Livestock Management Practices Research Articles

Contribution of Livestock Farming to Environmental Pollution in China

Purpose: The aim of the study was to examine contribution of livestock farming to environmental pollution in China. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that livestock farming in China significantly contributes to environmental pollution, posing substantial challenges to water, air, and soil quality. Intensive livestock operations, characterized by concentrated animal feeding operations (CAFOs), generate large quantities of animal waste containing nutrients and pathogens that contaminate water bodies. Additionally, livestock farming contributes to air pollution through emissions of ammonia and other pollutants, particularly in areas with intensive farming practices. Soil degradation and nutrient imbalance further exacerbate environmental degradation, with higher livestock densities associated with increased soil compaction and nutrient runoff. Unique Contribution to Theory, Practice and Policy: Tragedy of the Commons & Ecological Modernization Theory may be used to anchor future studies on contribution of livestock farming to environmental pollution in China. Promote the adoption of sustainable livestock management practices through capacity building, training, and extension services for farmers. Encourage the implementation of integrated farming systems that optimize resource use, minimize waste generation, and enhance environmental resilience. Strengthen regulatory frameworks and enforcement mechanisms to ensure compliance with environmental standards and promote responsible livestock production. Implement stringent measures to control water pollution from livestock waste, including mandatory wastewater treatment and monitoring programs.

Shepherding the past: High-resolution data on Neolithic Southern Iberian livestock management at Cueva de El Toro (Antequera, Málaga).

The feeding strategies of the first domesticated herds had to manage the risks arising from the novelty of livestock practices in territories often distant from the animals' primary habitats. The Iberian Peninsula is characterised by a great diversity of environments, which undoubtedly influenced these dynamics. At the beginning of the Neolithic period these led the possibility to combine diverse livestock farming practices based on different animal feeding habits. This variability is also consistent with the rythms of adoption of domesticated animals, being later on the northern area. In order to address this issue, this work focuses on the dietary regimes of early sheep herds from southern Iberia, an area for which information is currently scarce. This study utilises high-resolution radiocarbon dating and stable isotope data on teeth to investigate sheep husbandry management strategies in Cueva de El Toro (Antequera, Málaga). The radiocarbon dates on the analysed remains evidenced they were deposited at the site over a short period, supporting the recurrent use of the cave. The sequential analysis of oxygen and carbon isotopes in tooth enamel reveals distinct livestock management strategies, reproduction patterns, feeding habits, and mobility during this short period. This variability demonstrates that livestock management practices in the western Mediterranean are more diverse than previously considered. Furthermore, these findings support the hypothesis that early Neolithic communities in the southern Iberian Peninsula were able to adopt different feeding strategies within the same herd, depending on their ecological and productive needs.

IoT-based livestock tracking: Addressing challenges in Somali livestock farming

Livestock plays a vital role in Somalia's economy, contributing more than 60% of the country's gross domestic product. However, livestock production in Somalia faces many challenges, including conflict, insecurity, climate change and environmental degradation. These challenges can lead to livestock losses, which can significantly affect the livelihoods of livestock owners. This paper proposes an Internet of Things (IoT)-based livestock tracking system to help farmers locate their lost livestock. The system uses GPS and GSM/GPRS technology to track the location of livestock in real-time. The system also includes a boundary restriction feature that can be used to ensure that livestock remains within a designated area. The IoT-based livestock tracking system has the potential to address a number of challenges facing livestock production in Somalia. The system can help reduce livestock losses, improve livestock management practices, and increase productivity. The system is currently being field-tested in Somalia. The system successfully detects livestock crossing the border and transmits the livestock's location in real-time. Field test results show successful real-time tracking of livestock. The test data will be used to improve the system and assess its effectiveness in helping farmers locate their lost livestock.

