In this study, we aimed to assess the effect of flaking on the nutrient digestibility of corn grain in ruminants. In this regard, in vitro rumen fermentation, in situ rumen degradability, and in vivo metabolic experiments were performed. The automated gas production technique was used for the in vitro fermentation experiments. Six types of corn flakes with various degrees of gelatinization (32%, 41%, 48%, 66%, 86%, and 89%) were ground and incubated in rumen fluid to measure rumen fermentation characteristics and digestion rate. The in situ degradability of ground corn, whole corn, and corn flakes with 62% and 66% gelatinization was measured by incubation in the rumen of two cannulated Holstein cows. In vivo metabolic experiments were performed using 12 crossbred goats (29.8 kg ± 4.37) using a 3 × 3 Latin square design. The dietary treatments consisted of ground corn and flaked corn with 48% or 62% gelatinization. In vitro experiments showed that as the degree of gelatinization increased, the digestion rate increased linearly, while the discrete lag time decreased linearly (p < 0.05). The effective rumen dry matter degradability, determined by in situ fermentation, was 37%p lower in corn flakes than ground corn, assuming a passage rate of 6%/h (p < 0.01), and there was no difference between the two flakes. In the in vivo experiment, there was no difference in dry matter intake, average daily gain, feed efficiency, and nitrogen utilization among the treatment groups (p > 0.05); however, the crude fat digestibility was lower for corn flakes than for ground corn (p < 0.05). To summarize, the rate of fermentation of corn flakes increased as the degree of gelatinization increased. However, non-ground corn flakes had lower rumen digestibility and did not improve in vivo apparent nutrient digestibility, compared with ground corn.In contrast to the assumption that flaked corn provides more energy to ruminant animals than ground corn, we conclude that the digestibility and energy value of corn flakes are lower than those of ground corn if mastication does not sufficiently reduce the particle size of corn flakes.

This study aimed to determine the factors affecting the body weight (BW) of Hanwoo steers by collecting a large number of BW measurements using an automated weighing system (AWS). The BW of 12 Hanwoo steers was measured automatically using an AWS for seven days each month over three months. On the fourth day of the BW measurement each month, an additional BW measurement was conducted manually. After removing the outliers of BW records, the deviations between the AWS records (a) and manual weighing records (b) were analyzed. BW measurement deviations (a − b) were significantly (p < 0.05) affected by month, day and the time within a day as well as the individual animal factor; however, unexplained random variations had the greatest impact (70.4%). Excluding unexplained random variations, the difference between individual steers was the most influential (80.1%). During the day, the BW of Hanwoo steers increased before feed offerings and significantly decreased immediately after (p < 0.05), despite the constant availability of feeds in the feed bunk. These results suggest that there is a need to develop pattern recognition algorithms that consider variations in individual animals and their feeding patterns for the analysis of BW changes in animals.

The net energy requirement for lactation (NEL) equals the milk energy, which is the sum of the energy content from the energy-yielding nutrients in milk. The specific nutrients and their calories, however, vary depending on the feeding system. The objective of this study was to evaluate NEL prediction equations used in cattle feeding systems. A total of 11 equations from 6 feeding systems were assessed. For evaluation, a database was constructed based on the literature, and data for three nutrients (lactose, fat, and protein) were used to evaluate the equations. The equations were classified into three tiers based on the variables: Tier 1 (all three nutrients), Tier 2 (fat and protein), and Tier 3 (fat). NEL predicted by the equations were comparatively evaluated based on a reference value computed using Tyrrell and Reid’s equation. All equations showed high predictivity (in order, Tier 1, 2, and 3). Tier 1 equations showed a nearly perfect fit; however, for accurately predicting NEL, at least Tier 2 equations are recommended. The predictivity of theoretically derived equations was as high, or higher, as the predictivity of empirical equations. Thus, empirical development of an accurate equation to predict NEL, which requires a large amount of data, can be avoided.