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Ruminants, Volume 4, Issue 1 (March 2024) – 10 articles

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13 pages, 1754 KiB  
Review
Bibliometric Mapping of Academic Research Focusing on Animal Production and Climate Change in Association with Methane Emissions and Animal Productivity
by Akeem Babatunde Sikiru, Olayinka John Makinde, Bossima Ivan Koura, Stephen Sunday Egena Acheneje, John Olushola Alabi, Maria Ndakula Tautiko Shipandeni and Oludayo Michael Akinsola
Ruminants 2024, 4(1), 152-164; https://doi.org/10.3390/ruminants4010010 - 9 Mar 2024
Cited by 3 | Viewed by 1484
Abstract
Climate change is a pressing global challenge, and animal production is a major contributor to methane emissions. This study examines the academic landscape of research on CH4 emissions and animal productivity, with a focus on cattle, sheep, and goats. Using a bibliometric [...] Read more.
Climate change is a pressing global challenge, and animal production is a major contributor to methane emissions. This study examines the academic landscape of research on CH4 emissions and animal productivity, with a focus on cattle, sheep, and goats. Using a bibliometric analysis of 2500 documents published between 1987 and 2023, the study finds that research on this topic has increased significantly over time, with a record high in 2022. The leading countries in terms of research output are the United States, China, Brazil, Canada, and Italy. The study identifies several key research themes, including the impact of CH4 emissions on animal productivity parameters, the development of mitigation strategies, and the assessment of trade-offs and synergies between CH4 emissions reduction and other sustainability goals. The study concludes by highlighting the importance of continued research on CH4 emissions and animal productivity to develop and implement effective mitigation strategies. This study has important implications for policymakers, researchers, and the livestock industry. Policymakers can use the findings to inform the development of policies and regulations that support the reduction of CH4 emissions from animal production. Researchers can use the findings to identify gaps in the existing knowledge base and to develop new research directions. The livestock industry can use the findings to develop more sustainable production practices. By working together, policymakers, researchers, and the livestock industry can develop and implement effective mitigation strategies that reduce greenhouse gas emissions, protect the environment, and support sustainable food production. Full article
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<p>Trend in number of documents published in relation to total citation on CH<sub>4</sub> emissions and animal productivity (1987–2023). The figure illustrates the temporal evolution of research on CH<sub>4</sub> emissions and animal productivity, including growth rate, feed conversion ratio (FCR), milk yield, reproduction rate, dressing percentage, mortality rate, fertility rate, age at puberty, health and disease resistance, product quality, livestock reproductive efficiency, and animal behavior from 1987 to 2023. Some of the publications highlighted exploration of feeding and production management as means of abating methane emissions in livestock production. The <span class="html-italic">x</span>-axis represents the chronological time frame, while the <span class="html-italic">y</span>-axis shows the total number of documents published and total citations. The blue line graph demonstrates growth in the number of documents published in the specified field over this period. The overlaid orange lines represent the cumulative total of citations received by these publications. The figure reveals the relationship between the growth of research output and its corresponding impact, as measured by the accumulation of citations, providing insights into the research landscape’s development and scholarly influence over time.</p>
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<p>Clustering of countries publishing documents on CH<sub>4</sub> emissions and animal productivity. This figure showcases the clustering of countries engaged in producing documents related to CH<sub>4</sub> emissions and animal productivity. The clustering is based on the origin and affiliations of authors, spanning from 1987 to 2023. Notably, eight distinct clusters emerged, each spearheaded by a prominent country: China, Brazil, Canada, Italy, South Africa, the United States, France, and Australia. These clusters visually represent the global distribution of research contributions, highlighting the leading nations in this field during the specified time frame.</p>
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<p>Leading countries for publications on CH<sub>4</sub> emissions and animal productivity (1987–2023). This figure illustrates the leading countries that contributed to research on the interrelation between CH<sub>4</sub> emissions and animal productivity over the years 1987 to 2023. The height of the bar graph is proportionate to the number of documents published in the field and the average annual citation per document published from each of the countries. The chart provides valuable insights into the global distribution of research efforts related to this topic and identifies the key countries that significantly contributed to CH<sub>4</sub> emissions and animal productivity.</p>
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<p>Trend in number of documents published and citations on sheep, cattle, and goat production (1980–2023). The figure illustrates the temporal evolution of scholarly activity and research in the field of livestock production, focusing on sheep, cattle, and goat species. The graph spanned the years from 1980 to 2023, offering a comprehensive overview of the publication and citation trends over this period. The blue line represents the number of documents published, reflecting the annual quantity of research contributions pertaining to these livestock categories. The trendline revealed fluctuations and growth patterns in research output depicting the cumulative number of citations received by these documents; the citations are a key indicator of the impact and influence of the research. The trendline’s trajectory provides insights into the research’s reception and influence within the scientific community. The figure also highlights notable shifts (rising or declining trends) and major milestones in the accumulation of knowledge and scholarly engagement related to sheep, cattle, and goat production in the context of changing climate during the specified time frame as valuable information for understanding the evolving landscape of research in this field.</p>
