bluuebunny/crossref_metadata_2025_split huggingface dataset:
168 American mink Genome Sequencing Project
Authors: Miar Younes 
Published: September 2024

Abstract
The long-term goal of American Mink (Neogale vison) Genome Sequencing Project is to develop novel genomics tools to study the genome biology and evolution of mink. This research agenda is organized around three major goals: 1) development of first chromosome-level genome assembly with high quality for American mink to help understanding the biology and evolution of the order Carnivora, 2) designing the first mink single nucleotide polymorphism (SNP) genotyping array for genomic discovery in mink, and 3) using genomics to understand the genetic architecture of economically important traits in mink. An initial draft of the genome assembly is generated using 2,884,047 PacBio long reads. Integration of Hi-C data into the initial draft led to an assembly with 183 scaffolds and scaffold N50 of 220 Mb. This gap-free genome assembly of American mink (ASM_NN_V1) has a length of 2.68 Gb in which about 98.6% of the whole genome is covered by 15 chromosomes. Significant improvements are observed in genome contiguity, the number of scaffolds, and annotation compared with the first draft of mink genome assembly. A custom genotyping array is developed using the developed mink reference genome and DNA panel composed of 100 whole-genome sequences data of American mink from five color-types. This Affymetrix Mink 70K SNP array created 70,000 SNPs, which is sufficient for conducting genomic studies in the mink research program and beyond. This project would facilitate the discovery of genomic regions, markers and genetic networks underlying economically important traits in American mink, which will contribute novel insights into the genetic architecture of key traits and is a critical step to implement genomic selection in mink breeding programs. The results of this research have the potential to impact the mink industry, especially in North America, and improve the overall performance of the mink industry.
161 Development, deployment, and calibration of genomic selection tools to improve performance traits for Canadian beef cattle
Authors: Li Changxi , Basarab John A , Valente Tiago , Plastow Graham , Stothard Paul , Wang Yining , Vinsky Michael 
Published: September 2024

Abstract
Genomic selection has the potential to accelerate the genetic improvement rate for difficult/expensive to measure traits such as feed efficiency and fertility traits in beef cattle. To develop genomic selection tools for Canadian beef cattle, we collected or sourced phenotypic data of feed efficiency along with other traits including carcass merit, female feed intake and fertility related traits, and their genotypes from both research and commercial herds. The phenotypic data were consolidated and recorded for contemporary groups, leading to the development of data sets including 11,292 beef cattle with residual feed intake, dry matter intake, average daily gain, metabolic body weight, 7,299 to 8,081 cattle with carcass merit traits, and 1,802 to 2,792 cows with feed intake and fertility traits after quality controls. Genotype data of the animals from various single nucleotide polymorphisms (SNP) panels were merged to the same allele format of 50K, and then imputed to 770K SNPs, and eventually to whole genome sequence variants. The refined Canadian beef cattle data sets have not only allowed us to investigate genetic architectures of the beef performance traits but also enabled the development of genomic selection tools including molecular breeding values and multiple trait selection indexes with a moderate to moderately high accuracy for the traits through a cross-validation of within reference data set. The genomic selection tools have been successfully deployed to over 10,415 breeding candidates submitted by Canadian beef producers through multiple demonstration projects. The average accuracy of the molecular breeding values and multiple trait selection index of the commercial beef cattle ranged from 0.38 to 0.49 for feed efficiency and carcass merit traits, and from 0.26 to 0.38 for female feed intake and fertility traits. Current research is focusing on calibrating the genomic selection tools with larger data sets and with more advanced genomic prediction methods.
434 Strategies for improving the efficiency of rumen function
Authors: Gruninger Robert J , O’Hara Eóin , Terry Stephanie A , Payne Nikita A , Dubois Megan M , McAllister Tim A , Ribeiro Jr Gabriel O 
Published: September 2024

