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Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. Light is an important factor that affects the pecking behavior of poultry. A wooden box was built to investigate the effects of Light Emitting Diode (LED) light color (warm white and cold white) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used to simulate a red object and paired with another piece of paper (Paper 2 to 8) with a different color. Bigger number of the paired paper indicated greater color difference. The experiment consisted of three phases: adaptation, training, and test. In the adaptation phase, birds were selected for the adaptation to reduce the stress from the box. In the training phase, birds were trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Twenty-three birds were tested when the paired paper was changed from Paper 7 to 2. Each pair of paper included 12 trials for every bird, and response time to peck and proportion of choices of Paper 1 in the last 10 trials were collected. The results showed that broilers tested under 5 lux light had longer response times than broilers tested under 50 lux light (P < 0.05). When Paper 1 was paired with paper 7, broilers tested under warm white light had lower proportion of choices of Paper 1 than those tested under cold white light (P < 0.05). Color difference had a significant effect on response time of broilers (P < 0.05). Moreover, the proportion of choices of Paper 1 decreased to 50% (chance-level performance) when color of the paired paper was gradually similar to Paper 1. Conclusively, rearing broilers in warm white rather than cold white light with appropriate light intensity should be recommended to reduce damaging pecking behavior in broiler production. [ABSTRACT FROM AUTHOR]

Static quantification measures of chemical components are commonly used to make certain assumptions about forage or feed nutritive value and quality. In order for modern nutrient requirement models to estimate intake and digestibility more accurately, kinetic measures of ruminal fiber degradation are necessary. Compared to in vivo experiments, in vitro (IV) and in situ (IS) experimental techniques are relatively simple and inexpensive methods to determine the extent and rate of ruminal fiber degradation. This paper summarizes limitations of these techniques and statistical analyses of the resulting data, highlights key updates to these techniques in the last 30 yr, and presents opportunities for further improvements to these techniques regarding ruminal fiber degradation. The principle biological component of these techniques, ruminal fluid, is still highly variable because it is influenced by ruminally fistulated animal diet type and timing of feeding, and in the case of the IV technique by collection and transport procedures. Commercialization has contributed to the standardization, mechanization, and automation of the IV true digestibility technique, for example, the well-known DaisyII Incubator. There has been limited commercialization of supplies for the IS technique and several review papers focused on standardization in the last 30 yr; however, the IS experimental technique is not standardized and there remains variation within and among laboratories. Regardless of improved precision resulting from enhancements of these techniques, the accuracy and precision of determining the indigestible fraction are fundamental to modeling digestion kinetics and the use of these estimates in more complex dynamic nutritional modeling. Opportunities for focused research and development are additional commercialization and standardization, methods to improve the precision and accuracy of indigestible fiber fraction, data science applications, and statistical analyses of results, especially for IS data. In situ data is typically fitted to one of a few first-order kinetic models, and parameters are estimated without determining if the selected model has the best fit. Animal experimentation will be fundamental to the future of ruminant nutrition and IV and IS techniques will remain vital to bring together nutritive value with forage quality. It is feasible and important to focus efforts on improving the precision and accuracy of IV and IS results. [ABSTRACT FROM AUTHOR]

Enrichment increases the ability of a sow to express innate behavior in a barren environment and can reduce aggressive behaviors, stress, and promote animal welfare. The objective of this study was to determine sow interaction with different types of enrichment materials provided before farrowing. A total of 11 sows were randomly assigned an enrichment combination of cardboard, paper, and banana leaves. Sows received two enrichment types, then enrichment was switched for the following day, until all combinations were completed. The enrichment material hung from 2x4 wood using ropes that hung the enrichment and chains placed in the ceiling of the farrowing room that held the 2x4 above the pen of each sow. Therefore, enrichment was placed within the nose reach of each sow. Enrichment material (113 g) was placed in the pens at 0700 h and left for 24 h each day before changing with another enrichment combination. Any enrichment material remaining was weighed. Final weight varied by treatment (P = 0.01), with sows consuming the least of cardboard (24.9 ± 10.0 g) and having an equal preference for banana leaves and paper (74.1 ± 10.0 g and 50.1 ± 11.3 g, respectively). Scan sampling was utilized to measure the frequency of interaction with the enrichments. The scans were designed with the first hour recording every minute a sow interacted with enrichment and the following 3 hours recording every 10 minutes. Continuous observation for the first hour of placement was performed for count data. Each time the sow made contact with the enrichment with her nose, the observation was recorded. The results of the behavior observation suggested that time in contact with enrichment did not differ (P > 0.05), nor did the number of times the pig-initiated contact with enrichment (P > 0.05). In conclusion, sow preference showed that bananas and papers were preferred over cardboard. However, sows will engage with any enrichment material provided before farrowing. Future research should be done to see if any environmental factors play a role in sow's preference. [ABSTRACT FROM AUTHOR]

