Constructing better piggery buildings by identifying factors contributing to improved thermal control under hot climatic conditions

External and internal air temperatures were measured continuously for one year (between January 1999 and December 1999) in 48 piggery buildings in South Australia using self contained data-loggers with built-in sensors. Data was consolidated to correspond with the four seasons. Regression values between the external and internal temperatures were calculated for individual buildings for each season. Data was also collected on major housing features, including configuration of the buildings and management factors employed in them. The information collected was then analysed to quantify the effects of housing and management factors on the resulting environmental control using a multi-factorial statistical model. The overall mean air temperatures in all buildings corresponding to the four seasons were; 24°C (summer), 20°C (autumn), 18°C (winter), 21°C (spring) across all buildings. The regression values between external and internal temperatures were affected by the season, type of insulation material used in the buildings, the availability of extra heating or cooling equipment, height of buildings, roof pitch (angle), type of ridge ventilation control employed, stocking density, age of buildings and number of pigs housed per building. The effects of housing and management factors on thermal control capacity of buildings were quantified. These findings should aid the construction of better designed livestock buildings resulting in improved welfare and production efficiency in piggery buildings

Seroprevalence and potential risk factors associated with neospora spp. Infection among asymptomatic horses in Jordan

This study aimed to determine the seroprevalence and to identify risk factors associated with Neospora spp. infection in horses in Jordan. Management related data were collected from each farm and individual horses. Sera from 227 horses from 5 of 6 climatic regions in Jordan were analyzed for the presence of antibodies to Neospora spp. by ELISA kit. The study was performed during spring of 2010. The association between seropositivity and risk factors was analyzed. A total of 7 (3%) of 227 sera had antibodies for Neospora spp. There was a significant regional difference (P=0.018) between the 5 climatic regions. Positive cases were located in Amman and Irbid, while the other regions (Zarqa, Jordan Valley, and Wadi Mousa) had zero prevalence. The use of anthelmintics at least once a year resulted in a significant reduction of the seroprevalence to Neospora spp. (1.6% vs 9.8%). However, this might be a phenomenon by chance and a better hygiene since owners can invest in anthelmintics. Other risk factors such as age, gender, breed, usage, body condition score, grazing, presence of other animals mixed with the horses in the same property, and a history of previous diseases were not significantly associated with the seroprevalence to Neospora spp. infection. This is the first study to report on the presence of Neospora seropositive horses in Jordan. Further studies are warranted to better understand the role of certain risk factors in the transmission of Neospora spp. among horse population and to determine which Neospora spp. are responsible for the infection.Abdelsalam Q. Talafha, Sameeh M. Abutarbush, David L. Rutle

Sheep Updates 2006 - part 2

This session covers six papers from different authors: GENETICS 1. Novel selection traits - what are the possible side effects?, Darryl Smith, Kathryn Kemper, South Australian Research and Development Institute, David Rutley, University of Adelaide. 2. Genetic Changes in the Australian Merino since 1900, Sheep Genetics Australia Technical Committee, R.R. Woolaston Pullenvale, Queensland, D.J. Brown, Animal Genetics and Breeding Unit*, University of New England, K.D. Atkins, A.E. Casey, NSW Department of Primary Industries, A.J. Ball, Meat and Livestock Australia, University of New England 3. Influence of Sire Growth Estimated Breeding Value (EBV0 on Progeny Growth, David Hopkins, David Stanley, Leonie Martin, NSW Department Primary Industries, Centre for Sheep Meat Development, Arthur Gilmour, Remy van de Ven, NSW Department Primary Industries, Orange Agricultural Institute FINISHING 4. Predicting Input Sensitivity on Lamb Feedlot Profitability by Using Feedlot Calculator, David Stanley, NSW Department Primary Industries, Centre for Sheep Meat Development, Geoff Duddy, NSW Department Primary Industries, Yanco Agricultural Institute, Steve Semple, NSW Department Primary Industries, Orange Agricultural Institute, David Hopkins, NSW Department Primary Industries, Centre for Sheep Meat Development 5. Annual ryegrass toxicity (ARGT) in WA - 2006, David Kessell, Meat & Livestock Australia ARGT Project, Northam, WA 6. Poor ewe nutrition during pregnancy increases fatness of their progeny, Andrew Thompson, Department of Primary Industries, Victori

