Lesions causing lameness

There are a number of lesions (any pathological or traumatic discontinuity of tissue or loss of function of a part) (Blood and Studdert, Reference Blood and Studdert1999) that can cause lameness in dairy cows. These lesions are often found on the lateral claw of the hind foot (online Supplementary Table S1). The most frequent lesions identified in dairy cows housed indoors include sole ulcer, digital dermatitis and white line disease (online Supplementary Table S2). Few studies have been conducted on pasture-based dairy cows and it is difficult to draw conclusions regarding common foot lesions of these cows.

The implications of lameness

Lameness presents significant consequences for both dairy cows and dairy farmers. First and foremost, lameness is an essential welfare problem with multiple studies reporting signs of pain and distress in affected dairy cows. For example, lame cows demonstrate impaired mobility or abnormal gait (Whay et al., Reference Whay, Main, Green and Webster2003), spend less time standing or walking (Navarro et al., Reference Navarro, Green and Tadich2013) and graze for shorter periods compared to non-lame cows (Hassall et al., Reference Hassall, Ward and Murray1993). Consequently, dairy cow productivity is compromised. For example, Reader et al. (Reference Reader, Green, Kaler, Mason and Green2011) and Warnick et al. (Reference Warnick, Janssen, Guard and Grohn2001) report daily losses in milk production of up to 1.6 and 1.5 kg, respectively, while Green et al. (Reference Green, Hedges, Schukken, Blowey and Packington2002) observed a milk loss of 160 to 550 kg over an entire lactation. Dairy cow reproductive potential has also been shown to be compromised with delayed cyclicity (incidence 17%) (Garbarino et al., Reference Garbarino, Hernandez, Shearer, Risco and Thatcher2004) and extended calving to conception intervals up to 40 d longer than non-lame cows (online Supplementary Table S3; Collick et al., Reference Collick, Ward and Dobson1989; Bicalho et al., Reference Bicalho, Vokey, Erb and Guard2007).

Not surprisingly, lameness is considered to be one of the most important health conditions of economic significance affecting the dairy industry (Ettema et al., Reference Ettema, Ostergaard and Kristensen2010). In addition to reduced milk yield and compromised reproductive potential, the key factors contributing to the cost of a single case of lameness include treatment and increased labour costs, and forced culling (Esslemont and Kossaibati, Reference Esslemont and Kossaibati1996; Forbes, Reference Forbes2000; Whay et al., Reference Whay, Main, Green and Webster2003). A number of studies have estimated the costs of a case of lameness, these ranged from fl104 (Enting et al., Reference Enting, Kooij, Dijkhuizen, Huirne and Noordhuizen-Stassen1997), £113 (Kossaibati and Esslemont, Reference Kossaibati and Esslemont1997), £154 (Willshire and Bell, Reference Willshire and Bell2009), $75USD (Bruijnis et al., Reference Bruijnis, Hogeveen and Stassen2010), $178USD (Cha et al., Reference Cha, Hertl, Bar and Grohn2010) and $AU200–$300 (Jubb and Malmo, Reference Jubb and Malmo1991) (online Supplementary Table S4).

The detection of lameness and the diagnosis and treatment of lesions causing lameness

The observation of change in gait is typically the first indication that a cow is lame. This initial observation is typically performed by the dairy farmer during day-to-day farming practices. However, the literature suggests that the ability of the dairy farmer to observe lameness during day-to-day farming practices is relatively poor: according to studies by Wells et al. (Reference Wells, Trent, Marsh and Robinson1993), Espejo et al. (Reference Espejo, Endres and Salfer2006) and Leach et al. (Reference Leach, Whay, Maggs, Barker, Paul, Bell and Main2010), research-reported prevalence is up to three-fold higher than farmer-reported prevalence (online Supplementary Table S5).

In the management of foot lesions, ultimately, provision of appropriate intervention is the key. However, in order to establish appropriate treatment, accurate diagnosis is pivotal. A misdiagnosis may have no adverse consequence, if appropriate treatment is applied regardless of the diagnosis made. At the other end of the spectrum, incorrect diagnosis may result in more harm to both the cow (appropriate treatment is delayed, or unnecessary or harmful treatment is applied) and the dairy farmer (unnecessary financial repercussions, for example). Therefore, following the detection of a lame cow, it is important that the dairy farmer can identify the cause of lameness.

While there is a paucity of literature on farmer diagnosis of lameness lesions, available studies indicate that dairy farmers need more assistance in diagnosing and treating foot lesions causing lameness in their dairy herds. For example, Horseman et al. (Reference Horseman, Whay, Huxley, Bell and Mason2013) reported that most dairy farmers could not differentiate between solar abscess and white line disease. Arguably, this is not of significant consequence as it is agreed that treatment of the two diseases is very similar and therefore attempts to educate dairy farmers to diagnostically differentiate between them may add unwarranted complexity without significantly improving treatment outcomes. The alternative argument is that if dairy farmers develop the skills that can help them to understand the causes and pathologies of the two diseases, they may be able to choose a different approach to reduce the incidence of the two diseases and obtain better outcomes by applying a more specific treatment.

