Automatic oestrus detection system enables monitoring of sexual behaviour in sheep

Sheep Nocturnal Activity Dataset

Monitoring sheep’s behavior is of paramount importance, because deviations from normal patterns may indicate nutritional, thermal or social stress, changes in reproductive status, health issues, or predator attacks. The night period, despite being a more restful period in which animals are theoretically sleeping and resting, represents approximately half of the life cycle of animals; therefore, its study is of immense interest. Wearable sensors have become a widely recognized technique for monitoring activity, both for their precision and the ease with which the sensorized data can be analyzed. The present dataset consists of data from the sensorization of 18 Serra da Estrela sheep, during the nocturnal period between 18 November 2021 and 16 February 2022. The data contain measurements taken by ultrasound and accelerometry of the height from neck to ground, as well as measurements taken by an accelerometer in the monitoring collar. Data were collected every 10 s when the animals were in the shelter. With the collection of data from various sensors, active and inactive periods can be identified throughout the night, quantifying the number and average time of those periods.

Interactions between Ewes and Rams during Mating Can Be Used to Predict Lambing Dates Accurately, but Not Sire

Ewes often lamb over extended periods so the level of nutrition during pregnancy and lambing may be suboptimal for ewes that conceived later during mating. Predicting lambing dates would allow cohorts of ewes with similar gestational ages to be managed more precisely to achieve targets for ewe nutrition, feed on offer, mob sizes and access to shelter to improve lamb survival. The interactions between ewes and rams during mating have been used to predict the time of oestrus and lambing dates successfully, but this has not been tested at a commercial scale. In this study, proximity sensors were used to measure interactions between inexperienced Merino ewes (n = 317) and experienced rams (n = 9) during a 27-day mating period under commercial production conditions. When the gestation length was assumed to be 150 days, 91% of lambing dates were predicted within ±6 days of the actual birth date of lambs and 84% of lambing dates were predicted within ±3 days. The use of proximity sensors during mating was an effective means of predicting lambing dates, and there was no significant difference in accuracy for single bearing verses multiple bearing ewes. However, DNA parentage data showed the ram corresponding with the maximum daily interactions ratio was the sire for only 16% of all progeny, suggesting they could not be used to indicate the sire of the progeny.

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

Although the effects of human–dairy cattle interaction have been extensively examined, data concerning small ruminants are scarce. The present review article aims at highlighting the effects of management practices on the productivity, physiology and behaviour of dairy animals. In general, aversive handling is associated with a milk yield reduction and welfare impairment. Precision livestock farming systems have therefore been applied and have rapidly changed the management process with the introduction of technological and computer innovations that contribute to the minimization of animal disturbances, the promotion of good practices and the maintenance of cattle’s welfare status and milk production and farms’ sustainability and competitiveness at high levels. However, although dairy farmers acknowledge the advantages deriving from the application of precision livestock farming advancements, a reluctance concerning their regular application to small ruminants is observed, due to economic and cultural constraints and poor technological infrastructures. As a result, targeted intervention training programmes are also necessary in order to improve the efficacy and efficiency of handling, especially of small ruminants.

Review: Precision Livestock Farming technologies in pasture-based livestock systems

Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.

Proximity sensors provide an accurate alternative for measuring maternal pedigree of lambs in Australian sheep flocks under commercial conditions

