Prediction of parturition in Holstein dairy cattle using electronic data loggers

The objective of the present study was to assess the effect of parturition on behavioral activity [steps, standing time, lying time, lying bouts (LB), and duration of LB] 4 d before calving using electronic data loggers. Animals (n = 132) from 3 herds were housed in similar freestall barns using a prepartum pen 21 d before the expected calving date and were moved into a contiguous individual maternity pen for parturition. Electronic data loggers were placed on a hind leg of prepartum heifers (heifers, n = 33) and cows (cows, n = 99) at 7 ± 3 d before the expected calving date and removed at 14 ± 3 d in milk. Calving ease (scale 1–4), parity, calving date and time, and stillbirth (born dead or died within 24 h) were recorded. The number of steps (no./d), standing time (min/d), lying time (min/d), number of LB (no./d), and duration of LB (min/b) were recorded. Data were analyzed using MIXED procedures of SAS, adjusting for the herd effect. Only cows experiencing unassisted births (calving ease = 1) were included in the study. An activity index was developed to predict calving time. Heifers and cows with unassisted births had significantly higher number of steps and longer standing time, decreased lying time, and more LB of shorter duration 24 h before calving compared with d −4, −3, and −2. Additionally, the number of LB increased as both heifers and cows approached labor starting on d −2 and peaked at the day of calving. The time since the activity index increased over 50% to parturition did not differ between heifers and cows, and the activity index revealed the shift in activity on average 6 h 14 min (range from 2 h to 14 h 15 min) before calf birth. This study provided evidence that heifers and cows approaching parturition showed a similar, but distinct, behavioral pattern that can be observed on average 6 h before calf birth. The potential benefits of electronic data loggers as predictors of parturition along with proactive management practices should improve the overall survival and welfare of both the dam and calf

Neural predictive control of broiler chicken and pig growth

Active control of the growth of broiler chickens and pigs has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health in broiler chickens. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent animal growth using a nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predictive control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler and pig growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler and pig weights accurately followed the desired growth curves ranging from to +12% and to +20% of the standard curve for broiler chickens and pigs, respectively. The overall mean relative error between the desired and achieved broiler or pig weight was 1.8% for the period from day 12 to day 51 and 10.5% for the period from week 5 to week 21, respectively

Novel monitoring systems to obtain dairy cattle phenotypes associated with sustainable production

Improvements in production efficiencies and profitability of products from cattle are of great interest to farmers. Furthermore, improvements in production efficiencies associated with feed utilization and fitness traits have also been shown to reduce the environmental impact of cattle systems, which is of great importance to society. The aim of this paper was to discuss selected novel monitoring systems to measure dairy cattle phenotypic traits that are considered to bring more sustainable production with increased productivity and reduced environmental impact through reduced greenhouse gas emissions. With resource constraints and high or fluctuating commodity prices the agricultural industry has seen a growing need by producers for efficiency savings (and innovation) to reduce waste and costs associated with production. New data obtained using fast, in some cases real-time, and affordable objective measures are becoming more readily available to aid farm level monitoring, awareness, and decision making. These objective measures may additionally provide an accurate and repeatable method for improving animal health and welfare, and phenotypes for selecting animals. Such new data sources include image analysis and further data-driven technologies (e.g., infrared spectra, gas analysis), which bring non-invasive methods to obtain animal phenotypes (e.g., enteric methane, feed utilization, health, fertility, and behavioral traits) on commercial farms; this information may have been costly or not possible to obtain previously. Productivity and efficiency gains often move largely in parallel and thus bringing more sustainable systems

Quick, accurate, smart: 3D computer vision technology helps assessing confined animals' behaviour

