Lameness in Dairy Cattle: Effects on Animal Behavior, Welfare, and Milk Quality

Simple Summary

Lameness is a common and painful condition in dairy cows that makes it difficult for them to walk normally. This review explains how lameness affects almost every aspect of a cow's life. Lame cows often eat less, spend more time lying down, and move around less. They can show signs of pain through their posture and behavior. Their social interactions with other cows can also change. Detecting lameness early is important for the cow's well-being and the farm's productivity. New technologies, like sensors on collars and cameras, are being developed to help farmers identify lameness sooner so that cows can receive timely treatment and reduce their suffering. Understanding these effects is key to improving the lives of dairy cows.

Introduction

Lameness has long been recognized as a significant health and welfare challenge in dairy cattle populations (Whay et al., 1998). Early research, focusing on the economic impacts, highlighted that lameness leads to reduced milk production, decreased reproductive efficiency, and increased culling rates, imposing substantial financial burdens on dairy farms (Willshire & Bell, 2009). Alongside these economic implications, the welfare of affected cows has become an increasing concern. Studies have established that lameness is a painful condition that can significantly compromise an animal's well-being (Whay et al., 1998).

Chronologically, initial efforts to understand lameness relied primarily on visual assessments of gait and posture (Sprecher et al., 1997). These methods, while providing a basis for identifying lame cows, were recognized as subjective and prone to variability between observers (Winckler & Willen, 2001). As technology advanced, researchers began exploring more objective measures of lameness. Load cells and pressure-sensitive mats were employed to analyze weight distribution and gait patterns (Neveux et al., 2006; Pastell et al., 2010). Accelerometers, initially used for detecting estrus, were later adapted to monitor activity levels and identify changes associated with lameness (Munksgaard et al., 2006; Walker et al., 2008). For example, Taneja et al. (2020) utilized a commercially available radio-based long-range pedometer (LRP)—operating on the 433 MHz ISM band—attached to the front leg of dairy cows to monitor their activity (Figure 1). This setup enabled the collection of accelerometer data. The development of real-time location systems (RTLS) offered insights into cow movement and space use within barns (Gygax et al., 2007).

Figure 1. An example of a long-range pedometer attached to the front leg of dairy cows. Adapted from Taneja et al. (2020)

More recently, the integration of computer vision technology has opened new avenues for automated lameness detection through the analysis of gait, posture, and even facial expressions (Figure 2) (Song et al., 2008; Viazzi et al., 2013; Söderlind et al., 2025). The convergence of these technologies and analytical techniques underscores the ongoing effort to understand the multifaceted effects of lameness on dairy cows and to develop effective methods for early detection and management. This review aims to provide a comprehensive overview of the behavioral and welfare consequences of lameness in dairy cattle, drawing on current research to elucidate the far-reaching impacts of this prevalent condition.

Capturing cows’ facial expressions for the detection of lameness-1.PNG

Figure 2. Capturing cows’ facial expressions for the detection of lameness.

Adapted from Dhaliwal et al. (2025)

Effects of Lameness on Feeding Behavior

Lameness significantly alters the feeding behavior of dairy cows. Studies consistently show that lame cows exhibit a reduction in their overall daily feeding duration compared to their non-lame counterparts (González et al., 2008; Palmer et al., 2012; Norring et al., 2014; Barker et al., 2018). Barker et al. (2018) specifically found that the mean total daily feeding duration was significantly lower for lame cows. This reduction in feeding time can have direct consequences on nutrient intake and subsequent milk production (de Mol et al., 2013).

Interestingly, some research suggests that while lame cows feed for a reduced overall duration, they may exhibit faster eating rates (González et al., 2008; Palmer et al., 2012). This increased feeding speed could be a strategy to minimize the time spent standing and bearing weight on painful limbs at the feed bunk, potentially reducing discomfort or avoiding prolonged competition with other cows (Whay et al., 1998; Galindo & Broom, 2002). However, the accelerated eating rate may not compensate for the reduced total feeding time, leading to lower overall feed intake.

