Farms In Australia Research Articles

Predicting Wind Power Generation Using Hybrid Deep Learning With Optimization

Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks Optimization algorithm. The effectiveness of the proposed model is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.

The Game of Guwarra: A game theory-based decision-making framework for site selection of offshore wind farms in Australia

Global concerns around climate change and the volatility of conventional fuel prices have prompted researchers and technologists to make significant efforts to identify and exploit alternative energy sources that are cleaner and more sustainable. Wind energy has seen considerable development among these alternative energy sources, mainly due to its abundance and global availability for extraction and the existing knowledge within the aviation and aerospace fields. Many nations, including European countries, already operate offshore wind farms (OWFs) and are progressively carrying out new projects and expanding on other projects. The Australian offshore environment provides unique opportunities for wind energy extraction, particularly along the southern coast of mainland Australia and the regions around Tasmania, where substantially strong winds blow most of the year. A significant challenge to establishing wind farms is the selection of site locations with optimal outputs. This can become a complex decision-making problem if there are numerous options and no information from previous projects. This paper aims to develop a decision-making framework to select the optimal location for installing OWFs while addressing financial, performance-related, and availability-related objectives. This paper adopts a game-theoretical approach to develop a decision-support tool to account for the interdependencies of influencing factors and possible conflicts amongst the parties. The game model is applied to an OWF development case study in the Bass Strait, known for its dominant and strong winds.

890The prevalence and risk factors for Coxiella burnetii on commercial dairy goat farms in Australia

Abstract Background Despite the potentially important role that intensively managed dairy goats play in the spread of Q fever, the prevalence of Coxiella burnetii among dairy goat herds in Australia is largely unknown. The aim of this cross-sectional study was to estimate the prevalence of coxiellosis-positive dairy goat herds in Australia and to identify risk factors associated with coxiellosis positivity. Methods Owners or managers of commercial dairy goat herds were contacted and asked to complete a questionnaire about risk factors for coxiellosis and to provide a bulk tank milk (BTM) sample. BTM samples were tested using an enzyme-linked immunosorbent assay (ELISA) and real-time quantitative polymerase chain reaction (RT-PCR) targeting the Com1 and IS1111 sections of the C. burnetii genome. Questionnaire responses from coxiellosis positive and coxiellosis negative herds were compared using frequency cross-tabulations and multivariable logistic regression. Results Herd managers from 49 of the 61 commercial dairy goat herds in Australia took part in the study. Of this group, three BTM samples were found to be both ELISA and RT-PCR positive. Two BTM samples were ELISA positive but RT-PCR negative. There were 10 (95% CI 4.4 to 22) C. burnetii positive herds per 100 herds at risk. Conclusions The prevalence of coxiellosis among commercial dairy goat farms in Australia is relatively low. Key messages The Australian dairy goat industry should focus on biosecurity measures and risk management plans to reduce the probability of C. burnetii introduction.

Livestock data – Is it there and is it FAIR? A systematic review of livestock farming datasets in Australia

The global adoption of the FAIR principles for scientific data: findable, accessible, interoperable and reusable, has been relatively slow in agriculture, compared to other disciplines. A recent review of the literature showed that the use of precision farming technologies and the development and adoption of open data standards was particularly low in extensive livestock farming. However, a plethora of public datasets exist that have the potential to be used to inform precision farming decision tools. Using extensive livestock farming in Australia as example, we investigate the quantity and quality of datasets available via a systematic dataset review. This systematic review of datasets begins with a search of open data catalogues and querying these to find datasets. Software scripts are developed and used to query the Application Programming Interfaces (APIs) of many of the large data catalogues in Australia, while catalogues without public APIs are queried manually via available web portals. Following the systematic search, a combined list of all datasets is collated and tested for FAIRness and other quality metrics. The contribution of this work is the resulting overview of the state of open datasets within the livestock farming domain on the one hand, but also the development of a systematic dataset search strategy, reusable methods and software scripts.

