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The public controversy around genetically engineered (GE) products, aka genetically modified organisms (GMO), has been ongoing for almost 40 yr fueled in part by well-funded misinformation campaigns disseminated by activist organizations. Polls reveal that public perception of GE animals is generally negative, with biomedical applications being more positively perceived than agricultural applications. Opposition to GE animals is often conflated with opposition to use of animals in research in general, as well as opposition to aspects of intensive animal agriculture. In general, concerns about animal biotechnology are influenced by views around the moral status of animals, the boundary between “natural” and “unnatural,” and perceived risks and benefits of GE animals to health and the environment (personal and cultural characteristics); the purpose of the application (e.g., disease resistance > fast growth), the method(s) being used, and the motivation of the research group making the genetic modification (public or private); and the species being modified (animal characteristics). There were three factors that aggravated opposition to GE products including 1) a lengthy and expensive regulatory step uniquely associated with commercializing products developed using that breeding method, 2) competing business interests (e.g., organic, non-GMO project) that were able to monetarize fear around the method to extract value (rent seeking) and selling their value-added (more expensive) product that avoided the use of GE, and 3) there is a mandatory labeling requirement to track/differentiate products produced with (or without) that breeding method to enable value-added marketing. Even so, when GE products that people wanted were allowed to reach the market (e.g., Impossible Burger with GE heme, GloFish, AquAdvantage salmon), they met with commercial acceptance. Now it has been suggested that the public will similarly reject animal products derived from gene edited (GnEd) animals, but that idea has only recently been put to the test as such food products were not previously available. A 2022 Japanese paper noted that with regard to GnEd food (gene knock-out tomatoes and fish) there was a “gap between anticipated and actual public response”. They noted that “even though there were some petitions against the use of genome-editing made by consumer groups; they did not develop into a mass mobilization, and media coverage was mostly positive.” These GnEd food products were developed by startups from Japanese universities, required no unique regulatory step before commercialization or mandatory labeling with Japanese regulatory agencies transparently sharing a summary of the information and notification on their websites, and businesses were encouraged to share information on products to respect The right of consumers to know. Successful commercialization of GnEd animal products has so far occurred in countries (e.g., Argentina, Brazil, Japan) where regulations treat simple GnEd products with no “foreign DNA” to be “non-GMO” and analogous to conventional breeding.

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.

Ionophores are a separate class of non-medically 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 meta-regression 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 step-wise 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%.

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 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.

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.

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.

Claw lesions in dairy cows are a primary contributor to lameness, posing severe welfare concerns due to the distress and discomfort experienced by affected cows. Claw disorders have a significant negative impact on herd efficiency, milk yield, reproductive performance, and involuntary culling. Despite increased awareness of the detrimental effects of lameness, its incidence has continued to rise. Reducing the occurrence of lameness remains a major challenge, especially in defining and recording of claw traits. In South Africa (SA), claw data is limited to private hoof trimmers with paper-based recording. The objective of this research was to examine the occurrence and recording of claw lesions in Holstein cattle from the central region of South Africa. Claw-trimming data from five intensively managed dairy farms (average herd size: 1,073) using both dirt lot (DL) and free-stall (FS) housing systems were analyzed over a six-year period (2014 to 2019). The Claw Lesion Identification in Dairy Cattle brochure, co-developed by Zinpro Corporation and the International Lameness Committee, was used as the reference for lesion identification by a local hoof trimmer. Digital dermatitis (D) showed the greatest prevalence (64.02% of all recorded lesions), followed by sole ulcers (U; 8.59%), white line disease (W; 6.27%), and sole hemorrhage (H; 4.28%), with most lesions occurring in the rear feet. A chi-square test of independence between lesion and housing was rejected (P <0.0001). Correspondence analysis (CA) was then used to explore these dependencies. The first dimension of the graphical display explained 90.7% of the variation in the data. The CA indicated that the prevalence of D and F are strongly associated, and closely associated with U. The prevalence of these lesions is also associated with housing systems DL and FS. The CA also confirmed a close association between W and H, and the prevalence of these lesions in housing system DL+FS. Improving the precision of claw lesion identification and recording, especially with regard to lesions that tend to occur together, can furnish essential data to identify the most prevalent lesions in a population. This will enhance our comprehension of the causal factors behind these significant lesions and offer producers practical recommendations to use these data for prevention, early detection, management, and improvement of lameness.

