bluuebunny/crossref_metadata_2025_split huggingface dataset:
228 The effect of temperature-humidity index in different pregnancy stages on litter traits in Taiwan Landrace sows
Authors: Lin Kai-Hsiang , Flowers Billy , Jiang Jicai , Knauer Mark , Lin En-Chung 
Published: May 2024

Abstract
Because of climate change, annual average temperatures are gradually rising. Hence, increasing heat stress can impair the litter traits of Taiwanese sows located in tropical and subtropical zones. However, some pregnancy stages may be impacted more by heat stress than other periods. Thus, the purpose of this study was to demonstrate the effect of temperature-humidity index (THI) in different pregnancy stages on total number born (TNB), number born alive (NBA), and stillborn rate (STB). Data were collected in two Taiwanese farms from 2008 to 2021 for TNB, NBA, and STB, while weather data were collected from the closest respective weather station. There were 4,247 and 6,812 pure line Landrace record from farm 1 and farm 2, respectively. Pregnancy stages included 28 d before mating (BEFORE), mating to 30 d of pregnancy (EARLY), 31 to 70 d of pregnancy (MIDDLE), and 71 d of pregnancy to farrowing (LATE). Average THI was calculated in each pregnancy stage and regression coefficients were estimated by linear mixed models within ASReml. A two-trait analysis was used to estimate regression coefficients and genetic correlations among TNB, NBA, and STB. Results showed THI impacted (P &lt; 0.05) TNB and NBA BEFORE and EARLY, while THI influenced (P &lt; 0.05) STB EARLY and MIDDLE. Regression coefficients for TNB were -0.0069 and -0.0228 for BEFORE and EARLY, respectively. For NBA, regression coefficients were 0.0106 and -0.0391 for BEFORE and EARLY, respectively. Regression coefficients for STB were -0.0651 and 0.1332 for EARLY and MIDDLE, respectively. Heritability estimates for TNB, NBA, and STB were 0.168, 0.113, and 0.069, respectively. Genetic correlations between TNB with NBA and STB were 0.913 and 0.539, respectively. While the genetic correlation between NBA and STB was 0.158. In conclusion, THI impacted BEFORE and EARLY stages for TNB and NBA, while the stages of EARLY and MIDDLE were impacted by THI for STB. Hence, control of the environments in these specific stages for sows could improve the performance of litter traits.
229 Effects of sire line on survivability traits of commercial pigs sired by PIC 800 or a North American competitor Duroc Line from birth to market
Authors: Spath Isaiah T , Little Erin , Edler Roy , Zaragoza Luis E , Wuebker Michelle , Spindler Matthew , Wilson-Wells Danielle 
Published: May 2024

Abstract
This study aimed to evaluate differences in survivability traits on pigs originating from two sire lines from birth to market. A total of 28,426 commercial pigs sired by either PIC 800 boars (n = 13,960 pigs) or a North American competitor Duroc line (Sire A; n = 14,466 pigs) were used in this study. PIC Camborough sows from 2 sister-sow farms were bred using semen from both genetic lines was mated equally and randomly across sows to ensure sire lines and parity of dams were balanced across farms. Pigs were individually identified at birth by genetic line and cross-fostering was done following standard farm practices. At weaning (average wean age 22 d of age, average body weight 6.8kg), pigs from both lines were sent to nurseries or Wean-to-finish sites (n = 11,541 PIC 800 and n = 11,922 Sire A), comingling in the same barns and pens. Starting inventories for each genetic line were recorded at weaning for all sites, end of nursery (4 wk post weaning), and 1 to 2 wk before start of marketing (target market weight 127kg at 160 d of age). For each production period, information on mortality and morbidity traits (n = 10,600 PIC 800 and n = 10,974 Sire A) was collected. Data were analyzed using R separately for each production period (i.e., Pre-weaning, Grow-to-finish, and Wean-to-finish) and not pooled from birth to market due to some groups not being kept intact. Morbidity and mortality data were analyzed using binomial models with a fixed effect of sire line and a random effect of sow farm or nursery/wean-to-finish site for pre-wean traits or post-weaning traits, respectively. Pigs from farms without removal reason record were excluded from analysis of removal reasons (Table 1). Overall pre-wean mortality was less (P ≤ 0.05) for PIC 800 pigs than Sire A pigs (8.5 vs. 9.6%, for PIC 800 and Sire A, respectively), mostly due to having fewer (P ≤ 0.05) pigs laid-on (4.8 vs 5.9%, respectively). At 4 wk post weaning, PIC 800 pigs had less (P ≤ 0.05) fallout rate than Sire A pigs (2.5 vs 3.5%, for PIC 800 and Sire A, respectively), with no differences (P &gt; 0.05) in mortality rate (1.2 vs 1.3%, respectively). Grow-to-finish morbidity and mortality rates were less (P ≤ 0.05) for PIC 800 (2.1 and 1.3%, for morbidity and mortality rates, respectively) compared with Sire A (2.5 and 1.8%, respectively). Likewise, wean-to-finish morbidity and mortality rates were less (P ≤ 0.05) for PIC 800 (2.5 and 3.7%, for morbidity and mortality rates, respectively) compared with Sire A (3.0 and 4.7%, respectively). Results suggest PIC 800 sired pigs consistently had lower incidence of morbidity and mortality than Sire A pigs across all stages of production included in this study.
230 Using phenotypes with uncertain identification leveraging computer vision models
Authors: Shumaly Mina , Steibel Juan P 
Published: May 2024