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<p>Clustering of countries publishing documents on sheep, cattle, and goat production and climate change are being published from 1980 to 2023. This figure showcases the clustering of countries engaged in producing documents related to animal production and climate change. The clustering is based on the origin and affiliations of authors, spanning from 1980 to 2023. Notably, 10 distinct clusters emerged, each being led by prominent countries, including the United States, India, Brazil, Belgium, China, Spain, Canada, Australia, France, and Italy. These clusters visually represent the global distribution of research contributions, highlighting the leading nations in this field between 1980 and 2023. There was a diverse range of research from these countries, cutting across sheep, cattle, and goats, investigating the impact of climate change on land use and forage and feed scarcity. Some of the studies explore how changing precipitation patterns lead to droughts, floods, and land degradation, necessitating sustainable land management practices like rotational grazing. Some research also examines feed scarcity due to rising temperatures and altered precipitation, prompting exploration of heat-resistant forage varieties, alternative feed sources, and precision agriculture. These studies consider both global and regional contexts, highlighting the need for mitigation strategies in developed countries and adaptation strategies for resource-poor farmers in developing ones. Overall, the research output during the period 1980–2023 focuses on the sustainability of livestock production under changing climate conditions.</p>
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<p>Countries where documents on sheep, cattle, and goat production in relation to climate change are being published from 1980 to 2023. This figure illustrates the leading countries that have contributed to research on the interrelation between livestock production and climate change. The height of the bar graph is proportionate to the number of documents published in the field and the total citations for all the documents published from each country. The chart provides valuable insights into the global distribution of research efforts covering animal production and knowledge sharing across the world.</p>
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16 pages, 442 KiB  
Article
Assessment of Welfare in Transhumance Yak Hybrids (Chauris) in the Lower Himalayan Region of Nepal
by Sujan Sapkota, Richard Laven, Shanker Raj Barsila, Nikki Kells, Kristina Ruth Mueller and Dhurba DC
Ruminants 2024, 4(1), 136-151; https://doi.org/10.3390/ruminants4010009 - 8 Mar 2024
Viewed by 1973
Abstract
In order to develop a yak/chauri-specific welfare assessment protocol, we sent a set of 31 potential welfare measures to 120 Nepalese experts and asked them to identify the measures that they thought would be useful and propose additional useful measures. Eighty-three experts responded, [...] Read more.
In order to develop a yak/chauri-specific welfare assessment protocol, we sent a set of 31 potential welfare measures to 120 Nepalese experts and asked them to identify the measures that they thought would be useful and propose additional useful measures. Eighty-three experts responded, with 13 measures being identified by >50% of respondents as likely to be useful. These thirteen measures plus one new measure (hematology) were included in an assessment protocol that was tested in the second phase of this study in five chauri herds in two districts in northern Nepal. Animal-based evaluations along with sampling for mastitis, intestinal parasites, and hematology were undertaken during or just after morning milking. Resource- and record-based measures were assessed through structured interviews, with verifications on-site where possible. No chauris exhibited poor body conditions, skin injuries, significant locomotion issues, or significant subclinical mastitis. Fecal testing suggested a high prevalence of intestinal parasites at the herd level, while blood testing suggested no evidence of hematological abnormalities. However, for both results, we need more data to use these effectively as measures of welfare. The resource-based assessment revealed significant challenges across all resources, and veterinary services were reported as being inadequate. A high estimated annual mortality rate (10–21%) needs further investigation. This protocol provided a useful start towards developing a welfare assessment protocol for yak/chauri and identified issues that need addressing to optimize chauri welfare. Full article
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<p>Map showing the locations within Nepal of the farms where the welfare of chauris was assessed.</p>
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11 pages, 6862 KiB  
Communication
Ultrasonography and Postmortem Magnetic Resonance Imaging of Bilateral Ocular Disease in a Heifer
by Takeshi Tsuka, Yuji Sunden, Takehito Morita, Md Shafiqul Islam and Osamu Yamato
Ruminants 2024, 4(1), 125-135; https://doi.org/10.3390/ruminants4010008 - 8 Mar 2024
Viewed by 1361
Abstract
Bovine ocular diseases are typically characterized by the concurrent appearances of both macroscopic and intraocular abnormalities. This study examines the diagnostic efficacy of a combination of ultrasonography and magnetic resonance imaging (MRI) for the bilateral ocular disease observed in a 9-month-old Japanese Black [...] Read more.
