A model of diurnal grazing patterns and herbage intake of a dairy cow, MINDY: Model description

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d-1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Intensification strategies for temperate hot-summer grazing dairy systems in South America: Effects of feeding strategy and cow genotype

Pasture-based dairy systems present the opportunity to increase productivity per hectare through increasing stocking rate and forage utilization. However, in the temperate hot-summer region of South America, different productive strategies are being adopted by farmers. The aim of this study was to quantify the effect of feeding strategy (FS) and cow genotype (G) on individual animal and whole-farm biophysical performance. A design with 2 × 2 levels of intensification aiming to increase home-grown forage utilization and milk output per hectare was evaluated. The experiment was a randomized complete block design with a 2 × 2 factorial arrangement of treatments, combining 2 feeding strategies with varying proportions of grazing in the annual feeding budget [grass fixed (GFix) and grass maximum (GMax)] and 2 Holstein Friesian cow genotypes [New Zealand (NZHF) or North American Holstein Friesian (NAHF)]. The effects of FS, G, and their interaction were analyzed using mixed models. New Zealand Holstein Friesian cows presented lower individual milk yield and higher milk component concentrations, maintained higher average body condition score, and increased body weight (BW) throughout the experiment, while presenting a better reproductive performance compared with the NAHF cows. Although all farmlets were planned at the same stocking rate on a per kilogram of BW basis, the current stocking rate changed as a result of animal performance and grass utilization resulting in NZHF cows achieving greater BW per hectare. The superior stocking rate led to greater milk solids production and feed consumption per hectare for the systems with NZHF cows. The GFix feeding strategy resulted in greater total home-grown forage harvest and conserved forage surplus than GMax. Overall, it was feasible to increase stocking rate and increase milk production per hectare from home-grown forage with differing feeding strategies and Holstein Friesian cow genotypes within grazing systems located in the temperate hot-summer climate region of South America. The interactions reported between FS × G highlight the superior productivity per hectare of NZHF cows within the GMax feeding strategy based on maximizing grazed pasture, which could represent a competitive intensification strategy in terms of cost of production for this region.

Understanding intake on pastures: how, why, and a way forward

An assessment of dietary intake is a critical component of animal nutrition. Consumption of feed resources is the basis upon which feeding strategies and grazing management are based. Yet, as far back as 1948, researchers have lauded the trials and tribulations of estimation of the phenomenon, especially when focused on grazing animals and pasture resources. The grazing environment presents a unique situation in which the feed resource is not provided to the animal but, rather, the animal operates as the mechanism of harvest. Therefore, tools for estimation must be developed, validated, and applied to the scenario. There are a plethora of methods currently in use for the estimation of intake, ranging from manual measurement of herbage disappearance to digital technologies and sensors, each of which come with its share of advantages and disadvantages. In order to more firmly grasp these concepts and provide a discussion on the future of this estimation, the Forages and Pastures Symposium at the 2020 ASAS-CSAS-WSASAS Annual Meeting was dedicated to this topic. This review summarizes the presentations in that symposium and offers further insight into where we have come from and where we are going in the estimation of intake for grazing livestock.

Validation and Use of the RumiWatch Noseband Sensor for Monitoring Grazing Behaviours of Lactating Dairy Cows

Precision livestock farming (PLF) supports the development of sustainable dairy production. The sensors used in PLF provide valuable information for farm management, but they must be validated to ensure the accuracy. The goal of this study was to validate and use the RumiWatch sensor (RWS; Itin+Hoch GmbH, Liestal, Switzerland) to differentiate prehension bites, eating chews, mastication chews and rumination chews in pressure-based system. Twenty cows were used for 14 days to provide a validation dataset. The concordance correlation coefficient (CCC) was adopted to test the concordance between the RumiWatch sensor and video observation. The RumiWatch sensor performed well in counting prehension bites (CCC = 0.98), eating chews (CCC = 0.95) and rumination chews (CCC = 0.96), while it showed an acceptable concordance in counting mastication chews with video observation (CCC = 0.77). Moderate correlations were found between eating chews and daily milk production: daily milk production (kg/day) = 0.001151 × eating chews (chews/day) − 11.73 (R2 = 0.31; standard error (SE) = 8.88; p = 0.011), and between mastication chews and daily milk production: daily milk production (kg/day) = 0.001935 × mastication chews (chews/day) + 2.103 (R2 = 0.34; SE = 8.70; p = 0.007). Overall, the results indicated that the RumiWatch sensor can be confidently used to quantify and differentiate prehension bites, eating chews and rumination chews; in addition, ingestive behaviours explained up to 34% of the variation in milk production.

