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 (R2=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.

Several external markers can be used for estimating total faecal output in view of assessing ruminant intake at pasture. Among them, ytterbium (Yb) has been used for many years in various conditions. Polyethylene glycol (PEG) is a promising external marker because it can be rapidly determined using near-infrared spectroscopy (NIRS). The study consisted of 24 adult lactating dairy ewes over three periods (P1, P2 and P3), fed with three different diets: P1, total mixed ration (TMR); P2, Italian ryegrass (IRG); and P3, pasture. After an adaptation period, the ewes were administered a daily dose of ytterbium oxide (0.35 g/day) and PEG (20 g/day) for 2 weeks. During the last week, the daily organic matter intake (OMIOBS) was measured. Faecal samples were collected at milking time (0800 and 1600 h) to determine marker content, using only samples collected in the morning (PEGm) or by averaging samples (Yb, PEGma). Faecal marker content made it possible to assess total faecal output, either using the two recovery rates for PEG (0.98 or 0.87) or not. The OMIOBS was assessed on the basis of total faeces estimated with Yb (OMIYb) or PEG (OMIPEG), and the digestibility was calculated on the basis of feed analysis. With total TMR (P1), the OMIPEG, corrected with recovery rate (OMIPEGm98) or not corrected (OMIPEGm) was 2.40 kg/day and 2.50 kg/day, respectively, and was not different (P>0.05) from OMIOBS (2.51 kg/day), whereas OMIYb was lower (2.14 kg/day) (P<0.001). With IRG (P2), OMIPEGm98 (1.67 kg/day), OMIPEGm87 (1.51 kg/day) and OMIYb (1.59 kg/day) were not different (P>0.05) from OMIOBS (1.57 kg/day). With pasture (P3), the OMIPEGm (1.54 kg/day) and OMIPEGm98 (1.48 kg/day) were not different (P>0.05) from the OMI assessed from the biomass measurement (1.52 kg/day). The OMIYb (1.36 kg/day) was lower (P<0.05) but not different from OMIPEGm98 and OMIPEGm87. Spearman’s rank correlation between OMIOBS and other OMIs (predicted with Yb or PEG P1 and P2) showed that it is possible to rank animals using PEG when there is a sufficiently wide range of OMIOBS (1.65 to 2.8 kg/day in P1) but not within a narrower range (1.47 to 1.72 kg/day in P2). In conclusion, the present study confirms that PEG is a valuable external faecal marker, easy to prepare (solution), administer and determine (NIRS). It can be used to assess intake with numerous animals at pasture, but only for groups, and not to quantitatively estimate individual OMI.

The aim of the work was to compare the faecal output and digestibility estimated by two mathematical approaches with the actual amount of faeces excreted or feed digested by Siental cows. Experimental data (intakes and digestibility measured over 5 d) and faecal Cr concentrations (measured at 0, 4, 8, 12, 16, 24, 32, 48, 56, 72, 96, 120 and 144 h after a pulse dose of Cr-mordanted forage) were collected from published experiments and fitted to a multicompartmental (MC) model and a γ age-dependent (AD) model. From a statistical point of view, the MC model was very satisfactory while the AD model produced lower r2 and higher SE values and reached satisfactory statistical values only for higher DM intakes (lactating animals). The MC model produced higher correlations with the digestibility values while the AD model generated better correlations with the intake data; DM intake and digestibilities were more highly correlated with the model's parameters than neutral-detergent fibre terms. The regression between the estimated faecal outputs obtained with the two models showed an intercept closeto 0 (P>0·05) and angular coefficients near 1; there was a good correspondence of the estimates especially for the lowest values of output. The r2 values of the regressions were 0·800 and 0·829 for the MC and AD models respectively and their SE were 2·93 and 2·63. The ability of the two models to predict faecal output and digestibility was very similar, independent of the statistical accuracy of fitting the Cr- concentration data. The results indicate that variation of Cr concentration is the result of the entire digestive process, i.e. dilution and passage, which interact in a competitive or associative way.