Bovine ocular diseases are typically characterized by the concurrent appearances of both macroscopic and intraocular abnormalities. This study examines the diagnostic efficacy of a combination of ultrasonography and magnetic resonance imaging (MRI) for the bilateral ocular disease observed in a 9-month-old Japanese Black heifer. This case presented with bilateral strabismus and a white-colored lens structure in the right eye. A combination of ultrasonography and MRI revealed formations of corn-like and V-shaped membranous structures within the vitreous cavities of the left and right eyeballs, respectively. In the right eye, a cataract was suspected on both ultrasonogram and MRI. This case involved bilateral retinal detachments and strabismus similar to the signs of an autosomal recessive hereditary ocular disease; however, the cataract in the right eye differed from that hereditary disease. Finally, in genetic analysis, a known mutation of the WFDC1 gene was not detected. Ultrasonography is superior to MRI in demonstrating intraocular pathological changes. On the other hand, MRI is helpful for evaluating invasiveness of the ocular lesions to the peripheral structures. Thus, the combined use of these imaging modalities is recommended for diagnosing various bovine ocular diseases. Full article
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<p>Macroscopic appearances of the left eye (<b>a</b>) and right eye (<b>b</b>). (<b>a</b>) Severe strabismus is evident in the slightly protruded left eye. (<b>b</b>) Moderate strabismus is evident in the right eye with a whole, white-colored lens, indicating a cataract.</p>
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<p>Ultrasonographic appearances of the left eyeball (<b>a</b>,<b>b</b>) and right eyeball (<b>c</b>). (<b>a</b>) The lens is anechoic and surrounded by echogenic lines of the anterior and posterior lens capsules within the left eyeball. (<b>b</b>) The corn-like structure is heterogeneously echogenic and present between the posterior lens capsule and the deepest scleroretinal rim within the vitreous body of the left eyeball. (<b>c</b>) A V-shaped membranous structure is present and accompanied by two cystic structures within the vitreous body of the right eyeball. The enlarged lens is heterogeneously anechoic to hypoechoic and is lined by the thickened and irregular anterior and posterior lens capsules. Scale = 10 mm.</p>
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<p>Dorsal T1-weighted (<b>a</b>–<b>c</b>) and T2-weighted (<b>d</b>–<b>f</b>) views of the skull demonstrating the left and right eyeballs. (<b>a</b>) A V-shaped structure (empty and filled arrowheads) is seen within the vitreous body of the right eyeball. The tip of the V-shaped structure ends in the area of the optic nerve (arrow). (<b>b</b>) Two cystic structures are slightly evident in the center of one line of the V-shaped structure (empty arrowhead), despite no cystic structure being evident in another line (filled arrowhead) within the vitreous body of the right eyeball. A corn-like structure is not evident within the vitreous body of the left eyeball. (<b>c</b>) The right lens (filled arrow) is a spherical structure appearing entirely by a high signal intensity. The left lens (empty arrow) is normally visualized as a low signal intensity’s center surrounded by a high signal intensity’s line of the anterior and posterior lens structures. (<b>d</b>–<b>f</b>) Abnormal membranous structures are not evident within the vitreous bodies of the left and right eyeballs. The right lens is enlarged in the anteroposterior direction. Scale = 10 mm.</p>
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<p>Macroscopic view (<b>a</b>) and histopathology (<b>b</b>) of the lens within the right eyeball. (<b>a</b>) The lens is spherical, and diffusely clouded and white-colored. (<b>b</b>) In the cortex of the lens, mineralization (right upper area) and aggregated globular bodies (Morganian globules; inset) are scattered. The subcapsular region (left area) contains a mild proliferation of fibrous cells with an eosinophilic collagenous fibers deposition on the entire circumference of the lens structure (HE). Bar = 100 µm.</p>
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<p>Histopathology of the membranous substances within the left eyeball (<b>a</b>) and right eyeball (<b>b</b>). (<b>a</b>) Membranous structures within the left eyeball consist of the retina. The retina is detached from the pigment layer (left upper area). The optic disc is located in the left lower area. (<b>b</b>) Distorted membranous structures consist of the retina within the right eyeball. The retinae of both sides are atrophic; however, the layered construction is recognizable including a photoreceptor layer to the optic nerve layer as shown in the inset (HE). Bar = 500 µm.</p>
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<p>Forward (<b>a</b>) and reverse (<b>b</b>) sequences of the bovine <span class="html-italic">WFDC1</span> gene exon 2. Underlines show the wild-type nucleotide sequences at the position of g.10567100_10567101 in the bovine genome database (ARS-UCD1.2).</p>
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13 pages, 511 KiB  
Article
Evaluation of Precision Ingredient Inclusion on Production Efficiency Responses in Finishing Beef Cattle
by Santana R. Hanson, Erin. R. DeHaan, Forest L. Francis, Warren C. Rusche and Zachary K. Smith
Ruminants 2024, 4(1), 112-124; https://doi.org/10.3390/ruminants4010007 - 22 Feb 2024
Viewed by 1013
Abstract
Two randomized complete block design experiments evaluated the influence that varying degrees of ingredient inclusion accuracy in a finishing diet have on growth performance and carcass traits. Treatments included (1) normal inclusion tolerance with a 0.454 kg tolerance for all ingredients (CON) or [...] Read more.
