This study investigated the relationship between service quality factors, customer satisfaction, and reuse intention based on the degree of attachment to companion animals felt by caregivers when using dog grooming services. An online and offline survey with caregivers experienced in dog grooming services were conducted, and 304 responses were analyzed using SPSS 26.0 Statistics Program. The analysis revealed the following. First, sub-factors of dog grooming service quality: empathy, assurance, tangibles, and reliability, significantly affect customer satisfaction. Second, customers satisfaction with dog grooming services significantly affects their reuse intention. Third, only reliability demonstrates a moderating effect on attachment to companion dogs in influencing the relationship between customer satisfaction and service quality. These findings that service quality management is necessary to improve the business performance of dog-grooming services. Particularly, this study is meaningful in presenting the direction of service marketing centered on trust, as more guardians consider companion dogs as family.
We aimed to investigate the effect of environmental enrichment via toys on the behaviour and performance of weanling pigs. A total of 300 pigs (LYD) were housed in different pens with ten pigs per replicate and ten pigs per head divided into 3 groups. Group 1 was called “CON” and received no toys, group 2 was TOY-2, and pigs in this group had access to toys in the first 2 weeks, and lastly, pigs in TOY-4 were given toys in the fourth week. The pigs had access to feed and water ad-libitum. The individual pig behaviours in each group was recorded on days 14 and 28 (d 14 and 28) with a video camera for accuracy. The results showed higher (p<0.05) overall ADG in TOY-4 compared with CON, while the overall ADFI was higher (p<0.05) in TOY-supplemented groups compared to CON. Diarrhea incidence and fecal score were lower on D 14 in TOY-supplemented groups compared with CON. Behavioural features such as ear biting and fighting were lower (p<0.05) in TOY-supplemented groups compared with CON on D 14. Tail biting was lower (p<0.05) in TOY-2 compared with CON at D 14. Conversely, at D 28, tail biting was lower (p<0.05) in TOY-4 compared with CON. The ADG improved due to the toy supplied to reduce undesirable social behaviours. We concluded that the environmental enrichment of pens with toys can help to improve the welfare in weaning pigs, leading to a greater survivability and more production thereby improving farmer incomes.
This study was conducted with the aim of confirming the impact and relative contribution of extreme weather to dry matter yield (DMY) of silage corn in the central inland region of Korea. The corn data (n=1,812) were obtained from various reports on the new variety of adaptability experiments conducted by the Rural Development Administration from 1978 to 2017. As for the weather variables, mean aerial temperature, accumulated precipitation, maximum wind speed, and sunshine duration, were collected from the Korean Meteorological Administration. The extreme weather was detected by the box plot, the DMY comparison was carried out by the t-test with a 5% significance level, and the relative contribution was estimated by R2 change in multiple regression modeling. The DMY of silage corn was reduced predominantly during the monsoon in summer and autumn, with DMY damage measuring 1,500-2,500 kg/ha and 1,800 kg/ha, respectively. Moreover, the relative contribution of the damage during the monsoons in summer and autumn was 40% and 60%, respectively. Therefore, the impact of autumn monsoon season should be taken into consideration when harvesting silage corn after late August. This study evaluated the effect of extreme weather on the yield damage of silage corn in Korea and estimated the relative contribution of this damage for the first time.
On pig farms, the highest mortality rate is observed among nursing piglets. To reduce this mortality rate, farmers need to carefully observe the piglets to prevent accidents such as being crushed and to maintain a proper body temperature. However, observing a large number of pigs individually can be challenging for farmers. Therefore, our aim was to detect the behavior of piglets and sows in real-time using deep learning models, such as YOLOv4-CSP and YOLOv7-E6E, that allow for real-time object detection. YOLOv4-CSP reduces computational cost by partitioning feature maps and utilizing Cross-stage Hierarchy to remove redundant gradient calculation. YOLOv7-E6E analyzes and controls gradient paths such that the weights of each layer learn diverse features. We detected standing, sitting, and lying behaviors in sows and lactating and starving behaviors in piglets, which indicate nursing behavior and movement to colder areas away from the group. We optimized the model parameters for the best object detection and improved reliability by acquiring data through experts. We conducted object detection for the five different behaviors. The YOLOv4-CSP model achieved an accuracy of 0.63 and mAP of 0.662, whereas the YOLOv7-E6E model showed an accuracy of 0.65 and mAP of 0.637. Therefore, based on mAP, which includes both class and localization performance, YOLOv4-CSP showed the superior performance. Such research is anticipated to be effectively utilized for the behavioral analysis of fattening pigs and in preventing piglet crushing in the future.