Mankind directly controls the environment and lifestyles of several domestic species for purposes ranging from production and research to conservation and companionship. These environments and lifestyles may not offer these animals the best quality of life. Behaviour is a direct reflection of how the animal is coping with its environment. Behavioural indicators are thus among the preferred parameters to assess welfare. However, behavioural recording (usually from video) can be very time consuming and the accuracy and reliability of the output rely on the experience and background of the observers. The outburst of new video technology and computer image processing gives the basis for promising solutions. In this pilot study, we present a new prototype software able to automatically infer the behaviour of dogs housed in kennels from 3D visual data and through structured machine learning frameworks. Depth information acquired through 3D features, body part detection and training are the key elements that allow the machine to recognise postures, trajectories inside the kennel and patterns of movement that can be later labelled at convenience. The main innovation of the software is its ability to automatically cluster frequently observed temporal patterns of movement without any pre-set ethogram. Conversely, when common patterns are defined through training, a deviation from normal behaviour in time or between individuals could be assessed. The software accuracy in correctly detecting the dogs' behaviour was checked through a validation process. An automatic behaviour recognition system, independent from human subjectivity, could add scientific knowledge on animals' quality of life in confinement as well as saving time and resources. This 3D framework was designed to be invariant to the dog's shape and size and could be extended to farm, laboratory and zoo quadrupeds in artificial housing. The computer vision technique applied to this software is innovative in non-human animal behaviour science. Further improvements and validation are needed, and future applications and limitations are discussed.</p

Calf health from birth to weaning. I. General aspects of disease prevention

Calfhood diseases have a major impact on the economic viability of cattle operations. This is the first in a three part review series on calf health from birth to weaning, focusing on preventive measures. The review considers both pre- and periparturient management factors influencing calf health, colostrum management in beef and dairy calves and further nutrition and weaning in dairy calves

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Monitoring and control applications in determination of health and welfare related behavioural aspects of livestock

Welfare of animals in livestock houses is usually monitored manually by the farmer or ethologists by performing some standardized measures on chosen welfare indicators. Focusing on a single iceberg indicator is not sufficient to detect welfare by itself. On the other hand, a large set of measures need increased workload on manual observers or stockmen in order to provide a useful indication of an animal s quality of life.The objective of this thesis was to show that mathematical modelling and control techniques applied on sensor data lead to more frequent monitoring of health and welfare related responses of livestock animals. Animal responses were monitored with increasing frequency of data sampling, from manual observation once in a growth period to automatic capturing of one data point per second. It was aimed to prove that automated measurements give way to early warning systems as well as prediction of future conditions. In Chapter 2, the welfare of broiler chickens in terms of ease of locomotion was determined by experts going into poultry houses and assessing animals by an animal-based measure called gait score . Measurements were done manually by visual inspections at slaughter age. Assessing at the slaughter age was not an ideal way of monitoring animal welfare on farms. Therefore in chapter 3, measurements were done more frequently once per week during the growth period of the animals. In this chapter thermal comfort of broiler chickens were studied using measurements of a thermal camera. In Chapter 4, measurement frequency was increased to once per second in order to capture the dynamics of the variables under consideration. A camera system was used to detect locomotion and posture parameters of pregnant cows close to calving. A computer vision technique was developed to monitor the body variables of animals automatically. Images were acquired and analysed giving way to classify specific behaviours related to calving.In Chapter 5, real-time mathematical models were developed to describe the relation of the growth response of a broiler chicken to the feed intake. Weights of broiler chickens were measured by an automatic weighing scale with an increased measurement frequency of ten times per second. Feed intake was also measured continuously by a feeding system. The developed models enabled prediction of future weight responses of animals.In Chapter 6, responses of young chicks to temperature and ventilation changes were studied. Measurements were carried out once per second using a camera. It was shown that the mathematical modelling of dynamic responses not only enabled more frequent welfare assessment and anticipation but also could give an insight to the process of thermo-regulatory behaviour of broiler chicks.In Chapters 7 and 8, continuous weight response and feed intake of animals were used to develop a growth control procedure in an attempt to ensure that the chickens were following a desired growth trajectory. The idea behind was to test the control theory and also to explore the effects of early feed restriction on health, welfare and performance of broiler chickens. It was shown that active control of the growth of broiler chickens in commercial houses was possible.nrpages: 244status: publishe