Harnessing Tanzania's Rangelands to Mitigate Methane Emissions from Livestock Enteric Fermentation

Methane emissions from livestock being a major contributor to climate change as methane possesses more global warming potential than carbon dioxide, exacerbating the issue. Therefore, reducing methane emissions is crucial in addressing climate change and promoting sustainable agriculture. Tanzania is endowed with a large livestock population and vast rangelands. The resultant enteric methane emissions share in the agriculture sector was estimated to be 39.71% in 2014. This data suggests that interventions to lower methane emissions in agriculture sectors need to focus on livestock especially ruminants to provide significant reduction. The study found that normalisation of emissions can be facilitated by a set of measures, namely: a) increasing the energy content or quality of ruminant feed to reduce the intensity of methane production in the gut; b) addressing existing problems in pasture use, including deforestation, uncontrolled burning, overgrazing, water shortages and invasive species; c) improving feeding in combination with other livestock management practices to achieve the expected productive effect while reducing methane emissions. 

Risk Factors for Leptospirosis in Rural Communities of Bungoma County, Kenya: A Cross Sectional Survey

Leptospirosis is an important re-emerging zoonosis of worldwide public health concern. Leptospirosis is caused by a bacteria of the genus Leptospira. There was an outbreak of leptospirosis in humans in Bungoma County in 2004 with severe consequences. Leptospirosis is enzootic in cattle in Bungoma County. This study was conducted to determine the risk factors for leptospirosis in humans in Bungoma County. The risk factors considered in this study were; rainfall patterns, livestock management practices (grazing and watering system), home slaughter of livestock, consumption of un-inspected meat, rodent infestation in homes, and frequency of contact of livestock keepers with veterinary extension staff. A cross-sectional survey was carried out in Kimilili and Mt Elgon sub-counties of Bungoma County, Kenya. The study was conducted between April and July of 2017. The locations were then sampled, and 400 households selected using a systematic random sampling technique. Questionnaires were administered to heads of the identified households. Descriptive statistics and the chi-square test were used to analyze the data. Secondary data from medical records in Kimilili and Webuye health facilities were taken to show the disease trends in the year 2004. The study reveals the risk factors for leptospirosis in Bungoma County which include the close association of the community and their livestock (87.9%, n=400), rodent infestation in homes (92.3%, n=400), slaughter of animals in non-designated areas (83.8%, n=400), consumption of un-inspected meat (83.0%, n=400) and low contact of the population with veterinary extension staff (63.7%, n=400). The risk factors under consideration in this study were observed in over 80% of the study population. The study recommends that knowledge of leptospirosis and its risk factors be enhanced for effective control and prevention program.

Seroprevalence of reproductive and infectious diseases in cattle: the case of Madre de Dios in the Peruvian southeastern tropics.

The objective of this study was to determine the seroprevalence of reproductive and infectious diseases in tropical cattle in the Tambopata and Tahuamanu Provinces in the department of Madre de Dios, Peru. 156 bovines from 7 cattle farms were sampled. These farms used exclusive grazing for food and natural mating for reproduction and did not have sanitary or vaccination programs. The serum of blood samples was subjected to ELISA with commercial kits for the detection of antibodies against Neospora caninum, Mycobacterium avium subsp paratuberculosis (MAP), Leptospira interrogans, pestivirus bovine viral diarrhea virus-1, retrovirus bovine leukemia virus (BLV), orbivirus bluetongue virus (BTV), and herpesvirus bovine herpes virus-1 (BHV). The data were analyzed by means of association tests with χ2 (P < .05) and Spearman rank correlation (P < .05) in the SPSS v.15.0 software (IBM Corp). A low prevalence of antibodies to L interrogans, N caninum, M avium subsp paratuberculosis, bovine viral diarrhea virus-1 was found, but it was high to BTV, BLV, and BHV (100%, 53.85%, and 72.44%, respectively). The presence of BLV and BHV was higher in the Las Piedras District, bovines less than 5 years old, and cattle with breed characteristics of zebu and crossbred (P < .01). In addition, there was a significant correlation between both infections, showing 83.3% of BLV positivity that were also BHV positive (P < .01). The high prevalence of antibodies to BTV, BHV, and BLV could be due to livestock management practices, direct contact with infected animals, and variation of the presence of vectors and natural reservoirs in the context of climate change in the tropics.

Deep learning strategies with CReToNeXt-YOLOv5 for advanced pig face emotion detection.