Abstract
Ruminants are unique amongst livestock, having the ability to convert low cost and low-quality feedstuffs that cannot be consumed by humans, or non-ruminant animals, into high quality protein. Unfortunately, the low digestibility of these feeds also promotes reduced intake and performance, which limits their inclusion in ruminant diets. Variation in the ability of cattle to digest feed has also been considered one of the main factors affecting feed efficiency. In an extensive grazing system, greater feed intake and digestibility of forages are directly related to the performance of cattle fed. Development of technologies to enhance feed intake and digestibility of forage-based diets within the rumen is essential to increase their utilization in ruminant diets. Data will be presented from past, and current, efforts to understand inter-animal variability in feed digestibility, the linkages between the rumen microbiome and feed digestion efficiency, and to develop enzymes, and chemical pretreatment technologies that will enhance the digestion of lignocellulose in the rumen.
218 Comparison between vegetation indices to estimate biomass in cereal rye
Authors: Fernandes Pedro Henrique Jota , Xiong Yijie , Davies-Jenkins Shelby L , Zhao Biquan , Drewnoski Mary E 
Published: September 2024

Abstract
Accurate forage mass estimation can lead to improved pasture management, but it requires a significant investment of time on the ground sampling. The use of satellite images has potential to reduce the time needed and thus, expediting decision-making processes. Satellite measure various spectral wavelengths of light reflected by plants (i.e., reflectance), and using satellite images, various vegetation indices (VI) can be calculated, such as the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Spectral Feature Depth Vegetation Index (SFDVI). Each of these indices shows different vegetation properties via mathematical combinations of reflectance in two or more spectral wavelengths. For example, NDVI is calculated by reflectance in red and near infrared wavelengths, which is an effective VI to quantify vegetation greenness and vigor. This study aimed to compare the mentioned VI to evaluate their ability in estimating cereal rye (Secala cereale) mass and to compare them with a traditional method of biomass measurement, which relies on the standing height of the forage. From May 2 to 23, 2023, forage mass was measured in 18 paddocks (1.6 ha/paddock). Mass in the paddock was estimated by clipping four randomly located frames (0.49 m2) to ground level. The standing height of the forage was collected from 20 random points, measured in 0.625 cm increments, within each paddock. The forage mass of the paddocks measured from the clipped forage ranged from 212 to 3,382 kg/ha. PlanetScope satellite images from the day of forage mass sampling were obtained using the Planet Explorer platform. This satellite data was chosen due to its high spatial resolution (3 x 3 m2/pixel) and highly available data (theoretically daily). The four VI were calculated from images in QGIS software. A linear regression of each index against the forage mass estimated from the average of the clipped forage, was conducted in SAS using PROC REG. There was a significant (P &lt; 0.01) correlation between each VI with the forage mass. The adjusted R2 was 0.60, 0.58, 0.41, 0.78 and 0.76 for NDVI, EVI, GNDVI, SFDVI and standing height, respectively. The root mean square error (RMSE) was 518, 528, 629, 379, and 396 for NDVI, EVI, GNDVI, SFDVI, and standing height, respectively. All VI exhibited moderately promising results in cereal rye mass estimation. In particular, SFDVI proved to be superior with the greatest adjusted R² and least RMSE, followed by the traditional method of estimating forage mass by standing height. This comparable result between the use of SFDVI from satellite images and traditional ground measurement indicates that the use of VI, SFDVI, specifically in our study, from satellite images can be an effective means for a fast cereal rye mass estimation.
221 Multivariate statistics as a quick tool to screen perennial forages for forage value
Authors: Azuhnwi Blasius N , Omokanye Akim 
Published: September 2024

