Estimates of global population growth are often cited as a significant challenge for global food production. It is estimated that by 2050 there will be approximately two- billion additional people on earth, with the greatest proportion of that growth occurring in central Africa. To meet recommended future protein needs (60 g/d), approximately 120 million kg of protein must be produced daily. The production of ruminant meat (particularly beef cattle) offers the potential to aid in reaching increased global protein needs. However, advancements in beef cattle production are necessary to secure the industry’s future sustainability. This article draws attention to a subset of sustainable beef cattle production challenges, including the role of ruminant livestock in meeting global human protein needs, the environmental relationships of advanced beef cattle production, and big data and machine learning in beef cattle production. Considering the significant quantities of resources necessary to produce this form of protein, such advancements are not just a moral imperative but critical to developing advanced beef cattle production practices and predictive models that will reduce costs and liabilities and advance industry sustainability.

Technology that facilitates estimations of individual animal dry matter intake rates in group-housed settings will improve production and management efficiencies. Estimating dry matter intake in pasture settings or facilities where feed intake cannot be monitored may benefit from predictive algorithms that use other variables as proxies. This study examined the relationships between dry matter intake (DMI), animal performance, and environmental variables. Here we determined whether a machine learning approach can predict DMI from measured water intake variables, age, sex, full body weight, and average daily gain (ADG). Two hundred and five animals were studied in a drylot setting (152 bulls for 88 days and 53 steers for 50 days). Collected data included daily DMI, water intake, daily predicted full body weights, and average daily gain using In-Pen-Weighing Positions and Feed Intake Nodes. After exclusion of 26 bulls of low-frequency breeds and one severe (greater than 3 standard deviations) outlier, the final number of animals used for modeling were 178 (125 bulls, 53 steers). Climate data were recorded at 30-minute intervals throughout the study period. Random Forest Regression (RFR) and Repeated Measures Random Forest (RMRF) were used as a machine learning approaches to develop a predictive algorithm. Repeated Measures ANOVA (RMANOVA)was used as the traditional approach. Using the RMRF method, an algorithm was constructed that predicts an animal’s DMI within 0.75 kg. Evaluation and refining of algorithms used to predict DMI in drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive group field settings.

Summary: On day 12–13 of gestation, the preimplantation pig conceptus undergoes a dramatic morphologic change from an approximately 1‐cm sphere to a nearly 1‐m long thread. This transformation, referred to as elongation, occurs in just 12–24 h. Elongation is primarily the result of trophectodermal cell shape changes, as there is relatively little mitosis during this stage of development. Thus far, descriptions of elongation have been limited to histologic and immunofluorescent studies of cell morphology and gross biochemical evaluations. We hypothesized that the changes in trophectoderm morphology likely involves significant changes in gene expression. Therefore, we used RNA arbitrarily primed‐PCR (RAP‐PCR) to characterize potential differential gene expression by trophectodermal cells during pig conceptus elongation. We found that the porcine heterogeneous nuclear ribonucleoprotein (hnRNP) A2/B1 was shown to be differentially expressed by trophectodermal cells during elongation. We suggest that regulated alternative splicing may contribute to the morphogenetic process of elongation. genesis 26:9–14, 2000. Published 2000 Wiley‐Liss, Inc.