This study underscores the paramount importance of facial expressions in pigs, serving as a sophisticated mode of communication to gauge their emotions, physical well-being, and intentions. Given the inherent challenges in deciphering such expressions due to pigs' rudimentary facial muscle structure, we introduced an avant-garde pig facial expression recognition model named CReToNeXt-YOLOv5. The proposed model encompasses several refinements tailored for heightened accuracy and adeptness in detection. Primarily, the transition from the CIOU to the EIOU loss function optimized the training dynamics, leading to precision-driven regression outcomes. Furthermore, the incorporation of the Coordinate Attention mechanism accentuated the model's sensitivity to intricate expression features. A significant innovation was the integration of the CReToNeXt module, fortifying the model's prowess in discerning nuanced expressions. Efficacy trials revealed that CReToNeXt-YOLOv5 clinched a mean average precision (mAP) of 89.4%, marking a substantial enhancement by 6.7% relative to the foundational YOLOv5. Crucially, this advancement holds profound implications for animal welfare monitoring and research, as our findings underscore the model's capacity to revolutionize the accuracy of pig facial expression recognition, paving the way for more humane and informed livestock management practices.

A comparative study on time series data-based artificial intelligence approaches for classifying cattle feeding behavior

&lt;span&gt;Cattle feeding behavior analysis is crucial for optimizing livestock management practices and ensuring animal well-being. This study presents a comparative analysis of three models: two machine learning algorithms including random forest and support vector machine (SVM), in addition to a deep learning convolutional neural networks (CNN) model, for classifying cattle feeding behaviors (eating, ruminating, and other) using time series data generated from a 3-axis accelerometer. The results of this study highlight the performance of these methods in accurately categorizing cattle feeding behaviors and demonstrate the importance of precise and efficient livestock monitoring and contributing to the improvement of animal well-being and enhancing the overall effectiveness of livestock operations.&lt;/span&gt;

Impact of growth hormone receptor gene on measurements of dimen-sions and body weights of local Iraqi sheep

This research involved a sample of 50 local Iraqi sheep, consisting of 27 males and 23 females, aged between 5 and 17 months. Data collection occurred during two distinct seasonal periods: October 2022 and January 2023. The primary objective was to investigate the variations in the expression of the growth hormone receptor (GHR) and its influence on the physical attributes of local Iraqi sheep within this age range. The results showed that the GHR gene exhibited a marked increase in expression during the second seasonal timeframe with levels of 23.8 ± 0.2  and 29.7 ±1.4 for first period. This increase in gene expression was paralleled by substantial differences in the sheep's body weights, with values of 33.7376±0.732(kg) for the first seasonal period and 35.77±1.111(kg) for the second seasonal period. Similarly, the dressing percentage showed significant differences, with values of 47.71±0.49 (kg) for the first period and 50.11±0.59 (kg) for the second period. Furthermore, carcass weight demonstrated highly statistically significant variations, measuring 16.037±0.440 (kg) during the first seasonal period and 17.978±0.655 (kg) during the second seasonal period. Gender-based analyses revealed significant disparities, with males exhibiting greater weight measurements (kg) (36.764±1.002) compared to females (32.122±806.0). The same trend was observed in carcass weight (18.362±0.606 for males and 15.242±0.369 for females), dressing percentage (49.8±0.0062 for males and 47.8±0.0047 for females), and viscera weight (1.382±0.029 for males and 1.258±0.032 for females). In addition to these findings, body dimension measurements (cm) showed significant differences in lump height, with measurements of 67.81±0.891 in the first period and 64.88±0.717 in the second period. However, non-significant differences were observed in other body dimension measurements. These findings have implications for understanding the genetic factors affecting the growth and development of sheep which can inform breeding and livestock management practices.

