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AbstractSmallholder farmers face significant challenges in effectively managing their cattle due to the rapid outbreaks of theileriosis and other tick-borne diseases, resulting in severe economic losses. These outbreaks pose significant threats to food security and rural livelihoods, yet there is a lack of understanding of the factors affecting effective cattle disease management among smallholder farmers. In this paper, we determine the factors affecting effective cattle disease management in Mutare rural district, Zimbabwe. We quantify the economic losses associated with cattle diseases which we use as a proxy for effective cattle management. Multiple linear regression was used to analyse the data. Six wards were chosen at random among 36 wards in the district, and primary data was gathered from 90 randomly selected farmers. The results showed that cattle diseases cause significant losses in the rural Mutare district, with an average loss of US$ 1 288.87 per farmer during the two years under study. The level of education of the household head, access to an animal health officer, and training on cattle diseases are significant variables in reducing economic losses due to cattle diseases at 5% while formal credit access was found to be statistically significant at 1% significance level. To lessen the losses brought on by cattle diseases, we recommend the government raise the number of animal health officers, strengthen training programs on preventing and controlling cattle diseases, invest in the development of a comprehensive disease-reporting network, and the creation of policies that incentivize banks to lend money to farmers.

In the present study, an attempt was made to develop an expert system for the diagnosis of cattle diseases under field conditions by Veterinarians. A questionnaire tool was developed and used for collecting the scores for symptoms or signs of 13 cattle diseases for diagnosis. The tool was tested for face validity by pilot survey with experts and content validity by calculating Aiken’s value index. The scores for the signs or symptoms were collected from 178 field veterinarians from different states in India. The scores obtained were tested for reliability and consistency by using the split-half test [88.7%] and Cronbach’s alpha coefficient [92.7%], respectively. The weighted matrix was calculated for the 51 signs or symptoms obtained for 13 cattle diseases. The CaDDES, a web application was developed using PHP, HTML, JavaScript, and MySQL computer programming languages. This web application contains Home, Introduction, About CaDDES, Disease Diagnosis, Development Team, and Contact pages. The Veterinarians need to enter the basic details of cattle and select the signs or symptoms observed in the particular cattle under field conditions. The results page will display the three probable diseases diagnosed for the selected signs or symptoms. Further, the necessary samples to be collected for laboratory confirmation and preventive measures to be undertaken were also provided. This web application will be useful for field level diagnosis of cattle diseases by Veterinarians and later by farmers. It may also be useful as a surveillance application, which will improve cattle disease reporting in the future.

Geographical Information System (GIS) and remote sensing provide real-time data to stakeholders. GIS is new and modern tool that are essential for mapping, monitoring, and surveillance of animal diseases. This study aims to provide a digital map of cattle population and disease distribution. Using satellite imaging as mapping apparatus, this study map the distribution of cattle diseases. Animal health is key to livestock production and productivity. This study can be used as prevention and treatment measures efficiently and effectively. Based on the results of the study, the cattle population in Bowi Subur Village was 455 heads. About 78% of respondents graze their cattle extensively in a forage land while the other 22% keep their cattle intensively in the barn, 31 respondents (31%) stated that their cattle had health problems. The study showed that cattle diseases, such as helminth infestation, external parasite infestation, dystocia, and abortus, are varied. Both primary and secondary data showed that helminth infestation was the most common case. Bowi Subur Village has great potential for animal husbandry development especially ruminants in Manokwari Regency.

Lumpy skin disease virus (LSDV) is a member of the Capripoxvirus genus, mainly infecting cattle and buffalo, which until relatively recently was only endemic in parts of Africa and then spread to the Middle East and lately Europe and Asia. Lumpy skin disease (LSD) is a notifiable disease with a serious impact on the beef industry as it causes mortality of up to 10% and has impacts on milk and meat production, as well as fertility. The close serological relationship between LSDV, goat poxvirus (GTPV) and sheep poxvirus (SPPV) has led to live attenuated GTPV and SPPV vaccines being used to protect against LSD in some countries. There is evidence that the SPPV vaccine does not protect from LSD as well as the GTPV and LSDV vaccines. One of the LSD vaccines used in Eastern Europe was found to be a combination of different Capripoxviruses, and a series of recombination events in the manufacturing process resulted in cattle being vaccinated with a range of recombinant LSDVs resulting in virulent LSDV which spread throughout Asia. It is likely that LSD will become endemic throughout Asia as it will be very challenging to control the spread of the virus without widespread vaccination.

