Search Results (5,759)

Climate change models predict temperature increases, which may affect germination, an important stage in the recruitment of individuals in agroecosystems. Therefore, it is crucial to conduct research on how temperature will impact the germination of multipurpose native species. Leucaena diversifolia (Schltdl.) Benth. is native to America and is commonly cultivated around the world due to having a high protein content in seeds, and their trees are used in agrosilvopastoral systems because they fix nitrogen and provide shade and cattle feed. However, climate change affects the critical phases of its life cycle and influences its growth, reproduction, phenology, and distribution. To assess the germination performance of Leucaena diversifolia under different temperatures throughout thermal times, we estimated germination variables and determined cardinal temperatures and thermal time; we also analysed germination and potential distribution under two climate change scenarios. We found significant variations in seed germination (78–98%) and differences in cardinal temperatures (Tb = 5.17 and 7.6 °C, To = 29.42 and 29.54 °C, and Tc = 39.45 and 39.76 °C). On the other hand, the sub-optimal and supra-optimal temperature values showed little differences: 51.34 and 55.57 °Cd. The models used showed variations in germination time for the analysed scenarios and the potential distribution. We confirm that the populations and distribution of L. diversifolia will be altered due to climate changes, but the species retains the ability to germinate under warmer conditions. Full article

This study investigated species diversity and seasonal abundance of Stomoxyinae and tabanid flies, which are significant pests and vectors of animal pathogens, on a beef cattle and a buffalo farm in Nakhon Si Thammarat province, southern Thailand. During a one-year period from December 2020 to November 2021, flies were collected using Nzi traps from 6 a.m. to 6 p.m. over three consecutive days each month, resulting in the capture of 1912 biting flies, representing seven Stomoxyinae and nine tabanid species. The five most prevalent species were Tabanus megalops, Haematobia irritans exigua, Stomoxys calcitrans, Stomoxys indicus, and Stomoxys uruma. Fly density was notably higher on the beef cattle farm compared to the buffalo farm, with most species peaking during the rainy season, except for H. i. exigua, which was more abundant during the dry season. This study also examined the influence of temperature, relative humidity, and rainfall on fly density, revealing species-specific patterns. These findings offer updated insights into species diversity and seasonal trends, providing critical baseline data essential for the development of effective control strategies aimed at mitigating the impact of these flies on livestock health. Full article

Food production’s environmental, economic, and social challenges should be demystified through quantitative data. Therefore, the objective of this paper was to investigate the ecoregional sustainability of the Amazon biome from the perspective of the environmental life cycle, economic feasibility, and social life cycle analysis, emphasizing the pillars of sustainability in the production of three commodities: soybean, beef cattle, and Brazil nuts. Carbon footprint, net present value, and worker endpoint were the metrics evaluated. According to the results found in this study, the livestock presented greater environmental burdens in terms of carbon balance when compared to the production of Brazil nuts and soybean production with carbon balances in the order of 4.75 tCO₂eq/ha, −0.02 tCO₂eq/ha, and −1.20 tCO₂eq/ha, respectively. From an economic viewpoint, the extractive production of Brazil nuts presented the highest net profit per hectare/year (USD 559.21), followed by the agricultural system (USD 533.94) and livestock (USD 146.19). Finally, in relation to the social aspect of the production systems analyzed, the negative impacts linked to beef cattle production are related to the subcategories of forced labor and equal opportunities, and the positive impacts linked to soybean production are related to the subcategories of salary and benefits. The results highlight a genuine and sustainable balance in Brazil nuts extraction, presenting it as an investment for a sustainable future while demystifying the multifaceted information related to food production as a whole, in order to assist in decision-making and the formulation of public policies. Full article

