ARTICLE | doi:10.20944/preprints202305.0687.v1
Subject: Business, Economics And Management, Economics Keywords: livestock industry; livestock products; livestock powerhouse; issues and challenges; policy recommendations
Online: 10 May 2023 (04:50:22 CEST)
Accelerating the construction of livestock powerhouses is of great significance to better enhance the ability to guarantee the supply of livestock products and improve the quality, efficiency and competitiveness of the livestock industry. This study constructed an evaluation index system to assess the level of China’s livestock powerhouse and then performed an in-depth analysis of the issues and challenges facing the construction of livestock powerhouses. The research results showed that the level of China’s livestock powerhouse ranked 5th in the world, and the livestock industry was transitioning from being a large livestock country to a powerhouse, while there was still a large gap in terms of reaching the goal of constructing livestock powerhouses. There were significant differences in the level of livestock powerhouses across different livestock industries; the layer industry was the world leader in China, whereas the pig, sheep and goat, and broiler industries were stronger, while the beef cattle and dairy industries were weaker. There are still many challenges to accelerating the construction of livestock powerhouses in terms of supply security, scientific and technological support, operation systems, industry and supply chain resilience, international trade, policy support, etc. It is recommended to improve the institutional mechanism for the construction of livestock powerhouses, promote a high level of self-reliance and self-improvement in livestock science and technology, build a modern livestock operation system, enhance the resilience and security level of the industrial and supply chain, and consolidate and expand international trade and cooperation.
REVIEW | doi:10.20944/preprints202207.0200.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: microRNAs; Precision livestock science; animal welfare; livestock health; biomarkers; biosensor; pandemics
Online: 13 July 2022 (13:12:32 CEST)
Early disease detection in livestock allows for target treatment decreasing antibiotics use and allow advancements in precision veterinary medicine. MicroRNA (miRNA) -driven signaling cascades play a crucial role in the context of farm animal disease diagnostics and prediction, and their proper understanding remains a challenge. In livestock farm animals, only a small number of miRNAs have been fully validated with respect to disease conditions and physiological or behavioral traits. Low abundance of miRNAs in blood and bodily fluids, along with a small number of nucleotides, makes detection and discrimination tedious and challenging task in. miRNAs usually are homologous, owing to which detection specificity becomes next to impossible when screening for multiple miRNAs in the same analyte sample. Hence, a concurrent, multiplexing, approach becomes crucial for the development of on-farm point-of-care based detection systems. Comprehensive screening methods demand broad dynamic range and enhanced specificity. For on-farm handheld platform development, the ability to screen for multiple varieties of miRNA is essential. In this review paper, I provide an overview of the recent developments of miRNA sensing and the current bottlenecks in the realization of the sensors for detecting miRNAS as target analyte for various livestock disease detection applications. Due to the nascent stages of this research, the possibilities of exploiting miRNAs as a biomarker opens up ways to move from reactive to predictive possibilities in diseases detection in the modern digital livestock farming.
REVIEW | doi:10.20944/preprints202307.0473.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: precision livestock farming; smart agriculture; digital livestock management; animal welfare technology; sustainable livestock production; dairy cow monitoring; IoT in agriculture; agricultural big data
Online: 7 July 2023 (10:17:00 CEST)
This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.
ARTICLE | doi:10.20944/preprints202307.1949.v1
Subject: Environmental And Earth Sciences, Waste Management And Disposal Keywords: livestock; pig manure; solid–liquid separations
Online: 28 July 2023 (12:34:58 CEST)
This study evaluated various solid-liquid separation (SLS) processes used in pig farms for wastewater and livestock manure. SLS is crucial for recycling and purifying pig manure. Seven SLS processes were tested on 11 farms: centrifuge, centrifuge with coagulation agent, belt press with coagulation agent, drum screen, inclined screen, vibration screen, and screw press. Samples collected included raw manure, separated liquid, and solid manure after SLS processing, analyzed for pH, EC, moisture, CODMn, BOD5, TN, TP, K, TS, SS, NaCl, and heavy metals. Belt press with coagulation agent showed highest TS and SS reduction (78.8% and 96.9%). For TN and TP removal, belt press and centrifuge with coagulation agents achieved 41.0% and 94.2%, respectively. Belt press with coagulation agent removed 59.4% BOD5 and 66.0% CODMn. Centrifuge with coagulation agent removed 100% Zn and 98.6% Cu. Drum screen, inclined screen, vibration screen, screw press, and centrifuge without coagulation showed lower removal efficiency for nutrients, solids, Zn, and Cu compared to centrifugal and belt press with coagulation. Centrifugal and belt press with coagulation showed higher efficiency in removing nutrients, solids, and metals. Further studies are needed to understand its impact on linked biological or chemical processes.
ARTICLE | doi:10.20944/preprints202112.0322.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Development; area; livestock; beef cattle; corporation
Online: 21 December 2021 (11:31:54 CET)
Livestock is an integral part of agriculture which significantly contributes to the economic and socio-economic development. Based on its potential in natural resources and human resources, East Kalimantan Province has opportunity to become a beef cattle development area. The development of a corporate-based beef cattle breeding area is an approach which taken toward industrial and business-oriented beef cattle breeding. The concept of breeder corporation will create new strengths such as strengths in human resources, capital, and banking in business development, which can more open the opportunities for the success and growth of the breeder's business. The development of a corporate-based beef cattle breeding area provides opportunities, including: 1) improving the competitiveness and added value of the region and beef cattle commodities in order to support national sustainable food security; 2) strengthening the livestock business system in one area management in a holistic manner; and 3) strengthening breeders institutions in accessing information, technology, public facilities and infrastructure, capital, processing and marketing, so that the concept is expected to be applied in East Kalimantan Province.
ARTICLE | doi:10.20944/preprints202103.0068.v1
Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: air purification; animal production; porcine reproductive and respiratory syndrome; livestock health,; livestock biosecurity; swine diseases; ultraviolet light
Online: 2 March 2021 (10:08:23 CET)
Proper treatment of infectious air could potentially mitigate the spread of airborne viruses such as porcine reproductive and respiratory syndrome virus (PRRSV). The objective of this research is to test the effectiveness of ultraviolet (UV) in inactivating aerosolized PRRSV, specifically, four UV lamps, UV-A (365 nm, both fluorescent and LED-based), "excimer" UV-C (222 nm), and germicidal UV-C (254 nm), were tested. The two UV-C lamps effectively irradiated fast-moving PRRSV aerosols with short treatment times (<2 s). One-stage and two-stage UV inactivation models estimated the UV doses needed for target percentage (%) reductions on PRRSV titer. UV-C (254 nm) dose needed for 3-log (99.9%) reduction was 0.521 and 0.0943 mJ/cm2, respectively, based on one-stage and two-stage models. An order of magnitude lower UV-C (222 nm) doses were needed for a 3-log reduction, i.e., 0.0882 and 0.048 mJ/cm2, based on one-stage and two-stage models, respectively. However, the cost of 222-nm excimer lamps is still economically prohibitive for scaling-up trials. The UV-A (365 nm) lamps could not reduce PRRSV titers for tested doses up to 4.11 mJ/cm2. Pilot-scale or farm-scale testing of UV-C on PRRSV aerosols simulating barn ventilation rates are recommended based on its effectiveness and reasonable costs comparable to HEPA filtration.
ARTICLE | doi:10.20944/preprints202304.0495.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: wolf; Canis lupus; livestock; sheep; depredation; Latvia
Online: 18 April 2023 (05:40:10 CEST)
In Latvia, livestock depredation by wolves has increased during the last two decades. Most attacks had occurred in summer and autumn within wolf hunting season. Cumulative numbers of wolf attacks and number of affected sheep per year at regional forest management units were analyzed in relation to estimated wolf density, extent of culling and proportion of juveniles, as well as sheep density and estimated number of wild prey. The response variables (cumulative number of attacks and cumulative number of affected sheep) were modelled by a negative binomial regression, testing effects of every covariate separately and building models from the significant covariates. Depredation level was related to sheep density and estimated wolf population size. No reducing effect was found for culling, and even greater depredation rate was expected at higher proportions of culled wolves. Estimated number of wild prey or proportion of juvenile wolves had an insignificant effect. However, greater numbers of affected sheep were expected at higher red deer density, suggesting increased opportunistic livestock depredation when the red deer may locally outcompete the preferred wolf prey – roe deer.
REVIEW | doi:10.20944/preprints202211.0209.v1
Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: Bovine Tuberculosis; Human, Interface, Livestock, Wildlife,Ethiopia
Online: 11 November 2022 (02:39:22 CET)
Bovine tuberculosis (BTB) is endemic in Ethiopian cattle. BTB is caused by Mycobacterium bovis (M. bovis) and has economic and public health significance. which has significant impact on the health of livestock and human. It has been significantly a cause for great economic loss in animal production. Associated risk factors contributed to the prevalence of the disease in cattle and its transmission. Moreover, the majority of cattle owners lack awareness about the disease and its public health significance. The presence of multiple hosts including wild animals, inefficient diagnostic techniques, absence of defined national controls and eradication programs could impede the control of bovine TB. Awareness rising about the disease, its transmission andzoonotic implication however, in Ethiopia Bovine Tuberculosis in Human-Livestock-Wildlife Interface is not well studied in the country and there were no studies concerning the burden of the disease between human ,animal and wild life which is of great importance for reduction and control measures. This paper aims to review the potential health and economic impact of bovine tuberculosis control in order to safeguard human and animal population in Ethiopia
REVIEW | doi:10.20944/preprints202105.0340.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: Digital Biomarkers; Digital Phenotyping; Wearables; Sensors; Livestock
Online: 14 May 2021 (14:08:40 CEST)
Currently, large volumes of data are being collected on farms using multimodal sensor technol-ogies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored ef-ficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly ad-dressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future re-search is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.
