The aim of this study was to assess the effect of grazing (G) vs. zero-grazing (ZG), level of milk production, and quality and type of housing system [free stalls (FS) and straw yards (SY)] on the prevalence of lameness and leg injuries in dairy cows. Observations were made on 37 commercial dairy farms across Great Britain. A single visit of 5 d duration was made to each farm. During this visit, lameness scores and the incidence of swellings, rubs, and injuries to hocks and knees were recorded on all the peak- or mid-lactation cows. Aspects of the quality of housing and management that were likely to affect foot and leg health were recorded. There were more lame cows on ZG farms (39 +/- 0.02%) than on grazing (G) farms (15 +/- 0.01%), and lameness scores were higher on FS farms compared with SY farms (0.25 +/- 0.01 vs. 0.05 +/- 0.01). Cows on SY farms had fewer hock and knee injuries compared with FS farms. The frequency of knee swellings was higher on ZG farms (0.31 +/- 0.02) than on G farms (0.15 +/- 0.01). Aspects of the free-stall design affected foot and leg health. The number of hock swellings increased with increasing stall gradient (0.16 +/- 0.01 with no slope vs. 0.39 +/- 0.02 at a 0 to 1.5% slope). There was an interaction between the length of the free-stall lunging space and the hip width of the cow, indicating that the incidence of lameness is generally highest on farms with small free stalls and heavy cows. High levels of milk production did not affect lameness or leg injury. The results indicate that housing cows throughout the year potentially has a detrimental effect on foot and leg health. However, good free-stall design may reduce lameness and leg lesions.

This study compared the environmental impact of a range of dairy production systems in terms of their global warming potential (GWP, expressed as carbon dioxide equivalents, CO(2)-eq.) and associated land use, and explored the efficacy of reducing said impact. Models were developed using the unique data generated from a long-term genetic line × feeding system experiment. Holstein-Friesian cows were selected to represent the UK average for milk fat plus protein production (control line) or were selected for increased milk fat plus protein production (select line). In addition, cows received a low forage diet (50% forage) with no grazing or were on a high forage (75% forage) diet with summer grazing. A Markov chain approach was used to describe the herd structure and help estimate the GWP per year and land required per cow for the 4 alternative systems and the herd average using a partial life cycle assessment. The CO(2)-eq. emissions were expressed per kilogram of energy-corrected milk (ECM) and per hectare of land use, as well as land required per kilogram of ECM. The effects of a phenotypic and genetic standard deviation unit improvement on herd feed utilization efficiency, ECM yield, calving interval length, and incidence of involuntary culling were assessed. The low forage (nongrazing) feeding system with select cows produced the lowest CO(2)-eq. emissions of 1.1 kg/kg of ECM and land use of 0.65 m(2)/kg of ECM but the highest CO(2)-eq. emissions of 16.1t/ha of the production systems studied. Within the herd, an improvement of 1 standard deviation in feed utilization efficiency was the only trait of those studied that would significantly reduce the reliance of the farming system on bought-in synthetic fertilizer and concentrate feed, as well as reduce the average CO(2)-eq. emissions and land use of the herd (both by about 6.5%, of which about 4% would be achievable through selective breeding). Within production systems, reductions in CO(2)-eq. emissions per kilogram of ECM and CO(2)-eq. emissions per hectare were also achievable by an improvement in feed utilization. This study allowed development of models that harness the biological trait variation in the animal to improve the environmental impact of the farming system. Genetic selection for efficient feed use for milk production according to feeding system can bring about reductions in system nutrient requirements, CO(2)-eq. emissions, and land use per unit product.

Fearful behavioral responses to handling (temperament) are undesirably associated with ADG and meat quality in infrequently handled Bos indicus cattle. It has never been assessed whether these relationships exist in calmer Bos taurus breeds in systems where handling is more frequent. Such systems predominate in some countries where beef production is a major agricultural activity. During fattening, 144 crossbred cattle from Limousin and Aberdeen Angus sires were assessed for temperament using 4 approaches: response to movement along a race (race score; 4 occasions), restraint in a crush (crush score; 4 occasions), flight speed from the crush (flight speed; 4 occasions), and isolation in a pen with a human (isolation score; 1 occasion in yr 1, 2 occasions in yr 2). Measurements of ADG were made between birth and slaughter and between 16 and 18 mo of age during fattening. Fattening occurred indoors on a complete mixed diet fed for ad libitum intake. Meat quality was measured by pH, color, and Volodkevitch shear force and by a sensory panel. The repeatability of temperament traits was 0.17 (race score), 0.35 (crush score), 0.51 (flight speed), and 0.36 (isolation score). The proportion of the total variance of temperament traits attributable to the sire and the social group was low (0.003 to 0.402). However, the sire did affect behavior in all tests apart from the crush score (ranging from P = 0.02 to P < 0.001). Correlations between behavior in the different tests (ranging from r = 0.21 to 0.54, and P = 0.02 to P < 0.001) apart from between-flight speed and isolation score indicate that fearful behavior was consistently shown across assessment methods. A calm response in the crush score test was associated with a greater ADG during fattening (P = 0.05), whereas a calm response during the isolation test was associated with a greater ADG in cold carcass weight (P = 0.02). Animals with a calm isolation score had less tender meat as judged by the sensory panel (P = 0.03), but no other effects were apparent between temperament and meat quality measures, although several tendencies (0.06 ≤ P ≤ 0.10) were found. Temperament did not appear to relate to meat quality in this study of frequently handled Bos taurus genotypes, which is in contrast to other studies using different beef production systems. Genetic correlations between temperament and meat quality under these conditions could, although not measured in this study, still exist in the absence of phenotypic correlations. Copyright © 2011. American Society of Animal Science .

The aim of this study was to assess combining location, acceleration and machine learning technologies to detect estrus in dairy cows. Data were obtained from 12 cows, which were monitored continuously for 12 days. A neck mounted device collected 25,684 records for location and acceleration. Four machine-learning approaches were tested (K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), and classification and regression tree (CART)) to automatically identify cows in estrus from estrus indicators determined by principal component analysis (PCA) of twelve behavioral metrics, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, and drinking times. The study showed that the neck tag had a static and dynamic positioning accuracy of 0.25 ± 0.06 m and 0.45 ± 0.15 m, respectively. In the 0.5-h, 1-h, and 1.5-h time windows, the machine learning approaches ranged from 73.3 to 99.4% for sensitivity, from 50 to 85.7% for specificity, from 77.8 to 95.8% for precision, from 55.6 to 93.7% for negative predictive value (NPV), from 72.7 to 95.4% for accuracy, and from 78.6 to 97.5% for F1 score. We found that the BPNN algorithm with 0.5-h time window was the best predictor of estrus in dairy cows. Based on these results, the integration of location, acceleration, and machine learning methods can improve dairy cow estrus detection.

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management.