Time series model for real-time forecasting of Australian photovoltaic solar farms power output

Intermittent electrical power output from grid-connected solar farms causes intermittent and uncertain requirements for dispatchable power to balance power supply and demand. Accurate forecasting of electrical power output from solar farms can improve managing power generators connected to the grid. To forecast the electrical power output, a time series model is developed for two solar farms in Australia. The forecast model consists of a Fourier series that models seasonality and an autoregressive moving-average component that models the difference between the observed electrical power outputs and the Fourier series. Persistence detection is added to the model to improve forecast performance on clear days. Using minutely data, the model forecasts the electrical power output seven minutes ahead at every five-minute interval to comply with the requirements of the Australian Energy Market Operator (AEMO). Based on a 30-day testing period, the normalized mean absolute error (NMAE) skills of the time series model are 10.9% and 13.2% lower than those of the clear sky index persistence (CSIP) model. However, the normalized root mean squared error (NRMSE) skills of the time series model are approximately 3% and 12% higher than those of CSIP and the model currently used by AEMO, respectively. As the NRMSE skills are more indicative than the NMAE skills in reducing large forecast errors that would reduce electricity grid stability, the results suggest that AEMO can improve the management of the electricity grid with an inexpensive tool by adopting the developed model to forecast electrical power output of solar farms.

Predicting Immediate Marginal Milk Responses and Evaluating the Economics of Two-Variable Input Tactical Feeding Decisions in Grazing Dairy Cows.

Simple SummaryIn Australia, feeding grazing dairy cows concentrate and forage supplements is common. Dairy farmers face the challenge of profitably feeding their cows in situations where there is significant variation in feed costs and milk price. We used the results of grazing experiments to develop equations that predict the yield of milk fat and milk protein when different combinations of concentrates and pasture + forage are fed to grazing lactating dairy cows. We applied economic principles to these predictions to estimate the optimal combination of these feeds for given costs and prices. Feed is the largest variable cost in dairying. The allocation of pasture and supplements that are based on better estimates of milk responses to supplements should lead to increased profit for farmers.Feed is the largest variable cost for dairy farms in Australia, and dairy farmers are faced with the challenge of profitably feeding their cows in situations where there is significant variation in input costs and milk price. In theory, the addition of 5.2 MJ of metabolisable energy to a lactating cow’s diet should be capable of supporting an increase in milk production of one litre of milk of 4.0% fat, 3.2% protein and 4.9% lactose. However, this is almost never seen in practice, due to competition for energy from other processes (e.g., body tissue gain), forage substitution, associative effects and imbalances in rumen fermentation. Pasture species, stage of maturity, pasture mass, allowance and intake, stage of lactation, cow body condition and type of supplement can all affect the milk protein plus fat production response to additional feed consumed by grazing dairy cows. We developed a model to predict marginal milk protein plus fat response/kg DM intake when lactating dairy cows consume concentrates and pasture + forages. Data from peer reviewed published experiments undertaken in Australia were collated into a database. Meta-analysis techniques were applied to the data and a two-variable quadratic polynomial production function was developed. Production economic theory was used to estimate the level of output for given quantities of input, the marginal physical productivity of each input, the isoquants for any specified level of output and the optimal input combination for given costs and prices of inputs and output. The application of the model and economic overlay was demonstrated using four scenarios based on a farm in Gippsland, Victoria. Given that feed accounts for the largest input cost in dairying, allocation of pasture and supplements that are based on better estimates of marginal milk responses to supplements should deliver increased profit from either savings in feed costs, or in some cases, increased output to approach the point where marginal revenue equals marginal costs. Such data are critical if the industry is to take advantage of the opportunities to use supplements to improve both productivity and profitability.

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Foresighting Australian digital agricultural futures: Applying responsible innovation thinking to anticipate research and development impact under different scenarios