Prediction models for estimating feed intake (FI) or metabolizable energy intake (MEI) of pigs are available in the literature. During the recent two decades, several reports have addressed the influence of heat stress on FI. The objectives of the present work were to validate the accuracy of prediction equations for estimating FI or MEI of pigs in the literature using data from heat stress experiments and to develop novel equations for estimating FI and MEI of pigs under thermoneutral or heat stress environment. A total of 123 data from 25 research papers measuring the effects of heat stress on FI of pigs published between 2001 and 2021 were used for the validation of previous equations and the development of novel equations. In the 25 papers, the thermoneutral temperature (T) ranged from 17 to 24℃ and the heat stress T ranged from 21 to 35℃. The body weight (BW) of pigs ranged from 19 to 119 kg. In the first validation study, the equation for estimating FI was evaluated: FI, g/day = -1,264 + 73.6×BW - 0.26×BW² + 117×T - 2.40×T² - 0.95×T×BW. The slope representing a linear bias did not differ from 0 but the intercept representing a mean bias was greater than 0 (109 ± 36; P < 0.01), meaning that the predicted mean underestimated the FI. In the second validation study, the equation for estimating MEI using equations for lower critical temperature (LCT) and fraction of MEI: LCT, ℃ = 17.9 - 0.0375×BW and fraction of MEI = 1 - 0.012914 × [T - (LCT + 3)] - 0.001179 × [T - (LCT + 3)]2. The slope representing a linear bias did not differ from 0 but the intercept representing a mean bias was greater than 0 (731 ± 68; P < 0.001), meaning that the predicted mean underestimated the MEI. A novel equation for estimating MEI under thermoneutral or heat stress environment was developed: MEI, kcal/day = 2,603 + 137×BW - 0.189×BW² - 38.4×T - 0.556×T² - 1.165×T×BW (P < 0.001; R² = 0.83). An equation for estimating fraction of MEI under thermoneutral or heat stress environment was also developed: fraction of MEI = 1 - 0.00677 × [T - (LCT + 3)] - 0.000668 × [T - (LCT + 3)]² (P < 0.001; R² = 0.98). In conclusion, previously published equations underestimate FI and MEI and novel equations for estimating MEI under thermoneutral or heat stress environment have been developed in the present work. However, the novel equations have not been validated under cold stress. [ABSTRACT FROM AUTHOR]

Graduate education is an important aspect of the life of most academic scientists and a serious responsibility because it comes with the obligation to help students achieve their career and life goals. It can also be very fulfilling for the graduate mentor in terms of personal satisfaction and advancement of the research program. Learning to be a good major professor is an active process that depends on developing a formal framework of education and modifying that framework for each student based on past experiences and experimentation, advice from colleagues, and the individual personality of the student. Perhaps most important is for the graduate mentor to buy into the success and well-being of the student. Among the characteristics that a major professor could seek to instill in his or her students are critical and independent thinking, self-confidence, a thick skin, teamwork, laboratory skills and understanding, and the ability for hard work. Work to make science joyful by celebrating accomplishments, creating a fun environment in the lab, and stressing the societal value of science as compared to personal rewards or ambition. [ABSTRACT FROM AUTHOR]

Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytical studies are related to the mathematical property of structural identifiability, which refers to the theoretical ability to recover unique values of the model parameters from the measures defined in an experimental setup and use the model structure as the sole basis. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed (i.e., the problem has a unique solution). Structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements, and change of the model structure) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. However, a practitioner should be familiar with the model construction and software implementation process. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection) where experiment design is applied to guarantee identifiability. This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples were provided for learning purposes and to promote open science practices. [ABSTRACT FROM AUTHOR]

This paper presents the application of machine learning algorithms to identify pigs’ behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig’s back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for “eating,” 0.99 for “lying,” 0.93 for “walking,” and 0.91 for “standing” behaviors. The optimal WS was 7 s for “eating” and “lying,” and 3 s for “walking” and “standing.” The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance. [ABSTRACT FROM AUTHOR]

Skeletal muscle hypertrophy is a culmination of catabolic and anabolic processes that are interwoven into major metabolic pathways, and as such modulation of skeletal muscle metabolism may have implications on animal growth efficiency. Muscle is composed of a heterogeneous population of muscle fibers that can be classified by metabolism (oxidative or glycolytic) and contractile speed (slow or fast). Although slow fibers (type I) rely heavily on oxidative metabolism, presumably to fuel long or continuous bouts of work, fast fibers (type IIa, IIx, and IIb) vary in their metabolic capability and can range from having a high oxidative capacity to a high glycolytic capacity. The plasticity of muscle permits continuous adaptations to changing intrinsic and extrinsic stimuli that can shift the classification of muscle fibers, which has implications on fiber size, nutrient utilization, and protein turnover rate. The purpose of this paper is to summarize the major metabolic pathways in skeletal muscle and the associated regulatory pathways. [ABSTRACT FROM AUTHOR]