Two randomized complete block design experiments evaluated the influence that varying degrees of ingredient inclusion accuracy in a finishing diet have on growth performance and carcass traits. Treatments included (1) normal inclusion tolerance with a 0.454 kg tolerance for all ingredients (CON) or (2) variable inclusion tolerance where each ingredient was randomly increased or decreased but the targeted as-fed quantity for the daily delivery was met (VAR). In Experiment. 1, black Angus heifers (n = 60; initial shrunk BW = 460 ± 26.2 kg) were used in a 112 d experiment. Ten pens in total (5 pens/treatment, 6 heifers/pen) were used. The targeted diet (DM basis) consisted of high-moisture ear corn (75%), dried distiller’s grains (20%), and a liquid supplement (5%). As-fed inclusion rates for DDGS and LS varied from formulated targets by −20, −15, −10, −5, 0, +5, +10, +15 or +20%. The HMEC inclusion was adjusted so that the targeted as-fed amount of the diet was delivered daily. Treatment did not alter ADG, DMI, G:F, HCW, dressing percentage, rib-eye area, rib fat, USDA marbling score, KPH, yield grade, retail yield, empty body fat, or body weight at 28% estimated EBF, nor liver abscess prevalence or severity (p ≥ 0.15). In Exp. 2, Charolais–Angus cross steers (n = 128; initial shrunk BW = 505 ± 32.1 kg) were used in a 94 d experiment. Steers were assigned to pens (8 pens/treatment; 8 steers/pen) and one of the two management strategies used in Exp. 1 was employed. Ractopamine HCl was fed (300 mg per head daily) during the final 28 d. Diets consisted of (DM basis) dry-rolled corn (63%), dried distiller’s grains plus solubles (15%), liquid supplement (5%), grass hay (7%), and corn silage (10%). Ingredient inclusions were randomized in the same manner as Exp. 1, except LS inclusion was held constant. Corn silage inclusion was adjusted so that the targeted as-fed amount of the diet was delivered each day. Steers from VAR had increased (p = 0.01) DMI, but similar (p = 0.75) ADG resulting in reduced (p ≤ 0.02) G:F and growth-performance-predicted Net Energy for maintenance and gain. Treatment did not influence (p ≥ 0.38) HCW, dressing percentage, rib-eye area, rib fat, KPH, yield grade, retail yield, empty body fat, or body weight at 28% estimated EBF. A tendency for an increased USDA marbling score (p = 0.08) was noted in VAR. Under the conditions of this experiment, randomly altering ingredient proportions can impact growth performance and efficiency measures depending upon the type of finishing diet fed. Full article
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<p>Regression of daily intake variation (ED) determined according to [<a href="#B9-ruminants-04-00007" class="html-bibr">9</a>] regressed against gain-to-feed ratio (G:F). Treatments included (1) normal inclusion tolerance with a 0.454 kg tolerance for all ingredients (Constant) or (2) variable inclusion tolerance where each ingredient was randomly increased or decreased but the targeted as-fed quantity for the daily delivery was met (variable). Slopes and intercepts differed (<span class="html-italic">p</span> ≤ 0.01). For constant, G:F = −0.0060 (±0.00210)ED + 0.7767 (±0.22269), R<sup>2</sup> = 0.86, <span class="html-italic">p</span> = 0.01; for variable, G:F = 0.0027 (±0.00210)ED − 0.1467 (±0.22269), R<sup>2</sup> = 0.28, <span class="html-italic">p</span> = 0.17.</p>
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22 pages, 8958 KiB  
Article
The Use of Interactive Visualizations for Tracking Haplotypic Inheritance in Livestock
by Alana Selli, Stephen P. Miller and Ricardo V. Ventura
Ruminants 2024, 4(1), 90-111; https://doi.org/10.3390/ruminants4010006 - 21 Feb 2024
Viewed by 1216
Abstract
Our objective was to harness the power of interactive visualizations by utilizing open-source tools to develop an efficient strategy for visualizing Single Nucleotide Polymorphism data within a livestock population, focusing on tracking the transmission of haplotypes. To achieve this, we simulated a realistic [...] Read more.
Our objective was to harness the power of interactive visualizations by utilizing open-source tools to develop an efficient strategy for visualizing Single Nucleotide Polymorphism data within a livestock population, focusing on tracking the transmission of haplotypes. To achieve this, we simulated a realistic beef cattle population in order to obtain phased haplotypes and generate the necessary inputs for creating our visualizations. The visualization tool was built using Python and the Plotly library, which enables interactivity. We set out to explore three scenarios: trio comparison, visualization of grandparents, and half-sibling evaluation. These scenarios enabled us to trace the inheritance of genetic segments, identify crossover events, and uncover common regions within related and unrelated animals. The potential applications of this approach are significant, particularly for improving genomic selection in smaller breeding programs and farms, and it provides valuable insights for guiding more in-depth genomic region analysis. Beyond its practical applications, we believe this strategy can be a valuable educational tool, helping educators clarify complex concepts like Mendelian sampling and haplotypic diversity. Furthermore, we hope it will encourage livestock producers to adopt advanced technologies like genotyping and genomic selection, thereby contributing to the advancement of livestock genetics. Full article
(This article belongs to the Special Issue Beef Cattle Production and Management)
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<p>The structure of the simulation of beef cattle populations.</p>
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<p>The relation of files required to implement the visualization tool.</p>
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<p>How the similarity between haplotypes of the reference individual and of the other individuals is calculated.</p>
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<p>The initial screen of the visualization tool. Section (<b>A</b>) displays the haplotypes of the reference animal (the Sire). Section (<b>B</b>) shows all other individuals in the data set (the Dam and the Progeny). Section (<b>C</b>) allows for the application of filters. Letters in brackets exhibit which section the filters are applied to.</p>
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<p>Linkage disequilibrium decay on chromosome 1 in a simulated beef cattle population.</p>