As input costs increase, producers need to be mindful of management practices to maintain profit margins and it is important to understand current management practices. Therefore, the objective of this study was to investigate current beef marketing strategies and beliefs of beef producers in the state of Mississippi in 2019. This survey was distributed to cattle producers in Mississippi in the June/July issue of “Cattle Business in Mississippi” magazine with return postage. There also was a link included in the “Monday Memo” email sent to members of the Mississippi Cattlemen’s Association. The survey consisted of 22 questions that were divided into three sections: current practices, current cow-calf marketing practice, and general information. Responses from the survey were anonymous and confidential and were received via paper mail or electronic responses. Responses were stored and analyzed using Qualtrics. Data collected from this study were compared with 1999 data from a similar study conducted by Little et al. (2000). Out of 148 respondents, there was a 95% completion rate of surveys taken. Most respondents were 51 years and older with 54% of the respondents being part of the cattle industry for over 31 years. Ninety percent of respondents were members of the Mississippi Cattlemen’s Association and almost 60% were certified in Beef Quality Assurance. The majority, 122 of 148 producers, stated their cattle operation was not the primary source of income for their household. The average number of cows, bulls, stockers/yearling, replacement heifers and cattle on-feed on an operation was greater in the 2019 data compared with 1999. On average, the number of cows per operation increased from 40 to 68 animals, bulls increased from 2 to 5 animals, and replacement heifers increased from 7 to 17 animals from 1999 to 2019. There was also an average increase from 30 to76 animals of stocker/yearling and an increase from 2 to 27 animals of on-feed cattle. Most producers vaccinate (81%) and deworm (85%) according to recommendations and indicated a lack of labor and time as reasons for not implementing some common management practices such as preconditioning. Respondents mainly purchased calves directly from a producer (61%) or from special sales (59%). In 1999, over one-half of respondents purchased from an auction. When it came to selling cattle, 58% of respondents sold at auction regularly in 2019. In 1999, 73% of respondents sold at auctions. In 2019, 72% of respondents stated they did not retain ownership while in 1999 only 58% of respondents did not retain ownership. In conclusion, our results suggest there have been key changes within the cattle industry regarding herd size increases and a shift in selling and purchasing habits since 1999.

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.Identifiability analysis is a powerful, valuable tool for developing robust predictive models. With the help of freely available software, modelers in animal science can easily integrate this analysis into their model developments.When modeling biological systems, one major step of the modeling exercise is connecting the theory (the model) with the reality (the data). Such a connection passes through the resolution of the parameter identification (model calibration) problem, which aims at finding a set of parameters that best fits the variables predicted by the model to the data. Traditionally, the parameter identification step is often addressed like a downstream process (after data collection). Using this traditional approach, the modeler has minimal room for maneuvering to improve the model’s accuracy. This paper discusses the benefits of adopting an upstream approach (before data collection) during the model construction phase. This approach capitalizes on the identifiability analysis, a powerful tool seldom applied in dynamic models of the animal science domain, likely because of the lack of awareness or the specialized mathematical technicalities involved in the identifiability analysis. In this paper, we illustrate that the modeling community in animal science can easily integrate identifiability analysis in their model developments following a practitioner approach taking advantage of a variety of freely available software tools dedicated to identifiability testing.

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the “per animal” approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.Precision livestock farming (PLF) needs current technologies to adapt to suit its unique needs. We analyze cutting-edge sensor, networking, communication, and analytics advancements from the perspective of PLF.Precision technologies are revolutionizing animal agriculture by enhancing the management of animal welfare and productivity. To fully realize the potential benefits of precision livestock farming (PLF), the development and application of digital technologies are needed to facilitate the responsible and sustainable intensification of livestock production over the next several decades. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring audibility in value chains while assuaging concerns associated with labor shortages. In this paper, we analyze the multilayered network of sensors, actuators, communication, and analytics currently in use in PLF. We analyze the various aspects of sensing, communication, networking, and intelligence on the farm leveraging dairy farms as an example system. We also discuss the potential implications of advancements in communication, robotics, and artificial intelligence on the security and welfare of animals.

