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Black oat (Avena strigosa Schreb.) might be an attractive forage species in the northeastern United States, since it is generally more heat tolerant and disease resistant than other cool‐season grasses. Black oat is currently recommended for fall and winter production in USDA Plant Hardiness ones 8b–10a, which is beyond the northeastern United States (Zones 2a–6a). The objective was to evaluate 10 black oat breeding lines (referred to as "UF1" through "UF10") for forage accumulation, crude protein (CP), neutral detergent fiber (aNDF), acid detergent fiber (ADF), and in vitro digestible organic matter (IVDOM) concentrations. The experiment was carried out in April–July 2022 in Pennsylvania Furnace, PA. Triticale (× Triticosecale Wittmack cv. TriCal 342) and Legend 567 oat (Avena sativa L.) were included as controls, as well as Haden oat and Gunner triticale, as regionally recommended cultivars. The forage accumulation within the black oat germplasm ranged from 364 to 864 lb dry matter (DM) acre−1, observed in UF7 and UF9, respectively, during the first harvest. During the second harvest, forage accumulation within black oat ranged from 1048 to 1408 lb DM acre−1, from UF8 and UF1, respectively. Crude protein concentrations ranged from 16% to 23% across all black oats, with no differences found within the germplasm. The IVDOM concentrations averaged 78% across all treatments during the first harvest and decreased to 66% during the second harvest. Overall, this study showed that black oat merits further evaluation as forage species in the northeastern United States, but further studies are required to address management of the species. Core Ideas: Black oat is heat tolerant and therefore might be an alternative forage species for the northeastern United States.Further evaluation is required to establish production potential and nutritive value of black oat in the northeastern United States region.Best management practices for black oat are lacking and warranted, especially for future cultivar release. [ABSTRACT FROM AUTHOR]

To meet the expected demand for food while protecting animal welfare, environmental sustainability, and profitability, animal production efficiency must improve. Improvements in grazinglands management techniques can impact livestock production efficiency. The current stage of artificial intelligence development, mainly machine learning techniques, remote sensing (RS), and precision agriculture technologies, automatizes data collection and raises the monitoring capacity to support on‐farm decision‐making. This literature review presents current developments in precision livestock farming (PLF) applied to grazinglands monitoring and management, demonstrates some knowledge gaps, and discusses potential solutions of grazinglands management issues. Although the implementation of precision technologies in grazing systems is advancing rapidly, challenges, such as lack of reliable reference data and low variability of datasets used to calibrate models, are examples of constraints to be addressed in future studies. More effort in terms of relationship strengthening between farmers and researchers, benefits elucidation, cooperation among professionals with different expertise, and software or app development must be directed to make the knowledge accessible and largely implemented in field conditions. Core Ideas: Description and general perspective about grazinglands management practices are supported by precision technologies.We discus main knowledge gaps and challenges about precision livestock farming (PLF) in grazinglands.We suggest further research to overcome the main issues related to precision livestock farming in grazinglands.We present a summary of the potential and main usefulness or shortcoming for on‐farm implementation of technologies. [ABSTRACT FROM AUTHOR]

Both the vastness of pasturelands and the value they contain—e.g., food security, ecosystem services—have resulted in increased scientific and industry efforts to remotely monitor them via satellite imagery and machine learning (ML). However, the transferability of these models is uncertain, as modelers commonly train and test on site-specific or homogenized—i.e., randomly partitioned—datasets and choose complex ML algorithms with increased potential to overfit a limited dataset. In this study, we evaluated the accuracy and transferability of remote sensing pasture models, using multiple ML algorithms and evaluation structures. Specifically, we predicted pasture above-ground biomass and nitrogen concentration from Sentinel-2 imagery. The implemented ML algorithms include principal components regression (PCR), partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine regression (SVR), and a gradient boosting model (GBM). The evaluation structures were determined using levels of spatial and temporal dissimilarity to partition the train and test datasets. Our results demonstrated a general decline in accuracy as evaluation structures increase in spatiotemporal dissimilarity. In addition, the more simplistic algorithms—PCR, PLSR, and LASSO—out-performed the more complex models RF, SVR, and GBM for the prediction of dissimilar evaluation structures. We conclude that multi-spectral satellite and pasture physiological variable datasets, such as the one presented in this study, contain spatiotemporal internal dependence, which makes the generalization of predictive models to new localities challenging, especially for complex ML algorithms. Further studies on this topic should include the assessment of model transferability by using dissimilar evaluation structures, and we expect generalization to improve for larger and denser datasets. [ABSTRACT FROM AUTHOR]