CONTEXTPublic and private research institutions are grappling with the challenges and opportunities of embedding dimensions of responsible innovation within their research and development programs, including those seeking to transform agricultural productivity and sustainability through digital technologies. Central to meeting this challenge is building institutional, organisational and professional capacity for anticipation and reflexiveness within multidisciplinary research communities. Foresighting methodologies provide a means by which this might be usefully and practically enabled, whilst also shedding light on the broader social and ethical implications of alternative agricultural technology development pathways under uncertain environmental and industry futures. OBJECTIVEThis paper presents the results of a participatory foresighting exercise undertaken as part of a large, publicly funded multi-disciplinary research initiative designed to build a common big data infrastructure to harness the benefits of the digital revolution for the Australian agricultural and land sectors. We seek to explore what role digital technology will play in the future of Australian agriculture and to consider the social and ethical implications. METHODSWe ran a one-day foresighting workshop comprised of four steps – 1) horizon scanning to identify trends 2) selecting two drivers of change 3) producing a matrix to generate scenarios 4) building and refining scenarios. Participants explored different possible futures of farming in Australia, with a focus on scenarios involving socio-technical dimensions of digital agriculture to consider the implications of these futures for research practice and for farming communities. RESULTS AND CONCLUSIONSFour scenarios were developed, distinguished by the interplay of two critical but uncertain drivers of change identified by participants, namely: the degree of resource security or insecurity that future agricultural enterprises are likely to experience; and the degree to which farming sectors maintain traditional farm business models and associated value chains or transition to more diverse or innovative business models. The process highlighted the need to increase the capacity and opportunity for more reflexivity in research and development, if positive outcomes were to be achieved. SIGNIFICANCEThe scenarios we produced provide a catalyst for conversation about the implications of digital technology development in Australia and globally, for industry, policy and research and development. In particular, the scenarios highlight potential changes in farm business models, decision making, and beneficiaries and inequities of new technologies and other components of food value chains. The paper also serves as a guide and prompt for others, by demonstrating one way reflexivity can be achieved in organisations attempting innovation.

Very short-term forecasting of wind power generation using hybrid deep learning model

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora wind farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of 5-min intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory, GRU, autoregressive integrated moving average and support vector machine, which are tuned to optimise outcome. To further evaluate the efficacy of the proposed model, another data set taken from the Capital wind farm in Australia is used. It is observed that the hybrid deep learning model exhibits superior performance in both the data sets over other forecasting models to improve the accuracy of wind power forecasting, numerically for the Bodangora wind farm, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

Antimicrobial resistance and genomic characterisation of Escherichia coli isolated from caged and non-caged retail table eggs in Western Australia

Foodborne exposure to antimicrobial-resistant bacteria is a growing global health concern. Escherichia coli (E. coli) is well recognised as an indicator of food contamination with faecal materials. In the present study, we investigated the occurrence of E. coli in table eggs sold at retail supermarkets in Western Australia (WA). A total of 2172 visually clean and intact retail eggs were purchased between October 2017 and June 2018. A single carton containing a dozen eggs was considered as a single sample resulting a total of 181 samples. The shells and contents of each sample were separately pooled and tested using standard culture-based methods. Overall, generic E. coli was detected in 36 (19.8%; 95% confidence interval: 14.3; 26.4) of the 181 tested retail egg samples. We characterised 100 of the recovered E. coli isolates for their phenotypic antimicrobial resistance using minimum inhibitory concentration (MIC). A subset of E. coli isolates (n = 14) were selected on the basis of their MIC patterns, and were further characterised using whole genome sequencing (WGS). Fifty-seven (57%) of the recovered generic E. coli isolates (n = 100) were resistant to at least one of the 14 antimicrobials included in the MIC testing panel, of which 22 isolates (22%) showed multi-class resistance. The highest frequencies of non-susceptibility of E. coli isolated from WA retailed eggs were against tetracycline (49%) and ampicillin (36%). WGS revealed that tet(A) and blaTEM-1B genes were present in most of the isolates exhibiting phenotypic resistance to tetracycline and ampicillin, respectively. The majority (98%) of the characterised E. coli isolates were susceptible to ciprofloxacin and azithromycin, and none were resistant to the cephalosporin antimicrobials included in the MIC panel. Two isolates demonstrated reduced susceptibility to ciprofloxacin, with MICs of 0.125 and 0.25 mg/L, and WGS revealed the presence of plasmid mediated qnrs1 gene in both isolates. This is the first report on detection of non-wild-type resistance to fluoroquinolones in supermarket eggs in Australia; one of the two isolates was from a cage-laid eggs sample while the other was from a barn-laid retail eggs sample. Fluoroquinolones have never been permitted for use in poultry farms in Australia. Thus, the detection of low-level ciprofloxacin-resistant E. coli in the absence of local antimicrobial selection pressure at the Australian layer farms warrants further research on the potential role of the environment or human-related factors in the transmission of antimicrobial resistance. The results of this study add to the local and global understanding of antimicrobial resistance spread in foods of animal origin.

