Development of a model for the prediction of feed intake by dairy cows: 1. Prediction of feed intake

Zom, R.L.G.; André, G.; Vuuren, A.M. van

doi:10.1016/j.livsci.2011.08.014

Cited by 46 publications

(67 citation statements)

References 51 publications

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“…The majority of cows used in this study calved during autumn (36% of cows calved in September, October, or November). Therefore, the observed decrease in predicted daily methane emissions during the summer period could also be due to a decrease in feed intake, which commonly occurs at the end of lactation (Zom et al 2012). Predicted daily methane emissions also differed between years (Fig.…”

Section: Prediction Of Methane Emissionsmentioning

confidence: 99%

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

et al. 2016

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Abstract. Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 AE 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF 6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF 6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points · P0, first derivative MIR data points · P1, and first derivative MIR data points · P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (%2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions.

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Section: Prediction Of Methane Emissionsmentioning

confidence: 99%

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

et al. 2016

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“…Data on 100 lactating parity 1 and 2 cows collected on the Dutch farm Nij Bosma Zathe (Leeuwarden, the Netherlands) between May 2003 and December 2004 (Beerda et al, 2007) were also available. At this farm, differences in milking frequencies (2 or 3 times daily) and feeding strategy (fed a TMR with low-or high-energy content) existed; DM of the diet varied from 48 to 49/100 g. Data on 748 lactating parity 1 to 5 cows were collected on 6 different experimental herds (9 separate experimental sites) between 1991 and 2001 (Zom et al, 2012); average DM of the diets was 54/100 g. Data on a further 705 lactating parity 1 to 5 cows collected on 1 experimental farm between 2003 and 2011 were also available. All these data were collected in 21 different experiments.…”

Section: The Netherlandsmentioning

confidence: 99%

International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources

Berry

Coffey

Pryce

et al. 2014

Journal of Dairy Science

112

110

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Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herdspecific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered.

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“…De lagere melkproductie van koeien met het lipogene rantsoen kan verklaard worden door de lagere drogestof-en energie-inname van het lipogene rantsoen. Oorzaak voor de lagere energie-inname op het lipogene rantsoen was mogelijk het lagere drogestofgehalte van het lipogene rantsoen vergeleken met het glucogene rantsoen (Zom et al, 2012). Om het drogestofgehalte van het lipogene en glucogene vergelijkbaar te maken werd aan het lipogene rantsoen droge bietenperspulp toegevoegd, maar dit verhoogde de drogestof-en energieopname niet tot het niveau van het glucogene rantsoen.…”

Section: Effecten Van Een Lipogeen Of Glucogeen Rantsoen Op Persistenunclassified

Droogstand op maat : verkorten van de droogstand van melkvee: effecten op vervetting en persistentie, uiergezondheid, dierenwelzijn, melkproductie, economie en milieu

Knegsel¹,

Physiology²,

Hoeij³

et al. 2018

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Development of a model for the prediction of feed intake by dairy cows: 1. Prediction of feed intake

Cited by 46 publications

References 51 publications

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources

Droogstand op maat : verkorten van de droogstand van melkvee: effecten op vervetting en persistentie, uiergezondheid, dierenwelzijn, melkproductie, economie en milieu

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