ARTICLE | doi:10.20944/preprints202005.0169.v1
Subject: Computer Science And Mathematics, Software Keywords: digital platforms; digital auction; livestock systems; Zimbabwe
Online: 10 May 2020 (14:51:38 CEST)
Livestock contribute towards household food security in rural communities through income generation and provision of animal-source food. However, livestock system are fragile for example, in Zimbabwe, communities face challenges such as fewer buyers, poor infrastructure, and information asymmetry when selling livestock. Emerging digital platforms promise opportunities to address these challenge but only anecdotal evidence exist. This paper uses data from Beitbridge to explore the potential of digital platforms to revitalise the livestock auction system. Study findings show that digital platforms are designed with affordances which can help overcome challenges within the livestock system. However, these digital platforms are also fraught with hidden complexities such as power dynamics. Thus, despite digital platforms’ affordances, their design inherently extends beyond technical functions. Therefore, there is an urgent need for discussions exploring the contrast between affordances and complexities to enable target users to make informed decisions on the adoption and use of digital platforms.
REVIEW | doi:10.20944/preprints202003.0151.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: efficacy; ethno-veterinary; livestock; medicine; smallholder farmer
Online: 9 March 2020 (10:03:51 CET)
Often touted as an ancient and sustainable practice among indigenous livestock farmers in developing countries, the use of ethno-veterinary medicine is examined within the context of its efficacy. While there are undoubtedly positive implications for adopting knowledge and practice that align with nature, there is both prevalence and ambivalence to the adoption of indigenous plant knowledge and resources for the treatment of livestock infections and diseases. This situation is due to the lack of validation and standardization of the practice in low-income countries, requiring scholarly efforts in developing this indigenous knowledge system.
ARTICLE | doi:10.20944/preprints202002.0192.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: bacteria; fungi; livestock; microbiome; next generation sequencing
Online: 14 February 2020 (10:32:54 CET)
Ruminal microorganisms play a pivotal role in cattle nutrition. The discovery of the main microbes responsible for enhancing the gain of weight in beef cattle might be used in therapeutic approaches to increase animal performance and cause less environmental damages. Here, we examined differences in bacterial and fungal composition of rumen samples of Braford heifers raised in a natural grassland from Pampa Biome in Brazil. We aimed to detect microbial patterns in the rumen that could be correlated with the gain of weight. 16S and ITS1 genes were amplified from ruminal samples and sequenced to identify the closest microbial relatives within the microbial communities. A predictive model based on microbes responsible for the gain of weight was build and further tested using the entire dataset. The model detected a set of microorganisms associated with animals in the high gain of weight group, including the bacterial taxa RFN20, Prevotella, Anaeroplasma and RF16 and the fungal taxa Aureobasidium, Cryptococcus, Sarocladium, Pleosporales and Tremellales. Most of these organisms have been correlated to the production of substances that improve the ruminal digestion process. These findings provide new insights about cattle nutrition and suggest the use of these microbes to improve beef cattle breeding.
ARTICLE | doi:10.20944/preprints201810.0104.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Bale highlands; livestock; methane emissions; mixed farming
Online: 5 October 2018 (15:39:30 CEST)
The study was conducted in the potential mixed farming areas of Bale highland to estimate livestock methane emissions. Using multi-stage purposive sampling, 156 households of the three wealth groups were selected based on their livelihood assets as described under methodology. Structured questionnaires, focus group discussions, key informants interview and field visits were the employed methods during the study. Feed nutrient balance was estimated based on the demand and supply while the livestock methane emissions were estimated according to the IPCC guidelines. Descriptive statistics and one-way ANOVA tests were used to analyze the data. Cattle were the dominant (84.25%) livestock owned by the households. The estimated enteric CH4 emission rate from mature cattle, growing cattle, sheep >1 year, sheep ≤ 1 year, horse and donkey were significantly (P<0.001) higher for the better wealth group while mature cattle (69.78%) shared the highest rate. Though, higher emission rates credited to the large number of animals in the area, cattle stay crucial to the livelihoods of the households, beside the major sources of CH4. In conclusion, the estimated CH4 emissions should be focus areas of interventions. Therefore, proper husbandry and quality feed supply and promotion of farm level livestock technologies should be practiced wisely to increase productivity and protect the environment from emissions of the livestock sector.
ARTICLE | doi:10.20944/preprints201809.0600.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: emission inventory; livestock; greenhouse gases; air pollutant
Online: 30 September 2018 (06:04:22 CEST)
Livestock farming is a major source of greenhouse gas and ammonia emissions. In this study, we estimate methane, nitrous oxide and ammonia emission from livestock sector in the Red River Delta region from 2000 to 2015 and projection to 2030 using IPCC 2006 methodologies with the integration of local emission factors and provincial statistic livestock database. Methane, nitrous oxide and ammonia emissions in 2030 are estimated at 132 kt, 8.3 kt and 34.2 kt, respectively. Total global warming potential is 9.7 MtCO2eq in 2030, accounts for 33% greenhouse gas emissions from livestock in Vietnam. Pig farming is responsible for half of both greenhouse gases and ammonia emissions in the studied region. Other major livestock for greenhouse gas emission is cattle and for ammonia emission is poultry. Hanoi contributes for the largest emissions in the region in 2015 but will be caught up and surpassed by other provinces in 2030.
REVIEW | doi:10.20944/preprints202304.0409.v2
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Animal welfare; Virtual environments; Metaverse; Augmented reality; Precision Livestock Farming; Ethical farming; Sustainability; Livestock; Environmental impact; Digital agriculture
Online: 26 May 2023 (08:22:52 CEST)
The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.
ARTICLE | doi:10.20944/preprints202112.0466.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: farm animal; pig; livestock production; global warming; climate change; economic risk assessment; economic impact; resilience; livestock farming; adaptation
Online: 29 December 2021 (12:23:22 CET)
Economic risks for livestock production are caused by volatile commodities and market conditions, but also by environmental drivers like increasing uncertainties due to weather anomalies and global warming. These risks impact the gross margin of farmers and can stimulated investment decisions. For confined pig and poultry production, farmers can reduce the environmental impact by implementing specific adaptation measures to reduce heat stress. A simulation model driven by meteorological data was used to calculate heat stress impact as a projection for 2030. For a business-as-usual livestock building, the indoor climate for several adaptation measures was calculated. The weather-related value-at risk quantified the economic risks caused by global warming and the stochastic component of the weather. The results show that only energy-saving adaptation measures to reduce the inlet air temperature are appropriate to reduce the economic risk to the level of the year 1980. The efficiency of other adaptation measures to reduce heat stress is distinctly lower. The results in this study can support the decision making of farmers concerning adaptation management and investments. It can inform agricultural policy design as well as technological development.
ARTICLE | doi:10.20944/preprints202305.0881.v1
Subject: Biology And Life Sciences, Life Sciences Keywords: Climate change; livestock farmers; rural livelihoods; climate education.
Online: 12 May 2023 (04:33:58 CEST)
Climate change mainly affects production and consumption systems, such as: food, livelihoods, production (e.g., reduced milk production), water, and land use. The role of local knowledge has been recognized as important for decision-making under changing circumstances. This study was conducted in the northern part of the Ecuadorian Andes using a sample of 170 dairy-cattle-producing households. The objectives were: i) to characterize the rural livelihoods of dairy cattle farmers, ii) to evaluate access to climate information and perceptions of climate change, and iii) to determine the relationship between livelihoods and perceptions of climate change. Significant differences were identified between the groups evaluated in relation to the dairy farmers’ livelihoods. In addition, 85.29% of the respondents mentioned that climate information is important, but 67.83% do not trust the sources of information. It was found that there is a significant relationship between the level of education and age with the variables of climate change perceptions. This combined knowledge allows people to promote agri-environmental and educational policies to achieve climate literacy at a rural level.
ARTICLE | doi:10.20944/preprints202211.0540.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: organic fertilizer; livestock waste; compost; charcoal; agronomic effectiveness
Online: 29 November 2022 (07:12:54 CET)
Abundant animal manure in livestock areas has the potential to be used as organic fertilizer which can restore soil fertility by turning it into compost and biochar. The goal of this study was to as-sess how well soil fertility and red chili yield might be increased by using biochar and poschar made from various animal wastes. In this investigation, a factorial pattern and randomized block design were used. The first factor was the biochar treatment type, which included no biochar, biochar made from cow manure, biochar made from goat manure, and biochar made from chicken manure. The second factor was the type of poschar, which included no poschar, poschar made from cow manure, poschar made from goat manure, and poschar made from chicken manure. The findings of this study suggest that using biochar in conjunction with poschar can significantly improve soil parameters such as soil water content, pH, EC, humic acid, fulvic acid, C, N, P, K, and CEC. Red chilies grow and yield more per hectare when different types of biochar and poschar are used. The use of biochar from cow manure together with poschar from chicken manure shows the best agronomic effectiveness.
REVIEW | doi:10.20944/preprints202012.0324.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Climate; Conservation agriculture; GHG emission; Livestock husbandry; Mitigation
Online: 14 December 2020 (11:01:49 CET)
This paper aimed to assess climate-smart agricultural practices in Ethiopia, discuss the contribution of climate-smart agricultural practices for mitigation of greenhouse gas emissions, and examine determinant factors of climate-smart agricultural practices in mitigation of greenhouse gas emissions. Conservation agriculture, integrated soil fertility management, agroforestry, crop diversification, and improved livestock feed and feeding practices are among the best climate-smart agricultural practices in Ethiopia. Combination of the adoption of climate-smart agricultural practices such as no-tillage increased crop diversity and retaining crop residue on-farm have a mitigation potential of increased SOC in non-flooded crops that change in a significant ton of CO2e ha-1 year-1. In addition, a mitigation potential of CH4 in reduced irrigation of paddy rice farms was also changed in ton CO2e ha-1 year-1. It was found that productivity enhancing interventions in the tropics could reduce emission intensity in dairy systems by up to 0.9 t CO2e per milk. Agroforestry practices and the addition of organic fertilizers on the farm increased mitigation potential of 784093 t CO2e and 193050 t CO2e biomass of carbon and SOC per year respectively. Adoptions of climate-smart agricultural practices are affected by different factors such as farming factors, technology inaccessibility, environmental factors, policy design and social expertise, negative attitudes and motivations of farmers, farmers’ socio-demographic factors, and farmers' socioeconomic factors. To reverse the situation, preparation of targeted climate-smart agricultural practices to areas that are likely to provide the greatest GHG reduction potential and demonstration of these practices to other areas should be encouraged so that other farmers will learn for similar agro-ecologies.