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<p>Principal components analysis of seven simulated beef cattle populations. Numbers in parentheses correspond to the percentage of variance explained by the first two principal components, PC1 and PC2.</p>
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<p>Visualization of trios—progeny as the reference individual with either the paternal (<b>a</b>) or maternal (<b>b</b>) strand as reference. Only identical haplotypes are highlighted.</p>
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<p>The visualization of haplotypes with great positive effects, where the second strand of the second chromosome of the Sire was selected as a reference. Images show (<b>a</b>) the application of the effect filter &gt; 0.3; (<b>b</b>) the application of the effect filter &gt; 0.3 and the similarity filter &gt; 0.7; and (<b>c</b>) the application of the effect filter &gt; 0.3 and the similarity filter &gt; 0.9.</p>
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<p>The visualization of three generations. The grandfather (sire side) with either (<b>a</b>) the first strand or (<b>b</b>) the second strand selected as a reference. Only Identical Haplotypes are highlighted.</p>
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<p>The visualization of haplotypes from the second strand of the grandfather (Sire side) with effects (<b>a</b>) equal to or greater than 0.2 and (<b>b</b>) equal to or smaller than −0.2. Identical Haplotypes are set to yes.</p>
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<p>Histograms of the distribution of the haplotype similarity within groups of progeny with the (<b>a</b>) Top and (<b>b</b>) Bot Sires, considering both strands from the progeny and both strands from the Sire, in chromosome 1. The x-axis represents bins (including the lowest value) of the percentage of haplotypes in chromosome 1 that present a similarity equal to or greater than 90% or 100%. The y-axis represents the percentage of strands in the group that fall within each bin. The total number of strands evaluated was equal to 40 (10 progeny in each group ×2 strands from the progeny ×2 strands from the Sire).</p>
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11 pages, 2859 KiB  
Article
The Impact of Liver Abscesses on Performance and Carcass Traits in Beef Cattle: A Meta-Analysis Study
by Rodrigo de Nazaré Santos Torres, David Attuy Vey da Silva, Luis Arthur Loyola Chardulo, Welder Angelo Baldassini, Rafael Assis Torres de Almeida, Marco Tulio Costa Almeida, Rogério Abdallah Curi, Guilherme Luis Pereira, Jon Patrick Schoonmaker and Otavio Rodrigues Machado Neto
Ruminants 2024, 4(1), 79-89; https://doi.org/10.3390/ruminants4010005 - 12 Feb 2024
Viewed by 1821
Abstract
The use of high-grain diets in feedlots is associated with the development of acidosis and ruminitis, which can lead to the occurrence of liver abscesses (LAs). However, the effect of LA on carcass traits is not well known. This study assessed the effects [...] Read more.
The use of high-grain diets in feedlots is associated with the development of acidosis and ruminitis, which can lead to the occurrence of liver abscesses (LAs). However, the effect of LA on carcass traits is not well known. This study assessed the effects of LA on the performance and carcass traits of beef cattle. Nine peer-reviewed publications with forty-seven treatment means were included in the data set. The effects of the LA were evaluated by examining the weighted mean difference (WMD) between LA (animal with LA) and control treatment (animal without LA). Heterogeneity was explored by meta-regression, followed by a subgroup analysis of the scores and percentages of liver abscess and concentrate level in the feedlot diet. Animals affected by LA showed a reduction in dry matter intake (−1.03%) and feed efficiency (−1.82%). Animals with an LA score of “A” (one or two small abscesses) exhibited a decrease in carcass weight (WMD = 3.41 kg; p = 0.034) and ribeye area (WMD = −1.37 cm2; p = 0.019). When assessing the impact of LA on carcass traits, the most reliable finding indicates a 1.21% reduction in the ribeye area, with no adverse effects observed on subcutaneous fat thickness or the marbling score in the carcass. Full article
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<p>Flowchart showing results obtained from the search strategy and the selection of eligible studies for the meta-analysis on the effect of liver abscesses on performance and carcass traits in beef cattle.</p>
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<p>Forest plot of standardized mean difference (SMD) between the means calculated for the marbling score in beef cattle with and without liver abscesses. Liver abscess score is standardized after Elanco [<a href="#B15-ruminants-04-00005" class="html-bibr">15</a>] where: A = small abscesses (n = 1–2) or well-organized abscesses (n = 2–4) usually under one inch in diameter (the remainder of the liver looks healthy); A+ = large abscesses (n ≥ 1), with inflammation of surrounding liver tissue. Solid squares for the separate individual studies (denoted by roman numerals) represent weighting by the inverse of their respective variances. The horizontal lines represent the 95% confidence interval (CI) of each study. The open diamond represents the overall SMD and its width represents the associated 95% confidence interval. I-squared (I<sup>2</sup>) represents the proportion of total variation of effect size estimates due to heterogeneity, and % weight represents the contribution of the study to the overall effect size. Weights are from the random effects model. DL represents the treatment means weighted by the inverse of the variance, according to the method proposed by DerSimonian and Laird [<a href="#B25-ruminants-04-00005" class="html-bibr">25</a>] for a random effects model. The reference are Calderon-Corte and Zinn [<a href="#B17-ruminants-04-00005" class="html-bibr">17</a>], Depenbusch et al. [<a href="#B18-ruminants-04-00005" class="html-bibr">18</a>], Huck et al. [<a href="#B19-ruminants-04-00005" class="html-bibr">19</a>], May et al. [<a href="#B21-ruminants-04-00005" class="html-bibr">21</a>], Mir et al. [<a href="#B22-ruminants-04-00005" class="html-bibr">22</a>], Salim et al. [<a href="#B23-ruminants-04-00005" class="html-bibr">23</a>].</p>
