A Review of Equine Grazing Research Methodologies

Recently, there has been a renewed interest and an increase in published research on equine grazing and pasture management. The objectives of this paper are to review equine grazing research methodologies with an aim to encourage standardized research procedures. This review highlights standard methods used in equine pasture-related research, including use of climate data and soil type; seed bed preparation and grazing management; determining forage nutritive value; defining forage maturity; tracking horse health parameters; evaluating different grazing systems; and future technologies. This review covers in-depth discussions on determining forage biomass yield, forage populations, ground cover, persistence, forage intake, and grazing behavior. Specifically, mechanical harvesting, hand clipping, rising plate meter, and falling plate meter are all methods used to determine forage biomass yield. Frequency grid, point sampling, visual assessment, Natural Resources Conservation Service pasture condition score, and the double Dominant, Abundant, Frequent, Occasional, Rare (DAFOR) scale can be used to track forage populations, ground cover, and persistence of pasture species. Three primary methods have been used when estimating horse forage intake including the difference between pre- and postgrazing herbage mass, the difference in pre- and postgrazing bodyweight, and use of digestibility and fecal output markers. Equine grazing behavior has been described by investigating preference, pre- and post-sward height, and bite and mastication rate. Awareness of key methodologies should encourage movement toward research protocol standardization that will allow for scientific comparisons and application of pasture-related research results across the horse industry.     

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

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.     

Models for biomechanical analysis of jumping horses

The application of qualitative analysis techniques, currently used in sports biomechanics, may help equine biomechanics researchers identify the most appropriate directions for future research. This paper reviews existing qualitative biomechanical models that could be relevant to equine jumping research. The deterministic model approach¹ is identified as the most appropriate. This approach is used to derive deterministic models suitable for equine jumping evaluation. The horse's jump can be broken down into five discrete parts_— approach, take off, suspension, landing and departure. Each of these parts has specific determining factors, which have either a mechanical or a mathematical relationship. These models may be used to assist horse trainers identify and implement appropriate strategies for improvement in jumping horses.     

Action of immunostimulants on phagocytosis and derivative actions

One of the main objectives of immunopotentiators is their ability to enhance or to restore natural anti-infectious resistance of normal or immunocompromised hosts. Numerous experimental resistance models have been used in screening such potentiators. However, once such a substance has been selected, its potential and practical use will directly depend on the knowledge of the underlying resistance mechanisms induced, defining its cellular or molecular targets. Phagocytosis-polymorphonuclear or monocyte-macrophage dependent--is the most frequently described variable which have been analysed among the various potentially active mechanisms of non-specific resistance. Various in vivo and in vitro tests might be used and the target of several immunostimulants among the different steps of phagocytosis are described. However, numerous intrinsic limitations are associated with such tests and news models or tests are presented and discussed in order to gain more insights into the evaluation of active substances.     

Quantification of methane emitted by ruminants: a review of methods

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.     

Survey of Feeding Practices, Supplement Use, and Knowledge of Equine Nutrition among a Subpopulation of Horse Owners in New England

Nutrition is a critical component of equine health. Horse owners' knowledge of nutrition is likely to affect their feeding practices. The aim of this study was to survey feeding practices, dietary supplement use, and knowledge about equine nutrition in New England by surveying a subpopulation of horse owners (67/337 or 19.8%) who brought their horses to the Large Animal Hospital at the Cummings School of Veterinary Medicine at Tufts University between July and September 2008. All owners reported feeding hay, with the majority feeding grass or timothy hay. Most owners (96%) reported feeding a concentrate in addition to hay. Approximately 84% of owners reported including at least one dietary supplement in their horse's daily feeding. The most commonly used supplements were chondroprotectives, electrolytes, and multivitamins. Survey questions designed to assess the owner's knowledge of nutrition suggested that many owners may not have a basic understanding of principles of equine nutrition; less than 50% knew the daily water and hay requirements for a horse, and 69% lacked knowledge about the proper use of concentrates in a diet. Most of the surveyed owners consulted multiple sources of information concerning equine nutrition, including veterinarians (n = 36), trainers (n = 27), feed stores (n = 10), and the internet (n = 7). Although the major source of information was the veterinarian, it appears that the communication between horse owners and their veterinarian about optimal feeding practices could be enhanced. This survey demonstrated areas in the veterinarian–client dialog that need to be addressed when evaluating the health and well-being of the horse.     

The Equine Science Society: From the 1st to the 25th Symposium

In 1968, the group currently known as the Equine Science Society hosted its first symposium. At the meeting, there were 27 participants representing about a dozen institutions and 23 research presentations were given. In 2017, the Society is hosting its 25^th Symposium and will have over 200 presentations from individuals representing about 120 institutions. Throughout the history of the Society, the objectives have been to promote quality equine research, establish communication amongst those involved with equine research, teaching, extension, and production, conduct periodic symposia, and cooperate with other organizations having similar interests.     

