IMPORTANCE OF PRECISION LIVESTOCK FARMING IN ANIMAL WELFARE
Dr. Naushali Gujar1, Dr. Rajni Arora2
1M.V.Sc. Scholar, 2 Assistant Professor
Livestock Production Management, CVAS, Bikaner
INTRODUCTION –
Precision Livestock Farming (PLF) is generally associated with technologies that allow the real-time, automated and continuous monitoring of farmed animals such as cameras, sensors, and sound devices, which are increasingly powered by artificial intelligence and allow the collection and interpretation of data. The application of precision agriculture approaches in the livestock industry (i.e. precision livestock farming or PLF) uses advanced technologies aimed at automatic, real-time monitoring of animal behaviour, health, environmental impact and production.
The purpose is to detect deviations at an early stage and improve animal health, welfare and efficiency, expecting an improvement in production sustainability. Although one of the aims of PLF is to improve animal welfare, most of the systems monitor just one or only a few factors. The results of these measurements are compared to a general standard or farm-specific threshold, and a conclusion or alert is communicated to the farmer, prompting them to check the animal and take action if necessary. PLF uses a combination of tools and methods to measure different variables from each animal with high precision, supporting farmers to make decisions concerning the livestock production systems. Decisions are often based on the acquisition, collection, and analysis of quantitative data obtained by continuous real-time from animals and the environment. These tools include sensor technology cameras, microphones, wireless communication tools, Internet connections, and cloud storage among others.
PLF is potentially one of the most powerful developments amongst a few interesting new and upcoming technologies that have the potential to revolutionize the livestock farming industries. There are several levels of PLF, varying from collecting and analysing data at the group level down to monitoring individual animals, utilising sensors that can be static, moving or animal-mounted. The technology ranges from monitoring production and fertility to health and behaviour; some systems monitor environmental factors to control climate conditions and there are robotic systems that automate human handling such as milking, feeding and cleaning.
PLS in Animal Health and Welfare –
Animal health is of paramount importance in the livestock industry as it impairs production efficiency through growth retardation or even mortality, animal welfare through pain and discomfort, and it can even impair human health through the misuse of antibiotics or zoonosis. In fact, the large density of animals living so close to humans in some countries can transfer a high number of zoonosis diseases to humans. The monitoring of health problems in the early detection of clinical signs of diseases on the farm is one of the key issues from which PLF has arisen. Most diseases are easily treated when detected in an early phase, although prevention is always the priority.
Modern technologies such as sensors, big data, artificial intelligence (AI), and machine learning (ML) algorithms enable farmers to react to diseases after they become evident, or proactively using vet services, and also provide an opportunity to constantly monitor key animal health parameters such as movement, air quality, or consumption of feed and water. By constantly collecting these data and using advanced technology to predict deviations or abnormalities, farmers can identify, predict, and prevent disease outbreaks. Therefore, this technology has a significant cost advantage over older detection methods.
Animals can be monitored by methods based on the sound, with the potential to be automated for large-scale farming. A sound-based tool (Pig Cough Monitor™ (PCM), Soundtalks®, and Fancom B.V.) has been developed for automated pig cough detection that is based on a mathematical algorithm that processes all incoming sound and identifies the number of coughs automatically. Van Hertem evaluated the effect of using a microphone and subsequent advanced methods for labelling in the early detection of cough in calves and highlighted how the adoption of an algorithm with >90% precision allowed reducing the emergence of bovine respiratory disease (BRD).
In addition, distress can be vocalized by animals or shown though unusual activity. Vocalization could be measured via microphones, whereas activity could be observed and recorded using staff observations or surveillance cameras, with the interpretation of sounds and images to produce meaningful information
Nowadays, automated sensors and algorithms can reliably predict and reduce the risk of mastitis in cows. Air sensors in the poultry industry can predict the onset of Coccidiosis by constantly monitoring the concentration of volatile organic compounds in the air increase, as the number of infected birds increases. Air sensors could detect this change much earlier than a farmer or a vet could. In other cases, by carrying out image analysis and calculating model parameters from the image information, it was possible to develop an algorithm for automatic detection of lameness based on animal locomotion.
In the case of cattle health, a few common diseases can be identified using non-invasive, cheap sensor technologies. More complex sensor platforms exist, for instance, camera systems to detect back posture, and ingestible pills for heart rate.
Furthermore, the continuous feed and water registration in the farm makes it possible to assess the first freedom from hunger and thirst. Climate control sensors such as temperature sensors, relative humidity probes, and CO2 sensors will allow the automatic evaluation of thermal discomfort in the house.
Monitoring of Welfare in PLF –
Some attempts have been made to integrate several measures into an automated assessment of animal welfare. In a review on dairy cow technologies to automatically assess welfare, it was concluded that although manufacturers often claim to offer complete solutions, no system offers everything that could be achieved by using a full combination of all systems operating together, and almost without exception, the different technologies operate ‘stand-alone’ and will not communicate with each other.
In a broiler study, a Welfare Quality assessment was linked to vision data of a broiler flock. Relationships were found between deviations in distribution of the flock and footpad lesions, and between activity deviations and hock burns; activity and occupation pattern deviations were linked to the welfare assessments scores. Similar results were found in a study where broilers were assessed by human experts and where gait scores could be predicted from flock data and automatic activity monitoring.
The human-animal relationship is important for animal welfare and could be measured automatically. The eYeNamic camera system can measure activity and distribution of broilers in the farm when someone walks through—an alternative to the human avoidance test of the Welfare Quality Protocol.
PLF to Improve Animal Welfare
PLF systems can improve welfare by optimising nutrient supply, based on automated growth monitoring or weight measurements by early detection of disease, such as lameness or mastitis, as well as early detection of maladaptive behaviours such as feather pecking and tail-biting, and by improving housing conditions with devices such as robot scrapers and automated climate control systems.Webster argues that giving animals the ability to make choices that promote their own quality of life could help improve welfare, and this could be achieved with technologies that facilitate ‘choice’, such as individual feeding, robotic milking or voluntary showering facilities.
PLF systems may increase welfare if the farmer responds adequately to the PLF system alerts; however, good tools do not automatically guarantee good utilisation by a stockperson. In a field study with 23 farms that switched to an automatic milking system (AMS), many of the farmers reported that cows were calmer in comparison to cows milked in conventional milking systems (CMS). There was less of a herd hierarchy and less ‘bullying’; yields increased and after an initial adjustment period, lameness and mastitis levels decreased.
As per the European study on PLF, most of the 13 pig and poultry farmers that were interviewed emphasised that the personal contact with the animals cannot be replaced by video cameras, but that PLF systems can be a great help in daily life. One farmer responded that he understands his animals much better after starting to use PLF monitoring.
CONCLUSION –
PLF technologies also have the potential to improve farm animal welfare in several ways, such as improving the living environment, early detection of disease or by facilitating animal choice. A range of PLF sensors have been developed to improve the efficiency of animal production by optimising management. Data from these sensors could be integrated into automated welfare assessment systems although further research is needed to define and validate this approach.
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