ARTICLE | doi:10.20944/preprints202008.0539.v1
Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Correlation; Disease; Extension; Information-Score; Livestock; Perceived-Usefulness
Online: 25 August 2020 (05:01:42 CEST)
Smallholder livestock farmers utilize their existing communication networks as information sources. This study explored these information sources, frequency of contacts and perceived usefulness of information from these sources. Using descriptive and correlation analysis, it categorized respondents according to total information score and explored the relationship between their scores and socio-economic characteristics. Findings show that 65% of farmers in the area scored a high reliance on fellow farmers and extension officials. Mass media sources such as radio and television scored low on perceived usefulness. The correlation coefficients for age (-0.228), farming experience (0.183), extension visits (0.002) and information contacts (0.214) were significant (p<0.05). Level of education (0.256), herd size (0.067) and perceived usefulness of information contacts (0.252) were also significant (p<0.01). Gender, household size, income, cooperative participation and access to financial services were not correlated to the total information scores of respondents. It was recommended that mass media sources in the area be supported by extension communication specialists to disseminate livestock-health related information.
REVIEW | doi:10.20944/preprints202302.0473.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: Precision livestock farming; Digital livestock farming; Smart farming; Societal impacts; Data ownership; Open access; Sustainability; Animal ethics; Digital divide; Digital agriculture
Online: 27 February 2023 (10:16:27 CET)
The emergence of precision and digital livestock farming presents an opportunity for sustainable animal farming practices that enhance animal welfare and health. However, this transformation of modern animal farming through digital technology has several implications for the technological, social, economic, and environmental aspects of farming. It is crucial to analyze the ethical considerations associated with the digitalization of modern animal farming, particularly in the context of human-animal relationships and potential objectification. This analysis can help develop frameworks for improving animal welfare and promoting sustainability in animal farming.One of the primary ethical concerns of digital livestock farming is the potential for a digital divide between farmers who have access to advanced technologies and those who do not. This could lead to a disparity in animal welfare and health outcomes for different groups of animals. Additionally, the use of artificial intelligence in digital livestock farming may lead to a loss of personal connection between farmers and animals, which could impact the animal's well-being. Another ethical concern of digital livestock farming is the potential for objectification of animals as mere data points. The use of sensors and other monitoring technologies can provide valuable data on animal health and behavior, but it is important to remember that animals are sentient beings with complex emotional and social needs. The use of digital technologies should not lead to neglect of animal welfare or a lack of human responsibility towards animals.Furthermore, social context becomes essential while integrating technologies in the livestock farming to overcome ethics. By considering the cultural and societal norms of different communities, we can ensure that the use of digital technologies does not undermine these values. To address these ethical challenges, the development of standards and codes of conduct for the adoption and implementation of digital livestock farming tools and platforms can help ensure that animal welfare and sustainability are prioritized. This can help alleviate the privacy concerns of stakeholders and improve sustainability in animal farming practices. Additionally, the use of virtual and augmented reality technologies can provide a way to enhance human-animal interactions and provide more personalized care to animals, further promoting animal welfare.
COMMUNICATION | doi:10.20944/preprints202309.1616.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: wastewater surveillance; livestock; zoonotic pathogens; one health; animal epidemiology
Online: 25 September 2023 (04:58:41 CEST)
Wastewater surveillance, initially conceived in the early 20th century during typhoid fever outbreaks, has evolved into a powerful tool for monitoring public health-relevant analytes. Recent applications in tracking SARS-CoV-2 infection highlight its potential. Beyond humans, it can be extended to livestock populations due to the increasing demand for livestock products. Livestock intensification poses risks of zoonotic disease emergence. Wastewater surveillance offers non-invasive, cost-effective means to detect potential outbreaks. This approach aligns with the "One Health" paradigm, emphasizing the interconnectedness of human, animal, and ecosystem health. By monitoring viruses in livestock wastewater, early detection, prevention, and control strategies can be employed, safeguarding both animal and human health, economic stability, and international trade. This integrated One Health approach enhances collaboration and a comprehensive understanding of disease dynamics, supporting proactive measures in the Anthropocene era where animal and human diseases are on the rise.
DATA DESCRIPTOR | doi:10.20944/preprints202305.0777.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Data synthesis; Livestock health; PPR disease; Machine Learning; Prediction
Online: 11 May 2023 (03:56:47 CEST)
Data scarcity is a significant challenge in the field of Machine Learning (ML), as data collection can be expensive, time-consuming, and difficult, particularly in developing countries. This challenge is exaggerated on the need to use dataset for livestock disease predictions for early intervention and surveillance. To address this challenge, this paper presents a data synthesis method that has been used to accurately generate new data samples from few real-world data. With much data available to train the ML models, overfitting is eliminated. We present the use of Generative Adversarial Networks mainly the Conditional Tabular Generative Adversarial Network to synthesize categorical data for training machine learning models for prediction of the Pestes des Petits Ruminants (PPR) disease. The results showed that training score became 0.89 and the cross-validation score was 0.87 after synthesized data was used with Random Forest algorithm. The resulting dataset can be used to support the prediction and surveillance of the Pestes des Petits Ruminants (PPR) disease. The proposed method can also be applied to any domain with categorical data, and has the potential to improve the performance of machine learning models with increased data availability.
ARTICLE | doi:10.20944/preprints202304.1104.v1
Subject: Biology And Life Sciences, Parasitology Keywords: Babesia spp.; Theileria spp.; molecular detection; phylogeny; Livestock; Bangladesh
Online: 28 April 2023 (04:19:13 CEST)
Piroplasmosis, caused by Babesia spp. and Theileria spp., poses significant constraints for livestock production and upgradation in Bangladesh. Besides examining blood smears, few molecular reports are available from some selected areas in the country. Therefore, the actual scenario of piroplasmosis in Bangladesh is deficient. This study aimed to screen the piroplasms in different livestock species by molecular tools. A total of 276 blood samples were collected from cattle, gayals (Bos frontalis) and goats in five geographies of Bangladesh. Thereafter, screening was conducted through a polymerase chain reaction, and species were confirmed by sequencing. The prevalence of Babesia bigemina, B. bovis, B. naoakii, B. ovis, Theileria annulata and T. orientalis was 49.28%, 0.72%, 1.09%, 32.26%, 6.52% and 46.01%, respectively. The highest prevalence (79/109; 72.48%) of co-infections was observed with B. bigemina and T. orientalis. The phylogenetic analyses revealed that the sequences of B. bigemina, B. bovis, B. naoakii, B. ovis and T. annulata were included in one clade in the respective phylogram. In contrast, the sequences of T. orientalis were separated into two clades, corresponding to Type 5 and 7. To the best of our knowledge, this is the first molecular report on piroplasms in gayals and goats in Bangladesh.
REVIEW | doi:10.20944/preprints202301.0391.v2
Subject: Engineering, Energy And Fuel Technology Keywords: agrivoltaics; photovoltaics; biogas; renewable energy; agriculture; livestock; horticulture; aquaculture
Online: 16 March 2023 (04:13:23 CET)
Agrivoltaics (Agri-PV, AV) – the joint use of land for the production of agricultural products and energy – has recently been rapidly gaining popularity, as it can significantly increase income per unit of land area. In a broad sense, AV systems can include converters of not only solar, but also energy from any other local renewable source, including bioenergy. Current approach to AV represents an evolutionary development of agroecology and integrated PV power supply to the grid. That results in nearly doubled income per unit area. While AV could provide a basis for revolution in large-scale unmanned precision agriculture and smart farming which is impossible without on-site power supply, chemical fertilisation and pesticides reduction, and yield processing on-site. These approaches could change the logistics and the added value production chain in agriculture dramatically, and so, reduce its carbon footprint. Utilisation of decommissioned solar panels in AV could make the technology twice cheaper and postpone the need for bulk PV recycling. Unlike the mainstream discourse on the topic, this review feature is in focusing on the possibilities for AV to be stronger integrated into agriculture that could also help in relevant legal collisions (considered as neither rather than both components) resolution.
REVIEW | doi:10.20944/preprints202007.0417.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: adaptation physiology; sensors; precision livestock farming; wearable animal sensors
Online: 19 July 2020 (18:27:52 CEST)
Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, colour, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.
REVIEW | doi:10.20944/preprints202007.0040.v1
Subject: Engineering, Control And Systems Engineering Keywords: Digitalization; sensor technology; block chain technology; data models; livestock
Online: 3 July 2020 (12:29:39 CEST)
As the global human population increases, animal agriculture must adapt to provide more animal products while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this review is to discuss the digitalization of animal farming with Precision Livestock Farming (PLF) technologies, specifically biosensors, big data, and block chain technology. Biosensors are noninvasive or invasive sensors that monitor an animal’s health and behavior in real time, allowing farmers to monitor individual animals and integrate this data for population-level analyses. The data from the sensors is processed using big data-processing techniques such as data modelling. These technologies use algorithms to sort through large, complex data sets to provide farmers with biologically relevant and usable data. Blockchain technology allows for traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. With these PLF technologies, animal agriculture can become more transparent and regain consumer trust. While the digitalization of animal farming has the potential to address a number of pressing concerns, these technologies are relatively new. The implementation of PLF technologies on farms will require increased collaboration between farmers, animal scientists, and engineers to ensure that technologies can be used in realistic, on-farm conditions. These technologies will call for data models that can sort through large amounts of data while accounting for specific variables and ensuring automation, accessibility, and accuracy of data. Issues with data privacy, security, and integration will need to be addressed before there can be multi-farm databases. Lastly, the usage of blockchain technology in animal agriculture is still in its infancy; blockchain technology has the potential to improve the traceability and transparency of animal products, but more research is needed to realize its full potential. The digitalization of animal farming can supply the necessary tools to provide sustainable animal products on a global scale.
