Cow and herd-level risk factors associated with mobility scores in pasture-based dairy cows

Farm-level risk factors and treatment protocols for lameness in New Zealand dairy cattle

AIMS

To identify farm-level risk factors for dairy cow lameness, and to describe lameness treatment protocols used on New Zealand dairy farms.

METHODS

One hundred and nineteen farms from eight veterinary clinics within the major dairying regions of New Zealand were randomly enrolled into a cross-sectional lameness prevalence study. Each farmer completed a questionnaire on lameness risk factors and lameness treatment and management. Trained observers lameness scored cattle on two occasions, between October-December (spring, coinciding with peak lactation for most farms) and between January-March (summer, late lactation for most farms). A four-point (0-3) scoring system was used to assess lameness, with animals with a lameness score (LS) ≥2 defined as lame. At each visit, all lactating animals were scored including animals that had previously been identified lame by the farmer. Associations between the farmer-reported risk factors and lameness were determined using mixed logistic regression models in a Bayesian framework, with farm and score event as random effects.

RESULTS

A lameness prevalence of 3.5% (2,113/59,631) was reported at the first LS event, and 3.3% (1,861/55,929) at the second LS event. There was a median prevalence of 2.8% (min 0, max 17.0%) from the 119 farms. Most farmers (90/117; 77%) relied on informal identification by farm staff to identify lame animals. On 65% (75/116) of farms, there was no external provider of lame cow treatments, with the farmer carrying out all lame cow treatments. Most farmers had no formal training (69/112; 62%). Animals from farms that used concrete stand-off pads during periods of inclement weather had 1.45 times the odds of lameness compared to animals on farms that did not use concrete stand-off pads (95% equal-tailed credible interval 1.07-1.88). Animals from farms that reported peak lameness incidence from January to June or all year-round, had 0.64 times odds of lameness compared to animals from farms that reported peak lameness incidence from July to December (95% equal-tailed credible interval 0.47-0.88).

CONCLUSIONS

Lameness prevalence was low amongst the enrolled farms. Use of concrete stand-off pads and timing of peak lameness incidence were associated with odds of lameness.

CLINICAL RELEVANCE

Veterinarians should be encouraging farmers to have formal lameness identification protocols and lameness management plans in place. There is ample opportunity to provide training to farmers for lame cow treatment. Management of cows on stand-off pads should consider the likely impact on lameness.

A new approach and insights on modelling the impact of production diseases on dairy cow welfare

Animal welfare is becoming an important consideration in animal health-related decision-making. Integrating considerations of animal welfare into the decision-making process of farmers involves recognising the significance of health disorder impacts in relation to animal welfare. Yet little research quantifies the impact, making it difficult to include animal welfare in the animal health decision-making process. Quantifying the impact of health disorders on animal welfare is incredibly challenging due to empirical animal-based data collection constraints. An approach to circumvent these constraints is to rely on expert knowledge whereby perceived welfare impairment weights are indicative of the negative welfare effect. In this research, we propose an expertise-based method to quantify the perceived impact of sub-optimal mobility (SOM) on the welfare of dairy cows, because of its welfare importance. We first quantified perceived welfare impairment weights of SOM by eliciting expert knowledge using adaptive conjoint analysis (ACA). Second, using the perceived welfare impairment weights, we derived the perceived welfare disutility (i.e., perceived negative welfare effect) of mobility scores 1-5 (1 = optimal mobility, 5 = severely impaired mobility). Third, using the perceived welfare disutility per mobility score, we quantified the perceived welfare impact at case- and herd-level of SOM for different SOM severity. Results showed that perceived welfare disutility increased with each increase in mobility score. However, the perceived welfare impact of SOM cases with lower mobility scores was higher compared to SOM cases with higher mobility scores. This was because of the longer-lasting duration of the SOM cases with lower mobility scores. Moreover, the perceived herd-level welfare impact was largely due to SOM cases with lower mobility scores because of the longer duration and more frequent incidence compared to the SOM cases with higher mobility scores. These results entail that better welfare of dairy cows with respect to SOM can be achieved if lower mobility scores are detected and treated sooner. Our research demonstrates a novel approach that quantifies the perceived impact of health disorders on animal welfare when empirical evidence is limited.

