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Timely and accurate detection of ear tag dropout is crucial for standardized precision breeding, health monitoring, and breeding evaluation. Reserve breeding pigs exhibit high activity levels and frequent interactions, leading to a higher prevalence of ear tag dropout. However, detection is challenging due to motion blur, small tag size, and significant target scale variations. To address this, we propose a motion blur-aware multi-scale framework, Adapt-Cascade. First, a Weight-Adaptive Attention Module (WAAM) enhances the extraction of motion blur features. Second, Density-Aware Dilated Convolution (DA-DC) dynamically adjusts the convolutional receptive field to improve small ear tag detection. Third, a Feature-Guided Multi-Scale Region Proposal strategy (FGMS-RP) strengthens multi-scale target detection. Integrated into the Cascade Mask R-CNN framework with Focal Loss, Adapt-Cascade achieves 93.46% accuracy at 19.2 frames per second in detecting ear tag dropout in reserve breeding pigs. This model provides a high-accuracy solution for intelligent pig farm management.

Methicillin‐resistant Staphylococcus aureus (MRSA) has been recognised as an important cause of hospital‐associated infections in humans for several decades. MRSA is resistant to the most commonly used antibiotics. The MRSA lineage ST398 (MRSA ST398) has been recently described as a cause of infection for people occupationally exposed to pigs, by direct or indirect contact. MRSA ST398 can occasionally be introduced into hospitals as a result of community‐acquired human infections. To assess the occurrence and the diversity of MRSA in pig primary production, a European Union‐wide preliminary survey was carried out in parallel with a baseline survey on Salmonella spp. in holdings with breeding pigs to determine the prevalence of holdings positive for MRSA and MRSA ST398.

Using untreated livestock manure adversely affects the environment and human health. Although transforming untreated livestock manure into organic fertilizer can alleviate these adverse effects, the adoption rate of this technology is relatively low in rural areas. This study investigates whether information technology adoption, focusing on Internet use, increases farmers’ willingness to transform untreated livestock manure into organic fertilizer using data collected from pig breeding farmers in the Hubei province of China. A recursive bivariate probit model addresses the sample selection bias issues. Findings show that Internet use increases the probability of pig breeding farmers’ willingness to transform untreated manure by around 35–56 percentage points. Gender, farm size, credit availability, extension contact, environmental protection attitude and governmental regulation are also important factors driving pig breeding farmers’ willingness to transform untreated manure. Moreover, the impact of Internet use on farmers’ willingness to transform untreated pig manure tends to be higher for pig breeding farmers with a larger breeding size and longer breeding years. Our findings have significant policy implications that help to improve livestock manure management, environmental quality, and the well-being and health of livestock breeding farmers.

Pig farming is a major source of carbon emissions in China’s livestock industry, and the promotion and application of carbon neutral technologies dedicated to carbon reduction and sequestration is a key measure to promote the realization of carbon neutrality in pig farming industry. In the absence of a system for carbon emission reduction in agriculture, it is important to explore whether large-scale pig farming can spontaneously promote the application of carbon neutral technologies by pig farmers. Combined with the technical background of carbon neutrality in pig farming industry and using the field survey data of 468 pig farmers in Shandong Province, this paper empirically analyzed the effect of large-scale pig farming on the carbon neutral behavior of pig farmers. The findings are threefold. Firstly, because of the effect of economies of scale in the application of carbon neutral technologies, large-scale pig farming can promote the carbon neutral behavior of pig farmers. However, the effect of economies of scale in the application of carbon neutral technologies has a boundary, and the annual output of pigs should be maintained at 3000 to 4999. Secondly, among the effects of pig breeding scale on the application and its degree of composting project, state of planting and breeding cycle has no regulating effect. Thirdly, among the effects of pig breeding scale on the application and its degree of biogas engineering, both state of planting and breeding cycle and subsidy for construction of biogas digester have negative regulating effects. In this paper, the pig farming industry is taken as a typical example to reveal the endogenous incentive mechanism of promoting agricultural carbon emission reduction, which provides theoretical reference and experience enlightenment for the formulation of effective policies to promote agricultural carbon neutrality, which are different from industrial industries.