Abstract
There has been an increase in high throughput phenotyping using sensors in livestock farming. The major obstacle when analyzing phenotypic data in studies using cameras is identifying the animals using computer vision. To overcome this problem, an analysis method that uses keypoint detection in conjunction with classification algorithms can identify animals in images. However, these algorithms generally output a prediction probability for different classes, reflecting uncertainty. In most practices, the highest probability will be assigned as the putative identification (ID). In some cases, this may introduce incorrect identification and affect downstream decisions. Here, we propose an analytical approach to incorporate all the uncertainty in the putative ID distribution into downstream inferences. We used a publicly available keypoint detection dataset of 17 keypoints detected in 3,226 images from 30 diverse thoroughbred horses. We performed feature engineering with Linear Discriminant Analysis (LDA) using 5-fold cross-validation to build horse classifiers. The best model selected had an accuracy of 88.12% to identify animals in the testing set. Afterward, we performed Monte Carlo simulations to add a phenotype that was assumed independent of the image keypoints. This generated a set of plasmodes to evaluate methods that accounted for ID uncertainty. Each simulation included different means for two groups of 15 horses. In total nine simulation scenarios were considered including varying effect sizes of group mean difference and various repeatability values (horse variance to total variance ratio). We analyzed the simulated data with three different mixed models: 1) Fixed and random effects were based on using ground truth assignment of ID, 2) Fixed and random effects were based on using the putative ID with the highest posterior probability from the best classifier, 3) Finally, prediction probabilities were used to account for putative ID uncertainty. As expected, the model that used ground truth ID had the highest power to detect group differences, and it had the lowest root mean square error (rMSE) to estimate variance components across all scenarios. The linear model that used highest posterior probability putative ID, showed the lowest power and highest rMSE. For instance, as shown in Table 1, the mean rMSE value for group mean differences in the model using ground truth is 0.01 and the model using highest posterior probabilities is 0.13. The model that incorporated ID uncertainty had intermediate properties, showing a rMSE of 0.03 for group differences. Overall, incorporating uncertainty in Animal ID results in improved performance in ultimate test statistics used in animal studies. This is very important in breeding and precision livestock farming contexts where animal identification is challenging. We will further investigate this.
123 Phenomics enabled quantitative genetic modeling of complex traits
Authors: Morota Gota 
Published: May 2024

Abstract
With the development of high-throughput technologies, particularly for genomics and phenomics, biology has become a large-scale, data-rich field. High-throughput phenotyping (HTP) is an innovative technology that integrates advances in engineering or computer science into agriculture to optimize production while ensuring sustainability. In particular, the use of image-based HTP technologies allows us to collect a large number of phenotypes in an automated manner with less human labor and reduced costs, allowing us to efficiently evaluate phenotypes that were previously difficult to measure manually. The advent of HTP, coupled with the wealth of genomic data generated by next-generation sequencing technologies, provides new and exciting resources for studying complex traits. These new technologies also bring new challenges to quantitative genetics, namely the development of robust statistical frameworks that can accommodate spatial, temporal, heterogeneous, and high-dimensional phenotypic data. However, the impact of HTP-derived traits on genetic analysis in animal breeding and genetics has been limited. In this talk some real data examples of quantitative genetic modeling of image-derived traits will be presented.
105 Water quality analysis and flow rate comparison between wean-finish swine barns
Authors: Kittle Olivia C S , Leonard Suzanne M , Knauer Mark , Almond Glen 
Published: May 2024

