bluuebunny/crossref_metadata_2025_split huggingface dataset:
225 Kidney function and nitrogen excretion in Brown Swiss and Holstein dairy cows
Authors: Kessler Evelyne C C , Bruckmaier Rupert M M , Gross Josef J 
Published: September 2024

Abstract
The agricultural sector contributes to nitrogen (N) emissions since farm animals excrete considerable N amounts via feces and urine. To reduce N emissions by dairy cattle, methods predicting accurately individual N emissions are of great interest. Recent data showed that Brown Swiss (BS) cows have greater urea concentrations in milk and blood than Holstein (HO) cows. However, it is still unknown if greater BUN concentrations in a particular breed are congruently reflected by concomitantly increased contents of nitrogenous compounds in other media (e.g., urine and saliva). Thus, the present study simultaneously assessed biomarkers for kidney function like urea, and creatinine and symmetric dimethylarginine (SDMA) concentrations in several body fluids (i.e., blood, urine, milk, and saliva) as well as nitrogen excretion in feces in BS and HO cows under identical feeding conditions. Blood, saliva, urine, and feces were sampled in 31 multiparous BS and 46 HO cows kept under identical feeding and management conditions. Samples were collected at different lactational stages after the monthly DHIA control test-day. Concentrations of urea and creatinine were measured in serum, urine, and saliva. Symmetric dimethylarginine was determined in serum, which is an established indicator for glomerular filtration rate (GFR). Feces were analyzed for dry matter content and nitrogen concentrations. Data on milk urea and protein concentrations, and daily milk yield were obtained from the monthly DHIA test-day records. The effects of breed, time, and parity number on blood, saliva, urine, feces, and milk parameters were evaluated with the GLM procedure with breed, time, and parity number as fixed effects. Differences between BS and HO were assessed by the Tukey-corrected t-test at P &lt; 0.05. Dry matter intake and body weight were similar in BS and HO cows (P &gt; 0.05). No differences between BS and HO were observed for milk yield, fecal DM and nitrogen content. BS cows showed greater urea concentration (25.8 ± 0.7 vs 21.8 ± 0.7 mg/dL), and protein content in milk (3.70 ± 0.08 vs 3.45 ± 0.07%) than HO cows (P &lt; 0.01). Concentrations of urea, creatinine, and SDMA in serum, were greater in BS than in HO cows (P &lt; 0.01) with 5.46 ± 0.19 vs 4.72 ± 0.13 mmol/L for urea, 105.96 ± 2.23 vs 93.07 ± 1.50 mmol/l creatinine, and 16.78 ± 0.69 vs 13.39 ± 0.44 µg/dL SDMA, respectively. Urea and creatinine concentrations in urine and saliva did not differ among breeds. Despite greater urea and creatinine concentrations in blood, and milk urea content in BS compared with HO, excretion of these parameters did not differ between breeds in urine. Our results suggest a reduced renal glomerular filtration rate in BS compared with HO. The underlying mechanisms require further investigations.
385 Extracellular vesicle microRNAs in porcine seminal plasma reflect quality status
Authors: Dlamini Notsile H , Feugang Jean M N , Willard Scott T , Ryan Peter L 
Published: September 2024

Abstract
The pig industry relies heavily on artificial insemination as the primary breeding technique, using high-quality semen doses for genetic improvement. Semen consists of seminal plasma, a complex fluid that provides an optimal environment for sperm maturation, viability, and fertilization. Seminal plasma contains membranous structures called extracellular vesicles (EVs), facilitating intercellular communication. These seminal plasma-EVs (SP-EVs) contain microRNAs, which hold promise as predictive markers of semen quality. Thus, we hypothesized that the composition of SP-EVs might reflect the divergent quality status of corresponding spermatozoa. Therefore, this study aimed to explore the microRNA content of SP-EVs isolated from accepted (Passed) or rejected (Failed) boar semen for artificial insemination. Over 8 wk during the hot summer, raw semen was collected (n = 83) from sexually mature Duroc boars at a commercial boar stud (Prestage Farms, MS). Semen samples underwent sperm analysis to evaluate motility and morphology, classified as Passed or Failed quality based on predefined criteria (≥ 