92 Implications of Processing and Handling on the Functionality of Animal Derived Products for Pet Foods

Abstract The pet food industry has been closely tied to the livestock, meat, and rendering industries by using materials left over from human food production. Rendered animal fats are an excellent source of dietary fat and provide enhanced palatability when used to enrobe dry pet food. Mechanically deboned meat, viscera, and rendered protein meals are often utilized as a primary source of highly digestible, well-balanced proteins and vitamins and minerals. Although these products can be easily transported and stored, they are susceptible to oxidative and microbial degradation. Antioxidants and antimicrobials aid in preserving the quality of these ingredients but are only part of the solution. How these ingredients are produced, stored, and handled can impact quality. It is important to remember that this raw material stream starts with livestock management practices. Specifically, changes in nutritional practices in the livestock industry can impact the characteristics of the products used for both human and pet foods, especially when considering poultry and pork products. Demands by consumers in the meat industry have resulted in the popularity of vegetarian diets. For example, decreased amounts of rendered animal protein meals in diets fed to chickens and turkeys have shifted towards more unsaturated fats, ultimately decreasing oxidative stability. Handling animal products throughout their supply chain can also affect product quality and potential usability. After harvest, raw material is often transported and processed by two or more different facilities before arriving at the pet food manufacturer. At the meat and rendering facilities, food safety hurdles along with processing aids can limit biogenic amine or free fatty acid formation. Once oxidation and microbial degradation occur, they cannot be reversed. Therefore, addressing food safety or oxidation control upstream in the supply chain is critical. Since each facility is unique in how co-products are processed and handled, understanding how different antioxidant blends and how and where these are applied all impact final product quality; this carries through to the quality and shelf life of the pet food diet these ingredients are intended for. With most pet foods targeting 18 months to 2 years of shelf life, starting with high-quality ingredients is key to achieving these goals. The human food, rendering, and pet food industries remain closely tied together. Trends within the meat and rendering industry result in the potential for these ingredients to be more susceptible to quality and functionality changes. The pet food industry must continue to monitor and evaluate changes in livestock production practices and product handling. Proactive management of these ingredients throughout the supply chain can ensure that these ingredients remain high-quality, cost-effective ingredients for pet food.

PSIII-12 The Effects of PGF2 and PGF2 Plus Gnrh on Estrous Behavior and Pregnancy Outcomes in Dairy Goats

Abstract Artificial insemination protocols depend on efficient behavioral estrus detection and insemination time in dairy goats. Here we examined the effects of two synchronization protocols, prostaglandin F2α (PGF2α; protocol 1) and PGF2 plus gonadotropin-releasing hormone (GnRH; protocol 2) on estrus behavior, body temperature, pulse frequency, pregnancy protein B (PPB) concentrations, and pregnancy rates in Alpine and Saanen goats. Forty does (3.9 ± 0.28 year) were divided into two groups (20 each) with 10 Alpine and 10 Saanen females in each group. In protocol 1 goats were injected with 10 mg/goat PGF2α on day 0 and 11. In protocol 2, each goat was injected with 10 mg PGF2α on day 0, and 43 µg GnRH on day 9 and then 10 mg PGF2 α on day 11. Estrus signs were detected 24 to 36 hours after the second dose of PGF2α. Body temperature and pulse frequency were recorded on days 11, 12, 13, and 14 after the second dose of PGF2α. Goats that showed estrus signs were naturally bred with the same breed mature buck. Blood samples were collected on days 28, 35, and 42 post-breeding (PB) to assess PPB concentrations, and ultrasonography was performed on day 50 (PB) to confirm pregnancy. The percentage of female showed estrous was higher in protocol 1 than in 2 (70% vs. 60%). Irrespective of the breed, rectal and vulval temperature was higher in the protocol 2 than 1 at day 11 after PGF2 α injection; however, it was the opposite at days 12, 13, and 14. However, the pulse frequency was greater (P &amp;lt; 0.05) in protocol 2 at all the time point. Pregnancy rates were 71.4% and 75.0% for protocols 1 and 2, respectively. Irrespective to the synchronization protocols, the pregnancy rates were greater (P &amp;lt; 0.05) in Alpine than Saanen goats. In conclusion, both PGF2 and PGF2-GnRH protocols were effective in synchronizing estrus in Alpine and Saanen goats, with minor differences in estrus response rates and pregnancy outcomes. Breed-specific variations were observed within the protocols, indicating a potential influence of breed on the efficacy of estrous synchronization. This study contributes valuable insights to goat breeding programs and promotes more efficient and sustainable livestock management practices.