ARTICLE | doi:10.20944/preprints202005.0159.v1
Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: awareness; livestock farmer; ICT-source; market information; rural; smallholder
Online: 9 May 2020 (08:46:42 CEST)
The utility of ICTs for providing market information to rural smallholder farmers is growing rapidly, and access to reliable information and sources is considered crucial for beneficial market interaction. This study explored critical factors contributing to usage of electronic sources for market information search among rural smallholder livestock farmers. Using data collected from 129 respondents through a non-random sampling technique; descriptive and regression analysis was applied to identify key factors responsible for their awareness and use of ICT-based market information sources. Level of education was found to be a driver of awareness of ICT-based sources, and use of these sources was influenced by farmer-specific characteristics such as household size, education, income, membership of cooperatives and herd-size. The key ICT tools used was radio and mobile phones, widely available in the study area. Identified constraints to use of these ICTs include cost and patchy network signals in some areas. Policy interventions to reduce cost of mobile phone services and expansion of base stations; including practical recommendations for improved programming in radio and television offerings, are considered indispensable for greater uptake of e-information sources among smallholder livestock farmers.
ARTICLE | doi:10.20944/preprints202307.1253.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Greenhouse gas; Paris Agreement; Social Cost of Carbon; Livestock; Beef
Online: 18 July 2023 (15:11:49 CEST)
Among all Brazilian economic sectors, the livestock sector stands out due to its large production and high export volume. However, beef production is associated to significant environmental impacts, such as deforestation and greenhouse gas (GHG) emission. The Paris Agreement was settled to avoid that global mean temperature rise up to 1.5 – 2º C by 2100. In 2020, Brazil committed in its nationally determined contribution (NDC) to reduce its GHG emission by 43% until 2030. This study aims to identify the association of beef production and beef cattle emissions, as well as assess predictive GHG emission scenarios for 2030 and value these emissions. To translate the environmental impacts of beef production into economic impacts, and thus amplify the discussion, we valued GHG emissions using the social cost of carbon (SCC). The results showed that the business as usual (BAU) GHG emission derived from beef production would range between 0.423 and 0.634 GtCO2e in 2030, whereas the maximum emission estimated to meet the NDC should be 0.257 GtCO2e. The SCC revealed the opportunity to reduce between US $18.8 and $42.6 billion in the cost of BAU emissions from beef production in 2030 if the NDC is met. Lastly, assessing a scenario where climate targets and beef exports are prioritized, between 2-10 kg of beef per capita would be available in the domestic market in 2030. Our results reveal the need and urgency of changes in livestock production to emit less GHG per kg of beef produced, and the avoided monetary cost of reducing emissions.
ARTICLE | doi:10.20944/preprints202307.0207.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: photosensitization; feed diversity; feed selection; biodiversity; phototoxic; feed quality; livestock
Online: 4 July 2023 (11:16:56 CEST)
As most prominent plant-associated disease, photosensitization in large herbivores provides a substantial data base to evaluate the conditions under which animals are concerned. The purpose of this meta study was to investigate whether the level of feed plant diversity and feed choice influence the incidence of photosensitization. Case reports from 1900 to 2022 served as database. 113 publications described 178 cases of altogether 12 animal species, most of them being farm animals. 73.6% of the cases originated from three continents: South America, Australia and North-America. Of the 40 phototoxic agents, herbs represented the majority (63.6%). Brachiaria, Froelichia and other four genus were associated in almost 50% of the cases. Usually, animals received feed both of normal quality and in fresh state. Secondary photosensitization was most frequent only when associated with poor feed quality. If the animals had had access to high-diversity feed instead of low-diversity feed, the incidence was 27.5% smaller. If the animals had the choice between various kind of feed, the incidence was even reduced by 56.1%. Horses could select the least, however, suffered mainly from primary photosensitization. We conclude that farmers may prevent photosensitization in husbandry animals by allowing both more feed choice and feed diversity.
ARTICLE | doi:10.20944/preprints202306.0767.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Data analytics; Cluster analysis; Disease mapping; Distance metrics; livestock Disease
Online: 12 June 2023 (05:10:55 CEST)
This study investigates how Electronic Livestock Health Recording Systems (ELHRs) facilitates the detection of disease burden and make cluster analysis by applying data analytics tools and techniques. A sample size of 18333 livestock disease cases reported from 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for data collection. The results showed that ELHRs are important as livestock disease data preservers, saving costs, and facilitating the extraction of up-to-date and complete information. Euclidean and Manhattan distance performed well at 98%, while cosine distance measurement metrics performed poorly. Finally, with the application of the selected clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect an optimal number of disease clusters and meet the objectives of the study.
ARTICLE | doi:10.20944/preprints202304.1031.v1
Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: deep learning; convolutional neural networks; livestock; pose estimation; animal behavior
Online: 27 April 2023 (04:19:46 CEST)
Automatic and real-time pose estimation is important in monitoring animal behavior, health and welfare. In this paper, we utilized pose estimation for monitoring farrowing process to prevent piglet mortality and preserve the health and welfare of sow. State-of-the-art Deep Learning (DL) methods have lately been used for animal pose estimation. The aim of this paper was to probe the generalization ability of five common DL networks (ResNet50, ResNet101, MobileNet, EfficientNet and DLCRNet) for sow and piglet pose estimation. These architectures predict body parts of several piglets and the sow directly from input video sequences. Real farrowing data from a commercial farm was used for training and validation of the proposed networks. The experimental results demonstrated that MobileNet was able to detect seven body parts of the sow with median test error of 0.61 pixels.
ARTICLE | doi:10.20944/preprints202110.0043.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: Grass; Greenhouse Gases; Ruminal Degradation; Secondary Metabolites; Tropical Livestock Systems
Online: 4 October 2021 (10:40:05 CEST)
Enteric methane (CH4) emitted by ruminant species is known as one of the main greenhouse gases produced by the agricultural sector. The objective of this study was to evaluate the chemical composition, in vitro gas production, dry matter degradation (DMD), digestibility, CO2 production and CH4 mitigation potential of five tropical tree species with novel forage potential including: Spondias mombin, Acacia pennatula, Parmentiera aculeata, Brosimum alicastrum and Bursera simaruba mixed at two levels of inclusion (15 and 30%) with a tropical grass (Pennisetum purpureum). Crude protein content was similar across treatments (135 g kg-1 DM), while P. purpureum was characterized by a high content of acid detergent fiber (335.9 g kg-1 DM) and B. simaruba by a high concentration of condensed tannins (20 g kg-1 DM). Likewise, A. pennatula and P. aculeata were characterized by a high content of cyanogenic glycosides and alkaloids respectively. Treatments SM30-PP70 (30% S. mombin + 70% P. purpureum) and BA30-PP70 (30% B. alicastrum + 70% P. purpureum) resulted in superior digestibility than P. purpureum, while in the AP30-PP70 (30% A. pennatula + 70% P. purpureum) was lower than the control treatment (P≤0.05). At 24 and 48 h, treatments that contained P. aculeata and B. alicastrum produced higher CH4 ml g-1 DOM than P. purpureum (P≤0.05). The inclusion of B. simaruba at 30% reduced CH4 at 25% compared to P. purpureum. Tropical tree species can improve the nutritional quality of ruminant rations and reduce CH4 emissions to consequently contribute to the development of sustainable ruminant production systems that generate diverse ecosystem services.
REVIEW | doi:10.20944/preprints202101.0620.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: Digital twin; Precision Livestock Farming; digitosome; Digital cohort; animal farming
Online: 29 January 2021 (12:48:12 CET)
Digital twin technology is already improving efficiencies and reducing costs across multiple industries and sectors. As the earliest adopters, space technology and manufacturing sectors have made the most sophisticated gains with automobile and natural resource extraction industries following close behind with recent investments in digital twin technology. The application of digital twins within the livestock farming sector is the next frontier. The possibilities that this technology may fuel are nearly endless as digital twins can be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. Currently, many pioneers of digital twins in livestock farming are already applying sophisticated AI technology to monitor both animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and smarter business decisions for the farmer. Mental and emotional states of animals can be monitored using recognition technology that examines facial features such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis by individual farms. Digital twin application will need to overcome challenges and accept limitations that arise. However, regardless of these issues, the potential of digital twins promises to revolutionize livestock farming in the future.
ARTICLE | doi:10.20944/preprints201705.0092.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: livestock; stover; lucerne; maize; bull; animals; dry matter; protein; cottonseed
Online: 11 May 2017 (05:04:11 CEST)
The experiment was conducted at livestock research and development station surezai Peshawar during March 2012 to study the effect of cotton seed cake, Lucerne hay supplementation on intake of maize stover and weight gain by male sahiwal bull. Twelve (12) young Sahiwal bull breed, 280 kg average liveweight and 2 years of age were randomly put into 4 groups of 3 animals under intensive feeding system to determine the effect of different protein supplements on growth, and intake of chopped, dried maize stover. A control group was fed stover adlibitum only, and the other groups were fed daily 750 g cottonseed cake/head, 1 kg lucerne hay or 900 g of lucerne/cottonseed cake (66:34; w/w). Significant differences were observed on average daily live weight gains. Animals on lucerne and its mixture registered higher daily gains (243 g) and (330 g) respectively, followed by cottonseed cake (156 g); the control group lost weight (-8.0 g/d). Contrary to the live weight gains, animals fed on lucerne and its mixture had lower maize stover intakes, 3.35 kg DM/animal/day and 3.70 kg DM respectively, while those on cottonseed cake and the control group ingested respectively 4.72 kg DM and 4.16 kg DM maize Stover. It is concluded that during the critical period in the suburb of Peshawar, small-scale farmers can prevent loss in live weight by utilizing simple available rations.