The health and productivity of animals, as well as farmers’ financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno’s fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

Lumpy skin disease virus (LSDV) is a member of the Capripoxvirus genus, mainly infecting cattle and buffalo, which until relatively recently was only endemic in parts of Africa and then spread to the Middle East and lately Europe and Asia. Lumpy skin disease (LSD) is a notifiable disease with a serious impact on the beef industry as it causes mortality of up to 10% and has impacts on milk and meat production, as well as fertility. The close serological relationship between LSDV, goat poxvirus (GTPV) and sheep poxvirus (SPPV) has led to live attenuated GTPV and SPPV vaccines being used to protect against LSD in some countries. There is evidence that the SPPV vaccine does not protect from LSD as well as the GTPV and LSDV vaccines. One of the LSD vaccines used in Eastern Europe was found to be a combination of different Capripoxviruses, and a series of recombination events in the manufacturing process resulted in cattle being vaccinated with a range of recombinant LSDVs resulting in virulent LSDV which spread throughout Asia. It is likely that LSD will become endemic throughout Asia as it will be very challenging to control the spread of the virus without widespread vaccination.

The health and productivity of animals, as well as farmers' financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno's fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

Bovine brucellosis is a neglected zoonotic disease in developing countries, endemic, and a growing challenge to public health. The development of cost-effective control measures for the disease can only be affirmed by knowledge of the disease epidemiology and the ability to define its risk profiles. This study aimed to document the trend of bovine brucellosis and the control measures adopted following reported cases from 2014 to 2019 in South Africa. The data on confirmed cases of bovine brucellosis was retrieved from the website of the World Organization for Animal Health (WOAH). Descriptive analysis and the analysis of variance (ANOVA) were utilized for significance (p<0.05). The data retrieved revealed an overall average bovine brucellosis prevalence rate of 8.48%. There were significant differences in bovine brucellosis prevalence across the provinces in 2014, 2015, 2017, 2018, and 2019 (p<0.05). Provinces with generally lower annual temperatures, namely KwaZulu Natal, Western Cape, and Free State, had higher prevalences of bovine brucellosis, while disease trends for Gauteng, Northern Cape, and Eastern Cape provinces were not easy to establish due to missing data for some of the years falling within the study period. The study identified gaps in the reporting of bovine brucellosis and documentation of disease control measures in the country. The present study not only identified these gaps but also proposed the need for future studies that detect the prevalence of cattle brucellosis at the animal/herd level. This recommendation, if implemented, can provide further insights into the disease situation in South Africa and pave the way for more effective control measures. [ABSTRACT FROM AUTHOR]

Indonesia is a country that has many natural resources, especially mammals. The Papua and West Papua regions are large provinces with abundant natural resources and tremendous livestock potential. The availability of natural resources in the form of live cattle provides a great opportunity to develop animal husbandry in West Papua province. This research was conducted to create a new expert system with a knowledge base to solve the problems that occur and be useful for the community, especially cattle breeders. The current problem is the delay and lack of medical personnel in diagnosing cattle diseases, the distance that must be traveled, which is still very difficult to travel, and the lack of understanding of farmers in early handling when implications indicate animals. So, the Certainty Factor Method and Bayes Theorem with Forward-Chaining search are used to handle current problems. From the results of manual calculations, Certainty Factor Forward Chaining search is a method that has an uncertainty value of 99.84% for 3-day fever compared to Bayes Theorem Forward Chaining search with a value of 50% for worms, 50% for 3-day fever and 50% for nail rot, if applied then Certainty Factor Forward Chaining search is the most appropriate. Likewise, updating the knowledge base must be done from time to time. So that in the future, it can be compared with other methods and Android-based to facilitate current breeders. [ABSTRACT FROM AUTHOR]

Abstract Artificial intelligence (AI) was developed to distinguish cattle by their muzzle patterns and identify early cases of disease, including infectious bovine keratoconjunctivitis (IBK). It was tested on 870 cattle in four locations, with 170 developing IBK. The AI identified 169 of the 170 cases prior to their identification by veterinarians, and another 17 cases that remained free of IBK signs (sensitivity = 99.4%, specificity = 97.6%). These results indicate the AI can detect emerging IBK cases by muzzle images very early in the disease process and be used as an intervention tool in the prevention of IBK outbreaks.

