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.

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.

Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence (AI) tools based on pretrained models to segment lesions and detect breast cancer. The proposed workflow includes both the development of segmentation models and development of a series of classification models to classify ultrasound images as normal, benign or malignant. The pretrained models were trained and evaluated on the Breast Ultrasound Images (BUSI) dataset, a publicly available collection of grayscale breast ultrasound images with corresponding expert-annotated masks. For segmentation, images and ground-truth masks were used to pretrained encoder (ResNet18, EfficientNet-B0 and MobileNetV2)–decoder (U-Net, U-Net++ and DeepLabV3) models, including the DeepLabV3 architecture integrated with a Frequency-Domain Feature Enhancement Module (FEM). The proposed FEM improves spatial and spectral feature representations using Discrete Fourier Transform (DFT), GroupNorm, dropout regularization and adaptive fusion. For classification, each image was assigned a label (normal, benign or malignant). Optuna, an open-source software framework, was used for hyperparameter optimization and for the testing of various pretrained models to determine the best encoder–decoder segmentation architecture. Five different pretrained models (ResNet18, DenseNet121, InceptionV3, MobielNetV3 and GoogleNet) were optimized for multiclass classification. DeepLabV3 outperformed other segmentation architectures, with consistent performance across training, validation and test images, with Dice Similarity Coefficient (DSC, a metric describing the overlap between predicted and true lesion regions) values of 0.87, 0.80 and 0.83 on training, validation and test sets, respectively. ResNet18:DeepLabV3 achieved an Intersection over Union (IoU) score of 0.78 during training, while ResNet18:U-Net++ achieved the best Dice coefficient (0.83) and IoU (0.71) and area under the curve (AUC, 0.91) scores on the test (unseen) dataset when compared to other models. However, the proposed Resnet18: FrequencyAwareDeepLabV3 (FADeepLabV3) achieved a DSC of 0.85 and an IoU of 0.72 on the test dataset, demonstrating improvements over standard DeepLabV3. Notably, the frequency-domain enhancement substantially improved the AUC from 0.90 to 0.98, indicating enhanced prediction confidence and clinical reliability. For classification, ResNet18 produced an F1 score—a measure combining precision and recall—of 0.95 and an accuracy of 0.90 on the training dataset, while InceptionV3 performed best on the test dataset, with an F1 score of 0.75 and accuracy of 0.83. We demonstrate a comprehensive approach to automate the segmentation and multiclass classification of breast cancer ultrasound images into benign, malignant or normal transfer learning models on an imbalanced ultrasound image dataset.

Variation in individual animal feed intake is influenced by factors such as bunk management, digestive physiology, social hierarchy, and health status. While previous research has primarily examined feeding behavior in cattle offered high-concentrate diets, limited information exists regarding these relationships in high-forage systems. Residual Feed Intake (RFI), defined as the difference between an animal’s actual and expected feed intake based on metabolic mid-test body weight and average daily gain, serves as a key measure of feed efficiency. Animals with negative RFI values are classified as more efficient, whereas those with positive values are less efficient. This study investigated associations between feeding behavior and feed efficiency in yearling purebred Angus bulls (n = 232) and heifers (n = 58) consuming forage-based diets using a Vytelle feeding system. Upon arrival, bulls averaged 350.3 ± 3.6 kg and heifers averaged 287.5 ± 5.0 kg, with a subsequent 14-day acclimation followed by a 49-day ad libitum feeding period. In bulls, RFI was positively correlated with bunk visits (r = 0.34, p < 0.0001) and negatively correlated with duration (r = −0.16, p = 0.0124). In heifers, daily visits were negatively correlated with intake (r = −0.88, p < 0.0001), and RFI was negatively associated with RADG (r = −0.53, p < 0.0001).

Birdsfoot trefoil (Lotus corniculatus L.) (BFT) is a high quality, tanniniferous forage legume that has been shown to mitigate gastrointestinal nematode (GIN) infection in sheep. Reliable establishment of birdsfoot trefoil in existing pastures is difficult due to BFT’s poor seedling vigor, susceptibility to crown and root rot, and, perhaps chiefly, competition from other pasture plants. Most existing pastures in the Northeastern United States are primarily composed of cool-season grasses, many of which can be highly competitive. A common form of interplant competition is allelopathy, which occurs when secondary metabolites produced by a plant inhibit a necessary growth process of another plant. Allelopathic competition in cropping systems has been extensively studied, however very little has been done to assess allelopathic relationships between common cool-season forages and BFT. We investigated the presence and effect of allelopathic relationships of eight common cool-season forage grasses (Kentucky bluegrass, meadow fescue, orchardgrass, perennial ryegrass, reed canarygrass, smooth brome, tall fescue, and timothy) on BFT germination and radicle emergence. BFT seeds were germinated in a bioassay using serial dilutions of leaf tissue extracts and root leachates from the eight grasses, as well as from root and stem + leaf tissue from BFT to assess autotoxicity. We found that BFT germination and radicle length was allelopathically inhibited by the forage grasses and BFT. Inhibition varied widely by concentration, grass species, and source. Some grasses, such as orchardgrass and timothy, were inhibitory only at the highest concentration, while the more inhibitory grasses (reed canarygrass, tall fescue, and meadow fescue) were highly inhibitory at lower levels. BFT leaf + stem tissue extracts were found to inhibit BFT germination by up to 100%. Root leachate was overall much less inhibitory, though reed canarygrass root leachates inhibited BFT mean radicle length. Our results suggest that pasture establishment of BFT could be limited by the presence of severely inhibitory allelopathic relationships between BFT and the grasses into which it is commonly seeded. Growth patterns of forage grasses may be categorized as bunch-type or rhizomatous. Bunch-type grasses form discrete clumps and spread via tillering. Rhizomatous grasses are sod-forming and spread through rhizomes and stolons, increasing competition by decreasing space available to other pasture plants. Birdsfoot trefoil (Lotus corniculatus L.) (BFT) is a high quality, tanniniferous forage legume that has been shown to mitigate gastrointestinal nematode (GIN) infection in sheep. Pasture establishment of birdsfoot trefoil is chiefly inhibited by competition from other pasture plants. Though

Natural gas wells nearing the end of their production cycles represent both an environmental liability and an opportunity for sustainable land reuse in Appalachia. This study develops a fuzzy-logic geographic information system (GIS) model to identify well sites in West Virginia (WV) suitable for reclamation through small-ruminant agrivoltaic systems. The model integrates topographic, solar, and accessibility variables—slope, aspect, land cover, solar radiation, and distance to roads—each standardized by fuzzy membership functions and combined through fuzzy overlay to generate a continuous suitability surface. A biologically derived pairwise buffer analysis couples a 2.02 ha well-pad exclusion zone with a 1.48 ha grazing buffer representing the minimum forage area required for rotational grazing. Within a four-county case study, the model isolated 129 wells of high suitability, primarily in Tyler, Doddridge, and Harrison Counties. The framework provides a reproducible spatial decision tool for targeting post-extraction sites where solar infrastructure and managed grazing can co-locate, supporting both renewable-energy and land-restoration goals in the Appalachian region.