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.

Decision Making Tools: BeefTracker mobile app for tracking and analysis of beef herd pasture use and location

Beef Tracker is a web-based mapping platform that provides beef cattle ranchers a tool to demonstrate that cattle production fits within sustainable ecosystems and to provide regional data to update beef sustainability lifecycle analysis. After digitizing pastures, herd data (class and number of animals) are input on a mobile device in a graphical pasture interface, stored in the cloud, and linked via the web to a personal computer for inventory tracking and analysis. Pasture use calculated on an animal basis provides quantifiable data regarding carrying capacity and beef production. This data is sought by the National Beef Cattle Association to provide more accurate inputs for beef sustainability lifecycle analyses. This application is a useful way for large, complex ranching operations to have all employees remain informed as to cattle movements and ranch wide improvement projects. Better yet, as users make changes to their operation in BeefTracker, histories are automatically recorded and stored in the cloud. After initial testing by university range scientists and ranchers, we have enhanced the BeefTracker application to improve automation for increased ease of use. The following have been added: ability to access and edit the BeefTracker livestock inventory while disconnected from WiFi and cell service, ability to represent portions of a pasture in BeefTracker as irrigated and nonirrigated, and ability to report animal unit harvest (by pasture) calculated on an annual basis. This will provide quantifiable data regarding carrying capacity and subsequent beef production to provide more accurate data inputs for the beef sustainability lifecycle analysis, enhanced map synchronization, and improved security to allow a single individual to access multiple livestock operations without needing multiple user IDs and passwords.

Optimising the interrelationship between intake per cow and intake per hectare

Production and profit in grazed systems remain inherently constrained by the fundamental trade-off between maximising individual herbage intake and pasture utilisation. The primary objective of this paper is to investigate the trade-off between herbage intake per cow and intake per hectare, from the perspective of economic optimisation, for an intensive pasture-based dairy farm in New Zealand (NZ). A detailed optimisation model of a dairy farm is applied, to allow the complex animal–plant–supplement dynamics underlying this relationship to be explicitly considered. Model output confirmed the existence of the fundamental inverse relationship between individual cow intake and herbage utilisation, which arises from the underlying biophysical dynamics within a grazing system, in the context of pasture-based NZ dairy farms. Results indicated that profitable management relies on increasing total pasture eaten (grazing plus pasture silage harvested on farm) through the use of a relatively high stocking rate and moderate levels of pasture intake per cow. Indeed, for 450 kg liveweight cows, optimal pasture intake per cow is 5 t dry matter (DM) per cow for per hectare intakes of 12–16 t DM/ha and 4.5 t DM/cow for a per hectare intake of 17 t DM/ha. Thus, a goal to maximise both individual intake and herbage utilisation in grazing systems is misinformed; it is the latter that is principally important to farm profit. Indeed, seeking to maximise both simultaneously is not possible, and trying to maximise individual intake can impose an enormous cost. However, while high herbage utilisation increases profit, this can also be associated with greater nutrient leaching, reinforcing the importance of considering the environmental impacts of grazing management.

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.