Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Contemporary issues in agriculture and natural resource management (AGNR) span a wide spectrum of challenges and scales—from global climate change to resiliency in national and regional food systems to the sustainability of livelihoods of small-holder farmers—all of which may be characterized as complex problems. With rapid development of tools and technologies over the previous half century (e.g., computer simulation), a plethora of disciplines have developed methods to address individual components of these multifaceted, complex problems, oftentimes neglecting unintended consequences to other systems. A systems thinking approach is needed to (1) address these contemporary AGNR issues given their multi- and interdisciplinary aspects; (2) utilize a holistic perspective to accommodate all of the elements of the problem; and (3) include qualitative and quantitative techniques to incorporate “soft” and “hard” elements into the analyses. System dynamics (SD) methodology is uniquely suited to investigate AGNR given their inherently complex behaviors. In this paper, we review applications of SD to AGNR and discuss the potential contributions and roles of SD in addressing emergent problems of the 21st century. We identified numerous SD cases applied to water, soil, food systems, and smallholder issues. More importantly, several case studies are shown illustrating the tradeoffs between short-term and long-term strategies and the pitfalls of relying on quick fixes to AGNR problems (known as “fixes that backfire” and “shifting the burden”, well-known, commonly occurring, systemic structures—or archetypes—observed across numerous management situations [Senge, P.M. The Fifth Discipline, 1st ed.; Doubleday: New York, NY, USA, 1990.]). We conclude that common attempts to alleviate AGNR problems, across continents and regardless of the type of resources involved, have suffered from reliance on short-term management strategies. To effectively address AGNR problems, longer-term thinking and strategies aimed at fundamental solutions will be needed to better identify and minimize the often delayed, and unintended, consequences arising from feedback between management interventions and AGNR systems.

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH₄), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH₄ emissions. This review documents existing methods and methodologies to measure and estimate CH₄ emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH₄ have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH₄ for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH₄ from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH₄ emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice. There is a need to accurately and precisely quantify greenhouse gas (GHG) emissions, specifically methane (CH₄), to ensure correct reporting of GHG inventories and, perhaps more importantly, determine how to best mitigate CH₄ emissions. The objective of this study was to review existing methods and methodologies to quantify and estimate CH₄ emissions from ruminants. Historically, most techniques were developed for specific purposes that may limit their widespread use on commercial farms and for inventory purposes and typically required frequent calibration and equipment maintenance. Whole animal and head respiration chambers, spot sampling techniques, and tracer gas methods can be used to measure enteric CH₄ from individual animals, but each technique has its own inherent limitations. The measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the most complex variable creating many uncertainties. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources. Top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point.

Residual feed intake (RFI) testing has increased selection pressure on biological efficiency in cattle. The objective of this study was to assess the association of the rumen microbiome in inefficient, positive RFI (p-RFI) and efficient, negative RFI (n-RFI) Brahman bulls grazing ‘Coastal’ bermudagrass [Cynodondactylon (L.) Pers.]under two levels of forage allowance (high and low stocking intensity). Sixteen Brahman bulls were previously fed in confinement for 70 d to determine the RFI phenotype. Bulls were then allotted 60 d stocking on bermudagrass pastures to estimate RFI using the n-alkane technique. At the conclusion of the grazing period, rumen liquid samples were collected from each bull by stomach tube to evaluate the rumen microbiome. Extraction of DNA, amplification of the V4-V6 region of the 16S rRNA gene, and 454 pyrosequencing were performed on each sample. After denoising the sequences, chimera checking, and quality trimming, 4,573 ± 1,287 sequences were generated per sample. Sequences were then assigned taxonomy from the Greengenes database using the RDP classifier. Overall, 67.5 and 22.9% of sequences were classified as Bacteroidetes and Firmicutes, respectively. Within the phylum Bacteroidetes, Prevotella was the most predominant genus and was observed in greater relative abundance in p-RFI bulls compared with n-RFI bulls (P = 0.01). In contrast, an unidentified Bacteroidales family was greater in relative abundance for n-RFI bulls than p-RFI (26.7 vs. 19.1%; P = 0.03). Ruminococcaceae was the third most abundant family in our samples, but it was not affected by RFI phenotype. No effect of stocking intensity was observed for bacterial taxa, but there was a tendency for alpha diversity and operational taxonomic unit richness to increase with lower stocking intensity. Results suggested the rumen microbiome of p-RFI Brahman bulls has greater levels of Prevotella, but the bacterial community composition was unaffected by stocking intensity.