304 Precision Weighing Technologies to Measure Real-Time Drinking Behavior, Body Mass, and Growth in Steers Managed Using Virtual Fencing Technology in Extensive Pastures

Abstract Attaining sustainability of livestock production and understanding environmental synergies requires in-depth knowledge of grazing animal growth and behavior. A pilot study (2021-22) was conducted at the South Dakota State University Cottonwood Field Station as part of a broader project to evaluate precision livestock technology and environmental synergies on native rangelands. Yearling Angus steers (n = 262) were fitted with virtual fencing collars (Vence), weighed on a traditional chute scale, and allocated to one of six native grass pastures equipped with individual weighing (SmartScale) scales at the water source. Each pasture was assigned either Rotational (RG) or Continuous (CG) grazing strategy and one of three stocking rates (Low, Medium, and High, 0.3, 0.42, and 0.7 AUMs, respectively) in a 2x3 factorial design. The data were downloaded, (Rcore Team, 2023), and spurious weights identified and removed using Robust Regression (hwts &amp;gt; 0.99, Parsons et al. 2023). Effects of stocking rate and grazing strategy were analyzed on water visits, time spent drinking, and growth using a linear mixed effects model to calculate effect sizes. Differences between BW and ADG calculated using the smart-scale vs. traditional chute weights were evaluated using a paired t-test. Steers visited the smart-scale 3.44 ± 2.79 visits per day, with significant effect of RG vs. CG strategies (3.65 ± 0.09 vs. 2.89 ± 0.10 visits per day respectively, P &amp;lt; 0.05). Total water visit duration averaged 5.04 ± 7.58 minutes per day, with most visits occurring between 0600 and 2000 hours and no observations occurring between 2000 and 0000 hours. RG managed steers exhibited significantly higher water visit duration (5.28 ± 0.308 vs 4.16 ± 0.317 minutes/day, P &amp;lt; 0.05) compared with CG managed steers. Smartscale measured weights were significantly heavier than chute weights at the beginning and end of the study period, (7.47 ± 24.54 and 9.77 ± 46.25 kg respectively, P &amp;lt; 0.05). No differences in overall ADG were found between smartscale and traditional chute weights(P &amp;gt; 0.58), however, significant temporal interactions occurred (P &amp;lt; 0.05), which demonstrates that ADG varied over the trial duration. This created significant discrepancies in predicted vs. actual stocking rates in heavy stocked pastures (0.84 AUM, P &amp;lt; 0.05), indicating pastures exceeded their target stocking rate. Overall, we found steers readily acclimated to smart-scale weighing systems, and creates a viable technology to monitor watering behavior, real-time body weight, and ADG in extensively managed cattle. Further, we showed RG vs CG grazing strategies significantly affect animal drinking behavior, while stocking rate resulted in inequalities between expected vs. actual assigned animal unit months. Precision livestock technologies offer a vital solution towards enhancing sustainable livestock management practices and improving nutrition and modeling in extensive rangeland systems.

APPLICATION OF DIGITAL TECHNOLOGIES IN THE FIELD OF ANIMAL HUSBANDRY

In the modern world, the use of digital technologies and automation in agriculture is becoming increasingly widespread. According to the United Nations, by 2050 the world's population may reach 9.7 billion, and in 2080 it will peak at 10.4 billion people, so the demand for food will grow significantly. One of the possible solutions to meet this demand is the development and active implementation of digital technologies in animal husbandry. Modern digital technologies are transforming the industry and providing new opportunities for farmers to increase efficiency and productivity while minimizing their environmental impact. These technologies are also used to optimize farmers' work. One of the industries that benefits most from automation is precision animal husbandry. Precision animal husbandry is agriculture that uses technology to monitor and manage livestock with efficiency and sustainability. Using artificial intelligence-based automation, farmers can collect data about their livestock, such as health and nutritional information, in real time. This data can then be used to make informed decisions about animal care and management. Thanks to digital technology, farmers can improve and optimize their livestock management practices. For example, the health and well-being of animals can be monitored using drones, cameras, and AI sensors that can alert humans to potential problems. In addition, artificial intelligence can be used to make decisions about breeding and feeding strategies by tracking the performance of individual animals. Digital technologies are also being introduced to improve the efficiency of the farm itself. The use of AI-enabled robots can automate certain tasks, such as milking and feeding, allowing farmers to focus on other activities. The use of resources such as water and energy can also be optimized with AI, which can help mitigate the environmental impact of agriculture. Finally, the use of robots on farms helps to improve product traceability. AI-driven sensors can monitor the condition and location (and even identify) of animals to ensure product quality and safety, and increase the sustainability of livestock production.

