Neospora caninum is a protozoan parasite of animals. Until 1988, it was misidentified as Toxoplasma gondii. Since its first recognition in dogs in 1984 and the description of the new genus and species Neospora caninum in 1988, neosporosis has emerged as a serious disease of cattle and dogs worldwide. Abortions and neonatal mortality are a major problem in livestock operations, and neosporosis is a major cause of abortion in cattle. Although antibodies to N. caninum have been reported, the parasite has not been detected in human tissues. Thus, the zoonotic potential is uncertain. This review is focused mainly on the epidemiology and control of neosporosis in cattle, but worldwide seroprevalences of N. caninum in animals and humans are tabulated. The role of wildlife in the life cycle of N. caninum and strategies for the control of neosporosis in cattle are discussed.

/pdf/epidemiology-and-control-of-neosporosis-and-neospora-caninum-3wbu38pknw.pdf

Epidemiology and Control of Neosporosis and Neospora caninum

All living organisms are composed of major, minor, and trace elements, given by nature and supplied by geology.

Medical Geology: Impacts of the Natural Environment on Public Health

Genetic selection for residual feed intake (RFI) is an indirect approach for reducing enteric methane (CH4) emissions in beef and dairy cattle. RFI is moderately heritable (0.26 to 0.43), moderately repeatable across diets (0.33 to 0.67) and independent of body size and production, and when adjusted for off-test ultrasound backfat thickness (RFIfat) is also independent of body fatness in growing animals. It is highly dependent on accurate measurement of individual animal feed intake. Within-animal repeatability of feed intake is moderate (0.29 to 0.49) with distinctive diurnal patterns associated with cattle type, diet and genotype, necessitating the recording of feed intake for at least 35 days. In addition, direct measurement of enteric CH4 production will likely be more variable and expensive than measuring feed intake and if conducted should be expressed as CH4 production (g/animal per day) adjusted for body size, growth, body composition and dry matter intake (DMI) or as residual CH4 production. A further disadvantage of a direct CH4 phenotype is that the relationships of enteric CH4 production on other economically important traits are largely unknown. Selection for low RFIfat (efficient, −RFIfat) will result in cattle that consume less dry matter (DMI) and have an improved feed conversion ratio (FCR) compared with high RFIfat cattle (inefficient; +RFIfat). Few antagonistic effects have been reported for the relationships of RFIfat on carcass and meat quality, fertility, cow lifetime productivity and adaptability to stress or extensive grazing conditions. Low RFIfat cattle also produce 15% to 25% less enteric CH4 than +RFIfat cattle, since DMI is positively related to enteric methane (CH4) production. In addition, lower DMI and feeding duration and frequency, and a different rumen bacterial profile that improves rumen fermentation in −RFIfat cattle may favor a 1% to 2% improvement in dry matter and CP digestibility compared with +RFIfat cattle. Rate of genetic change using this approach is expected to improve feed efficiency and reduce enteric CH4 emissions from cattle by 0.75% to 1.0% per year at equal levels of body size, growth and body fatness compared with cattle not selected for RFIfat.

https://era.library.ualberta.ca/items/0ceda92a-2934-408e-bfad-0baa57f3d811/view/e3b7cba0-34dd-410e-9272-49e8ddefde14/A_7_s2_303.pdf

Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production

Residual feed intake (RFI) testing has increased selection pressure on biological efficiency in cattle. The objective of this study was to assess the association of the rumen microbiome in inefficient, positive RFI (p-RFI) and efficient, negative RFI (n-RFI) Brahman bulls grazing ‘Coastal’ bermudagrass [Cynodondactylon (L.) Pers.]under two levels of forage allowance (high and low stocking intensity). Sixteen Brahman bulls were previously fed in confinement for 70 d to determine the RFI phenotype. Bulls were then allotted 60 d stocking on bermudagrass pastures to estimate RFI using the n-alkane technique. At the conclusion of the grazing period, rumen liquid samples were collected from each bull by stomach tube to evaluate the rumen microbiome. Extraction of DNA, amplification of the V4-V6 region of the 16S rRNA gene, and 454 pyrosequencing were performed on each sample. After denoising the sequences, chimera checking, and quality trimming, 4,573 ± 1,287 sequences were generated per sample. Sequences were then assigned taxonomy from the Greengenes database using the RDP classifier. Overall, 67.5 and 22.9% of sequences were classified as Bacteroidetes and Firmicutes, respectively. Within the phylum Bacteroidetes, Prevotella was the most predominant genus and was observed in greater relative abundance in p-RFI bulls compared with n-RFI bulls (P = 0.01). In contrast, an unidentified Bacteroidales family was greater in relative abundance for n-RFI bulls than p-RFI (26.7 vs. 19.1%; P = 0.03). Ruminococcaceae was the third most abundant family in our samples, but it was not affected by RFI phenotype. No effect of stocking intensity was observed for bacterial taxa, but there was a tendency for alpha diversity and operational taxonomic unit richness to increase with lower stocking intensity. Results suggested the rumen microbiome of p-RFI Brahman bulls has greater levels of Prevotella, but the bacterial community composition was unaffected by stocking intensity.