REVIEW | doi:10.20944/preprints202309.0433.v1
Subject: Biology And Life Sciences, Plant Sciences Keywords: nitrogen; livestock feeding; biochar; greenhouse gas; sustainability; agriculture; game changer; environment
Online: 7 September 2023 (03:24:34 CEST)
Agriculture (crop production, land use, and livestock) is the second most important greenhouse gas (GHG) emitting sector after the energy sector. Agriculture is also recognized as the source and sink of GHGs. Evidence demonstrates that the application of high amounts of nitrogen-rich fertilizers enhances methane (CH4) and nitrous oxide (N2O) emissions, which are potent GHGs with a high global warming potential (GWP). Considering its global contribution to the climate crisis, reducing GHG emissions in agriculture would considerably lower its share of the global GHG emission records, which may lead to enormous benefits for the environment and food production systems. Several diverging and controversial views questioning the actual role of plants in the current global GHG budget continue to nourish the debate globally. We must acknowledge that considering the beneficial roles of major GHGs to plants at a certain level of accumulation, implementing GHG mitigation measures from agriculture is indeed a complex task. This review seeks to provide key approaches for GHG mitigation in the literature (environmentally friendly crop cultivation and residue management practices, improvement of plants nutrients/fertilizer use efficiency, exploring the genetic diversity for low GHG emission, soil methane-producing bacteria, integrated soil fertility management, improved livestock feeding efficiency, and production, etc.). This work gathers key approaches from 275 peer-reviewed publications, including experimental research papers, review articles, and books, discussing greenhouse gas emissions mechanisms and mitigation to unravel effective strategies for GHG mitigation, proven to be effective or carry the potential to mitigate GHG generation from agriculture. This review also discusses in depth the significance and the dynamics of strategies regarded as game changers with a high potential to enhance, in a sustainable manner, the resilience of agricultural systems. Agricultural GHG mitigation approaches discussed in this work can serve as game changers in global efforts to reducing GHG emissions and alleviating the impact of climate change through sustainable agriculture and informed-decision making.
ARTICLE | doi:10.20944/preprints202307.1932.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: precision livestock farming (PLF); image recognition model; feeding and fecal health
Online: 28 July 2023 (02:36:29 CEST)
This study proposes an image recognition method to assist swine farm managers in collecting health data related to pig feeding and excretion. Analyzing the correlation between sow health data and indoor air quality in the pigsties revealed significant influences of air quality indicators on sow health. Increased levels of TVOC, CO2, and temperature were found to negatively affect feeding health, while increased temperature, humidity, and PM10 were found to negatively impact excretion health. These findings provide a basis for evaluating variables when constructing future sow disease prediction models. Analyzing the correlation between pig health status and air quality can help swine farm managers improve sow production environments, reduce disease risks, enhance production efficiency, and provide valuable insights for future research on disease prediction models.
ARTICLE | doi:10.20944/preprints202302.0381.v1
Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: Rift Valley Fever; drones; Rwanda; livestock vaccine supply chain; zoonotic disease
Online: 22 February 2023 (09:01:39 CET)
Given the recent emergence of Rift Valley Fever (RVF) in Rwanda and its profound impact on livelihoods and health, improving RVF prevention and control strategies is crucial. Vaccinating livestock is one of the most sustainable strategies to mitigate the impact of RVF on health and livelihoods, yet vaccine supply chain constraints severely limit the effectiveness of vaccination programs. In the human health sector, unmanned aerial vehicles, i.e., drones, are increasingly being used to improve supply chains and last-mile vaccine delivery. We investigated perceptions on whether delivering RVF vaccines by drone in Rwanda might help to overcome logistical constraints in the vaccine supply chain. We conducted semi-structured interviews with stakeholders in the animal health sector and Zipline employees in Nyagatare District in the Eastern Province of Rwanda and used content analysis to identify key themes. We found that stakeholders in the animal health sector and Zipline employees believe that drones could improve RVF vaccination in Nyagatare. The main benefits study participants identified included decreased transportation time, improved cold chain maintenance, and cost savings.
REVIEW | doi:10.20944/preprints202302.0256.v1
Subject: Engineering, Energy And Fuel Technology Keywords: Thermochemical conversion; biological conversion; human excreta; waste-to-energy; livestock manure
Online: 15 February 2023 (08:45:16 CET)
Human and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods.
ARTICLE | doi:10.20944/preprints202012.0636.v1
Subject: Engineering, Civil Engineering Keywords: livestock manure; waste management; air pollution; air quality; biocoal; odor emission
Online: 24 December 2020 (15:14:59 CET)
The rural communities are affected by gaseous emissions from intensive livestock production. Practical mitigation technologies are needed to minimize emissions from stored manure and improve air quality inside barns. In our previous research, the one-time surficial application of biochar to swine manure significantly reduced emissions of NH3 and phenol. We observed that the mitigation effect decreased with time during the 30-day trials. In this research, we hypothe-sized that bi-weekly reapplication of biochar could improve the mitigation effect on a wider range of odorous compounds using larger scale and longer trials. The objective was to evaluate the effectiveness of biochar dose and reapplication on mitigation of targeted gases (NH3, odor-ous VOCs, odor, GHGs) from stored swine manure on a pilot-scale setup over 8-weeks. The bi-weekly reapplication of the lower biochar dose (2 kg/m2) showed much higher significant percent reductions of emissions for NH3 (33% without & 53% with reapplication) and skatole (42% without & 80% with reapplication), respectively. In addition, the reapplication resulted in the emergence of statistical significance to the mitigation effect for all other targeted VOCs. Spe-cifically, for indole, the % reduction improved from 38% (p=0.47, without reapplication) to 78% (p=0.018, with reapplication). For phenol, the % reduction improved from 28% (p=0.71, without reapplication) to 89% (p=0.005, with reapplication). For p-cresol, the % reduction improved from 31% (p=0.86, without reapplication) to 74% (p=0.028, with reapplication). For 4-ethyl phenol, the percent emissions reduction improved from 66% (p=0.44, without reapplication) to 87% (p=0.007, with reapplication). The one-time 2 kg/m2 and 4 kg/m2 treatments showed similar effectiveness in mitigating all targeted gases, and no statistical difference was found between the dosages. The one-time treatments showed significant % reductions of 33% & 42% and 25% & 48% for NH3 and skatole, respectively. The practical significance is that the higher (one-time) biochar dose may not necessarily result in improved performance over the 8-week manure storage, but the bi-weekly reapplication showed significant improvement in mitigating NH3 and odorous VOCs. The lower dosages and the frequency of reapplication on the larger-scale should be explored to optimize biochar treatment and bring it closer to on-farm trials.
REVIEW | doi:10.20944/preprints202309.0214.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Human-Centric AI in Livestock Farming; Sensor Technologies in Animal Welfare; Digital Livestock Farming; Objective Animal Welfare Indicators; AI-Driven Animal Health Monitoring; Farmer-Centric Technology Adoption
Online: 5 September 2023 (09:25:06 CEST)
In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.
ARTICLE | doi:10.20944/preprints202304.0031.v1
Subject: Business, Economics And Management, Economics Keywords: self-sufficiency degree; planetary health diet; land consumption; food sovereignity; livestock; consumption
Online: 4 April 2023 (02:22:26 CEST)
The way people in many countries eat today is disconnected to the resources and land locally available. In Europe, for instance, too much meat is eaten, but often cannot be fed by local resources. The percentage of non-local and non-seasonal food is tremendous, exploiting other regions and their water reservoirs. Current diets harm eco systems and people’s health. (Re-)regionalising food systems and aligning diets to planetary boundaries could be one way to reconnect people to the food they eat. Before demanding the (re-)regionalisation of food, it should be analysed whether current consumption patterns can be met at all with the regionally available agricultural land. We looked at the region Hesse in Central Germany, calculated and compared land consumption of current diets with the consumption as recommended by the Planetary Health Diet. Our focus is on livestock because land consumption to produce meat, dairy and eggs is relatively high. Our results show that the region is far from being able to feed the current livestock population, that it does not have the land to support the livestock needed to meet current consumption patterns, but that it could support a smaller livestock population according to the Planetary Health Diet, especially if farmers adopt crop rotation systems and extensive husbandry.
REVIEW | doi:10.20944/preprints202211.0058.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: Digital Agriculture; Precision Livestock Farming; Smart Farming; Artificial Intelligence; Sensors; Big Data
Online: 2 November 2022 (11:11:06 CET)
Sensor enabled big data and Artificial Intelligence platforms has the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand while maximizing the efficiency of production systems on the other. To stimulate a sustainable, digital, and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and develop new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI) based data acquisition and processing platform, the unmet current needs can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. There is a need for a multimodal, digitized, automated biosensor and health monitoring platform containing AI algorithms for optimized stress and disease prediction in farm animals. An experimental development of non-invasive wearable and wireless physiological sensor networks that can be deployed in agricultural environments would allow for real-time extraction and integration of physiological data and display on decision support dashboards for livestock workers. By testing the effectiveness of the system in generating meaningful longitudinal data for further research into physiological and behavioral characteristics of farm animals, novel insights about animal welfare can be developed. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to the significant innovation potential. Continuous monitoring of animal health and physiological functioning promotes more environmentally and socially acceptable developmental pathways. Once demonstrated, the portable animal health monitoring platform will close a critical gap in digitized livestock farming and position the agricultural industry as a frontrunner in the sector of farm animal monitoring and measurement systems. The multimodal wearable sensor-driven AI approach is expected to significantly improve the effectiveness of livestock decision support systems and the selection of resilient animals for the next generation and provide predictive data for end-users in livestock farming.
REVIEW | doi:10.20944/preprints202105.0364.v1
Subject: Engineering, Automotive Engineering Keywords: Poultry behaviour; target tracking; deep learning; precision livestock farming; poultry production systems.
Online: 16 May 2021 (22:43:58 CEST)
The world's growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animal on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of Artificial Intelligence (AI) assisted technology individualized and per-herd assessments of livestock are possible and accurate. Various studies have shown cameras linked with specialized systems of AI can properly analyze flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on the recent advancements, this review explores the aspects of AI in the detection, counting and tracking of the poultry in commercial and research-based applications.
ARTICLE | doi:10.20944/preprints201901.0111.v1
Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: Zoonoses, food-borne, disease control, public health, domestic livestock, pigs, One health
Online: 11 January 2019 (10:59:03 CET)
Non-typhoid salmonellosis is a common and problematic foodborne zoonotic disease in which pork and pork products can be an important potential source of infection. In order to prevent this disease important efforts to monitor the situation in the main source, livestock, are conducted in most developed countries. In the European Union EFSA and ECDC compile information at the member state level, even though important differences in production systems and surveillance systems exist. Here, Salmonella surveillance systems in one of the main sources of foodborne salmonellosis, swine, and humans in Spain were reviewed to identify potential gaps and discuss potential ways of integration under a One Health approach. Despite the extensive information generated through the surveillance activities source attribution can be only routinely performed through ad-hoc outbreak investigations, and national reports on human outbreaks do not provide sufficiently detailed information to gain a better understanding of the epidemiology of the pathogen. Human and animal monitoring of Salmonella would benefit from a better exchange of information and collaboration. Analysis of spatio-temporal trends in livestock and humans could help to identify likely sources of infection and to target surveillance efforts in areas with higher prevalence or where specific strains are found.