Abstract
Perennial forages seeded as straight grasses and legumes or in mixtures make a valuable contribution as feed for the beef cattle industry in Alberta. With new varieties constantly being released, there is need to quickly screen these for their forage value. Principal component analysis (PCA) is a multivariate analysis which can simplify a large dataset by identifying and representing patterns in fewer dimensions or factors. In total, 23 and 20 perennial straight grass and legume varieties respectively, and 15 commercial mixes were seeded as a complete randomized block design on small plots with 4 replicates on May 31, 2021 at Debolt, Alberta. Plots were harvested on July 12 and September 15, 2022 as first and second cut, respectively. The harvested biomass was weighed, and a sub-sample was air-dried to determine forage dry matter (DM). Dried forage samples were submitted for various forage quality analyses. A PCA was conducted on the correlation matrix of the data set of forage DM yield and quality. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity verified the sampling adequacy (KMO = 0.71 and P &gt; 0.001 respectively) for the analysis. The first three principal components (PC) had eigenvalues over Kaiser’s criterion of 1 and in combination explained 81 % of the variance (Table 1). Most of the legumes were greater in crude protein, yielded greater in forage DM, macro minerals (Ca, Mg and P) and micro minerals (Zn, Fe and Cu) compared with grasses and mixtures (Figure 1). Among the legumes, the following alfalfa varieties exhibited high forage value (i.e. combined both forage yield and quality): Peace Alfalfa, PV Ultima Alfalfa, Alfalfa Dalton, Algonquin, AC Yellow Head, Trueman Alfalfa. Noteworthy are the 3 non-alfalfa species (birdsfoot trefoil, sainfoin and cicer milkvetch) which loaded on the opposite quadrant to most of the alfalfa varieties. The grass varieties were more dispersed on the multivariate space of the first two PC thus exhibiting the inherent variability in forage value of the grass collection. A few grass species nonetheless distinguished themselves in terms of greater forage yield and quality and include AC Saltlander, Richard Timothy, Rocky Mountain Fescue and Hybrid Bromegrass. Most of the grasses were high in fiber and loaded primarily opposite to the positive forage value attributes. The mixtures loaded similarly to the grasses with a couple loading close to the legumes but most close to the grasses, revealing their high fiber content. The mixtures which loaded close to the legumes were legume-dominated mixtures such as Alfalfa/Sainfoin Mix, All LegumeMix, HayMix and LegumeMaster Mix which had between 90 to 100% legumes. PCA proved to be a useful tool in distinguishing treatments with high forage value as these corresponded with widely used varieties in Alberta.
Tables and Figures 2.pdf
394 Cover crops for efficient grazing systems
Authors: Suazo Sofia A , Pereira Jose D , Mullenix Kim , Smith W Brandon , Dillard S Leanne L 
Published: September 2024

Abstract
Grazing of cool-season cover crops provides a sustainable solution for extending the grazing season while potentially reducing the environmental impact of conventional row-crop farming practices. The primary objective of this study was to evaluate how different mixtures of cover crops affect stocker performance when grazed. The study was conducted across 18 paddocks that were sown with grass [‘Coker’ oat (Avena sativa)], grass + legume [‘Dixie’ crimson clover (Trifolium incarnatum)], or grass + legume + brassica [‘Turnip’ brassica (Brassica rapa subsp.; rapa)] at the Wiregrass Research and Extension Center in Headland, AL. These paddocks were randomly assigned to either grazed or un-grazed conditions (n = 3). In this study, yearling beef steers with an average body weight (BW) of 266 ± 44 kg were grazed in paddocks using the put and take method to maintain a forage allowance of 1 kg dry matter per kilogram of BW. Forage samples were collected biweekly to analyze biomass, botanical composition, and forage nutritive value. Grazing occurred from January 17 to March 22, 2023 (65 d). Steer BW were recorded at the beginning and end of the grazing season to calculate average daily gain. The data were subjected to analysis as a two-way ANOVA using Glimmix in SAS 9.4 (SAS Institute, Cary, NC), harvest date was considered a repeated measure. No treatment × grazing interaction was observed on forage yield performance [average (AVG) forage yield kg DM/ha; P = 0.460]. In addition, no difference was observed among the different cover crop mixtures (AVG forage yield kg DM/ha; P = 0.670). Grazed paddocks had a greater crude protein when compared with ungrazed paddocks (Grazed vs. Ungrazed %; P = 0. 017). However, there was no effect of cover crop mixture on crude protein (CP) concentration (AVG CP %; P = 0.670 for treatment effect). These results indicate that cool-season annual cover crops can provide sufficient forage yield and quality to support sufficient animal gains, even during the relatively short grazing season. Incorporating cover crops not only improves agricultural sustainability but can also enhance the resilience and long-term health of the agroecosystem.
369 Unlocking the mysteries of cow and calf grazing behavior on rugged rangeland pastures using GPS and accelerometer sensor technology
Authors: Sprinkle Jim E , Sullivan Landon , Jensen Kevin S , Ellison Melinda J , Hall John B , Willmore Carmen M , Sagers Joseph , Yelich Joel V , Glaze James B , Brennan Jameson R , Lewis Ronald M , Taylor Joshua B , Murdoch Brenda M , Stegemiller Morgan R , Tolleson Douglas R , Clark Pat , Roberts-Lew Meghan , Lamb Jim 
Published: September 2024