Ammonia and Particulate Matter Emissions at a Korean Commercial Pig Farm and Influencing Factors.

Quantifying emission factors of ammonia and particulate matter (PM) in livestock production systems is crucial for assessing and mitigating the environmental impact of animal production and for ensuring industry sustainability. This study aimed to determine emission factors of ammonia, total suspended particles (TSPs), PM10, and PM2.5 for piglets and growing-finishing pigs at a commercial pig farm in Korea. It also sought to identify factors influencing these emission factors. The research found that the emission factors measured were generally lower than those currently used in Korea, but were consistent with findings from individual research studies in the literature. Seasonal variations were observed, with ammonia emissions peaking in spring and autumn, and PM emissions rising in summer. Correlation analyses indicated that the number of animals and their average age correlated positively with both ammonia and PM emission factors. Ventilation rate was also positively correlated with PM emissions. Future extended field measurements across diverse pig farms will offer deeper insights into the emission factors of pig farms in Korea, guiding the development of sustainable livestock management practices.

Serological survey of immunoglobulin G from Toxoplasma gondii infection in dairy goats in East Java, Indonesia.

Toxoplasma gondii infection is a significant issue of veterinary public health because it is potentially transmitted through goat milk. Therefore, the use of control measures and routine monitoring of toxoplasmosis in dairy goats is necessary. Serological analysis using antibodies can detect T. gondii infection. This study aimed to conduct an epidemiological study of T. gondii in dairy goats using antibody detection and risk factor identification. This was a cross-sectional study. We performed a serological analysis of T. gondii infection in dairy goats to evaluate the prevalence of toxoplasmosis. Random sampling was performed, including 132 lactating dairy goats. Toxoplasma-modified agglutination test was used as a serological test for immunoglobulin G with a sensitivity of 98.55%, specificity of 86.21%, and accuracy of 94.9%. A structured questionnaire was used to collect risk factor data, which were analyzed using the Chi-square and Fisher's exact tests. The statistical package for the social sciences v. 21 was used for statistical analyses. The seroprevalence of T. gondii in Malang and Lumajang Regency was 100% and 90.7%, respectively. A significant difference in prevalence of T. gondii was observed between the two districts. Livestock management practices that significantly influenced T. gondii seroprevalence included water sources (p < 0.05; relative risk [RR] = 1.151; 95% confidence interval [CI]: 1.044-1.269). Farmers' characteristics that significantly influenced T. gondii seroprevalence included education (p < 0.05; RR = 1.125; 95% CI: 1.037-1.221), main occupation (p < 0.05; RR = 1.118; 95% CI: 1.035-1.207), and position in the organization of dairy goats farmers (p < 0.05; RR = 1.141; 95% CI: 1.022-1.274). In East Java, the prevalence of T. gondii in dairy goats is high. This study provides detailed information regarding risk factors associated with T. gondii seroprevalence in dairy goats in East Java, Indonesia.

Seeing Cattle like a State: Sedentist Assumptions of the Namibian Livestock Identification and Traceability System

Abstract Livestock identification and traceability systems (LITS) are an increasingly prominent component of national livestock development policies around the world. In theory, LITS allow governments to track and respond to disease and livestock theft efficiently. However, this paper argues that LITS are suffused with sedentist assumptions that are at odds with the livestock management practices of pastoralist communities. Drawing on qualitative interviews with implementing bureaucrats and affected pastoralist communities as well as one author's experience of growing up and managing cattle in a pastoralist community, we review the sedentist assumptions that animate the Namibian Livestock Identification and Traceability System (NamLITS) and describe how pastoralists in north-western Namibia perceive that NamLITS has affected their economic, social and political lives. We then show the strategies that pastoralists use to comply and circumvent NamLITS, and conclude with lessons for governments and development practitioners considering livestock tracing systems and mobile communities affected by them.