/pdf/relationship-between-the-rumen-microbiome-and-residual-feed-3pxutwwz8q.pdf

Relationship between the Rumen Microbiome and Residual Feed Intake-Efficiency of Brahman Bulls Stocked on Bermudagrass Pastures

The objectives of this study were to quantify the phenotypic variation in residual feed intake (RFI) in pregnant beef heifers offered a grass silage diet and to characterize their productivity. Seventy-three pregnant (mean gestation d 198, SD = 27 d) Simmental and Simmental × Holstein-Friesian heifers (mean initial BW 548, SD = 47.5 kg) were offered grass silage ad libitum. Heifer DMI, BW, BCS, skeletal measurements, ultrasonic fat and muscle depth, visual muscularity score, rumen fermentation, total tract digestibility, blood metabolite and hematology variables, feeding, and activity behavior were measured during an 84-d feed intake study. After parturition calf birth weight, calving difficulty, cow serum IgG, hematology variables, and calf humoral immune status were measured. In a subset of cows (n = 28), DMI, milk yield and various body composition variables were also measured approximately 3 wk postpartum. Phenotypic RFI was calculated for each animal as the difference between actual DMI and expected DMI. Expected DMI was computed for each animal by regressing average daily DMI on conceptus-adjusted mean BW(0.75) and conceptus-adjusted ADG over an 84-d period. Within breed, heifers were ranked by RFI into low (efficient), medium, and high (inefficient) groups by dividing them into thirds. Heifers with high RFI had 8.8 and 17.1% greater (P 0.05). Residual feed intake was positively correlated with DMI (r = 0.85) but not with feed conversion ratio, ADG, or BW. The RFI groups did not differ (P > 0.05) in skeletal size, BCS, ultrasonic fat depth, total tract digestibility, calf birth weight, calving difficulty, serum IgG concentrations, or milk yield. Visual muscularity scores, initial test and postpartum ultrasonic muscle depth were negatively correlated with RFI (P < 0.05). Including mean ultrasonic muscle depth into the base RFI regression model increased its R(2) (0.29 to 0.38). Pearson rank correlation between RFI and muscle-adjusted RFI was 0.93. The results show that efficient RFI heifers consumed less feed without any compromise in growth, body composition, or maternal traits measured.

Grass silage intake, rumen and blood variables, ultrasonic and body measurements, feeding behavior, and activity in pregnant beef heifers differing in phenotypic residual feed intake

tion systems, and marketing strategies. The enterprise budget, whole farm cash Budgeting is widely used in economic reflow, and income statement are fundamental search, policy evaluation, investment-project tools of farm and ranch management. The evaluation, and production management. Ap"Microcomputer Budget Management Sys- plications range form aiding very specific tem" (MBMS) is a microcomputer software management decisions to government policy package that facilitates the storage and use implications. of information for crop and livestock budg- A continued cost-price squeeze, fluctuating eting. It performs the calculations for severa input and output prices, and changes in techenterprise budgeting formats and for prepa- nology increase the demand for timely enration of whole farm resource use reports errise and finnil inform and financial statements. The MBMS also includes internal machinery and irrigation cost Computerized budget generator systems are calculation routines. one means of providing reliable, consistent, MBMS was developed for use by extension and up-to-date cost and return information. staff, researchers, lenders, consultants, and This paper describes microcomputer budget operators of diversified farms and ranches software, compares its capability to other with many enterprises that use enterprise and programs, illustrates reports through partial whole farm budgeting for analysis and plan- examples, and describes cost calculation proning activities. The flexibility and detailed cedures used by the software. nature of the program requires the user to have knowledge of enterprise budgeting and operation of complex computer programs. DESCRIPTION OF SOFTWARE This paper presents a discussion of the features and capabilities of the software and the The Microcomputer Budget Management computational procedures used in the cost System (MBMS) is a microcomputer software calculations. package that facilitates storage of information for corp and livestock enterprise budgeting.

