Significant intensification in livestock farming has become prevalent to meet the increasing meat production demand, resulting in a higher density of pigs in relatively small areas in a commercial swine building. The subsequent challenges of maintaining the quality of both routine management and environmental comfort of pigs to minimize the loss of both pigs’ health and welfare can be attained by implementing autonomous monitoring and intelligent management decisions based on precision livestock farming (PLF). A three-layer wireless sensor network (WSN) based on ZigBee technology has been devised to monitor four environmental parameters in real-time, namely: temperature, relative humidity, concentrations of carbon dioxide and ammonia in a commercial gestating sow house. The overall packet loss rate of the WSN system which reported 16,371 records from its 41 indoor slave nodes in a 10-min interval for three consecutive days was 4%. The carbon dioxide sensors had an average outlier rate of 6.5% after a series of preprocessing procedures. The spatial and temporal characteristics showed that the carbon dioxide level exceeded the limit of 2700 mg/m³ twice during both 07:00–08:00 and 14:00–15:00. Besides, the overall NH₃ concentration in the swine building was maintained in a relatively low-level range with a maximum of less than 8 mg/m³. In sum, the real-time monitoring and timely intervention of microclimate in this commercial gestating sow house can be achieved by deploying this WSN system, thereby making it possible to provide an intelligent decision on precise management of livestock automatically.

Since the first proposal to use spray-dried porcine plasma (SDPP) as an animal-based protein source feed additive for piglets in the late 1980s, a large number of studies have been published on the promotion effect of SDPP on piglets. SDPP contains biologically active components that support pig health during weaning stress and may be more economical to use compared to similar bovine-milk-derived protein sources. Unfortunately, animal blood proteins have been suspected as a source for African Swine Fever Virus (ASFV) spread in China. Furthermore, there are no offcially recognized methods for quantifying SDPP in complex feed mixtures. Therefore, it is essential to develop rapid, high-effciency analytical methods to detect SDPP. The feasibility of detecting SDPP using an electronic nose and near-infrared spectroscopy (NIRS) was explored and validated by a principal component analysis (PCA). Both discrimination experiments and prediction experiments were implemented to compare the detect feature of the two techniques. On this basis, partial least squares discriminant analysis (PLS–DA) under various preprocessing methods was used to develop a qualitative discriminant model for estimating the prediction performance. Before selecting a specific regression model for the quantitative analysis of SDPP, a continuum regression (CR) model was employed to explore and choose the potential most appropriate regression model for these two different types of datasets. The results showed that the optimal regression model adopted partial least squares regression (PLSR) with the Savitzky–Golay first derivative and mean-center preprocessing for the NIRS dataset (${\mathrm{R}}_{\mathrm{p}}^2$ = 0.999, RMSEP = 0.1905). Overall, combining the NIRS technique with multivariate data analysis methods shows more possibilities than an electronic nose for rapidly detecting the usage of SDPP in mixed feed samples, which could provide an effective way to identify the use of SDPP in feed mixtures.

We, the authors, wish to make the following corrections to our paper