This article provides a synopsis of the collection of papers provided by participants of the NE1727 Multistate project. Five reviews and thirteen primary research articles are included that provide contributions the group has made to understanding the role of the corpus luteum in reproduction, describing how the ovary influences fertility, delineating mechanisms controlling oocyte quality and early embryo development, and exploring new reproductive management schemes.This article provides a synopsis of the collection of papers provided by participants of the NE1727 Multistate project. Five reviews and thirteen primary research articles are included.

Our objectives were to compare the antifungal activity of 5 lignosulfonates, and 2 chitosans against fungi isolated from spoiled hay, and assess the effects of an optimized lignosulfonate, chitosan, and propionic acid (PRP) on high-moisture alfalfa hay. In experiment 1, we determined the minimum inhibitory concentration and minimum fungicidal concentration of 4 sodium lignosulfonates, 1 magnesium lignosulfonate, 2 chitosans, and PRP (positive control) against Aspergillus amoenus, Mucor circinelloides, Penicillium solitum, and Debaromyces hanseniiat pH 4 and 6. Among sodium lignosulfonates, the one from Sappi Ltd. (NaSP) was the most antifungal at pH 4. However, chitosans had the strongest fungicidal activity with the exception of M. circinelloidesat both pH 4 and 6. PRP had more antifungal effects than NaSP and was only better than chitosans for M. circinelloides. In experiment 2, we evaluated the effects of 3 additives (ADV): optimized NaSP (NaSP-O, UMaine), naïve chitosan (ChNv, Sigma–Aldrich), and PRP on high-moisture alfalfa hay. The experimental design was a randomized complete block design replicated 5 times. Treatment design was the factorial combination of 3 ADV× 5 doses (0, 0.25, 0.5, 1, and 2% w/w fresh basis). Additives were added to 35 g of sterile alfalfa hay (71.5 ± 0.23% DM), inoculated with a mixture of previously isolated spoilage fungi (5.8 log cfu/fresh g), and aerobically incubated in vitro for 23 d (25°C). After incubation, DM losses were reduced by doses as low as 0.25% for both NaSP-O and PRP (x¯=1.61) vs. untreated hay (24.0%), partially due to the decrease of mold and yeast counts as their doses increased. Also, hay NH3-N was lower in NaSP-O and PRP, with doses as low as 0.25%, relative to untreated hay (x¯=1.13 vs. 7.80% of N, respectively). Both NaSP-O and PRP increased digestible DM recovery (x¯=69.7) and total volatile fatty acids (x¯=94.3), with doses as low as 0.25%, compared with untreated hay (52.7% and 83.8 mM, respectively). However, ChNv did not decrease mold nor yeast counts (x¯=6.59 and x¯=6.16 log cfu/fresh g, respectively) and did not prevent DM losses relative to untreated hay. Overall, when using an alfalfa hay substrate in vitro, NaSP-O was able to prevent fungal spoilage to a similar extent to PRP. Thus, further studies are warranted to develop NaSP-O as a hay preservative under field conditions.In our first experiment, we assessed the antifungal activity of two major types of byproducts, one known as lignosulfonates (5 types), which are generated by paper mills, and another known as chitosans (2 types), which are generated from shellfish. These were tested against four fungi isolated from spoiled hay. We observed that acidic conditions are not necessary for chitosans but are crucial to activate the antifungal properties of lignosulfonates. Also, we found that sodium lignosulfonate from Sappi Ltd. was the most antifungal relative to other sodium lignosulfonates from other manufacturers. Chitosans had stronger fungicidal activity than propionic acid or lignosulfonates against all but one mold tested. In our second experiment, we compared the best treatments from experiment 1 against propionic acid using alfalfa hay as a substrate to grow the same fungi tested in experiment 1. None of the doses of chitosan prevented spoilage on high moisture hay, showing results similar to untreated hay. In contrast, an optimized sodium lignosulfonate and propionic acid prevented fungal spoilage of alfalfa hay with doses as low as 0.25%.An optimized paper mill byproduct prevented fungal spoilage of high moisture alfalfa hay to the same extent as propionic acid under aerobic conditions in vitro.