Prevalence of lameness on pasture-based New Zealand dairy farms: An observational study

To understand the current impact of lameness on a system, it is important to define lameness prevalence across a range of dairy farms in that system. Prevalence estimates from dairy systems where cows are permanently managed at pasture are uncommon, although the limited data suggest that they have a lower lameness prevalence than housed cattle. One hundred and 20 farms from eight of the major dairying regions of New Zealand were randomly enrolled into a cross-sectional lameness prevalence study. On each of the farms, trained observers lameness scored cattle on two occasions, between October-December (spring, coinciding with peak lactation for most farms) and between January-March (summer, late lactation for most farms). At each visit, all lactating animals were scored using a four-point 0-3 scoring system, and included animals that had previously been identified as lame by the farmer. Animals with a lameness score (LS) ≥2 were defined as lame. Mixed logistic regression models assessed the interaction between region and season and island and season, respectively, and differences between the lameness prevalence within farm across the two seasons reported descriptively. A total of 116,317 locomotion scores over two events were conducted across the 120 farms. At the spring scoring event, 2128/60,007 (3.5 %) cows had a LS ≥2 and 1868/56,310 (3.3 %) cows had a LS ≥ 2 at the summer scoring event. At the farm level, across both scoring events, median lameness prevalence was 2.8 (interquartile range 1.5 - 4.5) %, with a range of 0.0-17.0 %. The median farm-level prevalence of LS = 3 was 0.5 % with a range of 0-4.6 %. The effect of timing of scoring was modified by region (p < 0.001), and island (p = 0.006) and at the individual farm level, differences between spring and summer farm level lameness prevalence were generally small (interquartile range: -1.8 to 1.0 %) but potentially large on individual farms (range from -12.3 % to 7.6 %). The median farm-level lameness prevalence estimate of 2.8 % across a random representative sample of New Zealand dairy farms give confidence that the overall prevalence of cattle lameness on New Zealand dairy farms is low. This adds to the growing evidence that pasture is a good management system with respect to hoof health. The evidence of strong seasonality of lameness was lacking. Instead of using lameness scoring to identify farms with large lameness problems, lameness scoring should be encouraged to farmers as a tool to improve the identification of lame animals.

Effects of increased grazing intensity during the early and late grazing periods on the welfare of spring-calving, pasture-based dairy cows

The objective of this study was to identify potential effects of increased grazing intensity, characterized by differing pasture availability and stocking rate, on indicators of welfare during both early and late grazing periods. Seventy spring-calving, pasture-based Holstein-Friesian and cross-bred dairy cows, averaging 35 ± 16 d in milk on the first day of data collection, were assigned to 3 treatments (20-26 cows/treatment) representing a range in grazing intensity: LO (high pasture availability, 980 kg DM/ha opening cover, 2.75 cows/ha, 90:10% pasture:concentrate diet), MOD (medium pasture availability, 720 kg DM/ha opening cover, 2.75 cows/ha, 90:10% pasture:concentrate diet), and HI (low pasture availability, 570 kg DM/ha opening cover, 3.25 cows/ha, 80:20% pasture:concentrate diet); representative of current, best practice and proposed production levels respectively for this system. Welfare indicators were locomotion score, digital dermatitis and white line disease, rumen fill, ocular and nasal discharge, integument damage to the neck-back and hock regions, and lying time. Data were collected during a 5-wk early grazing period in spring (EG) and a 7-wk late grazing period in autumn (LG). Average daily lying time was recorded for 8 to 10 focal cows per treatment. Results demonstrated only minor treatment effects. Cows on MOD [odds ratio (OR) = 3.11] and HI (OR = 1.95) were more likely to display nasal discharge compared with LO. Cows on MOD tended to have more damage to the skin on the neck-back region than LO (OR = 4.26). Total locomotion score (maximum = 25) was greater on LOW (7.1 ± 0.20) compared with HI (6.5 ± 0.19). Average lame cow prevalence for EG and LG respectively was 15.3 ± 3.12% and 39.2 ± 3.00% (LO), 20.0 ± 2.58% and 24.2 ± 5.69% (MOD), and 14.9 ± 4.79% and 17.0 ± 3.44% (HI). Cows on HI were less likely to have impaired walking speed than either LO (OR = 0.24) or MOD (OR = 0.29). Cows on both HI (OR = 0.36) and MOD (OR = 0.40) were less likely to display impaired abduction or adduction compared with those on LO. An interaction between treatment and period revealed longer lying times for cows on LO (10.6 h/d ± 0.39) compared with both MOD and HI (8.7 ± 0.43 and 8.4 ± 0.41 h/d) during EG only. The greatest effects were across grazing periods, with all indicators except rumen fill and locomotion score demonstrating improvements from EG to LG. This suggests cows were able to cope well with increasing levels of grazing intensity, and that regardless of treatment, a greater number of days on pasture led to improvements in welfare indicators.

