Development of a real-time cattle lameness detection system using a single side-view camera

Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.

The prevalence of damaged tails in New Zealand dairy cattle

AIMS

To undertake a survey of the prevalence of tail deviations, trauma and shortening on a representative selection of New Zealand dairy farms, and to assess whether sampling based on milking order could be used instead of random sampling across the herd to estimate prevalence.

METHODS

This was a cross-sectional observational study, with 200 randomly selected farms enrolled across nine regions of New Zealand via selected veterinary practices (one/region). Veterinary clinics enrolled 20-25 farms each depending on region, with 1-2 trained technicians scoring per region. All cows (n = 92,348) present at a milking or pregnancy testing event were tail scored using a modified version of the New Zealand Veterinary Association Industry Scoring System. Palpated lesions were recorded as deviated (i.e. non-linear deformity), shortened (tail shorter than normal) or traumatic (all other lesions). The location of lesions was defined by dividing the tail into three equal zones: upper, middle and lower. A cow could have more than one lesion type and location, and/or multiple lesions of the same type, but for the prevalence calculation, only the presence or absence of a particular lesion was assessed. Prevalence of tail damage calculated using whole herd scoring was compared to random sampling across the herd and sampling from the front and back of the milking order. Bootstrap sampling with replacement was used to generate the sampling distributions across seven sample sizes ranging from 40-435 cows.

RESULTS

When scoring all cows, the median prevalence for deviation was 9.5 (min 0.9, max 40.3)%; trauma 0.9 (min 0, max 10.7)%, and shortening was 4.5 (min 1.3, max 10.8)%. Deviation and trauma prevalence varied between regions; the median prevalence of deviations ranged from 6% in the West Coast to 13% in Waikato, and the median prevalence of all tail damage from 7% in the West Coast to 29% in Southland. Sampling based on milking order was less precise than random sampling across the herd. With the latter and using 157 cows, 95% of prevalence estimates were within 5% of the whole herd estimate, but sampling based on milking order needed > 300 cows to achieve the same precision.

CONCLUSIONS AND CLINICAL RELEVANCE

The proportion of cows identified as having damaged tails was consistent with recent reports from New Zealand and Ireland, but at 11.5%, the proportion of cows with trauma or deviation is below acceptable standards. An industry-wide programme is needed to reduce the proportion of affected cows.

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities

Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.

Cattle Body Detection Based on YOLOv5-EMA for Precision Livestock Farming

Accurate cattle body detection is crucial for precision livestock farming. However, traditional cattle body detection methods rely on manual observation, which is both time-consuming and labor-intensive. Moreover, computer-vision-based methods suffer prolonged training times and training difficulties. To address these issues, this paper proposes a novel YOLOv5-EMA model for accurate cattle body detection. By incorporating the Efficient Multi-Scale Attention (EMA) module into the backbone of YOLO series detection models, the performance of detecting smaller targets, such as heads and legs, has been significantly improved. The Efficient Multi-Scale Attention (EMA) module utilizes the large receptive fields of parallel sub-networks to gather multi-scale spatial information and establishes mutual dependencies between different spatial positions, enabling cross-spatial learning. This enhancement empowers the model to gather and integrate more comprehensive feature information, thereby improving the effectiveness of cattle body detection. The experimental results confirm the good performance of the YOLOv5-EMA model, showcasing promising results across all quantitative evaluation metrics, qualitative detection findings, and visualized Grad-CAM heatmaps. To be specific, the YOLOv5-EMA model achieves an average precision (mAP@0.5) of 95.1% in cattle body detection, 94.8% in individual cattle detection, 94.8% in leg detection, and 95.5% in head detection. Moreover, this model facilitates the efficient and precise detection of individual cattle and essential body parts in complex scenarios, especially when dealing with small targets and occlusions, significantly advancing the field of precision livestock farming.

