Animal welfare and other ethical implications of precision livestock technology

In this commentary, we explore the risks and challenges associated with Precision Livestock Farming technologies based on an online workshop with over 70 international animal welfare experts, policy-makers, NGO, students, farmers and industry staff

Tehnologije preciznog stočarstva u praćenju dobrobiti životinja: pregledni rad

Precision Livestock Farming is the use of technology to help farmers monitor and manage their animals and their farm. Precision Livestock Farming technologies can be used to improve not only animal welfare and health, but also production. Automated measures reflecting the welfare of an animal can be related to its environment, and to the behaviour and physiology of the animal, as well as its position relative to environmental features. We need to ensure that the automatic measures we record reflect the type of behavioural or physiological changes we are interested in. Other aspects to consider are space and time, in terms of variable environmental conditions and animal-related changes that occur gradually. Different types of equipment can be used for measuring behaviour automatically, and these are either attached to, interacting with, or remote from the animal. A combination of these is often the most efficient method, but it is also more complex to manage. There are also species differences as to what is feasible. Small farms are unlikely to be able to afford the type of equipment used by larger enterprises, and we need to put more effort into finding Precision Livestock Farming technologies that can work for the smallholder. The use of Precision Livestock Farming technologies for efficient animal welfare monitoring in practice requires affordable, reliable, and easy-to-use equipment, providing data that reflect – adequately and in real-time – different aspects of the state of the welfare of animals within the herd.Precizno stočarstvo uključuje primjenu tehnologija koje stočarima pomažu pri praćenju i upravljanju njihovim životinjama i farmom. Tim se tehnologijama mogu unaprijediti ne samo zdravlje i dobrobit životinja nego i proizvodnja. Automatizirani načini mjerenja koji pokazuju dobrobit životinje mogu se odnositi na njezin okoliš, ponašanje i fiziologiju, kao i na njezin položaj s obzirom na značajke okoliša. Cilj je mjerenjem osigurati dobivanje ponašajnih i fizioloških promjena koje nas zanimaju. Drugi aspekti koje treba uzeti u obzir jesu prostor i vrijeme, s obzirom na promjenjive uvjete okoliša i promjene vezane uz životinju do kojih s vremenom dolazi. Za automatizirano mjerenje ponašanja mogu se upotrijebiti različiti tipovi opreme koji se mogu postaviti na životinju, koji su na neki način povezani sa životinjom ili mogu biti udaljeni od nje. Premda je najzahtjevnija, kombinacija ovih metoda obično je i najučinkovitija. Izvodivost osim toga ovisi i o vrsnim razlikama. Male farme obično si ne mogu priuštiti opremu kao što to mogu velike te je potrebno uložiti više napora u pronalaženje tehnologijskih rješenja kojima će se moći koristiti male farme. Njihova primjena za učinkovito praćenje dobrobiti životinja zahtijeva dostupnu i sigurnu opremu kojom se lako rukuje i kojom se pravodobno dobivaju odgovarajući podaci upotrebljivi za različite aspekte dobrobiti zivotinja unutar stadu

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kgCO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3  months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kgCO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3  months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems

Exploring the potential of Precision Livestock Farming technologies to help address farm animal welfare

The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers’ duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type (sensors, bolus, image or sound based, Radio Frequency Identification (RFID)), their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare

On the use of 3D camera to accurately measure volume and weight of dairy cow feed

The paper discusses the challenges facing the dairy industry due to increased farm sizes and reduced staff-to-animal ratios, which are impacting animal welfare. The development of precision livestock farming (PLF) technologies has gained momentum to address these challenges. PLF technologies can assess animal welfare and health status by monitoring animal behavior and biological changes, and alerting farmers of any issues. However, the applicability of PLF tools in other productive phases of the dairy cattle is still limited. The article focuses on the challenges of managing unweaned dairy calves, particularly the variability in relation to when calves start consuming solid feed, and how PLF technologies can be used to monitor individual calf intake and manage weaning at the individual level. The attention is mainly focused on the advantages of using automated feeders for unweaned dairy calves, including labor savings, greater precision in measurement and control of individual intake of liquid and solid feed, and higher preweaning growth rates. In particular, a method is proposed, involving a 3D depth camera and a proper algorithm to measure the volume and weight of eaten feed. The method is preliminarily assessed in tests conducted in laboratory, which highlight a remarkable concurrence (differences as low as 2 %) with respect to nominal values

Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry Sector

Tracking technologies offer a way to monitor movement of many individuals over long time periods with minimal disturbances and could become a helpful tool for a variety of uses in animal agriculture, including health monitoring or selection of breeding traits that benefit welfare within intensive cage-free poultry farming. Herein, we present an active, low-frequency tracking system that distinguishes between five predefined zones within a commercial aviary. We aimed to evaluate both the processed and unprocessed datasets against a “ground truth” based on video observations. The two data processing methods aimed to filter false registrations, one with a simple deterministic approach and one with a tree-based classifier. We found the unprocessed data accurately determined birds’ presence/absence in each zone with an accuracy of 99% but overestimated the number of transitions taken by birds per zone, explaining only 23% of the actual variation. However, the two processed datasets were found to be suitable to monitor the number of transitions per individual, accounting for 91% and 99% of the actual variation, respectively. To further evaluate the tracking system, we estimated the error rate of registrations (by applying the classifier) in relation to three factors, which suggested a higher number of false registrations towards specific areas, periods with reduced humidity, and periods with reduced temperature. We concluded that the presented tracking system is well suited for commercial aviaries to measure individuals’ transitions and individuals’ presence/absence in predefined zones. Nonetheless, under these settings, data processing remains a necessary step in obtaining reliable data. For future work, we recommend the use of automatic calibration to improve the system’s performance and to envision finer movements