M. L. Sullivan

Effect of shade area on performance and welfare of short-fed feedlot cattle

One hundred twenty-six Black Angus yearling heifers were used in a 119-d study to assess the effect of shade allocation (0, 2.0, 3.3, or 4.7 m(2)/animal) on the performance and welfare of feedlot cattle. Shade treatments were replicated 4 times and the no-shade treatment was replicated twice. Shade was provided by 70% solar block shade cloth, attached to a 4-m-high frame with a north-south orientation. Cattle were randomly allocated to a pen (9/pen; 19.2 m(2)/animal) within treatment. Performance was assessed using DMI, G:F, ADG, HCW, dressing percentage, and rump fat depth. Climatic data (ambient and black globe temperature, solar radiation, wind speed, relative humidity, and rainfall) were recorded. From these data, the heat load index (HLI) was calculated. When the daily maximum HLI (HLI(Max)) was <86, individual panting score (0 = no panting; 4 = open mouth, tongue extended), animal location (eating, drinking, under shade), and animal posture (standing or lying) were collected at 0600, 1200, and 1800 h. When HLI(Max) was ≥ 86, these data were collected every 2 h between 0600 and 1800 h. Feed intake was recorded weekly and water intake was recorded daily on a pen basis. When HLI(Max) was ≥ 86, mean panting score (MPS: mean of animals within treatment) was greatest (1.02; P < 0.001) for unshaded cattle compared with cattle in the shade treatments, which were similar (0.82; P = 0.81). During heat waves, the MPS of unshaded cattle was greater (2.66; P < 0.001) than that for shaded cattle. The MPS of cattle in the 2.0 m(2)/animal treatment (2.43 ± 0.13) was greater (P < 0.001) than that of cattle in the 3.3 (2.11 ± 0.13) and 4.7 m(2)/animal (2.03 ± 0.13) treatments. The MPS of cattle in the 3.3 and 4.7 m(2)/animal treatments were similar (P = 0.09). Number standing was similar (P = 0.98) between unshaded and shaded at 2.0 m(2)/animal treatments with 4.75 and 4.76 animals/pen, respectively. Fewer (P < 0.0001) were standing in the 3.3 (4.19 animals/pen) and 4.7 m(2)/animal (4.06 animals/pen) treatments. Fewer (P = 0.004) cattle were under the shade at 2.0 m(2)/animal (47.1%) compared with the number under the shade at 3.3 (53.7%) and 4.7 m(2)/animal (53.6%). Unshaded cattle had the smallest (0.085 ± 0.006) G:F ratio (P = 0.01), followed by cattle shaded at 4.7 m(2)/animal (0.104 ± 0.006; P ≤ 0.001). There was no difference (P = 0.12) between the 2.0 and 3.3 m(2)/animal treatments. There were no differences (P > 0.10) for final BW, HCW, dressing percentage, and rump fat depth. Cattle with access to shade had smaller panting scores, which suggests improved welfare, and had better feed efficiency. Shade reduced the intensity of the heat load but did not fully remove the effect of heat.

Isolation of Nocardia mexicana from focal proliferative tenosynovitis and arthritis in a steer

CASE REPORT An 18-month-old Charolais steer was presented with lameness and fluctuant swelling of the right stifle joint, which yielded neutrophils on fine-needle aspiration. A diagnosis of bacterial proliferative tenosynovitis and arthritis was made on postmortem and histological examination. Culture and 16S rRNA sequencing identified a Nocardia sp. with 99% homology with the corresponding DNA fragment of N. mexicana DSM 44952. Antimicrobial susceptibility testing revealed the isolate was susceptible to co-trimoxazole and third-generation cephalosporins. CONCLUSION We report the first case, both in Australia and internationally, of proliferative tenosynovitis and arthritis caused by Nocardia spp. infection in a bovine and the first report of pathology attributed to N. mexicana in a veterinary patient. Given the limited susceptibility of the bacteria, the poor antimicrobial penetration that would be expected and the morphological changes that had taken place in the joint; the steer would have required protracted antimicrobial treatment in addition to invasive debridement of the lesion. This case emphasises the importance of routinely performing cytology and extended incubation of cultures in cases of arthritis in order to make ethical and economically viable treatment decisions.

