Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows

Self Cite

The ability to accurately predict the conception outcome for a future mating would be of considerable benefit for producers in deciding what mating plan (i.e., expensive semen or less expensive semen) to implement for a given cow. The objective of the present study was to use herd- and cow-level factors to predict the likelihood of conception success to a given insemination (i.e., conception outcome not including embryo loss); of particular interest in the present study was the usefulness of milk mid-infrared (MIR) spectral data in augmenting the accuracy of the prediction model. A total of 4,341 insemination records with conception outcome information from 2,874 lactations on 1,789 cows from 7 research herds for the years 2009 to 2014 were available. The data set was separated into a calibration data set and a validation data set using either of 2 approaches: (1) the calibration data set contained records from all 7 farms for the years 2009 to 2011, inclusive, and the validation data set included data from the 7 farms for the years 2012 to 2014, inclusive, or (2) the calibration data set contained records from 5 farms for all 6 yr and the validation data set contained information from the other 2 farms for all 6 yr. The prediction models were developed with 8 different machine learning algorithms in the calibration data set using standard 10-times 10-fold cross-validation and also by evaluating in the validation data set. The area under curve (AUC) for the receiver operating curve varied from 0.487 to 0.675 across the different algorithms and scenarios investigated. Logistic regression was generally the best-performing algorithm. The AUC was generally inferior for the external validation data sets compared with the calibration data sets. The inclusion of milk MIR in the prediction model generally did not improve the accuracy of prediction. Despite the fair AUC for predicting conception outcome under the different scenarios investigated, the model provided a reasonable prediction of the likelihood of conception success when the high predicted probability instances were considered; a conception rate of 85% was evident in the top 10% of inseminations ranked on predicted probability of conception success in the validation data set.

Section: Predictive Ability Of Conceptionmentioning

confidence: 99%

Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows

Hempstalk

McParland

Berry

2015

Self Cite

“…The REI definition in the present study included an interaction between BW 0.75 and BCS, which is similar to Veerkamp et al (1995), Vallimont et al (2011), andMcParland et al (2014). This term was, however, not included in other REI definitions (e.g., Coleman et al, 2010;Pryce et al, 2014).…”

Section: Residual Traitsmentioning

confidence: 99%

“…Feed conversion ratio and feed conversion efficiency are the traditional measures of feed efficiency in growing and lactating animals, respectively. Residual feed intake, used predominately in growing animals as a measure of feed efficiency (Berry and Crowley, 2013), is now also being used in lactating dairy cow populations (Coleman et al, 2010;McParland et al, 2014;Pryce 469 et al, 2014). The definition of residual energy intake (REI) in lactating cows does, however, differ among studies (Coleman et al, 2010;McParland et al, 2014;Pryce et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Inter-relationships among alternative definitions of feed efficiency in grazing lactating dairy cows

Hurley

López‐Villalobos

McParland

et al. 2016

Self Cite

International interest in feed efficiency, and in particular energy intake and residual energy intake (REI), is intensifying due to a greater global demand for animalderived protein and energy sources. Feed efficiency is a trait of economic importance, and yet is overlooked in national dairy cow breeding goals. This is due primarily to a lack of accurate data on commercial animals, but also a lack of clarity on the most appropriate definition of the feed intake and utilization complex. The objective of the present study was to derive alternative definitions of energetic efficiency in grazing lactating dairy cows and to quantify the inter-relationships among these alternative definitions. Net energy intake (NEI) from pasture and concentrate intake was estimated up to 8 times per lactation for 2,693 lactations from 1,412 Holstein-Friesian cows. Energy values of feed were based on the French Net Energy system where 1 UFL is the net energy requirements for lactation equivalent of 1 kg of air-dry barley. A total of 8,183 individual feed intake measurements were available. Energy balance was defined as the difference between NEI and energy expenditure. Efficiency traits were either ratio-based or residual-based; the latter were derived from least squares regression models. Residual energy intake was defined as NEI minus predicted energy to fulfill the requirements for the various energy sinks. The energy sinks (e.g., NE L , metabolic live weight) and additional contributors to energy kinetics (e.g., live weight loss) combined, explained 59% of the variation in NEI, implying that REI represented 41% of the variance in total NEI. The most efficient 10% of test-day records, as defined by REI (n = 709), on average were associated with a 7.59 UFL/d less NEI (average NEI of the entire population was 16.23 UFL/d) than the least efficient 10% of test-day records based on REI (n = 709).Additionally, the most efficient 10% of test-day records, as defined by REI, were associated with superior energy conversion efficiency (ECE, i.e., NE L divided by NEI; ECE = 0.55) compared with the least efficient 10% of test-day records (ECE = 0.33). Moreover, REI was positively correlated with energy balance, implying that more negative REI animals (i.e., deemed more efficient) are expected to be, on average, in greater negative energy balance. Many of the correlations among the 14 defined efficiency traits differed from unity, implying that each trait is measuring a different aspect of efficiency.

“…Dehareng et al (2012) have shown that enteric methane was better predicted when directly working with FT-MIR spectra than the results based on fatty acid predictions. Also recently, the FT-MIR spectrum of milk was shown to be a good indicator of body energy status , energy intake and efficiency (McParland et al, 2014), and fertility diagnosis (Laine et al, 2013) in dairy cattle.…”

Section: Introductionmentioning

confidence: 98%

Standardization of milk mid-infrared spectra from a European dairy network

Grelet¹,

Pierna²,

Dardenne

et al. 2015

115

111

The goal of this study was to find a procedure to standardize dairy milk mid-infrared spectra from different Fourier transform mid-infrared spectrophotometers (different brands or models) inside a European dairy network to create new farm-management indicators (e.g., fertility, health, feed, environmental impact) based on milk infrared spectra. This step is necessary to create common spectral databases, allowing the building of statistical tools, to be used by all instruments of the network. The method used was piecewise direct standardization (PDS), which matches slave-instrument spectra on master-instrument spectra. To evaluate the possibility of using common equations on different instruments, the PDS method was tested on a set of milk samples measured on each machine, and an equation predicting fat content of milk is applied on all. Regressions were performed between master and slaves fat predictions, before and after PDS. Bias and root mean square error between predictions were decreased after PDS, respectively, from 0.3781 to 0.0000 and from 0.4609 to 0.0156 (g of fat/100mL of milk). The stability over time of these results was confirmed by an application of the coefficients created by PDS 1 mo later on the slave spectra. These preliminary results showed that the PDS method permits a reduction of the inherent spectral variability between instruments, allowing the merging of Fourier transform mid-infrared milk spectra from different instruments into a common database, the creation of new types of dairy farm management indicators, and the use of these common calibrations for all Fourier transform mid-infrared instruments of the European dairy network.