Search Results (6)

Mastitis is an important disease limiting milk production in dairy cows. Somatic cell score is commonly used as one of the main ways to gauge the level of mastitis in dairy cows, with higher somatic cell scores usually indicating possible mastitis. However, the main molecular markers affecting somatic cell scores remain unknown. The aim of this study was to investigate the association between single nucleotide polymorphisms in the SLC11A1 gene and somatic cell score in Chinese Holstein cows. In this study, 210 Chinese Holstein cows were genotyped and potential SNPs were detected by DNA sequencing, PCR-SSCP and PCR-RFLP analysis. Our results revealed two SNPs were identified in the CDS region of SLC11A1: c.723C>T and c.1144C>G. For the c.723C>T polymorphic site, two genotypes (AA, AB) were found and the genotype frequencies were 0.790 and 0.210, respectively. The results of the association analysis showed that the mean somatic cell score of the AA genotypes were significantly lower than those of the AB genotypes, suggesting that the A allele is a potential marker for improving mastitis resistance in Chinese Holstein cows. For the c.1144C>G polymorphic site, three genotypes (CC, CD, and DD) were found and the genotype frequencies were 0.629, 0.352 and 0.019, respectively. The association analysis revealed that the mean somatic cell score of CC genotypes was lower than that of CD and DD genotypes, however, no significant differences were observed among the various genotype groups when subjected to pair-wise comparisons. The bioinformatic analysis showed that these mutations affected the secondary and tertiary structure of SLC11A1 mRNA, suggesting that they may affect gene expression or protein translation and function. Finally, we predicted the SLC11A1 protein interaction network and found that SPI1, NOD2, TLR2 and S100A12 interacted with SLC11A1 and were reported as candidate genes associated with mastitis resistance. The results indicated that the SNP (c.723C>T) could be potential molecular marker for improving mastitis resistance traits in Chinese Holstein cows. We recommend further validation of this SNP in larger populations and its potential integration into breeding programs to enhance mastitis resistance in dairy cows. Full article

Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed. The model integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features (CARAFE) upsampling operator, enhancing the YOLOv8n framework’s attention to behavioral features, reducing interferences from complex backgrounds, and improving the ability to distinguish subtle behavior differences. Additionally, GSCW-YOLO incorporates a small-target detection layer and optimizes the Wise-IoU loss function, increasing its effectiveness in detecting distant small-target behaviors and transient abnormal behaviors in surveillance videos. Data for this study were collected via video surveillance under varying lighting conditions and evaluated on a self-constructed dataset comprising 9213 images. Experimental results demonstrated that the GSCW-YOLO model achieved a precision of 93.5%, a recall of 94.1%, and a mean Average Precision (mAP) of 97.5%, representing improvements of 3, 3.1, and 2 percentage points, respectively, compared to the YOLOv8n model. Furthermore, GSCW-YOLO is highly efficient, with a model size of just 5.9 MB and a frame per second (FPS) of 175. It outperforms popular models such as CenterNet, EfficientDet, and other YOLO-series networks, providing significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thus advancing the modernization of the dairy goat industry. Full article

In this systematic review, the PRISMA method was applied to examine publications from the last two decades that have investigated the noxious gaseous emissions from dairy barns. The aim was to analyse the outcomes from literature studies estimating the quantities of polluting gases produced in dairy barns, with a specific focus on ammonia (NH₃) emissions. Various studies, among those reviewed, have used mixed effects models, mass balance approaches and dispersion methods, revealing significant variability due to different experimental protocols and environmental contexts. Key challenges include the lack of standardised measurement techniques and the limited geographical coverage of research, particularly in climatically extreme regions. This review also explores proposed methods to reduce the associated effects through mitigation strategies. Estimation of NH₃ emissions is significantly influenced by the complex interactions between several factors; including animal management practices, such as controlling animal behavioural activities; manure management, like utilising practices for floor manure removal; the type of structure housing the animals, whether it is naturally or mechanically ventilated; and environmental conditions, such as the effects of temperature, wind speed, relative humidity, and ventilation rate on NH₃ release in the barn. These influential components have been considered by researchers and targeted mitigation strategies have been identified. Despite growing attention to the issue, gaps in the scientific literature were identified and discussed, particularly regarding the analysis of mitigation strategies and their long-term impacts (i.e., environmental, economic and productivity-wise). The purpose of this review is to help improve research into sustainable agricultural practices and technological innovations, which are fundamental to reducing NH₃ emissions and improving air quality in agricultural environments. Full article

The ambition of the Dutch Ministry of Agriculture is to stimulate the transition to circular agriculture. The objective of this paper is to develop and apply a farm level model toolbox for circular-agriculture policy assessment. Transition to circular agriculture affects farm management practices and outcome in the field of finance and economics, soil quality, use of finite resources, emissions, and biodiversity. Based on this, there is a need for an integrated assessment at farm level. Therefore, Bio Economic Farm Models should be at the core of the model toolbox. Model collaboration enables answering more complex questions and enlarges the scope of the analysis. Challenges of model collaboration are among others overlapping modules, different approaches (optimisation versus simulation), and existence of different networks of model developers and users. It is argued that a governance structure and networking will foster model collaboration. To stimulate transition to more circular agriculture practices and as a demonstration, the model toolbox was applied to assess the economic and environmental impacts of a tax on N from mineral fertiliser on a representative dairy and arable farm in a region in the Netherlands. It was found that a tax on N from mineral fertiliser has relatively large income effects, while the impacts on various environmental indicators are relatively limited. Full article

The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10⁻⁶ were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows. Full article

The aim of this study was to compare the impact of whole milk supplementation on gut microbiota and cardiometabolic biomarkers between lactose malabsorbers (LM) and absorbers (LA). We performed a pair-wise intervention study of 31 LM and 31 LA, 1:1 matched by age, sex, body mass index, and daily dairy intake. Subjects were required to add 250 mL/day whole milk for four weeks in their routine diet. At the beginning and the end of the intervention period, we collected data on gut microbiota and cardiometabolic biomarkers. Whole milk supplementation significantly increased Actinobacteria (P < 0.01), Bifidobacterium (P < 0.01), Anaerostipe (P < 0.01), and Blautia (P = 0.04), and decreased Megamonas (P = 0.04) in LM, but not LA. Microbial richness and diversity were not affected. The fecal levels of short-chain fatty acids (SCFAs) remained stable throughout the study. Body fat mass (P < 0.01) and body fat percentage (P < 0.01) reduced in both groups, but the changes did not differ between groups. No significant differences in other cardiometabolic markers were found between LM and LA. When compared with LA, whole milk supplementation could alter the intestinal microbiota composition in LM, without significant changes in fecal SCFAs and cardiometabolic biomarkers. Full article