Impacts of Reducing Protein Content in Milk Replacer on Growth Performance and Health of Young Calves.

In the present study, a drinking amount of 10 L of milk replacer (MR) was allowed to dairy calves in order to approach the natural drinking behavior. The question is: how much protein is required by calves in order to achieve an intended growth rate? For this reason, sixty-eight pre-weaned Holstein calves were divided into two groups and fed with 10 L/d of MR containing either 22% protein (MR22) or 19% protein (MR19) at an almost comparable energy intake. Effects on performance, metabolic status, and health were compared. Feed intake, growth performance, and health status were monitored during the pre-transition, transition, and postweaning phase (until 157 d of age). Total feed intake, and intake of MR, body weight (BW), and average daily gain (ADG) were not significantly different between MR22 and MR19 during the entire experimental period (p > 0.05). At d 42, calves in MR19 group showed greater serum levels of growth hormone (16.2 vs. 22.2 ng/mL; p = 0.02), insulin-like growth factor 1 (262 vs. 291 ng/mL; p = 0.03), and urea (2.86 vs. 3.04 mmol/L; p < 0.01). The results of the present study suggested that when high amounts of MR are provided, the protein content in MR can be reduced to 19% without any adverse effects on growth performance as well as on health status of dairy calves.

Quantification of Methane and Ammonia Emissions in a Naturally Ventilated Barn by Using Defined Criteria to Calculate Emission Rates.

Extensive experimentation on individual animals in respiration chambers has already been carried out to evaluate the potential of dietary changes and opportunities to mitigate CH4 emissions from ruminants. Although it is difficult to determine the air exchange rate of open barn spaces, measurements at the herd level should provide similarly reliable and robust results. The primary objective of this study was (1) to define a validity range (data classification criteria (DCC)) for the variables of wind velocity and wind direction during long-term measurements at barn level; and (2) to apply this validity range to a feeding trial in a naturally cross-flow ventilated dairy barn. The application of the DCC permitted quantification of CH4 and NH3 emissions during a feeding trial consisting of four periods. Differences between the control group (no supplement) and the experimental group fed a ration supplemented with condensed Acacia mearnsii tannins (CT) became apparent. Notably, CT concentrations of 1% and 3% of ration dry matter did not reduce CH4 emissions. In contrast, NH3 emissions decreased 34.5% when 3% CT was supplemented. The data confirm that quantification of trace gases in a naturally ventilated barn at the herd level is possible.

Comparative evaluation of equations predicting methane production of dairy cattle from feed characteristics.

Techniques that allow direct measurements on animals to quantify methane (CH4) emissions are costly and difficult to transfer to herd level. Mathematical approaches have been developed to predict methane emissions of cattle based on diet and intake characteristics, which were calibrated against largely varying calorimetry data. In this study, nine CH4 prediction equations were applied to five typical Central European dairy cow diets in order to compare their applicability. The five diets differed in respect of forage proportion and type. In a first attempt, regression equations were selected containing easily accessible data such as dry matter intake (DMI, kg/d) forage proportion, as well as neutral and acid detergent fibre that can also be extracted from on-farm datasets. Smallest differences to mean values were observed with the application of equations using neutral detergent fibre, while standard deviations were highest. Therefore, the best capability to differentiate between diets was shown, when using equations that operated with forage proportion and DMI. Nevertheless, the role of CH4 prediction equations should not be overestimated. The differences in CH4 estimates show that frequently used equations are still inaccurate and may only serve as implications to locate trends. It should be taken into consideration to expand datasets, involving future CH4 measurements, on animal and herd level, feeding typical, up-to-date regional diets in order to get more precise equations, suitable for a greater range of estimations. To ease and simplify the future applications, the prediction equations could be classified into groups, clearly stating by which data they were derived, for example, regional origin and diet composition.