Initial validation of an intelligent video surveillance system for automatic detection of dairy cattle lameness

Introduction

Lameness is a major welfare challenge facing the dairy industry worldwide. Monitoring herd lameness prevalence, and early detection and therapeutic intervention are important aspects of lameness control in dairy herds. The objective of this study was to evaluate the performance of a commercially available video surveillance system for automatic detection of dairy cattle lameness (CattleEye Ltd).

Methods

This was achieved by first measuring mobility score agreement between CattleEye and two veterinarians (Assessor 1 and Assessor 2), and second, by investigating the ability of the CattleEye system to detect cows with potentially painful foot lesions. We analysed 6,040 mobility scores collected from three dairy farms. Inter-rate agreement was estimated by calculating percentage agreement (PA), Cohen's kappa (κ) and Gwet's agreement coefficient (AC). Data regarding the presence of foot lesions were also available for a subset of this dataset. The ability of the system to predict the presence of potentially painful foot lesions was tested against that of Assessor 1 by calculating measures of accuracy, using lesion records during the foot trimming sessions as reference.

Results

In general, inter-rater agreement between CattleEye and either human assessor was strong and similar to that between the human assessors, with PA and AC being consistently above 80% and 0.80, respectively. Kappa agreement between CattleEye and the human scorers was in line with previous studies (investigating agreement between human assessors) and within the fair to moderate agreement range. The system was more sensitive than Assessor 1 in identifying cows with potentially painful lesions, with 0.52 sensitivity and 0.81 specificity compared to the Assessor's 0.29 and 0.89 respectively.

Discussion

This pilot study showed that the CattleEye system achieved scores comparable to that of two experienced veterinarians and was more sensitive than a trained veterinarian in detecting painful foot lesions.

The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle

This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.

A comparison of online and live training of livestock farmers for an on-farm self-assessment of animal welfare

One approach to strengthening the involvement of farmers or stockpersons in the evaluation and improvement of animal welfare is the implementation of an on-farm self-assessment. A valid comparison of the results with reference values, between or within farms, requires that training of the farmers and reliability testing have taken place. We investigated two different training methods (online vs. live) with a total of 146 livestock farmers from farms with dairy cows and calves, beef cattle, sows and suckling piglets, weaners and fattening pigs, laying hens, broiler chickens, and turkeys from all over Germany. Online tests were conducted by assessing photos/videos of each indicator of the assessment scheme to estimate the inter-rater reliability (prevalence-adjusted and bias-adjusted kappa, PABAK). The farmers were requested to provide information on their professional background and rate their motivation to participate in the training and their subjective training success, meaning their confidence in assessing each indicator later on-farm. They evaluated the feasibility of the training and its impact on their views and attitudes. In general, farmers achieved at least substantial inter-rater reliability (PABAK ≥ 0.61) in 86.8% of all initial tests; 13.4% of the tests were repeated once or more times, resulting in a significant improvement of the agreement, with 90.9% of the tests reaching a PABAK ≥ 0.61. However, reliability was higher for indicators with a lower number of score levels. The subjective evaluation of training success was, on average, positive (score = 74.8 out of 100). No effects of the training method or the farmers’ professional background on the inter-rater reliability or the subjective training success were detected. Furthermore, for both methods, farmers moderately agreed that the training had sharpened their views on the animals, encouraged them to implement the assessment on their farm, and made it clear that self-assessment supports animal management. Although the reported costs and time investment for the online training were significantly lower, the effort required for both methods and the ease of integration into the workflow were ranked as similarly acceptable. Overall, both training methods appear feasible for the training of farmers/stockpersons on the assessment of animal-based indicators.