In another study, Dutton-Regester (Reference Dutton-Regester2017) investigated the level of agreement relating to the diagnosis and treatment of foot lesions between a veterinarian and dairy farmer. She reported weak to moderate agreement between the pair, indicating that there were differences in opinion for diagnosing and treating lame cows. Of major concern was that the two most prevalent lesions (lesions of the sole and interdigital lesions) only achieved weak levels of agreement. This suggests that these lesions may frequently be misclassified by the dairy farmer, increasing the risk of incorrect treatment.

Dairy farmer perceptions towards lameness

The literature indicates that dairy farmers perceive lameness to be a relatively minor problem in their herds. For example, Leach et al. (Reference Leach, Whay, Maggs, Barker, Paul, Bell and Main2010) reported that while study investigators estimated the prevalence of lameness to be 36%, the majority of dairy farmers did not consider lameness to be a major problem within their herds. Similarly, Bennett et al. (Reference Bennett, Barker, Main, Whay and Leach2014) reported that of 163 dairy farmers, 93% did not consider lameness to be a major problem within their herds and (Bruijnis et al., Reference Bruijnis, Hogeveen, Garforth and Stassen2013) found that of 145 dairy farmers, most reported being content with the current foot health status on their farms.

This perception of lameness is likely to inhibit dairy farmer motivation to improve the management of lesions causing lameness; for where there is no perceived problem, motivation remains low (Dutton-Regester et al., Reference Dutton-Regester, Wright, Rabiee and Barnes2019). This is demonstrated by Bruijnis et al. (Reference Bruijnis, Hogeveen, Garforth and Stassen2013), reporting that farmers who believe their cows to have good foot health have lower intention to implement intervention. Conversely, farmers who believe their cows to have poor foot health have more interest in improving lameness detection and control strategies. Further, a recent study by Dutton-Regester et al. (Reference Dutton-Regester, Wright, Rabiee and Barnes2019) investigating dairy farmer intentions to make improvements to their current management practices of foot lesions, reported only moderate intention. They explain that this may be because dairy farmers feel that their current management of foot lesions is adequate as most (n = 50, 89%) indicated that they were already implementing at least one of the suggested management practices. Additionally, the incidence of lameness as estimated by the dairy farmers was low (when compared to estimates reported in the literature) with 75% (n = 42) suggesting that 10% or less of their herd was lame annually. Almost half of these dairy farmers (48%, n = 20/42) indicated that the incidence of lameness in their herds was 5% or lower annually.

Automated lameness detection technologies

Over the past two decades, automated lameness detection technologies have been extensively researched to demonstrate the accuracy and application of these systems at the farm level (See Alsaaod et al., Reference Alsaaod, Fadul and Steiner2019 and Rutten et al., Reference Rutten, Velthuis, Steeneveld and Hogeveen2013 for comprehensive reviews). These technologies can be categorised into four main classes: (i) Kinematic methods, which assess changes in the position of specific body segments over time, and include image-processing technologies, pressure-sensitive walkways, and accelerometers; (ii) Kinetic methods, where force is applied to the body, and include ground reaction force systems, force-scale weighing platforms and kinetic variations of accelerometers; (iii) Indirect methods including thermography, feeding behaviour detection technologies, grooming behaviour detection technologies and individual cow milk production measuring technologies (Alsaaod et al., Reference Alsaaod, Fadul and Steiner2019) as well as, more recently, (iv) machine learning (ML) algorithms. While the kinematic, kinetic and indirect methods have been reviewed elsewhere (see Alsaaod et al., Reference Alsaaod, Fadul and Steiner2019), here we briefly discuss advances in the application of ML for lameness detection.

