Next Article in Journal
Effect of Major Diseases on Productivity of a Large Dairy Farm in a Temperate Zone in Japan
Next Article in Special Issue
Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring
Previous Article in Journal
The Role of Exopolysaccharide-Producing Streptococcus thermophilus on Physical Properties of Stirred Skim Milk Gel
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Dairy
Volume 3
Issue 4
10.3390/dairy3040053
dairy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

by
Gerben Hofstra
1,*,
Judith Roelofs
2,
Steven Mark Rutter
3,
Elaine van Erp-van der Kooij
2 and
Jakob de Vlieg
4
1
Applied Biology Department, HAS University of Applied Sciences, Onderwijsboulevard 221, 5223 DE ’s-Hertogenbosch, The Netherlands
2
Animal Husbandry Department, HAS University of Applied Sciences, Onderwijsboulevard 221, 5223 DE ’s-Hertogenbosch, The Netherlands
3
Department of Animal Health, Behaviour and Welfare, Harper Adams University, Edgmond, Newport TF10 8NB, UK
4
Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
*
Author to whom correspondence should be addressed.
Dairy 2022, 3(4), 776-788; https://doi.org/10.3390/dairy3040053
Submission received: 21 September 2022 / Revised: 21 October 2022 / Accepted: 24 October 2022 / Published: 29 October 2022
(This article belongs to the Special Issue Advances in Digital Dairy)
Review Reports Versions Notes

Abstract

:
Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

1. Introduction

Since the beginning of this century, the general public has become more concerned with animal welfare and a growing number of European consumers now believe that welfare standards in livestock production should be improved [1,2]. This concern also translates to consumer purchase decisions and consumers are willing to pay a premium for credence attributes of animal welfare in beef and dairy products of up to 19 and 31%, respectively [3].
Animal welfare can be defined in several ways. The Five Freedoms, as first suggested by the Brambell Committee [4] and further developed by the British Farm Animal Welfare Council (FAWC), have been regarded as a standard for animal welfare for over half a century. Animals should have freedom from hunger and thirst, discomfort, pain, injuries or disease, freedom to express normal behaviour, and freedom from fear and distress [5]. In a large EU-funded research project animal welfare was later redefined as the four principles of Welfare Quality®: good housing, good health, good nutrition, and appropriate behaviour [6]. More recently, the Five Domains Model for animal welfare assessment, which was originally formulated in 1994 by Mellor [7] to facilitate structured, systematic, comprehensive, and coherent animal welfare assessments, is also often used. This model has already been updated several times to embrace scientific progress in ideas on animal welfare [8]. The Five Domains Model has a significant focus on subjective experiences, known as affects, which collectively contribute to an animal’s overall welfare state [9]. Based on these animal welfare frameworks, protocols have been developed to measure, for instance, cattle welfare [10]. However, these extensive assessment protocols have proven to be time-consuming and therefore costly, which limits their potential for practical application [11,12,13].
In recent years, several researchers have pointed out the potential of precision livestock farming (PLF) for animal welfare management and assessment [14,15,16,17,18,19,20,21,22]. However, no system exists yet that can provide the broad, multidimensional integration that is required to give a complete assessment of the welfare of any species, including cattle [17]. The automated monitoring of cattle welfare through technology has the potential to reduce the labour requirements and make the assessment more reliable by utilising continuous, real-time monitoring. These systems provide the farmer with more information on their animals, possibly resulting in higher production efficiency and improved animal welfare [14,17].
Automated monitoring and managing of individual cows start with recognizing the animal and its behaviour. Location, posture, and movement of the cow are key elements in this process. Multiple technologies are now available for the automated monitoring of the location of individual animals. These techniques, ranging from global positioning systems (GPS) to ultra-wideband (UWB), Bluetooth and even computer vision, yield the potential to manage and assess animal welfare. The location of a cow in a functional area, for instance, is closely related to the activity of the animal, e.g., feeding, lying, and drinking (basic needs) and thus location can be used to infer its behavioural budget [23,24]. Deviations from predetermined behavioural patterns in turn might be an indicator of disease or compromised welfare [25,26,27]. In addition, social relations can be inferred when comparing locations of group members in relation to each other [28,29]. However, each location determining technology has its constraints, such as range and accuracy, and might not have the same potential for determining animal welfare in every situation, e.g., indoor vs. outdoor. This review aims to provide an overview of the location determining techniques used for cattle and their potential for managing and assessing animal welfare. The different techniques and approaches currently applied for determining animal location will be discussed and the potential of these techniques for animal welfare management and assessment will be reviewed.

2. Location Determining Techniques

2.1. Global Navigation Satellite Systems (GNSS)

The Global Navigation Satellite Systems (GNSS) rely on radio signals from specialized satellites available at the given time of positioning. GNSS, such as the global positioning system (GPS), have been used for over 30 years to track domestic animals [30]. They have been used by ecologists to track and monitor the behavioural ecology of free-ranging animals [31,32], but also for monitoring cattle movement in the field [33,34]. GNSS works well outdoors when there is an unobstructed line of sight, although radio signal attenuation from walls causes standard GNSS receivers to perform poorly in indoor environments [35]. However, new technologies that enhance and/or repeat the GNSS signal indoors might have the potential to enhance indoor GNSS performance [36,37]. One of the concerns with poor signal strength is the inherent GNSS error when cows are inactive, which may overestimate daily travel by cattle by as much as 15% [38].
In general, when GNSS collars are used in cattle [39] they appear to have little to no effect on their behaviour [30]. Schleppe et al. [40] experimented with a GNSS ear tag; due to weight restrictions and consequently battery size, run-time was insufficient for practical applications. When using GNSS, the sampling frequency, which is often a trade-off with battery life, should be considered carefully since longer intervals may propagate flawed spatial interpretations [41]. When automatically classifying cattle behaviour, the data are commonly segmented using a fixed window size for feature extraction. However, a fixed window size might not always represent one single behaviour; thus, variable window sizes for feature extraction may lead to enhanced behaviour recognition [42].
For the management of animals in extensive grazing systems, it is suggested that monitoring grazing distribution via livestock GNSS trackers can, in some cases, improve stocking rate adjustment calculations [43]. Furthermore, GNSS data can give more insight into the relationship between cattle behaviours and pasture characteristics [44,45,46,47,48], which could, in turn, inform the development of management strategies to modify cattle distribution in the field, such as the strategic placement of water, shade and mineral points to decrease overgrazing and nutrient accumulation at resting sites [47]. In addition, social associations as monitored by GNSS data among individual cows may provide some insight into cattle grazing patterns, forage utilization and availability [49].
The detection of potential health issues such as lameness is possible when considering the modified exploratory dynamics of lame cows based on GNSS data in combination with accelerometers [50]. In combination with a birthing sensor, GNSS information can provide useful information about the location of calving events in the field, allowing the farmer to reduce potential injury to the calf caused by the mother or by environmental factors [51]. The spatial behaviour of cows and their calves can also help understand their behaviour in extensive grazing systems with potential applications to improve calf survival and performance [52]. Herd social structure based on GNSS data can provide cattle contact patterns that potentially have major implications for infection transmission within the herd [53] or between domestic cattle and wildlife species [54].
GNSS data offer the possibility to monitor animal behaviour, such as foraging, walking and resting, with an accuracy ranging from 57 to 87.5% depending on the circumstances [55,56,57,58,59,60,61,62]. However, the potential of GNSS for behavioural monitoring might benefit from combination with other sensors such as accelerometers [63,64,65,66,67,68,69,70,71,72]. GNSS data can also be utilized to understand and monitor cattle herd spatiotemporal behaviour [57] and consequently changes in herd dispersion–aggregation patterns [73,74]. The monitoring of this spatiotemporal behaviour of cows makes it possible to determine the social structure [63,73] and identify dominant animals in herd situations [73].
A virtual fence (VF) can be defined as a structure serving as an enclosure, a barrier, or a boundary without a physical barrier [75]. Some virtual fencing technologies use GNSS collars that deliver an audio cue when the animal approaches a GNSS-defined virtual boundary (e.g., [76,77]). An aversive electric stimulus is delivered by the device if, following the audio cue, the animal walks beyond the virtual boundary, but not if it stops walking or turns back. The animal learns to associate the audio cue with the pending electrical stimulus unless it changes its behaviour, and increasingly responds to the audio cue alone [78]. The general public has shown some concern about animal welfare when it comes to implementing virtual fencing technology [79]. Acosta et al. [80] have thus suggested replacing the electrical stimuli with tactile stimuli. However, several authors have confirmed that the VF does not impact welfare any greater than traditional physical electric fencing [81,82,83,84,85]. When the use of a VF promotes the use of, for instance, strip grazing instead of keeping animals indoors, it can improve animal welfare [86].

