Estimation of diet organic matter digestibility in grazing dairy cows

Precise and continual information on the energy supply from pasture is mandatory for managing grazing ruminants. Therefore, estimating the organic matter (OM) digestibility from faecal crude protein concentration using the regression equation, OM digestibility [%] = 79.76-107.7 · e^{(-0.01515 · faecal crude protein [g/kg OM])}, is known to be a suitable tool. However, essential information regarding faeces sampling times and the required number of samples are not yet available. We therefore analysed the OM digestibility data of an experiment with dairy cows grazing two pasture types and receiving two concentrate types over 6 d in three independent runs. Both pasture type and grazing day affected the OM digestibility estimates, whereas concentrate type and intake did not, indicating that this method reliably detects small changes in OM digestibility of pasture without being interfered by moderate concentrate supplementation, selective grazing behaviour or differences in feed intake. Likewise, as sampling time did not influence OM digestibility, faeces sampling once daily can be recommended to be sufficient for an accurate estimation of OM digestibility. The variance within pasture type and grazing day amounted for 1.1 percentage units of estimated OM digestibility, which enabled to define the minimum sample number required to detect given differences in OM digestibility with adequate statistical certainty. In conclusion, estimating OM digestibility from faecal crude protein concentration is an applicable and sensitive method to reliably detect differences in the quality of ingested pasture using a limited number of animals. Therefore, instructions for faeces sample collection  were provided.

Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.

Estimates of diet selection in cattle grazing cornstalk residues by measurement of chemical composition and near infrared reflectance spectroscopy of diet samples collected by ruminal evacuation

Six ruminally cannulated cows (570 ± 73 kg) fed corn residues were placed in a 6 × 6 Latin square to evaluate predictions of diet composition from ruminally collected diet samples. After complete ruminal evacuation, cows were fed 1-kg meals (dry matter [DM]-basis) containing different combinations of cornstalk and leaf and husk (LH) residues in ratios of 0:100, 20:80, 40:60, 60:40, 80:20, and 100:0. Diet samples from each meal were collected by removal of ruminal contents after 1-h and were either unrinsed, hand-rinsed or machine-rinsed to evaluate effects of endogenous compounds on predictions of diet composition. Diet samples were analyzed for neutral (NDF) and acid (ADF) detergent fiber, acid detergent insoluble ash (ADIA), acid detergent lignin (ADL), crude protein (CP), and near infrared reflectance spectroscopy (NIRS) to calculate diet composition. Rinsing type increased NDF and ADF content and decreased ADIA and CP content of diet samples (P < 0.01). Rinsing tended to increase (P < 0.06) ADL content of diet samples. Differences in concentration between cornstalk and LH residues within each chemical component were standardized by calculating a coefficient of variation (CV). Accuracy and precision of estimates of diet composition were analyzed by regressing predicted diet composition and known diet composition. Predictions of diet composition were improved by increasing differences in concentration of chemical components between cornstalk and LH residues up to a CV of 22.6 ± 5.4%. Predictions of diet composition from unrinsed ADIA and machine-rinsed NIRS had the greatest accuracy (slope = 0.98 and 0.95, respectively) and large coefficients of determination (r2 = 0.86 and 0.74, respectively). Subsequently, a field study (Exp. 2) was performed to evaluate predictions of diet composition in cattle (646 ± 89 kg) grazing corn residue. Five cows were placed in 1 of 10 paddocks and allowed to graze continuously or to strip-graze corn residues. Predictions of diet composition from ADIA, ADL, and NIRS did not differ (P = 0.99), and estimates of cornstalk intake tended to be greater (P = 0.09) in strip-grazed compared to continuously grazed cows. These data indicate that diet composition can be predicted by chemical components or NIRS by ruminal collection of diet samples among cattle grazing corn residues.