Context Proximity sensors were used recently to determine the maternal pedigree of lambs on a small plot with high accuracy. If this accuracy is maintained under commercial grazing conditions, this method could be a useful alternative to improving genetic gain in sheep, including reproduction traits. Aims To investigate using proximity sensors to determine the maternal pedigree of lambs and to define the level of interactions required to determine maternal pedigree confidently irrespective of differences in ewe age, lamb age, birth type, paddock size, flock size or stocking rate under commercial grazing conditions. Methods We compared maternal pedigree determined using the proximity sensors to DNA profiling (n = 10 flocks) and lambing rounds (n = 16 flocks). Ewes (n = 7315) and lambs (n = 8058) were fitted with proximity sensors under normal grazing conditions for each property for 1–3 days. Flocks varied in ewe age (adults, hoggets and ewe lambs), lamb age (up to 100 days old, except for 1 flock), birth type (singles, multiples), paddock size (0.25–320 ha), flock size (37–420 lambs) and stocking rate (2–100 dry sheep equivalents/ha, except for 1 flock). Key results An interaction ratio of >2 was required for a confident ewe–lamb match (ewe with the most interactions compared with the ewe with the second-most interactions for each lamb). Using this criterion, the average success of proximity sensors at matching a lamb to a ewe was 95% and the sensors were 97% accurate when compared with the pedigree results from lambing rounds or DNA. For lambs matched successfully, over 90% of this success was achieved in the first 7 h and over 99% in the first 20 h. While the success rate of matching a lamb to a ewe was not influenced significantly by ewe age, birth type, paddock size, flock size or stocking rate, the time to achieve sensor success was significantly quicker for singles than for twins and sensor accuracy was significantly higher for smaller paddocks with higher stocking rates. Conclusions Our results showed that proximity sensors can establish maternal pedigree effectively and accurately across a range of conditions experienced on commercial properties. Implications Private industry can now develop more cost-effective sensor technologies with greater confidence that will enhance recording of maternal pedigree and, hence, the rate of genetic gain across the sheep industry.

Review: Precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming

Precision livestock farming (PLF) technologies are becoming increasingly common in modern agriculture. They are frequently integrated with other new technologies in order to improve human-livestock interactions, productivity and economical sustainability of modern farms. New systems are constantly being developed for concentrated farming operations as well as for extensive and pasture-based farming systems. The development of technologies for grazing animals is of particular interest for the Mediterranean extensive sheep farming sector. Dairy sheep farming is a typical production system of the area linked to its historical and cultural traditions. The area provides roughly 40% of the world sheep milk, having 27% of the milk-producing ewes. Developed countries of the area (France, Italy, Greece and Spain - FIGS) have highly specialized production systems improved through animal selection, feeding techniques and intensification of production. However, extensive systems are still practiced alongside intensive ones due to their lower input costs and better resilience to market fluctuations. In the current article, we evaluate possible PLF systems and their suitability to be incorporated in extensive dairy sheep farming as practiced in the FIGS countries. Available products include: electronic identification systems (now mandatory in the EU) such as ear tags, ruminal boluses and sub-cutaneous radio-frequency identification; on-animal sensors such as accelerometers, global positioning systems and social activity loggers; and stationary management systems such as walk-over-weights, automatic drafter (AD), virtual fencing and milking parlour-related technologies. The systems were considered according to their suitability for the management and business model common in dairy sheep farming. However, adoption of new technologies does not take place immediately in small and medium scale extensive farming. As sheep farmers usually belong to more conservative technology consumers, characterized by an average age of 60 and a very transparent community, the dynamics do not favour financial risk taking involved with new technologies. Financial barriers linked to production volumes and resource management of extensive farming are also a barrier for innovation. However, future prospectives could increase the importance of technology and promote its wider adoption. Trends such as global sheep milk economics, global warming, awareness to animal welfare, antibiotics resistance and European agricultural policies could influence the farming practices and stimulate wider adoption of PLF systems in the near future.

Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy

This paper reviews the environmental impact of current livestock practices and discusses the advantages offered by Precision Livestock Farming (PLF), as a potential strategy to mitigate environmental risks. PLF is defined as: "the application of process engineering principles and techniques to livestock farming to automatically monitor, model and manage animal production". The primary goal of PLF is to make livestock farming more economically, socially and environmentally sustainable and this can be obtained through the observation, interpretation of behaviours and, if possible, individual control of animals. Furthermore, adopting PLF to support management strategies, may lead to the reduction of the environmental impact of farms. Currently, few studies reported PLF efficacy in reducing the environmental impact, however further studies are necessary to better analyze the actual potential of PLF as a mitigation strategy. Literature shows the potentiality of the application of PLF, as the introduction of PLF in farms can lead to a reduction of Greenhouse gases (GHG) and ammonia (NH3) emission in air, nitrates and antibiotics pollution in water bodies, phosphorus, antibiotics and heavy metals in the soil.