2.2. Ultra-Wideband (UWB)

The technique of ultra-wideband (UWB) has been around since the 19th century, but has only recently been used for the tracking of animals [87,88]. UWB is a wireless technology developed to transfer data at high rates over very short distances at very low power densities [89] and is one of the most reliable and accurate technologies available in the field of indoor positioning [90]. The UWB techniques used to track animals are spatially and temporally more accurate than GNSS technology [91]. The tracking of animals is mainly performed indoors but has also recently been successfully applied in field settings [92,93].
In many environments, the data from a UWB tracking system can contain unwanted noise due to the signal being masked or interfered with by the animals or by equipment in the barn [94]. Missing data from UWB devices might hinder reliable continuous monitoring and analysis of animal movement and social behaviour [95]. However, the quality of the data can be enhanced using filters [23,96,97] or with automated image analysis on density maps based on animals locations [94]. In this respect, the precise calibration and validation of a UWB tracking system is of the utmost importance [98].
Tracking cow activity with UWB and monitoring circadian rhythms might help detect health issues such as lameness and mastitis [99]. UWB data can provide information on, for instance, walking distance and lying time, which can be used for lameness detection [100]. It is also possible to monitor the proximity interaction network of a herd, which in the future might be used to detect individual differences in social associations; in turn, this could be used to identify health issues [29,101]. Data from UWB positioning systems can be used to measure animal behaviours [23,102,103] such as feeding behaviour [26,96,104] and oestrus behaviour [99,105,106,107,108]. However, Shane et al. [109] showed that one should be careful when inferring the behaviour based solely on the location data of the animal. Additional sensor data might be complimentary to UWB data. For instance, Ren et al. [28] were able to detect affiliative and agonistic social interactions with high accuracy when combining UWB with computer vision technology.

2.3. Radio Frequency Identification (RFID)

Radio frequency identification (RFID) was originally developed for identification purposes, but it can also be applied for the positioning and tracking of animals. RFID systems consist of a tag or transponder and a reader or transceiver that reads and writes data to a transponder [110,111]. RFID tags can be active, passive, or semi-passive. Passive and semi-passive RFID send their data by reflection or modulation of the electromagnetic field that was emitted by the reader, which limits the reading range to between 10 cm and 3 m. The battery of a semi-passive RFID is only used to power the sensor and data processing circuitry. An active RFID has a battery that enables higher signal strength and an extended communication range of up to 100 m, but is larger due to the presence of that battery and more electronic components [112,113]. The speed of travel of the animal [114,115] and body tissue near the transponders [116,117] have a strong influence on the reading performance of the system so the optimal position of the tag and reader should be considered [117]. Curran et al. [112] experimented with RFID-Radar where the reader calculates the location of transponders while interfacing with the reader instead of just recording its presence. In this capacity, it could be used as an indoor RFID-based location determination system, but Curran et al. deemed it impractical due to too many inaccurate readings.
RFID systems have been used to detect feeding behaviour [118,119,120], drinking behaviour [121,122] and supplement intake [123] based on proximity to the feed bunk, water point and mineral lick block, respectively. Toaf et al. [124] found that an RFID-based ear tag recording of brush proximity was not yet a reliable representation of grooming behaviour as some animals spent a relatively large portion of time standing idle close to the brush, resulting in false positives.

2.4. Wireless Sensor Networks

Wireless sensor networks (WSNs) use small, low-cost, low-power sensor nodes that communicate untethered over short distances [125,126]. WSNs have attracted more research efforts in the past few years and several standards in communications protocols such as Bluetooth and Zigbee have already been established [113]. These offer great opportunities for cattle monitoring [127,128,129] and localization [130,131,132]. There are several wireless communication technologies available for WSN, such as Bluetooth, Zigbee, BLE, LTE, WiFi and LoRa, each with its advantages and disadvantages [133,134]. For a wireless technology such as Zigbee, the network range is mostly between 10 and 30 m [135], which would be impractical for tracking purposes in large, outdoor environments; however, according to Feng et al. [136], it is preferable for indoor situations owing to its low power consumption. For long-range communication (>5 km), LPWAN technologies such as LoRa are preferable [136,137]. The system and the number of nodes needed for accurate communication depend on the environment [138]. Maroto-Malino et al. [139] developed a promising fusion system for a field setting with a few animals in the herd being fitted with a GPS collar for location determining and the rest of the animals being fitted with low-cost BLE tags to determine their relative location to the animals with the GPS collars. Nodes are becoming increasingly smaller and energy-efficient [140,141]. WSNs are often bioinspired, meaning that the network mechanisms are based on animal behaviour [142] and data collection may be real-time or more opportunistic [143].
Nadimi et al. [144] used a Zigbee WSN to determine the total number of animals roaming in a certain area of the field when using strip grazing and their total pasture time to use the presence of the animals as an indicator of the grass quality and quantity, which may help determine the right time to provide access to a new strip. WSN can be used to determine the position of animals and, in combination with a three-axis activity sensor, movement and activity status can be observed. This implies that it can be used for analysing animal behaviour at specific locations in the barn [135]. By detecting abnormally long walking distances, oestrus might be detected using a WSN [145], or by detecting if an animal is being mounted, standing oestrus might be detected [146].

2.5. Computer Vision Technology

Computer vision is a simulation of biological vision and is part of the field of artificial intelligence. In the field of animal science, computer vision is about deriving useful information from videos and translating the videos into new insights on e.g., animal behaviour through data science. A computer vision system extracts certain features from images while performing other subtasks, e.g., edge detection, corner detection, image segmentation and pattern recognition [147]. Although not primarily a location-determining technique, by mimicking the human eye, computer vision can identify individuals and thus identify an animal’s location. Biometric identification of animals through computer vision is possible [148,149,150] and also the tracking and counting of animals [151,152,153,154,155]. The use of a thermal sensor might even improve the tracking capabilities since it will make it easier to detect animals against the background and distinguish between overlapping individuals [156]. Computer vision relies on visual information only and thus is entirely non-invasive and non-intrusive [157]. Outdoors, cameras could be mounted on drones for locating and counting animals in the field [111].
The individual feed intake of dairy cows is an important variable in dairy farming which vision technology has the potential to measure [158,159,160]. Using low-cost RGB-D cameras, vision has the potential to measure the individual feed intake of dairy cows in a cowshed [161] but the deep learning models used do need to be tuned to different types of feed [162]. Computer vision can also be applied to determine behaviours such as lying [163,164] or feeding and standing behaviour [165]. Being able to monitor animal behaviour makes it possible to detect abnormal activities in case of disease [166,167].
Vision systems have a much wider scope than just determining location. They can also be utilized for lameness detection [168,169] or to detect facial expressions associated with health issues such as ‘pain face’ [170]. Using infrared cameras makes it possible to measure animal temperature to detect fever [171] indicating disease. Another possibility would be to detect the warm exhaled air to measure raised respiration rates [172] indicating heat stress or disease.

2.6. Challenges with Location Determining Technologies

There are several promising techniques available for determining location and animal monitoring in cattle, but all of them will have to compromise between performance and system efficiency in terms of battery life, size and cost (Table 1). Besides this compromise, there are issues such as standardization, data security, robustness and scalability, and educational challenges that need to be taken into account [173,174].

3. Welfare and Animal Location

According to the Welfare Quality system, welfare can be monitored in four domains: good housing, good nutrition, good health and appropriate behaviour. In the previous paragraphs, we have shown how different location determining techniques can play a role in monitoring welfare in these domains (Table 1). Good housing can be assessed by using location determining techniques to monitor space use, freedom of movement, standing behaviour (as a measure for insufficient or unsuitable lying space) and preference for certain drinkers or feeders. Good nutrition can be monitored by using the location determining technique to monitor the distribution of a herd in the field or to calculate time spent at the feeding rack or drinkers as an indicator of feeding and drinking behaviour. Health can be monitored by using the location determining technique to detect changes in behaviour, walking speed and social associations. Finally, location systems can give an indication of behavioural patterns, social interactions and time budgets for behaviour, which can be a strong factor in determining animal welfare. Several location determining techniques are promising for this purpose but, as discussed, might not function in every situation. GNSS has the longest history in animal tracking and thus has had more attention as to its use; however, it is mostly just suitable for outdoor applications. Systems that function well indoors, such as UWB and WSN, have difficulties in an outdoor setting due to a lack of networking connectivity. Currently, GNSS seems to have the most benefits for welfare monitoring based on location in a field setting. For an indoor setting, vision technology, which has been receiving increased attention in recent years, may hold the most potential for welfare monitoring. This is partly because it is much more than just a location determining technique, it is also a system that can measure and monitor behaviour. A future overall solution for location-based welfare monitoring might very well come from WSNs, which is still in its infancy for this purpose but could potentially work well both indoors and outdoors. Combining sensors (e.g., accelerometers) with any location determining technique in a sensor or data fusion system can give more detailed information on individual cows, and give an even more reliable and accurate indication of animal welfare. For example, an inertial measurement unit (IMU), which uses an accelerometer, gyroscope and magnetometer to accurately determine movement and relative position [175], can be used in a data fusion system with RFID [176] or GNSS [177] to give more precise information on behaviour. Another example is that an accelerometer alone might give information on standing idle but the added information on location helps determine whether this is a possible welfare issue. Idling in the queue in front of the AMS might be considered normal behaviour, whereas idling at the feeding rack or in the cubicle could indicate welfare issues such as disease, insufficient feed or unsuitable cubicles [178]. Still, it is a major challenge to manage and translate the huge amount of heterogeneous data produced by the various sensing technologies into new scientific insights.