Keeping profitable cows in the herd: A lifetime cost-benefit assessment to support culling decisions

Increasing the productive lifespan of dairy cows is important to achieve a sustainable dairy industry, but making strategic culling decisions based on cow profitability is challenging for farmers. The objective of this study was to carry out a lifetime cost-benefit analysis based on production and health records and to explore different culling decisions among farmers. The cost-benefit analysis was conducted for 22 747 dairy cows across 114 herds in Quebec, Canada for which feed costs and the occurrence of diseases were reported. Costs and revenues related to productive lifespan were compared among cohorts of cows that left their respective herd at the end of their last completed lactation or stayed for a complete additional lactation. Hierarchical clustering analysis was carried out based on costs and revenues to explore different culling decisions among farmers. Our results showed that the knowledge of lifetime cumulative costs and revenues was of great importance to identify low-profitable cows at an earlier lactation, while only focusing on current lactation costs and revenues can lead to an erroneous assessment of profitability. While culling decisions were mostly based on current lactation costs and revenues and disregarded the occurrence of costly events on previous lactations, there was variation among farmers as we identified three different culling decision clusters. Monitoring cumulative costs and revenues would help farmers to identify low-profitable cows at an earlier lactation and make the decision to increase herd productive lifespan and farm profitability by keeping the most profitable cows.

German Farmers' Awareness of Lameness in Their Dairy Herds

Lameness is one of the most challenging problems in the dairy industry. Control is impeded because farmers often underestimate the number of lame cows. The objectives of this study were to assess German farmers' awareness of lameness in their herds and to determine the associations between farmers' awareness and their management practices, farm characteristics as well as with farmers' education, personality traits and attitudes. As a part of a large cross-sectional study, veterinarians visited farms in three structurally different regions of Germany: north (n = 253), east (n = 252), and south (n = 260). The cows (n = 84,998) were scored for locomotion and farmers were asked to estimate the number of cows that were lame or did not walk soundly. The ratio of farmers' estimated prevalence and the veterinarians' observed prevalence (Farmer's Detection Index; FDI) was calculated. The median lameness prevalence assessed by the veterinarians was 23.1, 39.1, and 23.2%, and the median prevalence of lame cows estimated by the farmers was 9.5, 9.5, and 7.1% in the north, east, and south, respectively. On average, farmers were conscious of only 45.3% (north), 24.0% (east), and 30.0% (south) of their lame cows. Farmers managing their herds according to organic principles had a higher FDI than farmers who managed their herds conventionally. Surprisingly, no significant associations between FDI and factors concerning claw health management could be detected. Therefore, increased awareness did not seem to be necessarily linked to improved management. Moreover, the FDI was not significantly associated with farmers' education or herd size. In the south, more extraverted farmers had a lower FDI. Those farmers who totally agreed with the statement, “I am satisfied with my herd's health,” had a lower FDI than farmers who disagreed or were undecided. Moreover, farmers who disagreed or were undecided with the statement, “It affects me to see a cow in pain” had a higher FDI than those farmers who agreed to the statement. The results indicate that poor awareness of lameness was linked to the farmers' attitude and personality. Therefore, new approaches concerning the consultation regarding lameness control, such as the use of Motivational Interviewing, might be useful in the future.

Including milk production, conformation, and functional traits in multivariate models for genetic evaluation of lameness

Lameness is a serious health and welfare issue that can negatively affect the economic performance of cows, especially on pasture-based dairy farms. However, most genetic predictions (GP) of lameness have low accuracy because lameness data are often incomplete as data are collected voluntarily by farmers in countries such as Australia. The objective of this study was to find routinely measured traits that are correlated with lameness and use them in multivariate evaluation models to improve the accuracy of GP for lameness. We used health events and treatments associated with lameness recorded by Australian farmers from 2002 to early 2019. The lameness incidence rates in Holstein and Jersey cows were 3.3% and 4.6%, respectively. We analyzed the records of 36 other traits (milk production, conformation, fertility, and survival traits) to estimate genetic correlations with lameness. The estimated heritability ± standard error (and repeatability ± standard error) for lameness in both Holstein and Jersey breeds were very low: 0.007 ± 0.002 (and 0.029 ± 0.002) and 0.005 ± 0.003 (and 0.027 ± 0.006), respectively, in univariate sire models. For the GP models, we tested including measurements of overall type to prediction models for Holsteins, stature and body length for Jersey, and milk yield and fertility traits for both breeds. The average accuracy of GP, calculated from prediction error variances, were 0.38 and 0.24 for Holstein and Jersey sires, respectively, when estimated using univariate sire models and both increased to 0.43 using multivariate sire models. In conclusion, we found that the accuracy of GP for lameness could be improved by including genetically correlated traits in a multivariate model. However, to further improve the accuracy of predictions of lameness, precise identification and recording incidences of hoof or leg disorder, or large-scale recording of locomotion and claw scores by trained personnel should be considered.