Abstract
Water is an essential nutrient to sustain life and a vital component of biological functioning, impacting regulation of pig body temperature along with transporting nutrients to the body. Drinker availability, position, design, water flow rate and pressure, and water quality are major components of pig water intake. The ideal water flow rate for wean-to-finish pigs is 1 L m-1. Excessive flow rates could limit pig water intake and increase humidity in the barn, while too little water flow can lead to a decrease in water intake and biological functioning of the pig. Water should not contain harmful substances and should be readily accessible to pigs. Compounds in water are highly variable depending on region, water source, and pH. Quality of water influences feed consumption along with pig health. Water can contain a variety of microorganisms such as bacteria, viruses, algae, and protozoa; while not all microorganisms are harmful a highly contaminated water analysis is an index of poor water quality. In this study water quality and water flow rates were analyzed across five wean-finish sites comprising 16 barns for three turns of finishing pigs. Water flow rates were measured twice per turn, wk 1 and wk 17. Each barn contained 36 pens and flow rate measurements were collected in 8 pens evenly distributed within the barn. One suspended water source was available per pen, with two nipple drinkers per water source. Water flow rates were averaged over each turn within barns. Average water flow rates were 1.42 L m-1 with standard deviation of 0.44 L m-1. Overall barn water flow rates ranged from 0.78 to 2.19 L m-1. Barn flow rates were compared over time to evaluate performance and maintenance of facilities. Water quality samples were also collected and submitted for standard water analysis. Results will provide information on management strategies to assess water quality and water flow rates in wean-finish facilities.
100 Evaluating the effect of liver copper concentration on vaccine response in lightweight dairy-beef steers
Authors: Henderson Jacob A , McGill Jodi , Hansen Stephanie L 
Published: May 2024

Abstract
Dairy-beef crossbred calves often face greater copper (Cu) exposure than native beef calves, which may impact their response to vaccines. To investigate this, 28 weaned dairy-beef steers (90.25 ± 1.28 kg; ~8 wk old) were enrolled in a study to determine the effect of Cu status on the antibody response to vaccine. Steers were stratified by body weight (BW) and assigned to one of two dietary treatments: low Cu (no supplemental Cu) and high Cu (supplementing 20 mg Cu/kg DM). Steers were housed in pens of six to eight animals for 82 d before d 0 of the vaccination period to create distinct liver Cu groupings. Seven days before initial vaccine dose, liver biopsies were collected, and steers were grouped into low (n = 13) and high (n = 15) Cu treatments. Liver Cu for low Cu averaged 291 ± 24 mg/kg DM (range 240 to 376 mg/kg DM) and for high Cu averaged 665 ± 23 mg Cu/kg liver DM (range 519 to 893 mg/kg DM). All calves were vaccinated on d 0 and boostered 21 d later with Bovilis Vista 5 SQ (Merck Animal Health, Rahway, NJ) and an ovalbumin vaccine. Blood and BW were collected on these days and again on d 49 to end the study. Serum antibody titers for BRSV, BHV1, BVDV1, and BVDV2 were analyzed by the Iowa State University Veterinary Diagnostic Laboratory (Ames, IA). Response to ovalbumin vaccination was determined using an ELISA. Plasma Cu concentrations were determined on d 0, 21, and 49 via ICP-OES. Ovalbumin antibodies were determined by calculating the ratio of each sample to a positive control. Titers, ovalbumin response, and plasma Cu data from d 0, 21, and 49 were analyzed using the Mixed procedure of SAS 9.4 (Cary, NC) with the fixed effect of Cu treatment and day as repeated. Plasma Cu decreased across days (P = 0.0008). Given both treatments had adequate liver Cu, it is unsurprising plasma Cu did not differ by treatment (P = 0.2; 1.02 and 1.07 mg Cu/L for low and high Cu, respectively). Antibodies for BRSV, BHV1, and BVDV2 increased across days (P &lt; 0.0001), but there were no differences by treatment (P ≥ 0.12). Ovalbumin antibodies were affected by treatment by day (P = 0.04), where high Cu had greater response to ovalbumin than low Cu on d 21, but by d 49 ovalbumin antibodies were similar between Cu treatments. These results suggest excessive liver Cu concentration may not affect response to vaccination in dairy-beef steers; however, more research is needed to determine the effects of increased liver Cu concentration on oxidative stress and immune system function.
103 Effects of partial or total replacement of dried distiller’s grain with soybean meal on growth performance, carcass characteristics, sera urea nitrogen, and net energy utilization in finishing beef steers
Authors: Ross Cassidy , DeHaan Erin R , Francis Forest L , Francis Becca Grimes , Ribeiro Thiago , Smith Zachary K , Rusche Warren C 
Published: May 2024