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.

Genetic selection for traits that have a direct impact on profitability, such as productive longevity (PL), which blends cow longevity with regular reproductive performance, is fundamental for the economic success of beef cow-calf operations. The purpose of this study was to develop a data screening strategy and a statistical model to predict genetic merit for PL in a multibreed beef cattle population. Pedigree (n= 1,352,765) and phenotype (n= 978,382) information were provided by Leachman Cattle of Colorado, and genotypes (n= 26,342) were provided by the Zoetis commercial genotyping laboratory. A repeatability model (REP) including the systematic effects of age at first calving, year-season of progeny birth, pedigree-based retained heterosis, and parity number, as well as the random effects of the additive genetic, permanent environment, contemporary group, and residual were fitted to adjust PL. In addition, a random regression model (RRM) was fitted to investigate PL considering the same effects, with the difference that random effects were regressed on parity. Estimated breeding values (EBV) were obtained by single-step GBLUP (ssGBLUP) and transformed to predict differences in the number of calves through linear regression. Predictive performance was assessed in a group of 7,268 cows born in 2010. Heritability estimates for PL were relatively low, with values of 0.109 for REP and a decreasing trend for RRM with values ranging from 0.16 to 0.04. Repeatability for PL was of moderate magnitude, with values of 0.415 for REP and from 0.29 to 0.57 for RRM. Heritability estimates suggest that most of the phenotypic variation was accounted for by environmental factors, but long-term genetic selection could still be effective. REP was more efficient than RRM, showing the lower number of iterations and time to reach convergence with comparable solutions to RRM. Validation results showed that correlations between EBV and phenotypes (observed/precorrected) increased over the years ranging from 0.04 to 0.92. Repeatability values and the validation approach suggested that using a cow’s first record (second parity success or failure) is a reasonably good indicator of posterior performance for PL. Therefore, the inclusion of PL in a multibreed genetic evaluation program and incorporation into selection indexes with existing economic traits can enable more profitable selection and breeding decisions in beef cattle herds.This paper presents a concept called productive longevity (PL) that blends cow longevity with reproductive performance measured by the number of calves regularly produced in the herd. Identifying animals with high genetic merit for PL is crucial to inform selection and breeding decisions, assisting beef cattle producers to improve profitability.Productive longevity (PL) is a concept that blends cow longevity with reproductive performance quantified by the number of calves regularly produced over the defined lifespan. Specifically, PL was defined as the number of calves consecutively and regularly produced from the second to the eighth parity at 9 yr of age, assuming the first calf was produced at roughly 2 yr of age. Genetic selection for PL can improve producers’ profit by identifying animals with higher genetic merit to produce more calves regularly, and consequently reduce replacement rate and related costs. The purpose of this study was to develop a data screening strategy and a statistical model to predict genetic merit for PL in a multibreed beef cattle population. Selecting bulls with high genetic merit for PL should yield on average 1.2 additional progeny per daughter compared to bulls with low genetic merit. Moreover, when assessing the predictive performance of PL, it was observed that having approximately the first three calving dates contributing to PL are good indicators of cows’ posterior performance. Such results highlighted the benefits and importance of PL for beef cow-calf producers to inform selection decisions, especially when incorporated into economic selection indexes to maximize profitability.