Beef production needs to increase from 60 million to 130 million tons by 2050 to feed a growing world population, and 70% of this production increase is expected from beef industries located in subtropical and tropical regions of the world. Bos indicus-influenced cattle predominate in these regions but are often managed using practices developed for Bos taurus breeds reared in temperate climates. Hence, a fundamental step to meet the increasing global demand for beef is to develop specific management for B. indicus-influenced cattle in tropical or subtropical environments. Bos taurus and B. indicus are different subspecies, and diverge in social and biological functions due to selection pressure caused by complex evolutionary and domestication processes. Bos indicus cattle display different social responses compared with B. taurus counterparts, which must be taken into account by management planning as these traits directly impact cattle performance and welfare. In tropical and subtropical regions, warm-season perennial C4 grasses are the dominant forages, and their availability has a significant influence on the productivity of beef cattle systems. The resilience of C4 grasses under adverse conditions is one of their most important characteristics, even though these forages have reduced nutritive value compared with forages from temperate climates. Accordingly, nutritional planning in tropical and subtropical conditions must include management to optimize the quantity and quality of C4 forages. Nutritional requirements of cattle raised within these conditions also require special attention, including inherent metabolic compromises to cope with environmental constraints and altered energy requirements due to body composition and heat tolerance. Nutritional interventions to enhance beef production need to be specifically tailored and validated in B. indicus-influenced cattle. As an example, supplementation programs during gestation or early life to elicit fetal programming or metabolic imprinting effects, respectively, yield discrepant outcomes between subspecies. Bos indicus-influenced cattle produce carcasses with less marbling than B. taurus cattle, despite recent genetic and management advances. This outcome is mostly related to reduced intramuscular adipocyte volume in B. indicus breeds, suggesting a lesser need for energy stored intramuscularly as a mechanism to improve thermotolerance in tropical and subtropical climates.

This review provides an update of ecologically relevant phytochemicals for ruminant production, focusing on their contribution to advancing nutrition. Phytochemicals embody a broad spectrum of chemical components that influence resource competence and biological advantage in determining plant species' distribution and density in different ecosystems. These natural compounds also often act as plant defensive chemicals against predatorial microbes, insects, and herbivores. They may modulate or exacerbate microbial transactions in the gastrointestinal tract and physiological responses in ruminant microbiomes. To harness their production-enhancing characteristics, phytochemicals have been actively researched as feed additives to manipulate ruminal fermentation and establish other phytochemoprophylactic (prevent animal diseases) and phytochemotherapeutic (treat animal diseases) roles. However, phytochemical-host interactions, the exact mechanism of action, and their effects require more profound elucidation to provide definitive recommendations for ruminant production. The majority of phytochemicals of nutritional and pharmacological interest are typically classified as flavonoids (9%), terpenoids (55%), and alkaloids (36%). Within flavonoids, polyphenolics (e.g., hydrolyzable and condensed tannins) have many benefits to ruminants, including reducing methane (CH₄) emission, gastrointestinal nematode parasitism, and ruminal proteolysis. Within terpenoids, saponins and essential oils also mitigate CH₄ emission, but triterpenoid saponins have rich biochemical structures with many clinical benefits in humans. The anti-methanogenic property in ruminants is variable because of the simultaneous targeting of several physiological pathways. This may explain saponin-containing forages' relative safety for long-term use and describe associated molecular interactions on all ruminant metabolism phases. Alkaloids are N-containing compounds with vast pharmacological properties currently used to treat humans, but their phytochemical usage as feed additives in ruminants has yet to be exploited as they may act as ghost compounds alongside other phytochemicals of known importance. We discussed strategic recommendations for phytochemicals to support sustainable ruminant production, such as replacements for antibiotics and anthelmintics. Topics that merit further examination are discussed and include the role of fresh forages vis-à-vis processed feeds in confined ruminant operations. Applications and benefits of phytochemicals to humankind are yet to be fully understood or utilized. Scientific explorations have provided promising results, pending thorough vetting before primetime use, such that academic and commercial interests in the technology are fully adopted.