ARTICLE | doi:10.20944/preprints201810.0004.v1
Subject: Biology And Life Sciences, Horticulture Keywords: livestock care management; rotational/continuous grazing; technical advice; stocking rate; functional units
Online: 1 October 2018 (11:24:38 CEST)
The livestock sector can be a major contributor to the mitigation of greenhouse (GHG) emissions. Within the sector, beef production produces the largest proportion of the livestock sector’s direct emissions. The objective of this study was to assess the on-farm GHG emissions in semi-arid rangelands in Argentina and to identify the relationships between emissions and current farm management practices. A survey recorded detailed information on farm management and characteristics. Assessments of GHG emissions were based on the IPCC Tier 2 protocols . The relationships between farm management and GHG emissions were identified using General Linear Models. Cluster analysis was used to identify groups of farms that differed from others in emissions and farm characteristics. Emissions per product sold were low on farms that had improved livestock care management, rotational grazing, received technical advice, and had high animal and land productivities. Emissions per hectare of farmland were low on farms that had low stocking rates, low number of grazing paddocks, little or no land dedicated to improved pastures and forage crops, and low land productivity. Our results suggest that the implementation of realistic, relatively easy-to-adopt farming management practices has considerable potential for mitigating GHG emissions in semi-arid rangelands of central Argentina.
COMMUNICATION | doi:10.20944/preprints202308.1107.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Amazon region; livestock basin; physicochemical parameters; raw milk; sanitary parameters; somatic cell count
Online: 15 August 2023 (10:00:43 CEST)
The objective of this research was to evaluate the physicochemical and sanitary parameters of raw cow's milk intended for consumption in the Amazonas region, Peru. We sampled 31 collection centers and dairy processors. We evaluated physicochemical parameters (fat, protein, solids not fat, lactose, ash, pH, and density), somatic cell count, and microbiology (mesophilic aerobic bacteria and total coliforms) of raw milk. We applied an analysis of variance (α=0.05) and the comparison of means between the collection centers and livestock basins (p<0.05). In addition, we performed a cluster analysis, principal components and correlation. Depending on the collection center, we found differences in all the physicochemical parameters, somatic cell count and milk microbiology. In the livestock basin analysis, we found differences for somatic cells, fat, protein, solids, pH, coliforms and bacteria. The Jumbilla basin presented less somatic cells, less fat was found in the Levanto basin and higher protein in the Yambrasbamba basin. Lactose, ash, density, total coliforms and mesophilic aerobic bacteria did not vary according to basin. The cluster analysis yielded four groups, Group 1 is higher in ash and density, Group 2 and 3 are higher in fat, Group 3 with higher protein, but higher in total coliforms and mesophilic aerobic bacteria. Group 4 registered the highest pH and the lowest protein concentration. There was a positive correlation of fat with protein and solids not fat, lactose with solids not fat, ash with density. Negative correlations of somatic cells with fat and, protein and solids not fat with pH.
ARTICLE | doi:10.20944/preprints202307.0547.v1
Subject: Public Health And Healthcare, Health Policy And Services Keywords: antibiotics; antimicrobial resistance; knowledge; attitude; practice; animal health service provider; livestock farmer; Kenya.
Online: 10 July 2023 (08:35:53 CEST)
Antimicrobial resistance (AMR) remains a challenge in Kenya, while the extent remains unknown. To assess the knowledge, cultural beliefs, practices, and behavioral patterns among multisectoral stakeholders in Kenya. The cross-sectional survey was conducted in August 2021 among farmers, animal health service providers and AMR researchers. Regional digital data collection tool developed by FAO was shared and responses obtained through mail, phone calls and direct interviews. Descriptive and inferential analysis were conducted. Antimicrobials were mostly sourced from agro-veterinary shops and from veterinary professionals. Farmers, often implement self-treatment and reported overuse, unnecessary use, and sometimes fail to complete the dosage in livestock. More farmers reported to have heard about antibiotics as compared to antimicrobials, mostly from friends and radio program, however only 9.2% could correctly differentiate the two. Animal Health Service Providers (AHSP) were the source of information to farmers regarding AMR. AHSPs mainly relied on suppliers and distributors for information about antibiotics. Both farmers and AHSPs treated viral infections with antibiotics. One Health Partners (OHPs) had higher knowledge and largely favorable attitudes towards AMR. Up to 72.7% of OHPs from training institutions had AMR included in training curriculum however, they were optimistic livestock farmers and government were less concerned about AMR. Gaps in knowledge and practice on Antimicrobial Stewardship (AMS) were observed in all categories of stakeholders. Given the documented knowledge-practice gap, innovative solutions are needed for both AHSPs and farmers to promote good antimicrobial stewardship practices and to mitigate burdens of AMR. Outcomes of this research should deepen the understanding of critical information and trigger behavioral change in usage and stewardship of antimicrobials.
ARTICLE | doi:10.20944/preprints202306.1669.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Livestock monitoring; Open source UAV; Depth sorting; Kalman filter; Optical flow; Visual servo
Online: 23 June 2023 (11:53:25 CEST)
It is a challenging and meaningful task to carry out drone-based livestock monitoring in high-altitude and cold regions. The purpose of AI is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and DeepSORT algorithms for target detection and tracking, respectively. To address the existing problems of the DeepSORT algorithm, the following two optimizations are made: (1) Optical flow is used to compensate the Kalman filter for improvement of the prediction accuracy; (2) A low-confidence trajectory filtering method is adopted to reduce the influence of unreliable detection on target tracking. In addition, an visual servo controller for the UAV is designed to enable the automated tracking task. Finally, the system is tested using the Tibetan yaks living in the Tibetan Plateau as the tracking targets, and the results reveal the real-time multiple tracking ability and the ideal visual servo effect of the proposed system.
REVIEW | doi:10.20944/preprints202107.0368.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Precision Livestock Farming; Sensors; Animal Ethics; Animal Welfare; Society; Sustainability; Human-animal relationships
Online: 16 July 2021 (11:27:24 CEST)
The demand for animal products is expected to continue to rise, which requires the development of efficient livestock farming systems. Environmental, societal and economic concerns regarding this industry are however accumulating, addressing the large resource demand, pollutants and greenhouse gas emissions and health concerns that the livestock industry is responsible for. Precision livestock farming systems allow the continuous automatic monitoring of various physiological, behavioural and phenotypic parameters of animals in order to increase productivity and animal welfare while controlling and minimizing the environmental impact. There is a high potential for digital farming to be the solution for responsibly and ethically feeding the growing and urbanizing population. However, many problems and concerns are still present in this developing industry and remain relatively unaddressed, starting with the ethical aspects in regard to the animal, including its objectification, human-animal relationships and welfare and ending with the societal implications of this digitalization. Concrete frameworks, inter-disciplinary studies and global legislation need to be put in place in order to ensure the safety and protection of the animals, farmer and society. Here, implications of digital farming for the animals, farmers, society and the planet are critically reviewed with the future outlook of digital farms.
REVIEW | doi:10.20944/preprints202010.0453.v2
Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: oocyte competence; livestock production; assisted reproductive technology; embryo development; micromanipulation; in vitro production
Online: 17 November 2020 (12:41:51 CET)
The efficiency of producing embryos using in vitro technologies in livestock species rarely exceeds the 30 to 40% threshold, indicating that the proportion of oocytes that fail to develop after in vitro fertilization and culture is considerably large. Considering that the intrinsic quality of the oocyte is one of the main factors affecting blastocyst yield, the precise identification of non-invasive cellular or molecular markers that predict oocyte competence is of major interest to research and practical applications. The aim of this review was to explore the current literature on different non-invasive markers associated with oocyte quality in the bovine model. Apart from some controversial findings, the presence of cycle-related structures in ovaries, a follicle size between 6 and 10 mm, large number of surrounding cumulus cells, slightly expanded investment without dark areas, large oocyte diameter (>120 microns), dark cytoplasm, and the presence of a round and smooth first polar body have been associated to better competence. In addition, the combination of oocyte and zygote selection by BCB test and spindle imaging have the potential to further optimize the identification of oocytes with better developmental competence for in vitro-derived technologies in livestock species.
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: SARS-CoV-2; COVID-19; Angiotensin-converting enzyme 2; susceptibility; livestock; aquatic mammals
Online: 7 June 2020 (08:18:54 CEST)
SARS-CoV-2, the causal agent of the globally spreading COVID-19, is capable of infecting variable animals besides human being. We evaluated the potential susceptibility of important livestock, pets and aquatic mammals by performing a multi-species sequence analysis of ACE2 based on the reported affected and unaffected animals. We identified a triple amino acid pattern of ACE2, at position 30, 31 and 34, that might be associated with SARS-CoV-2 infection and H34 might be an indicator of the susceptibility to COVID-19.
ARTICLE | doi:10.20944/preprints201912.0225.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: double hurdle; ICTs; market participation; information source; probit; regression model; smallholder farmer; livestock
Online: 17 December 2019 (09:59:33 CET)
The study explored the contribution of ICT-based information sources to market participation among smallholder livestock farmers. Use of ICTs is considered paramount for providing smallholder farmers with required market information, in order to reduce market asymmetries. A Double Hurdle regression was utilized to analyze data collected from 150 smallholder livestock farmers in the study area. The results show that while use of ICT-based market information sources significantly influenced market participation, the effect of using ICT-based information sources on intensity of market participation was not significant. Other variables shown to influence both market participation and the intensity of market participation were age, additional income and membership of farmer cooperatives. This suggests the need to also consider other associated factors in the application of interventions which utilize ICT-based information sources in achieving planned market interventions.