Maternal and paternal genomes differentially affect myofibre characteristics and muscle weights of bovine fetuses at midgestation

Postnatal myofibre characteristics and muscle mass are largely determined during fetal development and may be significantly affected by epigenetic parent-of-origin effects. However, data on such effects in prenatal muscle development that could help understand unexplained variation in postnatal muscle traits are lacking. In a bovine model we studied effects of distinct maternal and paternal genomes, fetal sex, and non-genetic maternal effects on fetal myofibre characteristics and muscle mass. Data from 73 fetuses (Day153, 54% term) of four genetic groups with purebred and reciprocal cross Angus and Brahman genetics were analyzed using general linear models. Parental genomes explained the greatest proportion of variation in myofibre size of Musculus semitendinosus (80–96%) and in absolute and relative weights of M. supraspinatus, M. longissimus dorsi, M. quadriceps femoris and M. semimembranosus (82–89% and 56–93%, respectively). Paternal genome in interaction with maternal genome (P<0.05) explained most genetic variation in cross sectional area (CSA) of fast myotubes (68%), while maternal genome alone explained most genetic variation in CSA of fast myofibres (93%, P<0.01). Furthermore, maternal genome independently (M. semimembranosus, 88%, P<0.0001) or in combination (M. supraspinatus, 82%; M. longissimus dorsi, 93%; M. quadriceps femoris, 86%) with nested maternal weight effect (5–6%, P<0.05), was the predominant source of variation for absolute muscle weights. Effects of paternal genome on muscle mass decreased from thoracic to pelvic limb and accounted for all (M. supraspinatus, 97%, P<0.0001) or most (M. longissimus dorsi, 69%, P<0.0001; M. quadriceps femoris, 54%, P<0.001) genetic variation in relative weights. An interaction between maternal and paternal genomes (P<0.01) and effects of maternal weight (P<0.05) on expression of H19, a master regulator of an imprinted gene network, and negative correlations between H19 expression and fetal muscle mass (P<0.001), suggested imprinted genes and miRNA interference as mechanisms for differential effects of maternal and paternal genomes on fetal muscle.Ruidong Xiang, Mani Ghanipoor-Samami, William H. Johns, Tanja Eindorf, David L. Rutley, Zbigniew A. Kruk, Carolyn J. Fitzsimmons, Dana A. Thomsen, Claire T. Roberts, Brian M. Burns, Gail I. Anderson, Paul L. Greenwood, Stefan Hiendlede

Factors influencing water temperature on farms and the effect of warm drinking water on pig growth

Drinking water temperature was measured continuously for one year on 22 pig farms in South Australia (SA) and Queensland (QLD) and data were collected on major housing features and management factors employed in individual piggery buildings. The data collected enabled the likely effects of housing and management factors on resulting water temperature to be quantified and the industry to be made aware of the importance of providing drinking water within temperature range for efficient pig production and welfare. The data collected identified statistically significant housing and management factors associated with and contributing to suboptimal water temperature as seasons (P=0.0001), source of water (P=0.0001), position of piping (P=0.003), water pressure (P=0.042), size of in-shed water reservoir (P=0.0001) and diameter of the main (P=0.0001) and delivery pipes (P =0.0001). A controlled experiment was also conducted to complement these findings by quantifying the negative effect of sub-optimal (warm) drinking water temperature on pig growth rate. Two identical weaner rooms were selected for the on farm study. Genetics, nutrition, management, stocking rate and density were identical for both groups. Pigs in the treatment group received water heated to 28.3±OA °C while the control group received unheated water at 17.8±0.9 dc. Growth rate was suppressed by 58 grams/day in the group receiving the heated water. These results demonstrate the negative effect of warm water temperature on pig production and highlight potential ways of reducing the likelihood of providing warm drinking water to livestock

