Energy Expenditure and Activity of Different Types of Small Ruminants Grazing Varying Pastures in the Summer

Beker, A.; Gipson, T.A.; Puchała, R.; Askar, Ahmed; Tesfai, K.; Detweiler, G.; Asmare, A.; Goetsch, A.L.

doi:10.1080/09712119.2010.9707086

Cited by 32 publications

(22 citation statements)

References 5 publications

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“…In that method grazing plus walking time is the primary determinant of EE,%, with only small modifications due to dietary OM digestibility or TDN concentration, distance traveled and type of terrain. However, development of that method did not include an evaluation with data, which is addressed in the companion study of Beker et al (2010).…”

Section: Resultsmentioning

confidence: 98%

Effects of Stocking Rate, Breed and Stage of Production on Energy Expenditure and Activity of Meat Goat Does on Pasture

Beker

Gipson

Puchała

et al. 2009

Journal of Applied Animal Research

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Section: Resultsmentioning

confidence: 98%

Effects of Stocking Rate, Breed and Stage of Production on Energy Expenditure and Activity of Meat Goat Does on Pasture

Beker

Gipson

Puchała

et al. 2009

Journal of Applied Animal Research

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“…Conversely, identification of slow, single steps, such as occur when herbivores graze, can lead to appreciable displacement over time, so their identification can be important in dead‐reckoning approaches for resolving animal movement (Bidder et al., ). In addition, the ability to separate, for example, “grazing and walking” from “grazing without walking” should allow workers to recognise sub‐behaviours within behaviours, something that is considered by people observing animals (Beker et al., ) but which are normally overlooked in tag data (Martiskainen et al., ). The LoCoD method performed slightly less well with our example of long‐period behaviours than with short‐ or medium‐period behaviours (Tables and ) making it apparently less useful (although the behaviour was identified in <0.5% of the time taken for the manual or machine‐learning approach).…”

Section: Discussionmentioning

confidence: 99%

Give the machine a hand: A Boolean time‐based decision‐tree template for rapidly finding animal behaviours in multisensor data

et al. 2018

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The development of multisensor animal‐attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. The high volumes of data, are pushing us towards machine‐learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag‐equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. Overall behaviour recognition using our new approach was better than most other methods due to; (1) its ability to deal with behavioural variation and (2) the speed with which the task was completed because extraneous data are avoided in the process. We suggest that this approach is a promising way forward in an increasingly data‐rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work.

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“…Nonetheless, the data reported by Beker et al (2009a) indicated increases in AEC of 5.79 and 5.05% of ME m per hour spent grazing or eating and grazing or eating plus walking, respectively. These values resulted in the prediction of AEC of Angora, Boer, and Spanish goats in a companion experiment by Beker et al (2009b) with moderate accuracy (R 2 = 0.40 to 0.41) and without bias. The energy cost per hour of grazing or eating plus walking was near the 5% proposed by Sahlu et al (2004) and NRC (2007); however, consideration of neither distance traveled nor digestibility improved prediction.…”

Section: Grazing Management Practicesmentioning

confidence: 95%

“…It was suggested that because Boer goats were less familiar with vegetation in this region, more time was spent in testing different plant species for postingestive malaise (Forbes and Provenza, 2000). Beker et al (2009b) reported a shorter time spent grazing grass-forb pastures by Angora vs. Boer and Spanish goats, and Boer and Spanish goats exhibited similar grazing activities and AEC. The AEC by Angora goats and Rambouillet sheep was less than that by Boer and Spanish goats (16, 54, 50, and 19% of ME m of confined animals for Angora goats, Boer goats, Spanish goats, and Rambouillet sheep, respectively).…”

Section: Characteristics Of Grazing Goatsmentioning

confidence: 99%

Invited review: Feeding behavior of goats1,2

Goetsch

Gipson

Askar

et al. 2010

Journal of Animal Science

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Factors influencing the feeding behavior of goats include grazing management practices, type of vegetation and season, breed and stage of production, group size, and properties of diets fed in confinement. Considerable information has been gathered from visual observation during daylight. However, tools are now available to characterize the feeding behavior of goats while grazing and while in confinement throughout the day. Global positioning system collars can be used to assess horizontal and vertical distances traveled, up or down position of the head, and movement within pasture or rangeland areas. A commercially available leg activity monitor allows estimation of the number of steps and time spent standing, lying, and moving rapidly without grazing. However, these measurements do not directly determine grazing. Therefore, prediction equations based on visual observation must be developed. Classification tree analysis is a robust method in developing these equations because the decision tree can be pruned or expanded to provide the best fit. Another equipment system determines time spent eating, ruminating, and remaining idle from the pattern of jaw movement. In addition to use of n-alkanes as internal markers to estimate digestibility, their profile can provide an indication of the botanical composition of the selected diet. Automated feeding systems for confined goats permit determinations such as number of feeder visits and meals, eating time, and rate and pattern of feed intake. Heart rate measured while goats are in normal production settings can be used to predict total energy expenditure through multiplication by energy expenditure per heartbeat of individual animals. To partition the activity energy cost, an estimate of ME intake or measures of changes in body energy status and milk energy yield are needed to determine other sources of heat to be subtracted from total energy expenditure. These methods create the opportunity to gain a fuller understanding of factors influencing the feeding behavior of goats and the relationships with levels and efficiencies of production.

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Energy Expenditure and Activity of Different Types of Small Ruminants Grazing Varying Pastures in the Summer

Cited by 32 publications

References 5 publications

Effects of Stocking Rate, Breed and Stage of Production on Energy Expenditure and Activity of Meat Goat Does on Pasture

Effects of Stocking Rate, Breed and Stage of Production on Energy Expenditure and Activity of Meat Goat Does on Pasture

Give the machine a hand: A Boolean time‐based decision‐tree template for rapidly finding animal behaviours in multisensor data

Invited review: Feeding behavior of goats1,2

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