Use of Mechanistic Nutrition Models to Identify Sustainable Food Animal Production

To feed people in the coming decades, an increase in sustainable animal food production is required. The efficiency of the global food production system is dependent on the knowledge and improvement of its submodels, such as food animal production. Scientists use statistical models to interpret their data, but models are also used to understand systems and to integrate their components. However, empirical models cannot explain systems. Mechanistic models yield insight into the mechanism and provide guidance regarding the exploration of the system. This review offers an overview of models, from simple empirical to more mechanistic models. We demonstrate their applications to amino acid transport, mass balance, whole-tissue metabolism, digestion and absorption, growth curves, lactation, and nutrient excretion. These mechanistic models need to be integrated into a full model using big data from sensors, which represents a new challenge. Soon, training in quantitative and computer science skills will be required to develop, test, and maintain advanced food system models.

Animal responses to environmental variation: physiological mechanisms in ecological models of performance in deer (Cervidae)

Context Proper assessment of the consequences of environmental variation on animals depends on our ability to predict how they will perform under different circumstances. This requires two kinds of information. We need to know which environmental factors influence animal performance and their mode of action, i.e. whether a given factor acts alone or through interaction with other factors, directly or indirectly, instantaneously or after a delay and so on. This essentially correlative process falls within the domain of ecology. We also need to know what determines the direction, amplitude and limits of animal responses to environmental variation and change. This essentially experimental process falls within the domain of physiology. Physiological mechanisms are frequently poorly integrated within the correlative framework of ecological models. This is evident where programmed responses are attributed to environmental forcing and where the effect of environmental factors is evaluated without reference to the physiological state and regulatory capacity of the animal on which they act. Aims Here we examine ways in which the impacts of external (environmental) stimuli and constraints on performance are moderated by the animals (deer) on which they impinge. Key results The analysis shows (1) how trade-offs in foraging behaviour, illustrated by the timing of activity under the threat of predation, are modulated by integration of short-term metabolic feedback and animal emotions that influence the motivation to feed, (2) how the influence of thermal and nutritional challenges on performance, illustrated by the effect of weather conditions during gestation on the body mass of reindeer (Rangifer tarandus) calves at weaning, depends on the metabolic state of the female at the time the challenge occurs and (3) how annual cycles of growth, appetite and reproduction in seasonal species of deer are governed by innate circannual timers, such that their responses to seasonal changes in food supply are anticipatory and governed by rheostatic systems that adjust homeostatic set- points, rather than being purely reactive. Conclusions Concepts like ‘maintenance’ and ‘energy balance’, which were originally derived from non-seasonal domestic ruminants, are unable to account for annual cycles in metabolic and nutritional status in seasonal deer. Contrasting seasonal phenotypes (fat and anoestrous in summer, lean and oestrous in winter) represent adaptive solutions to the predictable challenges presented by contrasting seasonal environments, not failure of homeostasis in one season and its success in another. Implications The analysis and interpretation of responses to environment in terms of interaction between the external stimuli and the internal systems that govern them offer a more comprehensive, multifaceted understanding of the influence of environmental variation on performance in deer and open lines of ecological enquiry defined by non-intuitive aspects of animal function.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

The assessment of supplementation requirements of grazing ruminants using nutrition models

This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.