Piglets are born wet, and evaporation of that moisture decreases body temperature, increasing the mortality risk. The objective of this study was to compare the effect of 2 commercially-applicable piglet drying methods on rectal temperature over 24 h after birth. The study was carried out at a commercial sow facility using a CRD with 3 treatments: Control (piglets not dried); Desiccant (piglets dried at birth using a cellulose-based desiccant); Paper Towel (piglets dried at birth using paper towels). Piglets were weighed at birth and individually identified with a numbered ear tag. Rectal temperature was measured at 0, 10, 20, 30, 45, 60, 120, and 1440 min (24 h) after birth. Sows and litters were randomly allotted to treatments. Data were analyzed using a repeated measures model with PROC MIXED of SAS. Litter was the experimental unit and piglet was a subsample of the litter; the model included the fixed effects of treatment and repeated time, and the interaction. There was no effect (P > 0.05) of treatment on rectal temperature at birth, or at 10 min or 24 h after birth. The Desiccant and Paper Towel treatments had greater (P < 0.05) temperatures than the Control at all times of measurement from 20 to 120 min after birth. Piglets on the Desiccant treatment had greater (P < 0.05) temperatures than those on the Paper Towel treatment between 30 and 120 min after birth. In conclusion, drying piglets at birth with either a desiccant or paper towels increases rectal temperatures between 20 and 120 minutes after birth, with the greatest effect being for the desiccant. Further research is needed to compare other methods of increasing piglet temperature and to determine if drying of piglets impacts pre-weaning mortality. This research was funded by the National Pork Board. [ABSTRACT FROM AUTHOR]

A renewed interest in data analytics and decision support systems in developing automated computer systems is facilitating the emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling (HIMM) and agent-based models (iABM). Data analytics have evolved remarkably, and the scientific community may not yet fully grasp the power and limitations of some tools. Existing statistical assumptions might need to be re-assessed to provide a more thorough competitive advantage in animal production systems towards sustainability. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing HIMM. The progress of analytical tools was divided into three stages: collect and respond, predict and prescribe, and smart learning and policy making, depending on the level of their sophistication (simple to complicated analysis). The collect and respond stage is responsible for ensuring the data is correct and free of influential data points, and it represents the data and information phases for which data are cataloged and organized. The predict and prescribe stage results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. This stage represents the pinnacle of acquired knowledge that leads to wisdom, and AI technology is intrinsic. Although still incipient, HIMM and iABM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community still has some issues to be resolved, including the lack of transparency and reporting of AI that might limit code reproducibility. It might be prudent for the scientific community to avoid the shiny object syndrome (i.e. AI) and look beyond the current knowledge to understand the mechanisms that might improve productivity and efficiency to lead agriculture towards sustainable and responsible achievements. [ABSTRACT FROM AUTHOR]

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction. [ABSTRACT FROM AUTHOR]

Genetic improvement of animals plays an important role in improving the economic and environmental sustainability of livestock production systems. This paper proposes a method to incorporate mitigation of environmental impacts and risk preferences of producers into a breeding objective via economic values (EV s). The paper assesses the effects of using these alternative EVs of breeding goal traits on discounted economic response to selection and on environmental impacts at commercial farm level. The application focuses on a Brazilian pig production system. Separate dam- and sire-line breeding programs that supply parents in a 3-tier production system for producing crossbreds (fattening pigs) at commercial level were assumed. Using EVs that are derived from utility functions by incorporating risk aversion increases the cumulative discounted economic response to selection in sire-line selection (6%) while reducing response in dam-line selection (12%) compared with the use of traditional EVs. The use of EVs that include environmental costs increases the cumulative discounted social response to selection in both dam-line (5%) and sire-line (10%) selections. Emission of greenhouse gases, and excretion of nitrogen and phosphorus can be reduced more with genetic improvements of production traits than reproduction traits for the typical Brazilian farrow-to-finish pig farm. Reductions in environmental impacts do not, however, depend on the use of the different EVs (i.e. with and without taking into account environmental costs and risk). Both environmental costs and risk preferences of producers need to be considered in sire-line selection, and only environmental costs in dam-line selection to improve, at the same time, the economic and environmental sustainability of the Brazilian pig production system. [ABSTRACT FROM AUTHOR]