This study examined the impact of organic manures from different sources (poultry, sheep, and cattle) on lemon balm (Melissa officinalis L., Lamiaceae) during different harvests. Manure application increased the photosynthetic pigments levels (chlorophyll-a, 9–41%; chlorophyll-b, 24–60%), biomass (41–60%), and essential oil yield (60–71%). Sheep manure treatment exhibited the highest antioxidant capacity among all the manures tested. Through GC-MS and GC-FID analysis, 10 chemical constituents were identified in the essential oil, accounting together for 91–95% of the total composition. The primary chemical component was geranial (39–46%), followed by neral (28–35%), (E)-caryophyllene (4.7–11%), geranyl acetate (2.7–5.9%), and caryophyllene oxide (1.7–4.8%). The utilization of livestock manures significantly improved the quality of the essential oil in terms of neral and geranial percentages compared to the control. Notably, during mid-August and early September, there was a substantial rise in these valuable compounds. However, a decrease in geranyl acetate and oxygenated monoterpenes resulted in a decline in the antioxidant capacity to 3%. Consequently, it is recommended to utilize essential oils from the second and third harvests for industrial purposes. Overall, the use of livestock manures, especially sheep manure, as a nutrient source for lemon balm cultivation proves to be a viable approach for producing high-quality essential oils. Full article

The adoption of genomic selection is prevalent across various plant and livestock species, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models present a promising avenue to enhance prediction accuracy due to their ability to accommodate both linear and non-linear relationships. In this study, we evaluated four machine learning models—Random Forest, Support Vector Machine, Convolutional Neural Networks, and Multi-Layer Perceptrons—for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), and compared them with other conventional models—GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A, and Bayes B. The results demonstrated that the GBLUP model achieved the highest prediction accuracy for both BW and YW, whereas the Random Forest model exhibited a superior prediction accuracy for WW. Furthermore, GBLUP outperformed the other models in terms of model fit, as evidenced by the lower mean square error values and regression coefficients of the corrected phenotypes on predicted values. Overall, the GBLUP model delivered a superior prediction accuracy and model fit compared to the machine learning models tested. Full article

Six hundred million cases of disease and roughly 420,000 deaths occur globally each year due to foodborne pathogens. Current methods to screen and identify pathogens in swine, poultry, and cattle products include immuno-based techniques (e.g., immunoassay integrated biosensors), molecular methods (e.g., DNA hybridization and PCR assays), and traditional culturing. These methods are often used in tandem to screen, quantify, and characterize samples, prolonging real-time comprehensive analysis. Next-generation sequencing (NGS) is a relatively new technology that combines DNA-sequencing chemistry and bioinformatics to generate and analyze large amounts of short- or long-read DNA sequences and whole genomes. The goal of this project was to evaluate the quantitative capabilities of the real-time NGS Oxford Nanopore Technologies’ MinION sequencer through a shotgun-based sequencing approach. This investigation explored the correlation between known amounts of the analyte (lambda DNA as a pathogenic bacterial surrogate) with data output, in both the presence and absence of a background matrix (Bos taurus DNA). A positive linear correlation was observed between the concentration of analyte and the amount of data produced, number of bases sequenced, and number of reads generated in both the presence and absence of a background matrix. In the presence of bovine DNA, the sequenced data were successfully mapped to the NCBI lambda reference genome. Furthermore, the workflow from pre-extracted DNA to target identification took less than 3 h, demonstrating the potential of long-read sequencing in food safety as a rapid method for screening, identification, and quantification. Full article