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

Wind power has contributed significantly to the increase in electricity generation, but a decision-making tool capable of dealing with its variability and limited predictability is necessary. For this purpose, a novel self-adaptive approach for kernel recursive least-squares machines named multiple challengers is introduced in this work, which is successfully used to produce very short-term wind power forecasts at eight wind farms in Australia. The proposed method is based on a competitive tracking method, and the algorithm deals with some common difficulties of kernel methods, e.g., the increasing kernel matrix size associated with time and memory complexities and the overfitting problem. The proposed method always considers the new information received by the model, thus identifying changes in the time series, avoiding abrupt loss of information and maintaining a controlled number of examples since there is an adaptive selection of the active kernel. It works with the smallest dictionary possible, reducing the probability of overfitting. Five minute-ahead wind power forecasts are produced and evaluated in terms of point forecast skill scores and calibration. The results of the proposed method are compared with those provided by other kernel-based versions of the recursive least-squares algorithm, an online version of the extreme learning machine method, and the persistence time series model. An increase in the number of kernels used in the ensemble system can lead to better results when compared with those of single-kernel models.

HISTOPATHOLOGY of MIXED INFECTIONS in REDCLAW CRAYFISH (Cherax quadricarinatus) TISSUES

Redclaw crayfish ( C. quadricarinatus ) diseases have become an important issue with a number of reports from farms in Australia since the 1990s. About 30 Adult crayfish were housed at SVBS JCU and were given mixed food as required for 3wks. Crayfish samples were preserved at D 21 by injecting the cephalothorax and abdominal muscles with Davidson’s fixative (10mL of Davidson per 1g crayfish). Histological examination was conducted by longitudinally slicing the cephalothorax, abdomen, and gill tissues at approximately 2-3mm thickness to obtain separate histological sections of the tissues. Sections were cut at 5µm and stained with Mayer’s hematoxylin and eosin (H&amp;E), Gram-Twort, Periodic Acid-Schiff (PAS), and phosphotungstic acid-eosin. An Olympus EC microscope was used to view the sections and the photographs were taken using QCapture.Pro. The results showed melanisation in the hepatopancreatic tissues, hemolymph and gills; Haemocytic aggregation in the hemolymph; Fungal hyphae in the gills; Multiple nodule formation surrounded by melanisation with continued haemocytic aggregation and whirling, early stage of catabolism and bacterial colonies were identified in the muscles. In conclusion, histopathological analyses in this study that redclaw crayfish is susceptible for mixed infection from bacteria, fungi, and virus.

A survey of the visual impact and community acceptance of wind farms in Australia

ABSTRACT Although overall support for wind farms in Australia is strong, the rapid growth in their number and size has led to concerns about their visual impact, as evidenced by organised opposition and litigation. In March 2018, an Australia-wide Internet survey mainly of clubs and councils was conducted which asked respondents to rate the scenic quality of the landscape with and without a wind farm, and also to rate the acceptability or otherwise of the wind farm in scenes with the wind farm. Photographs of wind farms in NSW, Victoria and SA were used. From the response of nearly 800 participants a sample of 556, weighted to be representative of the Australian population, was analysed. While the respondents rated the scenic quality of landscapes with wind farms lower than scenes without them, they rated wind farms acceptable in virtually all landscapes, including high scenic quality coastal scenes. The finding suggests that the community is very tolerant of the visual impact of wind farms. Results are presented for 18 wind farms. This survey is unique in that it not only asked participants to rate the landscape with and without wind farms but it also asked them to rate the acceptability of the wind farm in each scene. To the author’s knowledge, this had not been accomplished previously in Australia.

