Relative availability of five inorganic magnesium sources in non-pregnant non-lactating Holstein cows.

Inorganic sources of Mg are commonly used in dairy cow diets, but their availability varies significantly. This study assessed the relative availability of 4 commonly used inorganic Mg sources and a novel alkalinizing proprietary mineral blend [PMB; Multesium (GLC Minerals, LLC, Green Bay, WI, USA)]. The study was a duplicated 6 × 6 Latin square, with 12 nonlactating, non-pregnant Holstein dairy cows assigned to a square based on BW and parity. Cows were fed 90% of their voluntary DMI (diet contained 0.21% Mg). Each experimental period lasted 7 d. On d 2 of each period, urinary catheters were fitted. Total urine collection started on d 3 for 48 h with samples collected and measured every 12 h. On d 4, 30 g of Mg were administered as boluses with gelatin capsules: negative control (one empty capsule), magnesium oxide (MgO), magnesium sulfate (MgSO4), calcium magnesium hydroxide [CaMg(OH)4], calcium magnesium carbonate [CaMg(CO3)2], and PMB [a blend of Ca and Mg sources that includes CaMg(CO3)2, CaMg(OH)4, and MgO]. Blood samples were collected at 0, 1, 2, 3, 12, and 24 h after treatment administration on d 4 of each treatment period. Urine and blood samples were analyzed for Mg and Ca concentration. Statistical analyses were conducted with PROC GLIMMIX including treatment, time, period, square, treatment × time, treatment × period, and time × period as fixed effects, and cow nested within square as a random effect in the model. Urinary Mg excretion for 4 of the Mg sources studied [PMB, MgO, CaMg(OH)4, and MgSO4] increased significantly, representing an increase of at least 40.8% relative to control. The supplementation of CaMg(CO3)2 did not significantly increase relative to control. There were no significant changes in blood Mg concentration with treatment; but, a significant treatment × time effect was observed. Calcium-rich sources [PMB, CaMg(OH)4, CaMg(CO3)2] had lower blood Mg concentrations at 12 or 24 h after treatment than control and CaMg(CO3)2. Based on urinary Mg excretion 24 h after treatment, 4 of the Mg sources evaluated (including PMB) showed a similar availability, however, the availability of the commercial CaMg(CO3)2 source included in our study was similar to the negative control (no-supplemented cows).

Assessing different cross-validation schemes for predicting novel traits using sensor data: an application to dry matter intake and residual feed intake using milk spectral data.

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.