70% and &lt; 70%, respectively). Seminal plasma was isolated through serial centrifugation, followed by SP-EV extraction using differential centrifugation. The isolated SP-EVs were characterized using transmission electron microscopy (TEM) for EV morphology and nanoparticle tracking analysis (NTA) for size and concentration. SP-EV miRNAs were extracted and identified via the Illumina NextSeq sequencing platform. Data were analyzed using SPSS software and significance was set at P &lt; 0.05. Both TEM and NTA demonstrated that SP-EVs had a spherical structure with a diameter range of 50 to 300 nm. A total of 437 miRNAs were expressed in the Failed group and 443 miRNAs in the Passed group. MiRNA differential expression analysis revealed 28 downregulated and two upregulated miRNAs in the Passed group compared with the Failed group. Validated miRNA gene targets were associated with signaling pathways, including N-glycan biosynthesis, p53 signaling, pancreatic cancer, and apoptosis. These findings suggest that SP-EVs carry specific microRNAs potentially linked to boar semen quality. Thus, microRNAs from SP-EVs have the potential to serve as biomarkers to assess sperm quality, thereby providing insight into therapeutic approaches to address male infertility. Additionally, seminal plasma-EVs can be useful nanocarriers for further research interventions. Work supported by USDA-ARS grant# 6066-31000-015-00D
71 An electronic device for enteric methane emissions monitoring
Authors: Mazo Sebastian Bedoya , Moreno Pulgarín Luisa Fernanda F , Agudelo John Fredy Ramirez , Guarín Montoya José Fernando , Guarnido-Lopez Pablo , Menendez Hector M 
Published: September 2024

Abstract
It is widely accepted that accumulation of greenhouse gases in the atmosphere, including methane (CH4), is promoting climate change. Cattle contribute significantly to CH4 emissions, mainly generated during ruminal fermentation. Measuring CH4 emissions is expensive and time-consuming, hampering individual, constant, and scalable monitoring at the farm level. Thus, developing innovative methodologies to measure CH4 emissions from cattle is crucial. These methodologies should enable the identification of lower-emitting animals and to assess the effectiveness of various mitigation strategies. This study aimed to develop a low-cost microcontroller-based device to record variations in CH4 concentrations in the air exhaled by animals during indoor intake periods. The device was assembled into a plastic box with an internal compartment for an MQ-4 sensor that measures CH4 concentration (ppm). The device is equipped with an air pump and a plastic tubing with a 4 mm diameter for air flow generation (2 L/min) from the feeder area to the sensor, ensuring individualized measurements. An ESP8266 module collects the sensor signal and sends it to a laptop through a Wi-Fi network. To test the device, emissions from 16 dairy cows were monitored at milking (0500 and 1600 h) during three consecutive days. Variations in CH4 concentration were recorded three times per second in a text file for each animal at milking times. The mean CH4 concentration among the cows was 666 ppm (20.4% CV). Figure 1 compares emissions between two animals: a low-emitter (458 ppm, 4.1% CV) and high-emitter cow (853 ppm, 9.2% CV). The findings reveal the capability of the device for monitoring CH4 emissions on an individual basis, underscoring its value in promoting sustainability at the farm level. Future iterations could benefit from integrating sensors for temperature, humidity, and atmospheric pressure to refine CH4 measurements. Additionally, distance measurement technologies could standardize the proximity of the air sampler to the animals, thereby enhancing the accuracy of the emissions captured. Implementing a CH4 background reader could allow for continuous monitoring of ambient conditions, adjusting the CH4 readings to account for fluctuations that might affect concentrations. Finally, incorporating animal identification tools, such as RFID tags or computer vision identification systems, could automate the process of associating CH4 emission data with specific animals, ensuring consistent data collection. These advancements would not only improve the precision of the device but also offer a more comprehensive understanding of individual enteric CH4 emissions.