In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring. Full article

Background/Objectives: Vaccines may improve the control and eradication of bovine tuberculosis. However, the evaluation of experimental candidates requires the assessment of the protection, excretion, transmission and biosafety. A natural transmission trial among likely infected animals was conducted. Methods: Seventy-four male heifers were randomly distributed (five groups) and vaccinated subcutaneously with attenuated strains (M. bovis Δmce2 or M. bovis Δmce2-phoP), a recombinant M. bovis BCG Pasteur (BCGr) or M. bovis BCG Pasteur. Then, they cohoused with a naturally infected bTB cohort under field conditions exposed to the infection. Results: A 23% of transmission of wild-type strains was confirmed (non-vaccinated group). Strikingly, first vaccination did not induce immune response (caudal fold test and IFN-gamma release assay). However, after 74 days of exposure to bTB, animals were re-vaccinated. Although their sensitization increased throughout the trial, the vaccines did not confer significant protection, when compared to the non-vaccinated group, as demonstrated by pathology progression of lesions and confirmatory tools. Besides, the likelihood of acquiring the infection was similar in all groups compared to the non-vaccinated group (p > 0.076). Respiratory and digestive excretion of viable vaccine candidates was undetectable. To note, the group vaccinated with M. bovis Δmce2-phoP exhibited the highest proportion of animals without macroscopic lesions, compared to the one vaccinated with BCG, although this was not statistically supported. Conclusions: This highlights that further evaluation of these vaccines would not guarantee better protection. The limitations detected during the trial are discussed regarding the transmission rate of M. bovis wild-type, the imperfect test for studying sensitization, the need for a DIVA diagnosis and management conditions of the trials performed under routine husbandry conditions. Re-vaccination of likely infected bovines did not highlight a conclusive result, even suggesting a detrimental effect on those vaccinated with M. bovis BCG. Full article

Lumpy Skin Disease (LSD) poses a significant threat to agricultural economies, particularly in livestock-dependent countries like India, due to its high transmission rate leading to severe morbidity and mortality among cattle. This underscores the urgent need for early and accurate detection to effectively manage and mitigate outbreaks. Leveraging advancements in computer vision and artificial intelligence, our research develops an automated system for LSD detection in cattle using deep learning techniques. We utilized two publicly available datasets comprising images of healthy cattle and those with LSD, including additional images of cattle affected by other diseases to enhance specificity and ensure the model detects LSD specifically rather than general illness signs. Our methodology involved preprocessing the images, applying data augmentation, and balancing the datasets to improve model generalizability. We evaluated over ten pretrained deep learning models—Xception, VGG16, VGG19, ResNet152V2, InceptionV3, MobileNetV2, DenseNet201, NASNetMobile, NASNetLarge, and EfficientNetV2S—using transfer learning. The models were rigorously trained and tested under diverse conditions, with performance assessed using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. Notably, VGG16 and MobileNetV2 emerged as the most effective, achieving accuracies of 96.07% and 96.39%, sensitivities of 93.75% and 98.57%, and specificities of 97.14% and 94.59%, respectively. Our study critically highlights the strengths and limitations of each model, demonstrating that while high accuracy is achievable, sensitivity and specificity are crucial for clinical applicability. By meticulously detailing the performance characteristics and including images of cattle with other diseases, we ensured the robustness and reliability of the models. This comprehensive comparative analysis enriches our understanding of deep learning applications in veterinary diagnostics and makes a substantial contribution to the field of automated disease recognition in livestock farming. Our findings suggest that adopting such AI-driven diagnostic tools can enhance the early detection and control of LSD, ultimately benefiting animal health and the agricultural economy. Full article

The study aimed to evaluate interleukin-8 (IL-8) as a biomarker for udder inflammation in dairy cows and to explore its associations with various metabolic parameters indicative of systemic inflammation and metabolic dysregulation. Dairy cows (multiparous) were categorized into five somatic cell count (SCC) classes: Class I (<100,000 cells/mL; n = 45), Class II (100,000–200,000 cells/mL; n = 62), Class III (201,000–400,000 cells/mL; n = 52), Class IV (401,000–1,000,000 cells/mL; n = 73), and Class V (>1,000,000 cells/mL; n = 56). The study quantified IL-8 levels and analyzed their correlations with NEFAs (non-esterified fatty acids), BHBA (beta-hydroxybutyrate), GGTP (gamma-glutamyltransferase), and AspAT (aspartate aminotransferase). IL-8 concentrations demonstrated a significant and progressive increase across the SCC classes, establishing a strong positive correlation with SCC (p < 0.01). Additionally, IL-8 levels exhibited positive correlations with GGTP (p < 0.01) and AspAT (p < 0.01), indicating that elevated IL-8 is associated with increased hepatic enzyme activities and potential liver dysfunction. Furthermore, IL-8 showed significant positive correlations with NEFAs (p < 0.01) and BHBA (p < 0.05), linking higher IL-8 levels to metabolic disturbances such as ketosis and negative energy balance. Variations in metabolic parameters, including NEFAs, BHBA, GGTP, and AspAT, across the SCC classes underscored the association between elevated SCC levels and metabolic dysregulation in dairy cows. These findings highlight the interrelated nature of the inflammatory responses and metabolic disturbances in dairy cattle, emphasizing that an elevated SCC not only signifies udder inflammation but also correlates with systemic metabolic alterations indicative of ketosis and liver damage. Full article