Assessment of the carbon footprint of four commercial dairy production systems in Australia using an integrated farm system model

ABSTRACT Integrated farm system model (IFSM) is a cost effective and efficient method of estimating greenhouse gas (GHG) emissions from dairy farms and analyzing how management strategies affect these emissions. An IFSM (DairyGHG model) was employed in this study to predict the GHG emission and assess the carbon footprints of four dairy farms in Southeast Queensland, Australia. Four representative commercial farms were selected: Farm 1 (220 cows; Jersey), Farm 2 (460 cows; Holstein Friesian), Farm 3 (850 cows; Holstein Friesian) and Farm 4 (434 cows; Holstein Friesian). The animal emission contribution to carbon footprints for Farm 1, 2, 3 and 4 were 54.2, 60.0, 59.6 and 38.6 % respectively for total output. Likewise the manure emission contribution to carbon footprints for Farm 1, 2, 3 and 4 were 30.6, 29.0, 29.0 and 58.3 % respectively. On the basis of per kg of energy corrected milk the amount of GHG produced for Farm 1, 2, 3 and 4 are 0.39 kg CO2Eq, 0.64 kg CO2Eq, 0.54 kg CO2Eq and 1.35 kg CO2Eq respectively. The method and database developed for these dairy farm GHG assessments may be considered an important step towards a harmonized methodology for the quantification of emissions in dairy farms.

Livestock as Sources of Greenhouse Gases and Its Significance to Climate Change

This chapter outlines the role of livestock in the production of greenhouse gases (GHGs) that contributes to climate change. Livestock contribute both di‐ rectly and indirectly to climate change through the emissions of GHGs such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). As animal production systems are vulnerable to climate change and are large contribu‐ tors to potential global warming, it is vital to understand in detail enteric CH4 emission and manure management in different livestock species. Methane emissions from livestock are estimated to be approximately 2.2 billion tonnes of CO2 equivalents, accounting for about 80% of agricultural CH4 and 35% of the total anthropogenic CH4 emissions. Furthermore, the global livestock sec‐ tor contributes about 75% of the agricultural N2O emissions. Other sources of GHG emission from livestock and related activities are fossil fuels used for as‐ sociated farm activities, N2O emissions from fertilizer use, CH4 release from the breakdown of fertilizers and from animal manure, and land-use changes for feed production. There are several techniques available to quantify CH4 emission, and simulation models offer a scope to predict accurately the GHG emission from a livestock enterprise as a whole. Quantifying GHG emission from livestock may pave the way for understanding the role of livestock to climate change and this will help in designing appropriate mitigation strat‐ egies to reduce livestock-related GHGs.

Modeling of Greenhouse Gas Emission from Livestock

The effects of climate change on humans and other living ecosystems is an area of on-going research. The ruminant livestock sector is considered to be one of the most significant contributors to the existing greenhouse gas (GHG) pool. However the there are opportunities to combat climate change by reducing the emission of GHGs from ruminants. Methane (CH4) and nitrous oxide (N2O) are emitted by ruminants via anaerobic digestion of organic matter in the rumen and manure, and by denitrification and nitrification processes which occur in manure. The quantification of these emissions by experimental methods is difficult and takes considerable time for analysis of the implications of the outputs from empirical studies, and for adaptation and mitigation strategies to be developed. To overcome these problems computer simulation models offer substantial scope for predicting GHG emissions. These models often include all farm activities while accurately predicting the GHG emissions including both direct as well as indirect sources. The models are fast and efficient in predicting emissions and provide valuable information on implementing the appropriate GHG mitigation strategies on farms. Further, these models help in testing the efficacy of various mitigation strategies that are employed to reduce GHG emissions. These models can be used to determine future adaptation and mitigation strategies, to reduce GHG emissions thereby combating livestock induced climate change.