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: part 4. Emission factors and study recommendations

[Abstract]: The internal concentrations and emission rates of ammonia (NH3 ), total bacteria, respirable endotoxins, and inhalable and respirable particles were monitored in 160 piggery buildings in four states of Australia (Queensland, Victoria, Western Australia, and South Australia) between autumn 1997 and autumn 1999. Emissions were calculated for individual buildings as a product of internal concentration and ventilation rate, which were estimated by a carbon dioxide balance method. Relative humidity and temperature were also measured. The overall mean emission rates of NH3 , total bacteria, respirable endotoxins, inhalable particles, and respirable particles per 500 kg live weight from Australian piggery buildings were 1442.5 mg h-1, 82.2 × 106 cfu h-1, 20.1× 103 EU h-1, 1306.7 mg h-1, and 254.7 mg h-1, respectively. Internal concentrations of key airborne pollutants have been reported in companion articles. Building characteristics and management systems used in the piggeries were documented at the time of sampling and used in the subsequent statistical modeling of variations in pollutant emission rates. The emissions model used all statistically significant factors identified during prior modeling conducted for individual pollutant concentrations and ventilation airflow. The identification of highly significant factors affecting emission rates and internal concentrations should aid the development of strategies for the industry to reduce emission rates from individual buildings, thus improving the environmental performance of piggery operations. In the second part of the article, specific recommendations are made based on the overall study results

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: part 3. Environmental parameters

[Abstract]: Between autumn 1997 and autumn 1999, we measured ventilation rates (using a CO2 balance method), air temperatures, and relative humidity (using self-contained dataloggers with built-in sensors) in 160 pig housing facilities in Queensland, South Australia, Victoria, and Western Australia, in each case over a 60 h period. In some buildings, the internal air velocities above the animals were also recorded. While the monitoring instruments were being set up, a detailed questionnaire was used to collect data on major housing features and management factors. This information was statistically analyzed to quantify the effects of housing and management factors on the resulting environment conditions using a multifactorial analysis. The overall mean air temperature, relative humidity, internal air velocity, and ventilation rate were 20.3°C, 58.9%, 0.12 m s-1, and 663.9 m3 h-1 500 kg-1 live weight, respectively, across all buildings. Internal building temperature and humidity were affected statistically by the type of insulation material used, the classification of buildings, and external climatic conditions. Ventilation rates were primarily affected by the type of ventilation system used, height (size) of ventilation openings, stocking density (kg m-3), and length, width, and height of buildings. These findings should aid the development of strategies for the industry to improve environmental control in piggery buildings

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: part 1. Study justification and design

[Abstract]: We undertook a literature search related to pig production facilities with two major aims: first, to review all the likely benefits that might be gained from air quality improvements; and second, to review previous research that had identified statistically significant factors affecting airborne pollutants and environmental parameters, so that these factors could be considered in a multifactorial analysis aimed at explaining variations in air pollutant concentrations. Ammonia, carbon dioxide, viable bacteria, endotoxins, and inhalable and respirable particles were identified as major airborne pollutants in the review. We found that high concentrations of airborne pollutants in livestock buildings could increase occupational health and safety risks, compromise the health, welfare, and production efficiency of animals, and affect the environment. Therefore, improving air quality could reduce environmental damage and improve animal and worker health. To achieve a reduction in pollutant concentrations, a better understanding of the factors influencing airborne pollutant concentrations in piggery buildings is required. Most of the work done previously has used simple correlation matrices to identify relationships between key factors and pollutant concentrations, without taking into consideration multifactorial effects simultaneously in a model. However, our review of this prior knowledge was the first important step toward developing a more inclusive statistical model. This review identified a number of candidate risk factors, which we then took into consideration during the development of multifactorial statistical models. We used a general linear model (GLM) to model measured internal concentrations, emissions, and environmental parameters in order to predict and potentially control the building environment