ARTICLE | doi:10.20944/preprints201801.0261.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: Risk management; Laos; livelihood; swidden; upland rice; rice bank; NTFPs; market economy; livestock
Online: 28 January 2018 (16:32:52 CET)
In areas with strongly seasonal climates, local people often use complex strategies to manage agricultural production shortages, including diverse activities such as hunting, selling and consuming non-agricultural products, and wage labor. We surveyed all the households in a village in northern Laos to evaluate how such livelihood activities varied during years with differing agricultural production conditions. We compared two years with normal rice production conditions (2010, 2012) and one year with a severe rice shortage (2011) due to a rodent outbreak. Earning wages inside and outside the village was the most important activity for mitigating rice shortages, followed by selling livestock and using/selling non-timber forest products. Villagers also borrowed rice from a village rice bank. Most cash income was earned from selling rice. We concluded that a balance of traditional risk management activities under the swidden system (e.g., raising livestock) with the more recent rice bank system and wages from the market economy will be critical for the sustainable development of mountain villages in northern Laos. Permanent crops and monocultures tend to make local livelihoods more dependent on a single crop, but maintaining the traditional swidden system will help local people to manage agricultural production shortages.
SHORT NOTE | doi:10.20944/preprints202211.0056.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: Precision Livestock Farming; Digital Agriculure; Smart Farming; In Ovo Sexing; Big Data; Artificial Intelligence
Online: 2 November 2022 (11:03:44 CET)
Current commercial, pre-commercial, and experimental in ovo techniques for the sex determination of fertilised eggs employ either minimally invasive biomolecular assays (extracting fluid via a small laser-drilled window in the eggshell, for detection of genetic or hormonal biomarkers), analysis of volatile compounds emitted from the eggshell, visible imaging, and reflectance or transmission spectroscopic analysis exploiting molecular optical fluorescence, polarisation, and scattering phenomena, including various combinations of these modalities. , to date no endeavour employing the NIR and FTIR based spectroscopic techniques has resulted in a commercially sustainable solution to the egg sexing problem. Besides achieving only subpar performance in overall accuracy, specificity, and sensitivity, the least invasive of the current state-of-the-art optical methods still requires, creating a transmission window (fenestration) of 12–15 mm diameter through to the mammillae layer of the shell, proximal to the external shell membrane, which can affect the incubation or post-hatch development viability of up to 10% of incubated eggs. Multimodal solution combining Raman spectroscopy and hyperspectral imaging has strong prospects to overcome the hard barriers existing before the perfection of a non-invasive in-line process for high reliability and rapid throughput for sex determination of eggs within 3 days of incubation. The method for sexing of chicken embryos needs to take a multipronged approach in collecting and analyzing spectral data that points to biomarkers using the machine learning approaches to look for nanomolar to picomolar concentrations of these in the fluid.
ARTICLE | doi:10.20944/preprints202012.0379.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: heat stress; farm animal; pig; livestock; global warming; climate change; risk assessment; economic impact
Online: 15 December 2020 (12:35:35 CET)
In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.
Subject: Biology And Life Sciences, Plant Sciences Keywords: ethnoveterinary medicines; Kinnaur; traditional knowledge; livestock; Sanctuary; shepherds; mountain people; aboriginal; tribal; herbalism; ritualism
Online: 29 January 2020 (03:57:56 CET)
The Himalayas are known for high floristic diversity and rich ethnobotanical practices. However, not all parts of the Himalayan regions are thoroughly studied. The present study aims to document the ethnoveterinary medicines used by migratory shepherds in Trans-Himalayan Rakchham-Chitkul Wildlife Sanctuary,Baspa (Sangla) valley of the Kinnaur district in Himachal Pradesh. The shepherds are very close to nature as they spend most of their time in forests with their livestock. Shepherding depends more on traditional healthcare practices based on local medicinal plants. In this study, we are reporting for the first time commonly used ethnoveterinary medicines in Rakchham and Chitkul Wildlife Sanctuary and their application, procedures of preparation, as well as listing 51 plant species. Such documentations are done first time in the Himachal Pradesh region of India as per our information. Our research emphasizes the urgent need to document traditional medicine preparation procedures from migratory shepherds. The required information on various ethnoveterinary medicines used by migratory shepherds was collected through personal field visits, participatory observations, interview and using a pretested questionnaire. It was observed that in all 51 species of ethnoveterinary were used by shepherds in Trans-Himalayan Rakchham-Chitkul Wildlife Sanctuary in Baspa (Sangla) valley of Kinnaur district. The results of this survey show that shepherds in tribal areas are highly dependent on ethnoveterinary remedies for their livestock which evolved over generations of practices for healthcare. There is an urgent need to document this vast knowledge of migratory shepherds concerning the use of ethnoveterinary remedies for animal health care in the regions of the Himalayas.
ARTICLE | doi:10.20944/preprints202001.0096.v1
Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: ph sensors; reticulorumen; blood gas; automatic milking system; real-time monitoring; precision livestock farming
Online: 10 January 2020 (10:08:05 CET)
We hypothesized possibility that inline registered reticulorumen pH can be as biomarker of cows reproduction and health status. Aim of this study was to evaluate the relationship of reticulorumen pH with biomarkers from automatic milking system (AMS) and some blood parameters and determinate reticulorumen pH as biomarker of cows reproduction and health status. According to cows reproductive status the cows were classified as belonging to the following four groups: 15-30 d. postpartum; 1-34 d. after insemination; 35 d. after insemination (non-pregnant); 35 d. after insemination (pregnant). According reticulorumen pH assay experimental animals were divided into four classes: 1) pH<6.22 (5.3% of cows), 2) pH - 6.22-6.42 (42.1% of cows), 3) pH - 6.42-6.62 (21.1% of cows), 4) pH >6.62 (10.5% of cows). Rumination time, body weight, milk yield, milk fat – protein ratio, milk lactose, milk somatic cell count (SCC), milk electrical conductivity of all quarters of udder were registered with the help of Lely Astronaut® A3 milking robots. The pH, temperature of the contents of cow reticulorumens and cow activity were measured using specific smaX-tec boluses. Blood gas parameters were analyzed using a blood gas analyzer (EPOC, Canada). We found that pregnant cows has higher reticulorumen pH during insemination time, comparing with non-pregnant. Cows with lower reticulorumen pH has lowest milk fat – protein ratio, and lactose concentration, and highest SCC. Cows with lowest reticulorumen pH has lowest blood pH. With increase reticulorumen pH, increases blood potasium and hematocrit, decreases CO2, saturation and sodium.
ARTICLE | doi:10.20944/preprints201801.0201.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Indian ocean; livestock; Extended-Spectrum β-Lactamase producing Enterobacteriaceae; risk factors; CTX-M; enzymes
Online: 22 January 2018 (12:02:53 CET)
In South Western Indian ocean (IO), Extended-Spectrum β-Lactamase producing Enterobacteriaceae (ESBL) are a main public health issue. In livestock, ESBL burden was unknown. The aim of this study was estimating the prevalence of ESBL on commercial farms in Reunion, Mayotte and Madagascar and genes involved. Secondly, risk factors of ESBL occurrence in broiler, beef cattle and pig farms were explored. In 2016-2017, commercial farms were sampled using boot swabs and samples stored at 4°C before microbiological analysis for phenotypical ESBL and gene characterization. A semi-directive questionnaire was performed. Prevalences observed in all production types and territories were elevated, except for beef cattle in Reunion which differed significantly. The most common ESBL gene was the CTX-M-1 subtype. Generalized linear models explaining ESBL occurrence varied between livestock production sectors and allowed identifying main protective (e.g., water quality control and detergent use for cleaning) and risk factors (e.g., recent antibiotic use, other farmers visiting the exploitation, pet presence). This study is the first to explore tools for antibiotic resistance management in IO farms. It provides interesting hypothesis to explore about antibiotic use in IO and ESBL transmission between pig, beef cattle and humans in Madagascar.
TECHNICAL NOTE | doi:10.20944/preprints202307.0705.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Computer Vision; Pixel Segmentation; Deep Learning; Convolutional Neural Network; Precision Livestock Farming; Feeding Behavior Analysis
Online: 11 July 2023 (11:52:38 CEST)
There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.
ARTICLE | doi:10.20944/preprints202307.1510.v1
Subject: Biology And Life Sciences, Aquatic Science Keywords: Pig emotion recognition (PER); convolution neural network (CNN); Xception; ResNet; deep neural network; domestic livestock; pigs
Online: 21 July 2023 (11:15:43 CEST)
The utilization of Pig Emotion Recognition (PER) driven by Artificial Intelligence (AI) promises to mitigate labor costs and alleviate stress among domestic pigs, thereby minimizing the need for consistent human intervention. Nevertheless, this research acknowledges the inherent limitations within the raw PER datasets, which often include irrelevant porcine features, hence impeding genuine progress in real-world evaluations. A significant proportion of PER datasets derive from sequential pig imagery obtained from video recordings, and a common pitfall in these studies is the unregulated shuffling of data. This lack of control can result in the overlap of data samples between training and testing groups, thereby yielding skewed experimental evaluations. To address these challenges, this paper introduces a novel solution in the form of the Semi-Shuffle-Pig Detector (SSPD) for PER datasets, with the intent to facilitate a less biased experimental output. By applying the SSPD, we can ensure that all testing data samples remain distinct from the training datasets, and any superfluous information from raw images is systematically discarded. This optimized method enhances the true performance of classification, providing unbiased experimental evaluations. Notably, our approach has led to a remarkable improvement in the Isolation After Feeding (IAF) metric by 20.2\% and achieved higher accuracy in segregating IAF and Paired After Feeding (PAF) classifications exceeding 92\%. This methodology, thereby, ensures the preservation of pertinent data within the PER system and eliminates potential biases in experimental evaluations. Consequently, it elevates the accuracy and reliability of real-world PER applications, resulting in a tangible positive impact on both pig welfare management and food safety standards.
