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Animal Sciences Commons

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Full-Text Articles in Animal Sciences

Identifying Early-Life Behavior To Predict Mothering Ability In Swine Utilizing Nutrack System, Savannah Millburn Nov 2022

Identifying Early-Life Behavior To Predict Mothering Ability In Swine Utilizing Nutrack System, Savannah Millburn

Department of Animal Science: Dissertations, Theses, and Student Research

Early recognition of indicator traits for swine reproduction and longevity supports economical selection decision making. Gilt activity is a key variable impacting a sow’s herd life and productivity. The purpose of this study was to examine early- life behaviors contributing to farrowing traits including gestation length (GL), number born alive (NBA), number weaned (NW), and herd life (HL). Herd life was a binary trait representing if a gilt was culled after one parity. Beginning at approximately 20 weeks of age, video recordings were taken on 480 gilts for 7 consecutive days and processed using the NUtrack system. Activity traits include …


Nabat Ml: Utilizing Deep Learning To Enable Crowdsourced Development Of Automated, Scalable Solutions For Documenting North American Bat Populations, Ali Khalighifar, Benjamin S. Gotthold, Erin Adams, Jenny Barnett, Laura O. Beard, Eric R. Britzke, Paul A. Burger, Kimberly Chase, Zackary Cordes, Paul M. Cryan, Emily Emily, Christopher T. Fill, Scott E. Gibson, G. Scott Haulton, Kathryn M. Irvine, Lara S. Katz, William L. Kendall, Christen A. Long, Oisin Mac Aodha, Tessa Mcburney, Sara Mccarthy, Matthew W. Mckown, Joy O'Keefe, Lucy D. Patterson, Kristopher A. Pitcher, Matthew Rustand, Jordi L. Segers, Kyle Seppanen, Jeremy L. Siemers, Christian Stratton, Bethany R. Straw, Theodore J. Weller, Brian E. Reichert Jul 2022

Nabat Ml: Utilizing Deep Learning To Enable Crowdsourced Development Of Automated, Scalable Solutions For Documenting North American Bat Populations, Ali Khalighifar, Benjamin S. Gotthold, Erin Adams, Jenny Barnett, Laura O. Beard, Eric R. Britzke, Paul A. Burger, Kimberly Chase, Zackary Cordes, Paul M. Cryan, Emily Emily, Christopher T. Fill, Scott E. Gibson, G. Scott Haulton, Kathryn M. Irvine, Lara S. Katz, William L. Kendall, Christen A. Long, Oisin Mac Aodha, Tessa Mcburney, Sara Mccarthy, Matthew W. Mckown, Joy O'Keefe, Lucy D. Patterson, Kristopher A. Pitcher, Matthew Rustand, Jordi L. Segers, Kyle Seppanen, Jeremy L. Siemers, Christian Stratton, Bethany R. Straw, Theodore J. Weller, Brian E. Reichert

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

  1. Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions.

  2. Here, we present NABat ML, an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and …


Individual Beef Cattle Identification Using Muzzle Images And Deep Learning Techniques, Guoming Li, Galen E. Erickson, Yijie Xiong May 2022

Individual Beef Cattle Identification Using Muzzle Images And Deep Learning Techniques, Guoming Li, Galen E. Erickson, Yijie Xiong

Department of Animal Science: Faculty Publications

The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support …


Whooping Crane Stay Length In Relation To Stopover Site Characteristics, Andrew J. Caven, Aaron T. Pearse, David A. Brandt, Mary J. Harner, Greg D. Wright, David M. Baasch, Emma M. Brinley Buckley, Kristine L. Metzger, Matthew R. Rabbe,, Anne E. Lacy Jan 2022

Whooping Crane Stay Length In Relation To Stopover Site Characteristics, Andrew J. Caven, Aaron T. Pearse, David A. Brandt, Mary J. Harner, Greg D. Wright, David M. Baasch, Emma M. Brinley Buckley, Kristine L. Metzger, Matthew R. Rabbe,, Anne E. Lacy

Proceedings of the North American Crane Workshop

Whooping crane (Grus americana) migratory stopovers can vary in length from hours to more than a month. Stopover sites provide food resources and safety essential for the completion of migration. Factors such as weather, climate, demographics of migrating groups, and physiological condition of migrants influence migratory movements of cranes (Gruidae) to varying degrees. However, little research has examined the relationship between habitat characteristics and stopover stay length in cranes. Site quality may relate to stay length with longer stays that allow individuals to improve body condition, or with shorter stays because of increased foraging efficiency. We examined this …