Abstract
Studies were undertaken to assess low residual feed intake (RFI; efficient, EFF) and high RFI (inefficient, INE) cattle grazing behavior and performance on sagebrush steppe rangeland in Idaho. Global positioning system (GPS) and 3-axis accelerometers were used. As nonlactating 2-yr-old cattle grazed dormant forage, INE cattle lost more body weight (BW; P &lt; 0.05) than EFF cattle. Subsequent research revealed that INE cattle tended (P = 0.07) to spend more time search-grazing than did EFF. Lactating cattle were evaluated during late spring and summer over 2 yr while grazing pastures with slopes approaching 60%, but mostly 5 to 25%. When temperatures were mild in late spring, INE cattle grazed 1.7 h/d more than EFF cattle (P &lt; 0.05), starting earlier and grazing later into the morning. As summer temperatures increased, no differences in morning grazing occurred (P &gt; 0.10), but INE cattle started grazing later in the afternoon (P &lt; 0.05) than did EFF cattle. On days when cattle were in mild heat load (MHL), INE cows spent more time resting at lower elevations (P &lt; 0.05). Inefficient cattle appeared to engage in more (P &lt; 0.10) search-grazing than did EFF cattle. In a 4-d spring and summer forage intake study in 2016 with the same cows, no differences (P &gt; 0.10) occurred for either forage intake or harvest rate. Treatment groups had similar (P &gt; 0.10) milk production, BW and body condition. A genome-wide association study identified genetic markers associated with terrain use during MHL, but not RFI efficiency. In a 2021-2022 study evaluating 3- to 8-yr-old lactating EFF and INE cows and their calves, both INE cows and calves tended to rise earlier (P &lt; 0.10) in the morning to start grazing when grazing flatter meadows in July 2022. This relationship did not hold true on steeper pastures, though INE cows tended (P = 0.08) to graze later into the morning. Daily activity was strongly correlated with MHL in July 2022. When the effects of MHL were removed by wind and cooler temperatures, INE cows walked 1.5 h/d more (P &lt; 0.01) than EFF cows with their calves accompanying them. On days with increasing heat load, EFF cows tended to walk more (P &lt; 0.10) than INE cows. On the day following the long travel day for INE cows, calves from INE cows remained a greater distance (P &lt; 0.01) from their dams at lower elevations (- 290 m) than did calves from EFF cows (+ 0.1 m). Calves from EFF cows utilized more south facing slopes (P &lt; 0.05) than did calves from INE cows in July of both years. The EFF cows and their calves appear to tolerate &gt; MHL than do INE cows and calves.
368 Applying physics and math to describe chemistry and biology at the plant/animal interface
Authors: Tolleson Douglas R 
Published: September 2024