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<p>Subgroup analysis of the effect of liver abscess on beef cattle performance. Liver abscess score is standardized after Elanco [<a href="#B15-ruminants-04-00005" class="html-bibr">15</a>] where: A = small abscesses (n = 1–2) or well-organized abscesses (n = 2–4) usually under one inch in diameter (the remainder of the liver looks healthy); A+ = large abscesses (n ≥ 1), with inflammation of surrounding liver tissue. WMD = weighted mean differences between the presence and absence of liver abscess.</p>
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<p>Subgroup analysis of the effect of liver abscess on carcass traits in beef cattle. Liver abscess score = liver abscess score, standardized (Elanco, 15): A= small abscesses (n = 1–2) or well-organized abscesses (n = 2–4) usually under one inch in diameter (the remainder of the liver looks healthy); A+ = large abscesses (n ≥ 1), with inflammation of surrounding liver tissue. WMD = weighted mean differences between the presence and absence of liver abscess.</p>
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32 pages, 2781 KiB  
Article
Evaluation and Development of a Nutrition Model to Predict Intake and Growth of Suckling Calves
by Geovana Camila Baldin, Caleb Hildebrand, Robert L. Larson and Phillip A. Lancaster
Ruminants 2024, 4(1), 47-78; https://doi.org/10.3390/ruminants4010004 - 28 Jan 2024
Viewed by 1279
Abstract
The objective of this study was to evaluate and develop equations to predict forage intake and growth of calves throughout the suckling period of beef calves grazing on forage or dairy calves fed harvested forage. Milk and forage intake and body weight data [...] Read more.
The objective of this study was to evaluate and develop equations to predict forage intake and growth of calves throughout the suckling period of beef calves grazing on forage or dairy calves fed harvested forage. Milk and forage intake and body weight data for individual animals were collected from published theses (one using bottle-fed dairy calves and one using suckling beef calves). A nutrition model was constructed using milk and forage intake equations and growth equations. Additional datasets were compiled from the literature to develop equations to adjust the original nutrition model for forage digestibility, milk composition, and growth. In general, the original nutrition model predicted the forage intake and body weight of dairy calves with moderate-to-high precision (CCC = 0.234 to 0.929) and poor accuracy (MB = −341.16 to −1.58%). Additionally, the original nutrition model predicted forage intake and body weight in beef calves with poor-to-moderate precision (CCC = 0.348 to 0.766) and accuracy (MB = 6.39 to 57.67%). Adjusted nutrition models performed better with the best model precisely (CCC = 0.914) predicting forage intake and precisely (CCC = 0.978) and accurately (MB = 2.83%) predicting body weight in dairy calves. The best adjusted nutrition model predicted forage intake and body weight with high precision (CCC = 0.882 and 0.935) and moderate accuracy (MB = −7.01 and −7.34) in beef calves. Nutrition models were able to adequately predict the forage intake and growth of calves with adjustments made to standard milk energy concentrations and growth equations. Full article
(This article belongs to the Special Issue Beef Cattle Production and Management)
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<p>Observed milk intake and predicted milk yield using milk yield equations over the suckling period in the dairy calf dataset. NASEM = milk yield Equation (1a–c); WOOD = milk yield Equation (2a–e); and BOTH = combination of NASEM used when estimated peak milk yield was &gt; 10 kg/d and WOOD used when estimated peak milk yield was ≤ 10 kg/d.</p>
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<p>Observed and predicted forage intake (<b>a</b>) and body weight (<b>b</b>) for the 5 forage intake equations over the suckling period in the dairy calf intake and body weight dataset using the original model. Eq91 = Equation (3); Eq67 = Equation (4); Eq25 = Equation (5); Eq17 = Equation (6a–c); and Eq21 = Equation (7).</p>
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<p>Observed and predicted forage intake (<b>a</b>) and body weight (<b>b</b>) for the 5 forage intake equations over the suckling period in the dairy calf intake and body weight dataset using the adjusted model. Eq91 = Equation (3); Eq67 = Equation (4); Eq25 = Equation (5); Eq17 = Equation (6a–c); and Eq21 = Equation (7).</p>
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<p>Observed and predicted milk intake using milk yield equations over the suckling period in the beef calf intake and body weight dataset. Calves were born in March and April. NASEM = milk yield Equation (1a–c) and WOOD = milk yield Equation (2a–e).</p>
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<p>Observed and predicted forage intake (<b>a</b>) and body weight (<b>b</b>) for 5 forage intake equations over the suckling period in the beef calf intake and body weight dataset using the original model. Calves were born in March and April. Eq91 = Equation (3); Eq67 = Equation (4); Eq25 = Equation (5); Eq17 = Equation (6a–c); and Eq21 = Equation (7).</p>
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<p>Observed and predicted forage intake (<b>a</b>) and body weight (<b>b</b>) for 5 forage intake equations over the suckling period in the beef calf intake and body weight dataset using the adjusted model. Calves were born in March and April. Eq91 = Equation (3); Eq67 = Equation (4); Eq25 = Equation (5); Eq17 = Equation (6a–c); and Eq21 = Equation (7).</p>
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25 pages, 1368 KiB  
Review
Enhancing Animal Production through Smart Agriculture: Possibilities, Hurdles, Resolutions, and Advantages
by Moammar Dayoub, Saida Shnaigat, Radi A. Tarawneh, Azzam N. Al-Yacoub, Faisal Al-Barakeh and Khaled Al-Najjar
Ruminants 2024, 4(1), 22-46; https://doi.org/10.3390/ruminants4010003 - 26 Jan 2024
Cited by 6 | Viewed by 8252
Abstract
Smart livestock farming utilizes technology to enhance production and meet food demand sustainably. This study employs surveys and case studies to gather data and information, subsequently analyzing it to identify opportunities and challenges. The proposed solutions encompass remote sensing, technology integration, farmer education, [...] Read more.
