Yaks Are Dependent on Gut Microbiota for Survival in the Environment of the Qinghai Tibet Plateau.

The yak (Poephagus grunniens) has evolved unique adaptations to survive the harsh environment of the Qinghai-Tibetan Plateau, while their gut microorganisms play a crucial role in maintaining the health of the animal. Gut microbes spread through the animal population not only by horizontal transmission but also vertically, which enhances microbial stability and inheritance between generations of the population. Homogenization of gut microbes in different animal species occurs in the same habitat, promoting interspecies coexistence. Using the yak as a model animal, this paper discusses the adaptive strategies under extreme environments, and how the gut microbes of the yak circulate throughout the Tibetan Plateau system, which not only affects other plateau animals such as plateau pikas, but can also have a profound impact on the health of people. By examining the relationships between yaks and their gut microbiota, this review offers new insights into the adaptation of yaks and their ecological niche on the Qinghai-Tibetan plateau.

Identifying Meat from Grazing or Feedlot Yaks Using Visible and Near-infrared Spectroscopy with Chemometrics.

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5-year-old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400-2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.