The use of legumes is an important strategy for animal feeding, especially during the dry season. The aim was to evaluate the effect of planting densities (40,000; 15,625 and 10,000 plants ha−1) and harvest heights (20 and 40 cm) on the morphology, accumulation and survival of Desmanthus (Desmanthus pernambucanus [L.] Thellung), cultivated in subhumid tropical region, as well as to estimate the repeatability of the evaluated characteristics and the optimal number of measurements. The treatments were randomized in blocks, with subdivided plots and four replicates. Planting densities were evaluated in the plots and the harvest heights in the subplots. Eight harvests were carried out with an interval of 84 days. Morphological and productive characteristics and survival were evaluated. Cultivation under density of 40,000 plants ha−1 produced taller plants, with higher leaf area index (LAI = 0.98), light interception (LI = 49%), individual accumulations (18.8 g DM plant−1) and by area (576.5 kg DM ha−1 harvest−1), although resulting in reduced plant stand (66%). Harvest heights do not affect accumulation and survival. It is possible to reduce the number of measurements for stem diameter, number of leaflets per leaf (R2 = 95%), plant height, canopy diameter, LAI, LI, leaf length and width, number of leaves per branch, branch diameter and LBR (R2 = 90%), optimizing resources for future research. Desmanthus has potential for use in protein banks, being harvested in the rainy season, conserved and supplied in addition to animals, but plant growth is minimal during the dry season under rainfed conditions. [ABSTRACT FROM AUTHOR]

Eighty-four Angus crossbred heifers (13 ± 1 mo of age, 329.5 ± 61.92 kg of body weight [BW]) were used in a generalized randomized block design with a 2 × 2 factorial arrangement of treatments. The factors evaluated were: 1) diet type (whole plant sorghum silage [ SS ] vs. byproducts-based [ BP ]), and 2) feed additive: Aspergillus oryzae prebiotic (AOP; 2 g/d) vs. Negative control (CTL; 0 g/d), resulting in four treatments: sorghum silage-control (SC), sorghum silage-AOP (SA), byproducts-control (BC), and byproducts-AOP (BA). Heifers were stratified by body weight (BW), randomly assigned to treatments (21 heifers per treatment) and housed in 12 pens equipped with two GrowSafe feed bunks each to measure individual dry matter intake (DMI). After a 14-d adaptation, BW was measured every 14 d for 56 d. Chewing activity was monitored through collar-mounted HR-Tags (heat-related tags). Following the performance period, apparent total tract digestibility was measured in 40 heifers, using indigestible neutral detergent fiber as a marker. Heifers fed with the BP diets had greater DMI (2.92% vs. 2.59% of BW, P < 0.01) and average daily gain (ADG ; 1.16 vs. 0.68 kg, P ≤ 0.01) than heifers fed with SS diets. Compared with BP-fed animals, heifers consuming the SS diets had 23 more visits/d to the feed bunks (P ≤ 0.01), consumed 53% less dry matter on each visit (P ≤ 0.01), and spent 39% more min chewing/d and 63% more min chewing/kg of DMI (P ≤ 0.01). However, chewing measured in min/kg of neutral detergent fiber intake was not affected by treatment (average 111.3 min/kg of NDF intake). Feeding AOP improved gain:feed (GF) by 15% in BP-fed heifers (0.120 vs. 0.104 kg/kg; P < 0.05). Inclusion of AOP increased organic matter digestibility (OMD) in SS diets (55.88% vs. 49.83%; P < 0.01), whereas it decreased OMD in BP diets (61.67% vs. 65.77%; P < 0.05). In conclusion, ADG and GF of BP-fed heifers was greater than SS-fed heifers, and GF was greater with AOP supplementation in BP-fed heifers. Improvement in GF in BP-fed heifers was likely not related to differences in nutrient digestibility as AOP inclusion did not enhance digestibility in the BP diet. Additionally, the effects of the AOP inclusion appear to be diet-dependent, where the 15% improvement in GF by AOP occurred in heifers fed with the more fermentable diet. Therefore, further research should explore the mechanisms responsible for the observed improvements in growth performance when feeding AOP to BP-fed heifers. [ABSTRACT FROM AUTHOR]