67 Precision identification and weight assessment of cattle using supervised machine learning on body surface keypoints
Authors: Menezes Guilherme , Negreiro Ariana , Ferreira Rafael , Higaki Shogo , Casella Enrico , Alves Anderson , Dórea Joao R R 
Published: September 2024

Abstract
Computer vision systems (CVS) offer identification solutions for animals with distinct coat patterns, but are less effective for solid-colored herds. In addition, they can be used to measure critical phenotypes, such as body weight (BW). Providing both BW and identification for solid-colored animals can help farmers make decisions. This study aimed to 1) develop an automatic CVS capable of identifying using the Euclidean distance between keypoints located at specific anatomical landmarks (e.g., bony prominences), and 2) predict the BW using features extracted from these keypoints. The keypoint model was trained to identify seven keypoints per cow, located at the left and right hips, left and right pin bones, tail head, sacral, and cervical vertebrae. These locations remain constant as body condition score of a cow changes. The Euclidean distance between these keypoints was used to generate biometric features for each cow. The keypoint detection model was trained using 3,928 images over 900 epochs with a batch size of 14 and validated using 391 images. This trained model was used to predict keypoints on a different set of 41 cows over 5 d, resulting in a total of 6,944 images which correspond to the dataset for cow identification. The Random Forest (RF) algorithm for cow identification was trained with the first 4 d (5,376 images), and the last day served as the test set (1,568 images). For the cow identification, a RF algorithm was employed on the Euclidean distance of the keypoints. The hyperparameters were determined through a grid search on the training set, utilizing 5-fold cross-validation. The hyperparameters ‘mtry’ (which ranged from 1 to 10) and ‘ntree’ (which ranged from 250 to 500, in increments of 50) were optimized. For the second objective, due to the completely new environment, the keypoint model was fine-tuned using transfer learning for 240 epochs with a batch size of 14, utilizing 310 images. The model predicted keypoints in 1,593 images from different beef-on-dairy animals. This dataset was used to train the BW prediction model. The Euclidean distances from the predicted images and sex were used as features. A RF model was trained using a leave-one-animal-out cross-validation approach. The hyperparameters were determined through a grid search on the training set, using 10-fold cross-validation. The ‘mtry’ parameter ranged from 2 to 16, in increments of 2. For animal identification, the model achieved an accuracy, precision, recall, and F1-score of 92.7%, 89.0%, 90.2%, and 92.7%, respectively. To predict BW, the RF model achieved an R² of 0.86 and a root mean squared prediction error of 36.9 kg, representing 6.7% of the observed values average. These results suggest that keypoints located on the dorsal body surface can identify and weigh individual animals, even those lacking distinct coat color patterns.
185 Effect of synchronized fixed-time AI and non-pregnant heifer delayed feedlot entry on reproductive performance and system economics
Authors: Landblom Douglas G , Hanna Lauren L , Parman Byron , Perry George A , Paisley Steve , Senturklu Songul 
Published: September 2024

Abstract
Heifer reproduction management research is being conducted at the NDSU–Dickinson Research Extension Center. Using crossbred heifers, the objective was to evaluate reproductive performance and economic efficiencies comparing a drylot synchronized fixed-time AI protocol with clean-up bulls to two grazing systems using a fixed-time AI protocol without clean-up bulls. Synchronization protocol:14-d CIDR-PG-GnRH (TAI 66 h ± 2 h). Fixed-time AI treatments: 1) Drylot Control (DLOT) with clean-up bulls, 2) Integrated diverse crop-heifer annual forage grazing (IAF, no clean-up bulls), and 3) Native range grazing (NR, no clean-up bulls). Following pregnancy ultrasound on d 85, non-pregnant heifers continued to bale-graze until late November, when they were transferred to the University of Wyoming SAREC feedlot, finished, harvested at Cargill Meat Solutions packing plant, Ft. Morgan, CO, and grid marketed. Data were analyzed using the MIXED procedure of SAS. Year 1 and 2 grazing days were 121, and 149, respectively. Synchronized TAI pregnancy rates yr 1 and 2 for DLOT, IAF, and NR were 59.4, 43.8, and 62.5%; 68.8, 46.9, and 34.4%, respectively. Total DLOT control pregnancy rates yr 1 and 2 were 90.6 and 100%. Grazing heifer feedlot performance Yr 1 did not differ for starting body weight (BW; 452 vs 436 kg, P = 0.29), ending BW (610 vs 600 kg, P = 0.74), gain (157 vs 164 kg, P = 0.69), average daily gain (ADG; 1.42 vs 1.48 kg, P = 0.70), dry matter (DM) feed intake (15.0 vs 14.7 kg, P = 0.73), F:G (4.81 vs 4.56 kg, P = 0.42), daily feed cost ($7.45 vs $9.47, P = 0.73), feed cost/kg BW gain ($3.11 vs $2.95, P = 0.51), and feed and yardage cost ($3.41 vs $3.24, P = 0.55). Year 2 grazing heifer feedlot performance for the IAF and NR were starting BW (501 vs 456 kg, P = 0.007), ending BW (670 vs 604 kg, P = 0.004), feedlot finishing gain (143 vs 123 kg, P = 0.05), ADG (1.08 vs 0.93 kg/day, P = 0.06), daily DM feed intake (20.1 vs 20.1 kg/d, P = 0.98), feed to gain ratio (7.13 vs 8.22 kg/d, P = 0.11), daily feed cost ($9.61 vs $9.54/d, P = 0.94), feed cost/kg BW gain ($4.05 vs $4.67/kg, P = 0.11), and feed and yardage cost/kg gain ($4.45 vs $5.13/kg, P = 0.092). IAF and NR Yr 1 HCW (P = 0.83), dressing % (P = 0.69), marbling score (P = 0.78) did not differ. Year 2, except for hot carcass weight (HCW; 379 vs 341 kg, P = 0.004), dressing pct (P = 0.99), fat depth (P = 0.29), marbling score (P = 0.94) did not differ. Systems 2-yr mean net return for DLOT, IAF, and NR were $592, $296, $379, respectively. Systems net return profitability was very good for all management comparisons, but reduced for IAF and NR due to IAF farming, finishing, and transportation costs, and NR finishing and transportation costs.