Machine learning, a sub-set of artificial intelligence, is an application that employs algorithms generated by computer systems to perform specific tasks without using explicit instructions but instead relying on statistical patterns and inference; learning increases over time as data are accumulated (Liakos et al., Reference Liakos, Moustakidis, Tsiotra, Bartzanas, Bochtis and Parisses2017). In veterinary medicine, ML has been used in a number of applications (Kalipsiz et al., Reference Kalipsiz, Gökçe and Cihan2017), including lameness detection in dairy cows (Liakos et al., Reference Liakos, Moustakidis, Tsiotra, Bartzanas, Bochtis and Parisses2017; Warner et al., Reference Warner, Vasseur, Lefebvre and Lacroix2018; Byabazaire et al., Reference Byabazaire, Olariu, Taneja and Davy2019). The most recent lameness application utilises leg-mounted sensors to measure step count, time in recumbency and positional changes (standing-lying-standing) over a set period of time to enable early detection of lameness (Liakos et al., Reference Liakos, Moustakidis, Tsiotra, Bartzanas, Bochtis and Parisses2017; Byabazaire et al., Reference Byabazaire, Olariu, Taneja and Davy2019). The predictive capability of ML makes this technology highly suitable for application on-farm, with great potential to improve the welfare of dairy cows. Recently, a Canadian study (Warner et al., Reference Warner, Vasseur, Lefebvre and Lacroix2018) demonstrated 90% specificity in dairy farms with high risk of lameness, suggesting only a small percentage of dairy cows were misclassified. However, ML is currently not without its' challenges, including heterogeneity in the type and frequency of data collection, feature customisation, and algorithm sensitivity and specificity of lameness detection (Byabazaire et al., Reference Byabazaire, Olariu, Taneja and Davy2019).

Overcoming barriers to on-farm implementation

A simulation study by Van De Gucht et al. (Reference Van De Gucht, Saeys, Van Meensel, Van Nuffel, Vangeyte and Lauwers2018) showed that high performance was one of the primary determinants of uptake by dairy farmers of new technologies. Regardless of this finding, relevant information regarding performance of investigated technologies is limited with many studies failing to report measures of accuracy, or the population sample size was too small to be meaningful thereby limiting the power and generalisability of results (Alsaaod et al., Reference Alsaaod, Fadul and Steiner2019). Further, our recent systematic review investigating lameness detection technologies found that pertinent information such as animal selection and spectrum of disease, as well as characteristics of dairy herds under investigation was poorly described in the majority of the studies reviewed, making it difficult to determine the quality of reported performance measures (Dutton-Regester et al., Reference Dutton-Regester, Barnes, Wright, Alawneh and Rabiee2018). Given that Van De Gucht et al. (Reference Van De Gucht, Saeys, Van Meensel, Van Nuffel, Vangeyte and Lauwers2018) demonstrated that high performance was a major determinant in farmers deciding to use new technologies, it is essential that future studies are designed to produce the highest quality information to enable farmers to be confident in their decision-making when embracing new technologies. As recommended in our systematic review, again we reiterate the importance of using the STARD guidelines (Standards for Reporting of Diagnostic Accuracy) when authors are investigating new lameness detection technologies.

In addition to performance, not surprisingly, Van De Gucht et al. (Reference Van De Gucht, Saeys, Van Meensel, Van Nuffel, Vangeyte and Lauwers2018) also showed cost to be an important determinant for the uptake of a new technology. This is consistent with findings from our recent study which found cost to be a potential barrier for some dairy farmers in making changes to their current management of lameness (Dutton-Regester et al., Reference Dutton-Regester, Wright, Rabiee and Barnes2019). Therefore, a reliable cost-benefit analysis of available technologies is pivotal for dairy farmers in deciding which technology can be financially sustainable in the long-term. Farmers' willingness to invest in new technologies will depend on the magnitude of return if they utilise these on their farms, which can be measured by willingness to pay (WTP). A survey by Bennett et al. (Reference Bennett, Barker, Main, Whay and Leach2014) explored UK dairy farmers' WTP to reduce the prevalence of lameness and reported these varied significantly between farmers, with mean WTP of UK£411 per lame cow and a median of UK£249. However, farmers expressed a substantial WTP to avoid the inconvenience associated with lameness control (median WTP UK£97 per lame cow). The variations in WTP could be due to differences in farming practice, farmers' perception of cost of lameness and other attributable risk factors at each farm. In order to enhance WTP, dairy farmers are required to have a better understanding of the direct and indirect costs of lameness and the potential benefits of automated detection technologies to be convinced that the chosen system would be a valuable investment.

Van De Gucht et al. (2017) also found that dairy farmers were favourably inclined to using automated lameness-detection technologies after learning more about the consequences of late detection of lameness and associated costs. This reinforces the idea of implementing a demonstration farm network, as proposed in our recent paper (Dutton-Regester et al., Reference Dutton-Regester, Wright, Rabiee and Barnes2019). This proposed network would have the capacity to show dairy farmers how lameness detection technologies can be successfully implemented on-farm. These farms could collect data regarding lameness incidence, lameness lesion type and severity, cost per case of lameness, duration of and repeat cases of lameness, milk yield per cow and calving to conception intervals, allowing farmers to compare their averages to those from farms utilising automated lameness detection technologies. Further, these demonstration farms would have the capacity to collect data regarding costs (both fixed coast such as purchase of equipment and variable costs including training and labour) associated with incorporating these technologies on-farm, giving dairy farmers the ability to assess the financial worth of implementing these technologies. By providing this information, dairy farmer perceptions concerning the importance of lameness may be heightened, removing the uncertainty around the advantages of automated lameness detection technologies.