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Inclusion of feed efficiency traits into the dairy cattle breeding programmes will require considering early lactation energy status to avoid deterioration in health and fertility of dairy cows. In this regard, energy status indicator (ESI) traits, for example, blood metabolites or milk fatty acids (FAs), are of interest. These indicators can be predicted from routine milk samples by mid-IR reflectance spectroscopy (MIR). In this study, we estimated genetic variation in ESI traits and their genetic correlation with female fertility in early lactation. The data consisted of 37 424 primiparous Nordic Red Dairy cows with milk test-day records between 8 and 91 days in milk (DIM). Routine test-day milk samples were analysed by MIR using previously developed calibration equations for blood plasma non-esterified FA (NEFA), milk FAs, milk beta-hydroxybutyrate (BHB) and milk acetone concentrations. Six ESI traits were considered and included: plasma NEFA concentration (mmol/l) either predicted by multiple linear regression including DIM, milk fat to protein ratio (FPR) and FAs C10:0, C14:0, C18:1 cis-9, C14:0 * C18:1 cis-9 (NEFAFA) or directly from milk MIR spectra (NEFAMIR), C18:1 cis-9 (g/100 ml milk), FPR, BHB (mmol/l milk) and acetone (mmol/l milk). The interval from calving to first insemination (ICF) was considered as the fertility trait. Data were analysed using linear mixed models. Heritability estimates varied during the first three lactation months from 0.13 to 0.19, 0.10 to 0.17, 0.09 to 0.14, 0.07 to 0.10, 0.13 to 0.17 and 0.13 to 0.18 for NEFAMIR, NEFAFA, C18:1 cis-9, FPR, milk BHB and acetone, respectively. Genetic correlations between all ESI traits and ICF were from 0.18 to 0.40 in the first lactation period (8 to 35 DIM), in general somewhat lower (0.03 to 0.43) in the second period (36 to 63 DIM) and decreased clearly (−0.02 to 0.19) in the third period (64 to 91 DIM). Our results indicate that genetic variation in energy status of cows in early lactation can be determined using MIR-predicted indicators. In addition, the markedly lower genetic correlation between ESI traits and fertility in the third lactation month indicated that energy status should be determined from the first test-day milk samples during the first 2 months of lactation.

Dairy cows have intense fluctuations in digestive, metabolic and hormonal systems around calving which predispose them to various disorders and health problems. The aim of the current experiment was to investigate feed and nutrient intake, rumen fermentation, rumen bacterial communities, milk production, milk fatty acid composition and plasma biomarker profiles of dairy cows to assess the modulation of these functions by in-feed resin acid inclusion. Thirty-six Nordic Red cows were used in a continuous feeding trial starting 3 weeks prepartum and lasting for 10 weeks into the lactation. The cows were fed grass silage ad libitum and the dietary treatments were 1) control with basal concentrate (CON), 2) CON supplemented with tall oil fatty acids (TOFA; 90 % fatty acids and 9% resin acids) at 7.0 g/cow/day and 3) CON supplemented with resin acid concentrate (RAC; 37.5% resin acids) at 1.7 g/cow/day. The mixture of resin acids in TOFA and RAC, consisting mostly of abietic and dehydroabietic acids, originated from coniferous tree species Pinus sylvestris L. and Picea abies L. Feed intake and milk production were measured throughout the experimental period. Milk and blood samples were collected at weeks 2, 3, 6 and 10, and rumen fluid was sampled at weeks 2 and 10 of lactation to analyse rumen fermentation and rumen bacterial communities. The dynamics in feed intake and milk production with progressing lactation showed typical curvilinear trends (P for time

In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations 1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping. ispartof: JOURNAL OF DAIRY SCIENCE vol:103 issue:7 pages:6422-6438 ispartof: location:United States status: published

