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Showing 1 to 6 of 6 for “"cattle behavior"”.

  1. Apply Machine Learning on Cattle Behavior Classification Using Accelerometer Data

    … a solution for timely and efficient measuring of cattle’s daily activities using wearable sensors and machine learning models.

    vt Repository record for Apply Machine Learning on Cattle Behavior Classification Using Accelerometer Data (opens in a new tab)

  2. Off-stream water sources for grazing cattle as a stream bank stabilization and water quality BMP

    A study was conducted in order to evaluate cattle behavior, stream bank erosion and water quality due to the installation of off-stream water sources for grazing cattle as an alternative to stream bank fencing. The study was located on two commercial cow-calf operations in southwest Virginia which …

    vt Repository record for Off-stream water sources for grazing cattle as a stream bank stabilization and water quality BMP (opens in a new tab)

  3. Cattle behavioral responses following castration and dehorning measured by accelerometers.

    … The objective of this research was to evaluate behavioral responses to these painful procedures. Two studies were conducted using three dimensional accelerometers to measure behavioral differences in cattle following dehorning and castration. The first study evaluated the potential of different …

    ksu Repository record for Cattle behavioral responses following castration and dehorning measured by accelerometers. (opens in a new tab)

  4. Factors Influencing the Amount of Time Spent By Cattle In Streams: Implications for TMDL Development

    The amount of time cattle spend standing in streams is one input parameter needed by computer models when total maximum daily load (TMDL) plans are being developed. This input parameter is estimated using professionals' best judgment because experimental data are not available, and estimations are …

    vt Repository record for Factors Influencing the Amount of Time Spent By Cattle In Streams: Implications for TMDL Development (opens in a new tab)

  5. Power Efficient Wireless Sensor Node through Edge Intelligence

    … a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget …

    vt Repository record for Power Efficient Wireless Sensor Node through Edge Intelligence (opens in a new tab)

  6. Effects of stocking rate and supplementation on pasture utilization, cow performance, and rumen environment in a pasture-based and automatic milking system

    … performance, AMS use, feed degradation, dairy cattle genotype, and pasture utilization. An investigation lasting 8 weeks was conducted using a completely randomized design with a 2 x 2 factorial of two stocking rate systems ( high stocking rate (HSR 2.89 cows/ha); low stocking rate (LSR 1.92 …

    msu Repository record for Effects of stocking rate and supplementation on pasture utilization, cow performance, and rumen environment in a pasture-based and automatic milking system (opens in a new tab)