Methionine is an essential dietary amino acid that can be supplied in different chemical forms: DL-methionine, L-methionine, and OH-methionine. It has been hypothesized that L-methionine might be used with greater efficacy for average daily gain (ADG) in weaning pigs due to their decreased enzymatic capacity to convert the other methionine forms. This study aimed to compare DL-methionine, L-methionine, and OH-methionine efficiency of utilization for ADG in weaned pigs through meta-analysis. The systematic review resulted in 2,167 papers screened for the title and abstract, with 24 papers meeting the inclusion criteria. The ADG was regressed as a function of the standardized ileal digestible (SID) methionine intake (Meti). The study was used as a random intercept, and regressions were weighted by the square root of the number of observations. Non-linear models were used to determine the Meti requirements. The average Meti requirement for pigs of 5 to 25 kg body weight (BW) was 2.09 g/d. The slopes of linear mixed-effects regression models were compared for the different methionine sources. Average initial BW was 7.99 (DL-methionine), 8.82 (L-methionine), and 7.06 kg (OH-methionine). Meti was 1.5 g/d for all sources whereas SID cysteine intake was 1.90, 2.23, and 1.93 g/d, for DL-methionine, L-methionine, and OH-methionine, respectively. Increase in Meti below the estimated requirement linearly increased ADG (ADG = 107 + 138 × Meti; P < 0.01). No difference between the slopes of methionine sources was observed, rejecting the hypothesis that L-methionine is used with greater efficiency in weaned pigs. Pigs receiving DL-methionine, L-methionine, and OH-methionine presented an estimated ADG of 319, 317, and 311 (SE = 19, 25 and, 26, respectively) g/d, respectively. In conclusion, methionine efficiency of utilization for ADG in post-weaned pigs is not affected by methionine sources. [ABSTRACT FROM AUTHOR]

The review paper, "Implications of placentation type on species-specific colostrum properties in mammals, "looks at how different species confer passive immunity to their young. Implications of placentation type on species-specific colostrum properties in mammals. Graph The feature infographic examines the ways that neonatal mammals acquire first immunity. [Extracted from the article]

In the dairy cow, negative energy balance affects milk yield and composition as well as animal health. Studying the effects of negative energy balance on dairy cow milk production is thus essential. Feed restriction (FR) experiments attempting to reproduce negative energy balance by reducing the quantity or quality of the diet were conducted in order to better describe the animal physiology changes. The study of FR is also of interest since with climate change issues, cows may be increasingly faced with periods of drought leading to a shortage of forages. The aim of this article is to review the effects of FR during lactation in dairy cows to obtain a better understanding of metabolism changes and how it affects mammary gland activity and milk production and composition. A total of 41 papers studying FR in lactating cows were used to investigate physiological changes induced by these protocols. FR protocols affect the entire animal metabolism as indicated by changes in blood metabolites such as a decrease in glucose concentration and an increase in non-esterified fatty acid or β-hydroxybutyrate concentrations; hormonal regulations such as a decrease in insulin and insulin-like growth factor I or an increase in growth hormone concentrations. These variations indicated a mobilization of body reserve in most studies. FR also affects mammary gland activity through changes in gene expression and could affect mammary cell turnover through cell apoptosis, cell proliferation, and exfoliation of mammary epithelial cells into milk. Because of modifications of the mammary gland and general metabolism, FR decreases milk production and can affect milk composition with decreased lactose and protein concentrations and increased fat concentration. These effects, however, can vary widely depending on the type of restriction, its duration and intensity, or the stage of lactation in which it takes place. Finally, to avoid yield loss and metabolic disorders, it is important to identify reliable biomarkers to monitor energy balance. [ABSTRACT FROM AUTHOR]

To meet the challenges of an increasing global population and multiple environmental stressors on agricultural production, it is essential to better understand how genotype (G) and environment (E) influence phenotype for traits of economic importance in agriculture. To accomplish this requires interdisciplinary teams consisting of researchers from crop and livestock sciences, genetics, genomics, computational and data sciences, and engineering. Towards this end, in 2020, Congress established the Agricultural Genome to Phenome Initiative (AG2PI). An important objective of this presentation is to encourage strong participation of animal science researchers and industry in ongoing AG2PI activities. The purpose of the initial AG2PI project, awarded to Iowa State University and the Universities of Arizona, Idaho and Nebraska, was to assemble and prepare a community to conduct AG2P research across the plant and animal kingdoms. The AG2PI project aims to: a) identify research gaps and opportunities, b) foster community solutions to these challenges, and c) rapidly disseminate findings for community use. Training workshops, field days, conferences, community surveys, and seed grants have all contributed to these goals. Opinions and ideas from the crop and livestock genetics communities are key to advancing AG2P science and related work. AG2PI has used online community surveys and in-person conferences to gather these and to facilitate transdisciplinary communications. In September 2022, AG2PI held a conference titled "Thinking Big: Visualizing the Future of AG2PI", in which attendees participated in focused small group discussions to identify which agricultural G2P-related topics are most critical for future R&D funding and which may be the most difficult to achieve. These discussions are summarized in a concept paper for the community, shared with USDA-NIFA, and submitted for publication. A follow-up conference was held June 14-15, 2023, in Kansas City, where people working in both the animal and plant genetics research communities and industry stakeholders attended. Outcomes from this conference will be summarized in this presentation, and requests for animal scientists to provide further input will be made. The future of agricultural genomics will be shaped by both the participants in AG2PI and the solutions developed by the AG2PI community. Funding for this work comes from USDA NIFA awards 2022-70412-38454, 2021-70412-35233, and 2020-70412-32615. [ABSTRACT FROM AUTHOR]