The xCELLigence system for real-time and label-free analysis of neuronal and dermal cell response to equine herpesvirus type 1 infection

Real-time cell electronic sensing (RT-CES) based on impedance measurements is an emerging technology for analyzing the status of cells in vitro. It allows label-free, real time monitoring of the biological status of cells. The present study was designed to assess dynamic data on the cell processes during equine herpesvirus type 1 (EHV-1) infection of ED (equine dermal) cells and primary murine neuronal cell culture. We have demonstrated that the xCELLigence system with dynamic monitoring can be used as a rapid diagnostic tool both to analyze cellular behavior and to investigate the effect of viral infection.     

Systems approaches to beef cattle production systems using modeling and simulation

Systems approach techniques have been applied to modeling production systems for beef cattle from the relatively micro‐level of tissues and organs to the macro‐level of farms and geographical regions. This paper reviews the various types of beef cattle production models already in operation in order to analyze beef cattle production systems and their components. It may be theoretically possible to construct system models which describe such complex production systems and can be generally used in various genetic, nutritional, management and economic situations as well as in training, extension and educational programs. Moreover, the systems approach can assist in the organization of information and identification of knowledge gaps and thereby open an avenue to multi‐disciplinary research projects.     

Opportunities for Increasing Utility of Models for Rangeland Management

A large number of empirical and mechanistic simulation models and decision support tools have been produced for rangelands. Collectively, these models have considerably increased our fundamental knowledge and understanding of the dynamics of ecosystem functions, processes, and structure. We explore three areas where models for rangeland management are often challenging for land managers and enterprise-level decision making: 1) coping with spatiotemporal and climatic variability in implementing scenario forecasting, risk assessments, and adaptive management; 2) addressing outputs of multiple ecosystem goods and services and determining whether they are synergistic or competitive; and 3) integrating experimental and experiential knowledge and observations into decision making. Increasing the utility of models for rangeland management remains a key frontier and a major research need for the modeling community and will be achieved less by further technical advances and model complexity and more by the use of existing topoedaphic databases, the capacity to readily incorporate new experimental and experiential knowledge, and the use of frameworks that facilitate outcome-based, adaptive decision making at the enterprise level with associated economic considerations. Opportunities exist for increasing the utility of models for decision making and adaptive rangeland management through better matching of model complexity with enterprise-level, decision-making goals. This could be accomplished by incorporating a fundamental understanding of herbivory, fire, and spatiotemporal interactions with weather patterns that affect multiple ecosystem functions. Most important, effective models would allow land managers in a changing and variable climate to 1) evaluate trade offs in producing multiple goods and services, 2) optimize the application of conservation practices spatially (comparing costs and benefits accrued across different timescales), and 3) incorporate manager capacity, including experience, skills, and labor input.     

Use of force sensing array technology in the development of a new equine saddle pad: Static and dynamic evaluations and technical considerations

Scientific approaches to the classical art of saddle-pad fitting with the horse have become available during the past few years. Force Sensing Array (FSA) technology has offered clinicians in the medical profession innovative systems for rehabilitation applications. With proven usefulness in the medical sector, the application of Force Sensing Array (FSA) technology in pressure mapping of the equine back and saddle has potential clinical and research applications in veterinary medicine. The objective in this study was to apply FSA technology in evaluation of an equine athletic saddle pad and pad liners and to document any observed/potential areas of error within the system that would affect objectivity of data collection/interpretation. All dynamic scans demonstrated a repeatable pattern of pressure distribution that is associated with gait, load distribution and horse limb placement. The in motion scans gave the best overall evaluation of effectiveness of the pad liners studied. This study did not define “normal” static or dynamic saddle-pad-horse pressure gradients or patterns. The pressure distribution pattern is the most valuable data to be gained from Force Sensing Arrays and should be the primary use of the device. Precise scientific methodology must be used in these type of studies. Potential exists for animal and operator induced error when using this technology.     

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

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.     

Approaches to modeling intramammary infections in dairy cattle

In this paper, three approaches (Markov processes, discrete-event simulation, and differential equations) to modeling intramammary infections (IMI; focusing on the dynamic changes between uninfected, subclinical, and clinical udder-health states) are described. The objectives were to describe the various approaches to modeling intramammary infections, determine if simulations of the examples of the three approaches yield stable prevalences, and discuss the approaches' limitations. The literature review showed that there is no agreement on the proportion of animals that change health states. The approach of discrete-event simulation modeling included the most cow-level risk factors and udder-health states (hence, was judged to replicated best the dynamics of the infection process) and yielded stable prevalences for all udder-health states. However, there remain parts of the dynamics that need further research. These include the pathogen-specific probabilities and times of occurrence for: regression of clinical IMI to subclinical IMI, flare-up of subclinical IMI to clinical IMI, and incidence of subclinical IMI. Also, the assumption in all current approaches of homogenous mixing is violated because the primary contact structure for contagious pathogens during milking is either between cows through residual infectious milk in the milking machine or within a cow by vacuum fluctuations or teat-cup liner slips. Better contact structures should be incorporated so that the effects of control strategies can be better-estimated. Moreover, the three modeling approaches discussed assumed that all non-infected quarters are susceptible to infection—which might be denied by work in genetic resistance.