REVIEW | doi:10.20944/preprints202107.0326.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Deepfake; Animal Welfare; Animal Emotions; Artificial Intelligence; Digital Farming; Animal Based Measures; Emotion Modeling; Livestock Health
Online: 14 July 2021 (11:49:38 CEST)
Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: livestock diseases; miRNAs; biomarkers; regulatory networks; mastitis; PRRSV; foot-and-mouth disease; Marek's disease; RNAi therapy
Online: 31 December 2020 (09:15:19 CET)
MicroRNAs (miRNAs) are small endogenous RNAs that regulate gene expression post-transcriptionally by targeting either the 3′ untranslated or coding regions of genes. They have been reported to play key roles in a wide range of biological processes. The recent remarkable developments of transcriptomics technologies, especially next-generation sequencing technologies and advanced bioinformatics tools, allow more in-depth exploration of messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) including miRNAs. These technologies have offered great opportunities for a deeper exploration of miRNA involvement in farm animal diseases, as well as livestock productivity and welfare. In this review, we provide an overview of the current knowledge of miRNA roles in farm animal diseases with a particular focus on diseases of economic importance. In addition, we discuss the steps and future perspectives of using miRNAs as biomarkers and molecular therapy for livestock disease management as well as the challenges and opportunities for understanding the regulatory mechanisms of miRNAs related to disease pathogenesis.
ARTICLE | doi:10.20944/preprints202006.0104.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: biochar; hydrogen sulfide; ammonia; livestock manure; agricultural safety; deep pit storage; waste management; air pollution; odor
Online: 7 June 2020 (15:54:48 CEST)
Hydrogen sulfide and ammonia are always a concern in the livestock industries, especially when farmers try to clear their manure storage pits. Agitation of manure can cause dangerously high concentrations of harmful agents such as H2S and NH3 to be emitted into the air. Biochar has the ability to sorb these gases. We hypothesized that applying biochar on top of manure can create an effective barrier to protect farmers and animals from exposure to NH3 and H2S. In this study, two kinds of biochar were tested, highly alkaline, and porous (HAP, pH 9.2) biochar made from corn stover and red oak biochar (RO, pH 7.5). Two scenarios of (6 mm) 0.25” and (12 mm) 0.5” thick layers of biochar treatments were topically applied to the manure and tested on a pilot-scale setup, simulating a deep pit storage. Each setup experienced 3-min of agitation using a transfer pump, and measurements of the concentrations of NH3 and H2S were taken in real-time and measured until the concentration stabilized after the sharp increase in concentration due to agitation. The results were compared with the control in the following 3 situations: 1. The maximum (peak) flux 2. Total emission from the start of agitation until the concentration stabilized, and 3. The total emission during the 3 min of agitation. For NH3, 0.5” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 63.3%, overall total emission by 70%, and total emissions during the 3-min agitation by 85.2%; 0.25” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 75.7%, overall, total emission by 74.5%, and total emissions during the 3-min agitation by 77.8%. 0.5” RO biochar treatment significantly reduced max by 8.8%, overall total emission by 52.9%, and total emission during 3-min agitation by 56.8%; 0.25” RO biochar treatment significantly reduced max by 61.3%, overall total emission by 86.1%, and total emission during 3-min agitation by 62.7%. For H2S, 0.5” HAP biochar treatment reduced the max by 42.5% (p=0.125), overall total emission by 17.9% (p=0.290), and significantly reduced the total emission during 3-min agitation by 70.4%; 0.25” HAP treatment reduced max by 60.6% (p=0.058), and significantly reduced overall and 3-min agitation’s total emission by 64.4% and 66.6%, respectively. 0.5” RO biochar treatment reduce the max flux by 23.6% (p=0.145), and significantly reduced overall and 3-min total emission by 39.3% and 62.4%, respectively; 0.25” RO treatment significantly reduced the max flux by 63%, overall total emission by 84.7%, and total emission during 3-min agitation by 67.4%.
ARTICLE | doi:10.20944/preprints202102.0253.v1
Subject: Biology And Life Sciences, Virology Keywords: airborne pathogens; animal production; infectious animal disease; livestock health; mass balance; swine diseases; viral aerosol; virus isolation
Online: 10 February 2021 (11:41:43 CET)
Porcine reproductive and respiratory syndrome virus (PRRSV) infections cause significant economic losses to swine producers every year. Aerosols containing infectious PRRSV are an important route of transmission, and proper treatment of air could mitigate the airborne spread of the virus within and between barns. Previous bioaerosol studies focused on the microbiology of PRRSV aerosols; thus, the current study addressed the engineering aspects of virus aerosolization and collection. Specific objectives were to (1) build and test a virus aerosolization system, (2) achieve a uniform and repeatable aerosol generation and collection throughout all replicates, (3) identify and minimize sources of variation, (4) verify that the collection system (impingers) performed similarly. The system for virus aerosolization was built and tested (Obj. 1). The uniform airflow distribution was confirmed using a physical tracer (<12% relative standard deviation) for all treatments and sound engineering control of flow rates (Obj. 2). Theoretical uncertainty analyses and mass balance calculations showed <3% loss of air mass flow rate between the inlet and outlet (Obj. 3). A comparison of TCID50 values among impinger fluids showed no statistical difference between any two of the three trials (p-value = 0.148, 0.357, 0.846) (Obj. 4). These results showed that the readiness of the system for research on virus aerosolization and treatment (e.g., by ultraviolet light), as well as its potential use for research on other types of airborne pathogens and their mitigation on a laboratory scale.
ARTICLE | doi:10.20944/preprints202103.0629.v1
Subject: Engineering, Civil Engineering Keywords: air pollution control; air quality; volatile organic compounds; nuisance smell; livestock agriculture; waste management; environmental technology; advanced oxidation; excimer; titanium dioxide
Online: 25 March 2021 (14:46:38 CET)
UV-A (ca. 365 nm wavelength, a.k.a. 'black light') photocatalysis has been investigated to comprehensively mitigate odor and selected air pollutants in the livestock environment. This study was conducted to confirm the performance of UV-A photocatalysis on the swine farm. The objectives of this research were to (1) scale-up of the UV-A photocatalysis treatment, (2) evaluate the mitigation of odorous gases from swine slurry pit, and (3) test different UV sources, (4) evaluate the effect of suspended particulate matter (PM), and (5) conduct preliminary economic analyses. We tested UV-A photocatalysis at a mobile laboratory-scale capable of treating ~0.2 - 0.8 m3·s-1 of barn exhaust air. The targeted gaseous emissions of barn exhaust air were significantly mitigated (p < 0.05) up to 40% reduction of measured odor; 63%, 44%, 32%, 40%, 66%, and 49% reduction of dimethyl disulfide, isobutyric acid, butanoic acid, p-cresol, indole, and skatole, respectively; 40% reduction of H2S; 100% reduction of O3; and 13% reduction of N2O. The PM mitigation effect was not significant. Formaldehyde levels did not change, and a 21% generation of CO2 was observed. The percent reduction of targeted gases decreased as the airborne PM increased. Simultaneous chemical and sensory analysis confirmed that UV-A treatment changed the overall nuisance odor character of swine barn emissions into weaker manure odor with 'toothpaste and 'mint' notes. The smell of benzoic acid generated in UV-A treatment was likely one of the compounds responsible for the less-offensive overall odor character of the UV-treated emissions. Results are needed to inform the design of a farm-scale trial, where the interior barn walls can be treated with the photocatalyst, and foul air will be passively treated as it moves through the barn.
ARTICLE | doi:10.20944/preprints202009.0614.v2
Subject: Engineering, Civil Engineering Keywords: air quality; air pollution; sustainable animal production; livestock and poultry; waste management; odor, ammonia; hydrogen sulfide; greenhouse gases; volatile organic compounds
Online: 26 October 2020 (09:33:12 CET)
Environmental impact associated with odor and gaseous emissions from animal manure is one of the challenges for communities, farmers, and regulatory agencies. Microbe-based manure additives treatments are marketed and used by farmers for mitigation of emissions. However, their performance is difficult to assess objectively. Thus, comprehensive, practical, and low-cost treatments are still in demand. We have been advancing such treatments based on physicochemical principles. The objective of this research was to test the effect of the surficial application of a thin layer (¼"; 6.3 mm) of biochar on the mitigation of gaseous emissions (as the percent reduction, % R) from swine manure. Two types of biochar were tested: highly alkaline and porous (HAP) biochar made from corn stover and red oak (RO), both with different pH and morphology. Three 30-day trials were conducted with a layer of HAP and RO (2.0 & 1.65 kg∙m-2, respectively) applied on manure surface, and emissions of ammonia (NH3), hydrogen sulfide (H2S), greenhouse gases (GHG), and odorous volatile organic compounds (VOCs) were measured. The manure and biochar type and properties had an impact on the mitigation effect and its duration. RO significantly reduced NH3 (19-39%) and p-cresol (66-78%). H2S was mitigated (16~23%), but not significantly for all trials. Significant (66~78%) reductions for p-cresol were observed for all trials. The phenolic VOCs had relatively high % R in most trials but not significantly for all trials. HAP reduced NH3 (4~21%) and H2S (2~22%), but not significantly for all trials. Significant % R for p-cresol (91~97%) and skatole (74~95%) were observed for all trials. The % R for phenol and indole ranged from (60~99%) & (29~94%) but was not significant for all trials. The impact on GHGs, isobutyric acid, and the odor was mixed with some mitigation and generation effects. However, larger-scale experiments are needed to understand how biochar properties and the dose and frequency of application can be optimized to mitigate odor and gaseous emissions from swine manure. The lessons learned can also be applicable to surficial biochar treatment of gaseous emissions from other waste and area sources.
ARTICLE | doi:10.20944/preprints202307.1059.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Internet of Things (IoT); Precision Livestock Farming (PLF); Animal welfare; Pig behavior; Mul-timodal data; Accelerometer sensors; Stress analysis; Gait analysis; Physiological monitoring; Sustainable farming practices
Online: 17 July 2023 (07:27:18 CEST)
This paper pioneers a novel exploration of environmental impacts in livestock farming, with a focus on pig farming's intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs, aged 86 to 108 days, using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores potential detection of gait abnormalities, correlations between pigs' age and their gait or activity patterns, and the evolution of pigs' walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time, and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs' physiological parameters. Sensor data is further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.