4. Conclusions

We conclude that location systems are a promising tool in determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use in animal welfare monitoring is needed. In a field setting, GNSS have the best potential for welfare monitoring based on location. Vision technology may hold the most potential for welfare monitoring in an indoor setting. In the future, sensor and data fusion systems may lead to objective ways of measuring animal welfare, which could greatly benefit both animals and farmers.

Author Contributions

Conceptualization, G.H.; methodology, G.H.; investigation, G.H.; writing—original draft preparation, G.H.; writing—review and editing, G.H., J.R., S.M.R., E.v.E.-v.d.K. and J.d.V. All authors have read and agreed to the published version of the manuscript.

Funding

Research funded by the Dutch Research Council (023.017.003).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. De Jonge, J.; van Trijp, H.C.M. Meeting Heterogeneity in Consumer Demand for Animal Welfare: A Reflection on Existing Knowledge and Implications for the Meat Sector. J. Agric. Environ. Ethics 2013, 26, 629–661. [Google Scholar] [CrossRef]
  2. European Union. Attitudes of Europeans towards Animal Welfare; European Commission: Brussels, Belgium, 2016. [Google Scholar]
  3. Yang, W.; Renwick, A. Consumer Willingness to Pay Price Premiums for Credence Attributes of Livestock Products—A Meta-Analysis. J. Agric. Econ. 2019, 70, 618–639. [Google Scholar] [CrossRef]
  4. Brambell, F.W.R. Report of the Technical Committee to Enquire into the Welfare of Animals Kept Under Intensive Livestock Husbandry Systems; Her Majesty’s Stationary Office: London, UK, 1965. [Google Scholar]
  5. FAWC. Report on Priorities for Animal Welfare Research and Development; FAWC: London, UK, 1993. [Google Scholar]
  6. Jones, B.; Manteca, X. Practical Strategies for Improving Farm Animal Welfare: An Information Resource; Welfare Quality: Lelystad, the Netherlands, 2009. [Google Scholar]
  7. Mellor, D.J.; Reid, C.S.W. Concepts of Animal Well-Being and Predicting the Impact of Procedures on Experimental Animals. In Experimental Research and Animal Welfare Collection; WellBeing International: Potomac, MD, USA, 1994. [Google Scholar]
  8. Mellor, D.J.; Beausoleil, N.J.; Littlewood, K.E.; McLean, A.N.; McGreevy, P.D.; Jones, B.; Wilkins, C. The 2020 Five Domains Model: Including Human–Animal Interactions in Assessments of Animal Welfare. Animals 2020, 10, 1870. [Google Scholar] [CrossRef] [PubMed]
  9. Mellor, D.J. Operational Details of the Five Domains Model and Its Key Applications to the Assessment and Management of Animal Welfare. Animals 2017, 7, 60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Welfare Quality. Welfare Quality Assessment Protocol for Cattle; Welfare Quality: Lelystad, the Netherlands, 2009. [Google Scholar]
  11. Knierim, U.; Winckler, C. On-Farm Welfare Assessment in Cattle: Validity, Reliability and Feasibility Issues and Future Perspectives with Special Regard to the Welfare Quality® Approach. Anim. Welf. 2009, 18, 451–458. [Google Scholar]
  12. Blokhuis, H.J.; Veissier, I.; Miele, M.; Jones, B. The Welfare Quality® Project and beyond: Safeguarding Farm Animal Well-Being. Acta Agric. Scand. A Anim. Sci. 2010, 60, 129–140. [Google Scholar] [CrossRef]
  13. De Vries, M.; Engel, B.; Den Uijl, I.; Van Schaik, G.; Dijkstra, T.; De Boer, I.J.M.; Bokkers, E.A.M. Assessment Time of the Welfare Quality® Protocol for Dairy Cattle. Anim. Welf. 2013, 22, 85–93. [Google Scholar] [CrossRef]
  14. Berckmans, D. Precision Livestock Farming Technologies for Welfare Management in Intensive Livestock Systems. OIE Rev. Sci. Tech. 2014, 33, 189–196. [Google Scholar] [CrossRef]
  15. Halachmi, I.; Guarino, M.; Bewley, J.; Pastell, M. Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production. Annu. Rev. Anim. Biosci. 2019, 7, 403–425. [Google Scholar] [CrossRef]
  16. Chapa, J.M.; Maschat, K.; Iwersen, M.; Baumgartner, J.; Drillich, M. Accelerometer Systems as Tools for Health and Welfare Assessment in Cattle and Pigs—A Review. Behav. Processes 2020, 181, 104262. [Google Scholar] [CrossRef]
  17. Van Erp-Van der Kooij, E.; Rutter, S.M. Using Precision Farming to Improve Animal Welfare. CAB Rev. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour. 2020, 15, 1–10. [Google Scholar] [CrossRef]
  18. Leliveld, L.M.C.; Provolo, G. A Review of Welfare Indicators of Indoor-Housed Dairy Cow as a Basis for Integrated Automatic Welfare Assessment Systems. Animals 2020, 10, 1430. [Google Scholar] [CrossRef] [PubMed]
  19. Lovarelli, D.; Bacenetti, J.; Guarino, M. A Review on Dairy Cattle Farming: Is Precision Livestock Farming the Compromise for an Environmental, Economic and Social Sustainable Production? J. Clean. Prod. 2020, 262, 121409. [Google Scholar] [CrossRef]
  20. Maroto Molina, F.; Pérez Marín, C.C.; Molina Moreno, L.; Agüera Buendía, E.I.; Pérez Marín, D.C. Welfare Quality® for Dairy Cows: Towards a Sensor-Based Assessment. J. Dairy Res. 2020, 87, 28–33. [Google Scholar] [CrossRef]
  21. Silva, S.R.; Araujo, J.P.; Guedes, C.; Silva, F.; Almeida, M.; Cerqueira, J.L. Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition. Animals 2021, 11, 2253. [Google Scholar] [CrossRef] [PubMed]
  22. Stygar, A.H.; Gómez, Y.; Berteselli, G.V.; Dalla Costa, E.; Canali, E.; Niemi, J.K.; Llonch, P.; Pastell, M. A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle. Front. Vet. Sci. 2021, 8, 4338. [Google Scholar] [CrossRef] [PubMed]
  23. Porto, S.M.C.; Arcidiacono, C.; Giummarra, A.; Anguzza, U.; Cascone, G. Localisation and Identification Performances of a Real-Time Location System Based on Ultra Wide Band Technology for Monitoring and Tracking Dairy Cow Behaviour in a Semi-Open Free-Stall Barn. Comput. Electron. Agric. 2014, 108, 221–229. [Google Scholar] [CrossRef]
  24. Chapa, J.M.; Lidauer, L.; Steininger, A.; Öhlschuster, M.; Potrusil, T.; Sigler, M.; Auer, W.; Azizzadeh, M.; Drillich, M.; Iwersen, M. Use of a Real-Time Location System to Detect Cows in Distinct Functional Areas within a Barn. JDS Commun. 2021, 2, 217–222. [Google Scholar] [CrossRef]
  25. Huhtala, A.; Suhonen, K.; Mäkelä, P.; Hakojärvi, M.; Ahokas, J. Evaluation of Instrumentation for Cow Positioning and Tracking Indoors. Biosyst. Eng. 2007, 96, 399–405. [Google Scholar] [CrossRef]
  26. Tullo, E.; Fontana, I.; Gottardo, D.; Sloth, K.H.; Guarino, M. Technical Note: Validation of a Commercial System for the Continuous and Automated Monitoring of Dairy Cow Activity. J. Dairy Sci. 2016, 99, 7489–7494. [Google Scholar] [CrossRef] [Green Version]
  27. Weigele, H.C.; Gygax, L.; Steiner, A.; Wechsler, B.; Burla, J.B. Moderate Lameness Leads to Marked Behavioral Changes in Dairy Cows. J. Dairy Sci. 2018, 101, 2370–2382. [Google Scholar] [CrossRef] [PubMed]
  28. Ren, K.; Bernes, G.; Hetta, M.; Karlsson, J. Tracking and Analysing Social Interactions in Dairy Cattle with Real-Time Locating System and Machine Learning. J. Syst. Archit. 2021, 116, 102139. [Google Scholar] [CrossRef]
  29. Chopra, K.; Hodges, H.R.; Barker, Z.E.; Vázquez Diosdado, J.A.; Amory, J.R.; Cameron, T.C.; Croft, D.P.; Bell, N.J.; Codling, E.A. Proximity Interactions in a Permanently Housed Dairy Herd: Network Structure, Consistency, and Individual Differences. Front. Vet. Sci. 2020, 7, 3715. [Google Scholar] [CrossRef] [PubMed]
