Behavior and foraging ecology of cattle: A review

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

A matter of age? How age affects the adaptation of lactating dairy cows to virtual fencing

Virtual Fencing (VF) can be a helpful technology in managing herds in pasture-based systems. In VF systems, animals wear a VF collar using global positioning, and physical boundaries are replaced by virtual ones. The Nofence (Nofence AS, Batnfjordsøra, Norway) collars used in this study emit an acoustic warning when an animal approaches the virtual boundaries, followed by an aversive electrical pulse if the animal does not return to the defined area. The stimuli sequence is repeated up to three times if the animal continues to walk forward. Although it has been demonstrated that animals successfully learn to adapt to the system, it is unknown if this adaptation changes with animal age and thus has consequences for VF training and animal welfare. This study compared the ability of younger and older dairy cows to adapt to a VF system and whether age affected activity behavior, milk yield, and animal long-term stress under VF management. The study was conducted on four comparable strip-grazing paddocks. Twenty lactating Holstein-Friesian cows, divided into four groups of five animals each, were equipped with VF collars and pedometers. Groups differed in age: two groups of older cows (>4 lactations) and two groups of younger ones (first lactation). After a 7-d training, paddock sizes were increased by successively moving the virtual fence during four consecutive grazing periods. Throughout the study, the pedometers recorded daily step count, time spent standing, and time spent lying. For the determination of long-term stress, hair samples were collected on the first and last day of the trial and the hair cortisol content was assessed. Data were analyzed by generalized mixed-effect models. Overall, age had no significant impact on animal responses to VF, but there were interaction effects of time: the number of acoustic warnings in the last period was higher in younger cows (P < 0.001), and the duration of acoustic warnings at training was shorter for older cows (P < 0.01). Moreover, younger cows walked more per day during the training (P < 0.01). Finally, no effects on milk yield or hair cortisol content were detected. In conclusion, all cows, regardless of age, adapted rapidly to the VF system without compromising their welfare according to the indicators measured.

Genomic Selection for Dairy Cattle Behaviour Considering Novel Traits in a Changing Technical Production Environment

Cow behaviour is a major factor influencing dairy herd profitability and is an indicator of animal welfare and disease. Behaviour is a complex network of behavioural patterns in response to environmental and social stimuli and human handling. Advances in agricultural technology have led to changes in dairy cow husbandry systems worldwide. Increasing herd sizes, less time availability to take care of the animals and modern technology such as automatic milking systems (AMSs) imply limited human-cow interactions. On the other hand, cow behaviour responses to the technical environment (cow-AMS interactions) simultaneously improve production efficiency and welfare and contribute to simplified "cow handling" and reduced labour time. Automatic milking systems generate objective behaviour traits linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of the genetic mechanisms influencing cow learning and social behaviour, in turn affecting herd management, productivity and welfare. Moreover, physiological and molecular biomarkers such as heart rate, neurotransmitters and hormones might be useful indicators and predictors of cow behaviour. This review gives an overview of published behaviour studies in dairy cows in the context of genetics and genomics and discusses possibilities for breeding approaches to achieve desired behaviour in a technical production environment.