Keeping Dairy Cows for Longer: A Critical Literature Review on Dairy Cow Longevity in High Milk-Producing Countries

Simple Summary

The ability of farms to produce milk sustainably is closely related to dairy cow longevity, i.e., the length of productive life. However, longevity is a very complex feature that depends on all the aspects of the lifespan of a cow and there is no standard definition nor metric to measure it. Measuring longevity is important because it influences the profitability and the environmental impact of farms as well as the welfare of the animals. The objectives of this paper were to review metrics used to measure longevity and describe its status among high milk-producing countries. Increasing dairy cow longevity would imply that an animal has an early age at first calving and a long and profitable productive life. Combining age at first calving, length of productive life, and margin over all (available) costs provides a complete evaluation of longevity. This paper also shows that dairy cow longevity has decreased in most high milk-producing countries over time, which confirm the concerns voiced by the dairy industry and other stakeholders. Increasing cow longevity would reduce health costs and increase cow profitability while improving both animal welfare and quality of life, contributing to a more sustainable dairy industry.

Abstract

The ability of dairy farmers to keep their cows for longer could positively enhance the economic performance of the farms, reduce the environmental footprint of the milk industry, and overall help in justifying a sustainable use of animals for food production. However, there is little published on the current status of cow longevity and we hypothesized that a reason may be a lack of standardization and an over narrow focus of the longevity measure itself. The objectives of this critical literature review were: (1) to review metrics used to measure dairy cow longevity; (2) to describe the status of longevity in high milk-producing countries. Current metrics are limited to either the length of time the animal remains in the herd or if it is alive at a given time. To overcome such a limitation, dairy cow longevity should be defined as an animal having an early age at first calving and a long productive life spent in profitable milk production. Combining age at first calving, length of productive life, and margin over all costs would provide a more comprehensive evaluation of longevity by covering both early life conditions and the length of time the animal remains in the herd once it starts to contribute to the farm revenues, as well as the overall animal health and quality of life. This review confirms that dairy cow longevity has decreased in most high milk-producing countries over time and its relationship with milk yield is not straight forward. Increasing cow longevity by reducing involuntary culling would cut health costs, increase cow lifetime profitability, improve animal welfare, and could contribute towards a more sustainable dairy industry while optimizing dairy farmers’ efficiency in the overall use of resources available.

Pairwise comparison locomotion scoring for dairy cattle

Conventional locomotion scoring is a subjective, absolute, and discrete assessment of locomotion. Here we assess pairwise comparison scoring to improve upon the limited intra- and interobserver consistency typical of conventional locomotion scoring. Five observers performed conventional 4-level locomotion scoring using 50 video recordings of dairy cattle, and also assessed 90 pairs of videos (composed from the same 50 recordings) using relative pairwise scoring. Intra- and interobserver consistency of pairwise scores [intraobserver: percentage agreement (PA) = 82%, κ = 0.63; interobserver: PA = 79%, κ = 0.57] were greater than of 4-level absolute scores (intraobserver: PA = 72%, κw = 0.74; interobserver: PA = 56%, κw = 0.59). Pairwise scores were scaled with an optimization method to obtain the position of the 50 recordings on a continuous locomotion scale. These continuous locomotion scores (CLS) were compared with the conventional mean absolute visual locomotion scores (VLS). Correlation between CLS and VLS was strong (τ = 0.69), and consistency between binarized CLS and binarized VLS was high (PA = 84%, κ = 0.66 for threshold VLS ≥1). Just noticeable difference (JND) for locomotion scoring was 0.3 on a 4-level scale ranging from 0 to 3. Pairwise scoring and scaling had the scoring consistency of binary absolute scoring with finer continuous granularity than 4-level absolute scoring. The pairwise scoring method, and associated scaling, offer a more consistent and informative alternative to conventional absolute multilevel locomotion scoring.