Threonine (Thr) is generally the second limiting amino acid (AA) in growing pig diets, and it has a vital role in supporting intestinal functions and the immune system. Additionally, providing pigs with the correct AA requirements decreases nitrogen excretion and improves growth performance. This study aimed to determine standardized ileal digestibility (SID) Thr requirements for individual post-weaned pigs. Additionally, the Thr supplementation effect on average daily gain (ADG) of weaning and growing [7.3 - 51.5 kg of body weight (BW)] pigs under a sanitary challenge was evaluated. Eleven papers published from 1993 to 2015 composed the dataset used to determine the SID Thr requirement in the nursery phase, and five studies conducted from 2014 to 2020 composed the enteric sanitary challenge dataset. The SID Thr was obtained from publications when available or estimated based on the ingredient composition using EvaPig software. The average individual pig SID Thr requirements were estimated by bootstrapping 1,000 repetitions of a linear-plateau mixed model where all parameters were considered random as a function of the trial code. A general positive-definite matrix was used to represent the between-factors covariance using the nlme package of the R software. The effect of supplementing SID Thr above the requirement to attenuate ADG losses during a challenge was evaluated by an effect size analysis using a mixed model of the R Metafor package. Due to the low number of studies assessing pigs' response to enteric challenges, a P < 0.25 was considered significant. In the post-weaning (14 kg average BW), the estimated ADG was 457 g/d (411 to 493 g/d) as a function of SID Thr individual requirement, which was 4.71 g/d for the average pig, with the least demanding pig requiring 4.4 g/d and the most demanding one requiring 5.1 g/d. For challenged pigs, the across-study heterogeneity was moderate (Higgs index = 36%). Increasing Thr intake by 18% above the estimated requirement attenuated (P = 0.15) ADG losses by 0.7 standard deviations (60 g) compared with pigs fed diets meeting Thr requirements. For pigs in individual precision feeding, the SID Thr requirement is estimated to be 1.03 g per 100 g of ADG for post-weaned pigs. The increase of SID Thr intake by 18% attenuated the ADG losses by 19% compared with non-supplemented pigs with enteric challenges. [ABSTRACT FROM AUTHOR]

Ionophores are a separate class of nonmedically important antibiotics used in animal production to increase growth rate. Narasin (Skycis; Elanco Animal Health, Greenfield, IN) is an ionophore labeled for increased rate of body weight gain and improved feed efficiency in growing-finishing swine. The mode of action of this ionophore is to increase energy availability by altering volatile fatty acid production in the hindgut in favor of propionate, which is the most efficient product of fermentation. Therefore, a metaregression analysis was conducted to evaluate the effects of narasin inclusion in growing-finishing pig diets to predict average daily gain and feed efficiency. A database was developed that contained 21 papers from 2012 to 2021 representing 308 observations including both individual period data as well as overall trial data. For statistical analysis two models were created. The individual period model considered only data with single continuous narasin feeding periods while the overall trial model considered complete data sets representing periods with and without narasin feeding. Regression model equations were developed with the GLIMMIX procedure of SAS (Version 9.4, SAS institute, Cary, NC). Predictor variables were assessed with a stepwise manual forward selection for model inclusion. Predictor variables were required to provide an improvement of at least 2 Bayesian information criterion units to be included in the final model. For individual period data for G:F, the model selected included concentration of narasin in the feed, ADG and ADFI of the control group not provided narasin, and average BW of the control group categorized into greater than 105 kg or less than 105 kg (Table 1). There were 2 competing models to predict improvement in overall G:F. For model 1, significant predictors included concentration of narasin in feed and ADG of the control group. Model 2 for overall G:F included concentration of narasin in feed, ADG and ADFI of the control group. For the individual period model that predicted ADG, the predictors were concentration of narasin in the feed, ADFI, G:F of the control group and BW category for feeding period. The best fitting ADG model using the overall trial data included concentration of narasin in feed, ADFI and G:F of the control group, and feeding duration categorized as longer than 65 days or shorter than 65 days. Together, these models can be used to predict the response to narasin supplementation in finishing pigs. Based on the results, the overall response to narasin inclusion rate for ADG and G:F is quadratic in nature. Additionally, as ADG and G:F increase, the response to narasin supplementation decreases. In summary, using average values from the database for predictor variables, this meta-analysis demonstrates narasin would be expected to improve overall G:F from 0.74 to 1.13% and ADG from 1 to 3%. [ABSTRACT FROM AUTHOR]