Abstract
The objective of this study was to determine if partial or complete replacement of dried distiller’s grains plus solubles (DDGS) with solvent-extracted soybean meal (SBM) influences growth performance, efficiency of dietary net energy utilization, sera urea-N (SUN) concentrations, or carcass traits in finishing beef steers. Continental × British steers [initial shrunk body weight (BW) = 382 ± 37 kg] were used in a 139-d experiment. Treatments included: DDGS fed at 20% DM (13.4% CP; DDGS), SBM replacing 50% of DDGS (14.4% CP; SBM50), and SBM replacing 100% of DDGS (15.4% CP; SBM100). Steers (n = 189) were weighed and allotted to 1 of 24 pens (n = 8 steers per pen; 8 pens per treatment) at the Ruminant Nutrition Center near Brookings, SD. Whole blood was collected and harvested as sera on d 77, 105, and 139 to determine SUN concentration. Growth and carcass data were analyzed as a randomized complete block design using the GLIMMIX procedure of SAS 9.4 with treatment and block (initial BW grouping) as fixed effects; SUN data were analyzed using repeated measures, pen was the experimental unit. Inclusion level was evaluated for linear and quadratic components by the method of orthogonal polynomials. Final BW was calculated by dividing hot carcass weight (HCW) by 0.625. No differences were noted (P ≥ 0.18) for final BW, average daily gain, dry matter intake, or feed efficiency. Dietary treatment tended to affect gain to feed ratio (G:F; quadratic; P = 0.10) and SBM increased the apparent efficiency of energy capture. Treatment had no effect (P ≥ 0.22) on HCW, rib fat, marbling score, USDA yield grade, or final BW adjusted to 28% empty body fat. Complete replacement of DDGS with SBM increased rib eye area by 1% (linear; P = 0.02), but decreased dressing percentage (linear, P = 0.03). Distribution of USDA quality or yield grades were unaffected by treatment (P ≥ 0.36). No treatment × day interaction was observed for SUN (P = 0.20). However, day (P &lt; 0.01) and treatment (P &lt; 0.01) effects were observed. Initial samples (d 77) differed between DDGS and SBM treatments, with the latter having greater SUN values (P &lt; 0.01). On d 105, the SBM100 treatment had greater SUN than DDGS, and SBM50 was intermediate (P &lt; 0.01). On d 139, steers from SBM100 had elevated increased SUN compared with SBM50 and DDGS treatments (P &lt; 0.01). Net energy values calculated from animal performance for DDGS agreed closely with tabular estimates. In this experiment, replacement of DDGS with SBM increased the observed to expected ratio of dietary NE, increased muscularity and tended to decrease DP with no other observed effects on growth performance or carcass traits.
97 Effects of crude protein content and supplementing probiotics in late gestation and lactation sow diets on performance of sows and suckling pigs
Authors: Chrapun Leah E , Lee Jinyoung , Mendoza S M S , Htoo John K , Nyachoti Martin 
Published: May 2024