  30. Rutter, S.M.; Beresford, N.A.; Roberts, G. Use of GPS to Identify the Grazing Areas of Hill Sheep. Comput. Electron. Agric. 1997, 17, 177–188. [Google Scholar] [CrossRef]
  31. Tomkins, N.; O’Reagain, P. Global Positioning Systems Indicate Landscape Preferences of Cattle in the Subtropical Savannas. Rangel. J. 2007, 29, 217–222. [Google Scholar] [CrossRef]
  32. Hebblewhite, M.; Haydon, D.T. Distinguishing Technology from Biology: A Critical Review of the Use of GPS Telemetry Data in Ecology. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 2303–2312. [Google Scholar] [CrossRef]
  33. Ganskopp, D. Manipulating Cattle Distribution with Salt and Water in Large Arid-Land Pastures: A GPS/GIS Assessment. Appl. Anim. Behav. Sci. 2001, 73, 251–262. [Google Scholar] [CrossRef]
  34. Liao, C.; Clark, P.E.; Shibia, M.; DeGloria, S.D. Spatiotemporal Dynamics of Cattle Behavior and Resource Selection Patterns on East African Rangelands: Evidence from GPS-Tracking. Int. J. Geogr. Inf. Sci. 2018, 32, 1523–1540. [Google Scholar] [CrossRef]
  35. Mautz, R. Overview of Current Indoor Positioning Systems. Geod. Cartogr. 2009, 35, 18–22. [Google Scholar] [CrossRef]
  36. Xu, R.; Chen, W.; Xu, Y.; Ji, S. A New Indoor Positioning System Architecture Using Gps Signals. Sensors 2015, 15, 10074–10087. [Google Scholar] [CrossRef] [Green Version]
  37. Li, X. A GPS-Based Indoor Positioning System with Delayed Repeaters. IEEE Trans. Veh. Technol. 2019, 68, 1688–1701. [Google Scholar] [CrossRef]
  38. Ganskopp, D.C.; Johnson, D.D. GPS Error in Studies Addressing Animal Movements and Activities. Rangel. Ecol. Manag. 2007, 60, 350–358. [Google Scholar] [CrossRef]
  39. Clark, P.E.; Johnson, D.E.; Kniep, M.A.; Jermann, P.; Huttash, B.; Wood, A.; Johnson, M.; McGillivan, C.; Titus, K. An Advanced, Low-Cost, GPS-Based Animal Tracking System. Rangel. Ecol. Manag. 2006, 59, 334–340. [Google Scholar] [CrossRef]
  40. Schleppe, J.B.; Lachapelle, G.; Booker, C.W.; Pittman, T. Challenges in the Design of a GNSS Ear Tag for Feedlot Cattle. Comput. Electron. Agric. 2010, 70, 84–95. [Google Scholar] [CrossRef]
  41. Johnson, D.D.; Ganskopp, D.C. GPS Collar Sampling Frequency: Effects on Measures of Resource Use. Rangel. Ecol. Manag. 2008, 61, 226–231. [Google Scholar] [CrossRef]
  42. Williams, M.L.; James, W.P.; Rose, M.T. Variable Segmentation and Ensemble Classifiers for Predicting Dairy Cow Behaviour. Biosyst. Eng. 2019, 178, 156–167. [Google Scholar] [CrossRef]
  43. Millward, M.F.; Bailey, D.W.; Cibils, A.F.; Holechek, J.L. A GPS-Based Evaluation of Factors Commonly Used to Adjust Cattle Stocking Rates on Both Extensive and Mountainous Rangelands. Rangelands 2020, 42, 63–71. [Google Scholar] [CrossRef]
  44. Plaza, J.; Palacios, C.; Sánchez-García, M.; Criado, M.; Nieto, J.; Sánchez, N. Monitoring Spatial Behavior of Pastoralist Sheep through Gps, Lidar Data and Vnir Image. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2020, 43, 169–176. [Google Scholar] [CrossRef]
  45. Putfarken, D.; Dengler, J.; Lehmann, S.; Härdtle, W. Site Use of Grazing Cattle and Sheep in a Large-Scale Pasture Landscape: A GPS/GIS Assessment. Appl. Anim. Behav. Sci. 2008, 111, 54–67. [Google Scholar] [CrossRef] [Green Version]
  46. Riaboff, L.; Couvreur, S.; Madouasse, A.; Roig-Pons, M.; Aubin, S.; Massabie, P.; Chauvin, A.; Bédère, N.; Plantier, G. Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics. Sensors 2020, 20, 4741. [Google Scholar] [CrossRef]
  47. Rivero, M.J.; Grau-Campanario, P.; Mullan, S.; Held, S.D.E.; Stokes, J.E.; Lee, M.R.F.; Cardenas, L.M. Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review. Sensors 2021, 21, 2696. [Google Scholar] [CrossRef] [PubMed]
  48. Tobin, C.; Bailey, D.W.; Trotter, M.G. Tracking and Sensor-Based Detection of Livestock Water System Failure: A Case Study Simulation. Rangel. Ecol. Manag. 2021, 77, 9–16. [Google Scholar] [CrossRef]
  49. Tobin, C.T.; Bailey, D.W.; Stephenson, M.B.; Trotter, M.G. Temporal Changes in Association Patterns of Cattle Grazing at Two Stocking Densities in a Central Arizona Rangeland. Animals 2021, 11, 2635. [Google Scholar] [CrossRef]
  50. Riaboff, L.; Relun, A.; Petiot, C.E.; Feuilloy, M.; Couvreur, S.; Madouasse, A. Identification of Discriminating Behavioural and Movement Variables in Lameness Scores of Dairy Cows at Pasture from Accelerometer and GPS Sensors Using a Partial Least Squares Discriminant Analysis. Prev. Vet. Med. 2021, 193, 105383. [Google Scholar] [CrossRef]
  51. Calcante, A.; Tangorra, F.M.; Marchesi, G.; Lazzari, M. A GPS/GSM Based Birth Alarm System for Grazing Cows. Comput. Electron. Agric. 2014, 100, 123–130. [Google Scholar] [CrossRef]
  52. Pearson, C.; Filippi, P.; Lush, L.; González, L.A. Automated Behavioural Monitoring Allows Assessment of the Relationships between Cow and Calf Behaviour and Calves’ Survivability and Performance. Appl. Anim. Behav. Sci. 2021, 245, 105493. [Google Scholar] [CrossRef]
  53. Fielding, H.R.; Silk, M.J.; McKinley, T.J.; Delahay, R.J.; Wilson-Aggarwal, J.K.; Gauvin, L.; Ozella, L.; Cattuto, C.; McDonald, R.A. Spatial and Temporal Variation in Proximity Networks of Commercial Dairy Cattle in Great Britain. Prev. Vet. Med. 2021, 194, 105443. [Google Scholar] [CrossRef] [PubMed]
  54. Hollingdale, E.; Pérez-Barbería, F.J.; Walker, D.M.P. Inferring Symmetric and Asymmetric Interactions between Animals and Groups from Positional Data. PLoS ONE 2018, 13, e0208202. [Google Scholar] [CrossRef]
  55. Schlecht, E.; Hülsebusch, C.; Mahler, F.; Becker, K. The Use of Differentially Corrected Global Positioning System to Monitor Activities of Cattle at Pasture. Appl. Anim. Behav. Sci. 2004, 85, 185–202. [Google Scholar] [CrossRef]
  56. Guo, Y.; Poulton, G.; Corke, P.; Bishop-Hurley, G.J.; Wark, T.; Swain, D.L. Using Accelerometer, High Sample Rate GPS and Magnetometer Data to Develop a Cattle Movement and Behaviour Model. Ecol. Modell. 2009, 220, 2068–2075. [Google Scholar] [CrossRef] [Green Version]
  57. Anderson, D.M.; Winters, C.; Estell, R.E.; Fredrickson, E.L.; Doniec, M.; Detweiler, C.; Rus, D.; James, D.; Nolen, B. Characterising the Spatial and Temporal Activities of Free-Ranging Cows from GPS Data. Rangel. J. 2012, 34, 149–161. [Google Scholar] [CrossRef]
  58. Homburger, H.; Schneider, M.K.; Hilfiker, S.; Lüscher, A. Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone. PLoS ONE 2014, 9, e114522. [Google Scholar] [CrossRef] [PubMed]
  59. De Weerd, N.; Van Langevelde, F.; Van Oeveren, H.; Nolet, B.A.; Kölzsch, A.; Prins, H.H.T.; De Boer, W.F. Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat. PLoS ONE 2015, 10, e129030. [Google Scholar] [CrossRef]
  60. Williams, M.L.; Mac Parthaláin, N.; Brewer, P.; James, W.P.J.; Rose, M.T. A Novel Behavioral Model of the Pasture-Based Dairy Cow from GPS Data Using Data Mining and Machine Learning Techniques. J. Dairy Sci. 2016, 99, 2063–2075. [Google Scholar] [CrossRef] [Green Version]
  61. Alawneh, J.; Barreto, M.; Bome, K.; Soust, M. Description of Behavioral Patterns Displayed by a Recently Weaned Cohort of Healthy Dairy Calves. Animals 2020, 10, 2452. [Google Scholar] [CrossRef] [PubMed]