Welfare of dairy cows

This Scientific Opinion addresses a European Commission's mandate on the welfare of dairy cows as part of the Farm to Fork strategy. It includes three assessments carried out based on literature reviews and complemented by expert opinion. Assessment 1 describes the most prevalent housing systems for dairy cows in Europe: tie-stalls, cubicle housing, open-bedded systems and systems with access to an outdoor area. Per each system, the scientific opinion describes the distribution in the EU and assesses the main strengths, weaknesses and hazards potentially reducing the welfare of dairy cows. Assessment 2 addresses five welfare consequences as requested in the mandate: locomotory disorders (including lameness), mastitis, restriction of movement and resting problems, inability to perform comfort behaviour and metabolic disorders. Per each welfare consequence, a set of animal-based measures is suggested, a detailed analysis of the prevalence in different housing systems is provided, and subsequently, a comparison of the housing systems is given. Common and specific system-related hazards as well as management-related hazards and respective preventive measures are investigated. Assessment 3 includes an analysis of farm characteristics (e.g. milk yield, herd size) that could be used to classify the level of on-farm welfare. From the available scientific literature, it was not possible to derive relevant associations between available farm data and cow welfare. Therefore, an approach based on expert knowledge elicitation (EKE) was developed. The EKE resulted in the identification of five farm characteristics (more than one cow per cubicle at maximum stocking density, limited space for cows, inappropriate cubicle size, high on-farm mortality and farms with less than 2 months access to pasture). If one or more of these farm characteristics are present, it is recommended to conduct an assessment of cow welfare on the farm in question using animal-based measures for specified welfare consequences.

Risk factors associated with indicators of dairy cow welfare during the housing period in Irish, spring-calving, hybrid pasture-based systems

In a dairy production system where cows are grazing for a large portion of their lactation, little attention has been afforded to investigating multiple indicators of welfare for risk factors associated with the housing period. Yet regardless of the length of the housing period, cows still experience the positive and negative welfare impacts of both indoor and outdoor environments in a hybrid system. Thus, the objective of this study was to identify risk factors for indicators of dairy cow welfare during the housing period in a spring-calving, hybrid pasture-based system. Herd-level scores for seven indicators of welfare (locomotion, body condition, ocular and nasal discharge, integument damage, tail injury and human avoidance response) were collected from 82 Irish dairy farms during the housing period (October - February). Data were analysed using multiple beta regression or zero-inflated beta regression to identify associations between these welfare indicators and measured herd-level housing, resource and management factors. Thirty-six unique risk factors were associated with one or more welfare indicators (P < 0.05). Analyses identified two risk factors for body condition < 3.0 and four for body condition > 3.5, the target range during the housing period. Four risk factors were identified for each of ocular discharge, nasal discharge and avoidance response of > 1 m from human approach. Six risk factors each were associated with the proportion of lame cows and integument damage to the head-neck-back or hindquarter regions. The greatest number of risk factors, 12, were associated with tail injury (broken, lacerated or incomplete tails). Risk factors associated with multiple indicators of welfare were cow comfort index (tail lacerations and hindquarter integument damage), cubicle width (broken and incomplete tails), shed floor slipperiness (lameness and head-neck-back integument damage), shed light-level (tail lacerations, avoidance response and below target body condition), shed passage width (broken tails and head-neck-back integument damage) and presence (incomplete tails) or absence (broken tails) of a collecting yard backing gate. With the large number of risk factors associated with tail injury, continued research is necessary to identify causes and determine prevention methods to contribute to improved overall welfare of dairy cows. Housing features meeting recommended guidelines from the literature were frequently associated with greater negative indicators of welfare. In light of this, housing guidelines may benefit from regular re-evaluation to ensure facilities meet the welfare needs of cows during the housing period.

