Extensively housed (i.e., semi-free and free-range) pigs provide meat products of high quality that generally enjoy increased prices in the market [151,152]. Due to the nature of the managerial techniques and the limited number of extensive pig farms, there has been little commercial demand for implementation of PLF in this sector [152]. However, PLF technologies could be proved beneficial, as they provide security against theft, wildlife [153] and records concerning animals’ health status and overall performance [7]. Furthermore, PLF could positively affect breeding, fattening performance and health status through monitoring and control, and strengthen consumer confidence by collecting data that refer to the characteristics of both the animal and farm [2]. Some PLF applications for free-ranging pigs are presented in the next paragraph.

Alexy and Horváth [152] presented results from the development of a continuous monitoring PLF tool for sows of the Mangalica breed that were extensively housed on a total area of 2.5 ha. RFID ear tags were attached to each sow and a monitoring area was designated. The extensive breeding site consisted of a tank drinker, a wooden feeder, the wallowing area (mostly created by the sows themselves), a wooden building used by the sows for resting, and five individual farrowing cottages. Four reading units were installed on a fence close to the wallowing area. A weather station recorded the climate data on an hourly basis. The system successfully recorded the hourly activity of the sows. They reported that the environment and the weather affected the activity of the wallowing site, as the sows tended to use it most in a temperature range between 0 to 4°C. Furthermore, it was stated that this particular activity was strongly connected with the animals’ welfare status. With regard to social behaviour, the sows tended to create small groups that visited and left the wallowing site simultaneously. However, the system’s evaluation parameters such as accuracy, precision, specificity, or efficiency were not provided. The researchers stated that additional reading units need to be installed in the pasture area and that additional sows will be marked with RFID ear tags. Aubé et al. [154] used hand-controlled video cameras and recorded and analysed sows’ posture (i.e., standing, sitting, kneeling or lying) and activity (i.e., grazing, rooting or any other behaviour). Furthermore, a GPS receiver was fixed between each sow’s shoulders and an accelerometer was installed on the lower part of one back leg for general activity assessment. An open-source geographic information system was used for GPS data processing, and they managed to successfully record frequency, duration, and the location of the foraging and resting behaviors of the sows, time spent on the pasture, and distance travelled. The authors reported that the applied method in their study was firstly used by Ringgenberg et al. [155], implying that simple systems used for indoor housing can potentially be used for free ranging animals. Van Damme et al. [156] used GPS receivers and successfully (p = 0.014) monitored the foraging and exploratory behaviours of free roaming pigs in Zambia. It should be noted that in both studies the authors only addressed the animal behaviour point of view and no PLF evaluation parameters for the systems were provided.

In January 2012, the European Union issued the Council Directive 1999/74/EC banning battery cages for egg production in the poultry sector. By 2019, hens housed in alternative systems including floor, aviary, free-range and organic reached 50% of their total population in Europe, as indicated by the European Commission Eggs Market Situation Dashboard. Such systems provide additional behavioural freedom for the everyday activities of poultry, resulting in improved welfare status [157,158]. Furthermore, it has been reported that free-range laying hens demonstrate improved plumage condition, final body weight and egg weight compared with their counterparts that are housed indoors [157,158,159,160,161]. It should be noted that even after switching to non-cage systems, welfare challenges such as keel bone damage [162] and damaging behaviours such as feather, toe and vent/cloacal pecking and cannibalism still persist [158,163,164,165]. Furthermore, in free range systems, the higher exposure to parasites, pathogens and predation contribute to poultry welfare impairment [161,166]. Wild animals can cause severe damage in free ranging systems. For example, red foxes, which are a common predator of chickens, can eliminate the whole flock within a single night, resulting in severe losses [167]. PLF technologies could potentially minimize these negative effects and improve welfare and performance status.