Smart livestock farming utilizes technology to enhance production and meet food demand sustainably. This study employs surveys and case studies to gather data and information, subsequently analyzing it to identify opportunities and challenges. The proposed solutions encompass remote sensing, technology integration, farmer education, and stakeholder engagement. The research delves into smart technologies in animal production, addressing opportunities, challenges, and potential solutions. Smart agriculture employs modern technology to improve efficiency, sustainability, and animal welfare in livestock farming. This includes remote monitoring, GPS-based animal care, robotic milking, smart health collars, predictive disease control, and other innovations. Despite the great promise of smart animal production, there are existing challenges such as cost, data management, and connectivity. To overcome these challenges, potential solutions involve remote sensing, technology integration, and farmer education. Smart agriculture provides opportunities for increased efficiency, improved animal welfare, and enhanced environmental conservation. A well-planned approach is crucial to maximize the benefits of smart livestock production while ensuring its long-term sustainability. This study confirms the growing adoption of smart agriculture in livestock production, with the potential to support the sustainable development goals and deliver benefits such as increased productivity and resource efficiency. To fully realize these benefits and ensure the sustainability of livestock farming, addressing cost and education challenges is essential. Therefore, this study recommends promoting a positive outlook among livestock stakeholders and embracing smart agriculture to enhance farm performance. Full article
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Figure 1
<p>Flowchart of the primary smart agricultural applications in livestock production.</p>
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<p>Flowchart of opportunities and challenges for smart livestock production farms.</p>
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<p>Flowchart of solutions and benefits for smart livestock production farms.</p>
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12 pages, 4722 KiB  
Article
Effect of Different Anthelmintic Drugs on the Development and Efficacy of Duddingtonia flagrans
by Sara Zegbi, Federica Sagües, Carlos Saumell, Laura Ceballos, Paula Domínguez, Inés Guerrero, Milagros Junco, Lucía Iglesias and Silvina Fernández
Ruminants 2024, 4(1), 10-21; https://doi.org/10.3390/ruminants4010002 - 11 Jan 2024
Cited by 1 | Viewed by 1357
Abstract
Nematophagous fungi are a biological control tool used against gastrointestinal nematodes in livestock. These fungi prey on free-living larvae in faeces and could be affected by active drugs excreted post-treatment. This study aimed to determine in vitro and under environmental conditions the effect [...] Read more.
Nematophagous fungi are a biological control tool used against gastrointestinal nematodes in livestock. These fungi prey on free-living larvae in faeces and could be affected by active drugs excreted post-treatment. This study aimed to determine in vitro and under environmental conditions the effect of the following anthelmintics on the fungus Duddingtonia flagrans: ivermectin, levamisole, albendazole, fenbendazole and ricobendazole. The in vitro effect of anthelmintics on fungal growth and predatory capacity was assessed in corn meal agar and coprocultures, respectively. Ivermectin (1, 2 and 10 ppm), fenbendazole (0.027, 0.054 and 1 ppm) and albendazole (1 ppm) significantly affected fungal development. The fungal efficacy against L3 was high in the control and levamisole coprocultures but decreased significantly in the presence of albendazole, fenbendazole, ricobendazole and ivermectin. The impact of levamisole on D. flagrans was further assessed under environmental conditions in autumn and winter; the fungal efficacy measured in faecal pats and the surrounding herbage was not affected by levamisole at any time. This study shows that using albendazole, fenbendazole, ricobendazole or ivermectin may compromise fungal activity, as these drugs affect the free-living stages of nematodes in faeces, but levamisole can be safely considered in parasite control strategies involving D. flagrans and anthelmintic treatments. Full article
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Figure 1
<p>Numbers of L3 recovered from coprocultures containing GIN eggs to which chlamydospores of <span class="html-italic">D. flagrans</span> and/or anthelmintics diluted in methanol had been added. Each bar represents the mean (<span class="html-italic">n</span> = 10) with its standard deviation, expressed as L3/10 g faeces. ***: <span class="html-italic">p</span> &lt; 0.001; Df: <span class="html-italic">D. flagrans</span>; ABZ: albendazole; FBZ: fenbendazole; RBZ: ricobendazole; IVM: ivermectin; LEV: levamisole; met: methanol.</p>