Perennial ryegrass breeding and the scaling issue: a review of system experiments investigating milk production and profit differences among cultivars

Results of studies designed to determine whether or not differences measured among perennial ryegrass cultivars in small-plot studies translate into differences in milk production and profit in dairy whole-system studies were reviewed. Only three experiments were identified that met the criteria for fully self-contained systems repeated over multiple years required to account for annual feed supply–demand balance, its interaction with animal intake and production, and the influence of inter-annual climate variability on these processes. Collectively, these studies provide evidence of improvement in animal production, associated with genetic gains from ryegrass breeding, albeit through shifts in factors such as heading date (as it affects herbage quality and grazing efficiency) and host plant by endophyte interactions, rather than through gains in dry-matter yield. The latter remains unresolved, despite substantial evidence for gains from small-plot trials of dry-matter yield increases in the order of 0.5% per annum. These studies also highlighted the number of factors that have to be taken into account in the design and conduct of such studies, including gaining clarity about the size of the differences that can be expected and ensuring sufficient statistical power. Implementing objective management rules that allow cultivars to express their potential and capture differences through the grazing animal will ensure sufficient measurement intensity to enable differences (if observed) in milk production and profit to be explained. This should guard against confounding factors such as the differential effects of insect pests on plant performance, and consequent changes in pasture botanical composition mediated by ryegrass endophyte strains. Despite these difficulties, more experiments of this type are required to quantify and, ultimately, increase the value being delivered by ryegrass breeding to pasture-based dairy production systems in temperate regions. Therefore, there is a need for whole-system studies to be undertaken to provide valuable new information and give farmers the confidence to invest in the use of new cultivars.

Grazing management: setting the table, designing the menu and influencing the diner

Pastoral livestock-production systems are under increasing environmental, social and consumer pressures to reduce environmental impacts and to enhance biodiversity and animal welfare. At the same time, farmers face the challenge of managing grazing, which is intimately linked with profitability. Recent advances in understanding grazing patterns and nutritional ecology may help alleviate such pressures. For instance, by managing grazing to (1) manipulate links between ingestive–digestive decisions and temporal patterns of nutrient excretion, (2) provide phytochemically diverse diets at appropriate temporal (the menu) and spatial (the table) scales and (3) influence the behaviour of animals (the diners) on the basis of their specific ‘personalities’ and needs, to overcome or enhance animal differences, thereby enhancing their and farm productivity and welfare, as well as our health. Under pastoral systems, synergies between animals’ and farmers’ grazing decisions have the potential to offer greater benefits to the animal, the environment and the farm than does simple and parsimonious grazing management based on a single component of the system. In the present review, we look at grazing and its management through an alternate lens, drawing ideas and hypotheses to stimulate thinking, dialogue and discussions that we anticipate will evolve into innovative research programs and grazing strategies. To do so, we combined experimental and observational studies from a wide range of disciplines with simulation-modelling exercises. We envisage a more holistic approach to manage grazing based on recent advances in the understanding of the nutritional ecology of grazing animals, and propose management practices that may enable pastoral livestock-production systems to evolve continually as complex creative systems.

Behavioral Timing without Clockwork: Photoperiod-Dependent Trade-Off between Predation Hazard and Energy Balance in an Arctic Ungulate

Occurrence of 24-h rhythms in species apparently lacking functional molecular clockwork indicates that strong circadian mechanisms are not essential prerequisites of robust timing, and that rhythmical patterns may arise instead as passive responses to periodically changing environmental stimuli. Thus, in a new synthesis of grazing in a ruminant (MINDY), crepuscular peaks of activity emerge from interactions between internal and external stimuli that influence motivation to feed, and the influence of the light/dark cycle is mediated through the effect of low nocturnal levels of food intake on gastric function. Drawing on risk allocation theory, we hypothesized that the timing of behavior in ruminants is influenced by the independent effects of light on motivation to feed and perceived risk of predation. We predicted that the antithetical relationship between these 2 drivers would vary with photoperiod, resulting in a systematic shift in the phase of activity relative to the solar cycle across the year. This prediction was formalized in a model in which phase of activity emerges from a photoperiod-dependent trade-off between food and safety. We tested this model using data on the temporal pattern of activity in reindeer/caribou Rangifer tarandus free-living at natural mountain pasture in sub-Arctic Norway. The resulting nonlinear relationship between the phasing of crepuscular activity and photoperiod, consistent with the model, suggests a mechanism for behavioral timing that is independent of the core circadian system. We anticipate that such timing depends on integration of metabolic feedback from the digestive system and the activity of the glucocorticoid axis which modulates the behavioral responses of the animal to environmental hazard. The hypothalamus is the obvious neural substrate to achieve this integration.