The transition period is a critical metabolic phase for dairy ruminants, especially those with high production levels. In spite of this, little is still known about dairy water buffalo. The aim of this study was to evaluate the effect of a commercial feed additive based on diatomaceous earth and hydrolyzed yeasts on health status, milk quality, and immune response of buffalo cows during the transition period. Eighty healthy Water buffaloes (Bubalus bubalis) of Italian Mediterranean breed were included in the trial. They were subdivided into two groups: one group received the additive (n= 40) while the control group (n= 40) received a placebo. The trial lasted 120 d, from 60 d before calving to 60 d in milk. Blood samples were collected from each buffalo at −60 (60 d from the expected calving), −30, 0 (calving), +15, +30, and +60 d (respectively, i.e., 15, 30, and 60 d in milking). The biochemical as well as the oxidative profile, and the antioxidant power and enzymatic activity were evaluated in the samples obtained. Moreover, acute phase proteins, reactive proteins, and interleukin plasma levels were determined. Peripheral blood mononuclear cells (PBMCs) and monocytes were isolated and viability, reactive oxygen species (ROS), and reactive nitrogen species were measured on PBMC and monocytes. The introduction of additives enhanced the total antioxidant capacity and enzyme activity, while no differences were observed in oxidation products throughout the trial. Additionally, it significantly reduced the synthesis of ROS in polymorphonuclear cells, supporting a potential positive response in animals experiencing inflammation. The impact of oxidation on the products was not evident. Despite higher enzyme levels in plasma, this did not necessarily correspond to significantly increased enzymatic activity but rather indicated a higher potential. From these results, it was evident that the transition period in buffaloes differs notably from what reported in the literature for cows, probably due to the absence of common postpartum production diseases in dairy cows and lower metabolic challenges linked to lower milk production in buffaloes. Few parameters exhibited notable changes during the transition period in buffaloes, notably certain antioxidant enzymes, PBMC viability, PBMC ROS production, and Hp levels.The observed heightened biological responsiveness in buffaloes, despite the absence of significant stress, implies the potential of diatomaceous earth and fungal cell walls to modulate physiological responses. These findings suggest avenues for further research on the nuanced impacts of these additives, offering insights into their role in promoting animal well-being during critical peripartum periods.The findings of this paper on the use of diatomaceous earth and yeast products during the transition period in buffaloes reveal that their inclusion does not significantly affect milk production, both qualitatively and quantitatively, or the overall health status of the animals. However, intriguingly, results pertaining to oxidative status and peripheral blood cells, stimulated ex vivo, indicate that even in the absence of pronounced stress during the peripartum period, the animals exhibit increased potential antioxidant response. These insights suggest a potential for enhancing physiological responses in transition period buffaloes, opening avenues for further research on the nuanced impacts of these additives and their implications for animal well-being.

Fall-calving primiparous beef females [body weight (BW): 451 ± 28 (SD) kg; body condition score (BCS): 5.4 ± 0.7] were individually-fed 100% (control; CON; n= 13) or 70% (nutrient restricted; NR; n= 13) of estimated metabolizable energy and metabolizable protein requirements from day 160 of gestation to calving. Post-calving, all dams were individually-fed tall fescue hay supplemented to meet estimated nutrient requirements for maintenance, growth, and lactation in Calan gates until day 149 of lactation, which limited calves to milk only. From day 150 of lactation until weaning at day 243, dams and calves were group-fed in drylots. Dam BW and metabolic status were determined every 21 d, and BCS and backfat (BF) were determined every 42 d of lactation until weaning. Pre-weaning calf BW, size, and metabolic status were determined every 21 d. Data were analyzed with nutritional plane, calving date, and calf sex (when P < 0.25) as fixed effects. Circulating metabolites included day and nutritional plane × day as repeated measures. We previously reported that post-calving, NR dams were 64 kg and 2.0 BCS less than CON, but calf BW and size at birth were not affected. During the first 147 d of lactation, NR dams gained more (P < 0.01) BW than CON and increased (P < 0.01) BCS, while CON decreased (P ≤ 0.01) BCS and BF. Previously, NR dams had lower (P< 0.01) circulating triglycerides on day 1 of lactation, tended to have lower (P= 0.08) triglycerides on day 21, and had lower (P≤ 0.04) non-esterified fatty acids (NEFA) on days 21 and 243 than CON. Maternal glucose and urea N were not affected (P ≥ 0.73). At weaning, NR dams weighed 17 kg less (P = 0.15), were 0.67 BCS lower (P < 0.01), and tended to have less (P = 0.06) BF. Calves born to NR dams weighed less (P = 0.02) than CON by day 42 of age and were 13% smaller (P < 0.01) at weaning. Calf girth measures diverged (P≤ 0.05) by day 21 of age, and skeletal size measures were less (P≤ 0.08) for calves born to NR dams at most timepoints after day 63 of age. Calves born to NR dams tended to have lower (P = 0.09) circulating urea N pre-weaning than CON, but glucose, triglycerides, and NEFA were not affected (P ≥ 0.16). In summary, first-parity beef females that were nutrient restricted during late gestation experienced compensatory growth and gained body condition during lactation but were still thinner at weaning. Nutrient restriction reduced pre-weaning calf growth, likely due to decreased milk production.First-parity beef females that were nutrient restricted during late gestation and then fed to meet estimated nutrient requirements during lactation experienced compensatory growth but were still in poorer body condition at weaning than controls. Despite a similar birth weight, calves born to nutrient restricted dams diverged in body weight by day 42 of age and weighed less at weaning.Nutrient requirements increase substantially during late gestation in the beef female; however, poor forage nutrient availability can result in undernutrition. For heifers, the added nutrient requirements needed to continue growing during their first pregnancy and lactation pose an even greater challenge. It is plausible that lingering effects of late gestational nutrient restriction may exist for the dam and calf pre-weaning. We report that first-parity beef females that were nutrient restricted during late gestation and then fed to meet estimated nutrient requirements during lactation recovered quickly metabolically and experienced compensatory growth, but still had less body condition at weaning than controls. Late gestational nutrient restriction did not affect calf size at birth but resulted in calf body weight and size measures diverging early in life. Ultimately, nutrient restriction resulted in a 13% decrease in weaning weight, which was likely due to decreased milk production (in a companion paper). Despite this, metabolic status of calves born to nutrient restricted dams was not greatly altered. In summary, first-parity beef females that were nutrient restricted during late gestation prioritized partitioning nutrients to maternal growth and energy reserves over milk production during lactation, but dams were thinner at weaning, and pre-weaning calf growth was slowed.