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<p>Radial growth of <span class="html-italic">D. flagrans</span> in CMA plates containing anthelmintics diluted in methanol (assay 1). Each point shows the mean (<span class="html-italic">n</span> = 12) and its standard deviation, expressed as mm of mycelial growth every 24 h. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001. Control: plates with fungus and without anthelmintics; Methanol: plates with fungus and only the diluent; IVM 2: ivermectin 2 ppm; LEV 1: levamisole 1 ppm; RBZ 1: ricobendazole 1 ppm; FBZ 1: fenbendazole 1 ppm; ABZ 1: albendazole 1 ppm.</p>
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<p>Radial growth of <span class="html-italic">D. flagrans</span> in CMA plates containing anthelmintics diluted in methanol (assay 2). Each point shows the mean (<span class="html-italic">n</span> = 10) and its standard deviation, expressed as mm of mycelial growth every 24 h. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; ****: <span class="html-italic">p</span> &lt; 0.0001. Control: plates with fungus and without anthelmintics; Methanol: plates with fungus and only the diluent; IVM 1: ivermectin 1 ppm; IVM 10: ivermectin 10 ppm; LEV 1: levamisole 1 ppm; RBZ 2.77: ricobendazole 2.77 ppm; FBZ0.027: fenbendazole 0.027 ppm; FBZ 0.054: fenbendazole 0.054 ppm; ABZ 0.027: albendazole 0.027 ppm; ABZ 0.054: albendazole 0.054 ppm; DMSO: plates with fungus and dimethylsulfoxide.</p>
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<p>Average daily growth rate of <span class="html-italic">D. flagrans</span> growing in CMA plates containing anthelmintics diluted in methanol (assay 1). Each bar represents the mean (<span class="html-italic">n</span> = 12) with its standard deviation, expressed as mm/24 h. **: <span class="html-italic">p</span> &lt; 0.001; ****: <span class="html-italic">p</span> &lt; 0.0001. Control: plates with fungus and without anthelmintics; Methanol: plates with fungus and only the diluent; IVM: ivermectin 2 ppm; LEV: levamisole 1 ppm; ABZ: albendazole 1 ppm; FBZ: fenbendazole 1 ppm; RBZ: ricobendazole 1 ppm.</p>
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<p>Average daily growth rate of <span class="html-italic">D. flagrans</span> growing in CMA plates containing anthelmintics diluted in methanol (assay 2). Each bar represents the mean (<span class="html-italic">n</span> = 10) with its standard deviation, expressed as mm/24 h. *: <span class="html-italic">p</span> &lt; 0.05; ****: <span class="html-italic">p</span> &lt; 0.0001. Control: plates with fungus and without anthelmintics; DMSO: plates with fungus and dimethylsulfoxide; Methanol: plates with fungus and only the diluent; IVM 1: ivermectin 1 ppm; IVM 10: ivermectin 10 ppm; LEV 1: levamisole 1 ppm; ABZ 0.027: albendazole 0.027 ppm; ABZ 0.054: albendazole 0.054 ppm; FBZ 0.027: fenbendazole 0.027 ppm; FBZ 0.054: fenbendazole 0.054 ppm; RBZ 2.77: ricobendazole 2.77 ppm.</p>
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9 pages, 266 KiB  
Communication
Chemical Composition and In Vitro Nutritive Evaluation of Pomegranate and Artichoke Fractions as Ruminant Feed
by Trinidad de Evan, Carlos N. Marcos and María Dolores Carro
Ruminants 2024, 4(1), 1-9; https://doi.org/10.3390/ruminants4010001 - 2 Jan 2024
Viewed by 2178
Abstract
The aim of this work was to assess the chemical composition and in vitro ruminal fermentation of samples (n = 3) of pomegranate (peels (PPs) and seeds (PSs)) and artichoke (hearts (AHs) and stems (ASs)) wastes. Dried orange pulp (DOP) and tomato pomace [...] Read more.
The aim of this work was to assess the chemical composition and in vitro ruminal fermentation of samples (n = 3) of pomegranate (peels (PPs) and seeds (PSs)) and artichoke (hearts (AHs) and stems (ASs)) wastes. Dried orange pulp (DOP) and tomato pomace (TP) were used as reference feeds. All wastes had low dry matter (DM; lower than 33.0 and 12.0% for pomegranate and artichoke, respectively). The DM of pomegranate fractions was rich in sugars (>42.0%) and contained low protein (<8.0%) and neutral detergent fiber (NDF; <27.0%), whereas that of both artichoke fractions had high protein (>18.0%) and NDF (>36.0%) and low sugars content (<9.2%). Pomegranate seeds were more rapidly and extensively fermented in vitro than PPs, but both were less degradable and contained less metabolizable energy (ME) than DOP (7.43, 11.0 and 12.5 MJ ME/kg DM, respectively). Although AHs were more rapidly fermented and produced more volatile fatty acids (VFAs) than ASs, both had lower ME content than TP (9.50, 7.25 and 12.5 MJ ME/kg DM). The analyzed wastes had lower ME content than other by-products, but they were extensively fermented by ruminal microorganisms and could be used as ruminant feeds. Full article
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