A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications

The computer vision technique has been rapidly adopted in cow lameness detection research due to its noncontact characteristic and moderate price. This paper attempted to summarize the research progress of computer vision in the detection of lameness. Computer vision lameness detection systems are not popular on farms, and the accuracy and applicability still need to be improved. This paper discusses the problems and development prospects of this technique from three aspects: detection methods, verification methods and application implementation. The paper aims to provide the reader with a summary of the literature and the latest advances in the field of computer vision detection of lameness in dairy cows.

Impact of Nutrients on the Hoof Health in Cattle

Lameness is currently one of the most important and economically demanding diseases in cattle. It is manifested in a change in locomotion that is associated with lesions, especially the pelvic limbs. The disease of the hoof is painful, affecting the welfare of dairy cows. Important factors that influence the health of the limbs include nutrition, animal hygiene, stable technology, and genetic and breeding predispositions. Nutrition is one of the basic preventive factors affecting the quality and growth of the hoof horn, and the associated prevalence of hoof disease. The strength and structure of the hoof horn are affected by the composition of the feed ration (amino acids, minerals, vitamins, and toxic substances contaminating the feed ration, or arising in the feed ration as metabolites of fungi).

Spatial and Ecological Farmer Knowledge and Decision-Making about Ecosystem Services and Biodiversity

Amid climate change, biodiversity loss and food insecurity, there is the growing need to draw synergies between micro-scale environmental processes and practices, and macro-level ecosystem dynamics to facilitate conservation decision-making. Adopting this synergistic approach can improve crop yields and profitability more sustainably, enhance livelihoods and mitigate climate change. Using spatially explicit data generated through a public participatory geographic information system methodology (n = 37), complemented by spatial analysis, interviews (n = 68) and focus group discussions (n = 4), we explored the synergies between participatory farmer-to-farmer agroecology knowledge sharing, farm-level decisions and their links with macro-level prioritization of conservation strategies. We mapped farm conditions and ecosystem services (ES) of two village areas with varying knowledge systems about farming. Results of the farm-level analysis revealed variations in spatial perception among farmers, differences in understanding the dynamics of crop growth and varying priorities for extension services based on agroecological knowledge. The ES use pattern analysis revealed hotspots in the mapped ES indicators with similarities in both village areas. Despite the similarities in ES use, priorities for biodiversity conservation align with farmers’ understanding of farm processes and practices. Farmers with training in agroecology prioritized strategies that are ecologically friendly while farmers with no agroecology training prioritized the use of strict regulations. Importantly, the results show that agroecology can potentially contribute to biodiversity conservation and food security, with climate change mitigation co-benefits. The findings generally contribute to debates on land sparing and land sharing conservation strategies and advance social learning theory as it pertains to acquiring agroecological knowledge for improved yield and a sustainable environment.

Farmers' perceptions and approaches to detection, treatment and prevention of lameness in pasture-based dairy herds in New South Wales, Australia

AIMS

The objective of this study was to investigate farmers' perception of lameness in comparison to the estimated prevalence of lameness in NSW pasture-based dairies to evaluate farmers' perceptions and approaches to detection, treatment and prevention of lameness.

METHODS

Across-sectional study was conducted on 62 pasture-based dairy farms across NSW, Australia. The prevalence of lameness in these farms was estimated using locomotion scoring (1-4 scale). A survey was also conducted, using a questionnaire and face-to-face interview, to explore farmers' perceived prevalence of lameness and approaches to treatment and prevention.