Two experiments were conducted to evaluate the effects of Pensacola bahiagrass (Paspalum notatum) hay treated with calcium oxide (CaO) and molasses on performance of growing beef cattle supplemented or not with cottonseed meal. In Exp. 1, growing Bos indicus influenced heifers (n = 59; 250 ± 29 kg body weight [BW]) and steers (n = 37; 256 ± 45 kg BW) were used. In Exp. 2, heifers (n = 56; 249 ± 26 kg BW) and steers (n = 8; 249 ± 20 kg BW) from Exp. 1 were used. Both experiments were randomized complete block designs and consisted of 56-d periods in which cattle were weighed every 14 d. On day 0, cattle were weighed after a 16-h water and feed withdrawal, stratified by sex, breed, and BW, and blocked by initial BW. Cattle were allotted to 24 and 16 dormant bahiagrass pastures (1.34 ha each) in Exp. 1 and Exp. 2, respectively. Pastures were located in two different areas within 0.52 km of each other and were stratified by location and randomly assigned (n = 8 pastures/treatment) to treatment. In Exp. 1 treatments were: 1) untreated dry hay (DH); 2) hay treated with 10% molasses (dry matter [DM] basis) + water (to 65% DM; MOL); or 3) hay treated with 5% CaO (DM basis) + 10% molasses (DM basis) + water (to 65% DM; CAO). In Exp. 2 only treatments MOL and CAO were applied, and cottonseed meal was provided at 0.3% of cattle BW/d (as fed basis). In both experiments, data were analyzed using pasture as the experimental unit. The model included the fixed effects of treatment, sex, and their interaction (Exp. 1). Location and block were included as random effects. In both experiments, initial and final BW were not affected by treatment (P ≥ 0.362 and P ≥ 0.283, respectively) or sex (P ≥ 0.512 and P ≥ 0.495, respectively) and no treatment × sex interaction was observed in Exp. 1 (P > 0.05). Additionally, no effects of treatment (P ≥ 0.515), sex (P ≥ 0.285), or treatment × sex interaction (Exp. 1; P = 0.582) were observed on average daily gain (average of −0.03 kg in Exp. 1 and 0.537 kg in Exp. 2). Bahiagrass hay treated with molasses alone or in combination with CaO failed to improve performance of growing beef cattle. However, when protein supplementation via cottonseed meal was provided, cattle did not experience weight loss. [ABSTRACT FROM AUTHOR]

Residual feed intake (RFI) is a measure of feed efficiency (FE). Differences in plasma metabolites concentration found in animals with contrasting RFI are potential biomarkers of FE. Furthermore, feed behavior patterns could also be used to identify animals with contrasting RFI. We hypothesize that the integration of plasma metabolomics data and feed behavior can predict the future animal performance. We used 302 bulls subjected to an ad libitum diet for 56-d while allocated in pens equipped with GrowSafe System to evaluate RFI. Blood samples were collected for metabolomics analysis at the beginning (d 0) and at the end (d 56) of the trial. Feeding behavior metrics including feeding bout (FB), and eating time were estimated. Subsequently, the bulls were classified as low RFI (LRFI) and high RFI (HRFI). The performance test showed no differences between LRFI and HRFI animals in the initial body weight (LRFI: 408.62 ± 47.43 Kg; HRFI: 407.64 ± 60.22; P = 0.41), final body weight (LRFI: 510.33 ± 58.28; HRFI: 500.82 ± 72.23; P = 0.46), and average daily gain (LRFI: 1.67 ± 0.28; HRFI: 1.54 ± 0.36; P = 0.42). But the dry matter intake was less for LRFI (6.45 ± 1.78) in comparison with HRFI animals (8.81 ± 2.63; P < 0.001). Metabolomics analysis performed on the top and bottom 30 bulls based on RFI, reveled increased concentrations of 18 metabolites in LRFI bulls on d 0, and 12 different metabolites on d 56. Among these compounds, Cer(d18:1/23:0) level was statistically greater on both d 0 (LRFI: 0.233 ± 0.016 µM; HRFI: 0.187 ± 0.014 µM, P = 0.039) and d 56 (LRFI: 0.267 ± 0.016 µM; HRFI: 0.205 ± 0.013 µM, P = 0.02), emerging as a potential biomarker distinguished by the multivariate analysis, capable of discerning between LRFI and HRFI animals. On the other hand, FB (LRFI: 13 events/d; HRFI: 15 events/d; P = 0.019) and eating time LRFI: 119 min/d; HRFI: 153 min/d; P < 0.001) were different between the two groups. The more efficient animals (LRFI) were observed to visit the feed bunk less frequently and spend less time consuming feed compared with the less efficient animals (HRFI). In conclusion, further exploration of the metabolites' pathways and their roles and recognition of feeding behavior patterns could provide valuable insights into the mechanisms contributing to FE. [ABSTRACT FROM AUTHOR]