Several papers report greater bioavailability of dietary trace mineral fed as organic in pigs compared with inorganic sources. However, only a few studies have tried to assess the variability of the trace mineral bioavailability among different organic sources. Growing barrows (n = 54; initial BW = 28.9 ± 1.3 kg) were used to determine the effects of trace mineral source on growth performance, bones mineralization and mineral status over the entire fattening period (d0-d73). Pigs were blocked by BW and randomly assigned to supplementation of trace minerals as inorganic (ITM) or one of 2 organic sources (OTM): glycine complex (GLY) or amino acids complex (AA). During the two feeding phases (growing: d0-d35; finishing: d35-d73), the experimental diets were supplemented with 5, 35 and 5 mg/kg of Cu, Zn and Mn, respectively. Pigs were fed restricted all through the experimental period. Feed intake was therefore comparable all through the experimental period for all pigs. Growth performance did not differ among the three experimental groups during the growing phase. However, Feed:Gain ratio measured during the finishing period tended to be affected by the dietary treatments (P=0.10). When grouping the GLY and AA pigs, the Feed:Gain ratio measured from d35 to d73 for the OTM pigs was significantly lower compared with ITM pigs (-0.07, P = 0.03). There was no significant effect of the dietary treatment on plasma trace mineral concentrations and alkaline phosphatase activity measured at day 34. At the end of the growing phase, plasma ceruloplasmin activity tended to be greater for the GLY and AA groups in comparison with the ITM pigs (+6.9%, P = 0.06). The zinc deposition in the proximal phalanx was significantly increased when the trace minerals were offered as organic compared with the group ITM (+10.9%, P = 0.06). Zinc content in the liver was greater for the pigs fed the OTM sources compared with the others (+6.0%, P = 0.10). The quantity of copper deposited in the liver did not differ significantly among the 3 trace mineral sources. This study indicates that the total replacement of inorganic Zn, Cu and Mn by organic sources brings benefits in terms of feed efficiency in fattening pigs. A better bioavailability as suggested by greater deposition levels in bone and liver as well as the increased J. Anim. Sci Vol. 101, Suppl. S2 103 circulating ceruloplasmin activity could explain the improvement of performance during the finishing period. Regardless of the variable considered, no significant differences were noticed between the two organic trace mineral sources. [ABSTRACT FROM AUTHOR]

The USDA-NIFA is developing a vision for agricultural genomics to phenomics research (AG2P) through two programs. In 2017, the Functional Annotation of Animal Genomes (FAANG) request for proposals was released, which has provided over $9 Million in funding to groups over the past five years to create public resources for the study of regulatory and other functional elements in agriculturally important species. This effort has been extended by other countries who have contributed another $20 Million+ (equivalent). These initial resource-building efforts are providing extensive new epigenetic data that are used to better understand the function of animal genomes and to enhance animal genetic improvement. However, such research demonstrates new infrastructure is needed across the agricultural enterprise to predict phenotypes from genome information. In 2020, NIFA inaugurated the Agricultural Genome to Phenome Initiative program (AG2PI), and an important objective of this presentation is to encourage strong participation of animal genomic researchers and industry in the AG2PI project. The purpose of AG2PI is to assemble and prepare a community to conduct AG2P research across the plant and animal kingdoms. The focus of the AG2PI is: a) identifying research gaps and opportunities, b) fostering community solutions to these challenges, and c) rapidly disseminating findings. Training workshops, field days, conferences, community surveys, and seed grants have all contributed to these goals. Opinions and ideas from the crop, livestock and aquaculture genetics communities are key to advancing G2P science and related work. The AG2PI uses online community surveys and in-person conferences to gather these and to facilitate transdisciplinary communications. In September 2022, AG2PI held a conference entitled "Thinking Big: Visualizing the Future of AG2PI", in which attendees focused small group discussions to identify which agricultural G2P-related topics are most critical for future R&D funding and which may be the most difficult to achieve. These discussions have been summarized in a concept paper for the community and shared with USDA-NIFA. A follow-up conference is planned for June 2023 in Kansas City, which we hope many members from animal genetics research and industry will attend. The future of agricultural genomics will be shaped by both the participants in AG2PI and the solutions developed by the AG2PI community. USDA NIFA awards 2022-70412-38454, 2021-70412-35233, and 2020-70412-32615. [ABSTRACT FROM AUTHOR]

Precision feeding (PF) with the daily mixing of 2 diets with different lysine content (high (H) or low (L)) was previously reported for growing pigs to reduce protein intake and N excretion compared with a conventional feeding (CF) based on a single diet (C). Using a simulation approach based on farm data, the objective of the present paper was to describe and evaluate a decision support system for the PF of gestating sows allowing the daily distribution of a tailored ration to each sow. Two datasets, 1 of 2,511 gestations (farm A) and 1 of 2,528 gestations (farm B), reporting sows' characteristics at insemination and objectives at farrowing were used as inputs for a Python model. This model, mainly based on InraPorc, calculates the nutrient requirements of each sow over gestation and simulates the impact of PF in comparison to CF. Simulated diets L, H, and C contained 3.0, 6.5, and 4.8 g/kg of standardized ileal digestible lysine (SID Lys) and 2.0, 3.3, and 2.5 g/kg of standardized total tract digestible phosphorus (STTD-P), respectively. The influence of farm, parity, gestation week, and their interactions, on calculated SID Lys and STTD-P requirements was analyzed applying a mixed model. The calculated SID Lys and STTD-P requirements increased markedly in the last third of gestation (P < 0.01) and were higher for primiparous than for multiparous sows, unless after week 14 for STTD-P requirement. The calculated SID AA and mineral requirements were lower for farm B than farm A (respectively, 2.94 vs. 3.08 g/kg for SID Lys and 1.30 vs. 1.35 g/kg for STTD-P, P < 0.01). On average, feed L represented 86% and 92% of the feed projected to be delivered by the PF strategy in farms A and B, respectively. Compared to CF, average calculated dietary SID Lys content was lowered by 27% and 32% with PF, for farms A and B, respectively, while average calculated dietary phosphorus content was lowered by 13% and 16%. The simulated proportions of sows in excess and deficient in SID Lys were reduced with PF. Compared to CF, the PF strategy allowed for a 3.6% reduction in simulated feed cost per sow during gestation, and reduced nitrogen and phosphorus intake (by 11.0% and 13.8%, respectively) and excretion (by 16.7% and 15.4%, respectively). To conclude, these simulations indicate that PF of gestating sow appears to be relevant to meet the amino acid requirement while reducing feed cost, and supplies and excretion of nitrogen and phosphorus. [ABSTRACT FROM AUTHOR]