Background: Mitochondria are considered the powerhouse of cells, and skeletal muscle cells are no exception. However, information regarding muscle mitochondria from different species is limited. Methods: Different muscles from cattle, pigs and chickens were analyzed for mitochondrial DNA (mtDNA), protein and oxygen consumption. Results: Bovine oxidative muscle mitochondria contain greater mtDNA (p < 0.05), protein (succinate dehydrogenase, SDHA, p < 0.01; citrate synthase, CS, p < 0.01; complex I, CI, p < 0.05), and oxygen consumption (p < 0.01) than their glycolytic counterpart. Likewise, porcine oxidative muscle contains greater mtDNA (p < 0.01), mitochondrial proteins (SDHA, p < 0.05; CS, p < 0.001; CI, p < 0.01) and oxidative phosphorylation capacity (OXPHOS, p < 0.05) in comparison to glycolytic muscle. However, avian oxidative skeletal muscle showed no differences in absolute mtDNA, SDHA, CI, complex II, lactate dehydrogenase, or glyceraldehyde 3 phosphate dehydrogenase compared to their glycolytic counterpart. Even so, avian mitochondria isolated from oxidative muscles had greater OXPHOS capacity (p < 0.05) than glycolytic muscle. Conclusions: These data show avian mitochondria function is independent of absolute mtDNA content and protein abundance, and argue that multiple levels of inquiry are warranted to determine the wholistic role of mitochondria in skeletal muscle. Full article

Background: Lameness in cattle negatively affects welfare and productivity. Early identification of lameness allows for prompt treatment, and mobility scoring allows for herd-level prevalence data to be monitored. The reliability of a four-point mobility scoring system was investigated when used by beef farmers and veterinary surgeons. Methods: An online questionnaire that contained forty video clips of beef cattle was created for mobility scoring performed by farmers and vets. Results: The Fleiss kappa coefficient for inter-observer agreement across all 81 respondents and all videos was 0.34, which showed fair agreement. Beef farmers generally had lower agreement than vets (0.29 vs. 0.38). Vets had significantly higher inter-observer reliability compared to beef farmers (p = 0.035). Overall, Cohen’s kappa coefficient for intra-observer agreement across all respondents varied from 0.085 (slight agreement) to 0.871 (almost perfect agreement). Limitations: The survey was only available online, which may have limited distribution and engagement. The recruitment of participants was not specific to differing levels of previous experience in mobility scoring. The mobility scoring was not performed in person, which could be more reflective of clinical application. Conclusions: The application of a four-point mobility scoring system for beef cattle had fair inter-observer reliability and a wide range of intra-observer reliability, but this is poorer than previously reported. This presents a challenge for the identification of lame beef cattle at both the individual and herd levels. Full article

