No abstract

Environmental concerns involving the dairy industry are shifting from an exclusive focus on water quality to encompass climate change and air quality issues. The dairy industry's climate change air emissions of concern are the greenhouse gases methane and nitrous oxide. With regard to air quality, the dairy industry's major emission contributions are particulate matter, volatile organic compounds, and ammonia. The emissions of these compounds from dairies can be variable because of a number of factors including weather conditions, animal type, management, and nutrition. To evaluate and compare emissions across the diverse operations that comprise the US dairy industry, emissions should be reported per unit of output (e.g., per kg of 3.5% fat-corrected milk). Accurately modeling emissions with models that can predict the complex bio-geochemical processes responsible for emissions is critical to assess current emissions inventories and develop mitigation strategies. Improving the dairy industry's production efficiency (e.g., improvements in management, nutrition, reproduction, and cow comfort) is an effective way to reduce emissions per unit of milk. With accurate process-based models, emissions reductions due to improved production efficiency could be reported per unit of milk and predicted on a farm-to-farm basis.

Simple SummaryWe describe the construction and operation of a unique system for measuring gaseous emissions that arise from the rumen and metabolism of cattle. This system allows for the collection of high quality data that can be used to improve emission inventories at the regional and national level. Additionally, the system can be used to test various emission mitigation techniques.AbstractRecent interest in greenhouse gas emissions from ruminants, such as cattle, has spawned a need for affordable, precise, and accurate methods for the measurement of gaseous emissions arising from enteric fermentation. A new head hood system for cattle designed to capture and quantify emissions was recently developed at the University of California, Davis. The system consists of two head hoods, two vacuum pumps, and an instrumentation cabinet housing the required data collection equipment. This system has the capability of measuring carbon dioxide, methane, ethanol, methanol, water vapor, nitrous oxide, acetic acid emissions and oxygen consumption in real-time. A unique aspect of the hoods is the front, back, and sides are made of clear polycarbonate sheeting allowing the cattle a full range of vision during gas sampling. Recovery rates for these slightly negative pressure chambers were measured ranging from 97.6 to 99.3 percent. This system can capture high quality data for use in improving emission inventories and evaluating gaseous emission mitigation strategies.

Water is an essential nutrient, but there are few recent studies that evaluate how much water individual beef cattle consume and how environmental factors affect an individual's water intake (WI). Most studies have focused on WI of whole pens rather than WI of individual animals. Thus, the objective of this study was to evaluate the impact of environmental parameters on individual-animal WI across different seasons and develop prediction equations to estimate WI, including within different environments and management protocols. Individual daily feed intake and WI records were collected on 579 crossbred steers for a 70-d period following a 21-d acclimation period for feed and water bunk training. Steers were fed in 5 separate groups over a 3-yr period from May 2014 to March 2017. Individual weights were collected every 14 d and weather data were retrieved from the Oklahoma Mesonet's Stillwater station. Differences in WI as a percent of body weight (WI%) were analyzed accounting for average temperature (TAVG), relative humidity (HAVG), solar radiation (SRAD), and wind speed (WSPD). Seasonal (summer vs. winter) and management differences (ad libitum vs. slick bunk) were examined. Regression analysis was utilized to generate 5 WI prediction equations (overall, summer, winter, slick, and ad libitum). There were significant (P < 0.05) differences in WI between all groups when no environmental parameters were included in the model. Although performance was more similar after accounting for all differences in weather variables, significant (P < 0.05) seasonal and feed management differences were still observed for WI%, but were less than 0.75% of steer body weight. The best linear predictors of daily WI (DWI) were dry mater intake (DMI), metabolic body weights (MWTS), TAVG, SRAD, HAVG, and WSPD. Slight differences in the coefficient of determinations for the various models were observed for the summer (0.34), winter (0.39), ad libitum (0.385), slick bunk (0.41), and overall models (0.40). Based on the moderate R 2 values for the WI prediction equations, individual DWI can be predicted with reasonable accuracy based on the environmental conditions that are present, MWTS, and DMI consumed, but substantial variation exists in individual animal WI that is not accounted for by these models.