Furthermore, lameness can affect a cow's responsiveness to food availability. Lame cows have been observed to be slower to approach the feed bunk when fresh feed is provided (Blackie et al., 2011; Yunta et al., 2012). This delayed response may be due to the pain associated with movement or a reluctance to compete for feed barrier space. The time of day also plays a role, with lame cows showing significantly lower mean total duration of feeding and higher total duration of nonfeeding in the afternoons compared with non-lame cows (Barker et al., 2018). This suggests that the impact of lameness on feeding behavior may be exacerbated during certain periods of the day.

Effects of Lameness on Lying and Resting Behavior

Lameness has a notable impact on the lying and resting behavior of dairy cows, although findings across different studies have been somewhat equivocal. Some research reports that lame cows increase their lying time (Singh et al., 1993; Galindo & Broom, 2002; Blackie et al., 2011). Blackie et al. (2011) observed that lame cows spent more time lying down, potentially as a mechanism to reduce discomfort and weight-bearing on affected limbs (Neveux et al., 2006).

However, other studies have found no significant difference in lying behavior between lame and non-lame cows (Ito et al., 2010; Yunta et al., 2012). Conversely, a subset of research indicates that lameness can lead to decreased lying time (Cook et al., 2008). Cook et al. (2008) suggested that discomfort in poorly designed freestalls might discourage lame cows from lying down, even if they are motivated to do so.

The inconsistencies in these findings may be attributed to various factors, including the severity of lameness, the type of housing and bedding, and individual cow responses to pain. For instance, lame cows may lie down more if provided with comfortable and spacious lying areas (Cook et al., 2008). The stage of lameness progression might also influence lying behavior, with cows potentially increasing lying time as lameness becomes more severe.

It is also important to consider the number and duration of lying bouts. While total lying time might not always differ significantly, lame cows could have more frequent but shorter lying bouts due to discomfort when standing up or lying down. Further research is needed to fully elucidate the complex relationship between lameness and the detailed characteristics of lying behavior in dairy cows.

Lameness and Locomotion

The most direct and observable effect of lameness is on a cow's locomotion. Lame cows exhibit a range of gait abnormalities, including uneven weight-bearing, shortened strides, and an arched back (Sprecher et al., 1997; Flower & Weary, 2009). These changes are compensatory mechanisms to alleviate pain and reduce pressure on the affected limbs (Scott, 1989). The severity of lameness can be quantified using locomotion scoring systems, such as the AHDB Dairy Mobility Score (Figure 3), which assesses a cow's gait on a scale from not lame to severely lame (Table 1; AHDB, 2017).

Regularly scoring your cows for mobility is the first step towards reducing lameness in your herd..PNG

Figure 3. Regularly scoring your cows for mobility is the first step towards reducing lameness in your herd. Mobility scoring helps identify cows with lameness issues early, allowing for timely intervention and management.

Source: https://ahdb.org.uk/knowledge-library/mobility-scoring-how-to-score-your-cows

Table 1. Mobility Scoring: How to Score Dairy Cows
Mobility Score	Description of Cow Behaviour	Suggested Actions
Good mobility Score 0	Walks with even weight-bearing and rhythm on all four feet, with a flat back. Long, fluid strides possible.	Routine (preventative) foot-trimming when/if required. Record mobility at next scoring session.
Imperfect mobility Score 1	Steps uneven (rhythm or weight-bearing) or strides shortened; affected limb or limbs not immediately identifiable.	Would benefit from routine (preventative) foot-trimming when/if required. Further observation recommended.
Impaired mobility Score 2	Uneven weight-bearing on a limb that is immediately identifiable and/or obviously shortened strides (usually with an arch to the centre of the back).	Lame and requires prompt treatment. Foot should be lifted to establish the cause of lameness before treatment. Should be attended to as soon as practically possible.
Severely impaired mobility Score 3	Unable to walk as fast as a brisk human pace (cannot keep up with the healthy herd). Lame leg easy to identify – limping; may barely stand on lame leg(s); back arched when standing and walking. Very lame.	This cow is very lame and requires urgent attention, nursing and further professional advice. Examine as soon as possible. Cow will benefit from treatment. Cow should not be made to walk far and kept on a straw yard or at grass. In the most severe cases, culling may be the only possible solution.
Adapted from AHDB (2017)

Recent advancements in technology have enabled more objective analysis of lameness-related locomotion changes. Accelerometers attached to the legs or neck can detect subtle alterations in movement patterns (Figure 4), providing continuous and quantitative data on gait parameters (Walker et al., 2008; Van Hertem et al., 2013).