  62. McIntosh, M.M.; Cibils, A.F.; Estell, R.E.; Gong, Q.; Cao, H.; Gonzalez, A.L.; Nyamuryekung’e, S.; Spiegal, S.A. Can Cattle Geolocation Data Yield Behavior-Based Criteria to Inform Precision Grazing Systems on Rangeland? Livest. Sci. 2022, 255, 104801. [Google Scholar] [CrossRef]
  63. Meckbach, C.; Elsholz, S.; Siede, C. An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture. Sensors 2021, 21, 7585. [Google Scholar] [CrossRef] [PubMed]
  64. Ungar, E.D.; Henkin, Z.; Gutman, M.; Dolev, A.; Genizi, A.; Ganskopp, D. Inference of Animal Activity from GPS Collar Data on Free-Ranging Cattle. Rangel. Ecol. Manag. 2005, 58, 256–266. [Google Scholar] [CrossRef]
  65. Ungar, E.D.; Schoenbaum, I.; Henkin, Z.; Dolev, A.; Yehuda, Y.; Brosh, A. Inference of the Activity Timeline of Cattle Foraging on a Mediterranean Woodland Using GPS and Pedometry. Sensors 2011, 11, 362–383. [Google Scholar] [CrossRef] [Green Version]
  66. Augustine, D.J.; Derner, J.D. Assessing Herbivore Foraging Behavior with GPS Collars in a Semiarid Grassland. Sensors 2013, 13, 3711–3723. [Google Scholar] [CrossRef]
  67. Spink, A.; Cresswell, B.; Kölzsch, A.; Van Langevelde, F.; Neefjes, M.; Noldus, L.P.J.J.; Van Oeveren, H.; Prins, H.; Van Der Wal, T.; De Weerd, N.; et al. Animal Behaviour Analysis with GPS and 3D Accelerometers. In Proceedings of the Precision Livestock Farming 2013, Leuven, Belgium, 10–12 September 2013; Berckmans, D., Vandermeulen, J., Eds.; EA-PLF: Parma, Italy, 2013; pp. 229–239. [Google Scholar]
  68. González, L.A.; Bishop-Hurley, G.J.; Handcock, R.N.; Crossman, C. Behavioral Classification of Data from Collars Containing Motion Sensors in Grazing Cattle. Comput. Electron. Agric. 2015, 110, 91–102. [Google Scholar] [CrossRef]
  69. Mora-Delgado, J.; Nelson, N.; Fauchille, A.; Utsumi, S. Application of GPS and GIS to Study Foraging Behavior of Dairy Cattle. Agron. Costarric. 2016, 40, 81–88. [Google Scholar] [CrossRef] [Green Version]
  70. Bailey, D.W.; Trotter, M.G.; Knight, C.W.; Thomas, M.G. Use of GPS Tracking Collars and Accelerometers for Rangeland Livestock Production Research. Transl. Anim. Sci. 2018, 2, 81–88. [Google Scholar] [CrossRef] [PubMed]
  71. Tofastrud, M.; Hegnes, H.; Devineau, O.; Zimmermann, B. Activity Patterns of Free-Ranging Beef Cattle in Norway. Acta Agric. Scand. A Anim. Sci. 2018, 68, 39–47. [Google Scholar] [CrossRef]
  72. Brennan, J.; Johnson, P.; Olson, K. Classifying Season Long Livestock Grazing Behavior with the Use of a Low-Cost GPS and Accelerometer. Comput. Electron. Agric. 2021, 181, 105957. [Google Scholar] [CrossRef]
  73. Harris, N.R.; Johnson, D.E.; McDougald, N.K.; George, M.R. Social Associations and Dominance of Individuals in Small Herds of Cattle. Rangel. Ecol. Manag. 2007, 60, 339–349. [Google Scholar] [CrossRef]
  74. Cheleuitte-Nieves, C.; Perotto-Baldivieso, H.L.; Wu, X.B.; Cooper, S.M. Association Patterns Reveal Dispersal-Aggregation Dynamics among Cattle in a South Texas Rangeland, USA. Ecol. Process. 2018, 7, 29. [Google Scholar] [CrossRef]
  75. Umstatter, C. The Evolution of Virtual Fences: A Review. Comput. Electron. Agric. 2011, 75, 10–22. [Google Scholar] [CrossRef]
  76. Campbell, D.L.M.; Haynes, S.J.; Lea, J.M.; Farrer, W.J.; Lee, C. Temporary Exclusion of Cattle from a Riparian Zone Using Virtual Fencing Technology. Animals 2019, 9, 5. [Google Scholar] [CrossRef] [Green Version]
  77. Lomax, S.; Colusso, P.; Clark, C.E.F. Does Virtual Fencing Work for Grazing Dairy Cattle? Animals 2019, 9, 429. [Google Scholar] [CrossRef] [Green Version]
  78. Lee, C.; Henshall, J.M.; Wark, T.J.; Crossman, C.C.; Reed, M.T.; Brewer, H.G.; O’Grady, J.; Fisher, A.D. Associative Learning by Cattle to Enable Effective and Ethical Virtual Fences. Appl. Anim. Behav. Sci. 2009, 119, 15–22. [Google Scholar] [CrossRef]
  79. Stampa, E.; Zander, K.; Hamm, U. Insights into German Consumers’ Perceptions of Virtual Fencing in Grassland-Based Beef and Dairy Systems: Recommendations for Communication. Animals 2020, 10, 2267. [Google Scholar] [CrossRef] [PubMed]
  80. Acosta, N.; Barreto, N.; Caitano, P.; Marichal, R.; Pedemonte, M.; Oreggioni, J. Research Platform for Cattle Virtual Fences. In Proceedings of the IEEE International Conference on Industrial Technology, Buenos Aires, Argentina, 26–28 February 2020; pp. 797–802. [Google Scholar]
  81. Campbell, D.L.M.; Lea, J.M.; Keshavarzi, H.; Lee, C. Virtual Fencing Is Comparable to Electric Tape Fencing for Cattle Behavior and Welfare. Front. Vet. Sci. 2019, 6, 445. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Kearton, T.; Marini, D.; Cowley, F.; Belson, S.; Keshavarzi, H.; Mayes, B.; Lee, C. The Influence of Predictability and Controllability on Stress Responses to the Aversive Component of a Virtual Fence. Front. Vet. Sci. 2020, 7, 523. [Google Scholar] [CrossRef] [PubMed]
  83. Lee, C.; Campbell, D.L.M. A Multi-Disciplinary Approach to Assess the Welfare Impacts of a New Virtual Fencing Technology. Front. Vet. Sci. 2021, 8, 7709. [Google Scholar] [CrossRef] [PubMed]
  84. Verdon, M.; Langworthy, A.; Rawnsley, R. Virtual Fencing Technology to Intensively Graze Lactating Dairy Cattle. II: Effects on Cow Welfare and Behavior. J. Dairy Sci. 2021, 104, 7084–7094. [Google Scholar] [CrossRef]
  85. Aaser, M.F.; Staahltoft, S.K.; Korsgaard, A.H.; Trige-esbensen, A.; Kristian, A.; Alstrup, O.; Sonne, C.; Pertoldi, C.; Bruhn, D.; Frikke, J.; et al. Is Virtual Fencing an Effective Way of Enclosing Cattle? Personality, Herd Behaviour and Welfare. Animals 2022, 12, 842. [Google Scholar] [CrossRef]
  86. Von Keyserlingk, M.A.G.; Amorim Cestari, A.; Franks, B.; Fregonesi, J.A.; Weary, D.M. Dairy Cows Value Access to Pasture as Highly as Fresh Feed. Sci. Rep. 2017, 7, 44953. [Google Scholar] [CrossRef] [Green Version]
  87. Schantz, H.G. A Brief History of UWB Antennas. In Proceedings of the IEEE Conference on Ultra Wideband Systems and Technologies, Reston, VA, USA, 16–19 November 2003; pp. 209–213. [Google Scholar]
  88. Gezici, S.; Tian, Z.; Giannakis, G.B.; Kobayashi, H.; Molisch, A.F.; Poor, H.V.; Sahinoglu, Z. Localization via Ultra-Wideband Radios: A Look at Positioning Aspects of Future Sensor Networks. IEEE Signal Process. Mag. 2005, 22, 70–84. [Google Scholar] [CrossRef]
  89. Rahayu, Y.; Rahman, T.A.; Ngah, R.; Hall, P.S. Ultra Wideband Technology and Its Applications. In Proceedings of the 5th IEEE and IFIP International Conference on Wireless and Optical Communications Networks, Surabaya, Indonesia, 5–7 May 2008; pp. 1–5. [Google Scholar]
  90. Alarifi, A.; Al-Salman, A.; Alsaleh, M.; Alnafessah, A.; Al-Hadhrami, S.; Al-Ammar, M.A.; Al-Khalifa, H.S. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors 2016, 16, 707. [Google Scholar] [CrossRef] [Green Version]
  91. Hindermann, P.; Nüesch, S.; Früh, D.; Rüst, A.; Gygax, L. High Precision Real-Time Location Estimates in a Real-Life Barn Environment Using a Commercial Ultra Wideband Chip. Comput. Electron. Agric. 2020, 170, 105250. [Google Scholar] [CrossRef]
  92. Byrne, D.T.; O’Brien, B.; O’Leary, N.; McDonagh, A.; Lidauer, L.; Kassebacher, T.; Shalloo, L. Validation of the Localisation Accuracy of the SMARTBOW Ear Tag in a Pasture Based Milking System. In Proceedings of the Precision Livestock Farming 2019, Cork, Ireland, 26–29 August 2019; pp. 484–487. [Google Scholar]