Glaesserella australis sp. nov., isolated from the lungs of pigs

Twenty-nine isolates of an unknown haemophilic organism were isolated from the lungs of pigs from 14 farms in Australia. Phylogenetic analyses based on the 16S rRNA gene, recN and rpoA showed a monophyletic group that was most closely related to Glaesserella parasuis and [Actinobacillus] indolicus. Whole genome sequence analysis indicated that the Glaesserella parasuis and this group, using the type strain HS4635T for comparison, showed a similarity of 30.9 % DNA-DNA renaturation. The isolates were Gram-stain-negative, NAD-dependent, CAMP-negative and were oxidase-positive, catalase-negative and produced indole but not urease. The isolates could be separated from all currently recognized haemophilic and non-haemophilic members of the family Pastuerellaceae. Key phenotypic properties were the production of indole, the lack of urease activity, production of β-galactosidase but not α-fucosidase, acid formation from (-)-d-arabinose, (+)-d-galactose, maltose and trehalose and a failure to produce acid from (-)-d-mannitol. Taken together, these data indicate that the isolates belong to a novel species for which the name Glaesserella australis sp. nov. is proposed. The type strain is HS4635T (=CCUG 71931T and LMG 30645T).

Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion.

Usefulness of milk mid-infrared spectroscopy for predicting lameness score in dairy cows

The objective of this study was to evaluate the ability of milk infrared spectra to predict cow lameness score (LMS) for use as an indicator of cow health on Australian dairy farms, or as an indicator trait for genetic evaluation purposes. The study involved 3,771 cows from 10 farms in Australia. Milk infrared spectra collected during the monthly herd testing were available in all the farms involved in the study. Lameness score was measured once in each herd, within 72 h from a test day, and merged to the closest spectra records. Lameness score was expressed on a scale from 0 to 3, where 0 is assigned to sound cows and scores 1 to 3 are assigned to cows with increased lameness severity. Partial least squares discriminant analysis was used to develop prediction models for classifying sound (score 0) and not-sound cows (i.e., cows walking unevenly, score greater than 0). Discriminant models were tested in a 10-fold random cross-validation process. Milk infrared spectra correctly classified only 57% of the cows walking unevenly and only 59% of the sound cows. When additional predictors (parity, age at calving, days in milk, and milk yield) were included in the prediction model, the model correctly classified 57% of the cows walking unevenly and 62% of the sound cows. The same model applied only to the cows in the first third of lactation correctly classified 66% of the cows walking unevenly and 57% of the sound cows. When the prediction model was used to identify lame cows (scores 2 and 3), only 49% of them were classified as such. These results are considered to be too poor to envisage a practical application of these models in the near future as on-farm tools to provide an indication of LMS. To investigate whether, at this stage, predictions of the LMS could be useful as large-scale phenotypes for animal breeding purposes, we estimated (co)variance components for actual and predicted LMS using 2,670 and 24,560 records, respectively. As the genetic correlation between actual and predicted LMS was not significantly different from zero, predictions of lameness from milk spectra and additional on-farm variables cannot be used, at this stage, as an indicator trait for actual LMS. More research is needed to find better strategies to predict lameness.

Soil carbon simulation confounded by different pool initialisation

Process-based models of soil organic carbon (C) define soil organic matter by conceptual C pools. In the Roth C model these pools are represented by the particulate, humus and resistant organic C fractions (POC, HOC, and ROC). Here, we used three different sets of estimates of the C fractions to initialise Roth C and assessed their effect on the model predictions of the 0–0.3 m C stocks in a 30-year simulation. We estimated the stocks of POC, HOC and ROC with (1) pedotransfer functions (PTF), (2) proximal sensing, and (3) digital soil mapping with data from sensing. We conducted experiments in three C estimation areas of a cattle grazing farm in Australia. We found that in all cases, the model predicted increasing total organic C (TOC) stocks by up to 15.2 Mg C ha−1. Using the PTF estimates, the uncertainty in the model predictions ranged from 4.9 to 7.1 Mg C ha−1. Using proximal sensing and digital soil mapping, the uncertainty decreased to 2.3–3.6 Mg C ha−1 and 3.2–5.4 Mg C ha−1, respectively. Our results show that model initialisation with proximal sensing and digital soil maps well represent the spatial variation of TOC and the C pools, so that simulation of changes in C stocks were more reliable. Using the PTF estimates resulted in biased predictions that underestimated the TOC stocks by 2.0–4.5 Mg C ha−1 over the simulation period. Informing a soil C model with measurements is key to building confidence in model-predictions of TOC stocks, providing useful feedback to management practices.

