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Measurement of nutrient digestibility is a key pillar in estimating the value of feeds for livestock. Conditions are not always amendable to estimating feed digestibility through total collection methods, and as a result digestibility markers are frequently employed. The first “Digestibility markers” were reported in the early 1900s where colored glass beads were used to estimate the flow of digesta through the digestive tract. Subsequently, a myriad of digestibility markers has been assessed with the internal markers; acid insoluble ash, acid detergent lignin, indigestible neutral detergent fiber and n-alkanes and the external markers titanium dioxide and chromic oxide being the most common. Chromic oxide (Cr2 O3) was first proposed as a digestibility maker in 1918 and continues to be widely used in modern day metabolism, confined performance and grazing livestock experiments. It was the demand to use chromic acid as a digestibility marker that led Terry and Mira Fenton to publish the article “An improved procedure for the determination of chromic oxide in feed and feces” in the Canadian Journal of Animal Science in 1979. This became the most cited article in the history of the journal with over 800 citations. The procedure eliminated the use of nitric acid in a pre-digestion step and ashing reduced the organic matter content, making it less likely that perchloric acid would be converted to anhydrous perchloric acid which poses an explosive hazard. The procedure continues to be widely used to measure Cr2 O3 as a digestibility marker and has made a significant contribution to our assessment of the value of a wide range of feeds for Canadian livestock. [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]

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]

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]

The scientific publication system should evolve into practices that enhance free dissemination and full access to research findings. At the same time, it should ensure reproducibility and transparency and safeguard scientific integrity from the detrimental effects of the current "publish or perish" culture. The objective of this contribution is to introduce the Peer Community In (PCI) Animal Science initiative (https://animsci.peercommunityin.org/), which represents an alternative to the current publication system under the umbrella of the "Peer Community In" project (https://peercommunityin.org/). PCI Animal Science is an international community of researchers working in animal science and related areas and it promotes open science and research transparency. Although PCI Animal Science is not a scientific journal, it operates similarly with editors (here: recommenders) and reviewers. Currently, the PCI Animal Science community has 64 recommenders from 20 countries. PCI Animal Science is a non-profit initiative, run and managed by researchers. The PCI Animal Science community performs, at no cost, rigorous open reviews of preprints that have been deposited on repositories such as bioRxiv and Zenodo from a wide range of research areas related to animal science. Based on independent reviews, a recommender decides whether a paper is recommended or not. Recommended preprints are peer-reviewed and citable stand-alone articles of high scientific value that do not need publication in traditional journals. However, if the authors wish, they can also publish their recommended preprint in the Diamond Open Access Peer Community Journal (https://peercommunityjournal.org/section/animsci/) at no cost. Authors can also submit their recommended manuscript to PCI-friendly journals (i.e., journals that consider the PCI evaluation in their own review processes) or to other journals. This contribution shows the workflow of the evaluation of manuscripts by PCI Animal Science and the advantages of adopting this new publishing model. [ABSTRACT FROM AUTHOR]

This article provides a science-based, data-driven perspective on the relevance of the beef herd in the U.S. to our society and greenhouse gas (GHG) contribution to climate change. Cattle operations are subject to criticism for their environmental burden, often based on incomplete information disseminated about their social, economic, nutritional, and ecological benefits and detriments. The 2019 data published by the U.S. Environmental Protection Agency reported that U.S. beef cattle emitted 22.6% of the total agricultural emissions, representing about 2.2% of the total anthropogenic emissions of CO2 equivalent (CO2e). Simulations from a computer model developed to address global energy and climate challenges, set to use extreme improvements in livestock and crop production systems, indicated a potential reduction in global CO2e emissions of 4.6% but without significant enhancement in the temperature change by 2030. There are many natural and anthropogenic sources of CH4 emissions. Contrary to the increased contribution of peatlands and water reservoirs to atmospheric CO2e, the steady decrease in the U.S. cattle population is estimated to have reduced its methane (CH4) emissions by about 30% from 1975 to 2021. This CH4 emission deacceleration of 2.46 Mt CO2 e/yr2 might be even more significant than reported. Many opportunities exist to mitigate CH4 emissions of beef production, leading to a realistic prospect of a 5% to 15% reduction in the short term after considering the overlapping impacts of combined strategies. Reduction strategies include feeding synthetic chemicals that inactivate the methyl-coenzyme M reductase (the enzyme that catalyzes the last step of methanogenesis in the rumen), red seaweed or algae extracts, ionophore antibiotics, phytochemicals (e.g., condensed tannins and essential oils), and other nutritional manipulations. The proposed net-zero concept might not solve the global warming problem because it will only balance future anthropogenic GHG emissions with anthropogenic removals, leaving global warming on a standby state. Recommendations for consuming red meat products should consider human nutrition, health, and disease and remain independent of controversial evidence of causational relationships with perceived negative environmental impacts of beef production that are not based on scientific data. [ABSTRACT FROM AUTHOR]

With most of the student attrition occurring early in undergraduate educational programs (Braunstein et al. 1997) it is necessary to interest and motivate students early on. The demographics of animal science students have shifted to students with minimal background in food producing animals. This presents a unique challenge as the current student population represents a diverse array of backgrounds and prior experiences. As a result, students enroll in undergraduate animal science programs with various expectations for their undergraduate degree and a focus primarily on careers in veterinary medicine. To engage all students, interest and motivation need to be generated. This review will use motivational frameworks as outlined by the self-determination theory, expectancy value theory, and interest, to explain the impact of the proposed solutions. Active learning classroom strategies are linked to increased knowledge compared with traditional, passive classrooms (Wells et al. 2019). Active learning shifts from a traditional teaching model to a student-centered model, which transitions instructors to facilitators of learning. This review summarizes current proposed pedagogies that have been researched in animal science classrooms such as experiential learning, flipped classrooms, hands-on animal experience, undergraduate research experiences, mentorship opportunities, capstone experiences, service-learning experiences, team-based learning, and cooperative learning. The limitations of these proposed pedagogies and the future research needed are also discussed. [ABSTRACT FROM AUTHOR]

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings. [ABSTRACT FROM AUTHOR]

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics—in addition to the traditional considerations when conducting an animal science study—makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type. [ABSTRACT FROM AUTHOR]

The American beef bulls have been neglected in terms of animal science research. Metabolomics is a respected for of science that will aid in developing hypothesis for future research in beef bulls. The objective of this study was to identify metabolites within a 65-day breeding season that were affect by breeding season duration and workload. Over a two-year period, 8 purebred Angus bulls owned and housed by the Cal Poly beef unit were surveyed using serum (collected via the coccygeal vein) and seminal fluid (collected via electroejaculation) at the beginning and end of a 65-day post-AI breeding season. Samples underwent HPLC-MS to observe metabolites within the fluids. Data were stratified for sample day and workload and compared using t-test for day and ANOVA for workload. Within serum, 8 metabolites of interest were observed. Effects included differences between workload groups in the abundance of guanosine (P = 0.01) a metabolite associated with purine metabolism. Within seminal fluid, 13 metabolites of interest were observed including Inosine, a metabolite also involved in purine metabolism and fluctuating based on workload (P = 0.05). Purine metabolism will be of interest to future research of beef bulls. [ABSTRACT FROM AUTHOR]