed by breeding values (BVs) of production traits, type traits and somatic cell score. Knowing selection for milk yield has been proved to deteriorate fertility performances, aim of the Italian Holstein, Brown and Jersey Breeders Association (ANAFIBJ) is to develop a fertility index inspired to the model they use for Italian Holstein breed. The model used considers different phenotypes for heifer and cow evaluation, but while for Holstein only the first three parities are considered, for Jersey all parities are considered to overcome the lower size of the population. Starting from two different datasets, containing data on inseminations and lactations we derived several fertility traits. From the inseminations dataset we harvested information on the dates of services, which we grouped in baches of a maximum length of 300 days. The groups of inseminations have then been attached to the calvings happened in an interval of 260–300 days from at least one of the services in the group. Phenotypes estimated for both heifers and cows are Non-Return Rate at 56 days (NRR56) and Interval from First to Last service (IFL); Age at First Insemination (AFI) has been computed only for heifers, while Days from delivery To First Service (DTFS) and Equivalent Milk Yield 305 (EMY305) only for cows. The values of the phenotypes have been filtered to be in line with biological and economic criteria of cattle farming. The final dataset is composed of 35081 records for heifers and 100285 records for cows. Correlations and Co-Variances between the phenotypes have been calculated. For Heifers we observe a slight positive correlation between AFI and NRR56 (ρxy = −0.061) and a negative correlation between NRR56 and IFL (ρxy = −0.349); IFL and AFI are negatively correlated (ρxy = −0.048). For cow fertility traits NRR56 resulted to be positively correlated with DTFS (ρxy = 0.088) and negatively correlated with EMY305 (ρxy = −0.092) and IFL (ρxy = −0.293); DTFS resulted negatively correlated to IFL (ρxy = −0.015) and EMY305 (ρxy = −0.045), while IFL and EMY305 resulted positively correlated (ρxy = 0.083). The next step is to calculate genetic parameters and EBVs for fertility traits for Italian Jersey

Validation of genomic breeding values for feed efficiency using field data: experience from UNIBO experimental herd

A strategy to maximize genetic gain in dairy farming is to identify genetically elite females through genomic testing. Indeed, direct genomic values (DGV) are early accessible at a cost-effective also in young stock and are subsequently blended to estimated breeding values (GEBV) once prediction of genetic merit is undertaken when phenotypes are available. These criteria can be used by farmers to rank females and service the best ones with (sexsorted) semen of elite bulls to achieve greater gain while simultaneously reducing the number of non-productive animals. Feed efficiency is nowadays the most popular issue for animal scientists working with ruminants. The purpose of the current research was to validate feed efficiency breeding values using real dry matter intake (DMI) data. For this purpose, we quantified the association between DGV/GEBV of predicted feed efficiency (PFE) and on-field feed intake data. Up to date, the total number of genotyped subjects at the UNIBO experimental herd is 279. Cows are routinely enrolled into experimental nutritional trials in which individual DMI (kg/d) information are collected on a daily basis. DGV/GEBV for PFE are calculated by ANAFIBJ within the national genetic and genomic evaluation; proofs are standardized to a mean of 100 and standard deviation of 5. Data were analysed with a linear mixed model, separately for primiparae and pluriparae, by adjusting the dependant variable DMI for the fixed effects of daily milk yield (linear covariate), PFE DGV/GEBV (two classes: <100 or ≥100), and days-post-calving (<120 or ≥120); random terms were cow nested within experimental trial and contemporary group (experimental trial-test date). Mean PFE was 99.50 (±1.48) while daily individual DMI averaged 23.48 (±3.44) and 26.91 (±3.14) kg/d for primiparae and pluriparae, respectively. The Pearson’s correlation coefficient between PFE and feed intake was −0.14, suggesting that cows with higher genetic merit have lower DMI. Differences between least squares means of the two PFE levels were −0.23 (±1.10; p > 0.05) and −0.26 (±0.75; p > 0.05) kg DMI/d in first- and later-parity cows, respectively. Although not significantly different, estimates indicated that, irrespective of systematic effects, cows with higher genetic merit for PFE have a lower individual daily intake compared to those with a lower genetic merit. Future efforts should be pursued to augment the sample size in order to increase the robustness of these estimates