Planning to engage the community on renewables: insights from community engagement plans of the Australian wind industry

ABSTRACT Wind farms in Australia have encountered a range of community responses, sometimes resulting in opposition. This has caused the wind energy industry to evaluate and amend their community engagement practices. Community Engagement Plans detail wind farm developers’ internal intentions’ for stakeholder communication and engagement. This research sought insights into these intentions and the success of the selected engagement approaches through an examination of 32 plans. The article presents trends in wind developer practice and understanding of community engagement and benefit sharing, through analysis of these strategic documents that are rarely shared externally. The findings reveal a strong intention by wind farm developers to respond to and meet, or exceed, community expectations. The plans were heavily influenced by recent guidance documents from the Australian renewable energy sector and government. Analysing the plans revealed an increasing use of a diversity of community engagement and benefit sharing practices, and a recognition of the value of engaging more meaningfully with local communities. These findings are relevant to wind and other renewable energy developers scoping new or expanded projects, as well as government organisations seeking to maximise the contribution of wind-generated energy.

An investigation of dairy calf management practices, colostrum quality, failure of transfer of passive immunity, and occurrence of enteropathogens among Australian dairy farms

Calf preweaning morbidity and mortality risks have been reported as high in several countries, with average values approximating 35 and 7%, respectively. However, limited data are available for calf morbidity and mortality risks on Australian dairy farms. The aims of this study were (1) to investigate current calf management practices on dairy farms in Australia and their association with herd-level morbidity and mortality using a questionnaire-based, cross-sectional study; and (2) to estimate the prevalence of common enteropathogens causing diarrhea, the failure of passive transfer of immunity, and poor colostrum quality in a sample of Australian dairy farms. We analyzed 106 completed questionnaires and samples from 23 farms (202 fecal, 253 calf serum, and 221 colostrum samples). Morbidity and mortality risks reported by farmers in preweaned heifers were 23.8 and 5.6%, respectively. These risks were above the Australian dairy industry targets in 75.5 and 66.7% of respondents. The zoonotic pathogens Cryptosporidium spp. and Salmonella spp. were the most prevalent enteropathogens, with a true prevalence of 40.9 and 25.2%, respectively. Salmonella O-group D was present in 67.9% of Salmonella-positive samples, followed by O-groups B (17.9%) and C (10.7%). Failure of transfer of passive immunity (IgG <10 g/L) was observed in 41.9% of calves (mean herd-level prevalence of 36.2%), and only 19.5% of colostrum samples met the standards for immunoglobulin content and microbiological quality. Collectively, these data indicate that there is still considerable room for improvement in calf-rearing practices on Australian dairy farms, particularly with regard to colostrum management and feeding hygiene.

The Effect of Routine Postpartum Uterine Lavage on Endometrial Cytology, Culture, and Pregnancy Rates in Thoroughbred Broodmares

Minimizing the interval between parturition and successful rebreeding of thoroughbred broodmares is important in maintaining annual foal production and broodmare profitability. Routine postpartum uterine lavage may help achieve this by aiding uterine clearance and involution in the early postpartum period and increasing pregnancy rates. This retrospective study investigates the effect of postpartum uterine lavage in 32 thoroughbred mares housed on a single farm in Australia in 2014 and 2015. In the control year (2014), postpartum uterine lavage was not performed. In the treatment year (2015), all mares received a uterine lavage 3 days postpartum. In both years, endometrial swabs were obtained for culture and cytology at the first postpartum estrus (foal heat swab) and the second postpartum estrus (second heat swab), prior to the mare being bred by natural cover at the second postpartum estrus. There was no significant difference in bacterial culture rates from foal heat or second heat swabs between the control and treatment years. Assessment of endometrial cytology of the foal heat swab and second heat swab revealed a significantly higher proportion of polymorphonuclear cells (PMNs) in the control year compared with the treatment year. Positive culture and cytology rates were significantly higher at foal heat than at the second postpartum heat in both years. No association of year, cytology, or culture with pregnancy rates was identified. In conclusion, routine postpartum uterine lavage significantly reduces the presence of endometrial PMNs at first and second postpartum estrus but does not reduce endometrial culture rates or increase pregnancy rates.