In high yielding dairy cattle, severe postpartum negative energy status is often associated with metabolic and infectious disorders that negatively affect production, fertility and welfare. Mobilization of adipose tissue associated with a negative energy status is reflected through an increased level of non-esterified fatty acids (NEFA) in the blood plasma. Earlier, identification of a negative energy status through the detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently there have been attempts to predict blood NEFA concentration from milk samples. This study aimed to develop and validate a model to predict the blood plasma NEFA concentration using milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in weeks 2, 3 and 20 post-partum for 192 lactations in 3 different herds. The blood plasma samples were taken in the morning, while representative milk samples were collected during the morning and evening milk session on the same day. To predict the blood plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the two independent herds to explore their robustness and wide applicability. The final model can accurately predict blood plasma NEFA concentrations below 0.6 mmol/L with a root mean square error of prediction (RMSE) of less than 0.143 mmol/L. However, for blood plasma with more than 1.2 mmol/L NEFA, the model clearly underestimates the true level. Additionally, it was found that morning blood plasma NEFA levels were predicted with a significantly higher accuracy (p = 0.009) using MIR spectra of evening milk samples compared to morning samples, with RMSEP values of respectively 0.182 and 0.197 mmol/L and R2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for a negative energy status, indicated with detrimental morning blood plasma NEFA levels (> 0.6 mmol/L), could be identified with a sensitivity and specificity of respectively 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens opportunities for regular metabolic screening and improved resilience phenotyping.

Digestibility traits included in this study were dry matter digestibility (DMD, g/kg), which was calculated based on the indigestible neutral detergent fibre (iNDF, g/kg of dry matter) content in faeces (iNDFf) and in diet (iNDFd), and iNDFf predicted directly from faecal samples by near infrared reflectance spectroscopy (NIRS). The data set was collected at three research herds in Finland and one in Norway including in total 931 records from 328 lactating Nordic Red Cattle and Holstein cows. Observations were associated with different accuracy, due to the differences in sampling protocols used for collecting faecal samples. Heritability estimates varied between different sampling protocols and ranged from 0.14 ± 0.06 to 0.51 ± 0.24 for DMD and from 0.13 ± 0.05 to 0.48 ± 0.18 for iNDFf. Estimated genetic standard deviations were 10.5 g/kg and 6.2 g/kg dry matter for DMD and iNDFf, respectively. Results of our study indicated that recording only the iNDF content in the faeces is sufficient to determine genetic variation in cows’ ability to digest feed. The coefficient of genetic variation for DMD was rather small (1.7%), but could be utilized if it is supported by a positive analysis of benefits over costs. Genetic parameters for cow-specific digestibility predicted by near infrared reflectance spectroscopy

In this study, we aimed to estimate and compare the genetic parameters of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) as 3 feed efficiency-related traits across lactation in 3 dairy cattle breeds (Holstein, Nordic Red, and Jersey). The analyses were based on weekly records of DMI, ECM, and BW per cow across lactation for 842 primiparous Holstein cows, 746 primiparous Nordic Red cows, and 378 primiparous Jersey cows. A random regression model was applied to estimate variance components and genetic parameters for DMI, ECM, and BW in each lactation week within each breed. Phenotypic means of DMI, ECM, and BW observations across lactation showed to be in very similar patterns between breeds, whereas breed differences lay in the average level of DMI, ECM, and BW. Generally, for all studied breeds, the heritability for DMI ranged from 0.2 to 0.4 across lactation and was in a range similar to the heritability for ECM. The heritability for BW ranged from 0.4 to 0.6 across lactation, higher than the heritability for DMI or ECM. Among the studied breeds, the heritability estimates for DMI shared a very similar range between breeds, whereas the heritability estimates for ECM tended to be different between breeds. For BW, the heritability estimates also tended to follow a similar range between breeds. Among the studied traits, the genetic variance and heritability for DMI varied across lactation, and the genetic correlations between DMI at different lactation stages were less than unity, indicating a genetic heterogeneity of feed intake across lactation in dairy cattle. In contrast, BW was the most genetically consistent trait across lactation, where BW among all lactation weeks was highly correlated. Genetic correlations between DMI, ECM, and BW changed across lactation, especially in early lactation. Energy-corrected milk had a low genetic correlation with both DMI and BW at the beginning of lactation, whereas ECM was highly correlated with DMI in mid and late lactation. Based on our results, genetic heterogeneity of DMI, ECM, and BW across lactation generally was observed in all studied dairy breeds, especially for DMI, which should be carefully considered for the recording strategy of these traits. The genetic correlations between DMI, ECM, and BW changed across lactation and followed similar patterns between breeds.