Review: Assessment of dairy cow welfare at pasture: measures available, gaps to address, and pathways to development of ad-hoc protocols

Pasture is generally perceived as positive for dairy cow welfare, but it nevertheless exposes cows to heat, parasites, and other challenges. This review is intended for people ready to design comprehensive protocols for assessing the welfare of dairy cows at pasture. We provide an overview of the benefits and risks of pasture for cows, and then go on to identify the available and feasible measures for assessing cow welfare at pasture and the gaps that need to be addressed to develop specific welfare measures. Some of the measures from on-farm welfare assessment protocols designed for indoor use (e.g. Welfare Quality®) are relevant for cows at pasture (e.g. lameness scoring). However, the timing, location and/or method for certain measures (e.g. observation of social behaviour) need to be adapted to the pasture context, as cows at pasture can roam over a large area. Measures to address specific pasture-related risks (e.g. heat stress, biosecurity) or benefits (e.g. expression of a wide range of behaviours) should be implemented in order to capture all dimensions of cow welfare at pasture. Furthermore, cow welfare is liable to vary over the grazing season due to changes in weather conditions, grass quality and pasture plots that induce variations in lying surface conditions, food availability, distance to walk to the milking parlour, and so on. It is therefore important to investigate the variability in different welfare measures across the pasture season to check whether they hold stable over time and, if not, to determine solutions that can give an overview across the grazing season. Sensors offer a promising complement to animal and environment observations, as they can capture long-term animal monitoring data, which is simply not possible for a one-day welfare-check visit. We conclude that some measures validated for indoor situations can already be used in pasture-based systems, while others need to be validated for their fitness for purpose and/or use in pasture conditions. Furthermore, thresholds should probably be determined for measures to fit with pasture contexts. If all measures can be made adaptable to all situations encountered on farms or variants of the measures can at least be proposed for each criterion, then it should be possible to produce a comprehensive welfare assessment protocol suitable for large-scale use in near future.

Lameness prevalence and management practices on Irish pasture-based dairy farms

Background

Lameness is a painful disease, which negatively impacts dairy cow production and welfare. The aim of this observational study was to determine herd lameness prevalence, describe current lameness management practices and identify the presence of established risk factors for lameness on Irish pasture-based dairy farms. Farms were visited once during grazing (99 farms) and again during housing (85 farms). Lameness scoring was carried out at each visit (AHDB 0–3 scale); cows were classified as lame if they scored two or three. Farm management practices and infrastructure characteristics were evaluated via farmer questionnaires and direct measurements of farm infrastructure.

Results

Median herd-level lameness prevalence was 7.9% (interquartile range = 5.6 – 13.0) during grazing and 9.1% (interquartile range = 4.9 – 12.0) during housing; 10.9% of cows were lame at a single visit and 3.5% were lame at both visits (chronically lame or had a repeat episode of lameness). Fifty-seven percent of farmers were not familiar with lameness scoring and only one farm carried out lameness scoring. Only 22% of farmers kept records of lame cows detected, and 15% had a lameness herd health plan. Twenty-eight percent of farmers waited more than 48 h to treat a lame cow, and 21% waited for more than one cow to be identified as lame before treating. Six percent of farmers carried out routine trimming and 31% regularly footbathed (> 12 times per year). Twelve percent put severely lame cows in a closer paddock and 8% stated that they used pain relief to treat severely lame cows. Over 50% of farms had at least one cow track measurement that was classified as rough or very rough, and cow tracks were commonly narrow for the herd size. On 6% of farms, all cubicle beds were bare concrete (no matting or bedding) and on a further 6% of farms, there was a combination of cubicles with and without matting or bedding. On 56% of farms, all pens contained less than 1.1 cubicles per cow and on 28% of farms, a proportion of pens contained less than 1.1 cubicles per cow.

Conclusions

Overall, this study identified infrastructure and management practices which could be improved upon. The comparatively low lameness prevalence demonstrated, compared to fully housed systems, also highlights the benefits of a pasture-based system for animal welfare; however, there remains scope for improvement.

