The Research Column

Dry matter intake (DMI) plays a major role in characterizing feed efficiency in dairy cattle and has been widely used in dairy nutrition research. As feed represents the largest operating cost in dairy production, feed efficiency has gained increased attention for genetic selection.

Waste milk (WM) may be colostrum and milk derived from cows undergoing treatment for several ailments, including clinical mastitis, foot and reproductive diseases etc. Additionally, WM may have elevated SCC, rendering it unsuitable for commercial use. Yet despite this adversity, the use of WM in suckling programs is worldwide a common practice, since producers perceive the use thereof as an economical feed alternative which can effectively replace calf nutrition derived from bulk tank milk (BTM) or milk replacer.

An 8 × 8 incomplete Latin square design was conducted with 48 lactating Danish Holstein cows over 6 periods of 21 days each. Eight diets were 2 × 2 × 2 factorially arranged: FAT (30 or 63 g crude fat/kg DM), NITRATE (0 or 10 g nitrate/kg DM), and 3-NOP (0 or 80 mg 3-NOP/kg DM), and cows were fed ad libitum. Milk samples were analyzed for general composition, fatty acids (FA) and thermal properties of milk fat.

A total of 42 Holstein heifer calves on a commercial dairy farm were enrolled in groups of three to different housing treatments; IH (n = 21) or GH (n = 21). Each treatment was composed of seven groups of three calves each. Calves in the GH treatment were housed in groups of three from six to ten days until 70 days of age. Individual pens consisted of one polyethylene hutch with a 1.5 m × 1.2 m outside exercise area. Group pens were constructed by assembling three polyethylene hutches with a 1.5 m × 3.6 m outside exercise area of wire panel fencing.

Butters were produced using milks collected from three feeding systems: outdoor pasture grazing (high pasture allowance); indoor TMR (no pasture allowance); and a partial mixed ration (medium pasture allowance) system, which involved outdoor pasture grazing during the day and indoor TMR feeding at night. Butters were manufactured during early, mid, and late lactation.

Milk production data of 23 908 first lactation and multi lactation cows from 87 herds were used. Persistency was measured by a lactation curve characteristic decay, representing the time taken to halve milk production after peak yield. Decay was calculated for eight DPC (0, 30, 60, 90, 120, 150, 180, and 210 days after DIMc), which served as the dependent variable. Independent variables included DPC, DIMc (≤60, 61–90, 91–120, 121–150, 151–180, 181–210, >210 days), parity group, DPC × parity group, DPC × DIMc, and variables from 30 days before DIMc as covariates. 

The authors measured AGEPprog, height, length, and BW in approximately 5 000 Holstein-Friesian or Holstein-Friesian × Jersey crossbred yearling heifers across 54 pasture-based herds managed in seasonal calving production systems.

In recent years, the Montbéliarde (MO), Viking Red (VR), and Holstein (HO) breeds have been marketed for three-breed rotational crossbreeding. The MO and VR breeds have placed more selection emphasis on fertility, health, and longevity for decades than has the HO breed, while maintaining substantial selection emphasis on increased milk solids. Research on lactation-curve characteristics of crossbred dairy cows is however limited. Also, the persistency of production for MO-sired and VR-sired crossbred cows compared with their HO herdmates has not been studied.

Several other factors beyond temperature also affect heat exchange, including thermal radiation, air flow, and air moisture content. Although temperature is the primary driving force of heat exchange, it is generally agreed that temperature alone is not an adequate indicator of the environmental impact, because other factors can influence the perception of heat.

Of the newer developments are the availability of high-dimensional omics, such as the metagenome, metabolome and transcriptome, which provide the opportunity to incorporate such data, in addition to genomic data, to improve the prediction of feed efficiency. The inclusion of microbial data in genomic models enables unravelling the contribution of a particular host genome and its microbiome to the phenotype of interest.