A mechanistic, dynamic model was developed to calculate body composition in growing lambs by calculating heat production (HP) internally from energy transactions within the body. The model has a fat pool (f) and three protein pools: visceral (v), nonvisceral (m), and wool (w). Heat production is calculated as the sum of fasting heat production, heat of product formation (HrE), and heat associated with feeding (HAF). Fasting heat production is represented as a function of visceral and nonvisceral protein mass. Heat associated with feeding (HAF) is calculated as ((1 − km) x MEI), where kmis partial efficiency of ME use for maintenance, and MEI = metabolizable energy intake) applies at all levels above and below maintenance. The value of kmderived from data where lambs were fed above maintenance was 0.7. Protein change (dp/dt) is the sum of change in the m, v, and wpools, and change in fat is equal to net energy available for gain minus dp/dt. Heat associated with a change in body composition (HrE) is calculated from the change in protein and fat with estimated partial efficiencies of energy use of 0.4 and 0.7 for protein and fat, respectively. The model allows for individuals to gain protein while losing fat or vice versa.When evaluated with independent data, the model performed better than the current Australian feeding standards (Freer et al., 2007) for predicting protein gain in the empty body but did not perform as well as for gain of fat and fleece-free empty body weight. Models performed similarly for predicting clean wool growth. By explicit representation of the major energy using processes in the body, and through simplification of the way body composition is computed in growing animals, the model is more transparent than current feeding systems while achieving similar performance. An advantage of this approach is that the model has the potential for wider applicability across different growth trajectories and can explicitly account for the effects of systematic changes on energy transactions, such as the effects of selective breeding, growth manipulation, or environmental changes.This paper presents a revised dynamic, mechanistic model of heat production and body composition in sheep where heat production is calculated internally as the sum of fasting heat production, heat associated with feeding, and heat from changes in bodily protein and fat pools. The resulting model reflects the variation in heat production that arises both from feed, the animal, and the intersection of these two sources, and is a simpler and more flexible way to predict energy requirements and heat production in growing ruminants than traditional feeding systems.Based on prior work by Oltjen et al. (2006), a revised dynamic, mechanistic model was developed to improve the prediction of the composition of protein and fat in the body of growing ruminants. The revised model calculates heat production (HP) internally as a function of fasting HP, heat associated with feeding, and HP from changes in fat and protein within the body. Heat associated with product formation is calculated from changes in body protein and fat, with separate efficiencies for each, while heat associated with feeding is a constant proportion of metabolizable energy intake and applies at all levels of feeding above and below maintenance. When evaluated against novel data, the revised model performed similarly to current Australian feeding standards (Freer et al., 2007) Unlike the Freer model, the revised model captures variation in HP arising from feed as well as gain of protein and fat. The revised model explicitly represents protein in the body as two pools with markedly different rates of energy expenditure, improving representation of the underlying biology compared to current feeding systems. This provides a more flexible way to predict energy requirements and body composition in growing animals while achieving similar performance to current feeding systems.