Figure 4. The left photo illustrates a neck collar. The collar contains a mobile sensor for tracking the spatial location of cows within the barn. It is maintained in position at the top of the cow's neck through a counter-weight at the bottom of the collar.

The middle image (a) shows an example of FIBION Sens motion sensor used to monitor the locomotor activity of cows. The right image (b) shows a cow with a FIBION Sens motion sensor attached to the right hind limb using a Velcro strap and tuck tape.

Adapted from Barker et al. (2018) and Dhaliwal et al. (2025)

Pressure mats and force plates can measure the force exerted by each limb during standing and walking, revealing asymmetries in weight distribution indicative of lameness (Pastell et al., 2008, 2010).

Computer vision technology offers another promising avenue for automated gait analysis. By analyzing video recordings of walking cows, algorithms can track key body points, such as the hooves, back, and head, and quantify gait parameters like stride length, step overlap, and back arch (Figure 5) (Song et al., 2008; Zhao et al., 2018).

the back is arched when the cow stands or walks.PNG

Figure 5. The characteristics that a lame cow may exhibit: (a) the back is arched when the cow stands or walks, (b) the head swings when the cow walks, and (c) the hindlimb cannot fall where the forelimb is (poor overlap). Adapted from Li et al. (2024)

Kang et al. (2020) proposed a novel approach based on analyzing the supporting phase of a cow's hoof. They found a strong correlation between changes in the supporting phase and the degree of lameness. These technological tools provide valuable insights into the subtle kinematic changes associated with lameness, enabling earlier and more accurate detection than subjective visual assessments.

Pain and Stress Indicators in Lame Cows

Lameness is a painful condition that elicits physiological and behavioral stress responses in dairy cows [Whay et al., 1998]. Behavioral indicators of pain in lame cows can include arched back posture, reluctance to bear weight on the affected limb, and altered head movements (Flower & Weary, 2009; Söderlind et al., 2025). Söderlind et al. (2025) found a positive correlation between Cow Pain Scale (CPS) total scores and Sprecher lameness scores, suggesting that specific behaviors can indicate orthopedic pain.

Physiological markers of stress, such as elevated cortisol levels, have also been associated with lameness (Bustamante et al., 2015; Warner et al., 2021). Kleinhenz et al. (2019) observed increased plasma cortisol concentrations in cows with induced lameness. Warner et al. (2021) also reported lower cortisol area under the effect curve following treatment with analgesic drugs in lame cows.

Furthermore, studies using infrared thermography (IRT) have shown that lame feet may exhibit altered surface temperatures due to inflammation and changes in blood flow (Stokes et al., 2012; Alsaaod et al., 2014; Ozturan & Akin, 2025). Ozturan & Akin (2025) found significant temperature variations across different claw regions in healthy cows post-trimming, providing baseline data that could be used to identify thermal changes associated with lameness (Figure 6).

Solar aspect and thermographic view of the foot..PNG

Figure 6. Solar aspect and thermographic view of the foot. (a) The measured anatomical regions of the lateral and medial claws, along with the foot dermis. (b) Thermographic view of the foot showing the temperature distribution across the claws and surrounding areas.

Adapted from Ozturan & Akin (2025)

The Cow Pain Scale (CPS), which includes facial expressions, has been evaluated as a tool for detecting orthopedic pain in lame dairy cows (Söderlind et al., 2025). While some facial expressions, such as orbital tightening and muzzle tension, were associated with lameness, the reliability of individual scale items varied, suggesting that a combination of behavioral and physiological measures may provide a more comprehensive assessment of pain and stress in lame cows.

Impact of Lameness on Social Behavior

Lameness can influence the social behavior of dairy cows within a herd. Lame cows may be less likely to initiate aggressive interactions at the feed bunk and may be found at the back of the herd while moving (Galindo & Broom, 2002). This could be due to pain limiting their ability to compete for resources or to maintain pace with the rest of the group.