Historical Evolution of Cattle Management and Herd Health of Dairy Farms in OECD Countries

This work aimed to review the important aspects of the dairy industry evolution at herd level, interrelating production with health management systems. Since the beginning of the industrialization of the dairy cattle sector (1950s), driven by the need to feed the rapidly growing urban areas, this industry has experienced several improvements, evolving in management and technology. These changes have been felt above all in the terms of milking, rearing, nutrition, reproductive management, and design of facilities. Shortage of labor, emphasis on increasing farm efficiency, and quality of life of the farmers were the driving factors for these changes. To achieve it, in many areas of the world, pasture production has been abandoned, moving to indoor production, which allows for greater nutritional and reproductive control of the animals. To keep pace with this paradigm in milk production, animal health management has also been improved. Prevention and biosecurity have become essential to control and prevent pathologies that cause great economic losses. As such, veterinary herd health management programs were created, allowing the management of health of the herd as a whole, through the common work of veterinarians and farmers. These programs address the farms holistically, from breeding to nutrition, from prevention to consultancy. In addition, farmers are now faced with a consumer more concerned on animal production, valuing certified products that respect animal health and welfare, as well as environmental sustainability.

Identifying cow - level factors and farm characteristics associated with locomotion scores in dairy cows using cumulative link mixed models

Lameness is a tremendous problem in intensively managed dairy herds all over the world. It has been associated with considerable adverse effects on animal welfare and economic viability. The majority of studies have evaluated factors associated with gait disturbance by categorising cows into lame and non-lame. This procedure yet entails a loss of information and precision. In the present study, we extend the binomial response to five categories acknowledging the ordered categorical nature of locomotion assessments, which conserves a higher level of information. A cumulative link mixed modelling approach was used to identify factors associated with increasing locomotion scores. The analysis revealed that a low body condition, elevated somatic cell count, more severe hock lesions, increasing parity, absence of pasture access, and poor udder cleanliness were relevant variables associated with higher locomotion scores. Furthermore, distinct differences in the locomotion scores assigned were identified in regard to breed, observer, and season. Using locomotion scores rather than a dichotomised response variable uncovers more refined relationships between gait disturbances and associated factors. This will help to understand the intricate nature of gait disturbances in dairy cows more deeply.

The estimation and interpretation of ordered logit models for assessing the factors connected with the productivity of Holstein-Friesian dairy cows in Egypt

The incorporation of novel technologies such as artificial intelligence, data mining, and advanced statistical methodologies have received wide responses from researchers. This study was designed to model the factors impacting the actual milk yield of Holstein-Friesian cows using the proportional odds ordered logit model (OLM). A total of 8300 lactation records were collected for cows calved between 2005 and 2019. The actual milk yield, the outcome variable, was categorized into three levels: low (< 4500 kg), medium (4500-7500 kg), and high (> 7500 kg). The studied predictor variables were age at first calving (AFC), lactation order (LO), days open (DO), lactation period (LP), peak milk yield (PMY), and dry period (DP). The proportionality assumption of odds using the logit link function was verified for the current datasets. The goodness-of-fit measures revealed the suitability of the ordered logit models to datasets structure. Results showed that cows with younger ages at first calving produce two times higher milk quantities. Also, longer days open were associated with higher milk yield. The highest amount of milk yield was denoted by higher lactation periods (> 250 days). The peak yield per kg was significantly related to the actual yield (P < 0.05). Moreover, shorter dry periods showed about 1.5 times higher milk yield. The greatest yield was observed in the 2nd and 4th parities, with an odds ratio (OR) equal to 1.75, on average. In conclusion, OLM can be used for analyzing dairy cows' data, denoting fruitful information as compared to the other classical regression models. In addition, the current study showed the possibility and applicability of OLM in understanding and analyzing livestock datasets suited for planning effective breeding programs.

Simulating the mechanics behind sub-optimal mobility and the associated economic losses in dairy production

Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2-5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2-5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied considerably between the 5th and 95th percentiles: €-6174 and €19,499, and had a mean of €6281. This loss was composed of additional indirect culling due to SOM (∼65%) and changes in the overall herd milk production (∼35%) because of additional younger replacement heifers entering the herd due to increased culling rates. The bio-economic model presented novel results with respect to indirect economic losses arising due to SOM. The results can be used to stimulate farmer awareness and promote better SOM management.

Machine learning approaches for the prediction of lameness in dairy cows

Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.