  93. Li, W.; Bakker, J.D.; Li, Y.; Zheng, S.; Li, F.Y. Applying a High-Precision Tracking System to Distinguish the Spatiotemporal Patterns of Animal Movement in Grassland Ecology. Biol. Conserv. 2021, 255, 109016. [Google Scholar] [CrossRef]
  94. Meunier, B.; Pradel, P.; Sloth, K.H.; Cirié, C.; Delval, E.; Mialon, M.M.; Veissier, I. Image Analysis to Refine Measurements of Dairy Cow Behaviour from a Real-Time Location System. Biosyst. Eng. 2018, 173, 32–44. [Google Scholar] [CrossRef]
  95. Ren, K.; Nielsen, P.P.; Alam, M.; Rönnegård, L. Where Do We Find Missing Data in a Commercial Real-Time Location System? Evidence from 2 Dairy Farms. JDS Commun. 2021, 2, 345–350. [Google Scholar] [CrossRef]
  96. Pastell, M.; Frondelius, L. A Hidden Markov Model to Estimate the Time Dairy Cows Spend in Feeder Based on Indoor Positioning Data. Comput. Electron. Agric. 2018, 152, 182–185. [Google Scholar] [CrossRef]
  97. Chewasoonthorn, P.; Kwanmuang, S. Error Correction Method for 2D Ultra-Wideband Indoor Wireless Positioning System Using Logarithmic Error Model. Int. J. Electron. Commun. Eng. 2021, 15, 340–343. [Google Scholar]
  98. Melzer, N.; Foris, B.; Langbein, J. Validation of a Real-Time Location System for Zone Assignment and Neighbor Detection in Dairy Cow Groups. Comput. Electron. Agric. 2021, 187, 106280. [Google Scholar] [CrossRef]
  99. Veissier, I.; Mialon, M.M.; Sloth, K.H. Short Communication: Early Modification of the Circadian Organization of Cow Activity in Relation to Disease or Estrus. J. Dairy Sci. 2017, 100, 3969–3974. [Google Scholar] [CrossRef]
  100. Frondelius, L.; Kajava, S.; Lindeberg, H.; Mononen, J.; Pastell, M. Measuring the Effect of Hoof Lesions on Cow’s Walking, Lying and Eating Behavior. In Proceedings of the Precision Livestock Farming 2015—Papers Presented at the 7th European Conference on Precision Livestock Farming, Milan, Italy, 15–18 September 2015; pp. 363–369. [Google Scholar]
  101. Dantzer, R. Cytokine-Induced Sickness Behavior: Where Do We Stand? Brain. Behav. Immun. 2001, 15, 7–24. [Google Scholar] [CrossRef] [Green Version]
  102. Ipema, A.H.; Van De Ven, T.; Hogewerf, P.H. Validation and Application of an Indoor Localization System for Animals. In Proceedings of the Precision Livestock Farming 2013, Leuven, Belgium, 10–12 September 2013; pp. 135–144. [Google Scholar]
  103. Wolfger, B.; Jones, B.W.; Orsel, K.; Bewley, J.M. Technical Note: Evaluation of an Ear-Attached Real-Time Location Monitoring System. J. Dairy Sci. 2017, 100, 2219–2224. [Google Scholar] [CrossRef]
  104. Oberschätzl, R.; Haidn, B.; Peis, R.; Kulpi, F.; Völkl, C. Validation of Automated Processed Position Data for Assessing Dairy Cow Behaviour. Landtechnik 2015, 70, 3–8. [Google Scholar] [CrossRef]
  105. Homer, E.M.; Gao, Y.; Meng, X.; Dodson, A.; Webb, R.; Garnsworthy, P.C. Technical Note: A Novel Approach to the Detection of Estrus in Dairy Cows Using Ultra-Wideband Technology. J. Dairy Sci. 2013, 96, 6529–6534. [Google Scholar] [CrossRef] [PubMed]
  106. Arcidiacono, C.; Porto, S.M.C.; Mancino, M.; Cascone, G. A Software Tool for the Automatic and Real-Time Analysis of Cow Velocity Data in Free-Stall Barns: The Case Study of Oestrus Detection from Ultra-Wide-Band Data. Biosyst. Eng. 2018, 173, 157–165. [Google Scholar] [CrossRef]
  107. Benaissa, S.; Tuyttens, F.A.M.; Plets, D.; Trogh, J.; Martens, L.; Vandaele, L.; Joseph, W.; Sonck, B. Calving and Estrus Detection in Dairy Cattle Using a Combination of Indoor Localization and Accelerometer Sensors. Comput. Electron. Agric. 2020, 168, 105153. [Google Scholar] [CrossRef]
  108. Wang, J.; Bell, M.; Liu, X.; Liu, G. Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data. Animals 2020, 10, 1160. [Google Scholar] [CrossRef] [PubMed]
  109. Shane, D.D.; White, B.J.; Larson, R.L.; Amrine, D.E.; Kramer, J.L. Probabilities of Cattle Participating in Eating and Drinking Behavior When Located at Feeding and Watering Locations by a Real Time Location System. Comput. Electron. Agric. 2016, 127, 460–466. [Google Scholar] [CrossRef]
  110. Li, S.; Visich, J.K.; Khumawala, B.M.; Zhang, C. Radio Frequency Identification Technology: Applications, Technical Challenges and Strategies. Sens. Rev. 2006, 26, 193–202. [Google Scholar] [CrossRef] [Green Version]
  111. Herlin, A.; Brunberg, E.; Hultgren, J.; Rydberg, A.; Skarin, A. Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture. Animals 2021, 11, 829. [Google Scholar] [CrossRef]
  112. Curran, K.; Norrby, S. RFID-Enabled Location Determination: Within Indoor Environments. Int. J. Ambient Comput. Intell. 2009, 1, 63–86. [Google Scholar] [CrossRef] [Green Version]
  113. Ruiz-Garcia, L.; Lunadei, L.; Barreiro, P.; Robla, J.I. A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends. Sensors 2009, 9, 4728–4750. [Google Scholar] [CrossRef] [Green Version]
  114. Gebhardt-Henrich, S.G.; Fröhlich, E.K.F.; Burose, F.; Fleurent, J.; Gantner, M.; Zähner, M. Registrierung Des Auslaufverhaltens Einzelner Legehennen Mit Einem RFID-System. Individual Tracking of Laying Hens with an RFID-System. Landtechnik 2014, 69, 301–307. [Google Scholar]
  115. Siegford, J.M.; Berezowski, J.; Biswas, S.K.; Daigle, C.L.; Gebhardt-Henrich, S.G.; Hernandez, C.E.; Thurner, S.; Toscano, M.J. Assessing Activity and Location of Individual Laying Hens in Large Groups Using Modern Technology. Animals 2016, 6, 10. [Google Scholar] [CrossRef] [PubMed]
  116. Adrion, F.; Kapun, A.; Holland, E.M.; Staiger, M.; Löb, P.; Gallmann, E. Novel Approach to Determine the Influence of Pig and Cattle Ears on the Performance of Passive UHF-RFID Ear Tags. Comput. Electron. Agric. 2017, 140, 168–179. [Google Scholar] [CrossRef]
  117. Dogan, H.; Basyigit, I.B.; Yavuz, M.; Helhel, S. Signal Level Performance Variation of Radio Frequency Identification Tags Used in Cow Body. Int. J. RF Microw. Comput. Eng. 2019, 29, e21674. [Google Scholar] [CrossRef]
  118. Schwartzkopf-Genswein, K.S.; Huisma, C.; McAllister, T.A. Validation of a Radio Frequency Identification System for Monitoring the Feeding Patterns of Feedlot Cattle. Livest. Prod. Sci. 1999, 60, 27–31. [Google Scholar] [CrossRef]
  119. Adrion, F.; Keller, M.; Bozzolini, G.B.; Umstatter, C. Setup, Test and Validation of a UHF RFID System for Monitoring Feeding Behaviour of Dairy Cows. Sensors 2020, 20, 7035. [Google Scholar] [CrossRef] [PubMed]
  120. Foldager, L.; Trénel, P.; Munksgaard, L.; Thomsen, P.T. Technical Note: Random Forests Prediction of Daily Eating Time of Dairy Cows from 3-Dimensional Accelerometer and Radiofrequency Identification. J. Dairy Sci. 2020, 103, 6271–6275. [Google Scholar] [CrossRef] [PubMed]
  121. Williams, L.R.; Fox, D.R.; Bishop-Hurley, G.J.; Swain, D.L. Use of Radio Frequency Identification (RFID) Technology to Record Grazing Beef Cattle Water Point Use. Comput. Electron. Agric. 2019, 156, 193–202. [Google Scholar] [CrossRef]
  122. Williams, L.R.; Moore, S.T.; Bishop-Hurley, G.J.; Swain, D.L. A Sensor-Based Solution to Monitor Grazing Cattle Drinking Behaviour and Water Intake. Comput. Electron. Agric. 2020, 168, 105141. [Google Scholar] [CrossRef]