Abstract
Monitoring the diet consumed by grazing animals and relating that to health and performance is a task dating back to the origins of herding and pastoralism. Visual assessment of plants and animals by knowledgeable individuals has and will likely continue to be the most common manner of obtaining this information. It is part of the culture of livestock rearing and so even with the advent of modern analytical methods, adoption may be slow until the demographics of the industry change toward practitioners who have grown up with technology as part of their culture. Recent discussion within the rangeland profession has revolved around the art and science of grazing management, and thus grazing animal nutrition. These two skill sets are not mutually exclusive. I contend that art is the skilled application of science. With respect to the science, near infrared spectroscopy (NIRS) has been applied to determine the chemical content of feeds and forages. Fecal NIRS has been used to determine diet quality and composition in herbivores and results of this analysis can generate inputs to nutritional balance software that subsequently project animal performance. As described in the title, this presentation will involve a discussion of physics, math, chemistry, and biology. Regarding physics, the absorbance and reflection of near infrared energy by a population of chemical bonds ultimately described mathematically via multivariate statistics. Statistics that quantify the relationships between spectra and reference methods. Chemistry in this application quantifies constituents of interest in plant tissue consumed which are related to biology by way of intake, digestion, metabolism, and elimination of said grazed material. When projections of grazing animal performance are merged with landscape scale remotely sensed vegetation indices, the result is a powerful decision support system, provided by science, that facilitates the skill of the grazier; the artist if you will.
279 Estimating the impact of genomic selection for thermotolerance and in utero heat stress on piglet body weight from birth to bacon
Authors: Sullivan Caitlyn R , Brito Luiz F , Schinckel Allan P , Byrd MaryKate H , Hernandez Rick O , Diggs Shelby L , Maltecca Christian , Tiezzi Francesco , Johnson Jay S 
Published: September 2024

Abstract
The maternal response to heat stress (HS) has detrimental effects on swine offspring in the form of in utero heat stress (IUHS). Long-term effects of IUHS include reduced postnatal growth performance and lean muscle deposition. As such, developing mitigation strategies that reduce IUHS is essential to maximize economic return. Therefore, the study objective was to investigate whether genomic selection for HS tolerance (TOL) or HS sensitivity (SEN) would impact the postnatal growth of IUHS and in utero thermoneutral (IUTN) pigs from birth to market. We hypothesized that the IUHS pigs born to TOL dams would have improved growth relative to IUHS pigs born to SEN dams, but overall, IUHS pigs would have reduced growth compared with IUTN pigs. Pregnant gilts (n = 15 TOL and 13 SEN) were exposed to thermoneutral (TN; 17-20ºC; n = 7 TOL and 6 SEN) or heat stress (HS; cycling 26 to 36ºC; n = 8 TOL and 7 SEN) conditions from d 6 to 70 of gestation, and then all gilts were exposed to TN conditions until farrowing. Thirty-six offspring (barrows) were selected to represent each possible treatment combination: TOL+IUTN (n = 9), TOL+IUHS (n = 9), SEN+IUTN (n = 9), and SEN+IUHS (n = 9). Barrows were group-housed and fed a corn-soybean based diet that was provided with water ad libitum. Body weight (BW) of all barrows was measured at d 1, 21, 28, 35, 42, 49, 56, 84, 105, 126, 147, 168, and 189 of life to evaluate growth from birth (d 1) to market (d 189). Data were analyzed using the PROC GLIMMIX procedure of SAS with pig as the experimental unit and random effects being pen and litter. Body weights for each treatment group were fitted to a generalized Michaelis-Menten (GMM) function using the Nonlinear MIXED procedure of SAS with pig specific random effects for mature BW. On d 1, 126, and 168, IUHS pigs tended to have reduced BW (P &lt; 0.10; 1.19 ± 0.07, 58.37 ± 4.94, 91.18 ± 7.26 kg, respectively) when compared with IUTN pigs (1.32 ± 0.07, 68.95 ± 4.94, 106.25 ± 7.26 kg, respectively). Overall, final BW was reduced (P = 0.04) for IUHS (111.08 ± 7.91 kg) versus IUTN (128.79 ± 7.91 kg) pigs, and estimated days to a market weight of 125 kg tended to be greater (P = 0.06) for IUHS (219.8 ± 10.8 d) versus IUTN (188.1 ± 10.21 d) barrows. The GMM model predicted that IUHS+TOL and IUTN+TOL barrows had different shaped BW growth curves with decreased age to achieve one-half of their predicted mature BW than IUHS+SEN and IUTN+SEN barrows. In conclusion, IUHS resulted in reduced growth rates overall; however, TOL improved some aspects of growth.