Corrigendum to “Using a three-axis accelerometer to identify and classify sheep behaviour at pasture” [Appl. Anim. Behav. Sci. 181 (2016) 91–99]

Alvarenga, Flavio A. P.; Borges, Isabela Nascimento; Palkovič, Lukáš; Rodina, Jozef; Oddy, V. H.; Dobos, Robin

doi:10.1016/j.applanim.2016.08.001

Cited by 2 publications

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“…Increased behavior monitoring would be extremely beneficial during parturition, as mortality will affect both animal welfare and farm productivity. In sheep, accelerometers have previously been used to detect behavioral states such as high and low general activity or some combinations of lying, standing, grazing, walking and/or running [3,17,24]. Use of an accelerometer, with machine learning, has been found to accurately predict 91% of lambing events within 3 h of birth based on body posture alone [25].…”

Section: Discussionmentioning

confidence: 99%

Changes in Sheep Behavior before Lambing

et al. 2021

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The aim of this study was to assess the duration and frequency of behavioral observations of pregnant ewes as they approached lambing. An understanding of behavioral changes before birth may provide opportunities for enhanced visual monitoring at this critical stage in the animal’s life. Behavioral observations for 17 ewes in late pregnancy were recorded during two separate time periods, which were 4 to 6 weeks before lambing and before giving birth. It was normal farm procedure for the sheep to come indoors for 6 weeks of close monitoring before lambing. The behaviors of standing, lying, walking, shuffling and contraction behaviors were recorded for each animal during both time periods. Over both time periods, the ewes spent a large proportion of their time either lying (0.40) or standing (0.42), with a higher frequency of standing (0.40) and shuffling (0.28) bouts than other behaviors. In the time period before giving birth, the frequency of lying and contraction bouts increased and the standing and walking bouts decreased, with a higher frequency of walking bouts in ewes that had an assisted lambing. The monitoring of behavioral patterns, such as lying and contractions, could be used as an alert to the progress of parturition.

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Section: Discussionmentioning

confidence: 99%

Changes in Sheep Behavior before Lambing

et al. 2021

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ASAS-NANP symposium: mathematical modeling in animal nutrition—Making sense of big data and machine learning: how open-source code can advance training of animal scientists

Brennan,

Menendez,

Ehlert

et al. 2023

Journal of Animal Science

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Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.

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Corrigendum to “Using a three-axis accelerometer to identify and classify sheep behaviour at pasture” [Appl. Anim. Behav. Sci. 181 (2016) 91–99]

Cited by 2 publications

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Changes in Sheep Behavior before Lambing

Changes in Sheep Behavior before Lambing

ASAS-NANP symposium: mathematical modeling in animal nutrition—Making sense of big data and machine learning: how open-source code can advance training of animal scientists

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