TRIENNIAL LACTATION SYMPOSIUM: Systems biology of regulatory mechanisms of nutrient metabolism in lactation

A major role of the dairy cow is to convert low-quality plant materials into high-quality protein and other nutrients for humans. We must select and manage cows with the goal of having animals of the greatest efficiency matched to their environment. We have increased efficiency tremendously over the years, yet the variation in productive and reproductive efficiency among animals is still large. In part, this is because of a lack of full integration of genetic, nutritional, and reproductive biology into management decisions. However, integration across these disciplines is increasing as the biological research findings show specific control points at which genetics, nutrition, and reproduction interact. An ordered systems biology approach that focuses on why and how cells regulate energy and N use and on how and why organs interact through endocrine and neurocrine mechanisms will speed improvements in efficiency. More sophisticated dairy managers will demand better information to improve the efficiency of their animals. Using genetic improvement and animal management to improve milk productive and reproductive efficiency requires a deeper understanding of metabolic processes throughout the life cycle. Using existing metabolic models, we can design experiments specifically to integrate data from global transcriptional profiling into models that describe nutrient use in farm animals. A systems modeling approach can help focus our research to make faster and larger advances in efficiency and determine how this knowledge can be applied on the farms.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction

Several attempts have been made to quantify microbial protein flow from the rumen; however, few studies have evaluated tradeoffs between empirical equations (microbial N as a function of diet composition) and more mechanistic equations (microbial N as a function of ruminal carbohydrate digestibility). Although more mechanistic approaches have been touted because they represent more of the biology and thus might behave more appropriately in extreme scenarios, their precision is difficult to evaluate. The objective of this study was to derive equations describing starch, neutral detergent fiber (NDF), and organic matter total-tract and ruminal digestibilities; use these equations as inputs to equations predicting microbial N (MicN) production; and evaluate the implications of the different calculation methods in terms of their precision and accuracy. Models were evaluated based on root estimated variance σˆe and concordance correlation coefficients (CCC). Ruminal digestibility of NDF was positively associated with DMI and concentrations of NDF and CP and was negatively associated with concentration of starch and the ratio of acid detergent fiber to NDF (CCC=0.946). Apparent ruminal starch digestibility was increased by omasal sampling (compared with duodenal sampling), was positively associated with forage NDF and starch concentrations, and was negatively associated with wet forage DMI and total dietary DMI (CCC=0.908). Models were further evaluated by calculating fit statistics from a common data set, using stochastic simulation, and extreme scenario testing. In the stochastic simulation, variance in input variables were drawn from a multi-variate random normal distribution reflective of input measurement errors and predicting MicN while accounting for the measurement errors. Extreme scenario testing evaluated each MicN model against a data subset. When compared against an identical data set, predicting MicN empirically had the lowest prediction error, though differences were slight (σˆe 23.3% vs. 23.7 or 24.3%), and highest concordance (0.52 vs. 0.48 or 0.44) of any approach. Minimal differences were observed between empirical MicN prediction (σˆe 25.3%; CCC 0.530) and MicN prediction (σˆe 25.3%; CCC 0.532) from rumen carbohydrate digestibility in the stochastic analysis or extreme scenario testing. Despite the hypothesized benefits of a more mechanistic prediction approach, few differences between the calculation approaches were identified.

Modelling preference and diet selection patterns by grazing ruminants: a development in a mechanistic model of a grazing dairy cow, MINDY

The work presented here represents additions to the mechanistic and dynamic model of a grazing dairy cow (MINDY). The additions include a module representing preference and selection, based on two theories, namely, post-ingestive feedback and discomfort. The model was evaluated by assessing its ability to simulate patterns of preference and selection in response to a variety of feeding management. The improvements detailed here enable a realistic simulation of patterns of food selection by grazing ruminants, based on a range of feeding situations from different studies with cattle and sheep. These simulations indicate that the concepts encoded in MINDY capture several of the underlying biological mechanisms that drive preferences and selective behaviour. Thus, simulations using MINDY allow prediction of daily and diurnal patterns of selection based on preference, derived from some post-ingestive feedbacks and total discomfort. Estimates of herbage intake and parallel measurements of ingestive behaviour, rumen function and metabolism in grazing ruminants pose experimental and technical difficulties, and matching these processes to animal preference and selective behaviour is a greater challenge. As a consequence, advances in knowledge of foraging behaviour and dietary choice are slow and costly. On completion of more thorough testing, MINDY can be used as a tool for exploratory mechanistic research, to design and organise experimental programs to address a range of factors that control intake and its ecology, helping advance knowledge faster and at a low cost.