Placental efficiency (PE) describes the relationship between placental and fetal weights (fetal wt/placental wt). Within litters, PE can vary drastically, resulting in similarly sized pigs associated with differently sized placentas, up to a 25% weight difference. However, the mechanisms enabling the smaller placenta to grow a comparable littermate are unknown. To elucidate potential mechanisms, morphological measurements and gene expression profiles in placental and associated endometrial tissues of high PE and low PE feto-placental units were compared. Tissue samples were obtained from eight maternal line gilts during gestational day 95 ovario-hysterectomies. RNA was extracted from tissues of feto-placental units with the highest and lowest PE in each litter and sequenced. Morphological measurements, except placental weight, were not different (P > 0.05) between high and low PE. No DEG were identified in the endometrium and 214 DEG were identified in the placenta (FDR < 0.1), of which 48% were upregulated and 52% were downregulated. Gene ontology (GO) analysis revealed that a large percentage of DEG were involved in catalytic activity, binding, transporter activity, metabolism, biological regulation, and localization. Four GO terms were enriched in the upregulated genes and no terms were enriched in the downregulated genes (FDR < 0.05). Eight statistically significant correlations (P < 0.05) were identified between the morphological measurements and DEG. Morphological measures between high and low PE verified comparisons were of similarly sized pigs grown on different sized placentas, and indicated that any negative effects of a reduced placental size on fetal growth were not evident by day 95. The identification of DEG in the placenta, but absence of DEG in the endometrium confirmed that the placenta responds to the fetus. The GO analyses provided evidence that extremes of PE are differentially regulated, affecting components of placental transport capacity like nutrient transport and blood flow. However, alternative GO terms were identified, indicating the complexity of the relationship between placental and fetal weights. These findings support the use of PE as a marker of placental function and provide novel insight into the genetic control of PE, but further research is required to make PE production applicable.

Dry matter intake (DMI) is a measure critical to managing and evaluating livestock. Methods exist for quantifying individual DMI in dry lot settings that employ expensive intake systems. No methods exist to accurately measure individual DMI of grazing cattle. Accurate prediction of DMI using machine learning (ML) promotes improved production and management efficiency. It also opens the door to empowering producers to validate and verify intakes in order to participate in incentive programs for delivering ecosystem service credits. We explored gradient boosting-based approaches to predict DMI in beef cattle using actual animal intake and climate datasets of 12,056 daily records from 178 cattle fed at West Virginia University from 2019 to 2020. The tested and developed methods include gradient boosting regression (GBR), Light boosting regression (LGB), extreme GBR (XGB), and Gaussian process boosting (GPBoost) models and 2 baseline models: 1. Nutrient Requirements of Beef Cattle Equation 1 & 2. mixed linear model regression (MLM). The GPBoost models were developed considering the random effects associated with animal ID and date. Moreover, we developed an end-to-end ML operations (MLOps) pipeline to streamline the ML steps using crucial components, such as MLflow and Dockerization. The best-performing model was determined by comparing the common evaluation metrics such as root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error. The RMSE values on the test data of the optimized models ranged from 1.18 to 1.54 kg. The focus was developing a generalized algorithm that models covariates associated with animal ID and date that would generalize well on unseen data. The GPBoost models exhibited the best bias and variance compared to the other models (MLM, GBR, LGB, XGB). The R2 of the GPBoost on the training and test datasets were 0.58 and 0.55, respectively. The GPBoost model generalized well on the test dataset and train dataset with MAE values of 0.92 and 0.90 kg, respectively. We implemented an end-to-end MLOps pipeline with MLflow and Docker, enabling experiment tracking, model registry, reproducibility, scalability (to deploy on multiple computers), and seamless deployment. This approach offers a reliable and scalable solution for accurate DMI prediction, enhancing livestock management, and facilitating participation in ecosystem service credits.

Improving the efficiency, economic viability, and environmental sustainability of beef cattle production requires tools to identify resource-efficient animals. Validated tools to measure, monitor, and verify individual feed and water intake are needed. Here, we verify the validity of the Vytelle In-Pen Weighing Position to passively collect daily full body weights and assess the use of an integrated flow meter with a commercial waterer as a tool to measure daily water intake. This study involved 103 bulls (40 Charolais and 63 Angus) and 54 heifers (25 Charolais and 29 Angus). These animals were fed in a facility with feed intake nodes, In-Pen Weighing, and metered waterers. Body weights collected on the chute scale and total water usage measured by a residential water meter were used to evaluate body weight and water intake measured at the In-Pen Weighing Positions. We confirmed that In-Pen Weighing is highly correlated to chute weighing (Spearman’s correlation coefficient, ρ = 0.99, p < 0.0001). We observed high correlation of total water use measured at the In-Pen Weighing units with the facility’s total water use (ρ = 0.9999, p < 0.0001). This validates the accuracy of the in-pen water meters, but not the precision of applying water consumption to individual animals. The use of such passive monitoring equipment has the potential to help improve the sustainability of animal agriculture.