  123. Simanungkalit, G.; Barwick, J.; Cowley, F.; Dawson, B.; Dobos, R.; Hegarty, R. Use of an Ear-Tag Accelerometer and a Radio-Frequency Identification (RFID) System for Monitoring the Licking Behaviour in Grazing Cattle. Appl. Anim. Behav. Sci. 2021, 244, 105491. [Google Scholar] [CrossRef]
  124. Toaff-Rosenstein, R.L.; Velez, M.; Tucker, C.B. Technical Note: Use of an Automated Grooming Brush by Heifers and Potential for Radiofrequency Identification-Based Measurements of This Behavior. J. Dairy Sci. 2017, 100, 8430–8437. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Raghavendra, C.S.; Sivalingam, K.M.; Znati, T. Wireless Sensor Networks; Springer: New York, NY, USA, 2004; ISBN 9781119130536. [Google Scholar]
  126. Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. Wireless Sensor Networks: A Survey. Comput. Networks 2002, 38, 393–422. [Google Scholar] [CrossRef] [Green Version]
  127. Mudziwepasi, S.K.; Scott, M.S. Assessment of a Wireless Sensor Network Based Monitoring Tool for Zero Effort Technologies: A Cattle-Health and Movement Monitoring Test Case. In Proceedings of the 2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), Ota, Nigeria, 29–31 October 2014; IEEE: Ota, Nigeria, 2014; pp. 1–6. [Google Scholar]
  128. Sharma, B.; Koundal, D. Cattle Health Monitoring System Using Wireless Sensor Network: A Survey from Innovation Perspective. IET Wirel. Sens. Syst. 2018, 8, 143–151. [Google Scholar] [CrossRef]
  129. Nikodem, M. Bluetooth Low Energy Livestock Positioning for Smart Farming Applications. In Proceedings of the Computational Science—ICCS 2021, Krakow, Poland, 16–18 June 2021; pp. 55–67. [Google Scholar]
  130. Tøgersen, F.A.; Skjøth, F.; Munksgaard, L.; Højsgaard, S. Wireless Indoor Tracking Network Based on Kalman Filters with an Application to Monitoring Dairy Cows. Comput. Electron. Agric. 2010, 72, 119–126. [Google Scholar] [CrossRef]
  131. Huircán, J.I.; Muñoz, C.; Young, H.; Von Dossow, L.; Bustos, J.; Vivallo, G.; Toneatti, M. ZigBee-Based Wireless Sensor Network Localization for Cattle Monitoring in Grazing Fields. Comput. Electron. Agric. 2010, 74, 258–264. [Google Scholar] [CrossRef]
  132. Trogh, J.; Plets, D.; Martens, L.; Joseph, W. Bluetooth Low Energy Based Location TRacking for Livestock Monitoring. In Proceedings of the 8th European Conference on Precision Livestock Farming (EC-PLF 2017), Nantes, France, 12–14 September 2017. [Google Scholar]
  133. Karunarathne, G.G.K.W.M.S.I.R.; Kulawansa, K.A.D.T.; Firdhous, M.F.M. Wireless Communication Technologies in Internet of Things: A Critical Evaluation. In Proceedings of the International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018, Plaine Magnien, Mauritius, 6–7 December 2018. [Google Scholar]
  134. Singh, D.K.; Sobti, R. Wireless Communication Technologies for Internet of Things and Precision Agriculture: A Review. In Proceedings of the IEEE International Conference on Signal Processing, Computing and Control, Solan, India, 7–9 October 2021; IEEE: Solan, India, 2021; pp. 765–769. [Google Scholar]
  135. Molapo, N.A.; Malekian, R.; Nair, L. Real-Time Livestock Tracking System with Integration of Sensors and Beacon Navigation. Wirel. Pers. Commun. 2019, 104, 853–879. [Google Scholar] [CrossRef]
  136. Feng, X.; Yan, F.; Liu, X. Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture. Wirel. Pers. Commun. 2019, 108, 1785–1802. [Google Scholar] [CrossRef]
  137. Jawad, H.M.; Nordin, R.; Gharghan, S.K.; Jawad, A.M.; Ismail, M. Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors 2017, 17, 1781. [Google Scholar] [CrossRef] [Green Version]
  138. Gay-Fernández, J.A.; Cuiiñas, I.; Sánchez, M.G.; Alejos, A.V. Radio-Electric Validation of an Electronic Cowbell Based on ZigBee Technology. IEEE Antennas Propag. Mag. 2011, 53, 40–44. [Google Scholar] [CrossRef]
  139. Maroto-Molina, F.; Navarro-García, J.; Príncipe-Aguirre, K.; Gómez-Maqueda, I.; Guerrero-Ginel, J.E.; Garrido-Varo, A.; Pérez-Marín, D.C. A Low-Cost IOT-Based System to Monitor the Location of a Whole Herd. Sensors 2019, 19, 2298. [Google Scholar] [CrossRef] [Green Version]
  140. Burman, K.S.; Schmidt, S.; Houssaini, D.E.; Kanoun, O. Design and Evaluation of a Low Energy Bluetooth Sensor Node for Animal Monitoring. In Proceedings of the 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, Monastir, Tunisia, 22–25 March 2021; pp. 971–978. [Google Scholar]
  141. Makario, J.; Maina, C.W. A Bluetooth Low Energy (BLE) Based System for Livestock Tracking and Localization. In Proceedings of the 2021 IST-Africa Conference, IST-Africa 2021, Dublin, Ireland, 10–14 May 2021; IST-Africa Institute and Authors: Dublin, Ireland, 2021; pp. 1–7. [Google Scholar]
  142. Sendra, S.; Parra, L.; Lloret, J.; Khan, S. Systems and Algorithms for Wireless Sensor Networks Based on Animal and Natural Behavior. Int. J. Distrib. Sens. Networks 2015, 2015, 625972. [Google Scholar] [CrossRef]
  143. Kwong, K.H.; Wu, T.T.; Goh, H.G.; Sasloglou, K.; Stephen, B.; Glover, I.; Shen, C.; Du, W.; Michie, C.; Andonovic, I. Practical Considerations for Wireless Sensor Networks in Cattle Monitoring Applications. Comput. Electron. Agric. 2012, 81, 33–44. [Google Scholar] [CrossRef]
  144. Nadimi, E.S.; Søgaard, H.T.; Bak, T.; Oudshoorn, F.W. ZigBee-Based Wireless Sensor Networks for Monitoring Animal Presence and Pasture Time in a Strip of New Grass. Comput. Electron. Agric. 2008, 61, 79–87. [Google Scholar] [CrossRef]
  145. Bloch, V.; Pastell, M. Monitoring of Cow Location in a Barn by an Opensource, Low-Cost, Low-Energy Bluetooth Tag System. Sensors 2020, 20, 3841. [Google Scholar] [CrossRef] [PubMed]
  146. Chen, C.H.; Chen, M.C. A Novel Position Estimation Method Using Accelerometer Based Error Correction. Eng. Comput. 2016, 33, 1784–1799. [Google Scholar] [CrossRef]
  147. Chen, C.; Zhu, W.; Norton, T. Behaviour Recognition of Pigs and Cattle: Journey from Computer Vision to Deep Learning. Comput. Electron. Agric. 2021, 187, 106255. [Google Scholar] [CrossRef]
  148. Hitelman, A.; Edan, Y.; Godo, A.; Berenstein, R.; Lepar, J.; Halachmi, I. Biometric Identification of Sheep via a Machine-Vision System. Comput. Electron. Agric. 2022, 194, 106713. [Google Scholar] [CrossRef]
  149. Phyo, C.N.; Zin, T.T.; Hama, H.; Kobayashi, I. A Hybrid Rolling Skew Histogram-Neural Network Approach to Dairy Cow Identification System. In Proceedings of the International Conference Image and Vision Computing New Zealand, Auckland, New Zealand, 19–21 November 2018; IEEE: Auckland, New Zealand, 2018. [Google Scholar]
  150. Lv, F.; Zhang, C.; Lv, C. Image Recognition of Individual Cow Based on SIFT in Lαβ Color Space. MATEC Web Conf. 2018, 176, 01023. [Google Scholar] [CrossRef]
  151. Tiwari, A.; Sachdeva, K.; Jain, N. Computer Vision and Deep Learning- Based Framework for Cattle Monitoring. In Proceedings of the IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Uttarakhand, India, 11–13 November 2021; IEEE: Uttarakhand, India, 2021; pp. 20–25. [Google Scholar]
  152. Xu, B.; Wang, W.; Falzon, G.; Kwan, P.; Guo, L.; Chen, G.; Tait, A.; Schneider, D. Automated Cattle Counting Using Mask R-CNN in Quadcopter Vision System. Comput. Electron. Agric. 2020, 171, 105300. [Google Scholar] [CrossRef]
  153. Su, Q.; Tang, J.; Zhai, J.; Sun, Y.; He, D. Automatic Tracking of the Dairy Goat in the Surveillance Video. Comput. Electron. Agric. 2021, 187, 106254. [Google Scholar] [CrossRef]