The potential benefits of significant livestock health diagnosis is enormous. Animal husbandry being the most important aspect of India's agricultural growth, farmers suffer on a regular basis as a result of their cattle's bad health and lack of qualified veterinary doctors in their region. At a breakneck pace of Big data and Machine Learning, data mining and analysis are implemented widely in the field of animal husbandry because of the fact that disease and bioterrorism constitute a danger to cattle produce and food supply's safety. In this review paper, we will be comparing the different data science techniques. [ABSTRACT FROM AUTHOR]

ABSTRACT: Urea is an organic compound characterized as a white, solid, and hygroscopic substance. It is recognized as a source of non-protein nitrogen (NPN) and is widely used as a partial replacement for protein in cattle diets due to the ability of the ruminal microbiota to convert it into microbial protein. Despite the advantages of using urea, it also has limitations, particularly the proximity between metabolizable and toxic or fatal doses. Furthermore, for safe use, a period of adaptation is necessary for the animals. Poisoning is characterized by rapid and generally fatal development, which is frequent in non-adapted animals but can also occur in those with previous adaptations. The aim of this study was to characterize the clinical, epidemiological, and pathological aspects of urea poisoning through a brief review and a retrospective study. In addition, interviews were conducted with veterinarians who frequently send diagnostic material to the Laboratory of Anatomic Pathology of the “Faculdade de Medicina Veterinária e Zootecnia” (LAP-FAMEZ) to assess their perception of the outbreaks of urea poisoning. The objective was to obtain a comparative scenario between published cases and those received by the laboratory while considering the real situation of this condition in the field. During this retrospective study, only four outbreaks were investigated; in one, the diagnosis was possible through experimental reproduction. Of 35 interviewees, 88.9% said they had seen more than one case compatible with urea poisoning, but 87.5% did not perform a necropsy and/or send material to confirm the diagnosis. The results show that the reality of urea poisoning may be very distant from that reported in previous studies due to the difficulty often observed in the diagnostic approach, so we developed a flowchart aiming to provide a useful guide for field veterinarians.

Abstract Background Lumpy skin disease (LSD), a disease of cattle and buffaloes, has recently become widely prevalent in Egypt. The aim of this study was to ascertain the potential role of Rhipicephalus (Boophilus) annulatus ticks in the transmission of this disease. Samples collected from suspected lumpy skin disease virus (LSDV) infected cows that had previously been vaccinated with the Romanian sheep pox virus (SPPV) in various Egyptian governorates were obtained between May to November over two consecutive years, namely 2018 and 2019. Ticks were morphologically identified and the partial cytochrome oxidase subunit I gene (COI) were sequenced, revealing that they were closely related to R. (Boophilus) annulatus. The G-protein-coupled chemokine receptor (GPCR) gene of the LSDV was used to test hard ticks. Results Two positive samples from Kafr El-Sheikh province and one positive sample from Al-Behera province were reported. BLAST analysis revealed that the positive samples were closely related to the Kazakhstani Kubash/KAZ/16 strain (accession number MN642592). Phylogenetic analysis revealed that the GPCR gene of the LSDV recently circulating in Egypt belongs to a global cluster of field LSDV with a nucleotide identity of 98–100%. LSDV isolation was successfully performed four days after inoculation using 9 to 11-day-old embryonated chicken eggs showing characteristic focal white pock lesions dispersed on the choriallantoic membrane after three blind passages. Intracytoplasmic inclusion bodies, cell rupture, vacuoles in cells, and virus particles ovoid in shape were demonstrated by electron microscopy. Conclusion In this study the role of hard ticks in the transmission of the LSDV to susceptible animals in Egypt was revealed and confirmed by various methods.