As reported by Rowe et al. [168], more than 42% of the PLF systems use image analysis to assess welfare in poultry. This phenomenon is mainly attributed to the fact that image and video analysis and processing are inexpensive ways to record and analyse the behaviour of the birds without disturbing them [158]. Similarly, Campbell et al. [169] used a series of cameras to capture the indoor rearing pens and range area of each pen, and successfully classified the dust bathing and foraging behaviours, as well as the time the birds spent interacting with enrichment materials and the time the chicks spent expressing play behaviours with each other. Unfortunately, no information concerning the precision, efficiency, accuracy, or specificity of the system was provided. Montalcini et al. [170] developed a combined camera-based and RFID tracking system that automatically monitors individual bird movement over long periods of time for free-ranging commercial farms with an accuracy of 99%. The system overestimated the number of transitions carried out by the birds per zone (i.e., three stacked tiers of a commercial aviary, a littered floor and the winter garden), explaining only 23% of the actual variation, hence further research is needed to improve the performance of this application. Various camera-based methods can be found in the literature including wildlife interactions with free-ranging ducks [171] and chickens [172,173], activity [174,175] and ranging behaviour [176,177,178] monitoring, counting, or detecting of dead chickens [179], weight estimation [180], shelter preference behaviour monitoring [181], enrichment utilization monitoring [182], and meat colour and quality classification [183,184]. All of the methods are still under development and therefore further research is needed for the development of a commercial application.

Another widely spread PLF application in poultry is RFID systems. A variety of different sizes and settings have been developed, and they are available for commercial use, focusing on individual behaviour recording [185,186,187], feed intake monitoring [188], individual range use [186,189,190,191,192,193], range behaviour tracking [160,169,194,195,196], response to stressors monitoring [197], welfare assessment [198], range behaviour and health status evaluation [199], individual identification [200], individual movement parameters monitoring such as speed, ability to snatch feed and resting behaviour for disease detection [201], body weight, feed intake, egg production and quality evaluation [202], behavioural preferences, and indoor and outdoor resource utilization monitoring [192,203]. It should be noted that an alternative system to RFID technology consisting of a small, light-based monitoring system was developed by Buijs et al. [204]. The system demonstrated 89% or better accuracy for hens’ position detection. Hedman et al. [205] developed a GPS-based system for individual chickens’ position and movement monitoring but did not provide any PLF evaluation parameters. Finally, Stadig et al. [206] developed an automated Ultra-Wideband positioning system for location monitoring with an accuracy of 68%. Further research is needed for the improvement of the system’s internal characteristics and accuracy.

More complex systems have been introduced during the previous decade, including automatic egg collection robots [207,208], behaviour monitoring [209], dead chicken removal robots [210], and guardian dog monitoring using a combination of GPS and camera equipment for auto-guidance for the repulsion of wildlife such as red foxes [167]. Gilsdorf et al. [153] also reviewed a variety of different technologies concerning the use of frightening devices for wildlife repulsion and therefore wildlife damage management. They reported that today’s ultrasonic devices are ineffective at repelling birds and mammals. However, the potential of a combination of frightening devices could provide a cost-effective integrated system that considerably reduces wildlife damage. However, only a few commercial applications have been released due to their complexity and limited field testing. Furthermore, a thorough economic analysis for the systems’ total costs is essential for the development of commercial products [4].

The constantly increasing global need for higher quality food and improved animal welfare status based on sustainable farming systems highlights the necessity of high‐quality livestock management. PLF technologies have shown great potential in addressing this issue in an animal‐friendly manner, while simultaneously providing the farmers with information that further assists them in decision making. The application of such technologies is directed towards the automatization of simple procedures, the minimization of labour and environmental impact, and the improvement of animal welfare. PLF applications can only serve as decision making support tools for farmers, since automatic decisions for efficient handling and critical health and welfare issues are not feasible at present. Furthermore, although various PLF applications for grazing animals are available commercially, their use is limited and can be found mainly in cattle production rather than in small ruminants or other species. This is likely attributed to individual animal value and producers’ reluctance due to financial constraints, unresolved welfare concerns, lack of specialized nearby service, and complexity in using the technologies. The limited testing and the lack of cost‐benefit evaluation make these technologies undesirable for farmers. Future PLF research should focus on improving the systems’ evaluation parameters and should be based on realistic and thorough economic analysis, emphasizing their beneficial impact. In parallel, ʺfriendlyʺ software and effective marketing techniques should be applied to persuade more farmers to adopt the technologies.