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The main objectives of this study were to perform a systematic review of genomic regions associated with various behavioral traits in the main farmed mammals and identify key candidate genes and potential causal mutations by contrasting the frequency of polymorphisms in cattle breeds with divergent behavioral traits (based on a subjective clustering approach). A total of 687 (cattle), 1391 (pigs), and 148 (sheep) genomic regions associated with 37 (cattle), 55 (pigs), and 22 (sheep) behavioral traits were identified in the literature. In total, 383, 317, and 15 genes overlap with genomic regions identified for cattle, pigs, and sheep, respectively. Six common genes (e.g., NR3C2, PITPNM3, RERG, SPNS3, U6, and ZFAT) were found for cattle and pigs. A combined gene-set of 634 human genes was produced through identified homologous genes. A total of 313 out of 634 genes have previously been associated with behavioral, mental, and neurologic disorders (e.g., anxiety and schizophrenia) in humans. Additionally, a total of 491 candidate genes had at least one statistically significant polymorphism (p-value < 0.05). Out of those, 110 genes were defined as having polymorphic regions differing in greater than 50% of exon regions. Therefore, conserved genomic regions controlling behavior were found across farmed mammal species and humans.

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

NH3 emission has become one of the key factors for aerobic composting of animal manure. It has been reported that adding microbial agents during aerobic composting can reduce NH3 emissions. However, environmental factors have a considerable influence on the activity and stability of the microbial agent. Therefore, this study used cornstalk biochar as carriers to find out the better biological immobilization method to examine the mitigation ability and mechanism of NH3 production from laying hen manure composting. The results from different immobilized methods showed that NH3 was reduced by 12.43%, 5.53%, 14.57%, and 22.61% in the cornstalk biochar group, free load bacteria group, mixed load bacteria group, and separate load bacteria group, respectively. Under the simulated composting condition, NH3 production was 46.52, 38.14, 39.08, and 30.81 g in the treatment of the control, mixed bacteria, cornstalk biochar, and cornstalk biochar separate load immobilized mixed bacteria, respectively. The cornstalk biochar separate load immobilized mixed bacteria treatment significantly reduced NH3 emission compared with the other treatments (p < 0.05). Compared with the control, adding cornstalk biochar immobilized mixed bacteria significantly decreased the electrical conductivity, water-soluble carbon, total nitrogen loss, and concentration of ammonium nitrogen (p < 0.05), and significantly increased the seed germination rate, total number of microorganisms, and relative abundance of lactic acid bacteria throughout the composting process (p < 0.05). Therefore, the reason for the low NH3 emission might be due not only to the adsorption of the cornstalk biochar but also because of the role of complex bacteria, which increases the relative abundance of lactic acid bacteria and promotes the acid production of lactic acid bacteria to reduce NH3 emissions. This result revealed the potential of using biological immobilization technology to reduce NH3 emissions during laying hen manure composting.