  154. Su, Q.; Tang, J.; Zhai, M.; He, D. An Intelligent Method for Dairy Goat Tracking Based on Siamese Network. Comput. Electron. Agric. 2022, 193, 106636. [Google Scholar] [CrossRef]
  155. Bello, R.W.; Mohamed, A.S.A.; Talib, A.Z.; Olubummo, D.A.; Enuma, O.C. Computer Vision-Based Techniques for Cow Object Recognition. IOP Conf. Ser. Earth Environ. Sci. 2021, 858, 012008. [Google Scholar] [CrossRef]
  156. Kim, W.; Cho, Y.B.; Lee, S. Thermal Sensor-Based Multiple Object Tracking for Intelligent Livestock Breeding. IEEE Access 2017, 5, 27453–27463. [Google Scholar] [CrossRef]
  157. Bhole, A.; Udmale, S.S.; Falzon, O.; Azzopardi, G. CORF3D Contour Maps with Application to Holstein Cattle Recognition from RGB and Thermal Images. Expert Syst. Appl. 2022, 192, 116354. [Google Scholar] [CrossRef]
  158. Shelley, A.N. Monitoring Dairy Cow Feed Intake Using Machine Vision. Theses Diss. Comput. Eng. 2013, 24, 99. [Google Scholar]
  159. Shelley, A.N.; Lau, D.L.; Stone, A.E.; Bewley, J.M. Short Communication: Measuring Feed Volume and Weight by Machine Vision. J. Dairy Sci. 2016, 99, 386–391. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  160. Bloch, V.; Levit, H.; Halachmi, I. Assessing the Potential of Photogrammetry to Monitor Feed Intake of Dairy Cows. J. Dairy Res. 2019, 86, 34–39. [Google Scholar] [CrossRef]
  161. Bezen, R.; Edan, Y.; Halachmi, I. Computer Vision System for Measuring Individual Cow Feed Intake Using RGB-D Camera and Deep Learning Algorithms. Comput. Electron. Agric. 2020, 172, 105345. [Google Scholar] [CrossRef]
  162. Saar, M.; Edan, Y.; Godo, A.; Lepar, J.; Parmet, Y.; Halachmi, I. A Machine Vision System to Predict Individual Cow Feed Intake of Different Feeds in a Cowshed. Animal 2022, 16, 100432. [Google Scholar] [CrossRef]
  163. Porto, S.M.C.; Arcidiacono, C.; Anguzza, U.; Cascone, G. A Computer Vision-Based System for the Automatic Detection of Lying Behaviour of Dairy Cows in Free-Stall Barns. Biosyst. Eng. 2013, 115, 184–194. [Google Scholar] [CrossRef]
  164. Cangar, Ö.; Leroy, T.; Guarino, M.; Vranken, E.; Fallon, R.; Lenehan, J.; Mee, J.; Berckmans, D. Automatic Real-Time Monitoring of Locomotion and Posture Behaviour of Pregnant Cows Prior to Calving Using Online Image Analysis. Comput. Electron. Agric. 2008, 64, 53–60. [Google Scholar] [CrossRef]
  165. Porto, S.M.C.; Arcidiacono, C.; Anguzza, U.; Cascone, G. The Automatic Detection of Dairy Cow Feeding and Standing Behaviours in Free-Stall Barns by a Computer Vision-Based System. Biosyst. Eng. 2015, 133, 46–55. [Google Scholar] [CrossRef]
  166. Gao, T.; Kasabov, N. Adaptive Cow Movement Detection Using Evolving Spiking Neural Network Models. Evol. Syst. 2016, 7, 277–285. [Google Scholar] [CrossRef]
  167. Nasirahmadi, A.; Edwards, S.A.; Sturm, B. Implementation of Machine Vision for Detecting Behaviour of Cattle and Pigs. Livest. Sci. 2017, 202, 25–38. [Google Scholar] [CrossRef]
  168. Kang, X.; Zhang, X.D.; Liu, G. Accurate Detection of Lameness in Dairy Cattle with Computer Vision: A New and Individualized Detection Strategy Based on the Analysis of the Supporting Phase. J. Dairy Sci. 2020, 103, 10628–10638. [Google Scholar] [CrossRef]
  169. Zhao, K.; Bewley, J.M.; He, D.; Jin, X. Automatic Lameness Detection in Dairy Cattle Based on Leg Swing Analysis with an Image Processing Technique. Comput. Electron. Agric. 2018, 148, 226–236. [Google Scholar] [CrossRef]
  170. Noor, A.; Zhao, Y.; Koubaa, A.; Wu, L.; Khan, R.; Abdalla, F.Y.O. Automated Sheep Facial Expression Classification Using Deep Transfer Learning. Comput. Electron. Agric. 2020, 175, 105528. [Google Scholar] [CrossRef]
  171. Jaddoa, M.A.; Gonzalez, L.; Cuthbertson, H.; Al-Jumaily, A. Multiview Eye Localisation to Measure Cattle Body Temperature Based on Automated Thermal Image Processing and Computer Vision. Infrared Phys. Technol. 2021, 119, 103932. [Google Scholar] [CrossRef]
  172. Jorquera-Chavez, M.; Fuentes, S.; Dunshea, F.R.; Warner, R.D.; Poblete, T.; Jongman, E.C. Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle. Animals 2019, 9, 1089. [Google Scholar] [CrossRef] [Green Version]
  173. Friha, O.; Ferrag, M.A.; Shu, L.; Maglaras, L.; Wang, X. Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies. IEEE CAA J. Autom. Sin. 2021, 8, 718–752. [Google Scholar] [CrossRef]
  174. Ojha, T.; Misra, S.; Raghuwanshi, N.S. Internet of Things for Agricultural Applications: The State of the Art. IEEE Internet Things J. 2021, 8, 10973–10997. [Google Scholar] [CrossRef]
  175. Ahmad, N.; Ghazilla, R.A.R.; Khairi, N.M.; Kasi, V. Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications. Int. J. Signal Process. Syst. 2013, 1, 256–262. [Google Scholar] [CrossRef] [Green Version]
  176. Achour, B.; Belkadi, M.; Saddaoui, R.; Filali, I.; Aoudjit, R.; Laghrouche, M. High-Accuracy and Energy-Efficient Wearable Device for Dairy Cows’ Localization and Activity Detection Using Low-Cost IMU/RFID Sensors. Microsyst. Technol. 2022, 28, 1241–1251. [Google Scholar] [CrossRef]
  177. Andriamandroso, A.L.H.; Lebeau, F.; Beckers, Y.; Froidmont, E.; Dufrasne, I.; Heinesch, B.; Dumortier, P.; Blanchy, G.; Blaise, Y.; Bindelle, J. Development of an Open-Source Algorithm Based on Inertial Measurement Units (IMU) of a Smartphone to Detect Cattle Grass Intake and Ruminating Behaviors. Comput. Electron. Agric. 2017, 139, 126–137. [Google Scholar] [CrossRef]
  178. Haley, D.B.; De Passillé, A.M.; Rushen, J. Assessing Cow Comfort: Effects of Two Floor Types and Two Tie Stall Designs on the Behaviour of Lactating Dairy Cows. Appl. Anim. Behav. Sci. 2001, 71, 105–117. [Google Scholar] [CrossRef]
Table
Table 1. Comparison of current location determining techniques used for cattle and details of the research conducted to date in relation to the Welfare Quality principles.
Table 1. Comparison of current location determining techniques used for cattle and details of the research conducted to date in relation to the Welfare Quality principles.
GNSSUWBRFIDWSNVision
Works inside barn-++++++
Works in the field++--+-
Battery life-++++n/a
Size transponders++-+n/a
Measuring welfare1,2,3,43,412,41,2,3,4
-: poor, +: adequate, ++: good, n/a: not applicable 1: Good Housing, 2: Good Nutrition, 3: Good Health, 4: Appropriate Behaviour.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hofstra, G.; Roelofs, J.; Rutter, S.M.; van Erp-van der Kooij, E.; de Vlieg, J. Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review. Dairy 2022, 3, 776-788. https://doi.org/10.3390/dairy3040053

AMA Style

Hofstra G, Roelofs J, Rutter SM, van Erp-van der Kooij E, de Vlieg J. Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review. Dairy. 2022; 3(4):776-788. https://doi.org/10.3390/dairy3040053

Chicago/Turabian Style

Hofstra, Gerben, Judith Roelofs, Steven Mark Rutter, Elaine van Erp-van der Kooij, and Jakob de Vlieg. 2022. "Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review" Dairy 3, no. 4: 776-788. https://doi.org/10.3390/dairy3040053

APA Style

Hofstra, G., Roelofs, J., Rutter, S. M., van Erp-van der Kooij, E., & de Vlieg, J. (2022). Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review. Dairy, 3(4), 776-788. https://doi.org/10.3390/dairy3040053

Article Metrics

Back to TopTop