Sustainable agricultural practices are needed to find a solution to the problem of soil erosion and decreased soil quality. A study was conducted during the 2021/2022 and 2022/2023 cropping seasons to evaluate the synergistic effect of the tillage system (TS) and manure rates (MR) on selected soil properties at the University of Limpopo Experimental Farm (Syferkuil) and University of Venda Experimental Farm (UNIVEN). The experiment had a split plot design with three replications. The main plots used conventional (CON) and in-field rainwater harvesting (IRWH) tillage systems, while subplots used poultry and cattle manure at rates of 0, 20, and 35 t ha⁻¹. Bulk density (BD), aggregate stability (AS), pH, total N, organic carbon (OC), available P, and exchangeable cations (Ca, Mg, and K) were determined. IRWH significantly increased AS in the 0–20 cm soil layer at Syferkuil. TS × MR interaction significantly influenced AS and total N in the 20–40 cm soil layer during the 2022/2023 season at Syferkuil. IRWH significantly increased Mg content in the 2021/2022 season and total N, OC, and Mg content in the 2022/2023 season at Syferkuil over CON. At UNIVEN, CON significantly increased total N, whereas IRWH increased available P in the 2022/2023 season. MR significantly increased AS, exchangeable Ca, Mg, and K at both sites. At Syferkuil, MR significantly increased total N, OC, and available P during both seasons, whereas at UNIVEN the significant increase was observed on OC and available P during both seasons and total N in the 2021/2022 season. It was found that IRWH and poultry manure (35 t ha⁻¹) improved most soil properties at both sites; however, this study recommends long-term experiments to investigate the combined effect of IRWH and manure rate on soil properties to validate the findings observed in this study. Full article

The purpose of this research was to validate a urine sensor (Lincoln University PEETER V2.0, Canterbury, New Zealand) that records the time and volume of urination events for dairy cows in addition to collecting a proportional urine sample from all urination events. Sixteen multiparous Holstein × Jersey mid-lactating cows (101 ± 5 days in milk, 498 ± 24.2 kg body weight, 26.2 ± 3.07 kg/d milk yield; mean ± standard deviation) were allocated herbage diets ranging in protein and sodium content to generate a range of urine volumes and urine nitrogen (UN) concentrations. Total collection of individual urination events occurred during a 72-h measurement period where PEETER V2.0 sensors were attached to cows. A mixed model ANOVA using lme4 package (version 1.1-35.5) in R (version 4.3.3) were used to compare the means. The average urine event size was 2.65 ± 1.1 L for total collection by observers and 2.68 ± 1.1 L as recorded by the sensor (mean ± standard deviation; p = 0.730). The urine nitrogen concentration was 5.76 ± 1.2 g N/L for samples collected by observers and 5.85 ± 1.3 g N/L for the samples collected by the sensor (p = 0.583). The calculated UN excretion was 156 ± 45.1 g/day for direct measurements and 162 ± 40.0 g/day for the sensor (p = 0.539. Contrasts with simultaneously measured data were undertaken using Lin’s Concordance Correlation Coefficient (CCC) and a Pearson correlation coefficient (r). Correlations between the actual values and sensor values were strong, with little to moderate variability in the urine volume (CCC = 0.936, r = 0.937; n = 222), UN concentration (CCC = 0.840, r = 0.837, n = 48) and total UN excretion (CCC = 0.827, r = 0.836, n = 24). Considering the findings, the PEETER V2.0 urine sensor has the potential to reliably measure urine volumes and UN concentrations for estimations of the UN excretion of dairy cattle under grazing systems. Full article

Helminth infections pose a significant global health challenge, as existing drugs often lack efficacy and may be contraindicated in some populations. Progress in the development of new drugs is hindered by the lack of innovative models for use in drug research. Metarhabditis blumi nematodes, which are associated with parasitic otitis in cattle, can severely affect the nervous system, leading to death. The treatment and control of this pathology face similar limitations to those for other parasitic diseases. Our study aimed to standardize M. blumi as a model for evaluating the anthelmintic activity of new drugs. Larvae (L3) and adult worms were treated with the reference drugs albendazole (16 µM) and ivermectin (2.5 µM) diluted in an NGM medium for 24 h, and various parameters were evaluated. Motility and mobility were analyzed using a video tracking and analysis program. Morphological and ultrastructural characterizations were performed after chemical fixation using light and scanning electron microscopy (SEM). The results showed that ivermectin was more effective than albendazole in treating M. blumi adults and L3. The SEM images revealed drug-induced ultrastructural changes. Compared to previous studies using the established Caenorhabditis elegans model, M. blumi demonstrated greater resistance to both albendazole and ivermectin. We conclude that M. blumi is a viable model for drug discovery assays and a valuable new experimental model for various biological studies, highlighting that, unlike C. elegans, M. blumi is associated with parasitism. Full article