Association patterns of visually-observed cattle on Montana, USA foothill rangelands

Stephenson, Mitchell B.; Bailey, Derek W.; Jensen, D. P.

doi:10.1016/j.applanim.2016.02.007

Cited by 28 publications

(30 citation statements)

References 38 publications

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“…In a study about association patterns of visually observed cattle, Stephenson et al [21] concluded that herds with 40 or less cows did not exhibit preferential or avoidance associations. This means that they lived together as a single group.…”

Section: A Rationale Of the Clustering Modelmentioning

confidence: 99%

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Byabazaire

Olariu²,

Taneja

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Lameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs and complex equipment in currently available solutions, and as a result, we propose an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to identify lame dairy cattle. As part of a real world trial in Waterford, Ireland, 150 dairy cows were each fitted with a long range pedometer. The mobility data from the sensors attached to the front leg of each cow is aggregated at the fog node to form time series of behavioral activities (e.g. step count, lying time and swaps per hour). These are analyzed in the cloud and lameness anomalies are sent to farmer's mobile device using push notifications. The application and model automatically measure and can gather data continuously such that cows can be monitored daily. This means there is no need for herding the cows, furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application scales. The initial results indicate that we can predict lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness.

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Section: A Rationale Of the Clustering Modelmentioning

confidence: 99%

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Byabazaire

Olariu²,

Taneja

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

View full text Add to dashboard Cite

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“…Stephenson et al (2016) reported that in herds of 40 or less cows no strong or weak association pattern was detected using visual observations suggesting that these animals had equal association with all other cows in the pasture. Previous work has indicated that small herds of cattle tend to stay and graze relatively close to each other, and larger groups (i.e., more than 40 animals) tend to show sub-grouping behavior and use different parts of the pasture (Harris et al 2007;Stephenson et al 2016). However, there is a need to determine if current tracking technology such as global positioning systems (GPS) and association analysis tools such as ASSOC1 can detect more subtle dispersal-aggregation dynamics in a small cattle herd and whether any effects of social dominance can be observed.…”

Section: Introductionmentioning

confidence: 99%

“…Dominance occurs when the behavior of one animal is affected by the presence or threat of another animal (Šárová et al 2010). In small, well established groups, dominant animals can limit the access of subordinates to food, water, and shelter when these resources are limited in space and time and can be defended (Friend and Polan 1974;Arave and Albright 1981;Grant and Albright 2001;Estevez et al 2007;Stephenson et al 2016). Social hierarchies may be indiscernible during times of ample resource availability but become more evident during periods of low resource availability.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have addressed questions regarding the relationship between spatial interactions and social dominance in a cattle herd (Stricklin 1983;Grant and Albright 2001;Šárová et al 2010;Stephenson et al 2016) as well as independence among individuals (Stephenson and Bailey 2017) in herds of various sizes. Stephenson et al (2016) reported that in herds of 40 or less cows no strong or weak association pattern was detected using visual observations suggesting that these animals had equal association with all other cows in the pasture. Previous work has indicated that small herds of cattle tend to stay and graze relatively close to each other, and larger groups (i.e., more than 40 animals) tend to show sub-grouping behavior and use different parts of the pasture (Harris et al 2007;Stephenson et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Association patterns reveal dispersal-aggregation dynamics among cattle in a South Texas Rangeland, USA

Cheleuitte-Nieves

Perotto‐Baldivieso

et al. 2018

Ecol Process

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Introduction: The spatial association dynamics of free-ranging cattle herds are not fully understood; however, they can have a direct influence on the spatial patterns of resource utilization. The aim of our study was to examine new analytical methods of identifying the spatio-temporal patterns of behavioral dynamics that determine cattle herd dispersal in the semi-arid rangelands of South Texas. We fitted 10 free-ranging cows with global positioning system collars and obtained positions every 5 min for each animal for 21-day trials, twice during the summer and winter period. We used an association pattern recognition software (ASSOC1) and the herd center of gravity to identify the spatial and temporal thresholds that defined dispersion-aggregation patterns and individual position to determine their relation to social dominance. Results: The association pattern defining herd membership was that animals spent 70% of their time within 200 m of each other. Dominance ranking did not appear to influence association membership or position within the herd. The cattle showed a more dispersed distribution during summer, but in winter, herd members behaved in a more aggregated pattern. This distribution is contrary to patterns described in more northerly and mountainous regions. Conclusions: The spatial thresholds of the cattle herd and the overall distance of all members to the center of the herd were smaller during winter and larger during summer, indicating that this study herd congregated during the winter and dispersed during the summer. Although this study uses a herd of 10 individuals in a 100 ha pasture to evaluate spatio-temporal dynamics, our results provide evidence of the ability of current tracking and spatial association tools to detect and quantify seasonal changes in cattle herd dispersion-aggregation patterns. The use of these data collection and analysis methods could prove useful in larger cattle herds, increase our understanding of herd spatiotemporal behavior, and subsequently help in the development of improved management practices.

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“…Stephenson et al [13] determined that smaller herds (i.e., 27 to 40 cow-calf pairs) tended to function as a single unit with many of the cattle being observed within the same grazing group (i.e., cattle within 30 m) at least once over a one to two month summer grazing period. In contrast, larger herds (i.e., 53 to 240 cows) tended to form into multiple groups with associations between dyads that were stronger than what would be expected from chance.…”

Section: Association Of Gps-tracked Cattlementioning

confidence: 99%

Do Movement Patterns of GPS-Tracked Cattle on Extensive Rangelands Suggest Independence among Individuals?

Stephenson

Bailey

2017

Agriculture

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Abstract:In behavioral studies, cattle within the same pasture are not considered as independent experimental units because of the potential confounding effects of the herd's social interactions. However, evaluating cattle behavior on extensive rangelands is logistically challenging for researchers, and treating individual animals as independent experimental units may be beneficial for answering specific research questions. The objective of this study was to evaluate the association patterns among global positioning system (GPS)-tracked cattle at six different study sites in the western United States. A Half-Weight Index (HWI) association value was calculated for each pair of GPS-tracked cows (i.e., dyad) to determine the proportion of time that cattle were within 75 m and 500 m of each other. Cattle at two study sites exhibited relatively low mean HWI-association values (i.e., less than 0.23 HWI); whereas, cattle at other study sites tended to have greater mean HWI associations (i.e., greater than 0.35 HWI). Distinguishing features between study sites with low and high association values were the management of cattle prior to the study, herd size, pasture size, and the number of watering points. However, at all ranches except one, at least 75% of all dyadic associations had HWI values of less than 0.5 at 500 m, indicating that most of the GPS-tracked cows were greater than 500 m from each other for over 50% of tracking period. While interactions among cattle in the same pasture are often inevitable, our data suggests that under some situations, movement patterns of a sub-set of individual GPS-tracked cows may have levels of independence that are sufficient for analysis as individual experimental units. Understanding the level of independence among GPS-tracked cattle may provide options for analysis of grazing behavior for individual cattle within the same pasture.

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Association patterns of visually-observed cattle on Montana, USA foothill rangelands

Cited by 28 publications

References 38 publications

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Association patterns reveal dispersal-aggregation dynamics among cattle in a South Texas Rangeland, USA

Do Movement Patterns of GPS-Tracked Cattle on Extensive Rangelands Suggest Independence among Individuals?

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