Predicting feed intake and feed efficiency in lactating dairy cows using digesta marker techniques

Abstract

Direct measurement of individual animal dry matter intake (DMI) remains a fundamental challenge to assessing dairy feed efficiency (FE). Digesta marker, is currently the most used indirect technique for estimating DMI in production animals. In this meta-analysis we evaluated the performance of marker-based estimates against direct or observed measurements and developed equations for the prediction of FE (g energy-corrected milk (ECM)/kg DMI). Data were taken from 29 change-over studies consisting of 416 cow-within period observations. Most studies used more than one digesta marker. So, for each observed measurement of DMI, faecal dry matter output (FDMO) and apparent total tract dry matter digestibility (DMD), there was one or more corresponding marker estimate. There were 924, 409 and 846 observations for estimated FDMO (eFDMO), estimated apparent total tract DMD (eDMD) and estimated DMI (eDMI), respectively. The experimental diets were based mainly on grass silage, with soya bean or rapeseed meal as protein supplements and cereal grains or by-products as energy supplements. Across all diets, average forage to concentrate ratio on a dry matter (DM) basis was 59 : 41. Variance component and repeatability estimates of observed and marker estimations were determined using random factors in mixed procedures of SAS. Between-cow CV in observed FDMO, DMD and DMI was, 10.3, 1.69 and 8.04, respectively. Overall, the repeatability estimates of observed variables were greater than their corresponding marker-based estimates of repeatability. Regression of observed measurements on marker-based estimates gave good relationships (R 2 = 0.87, 0.68, 0.74 and 0.74, relative prediction error = 10.9%, 6.5%, 15.4% and 18.7%for FDMO, DMD, DMI and FE predictions, respectively). Despite this, the mean and slope biases were statistically significant (P < 0.001) for all regressions. More than half of the errors in all regressions were due to mean and slope biases (52.4% 87.4%, 82.9% and 85.8% for FDMO, DMD, DMI and FE, respectively), whereas the contributions of random errors were small. Based on residual variance, the best model for predicting FE developed from the dataset was FE (g ECM/kg DMI) = 1179(±54.1) +38.2 (±2.05) × ECM(kg/day) − 0.64(±0.051) × BW (kg) − 75.6(±4.39) × eFDMO (kg/day). Although eDMD was positively related to FE, it only showed a tendency to reduce the residual variance. Despite inaccuracy in marker procedures, eFDMO from external markers provided a reliable determination for FE measurement. However, DMD estimated by internal markers did not improve prediction of FE, probably reflecting small variability. ImplicationsThe main obstacle to assessing dairy feed efficiency (FE) is an accurate individual animal feed intake measurement which direct recording is laborious and expensive. Therefore, indirect methods using digesta markers to estimate intake have been developed. In this study, we used a metaanalytical approach to develop models that could be used to predict feed intake and FE by means of markers. Overall, mar...

Between-cow variation in the components of feed efficiency

“…The estimated value in the present study is consistent with the acrosslactation repeatability estimate of 0.66 documented in 5,162 lactating dairy cows (Berry et al, 2014). In a recent study, Guinguina et al (2019) also reported a repeatability estimate of 0.65 for DMI in lactating dairy cows in digestion studies. We estimated a repeatability Correlations were controlled for feeding level, experiment, diet within experiment, and period within experiment effects.…”

Effect of using Acacia mearnsii tannin extract as a feed additive on nutritional variables and productive performance in dairy cows grazing a temperate pasture

“…Moreover, both fecal output and herbage intake were estimated by double marker technique using Cr 2 O 3 and iNDF as markers, respectively, which are also reported to have bias associated to herbage and fecal sampling protocol and marker recovery in feces (Guinguina, Ahvenjärvi, Prestløkken, Lund, & Huhtanen, 2019;Velásquez et al, 2018).…”

Prediction of dry matter intake and gross feed efficiency using milk production and live weight in first-parity Holstein cows

“…A model including BFY, MY and LW achieved the best accuracy of prediction, compared to one with only one of these traits. These traits have also been found to be associated with GFE and, therefore, good predictor traits for GFE in several other studies (Linn et an exceptionally strong prediction power (R 2 = 0.98), which was much higher than for one developed previously from multiparous Holstein data (R 2 = 0.76) (Guinguina et al 2019). Beard (2018) developed models with much lower prediction ability (R 2 = 0.45) using primiparous Canadian Holstein data, and including milk yield, milk components and live weight only.…”

“…There was a low antagonistic relationship (-0.23) between LW and GFE, which was however not signi cant. This negative association is a well-documented phenomenon, and is attributable to the fact that larger cows demand more nutrients for body maintenance, resulting in less feed being available for milk production (Linn et al, 2009;Vallimont et al, 2011;Ben Meir et al, 2018;Guinguina et al, 2019). Thus, LW could contribute towards the prediction of GFE.…”

“…Thus, LW could contribute towards the prediction of GFE. A recent study by Guinguina et al (2019) found the inclusion of LW to be useful in models for predicting GFE.…”