/pdf/microcomputer-budget-management-system-2lpmittpm6.pdf

Microcomputer budget management system

A computerized spreadsheet was used to model economic losses attributed to changes in production parameters for a typical 42 head Texas beef herd following a simulated N. caninum infection. Texas beef herds that completed Standardized Performance Analysis (SPA) records between 1991-1997 supplied the representative production information for economic analysis. SPA production parameters that were used included pregnancy percentage, calving percentage (live calves born), calf death loss between birth and weaning (%), weaned calf crop (%), pounds of calf weaned per female exposed to bulls at start of breeding season, and replacement percentage. The prevalence of N. caninum across and within herds, the risk of increased abortions and stillbirths in infected cows, and the percentage of aborting cows that were culled were collectively superimposed onto the production information in the model either as static estimates (deterministic model) or changing estimates (stochastic model) to cause change in production parameters. Using a deterministic approach and compared to a non-infected herd, a herd with a 20% prevalence of infection experienced an estimated 2.4% lower calving percentage and an overall estimated 2.3% lower weaned calf crop. Estimated weaned calf weight per exposed female was 12.3 lb (5.6 kg) less in an infected herd. Predicted economic loss was $13.75 per head ($577.50 herd loss). Using software that facilitated stochastic modelling, average predicted loss was between $23.29 per head ($978.18 herd loss) and $35.21 per head ($1478.82 herd loss). The total predicted economic loss to the Texas beef industry from this disease was estimated to be $7.6 million using the deterministic economic model. When prevalence figures were allowed to vary, the most likely financial losses to the Texas beef industry were predicted to be between $15 million and $24 million.

Estimated production and economic losses from Neospora caninum infection in Texas beef herds

Selected summary cost of production data gathered in Texas using the NCBA-IRM-SPA guidelines were reported and utilized to estimate a cost function for the Texas cow-calf industry. Results indicate that the economic cost of production for the cow-calf industry averaged $102.59 per cwt. over the 1992-1998 time period. It was found that a 1% increase in the per acre price of grazing results in a 0.141% increase in total cost for Texas cow-calf producers. In addition, the analysis found that a 1% increase in the per pound price of raised and purchased feed results in an expected 0.055% increase in total cost for Texas cow-calf producers.

https://txjanr.agintexas.org/index.php/txjanr/article/download/190/177

Cost of Production Analysis for the Texas Cow-Calf Industry

The role of the economist in agriculture has increased in significance in the 1970s and 80s. A review of the recent history of agriculture begins with the period of stability and relative certainty of agricultural input and product prices in the 1950s. But by the 1970s, the economic impacts of the oil crisis, inflation, and orientation to free market prices of agricultural products necessitated a transition in the role and importance of the agricultural economist. Economists became vital to farmers who had to make economic decisions on appropriate cropping patterns, when and how to market products, and how to invest wisely.

/pdf/research-and-extension-issues-in-production-economics-1uf162njma.pdf

Research and Extension Issues in Production Economics

OBJECTIVE: To determine gross income lost that was attributable to thin cows in a beef cattle herd, to estimate the cost of added nutrition necessary to prevent thin cows in the herd, and to determine the financial outcome of the improved nutritional practices. DESIGN: Prospective, observational study. ANIMALS: Four hundred and twenty-two Santa Gertrudis cows and their calves. PROCEDURE: At pregnancy examination in the fall of 1992, cows were assigned a body condition score (BCS), using a scale of 1 (emaciated) to 9 (obese), and the ratio of the productivity of BCS-3 and BCS-4 cow groups (thin cows), compared with the mean productivity of BCS-5 and BCS-6 cows (cows in good condition), was determined. Measures of productivity evaluated included pregnancy rates, weaning weights, and prices per hundredweight of calves. The performance ratios of BCS-3 and BCS-4 cows were multiplied by the mean gross income of BCS-5 and BCS-6 cows to calculate their gross income. This was then subtracted from the mean income of BCS-5 and BCS-6 cows to estimate the amount of lost gross income per thin cow. The cost of a nutritional program that would prevent thin cows in the herd was subtracted from the lost gross income of the thin cows to yield the amount of increased net income that could be generated from a nutritional program that would maintain cows in the herd at a BCS of 5 or 6. RESULTS: Cows with a BCS of 3 were 0.48 as productive, and cows with a BCS of 4 were 0.74 as productive as the average of the BCS-5 and BCS-6 cows combined. Each BCS-3 cow generated $215.06 less, and each BCS-4 cow generated $107.53 less gross income than the average gross income of BCS-5 and BCS-6 cows. The added cost of nutrition that would have reconditioned BCS-3 and BCS-4 cows to a BCS of 5.5 was $91.48/BCS-3 cow and $43.67/BCS-4 cow. Implementation of the reconditioning nutrition program the previous fall would have resulted in an extra net income of $123.58/BCS-3 cow and $63.86/BCS-4 cow, received over a 2-year period. The 262 thin cows in the herd accounted for a total net income loss of $19,897. CLINICAL IMPLICATIONS: The time of pregnancy examination is a strategic intervention point to estimate the past negative economic impact of thin cows and to implement a plan to prevent these losses in the future. Pregnancy examinations should be performed at least 100 days before the beginning of the calving season, and thin pregnant cows should be sorted into a special group and fed a reconditioning diet that will improve their body condition to an average BCS of 5.5 by the onset of the calving season.

Use of performance ratios to calculate the economic impact of thin cows in a beef cattle herd.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

James M. McGrann

Author Tools

Chat about Author