RESULTS

The prevalence of lameness estimated by farmers was 3.7 times less (mean: 5%; range 0% to 26%) than that determined by locomotion scoring (mean: 19.1%; range 5.0%-44.5%). Approaches to treatment included antimicrobial therapy, hoof inspection with or without application of wooden blocks. In 28% of the farms, the lame cows were managed by farmers or farm staff with no official training in treatment of lame cows. The mean interval from detection of lameness to examination of the affected hoof was almost 55 hours (range 2-720 hours). A very low percentage of farms kept lameness records or implemented lameness preventive strategies such as footbaths and prophylactic foot trimming.

CONCLUSIONS

Farmers and farm managers were found to underestimate the prevalence of lameness which could be due to the low level of awareness and can contribute to subsequent lack of implementation of prophylactic procedures and preventive management strategies for lameness. These findings accentuate the need to improve farmers' ability to detect lame cows and to emphasise the importance of recording in order to facilitate the management of lameness in dairy herds.

Animal science Down Under: a history of research, development and extension in support of Australia’s livestock industries

This account of the development and achievements of the animal sciences in Australia is prefaced by a brief history of the livestock industries from 1788 to the present. During the 19th century, progress in industry development was due more to the experience and ingenuity of producers than to the application of scientific principles; the end of the century also saw the establishment of departments of agriculture and agricultural colleges in all Australian colonies (later states). Between the two world wars, the Council for Scientific and Industrial Research was established, including well supported Divisions of Animal Nutrition and Animal Health, and there was significant growth in research and extension capability in the state departments. However, the research capacity of the recently established university Faculties of Agriculture and Veterinary Science was limited by lack of funding and opportunity to offer postgraduate research training. The three decades after 1945 were marked by strong political support for agricultural research, development and extension, visionary scientific leadership, and major growth in research institutions and achievements, partly driven by increased university funding and enrolment of postgraduate students. State-supported extension services for livestock producers peaked during the 1970s. The final decades of the 20th century featured uncertain commodity markets and changing public attitudes to livestock production. There were also important Federal Government initiatives to stabilise industry and government funding of agricultural research, development and extension via the Research and Development Corporations, and to promote efficient use of these resources through creation of the Cooperative Research Centres program. These initiatives led to some outstanding research outcomes for most of the livestock sectors, which continued during the early decades of the 21st century, including the advent of genomic selection for genetic improvement of production and health traits, and greatly increased attention to public interest issues, particularly animal welfare and environmental protection. The new century has also seen development and application of the ‘One Health’ concept to protect livestock, humans and the environment from exotic infectious diseases, and an accelerating trend towards privatisation of extension services. Finally, industry challenges and opportunities are briefly discussed, emphasising those amenable to research, development and extension solutions.

Influence of Lameness on the Lying Behaviour of Zero-Grazed Lactating Jersey Dairy Cattle Housed in Straw Yards

Thirty-five lactating Jersey cows were recruited to the study. They were grouped according to locomotion score (LS), where low scores indicate normal gait. LS-1 (n = 12), LS-2 (n = 12) and LS-3 (n = 11) were used. Locomotion scores were balanced for parity and stage of lactation. Lying behaviour was recorded using IceTag™ data loggers attached to the cows for four consecutive days. The study animals remained in the straw based yards with grooved concrete flooring throughout the duration of the study. All data were normally distributed and assessed using a one-way ANOVA with a post hoc Tukey test. There were no statistically significant differences between locomotion score and the time spent lying, active and standing of zero-grazed lactating Jersey dairy cattle housed on straw yards. Lame cows (LS-3) had significantly shorter lying bouts than sound cows (LS-1) (34 min vs. 42 min, respectively). There has been limited research to date measuring the lying behaviour of cattle on straw and into the Jersey breed. The cows had longer than expected standing times and an increased frequency of lying bouts. This may have been attributed to the stocking density in which the cows were kept. We also reported a prevalence of lameness within the herd of 38%.