Molecular and serological prevalence of Coxiella burnetii in bovine dairy herds in southern Chile: A PCR and ELISA-based assessment of bulk tank milk samples

Coxiella burnetii (C. burnetii) is a highly resilient zoonotic bacterium responsible for Q fever, a disease which occurs worldwide, with the exception of New Zealand. However, in Chile, the prevalence and impact of C. burnetii in cattle herds remain poorly understood due to limited research. This study aimed to assess the presence of C. burnetii in dairy cattle herds in southern Chile, using two diagnostic methods on bulk tank milk samples. The results of the study revealed a high prevalence of C. burnetii infection in the analyzed herds. Of the 271 milk tank samples tested, 76% (208/271, CI: 71.1–81.5) tested positive using ELISA, while 73% (200/271, CI: 68.0–78.8) tested positive using qPCR. These findings indicate a significant presence of C. burnetii in the cattle herds studied. Despite the high prevalence observed, no new Q fever outbreaks have been reported in the study area. This discrepancy highlights the need for further research to better understand the transmission dynamics, environmental factors, and livestock management practices associated with C. burnetii infection. These studies will contribute to the development of effective prevention and control strategies and promote public health regarding Q fever.

Animal Vocal Recognition-Based Breeding Tracking and Disease Warning

&lt;p&gt;In this study, we investigate the application of a deep learning framework for the recognition of pig vocalizations. This innovative approach aims to actively monitor and evaluate the diverse states of pigs, with an overarching objective to improve the efficiency of pig farming through prompt identification and resolution of issues. In our comprehensive data collection effort, we carefully gathered a vast assortment of vocal samples from 50 pigs, representative of four distinct states: normal, frightened, coughing, and sneezing. We then meticulously analyzed this vocal data using Mel Frequency Cepstral Coefficients (MFCC). For accurate recognition of pig vocalizations, we devised a fusion model that combines the strengths of Residual Networks (ResNet) and Long Short-Term Memory Networks (LSTM). This model was subsequently tailored, trained, and optimized to meet our specific requirements. Upon rigorous evaluation, we found our model to exhibit exceptional performance in pig vocal recognition tasks, thereby reinforcing the potential of deep learning methodologies in revolutionizing the livestock industry. This research notably underscores the potential of deploying efficient real-time health monitoring systems, offering a promising avenue towards modernizing livestock management practices.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Adoption of improved management practices of livestock: Case of small-scale farmers in rural Bangladesh

This study examines the drivers of improved livestock management practices (ILMP) adoption in rural Bangladesh using data from the International Food Policy Research Institute's (IFPRI) Bangladesh Integrated Household Survey (BIHS). This study investigated four improved management practices: artificial insemination, concentrate feed, vaccination, and deworming. The binary logit and multivariate probit (MVP) models were used to analyse the data. According to the findings, approximately 65% of farmers practiced deworming, but only about 20% of farmers vaccinated their livestock. Logit regression analysis suggested that rural households with mobile phones and televisions were 5.2% and 3.8% more likely to adopt. Furthermore, compared to their peers, farmers who have maintained contact with livestock extension agents are 11% more likely to adopt. The MVP model indicated that likelihood of using concentrate feed increases with income, while artificial insemination is more prevalent among farmers who raise livestock for sale. Policy implication included the need for more extension agent-delivered awareness development programmes to educate livestock farmers on the benefits of ILMP. Scale-appropriate management practices can also play an important role. Farmers should be guaranteed of the availability of livestock feed and veterinary services at reasonable prices to promote adoption.

Epidemiological connectivity between humans and animals across an urban landscape.

Urbanization is predicted to be a key driver of disease emergence through human exposure to novel, animal-borne pathogens. However, while we suspect that urban landscapes are primed to expose people to novel animal-borne diseases, evidence for the mechanisms by which this occurs is lacking. To address this, we studied how bacterial genes are shared between wild animals, livestock, and humans (n = 1,428) across Nairobi, Kenya-one of the world's most rapidly developing cities. Applying a multilayer network framework, we show that low biodiversity (of both natural habitat and vertebrate wildlife communities), coupled with livestock management practices and more densely populated urban environments, promotes sharing of Escherichia coli-borne bacterial mobile genetic elements between animals and humans. These results provide empirical support for hypotheses linking resource provision, the biological simplification of urban landscapes, and human and livestock demography to urban dynamics of cross-species pathogen transmission at a landscape scale. Urban areas where high densities of people and livestock live in close association with synanthropes (species such as rodents that are more competent reservoirs for zoonotic pathogens) should be prioritized for disease surveillance and control.