Dry matter intake (DMI) is a key component of feed efficiency in dairy cattle. In this study, we estimated genetic parameters of DMI over the first 24 lactation weeks in 3 dairy cattle breeds: Holstein, Nordic Red, and Jersey. In total, 1,656 primiparous cows (717 Holstein, 663 Nordic Red, and 276 Jersey) from Denmark, Finland, and Sweden were studied. For each breed, variance components, heritability, and repeatability for weekly DMI were estimated in 6 consecutive periods of the first 24 lactation weeks based on a repeatability animal model. Genetic correlations for DMI between different lactation periods were estimated using bivariate models. Based on our results, Holstein and Nordic Red cows had similar DMI at the beginning of lactation, but later in lactation Holstein cows had a slightly higher DMI than Nordic Red cows. In comparison, Jersey cows had a significantly lower DMI than the other 2 breeds within the first 24 lactation weeks. Heritability estimates for DMI ranged from 0.20 to 0.40 in Holsteins, 0.25 to 0.41 in Nordic Red, and 0.17 to 0.42 in Jerseys within the first 24 lactation weeks. Genetic and phenotypic variances for DMI varied along lactation within each breed and tended to be higher in the middle of lactation than at the beginning of the lactation. High genetic correlations were noted for DMI in lactation wk 5 to 24 in all 3 breeds, whereas DMI at early lactation (lactation wk 1 to 4) tended to be genetically different from DMI in the middle of lactation. The 3 breeds in this study might differ in their genetic variances for DMI, but the differences were not statistically significant in most of the studied periods. Breed differences for the genetic variance tended to be more obvious than for heritability. The potential breed differences in genetic variation for DMI should be considered in a future study using feed intake information from multiple breeds.

Little information is available concerning the effects of offering additional concentrates with total mixed ration (TMR) on milk production. The aim of the present study was to compare TMR representing simplified feeding (TMR1) with TMR combined with a decreasing amount of additional concentrate (TMR2C). Finnish Ayrshire cows (39) were housed in a loose housing barn. TMR1 consisted of a mixed ration of grass silage (0.49) and concentrates (0.51). In TMR2C, the same grass silage and concentrate were mixed in a ratio of 55:45. In TMR2C, cows were offered additional concentrates from automatic feeders differing in composition from the concentrate in TMR1 [6.5 kg d-1 (first 100 days, Phase 1), 3.0 kg d-1 (subsequent 50 days, Phase 2) and no concentrate thereafter (Phase 3)]. During the whole experiment (224 days), total consumption of concentrates per cow averaged 2426 kg dry matter (TMR1) and 2414 kg dry matter (TMR2C). There were no significant differences in mean total dry matter, metabolizable energy, crude protein or absorbed amino acid intakes. During Phase 2, total intake of all cows fed TMR2C was one kg lower (P = 0.10) than for cows fed TMR1. This was due to differences in total feed intake of multiparous cows. Average yields (kg d-1) of milk, energy corrected milk, protein, fat and lactose were not significantly different between diets. During Phase 2, primiparous cows tended to produce more energy corrected milk on TMR2C than on TMR1. The results showed that both TMR1 and TMR2C were equal feeding strategies for early lactating cows and cows did not benefit from greater concentrate consumption in early stage of lactation when total consumption of concentrates was similar., Suurissa tuotantoyksiköissä pihatto-olosuhteissa lypsylehmätruokitaan yleisesti seosrehulla, mutta pihatoissa on usein käytössä myös väkirehukioskit. Ulkomaisia tai kotimaisia koetuloksia kioskiruokinnan yhdistämisestä seosrehuruokintaan ei kuitenkaan ole käytettävissä. Tämän tutkimuksen tarkoituksena oli selvittää väkirehukioskin merkitystä seosrehuruokinnan täydentäjänä pihatossa. Tutkimuksessa verrattiin toisiinsa pelkkää seosrehuruokintaa ja ruokintaa, jossa laimeampaa perusseosta täydennettiin lypsykauden alussa väkirehuautomaatista saatavalla lisäväkirehuannoksella. Kokeessa oli kaikkiaan 39 ayrshirelehmää. Puolet lehmistä sai ainoastaan seosrehua, jossa väkirehun osuus kuiva-aineesta oli 51 %, ja puolet sai laimeampaa seosta, jossa väkirehun osuus kuiva-aineesta oli 45 %. Laimeampaa seosta saaneet lehmät saivat lisäksi lypsykauden alussa väkirehua kioskista. Päivittäinen lisäväkirehuannos oli laktaatiopäivinä 1–100 useammin poikineilla 6,5 kg ja ensikoilla 5,5 kg. Laktaatiopäivinä 101–150 oli kioskiväkirehuannos useammin poikineilla 3,0 kg ja ensikoilla 2,5 kg. Loppulypsykaudella (yli 150 päivää poikimisesta) lisäväkirehua ei annettu. Kioskilisä ei vaikuttanut keskimääräiseen kuiva-aineen syöntiin tai ravintoaineiden saantiin. Tarkasteltaessa syöntejä jaksoittain havaittiin, että ensimmäisen 100 lypsypäivän aikana syönti kehittyi samaa vauhtia molemmissa koeryhmissä. Sen sijaan seuraavan 50 päivän aikana ainoastaan seosrehua saaneet lehmät söivät enemmän kuin lehmät, jotka saivat lisärehua kioskeista. Tulos on kuitenkin vain suuntaa antava. Kun lisärehuryhmän kioskiannosta pudotettiin 100 ja 150 lypsypäivien jälkeen, niin lehmät eivät pystyneet lisäämään seoksen syöntiä vastaavaa määrää, ja näin kokonaissyönnit jäivät alhaisemmiksi kuin ainoastaan seosrehua saaneella ryhmällä. Koekauden keskimääräisissä maito-, energiakorjattumaito-, rasva- ja valkuaistuotoksissa ei ollut ryhmien välillä eroa. Myöskään lehmien kunnon kehityksessä ei ollut eroa ruokintojen välillä. Johtopäätöksenä voidaan todeta, että molemmat ruokintastrategiat soveltuivat hyvin lehmien ruokintaan.