The microbiome has been linked to animal health and productivity, and thus, modulating animal microbiomes is becoming of increasing interest. Antimicrobial growth promoters (AGP) were once a common technology used to modulate the microbiome, but regulation and consumer pressure have decreased AGP use in food animals. One alternative to antimicrobial growth promoters are phytotherapeutics, compounds derived from plants. Capsaicin is a compound from the Capsicumgenus, which includes chili peppers. Capsaicin has antimicrobial properties and could be used to manipulate the gastrointestinal microbiome of cattle. Both the rumen and fecal microbiomes are essential to cattle health and production, and modulation of either microbiome can affect both cattle health and productivity. We hypothesized that the addition of rumen-protected capsaicin to the diet of cattle would alter the composition of the fecal microbiome, but not the rumen microbiome. To determine the impact of rumen-protected capsaicin in cattle, four Holstein and four Angus steers were fed rumen-protected Capsicumoleoresin at 0 (Control), 5, 10, or 15 mg kg−1diet dry matter. Cattle were fed in treatment groups in a 4 × 4 Latin Square design with a 21-d adaptation phase and a 7-d sample collection phase. Rumen samples were collected on day 22 at 0-, 2-, 6-, 12-, and 18-h post-feeding, and fecal swabs were collected on the last day of sample collection, day 28, within 1 h of feeding. Sequencing data of the 16s rRNA gene was analyzed using the dada2 pipeline and taxa were assigned using the SILVA database. No differences were observed in alpha diversity among fecal or rumen samples for either breed (P> 0.08) and no difference between groups was detected for either breed in rumen samples or for Angus steers in fecal samples (P> 0.42). There was a difference in beta diversity between treatments in fecal samples of Holstein steers (P< 0.01), however, a pairwise comparison of the treatment groups suggests no difference between treatments after adjusting for multiple comparisons. Therefore, we were unable to observe substantial overall variation in the rumen or fecal microbiomes of steers due to increasing concentrations of rumen-protected capsaicin. We do, however, see a trend toward increased concentrations of capsaicin influencing the fecal microbiome structure of Holstein steers despite this lack of significance.This paper presents a study of the microbiome of Angus and Holstein steers fed rumen-protected Capsicumoleoresin. Capsaicin does not appear to affect the rumen microbiome of either breed but might affect the fecal microbiome of Holstein steers.The microbiome is the collection of microbes present in an animal’s body and has been discovered to be directly connected to animal health and productivity. In production animals, such as feedlot cattle, the microbiome can be modulated by antimicrobials to promote growth, but increasing consumer pressure to reduce antimicrobial use has producers seeking alternatives. Capsaicin is a phytotherapeutic derived from chili peppers that can be used to modulate the microbiome due to its antimicrobial properties. Eight steers were fed rumen-protected Capsicumoleoresin to determine its effect on average daily gain. In addition, rumen and fecal samples were collected for microbiome testing. No differences were detected in the rumen microbiomes between cattle fed capsaicin (treatment) or those that received no capsaicin (control). While no overall effect was observed on the fecal microbiome of cattle fed different doses of capsaicin or control, we did observe changes in fecal beta diversity due to capsaicin treatment in Holstein steers fed greater doses. The fecal microbiome structure of Holsteins fed greater dosages of capsaicin differed from those fed control or low doses, as observed by the presence of two distinct clusters. This observation suggests an impact of greater doses of capsaicin treatment on microbiome structure.