Furthermore, lame cows might experience reduced social interaction with other herd members due to their decreased mobility and potential avoidance by sound cows (Galindo & Broom, 2002). Their altered behavior could lead to social isolation and further compromise their welfare.

Lameness might also affect the hierarchy within the herd. Dominant cows that become lame may lose their status due to their physical limitations, while subordinate cows might avoid interacting with lame individuals. These shifts in social dynamics can create additional stress for lame cows, impacting their access to food, water, and comfortable resting areas.

Understanding the social consequences of lameness is important for developing comprehensive management strategies that address the physical discomfort and the social integration and well-being of affected cows within the herd environment.

Automated Detection of Lameness

The need for early and efficient lameness detection has driven the development of various automated monitoring systems (Van Nuffel et al., 2015). Sensor-based systems, including accelerometers and RTLS, offer the potential for continuous and objective monitoring of cow behavior and locomotion, which can be analyzed using machine learning algorithms to detect subtle changes indicative of lameness (Taneja et al., 2020; Van Hertem et al., 2013; Barker et al., 2018). Taneja et al. (2020) developed an IoT application using machine learning on accelerometer data to detect lameness with high accuracy, even before visual signs were apparent.

Computer vision technology also plays a crucial role in automated lameness detection. Analyzing video data using deep learning algorithms can identify gait abnormalities, postural changes, and even facial expressions associated with lameness (Kang et al., 2021; Li et al., 2024; Söderlind et al., 2025). Li et al. (2024) proposed a novel method based on keyframe positioning and posture analysis using deep learning to detect lameness with high accuracy. Sheng et al. (2025) explored the use of crowd-sourced data and pairwise comparisons to construct a lameness hierarchy, providing a reliable and granular evaluation method for training automated detection models.

Integrating multiple sensor modalities, such as accelerometer data with RTLS or computer vision, may further enhance the accuracy and robustness of automated lameness detection systems (Berckmans, 2014; Dhaliwal et al., 2025). Dhaliwal et al. (2025) introduced a bimodal AI framework combining facial biometric data and accelerometer-based movement metrics for early lameness detection with high accuracy. These advancements in automated detection technologies promise to enable timely interventions and improve the welfare of dairy cattle.

Discussion and Implications

Research highlights the far-reaching consequences of lameness on dairy cattle behavior and welfare. Lame cows experience a cascade of negative effects, impacting their feeding and lying behavior, locomotion, social interactions, and overall well-being. The observed reductions in feeding duration (Barker et al., 2018) and altered lying patterns (Blackie et al., 2011) are indicative of the physical discomfort and pain associated with lameness (Whay et al., 1998). The gait abnormalities, objectively quantified through technological advancements (Kang et al., 2020), directly manifest this underlying pain and the cow's attempt to redistribute weight (Scott, 1989).

The studies consistently demonstrate that lameness is not merely a physical ailment but a significant welfare issue. It elicits stress responses reflected in physiological markers like cortisol (Kleinhenz et al., 2019) and behavioral cues detectable through pain scales (Söderlind et al., 2025). Furthermore, the impact of lameness extends beyond the individual cow, affecting social dynamics within the herd (Galindo & Broom, 2002) and emphasizing the interconnectedness of animal welfare.

The advancements in automated detection technologies (Taneja et al., 2020; Sheng et al., 2025) offer promising solutions for addressing the challenges of early lameness identification, which is crucial for effective treatment and minimizing the long-term negative impacts. Integrating diverse data sources and sophisticated analytical techniques is key to developing robust and reliable on-farm monitoring systems (Dhaliwal et al., 2025).

The implications of these findings are significant for dairy farm management. Early detection and prompt treatment of lameness are essential for improving cow welfare by reducing pain and suffering (Leach et al., 2012) and mitigating economic losses associated with reduced productivity and increased culling (Willshire & Bell, 2009). Implementing regular lameness scoring, utilizing automated monitoring systems, and ensuring appropriate housing and bedding conditions are crucial steps in proactive lameness management (Sadiq et al., 2020).

Conclusion

Ultimately, a greater understanding of the multifaceted effects of lameness and the adoption of effective detection and prevention strategies are vital for promoting both the welfare and the sustainability of the dairy industry.

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