Tutkimuksen tavoitteena oli selvittaa mittausten kayttokelpoisuutta 3-10 kuukauden ikaisten hiehojen elopainon maarittamisessa. Elopainon kehityksen seuraaminen jo alkukasvatuskaudella on valttamatonta, silla utareen kehityksen kannalta kriittinen kasvunvaihe alkaa noin 3 kk iassa. Kokeessa mitattiin elopaino, rinnanymparys, pituus ja sakakorkeus 3-9,5 kk ikaisilta hiehoilta, joiden keskimaarainen paivakasvu oli650 g. Mittojen ja elopainon valisia yhteyksia pyrittiin selvittamaan regressioanalyysilla. Rinnanymparyksen perusteella pystyttiin parhaiten ennustamaan elopainoa (R2 =0,969). Kun rinnanymparyksen lisaksi regressiomalliin lisattiin seka pituus etta sakakorkeus, lisaantyi ennusteen varmuus vain vahan (R2 = 0,983). Hiehojen elopainoa suositeltiin arvioitavan rinnanymparyksen perusteella kaavalla EP = exp[1,0453+0,04196(RY)-0,00007713(RY)2 ], missa EP = elopaino ja RY = rinnanymparys. Yhtaloa voidaan kayttaa, kun rinnanymparys on 95-140 cm. Yhtaloa testattiin toisella hiehoaineistolla. Testiaineiston hiehoja oli kasvatettu kahdella ruokintatasolla. Alhaisella tasolla hiehojen kasvu oli 668 g/d ja korkealla 848 g/d. Elopainon ja rinnanymparyksen valisen yhteyden todettiin olevan riippuvainen ruokinnan voimakkuudesta. Malli ennusti alhaisen ryhman hiehojen elopainon suhteellisen luotettavasti, mutta aliarvioi voimakkaasti ruokittujen hiehojen elopainon. Koska esitetty elopainon arvioimisyhtalo perustuu rajoitetusti ruokittujen hiehojen mittauksiin, antaa se parhaan ennusteen hiehoille, joiden paivakasvut ovat 550-700 g.