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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]

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]

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]

In recent times, there has been a growing focus on the well-being and healthcare of small ruminants, particularly in relation to the issue of anemia due to infection with blood-feeding gastrointestinal nema-todes, such as Haemonchus contortus. The objective of this study was to assess the hematocrit levels in blood samples obtained from small ruminants, specifically goats. Additionally, the study is an attempt to design and create a quick sensor for identifying anemia, which could be conveniently used on farms. A total of 75 mature, intact male Spanish goats were subjected to hematocrit analysis to ascertain their spectrum of hematocrit values and association with anemic conditions. Simultaneously, a unique sensor with user-friendly features was developed to promptly provide farmers with feedback on the anemic status of animals using AI-based machine learning algorithms, enabling timely intervention. The sensor utilized a semi-invasive approach with minimal blood sample requirement. 30 ^L of blood was dropped on glycerol soaked Whatman filter paper No. 1, and images were taken at 90 sec, 150 sec, and 270 sec to capture the blood pattern on filter paper soaked in glycerol using a cell phone. These images were correlated with actual hematocrit value obtained from conventional hematocrit analysis. A RMSprop with adjusted learning rate convolution neural network (RMSprop-CNN) based images classification model was developed for the classification of different patterns at different levels of hematocrit. The model was trained on a total of 1,000 images with a training and testing split of 80:20 ratio. For the optimization process, the Adam optimizer with a learning rate of 0.001 is employed. The model is compiled with a categorical cross-entropy loss function, aiming to improve its accuracy metric over training iterations. The initial studies demonstrated a detection accuracy level of 70.31 % at 10 epochs in identifying different hematocrit levels (Level 1; healthy, to Level 5; severely anemic) but improved significantly up to 94.89 % by 100 epochs, resulting in a notable reduction in the time and level of knowledge previously necessary for conducting such evaluations. The present study not only provides valuable insights into the hematological characteristics of small ruminants, but also establishes a foundation for the development of a straightforward and efficient method for detecting anemia. Consequently, this advancement has the potential to enhance the overall care and well-being of animals within agricultural environments. [ABSTRACT FROM AUTHOR]

In early lactation, susceptibility to disease is greatest, impacting cow health and productivity and leading to economic losses. Mastitis is the most economically costly disease to the dairy industry and is most frequent at this time. The objective of this paper is to discuss the energetic fuels used by leukocytes in the metabolic response during mastitis that may reveal potential mechanisms linking mastitis with the development of subsequent metabolic diseases for dairy cows during lactation. Glucose and glutamine are the primary fuels used by leukocytes and are essential substrates for optimal leukocyte function. Yet because these substrates are in high demand to support milk synthesis during early lactation, their supply to leukocytes may be compromised and may partly contribute to immunosuppression observed at this time. Production-related metabolic diseases during early lactation, such as ketosis and hepatic lipidosis, can also adversely affect health and productivity. Risk of subsequent disease for cows during mastitis has not been fully elucidated. Regardless of stage of lactation and physiological state, increases in circulating NEFA and glucose and decreases in ketones during an intramammary inflammation in dairy cows have been reported. In addition, previous work indicates that hepatic metabolism may be impaired during inflammation. These results indicate a potential link between mastitis and the risk of subsequent metabolic disease for dairy cows during lactation. This paper will discuss the complex relationships between metabolism and immune function and how these immunometabolic interactions relate mastitis with increased risk of subsequent disease during early lactation. [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]