Managing the herbage utilisation and intake by cattle grazing rangelands

Rangelands throughout the world provide clean water, fix solar energy in plants, sequester carbon, and offer recreational opportunities, with other ecosystem goods and services, including food from wild and domestic herbivores. Grazing rangelands with cattle requires constant management to balance the economic sustainability of the farm with other ecological services that rangelands provide. The challenges in management arise from the diversity of the rangeland forage resources at extremely large spatial and temporal scales. To be able to predict the performance of cattle grazing in extensive rangeland environments, estimating herbage intake is paramount because it quantifies energy intake and performance. Nutrient demand is the major driver of herbage intake, and characteristics of the sward and terrain of the landscape dictate how this demand is met. System models that integrate changes in weather patterns and herbage over long periods of time will allow farmers and scientist to monitor changes in herbage mass and utilisation. Dynamic models that include herbage growth components sensitive to weather patterns and animal demands are needed to predict how long-term changes in beef herd management will affect performance and range condition. Vegetation indexes captured across biomes with satellites can accurately quantify the dynamics of aboveground net primary production and changes in nutritional value with confidence. The computer software, PCRANCH, is a program for simulating cow–calf herd dynamics over long periods of time. The models within the PCRANCH software can simulate herbage growth and animal utilisation at large spatial and temporal scales needed for rangeland management and allow ranchers to evaluate the impacts of management on other ecological services. Knowing the long-term impact of management changes on swards enable ranchers to anticipate the ecological and economic benefits of improvements or demonstrate a protection of current ecological services.

The catastrophe of meal eating

Optimisation of feed intake is a major aim of pasture and range management for ruminants and understanding what influences feeding behaviour may play an important role in satisfying this aim. An obstacle to such understanding is the fact that feeding is a two-state variable (eating or not eating, albeit with changes in rate of eating during meals), whereas the likely influencing factors are mostly continuous variables. These include gut-fill, concentrations and rates of utilisation of nutrients and metabolites, and changes in nutrient demand due to growth, reproduction and environment, both climatic and social. Catastrophe theory deals mathematically with situations in which an outcome is discontinuous (e.g. eating or not eating) and influencing variables (‘control’ variables in terms of catastrophe theory) are continuously variable (e.g. physiological and environmental factors affecting feeding). We discuss models of feeding and develop an approach in which the Type 2 catastrophe, illustrated by the bifurcation or cusp diagram, is adapted to use negative feedbacks and capacity to handle food and nutrients as the two controlling factors. Ease of prehension, as expressed by rate of eating, is modelled, as are pauses within, as well as between, meals. Quantification has not yet been attempted and the approach is presented to stimulate new thinking about the modelling and prediction of feeding behaviour and meal dynamics.

Synthesis: foraging decisions link plants, herbivores and human beings

Herbivores make decisions about where to forage and what combinations and sequences of foods to eat, integrating influences that span generations, with choices manifest daily within a lifetime. These influences begin in utero and early in life; they emerge daily from interactions among internal needs and contexts unique to biophysical and social environments; and they link the cells of plants with the palates of herbivores and humans. This synthesis summarises papers in the special issue of Animal Production Science that explore emerging understanding of these dynamics, and suggests implications for future research that can help people manage livestock for the benefit of landscapes and people by addressing (1) how primary and secondary compounds in plants interact physiologically with cells and organs in animals to influence food selection, (2) temporal and spatial patterns of foraging behaviours that emerge from these interactions in the form of meal dynamics across landscapes, (3) ways humans can manage foraging behaviours and the dynamics of meals for ecological, economic and social benefits, and (4) models of foraging behaviour that integrate the aforementioned influences.