456 Allocating distribution of pasture utilization across the grazing landscape in grazing steers equipped with virtual fencing collars
Authors: Parsons Ira L , Brennan Jameson R , Menendez Hector M , Moreno Elias R Velasquez , Huseman Aletta , Dotts Hadley 
Published: September 2024

Abstract
Spatial utilization of pasture landscapes by grazing animals is often heterogeneous and driven by complex environmental and physiological interactions between plants and animals. Synergistic energy pathways can be modeled using mechanistic relationships describing resource distribution, animal behavior, metabolic energy rate, and pasture utilization using biometric sensors. The objective of this study was to demonstrate the ability to measure the energy landscape by mapping fine-scale contributions to animal energy expenditure, nutrient acquisition, growth, and overall economic profitability. We utilized animal trajectories and location fixes recorded by virtual fencing collars (Vence) daily body weight (BW) using precision livestock scales (Smartscale, C-Lock Inc.) and oxygen consumption (GreenFeeds, C-Lock Inc.) on steers (n = 127), managed as part of a broader project to evaluate environmental synergies in extensive grazing systems. Steers were allocated to one of six native grass pastures assigned to either rotational or continuous grazing strategies, and one of three stocking rates (low, moderate, high) in a 2 x 3 factorial arrangement. Space utilization was quantified as the frequency of fixes located within each cell of a 5 x 5-meter Grid and the distance between grid cells and water was calculated using the Pythagorean theorem. Movement behavior was quantified using Program R to calculate step length, the distance between two temporally continuous fix points utilizing the Pythagorean Theorem, and turn angles calculated as the cosine of the pre- and pro-ceeding step lengths where larger values indicated greater deviation from a straight trajectory. A mixed linear regression model was fitted using the lmer function in the lme4 package. Space utilization decreased with increased distance from water (P &lt; 0.05), with rotational grazing strategies increasing concentration around water sources (P &lt; 0.05). This indicates an opportunity to utilize precision livestock technology to facilitate pasture management and increase space utilization in grazing cattle.
453 Fluxes of greenhouse gases from pastures and methane emissions from beef cattle in Australia
Authors: Shirvan Milad Bagheri , Gonzalez Luciano A , Burgess Jillian E M 
Published: September 2024

Abstract
The carbon footprint of beef cattle production may need to consider both the fluxes of greenhouse gases (GHG) of pasture ecosystems and cattle enteric emissions because these are the largest contributors to the carbon balance. The objective of the present study was to measure the fluxes of greenhouse gases from pastures and enteric methane (CH4) emissions from cattle throughout long periods of time under commercial conditions. The fluxes of CH4, carbon dioxide (CO2), and nitrous oxide (N2O) were measured throughout a period of 3 yr in multiple pasture types ranging from native to sown, and from temperate to summer pastures in New South Wales, Australia. A set of 8 dynamic chambers that automatically close for 10 min every 1.5 h were used for this purpose. The chambers measured the change in the concentration of GHG to allow for the estimation of emissions (positive flux) or sinks (negative flux). All data was then summarized to estimate daily fluxes and the global warming potential (CO2e) was calculated multiplying CH4 by 28 and N2O by 265. The final dataset contained 2,709 d of measurements across the 8 chambers. The GreenFeed system was used to measure CH4 production from cattle under varying conditions fed various diets throughout the same period. Cattle categories included weaners, heifers and steers, bulls and cows under both grazing or pen conditions. Cattle were fed a variety of diets ranging from low quality forages to high quality forages, grain supplementation, and grain-based diets. Data were averaged across many days for each animal and the final dataset contained 1,014 measurements. The CO2 flux from pastures ranged from -855 (uptake) to +339 kg CO2ּ ha-1ּ d-1 with a mean of -10.4 ± 2.19 kg CO2ּ ha-1ּ d-1. The minimum, mean and maximum CH4 fluxes were -192 (uptake), 0.015 ± 0.488, 434 g CH4 ּha-1ּ d-1, respectively, whereas NO2 values -146 (uptake), 17.1 ± 1.23, 1,293 g N2O ּ ha-1ּ d-1, respectively. These fluxes resulted in a GWP ranging from -855 (uptake), -5.83 ± 2.222, 445 kg CO2e ּha-1ּ d-1suggesting that pastures were on average a net sink of GHG throughout the present study. The CH4 production from cattle ranged from 43.6 to 404.6, with an average CH4 production of 192 ± 90.3 g CH4ּ animal-1ּ d-1, which is equivalent to 5.37 kg CO2eqּ animal-1ּ d-1. These results suggest that a hectare of livestock pastures offsets more than the CH4 produced by the average beef cattle in the present study. The fluxes of GHG from vegetation may need to be considered when estimating the contribution of livestock production to global warming.