Salmonella is a major cause of food‐borne illness in humans. Farm animals and foods of animal origin are important sources of human Salmonella infections. This European Union‐wide Salmonella baseline survey was conducted in 2008 in holdings with breeding pigs. A total of 1,609 holdings housing and selling mainly breeding pigs (breeding holdings) and 3,508 holdings housing breeding pigs and selling mainly pigs for fattening or slaughter (production holdings) from 24 European Union Member States and two non‐Member States, were randomly selected and included in the survey. In each selected breeding and production holding, fresh voidedpooled faecal samples were collected from 10 randomly chosen pens, yards or groups of breeding pigs over six months of age, representing the different stages of the breeding herd. The pooled faecal samples from each holding were tested for Salmonella and the isolates were serotyped. The overall European Union prevalence of Salmonella‐positive holdings with breeding pigs was 31.8% and all but one of the 24 participating Member States detected Salmonella in at least one holding. The European Union prevalence of Salmonella‐positive breeding holdings was 28.7%, and prevalence varied from 0% to 64.0% among Member States. The European Union prevalence of Salmonella‐positive production holdings was 33.3%, while the Member States’ prevalence varied from 0% to 55.7%. The number of different Salmonella serovars isolated in breeding holdings and production holdings in the European Union was 54 and 88, respectively. Salmonella Derby and Salmonella Typhimurium predominated in both types of holdings. Breeding pigs may be an important source of dissemination of Salmonella throughout the pig‐production chain. The results of this survey provide valuable information for setting a Salmonella reduction target for breeding pigs and for assessing the impact of Salmonella transmission originating from holdings with breeding pigs

Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.

China has a vast territory and abundant resources, and there are significant differences in the development of pig breeding in different regions. Chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) produced in the process of pig breeding will affect China’s environmental quality. In view of this, based on the Minimum Distance to Weak efficient frontier model, this paper constructs Metafrontier-Malmquist-Luenberger (MML) index considering negative output under the common frontier to comprehensively evaluate the green total factor productivity of Chinese pig breeding (GTCP). This has guiding significance for improving China’s pork production and reducing pollution emissions. The results manifest that (1) no matter under the common frontier or the group frontier, GTCP presents large temporal and spatial differentiation characteristics. Compared with the central region and the western region, the eastern region has obvious advantages in GTCP. (2) GTCP has shown an upward trend as a whole, which is mainly due to the technical progress. (3) Compared with small-scale and medium-sized GTCP, large-scale GTCP has apparent superiorities. Based on the above outcomes, this paper finally raises policy recommendations for improving GTCP: (1) give full play to the advantages of pig breeding in different regions, (2) increase the research and introduction of pig breeding clean technology and improve the application efficiency, and (3) give full play to the scale effect and vigorously develop large-scale pig breeding.

Background Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species. Results In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2 , LEPROT , PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs. Conclusion Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.

Pork is a major source of animal protein worldwide, and its quality is influenced by pre- and post-slaughter procedures. Advances in molecular biology, particularly gene expression studies, support genetic improvement programs by enabling precise strategies to enhance meat quality and economic sustainability. This study evaluated meat quality traits and candidate gene expression in muscle and subcutaneous adipose tissue from different genetic lineages and sexes. A total of 120 pigs from three lineages—Line D (½ Duroc × ½ DB90), Line P (½ Pietrain × ½ DB90), and Line H (½ [Duroc and Pietrain] × ½ DB90); including immunocastrated males (IM) and females, were randomly selected. Meat quality was assessed using physicochemical parameters, and gene expression analysis was performed in 36 pigs using RT-qPCR with B2M, TBP, and RPL4 as references, and COL1A1, PRKAR2A, CAST, ADIPOQ, and PPARGC1A as targets. Lineage influenced drip loss and intramuscular fat, while lineage–sex interaction affected tenderness and color (L* and b*), and sex influenced b*. In muscle, sex affected COL1A1 and PRKAR2A, and lineage influenced COL1A1 and CAST. In adipose tissue, only CAST was lineage-dependent.

This study aimed to optimise the layout of pig breeding in the south water network area and guide the transfer of pig production capacity to the areas with large environmental capacity and the main maize-producing areas. Combining with the alarm value of equivalent pig manure load on cultivated land, the comprehensive advantage index of maize and GIS (Geographic Information System), this study analysed the spatial layout planning of the pig northward movement in China. Results showed that the alarm value of equivalent pig manure load on cultivated land in other parts of the study area exceeded 0.40, except in Jilin, Heilongjiang and the Inner Mongolia Autonomous Region. The highest alarm value of equivalent pig manure load on cultivated land value of 1.96 was observed in Fujian Province, followed by Jiangxi (1.09) and Hubei (0.42). A total of 573.996 million excess pig equivalents were present in the study area. In the provinces with potential pig equivalent, Jilin, Heilongjiang and the Inner Mongolia Autonomous Region can carry the potential pig equivalent of 306.270 million. The priority target areas of the pig northward movement were Jilin, Heilongjiang and the Inner Mongolia Autonomous Region according to the comprehensive advantage index of maize. Combining with the spatial analysis method of GIS, the scheme of the pig northward movement was obtained. According to the spatial layout planning of the pig northward movement, Jilin and Heilongjiang carried 15.339 and 135.36 million pig equivalents, respectively, thereby reaching the maximum number of pig equivalents they can carry, whereas the Inner Mongolia Autonomous Region can carry 80.086 million pig equivalents. The results can provide a scientific basis for the spatial layout planning of pig breeding and environmental pollution protection in China.