Abstract
The objective of this study was to investigate the effects of crude protein (CP) content and supplementation with Bacillus-based probiotics (PRO) in late gestation and lactation sow diets on performance of sows and suckling pigs. Pregnant sows (n = 120; 60 gilts and 60 2+ parity) were assigned to 4 dietary treatments in a 2 x 2 factorial arrangement (2 CP levels, with or without probiotics) from d 85 of gestation to d 21 of lactation. The experiment was conducted in 4 consecutive periods (half sows and half gilts per period). The diets consisted of 1) a corn-soybean meal (SBM)-based low CP (LCP; 12% CP) diet; 2) LCP + 0.05% probiotic (LCP-PRO); 3) a corn-SBM-based high CP (HCP; 15% CP) diet; and 4) HCP + 0.05% probiotic (HCP-PRO). The diets were formulated for 2 phases: gestation (d 85 – 115) and lactation (d 1 – 21). Sow body weight (BW) and backfat thickness were recorded on d 85 and 111 of gestation and on d 1 and 21 of lactation. Sow blood and milk were sampled on d 1 and 21 of lactation to analyze plasma urea nitrogen (PUN) concentration, and milk fat, protein, and lactose, respectively. Data were analyzed using the PROC MIXED of SAS. The statistical model included diet as fixed effect and period as random effect. There were no significant effects of CP and PRO on sow BW or backfat thickness. There was an interaction (P &lt; 0.05) between CP and PRO on the number of stillbirths, where the number of stillbirths did not differ between LCP and LCP-PRO groups, while the number decreased in sows fed HCP-PRO diet compared with HCP diet. Sows fed LCP diets had reduced (P &lt; 0.05) PUN concentrations on d 1 and d 21 of lactation compared with those fed HCP diets. However, no difference was found in fat, protein, or lactose content in both colostrum (d 1) and milk (d 21). Piglets from the sows fed LCP diets showed heavier (P &lt; 0.05) birth weights (1.58, 1.57, 1.53, and 1.52 kg for diets 1 to 4) than those in HCP groups, however, the average daily gain (ADG) of week 1 (188, 166, 187, and 187 g for diets 1 to 4) was less (P &lt; 0.05) for piglets in LCP groups than HCP groups. No other effects were observed on piglet growth. In conclusion, CP and PRO did not affect sow BW and backfat thickness. There was an interaction effect of CP and PRO on reproductive performance, where stillbirths did not differ between LCP and LCP-PRO groups; however, stillbirths decreased with PRO in HCP diet. Moreover, feeding LCP diets to sows did not influence milk nutrient contents and overall growth performance in suckling piglets.
96 Evaluating the impact of macroenvironment temperature on thermoregulatory and milk quality parameters in lactating sows and their litter
Authors: Cecil Mekenzie R , Neeno Samantha M , Byrd MaryKate , field Tyler C C , Marchant Jeremy N , Schinckel Allan P , Richert Brian T , Ni Ji-Qin , III Robert M M Stwalley , Johnson Jay S 
Published: May 2024

Abstract
Farrowing facility macroenvironment temperatures (TMacro) are often maintained at the upper end of the thermoneutral zone (24–25°C) of lactating sows, which may result in heat stress and reduced milk quality and quantity. Therefore, the study objective was to evaluate the impact of TMacro on thermoregulatory and milk quality parameters in lactating sows and their litters. In two repetitions, 37 lactating sows (parity = 3.1 ± 1.7) and their litters (d 1 litter size = 12.0 ± 1.0) were housed within 1 of 3 farrowing rooms set to different TMacro: LOW (n = 12 sows and litters; 15.0 ± 0.4°C), MID (n = 13 sows and litters; 19.3 ± 0.2°C), and HIGH (n = 12 sows and litters; 24.6 ± 0.8°C). Lactating sows and litters were monitored from d 3 of lactation until weaning (19.8 ± 0.9 d of age). All litters were provided with a 0.34 × 1.52 m heating pad. Sow respiration rates (RR, breaths per minute, BPM) were measured daily at 0800, 1200, 1600 and 2000 h by counting the number of flank movements. Vaginal temperature (TV) was measured in all sows at 10-min intervals using implanted data loggers. One sentinel piglet from each litter was implanted with a temperature recording bolus at d 2 of age to monitor core body temperature in 15-min intervals. Milk samples were collected from all sows on d 6, 12, and 18 of lactation and analyzed for true protein %, total solid %, and fat %. Data were analyzed using PROC GLIMMIX. The litter was considered the experimental unit for piglet measures, and the sow was considered the experimental unit for sow measures. Respiration rates were greater (P &lt; 0.01) for HIGH (48 ± 2 bpm) sows versus MID (33 ± 2 BPM) and LOW (27 ± 2 bpm) sows, but no differences were detected between MID and LOW sows. A TMacro treatment by lactation day effect was observed for TV (P = 0.02), where HOT sows had greater TV on lactation d 15 (+0.31°C), 18 (+0.43°C) and 19 (+0.34°C) versus MID and LOW sows. Piglet core body temperature was reduced (P &lt; 0.01) in the HIGH (39.07 ± 0.06 °C) versus the MID (39.31 ± 0.06 °C) and LOW (39.33 ± 0.07 °C) treatment groups, but it was similar for MID and LOW litters. Fat % was reduced for COLD (6.15 ± 0.52%) versus MID (7.35 ± 0.50%) sows on d 12, but no differences were observed when compared with HIGH sows (6.97 ± 0.52%). In conclusion, the HIGH treatment resulted in physiological heat stress for lactating sows and lowered piglet core body temperature, but it had minimal effects on measures of milk quality.