97 Influence of cashew nutshell extract (CNSE) on enteric methane emission, feeding behavior, and nutrients digestibility in beef steers receiving a finishing diet
Authors: Cuervo Wilmer , Tarnonsky Federico , Fernandez-Marenchino Ignacio , Maderal Araceli B , Fernandez-Lehman Andres , Podversich Federico , Schulmeister Tessa M M , Gonzalez Lucas , Erazo-Mendez Cristian , DiLorenzo Nicolas 
Published: September 2024

Abstract
Feedlots have a crucial role in meeting the increasing global demand for meat. Despite ongoing efforts to improve feed efficiency in finishing diets, cattle in feedlots may experience methane (CH4) losses ranging from 2 to 12% of dietary gross energy (GE). Identifying strategies to mitigate energy losses in finishing diets is imperative for ensuring the sustainability of beef production. Anacardic acid, contained in the cashew nutshell extract (CNSE) has shown promising results in reducing CH4 emissions in forage-based diets; however, its impact on high-grain diets remains to be analyzed. Therefore, this study aimed to evaluate CNSE addition (5 gּ steer-1ּ d-1) in a finishing diet (85:15% grain: forage) on in vivo enteric CH4 emissions, apparent total tract digestibility, and feeding behavior. It was hypothesized that CNSE addition to a finishing diet would reduce enteric CH4 emissions without reducing feed intake or nutrient digestibility. To this aim, Angus crossbred steers (n = 16; 599 ± 40 kg) were used in a crossover design with two periods of 56 d each. Steers were adapted (14 d) to the finishing diet and assigned to receive CNSE or no additive (CON). The diet was offered twice daily as a total mixed ration (TMR) in the feedbunk, while CNSE was independently delivered through the Super SmartFeed system (C-Lock, Inc, Rapid City, SD) using ground corn gluten feed as a carrier. In vivo CH4 emissions, apparent total tract digestibility of nutrients, and feeding behavior were analyzed. Data were recorded during 35 d after the adaptation, followed by 7 d of washout, and analyzed as a crossover design using the MIXED procedure of SAS. Compared with CON, the addition of CNSE increased meal duration (P = 0.03), bunk visit duration (P &lt; 0.01), and dry matter intake (DMI) of TMR (P = 0.03) by 25, 23, and 7%, respectively. Dry matter digestibility was increased (P = 0.02) by CNSE addition. Steers receiving CNSE, had a reduction in enteric CH4 emission rate (g/d; P &lt; 0.001), yield (g/kg of DMI; P &lt; 0.001), and intensity (g/kg of average daily gain; P &lt; 0.01) in 29, 32, and 39% respectively. The loss of dietary GE as CH4 was reduced in CNSE (3.2 vs. 4.7%) compared with CON (P &lt; 0.01). This study showed that CNSE at the evaluated inclusion rate, reduced CH4 yield and emission intensity by more than 31% in beef steers receiving a finishing diet. Moreover, given that CNSE addition promoted a longer feed bunk occupancy and a greater TMR intake, it could be suggested that the observed CH4 mitigation may be linked to shifts in ruminal fermentation rather than changes in DMI.