RESUMO
Determination of energy requirements for growth depends on measuring the composition of body weight (BW) gain. Previous studies have shown that the composition of gain can be altered in young dairy calves by composition of the milk replacer diet. Here, our objective was to determine body composition and the composition of empty body gain in young calves fed increasing amounts of a milk replacer containing adequate CP. Male Holstein calves underwent an adjustment period of 14 d after birth in which they were fed whole waste milk at 10% of BW. Calves were then stratified by BW and randomly assigned to either an initial harvest group (n = 11) or to groups fed 1 of 3 milk replacer amounts and harvested after 35 d of growth. All treatments consumed the same milk replacer containing 24.8% CP (dry matter [DM] basis; from all milk proteins) and 18.9% fat, reconstituted to 12.5% solids. Treatments were milk replacer fed at 1.25% of BW (DM basis; n = 6), 1.75% of BW (n = 6), or 2.25% of BW (n = 8), adjusted weekly as calves grew. Calves fed at 1.25% or 1.75% of BW were fed twice daily and those fed 2.25% of BW were fed 3 times daily. No starter was offered. Post harvest, the bodies of calves were separated into 4 fractions: carcass; total viscera minus digesta; head, hide, feet, and tail; and blood. The sum of those 4 fractions was empty BW, which increased linearly as amount of milk replacer increased. Final heart girth and body length, but not withers height, increased linearly as intake increased. Gain:feed increased linearly with increasing milk replacer. Feeding more milk replacer increased the amounts of lean tissue and fat in the body. The percentages of water and protein in the final body decreased linearly, whereas fat percentage and energy content increased linearly as intake increased. As gain increased, the percentage of protein in gain decreased and the percentage of fat increased, resulting in an increase of energy content of EBW gain. Efficiency of energy use (retained energy:gross energy intake) increased linearly but retained energy:metabolizable energy available for growth was not different among treatments. Efficiency of protein use increased quadratically as feeding rate increased; there was no further increase at 2.25% of BW. Plasma insulin-like growth factor 1, insulin, and glucose increased linearly, whereas urea-N decreased linearly, as milk replacer intake increased. Our data document changes in body composition that affect estimates of retained energy in the bodies of calves harvested at a common age. These data are important for calculations of energy requirements for young calves.
RESUMO
Feed efficiency is important for economic profitability of dairy farms; however, recording daily dry matter intakes (DMI) is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (Model M1), a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (Model M2), and an extended model that incorporated both energy sinks and MIR data (Model M3). Models M2 and M3 also included various cow level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks vs. averaged across 28-d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (Models M3 vs M2) significantly (P < 10-10) reduced average RMSE to 1.59 kg and increased average R2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes, i.e., from cow-independent to experiment-independent to herd-independent schemes, the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.