Traditional feed composition tables have been a useful tool in the field of animal nutrition throughout the last 70 yr. The objective of this paper is to discuss the challenges and opportunities associated with creating large feed ingredient composition tables. This manuscript will focus on three topics discussed during the National Animal Nutrition Program (NANP) Symposium in ruminant and nonruminant nutrition carried out at the American Society of Animal Science Annual Meeting in Austin, TX, on July 11, 2019, namely: 1) Using large datasets in feed composition tables and the importance of standard deviation in nutrient composition as well as different methods to obtain accurate standard deviation values, 2) Discussing the importance of fiber in animal nutrition and the evaluation of different methods to estimate fiber content of feeds, and 3) Description of novel feed sources, such as insects, algae, and single-cell protein, and challenges associated with the inclusion of such feeds in feed composition tables. Development of feed composition tables presents important challenges. For instance, large datasets provided by different sources tend to have errors and misclassifications. In addition, data are in different file formats, data structures, and feed classifications. Managing such large databases requires computers with high processing power and software that are also able to run automated procedures to consolidate files, to screen out outlying observations, and to detect misclassified records. Complex algorithms are necessary to identify misclassified samples and outliers aimed to obtain accurate nutrient composition values. Fiber is an important nutrient for both monogastrics and ruminants. Currently, there are several methods available to estimate the fiber content of feeds. However, many of them do not estimate fiber accurately. Total dietary fiber should be used as the standard method to estimate fiber concentrations in feeds. Finally, novel feed sources are a viable option to replace traditional feed sources from a nutritional perspective, but the large variation in nutrient composition among batches makes it difficult to provide reliable nutrient information to be tabulated. Further communication and cooperation among different stakeholders in the animal industry are required to produce reliable data on the nutrient composition to be published in feed composition tables. [ABSTRACT FROM AUTHOR]

The objective of this paper is to report observations from a 10-week summer training program on Data Management in Animal and Plant Sciences. A total of six undergraduate students from three universities in the Texas A&M University System [Texas A&M University (TAMU)-College Station, TAMUKingsville, and TAMU-Corpus Christi] representing disciplines of Animal Science, Plant Science, and Computer Science learned handling and interpretation of sensor-based data derived from both animal sensors and UAV images of plant fields. Students attended weekly training sessions on data collection, management, processing, and visualization software including ArcGIS, MySQL and power BI. In addition, students also worked as a team and developed a database project and gave an oral presentation on their team project. On the pretraining survey, two students indicated that they had a background in animal science, two students indicated a background in plant science, one student in computer science, and one student with experience in spatial data. To begin with, the students had a mean score of 4.3 (range 0-8; on a scale of 0-10) for knowledge of data management in agriculture. One third of the students indicated that they did not have data handling experience, and one half of the students had some data analysis experience whereas one student indicated partial experience of working with data. In general, students indicated that they were very satisfied with the internship experience. On a scale of 0-10, the mean satisfaction score was 8.75 (range 7.5-10). Students indicated that they were more confident to work with and talk about data from animal systems (mean 8.5; range 7-10) and plant systems (mean = 8.4; range = 7-10). All students agreed that they learned at least one new concept related to animal and plant data ecosystems. Students indicated that the program was a good start for understanding the overall data architecture, indicated progress on data handling and thought helpful to understand opportunities in agriculture. All students agreed that their understanding of data management in agriculture changed significantly because of the course. Consequently, four students indicated that they were interested in career opportunities related to data in agriculture whereas two students indicated their interest in application of the developed tools for the on-farm use. When evaluating effect of the cross disciplinary training, students agreed that the training was helpful in learning concepts outside of their own discipline with the mean score 9.4(range 8-10) on a scale of 0-10. All students indicated that they learned team management skills and skills working with people that are different from their own. Students suggested improvement on the communications, prerecorded videos, in-person meetings, weekly reporting reviews in the future iterations for students to benefit more from the program. [ABSTRACT FROM AUTHOR]

A meta-analysis was conducted to evaluate the effects of branched-chain amino acids (BCAA), their interactions, and interactions with large neutral amino acids (LNAA) to develop prediction equations for growth performance of pigs. Data from 25 papers, published from 1995 to 2018, for a total of 44 trials and 210 observations were recorded in a database. Diets were reformulated using the NRC (2012) loading values to estimate nutrient concentrations. The response variables were average daily gain (ADG), average daily feed intake (ADFI), and gain-to-feed ratio (G:F). The predictor variables tested included average body weight (BW), crude protein, neutral detergent fiber, Ile:Lys, Leu:Lys, Val:Lys, BCAA:Lys, Ile:Leu, Val:Leu, Ile:Val, (Ile+Val):Leu, Trp:Lys, Leu:Trp, Ile:Trp, Val:Trp, BCAA:Trp, Met:Lys, Leu:Met, Ile:Met, Val:Met, BCAA:Met, His:Lys, Leu:His, Ile:His, Val:His, BCAA:His, Thr:Lys, Leu:Thr, Ile:Thr, Val:Thr, BCAA:Thr, (Phe+Tyr):Lys, Leu:(Phe+Tyr), Ile:(Phe+Tyr), Val:(Phe+Tyr), BCAA:(Phe+Tyr), LNAA:Lys, Leu:LNAA, Ile:LNAA, Val:LNAA, and BCAA:LNAA. Amino acids were expressed on standardized ileal digestible basis. The MIXED procedure of SAS (SAS Institute Inc. Cary, NC) was used to develop the equations. The inverse of squared SEM was used to account for heterogeneous errors using the WEIGHT statement. Models were selected with a step-wise manual forward selection. In order to be included in the final model, predictor variables had to be statistically significant (P < 0.05) and provide an improvement of at least 2 points in Bayesian information criterion. The optimum equations were: ADG, g = – 985.94 + (15.2499 × average BW (kg)) – (0.08885 × average BW × average BW) + (1.063 × Leu:Lys) + (20.2659 × Ile:Lys) – (0.1479 × Ile:Lys × Ile:Lys) + (9.2243 × (Ile+Val):Leu) – (0.03321 × (Ile+Val):Leu × (Ile+Val):Leu) – (0.4413 × Ile:Trp); G:F, g/kg = 648.3 – (6.2974 × average BW (kg)) + (0.02051 × average BW × average BW) + (0.5396 × Ile:Lys) + (1.7284 × Val:Lys) – (0.00795 × Val:Lys × Val:Lys) – (1.7594 × Met:Lys); and ADFI, kg = predicted ADG/predicted G:F. Overall, the prediction equations suggest that increasing Leu:Lys negatively impacts ADG due to a reduction in G:F and ADFI caused by insufficient levels of other BCAA and LNAA relative to Leu. According to the model, the addition of Val, Ile, and Trp, alone or in combination, has the potential to counteract the negative effects of high dietary Leu concentrations on growth performance. [ABSTRACT FROM AUTHOR]

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction. [ABSTRACT FROM AUTHOR]

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies. [ABSTRACT FROM AUTHOR]

For genomic predictors to be of use in genetic evaluation, their predicted accuracy must be a reliable indicator of their utility, and thus unbiased. The objective of this paper was to evaluate the accuracy of prediction of genomic breeding values (GBV) using different clustering strategies and response variables. Red Angus genotypes (n = 9,763) were imputed to a reference 50K panel. The influence of clustering method [k-means, k-medoids, principal component (PC) analysis on the numerator relationship matrix (A) and the identical-by-state genomic relationship matrix (G) as both data and covariance matrices, and random] and response variables [deregressed estimated breeding values (DEBV) and adjusted phenotypes] were evaluated for cross-validation. The GBV were estimated using a Bayes C model for all traits. Traits for DEBV included birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT). Adjusted phenotypes included BWT, YWT, and ultrasonically measured intramuscular fat percentage and REA. Prediction accuracies were estimated using the genetic correlation between GBV and associated response variable using a bivariate animal model. A simulation mimicking a cattle population, replicated 5 times, was conducted to quantify differences between true and estimated accuracies. The simulation used the same clustering methods and response variables, with the addition of 2 genotyping strategies (random and top 25% of individuals), and forward validation. The prediction accuracies were estimated similarly, and true accuracies were estimated as the correlation between the residuals of a bivariate model including true breeding value (TBV) and GBV. Using the adjusted Rand index, random clusters were clearly different from relationship-based clustering methods. In both real and simulated data, random clustering consistently led to the largest estimates of accuracy, while no method was consistently associated with more or less bias than other methods. In simulation, random genotyping led to higher estimated accuracies than selection of the top 25% of individuals. Interestingly, random genotyping seemed to overpredict true accuracy while selective genotyping tended to underpredict accuracy. When forward in time validation was used, DEBV led to less biased estimates of GBV accuracy. Results suggest the highest, least biased GBV accuracies are associated with random genotyping and DEBV. [ABSTRACT FROM AUTHOR]