03/22/2023

1. Benicio, L. M. ,K. O. S. Miranda, T. M. Brown-Brandl, I. C. F. S. Condotta, Y. Xiong. 2022. Obtaining broiler chickens’ weight through depth image In10th European Conference on Precision Livestock Farming, ECPLF 2022 Vienna, Vienna, Austria. 151-158.


2. Brown-Brandl, Tami & Hayes, Morgan & Rohrer, Gary & Eigenberg, Roger. (2023). Thermal comfort evaluation of three genetic lines of nursery pigs using thermal images. Biosystems Engineering. 225. 1-12. 10.1016/j.biosystemseng.2022.11.002.


3. Codling JR, Dong Y, Bonde A, Bannis A, Macon A, Rohrer G, Miles J, Sharma S, Brown-Brandl T, Noh HY, Zhang P. 2022 Sow posture and feeding activity monitoring in a farrowing pen using ground vibration. In 10th European Conference on Precision Livestock Farming ECPLF, 2022, Vienna, Austria. p 240-248


4. Ferreira, R. E., Bresolin, T., Rosa, G. J., & Dórea, J. R. (2022). Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms. Computers and Electronics in Agriculture, 201, 107272.


5. Han, J., Dorea, J. R., Norton, T., Parmiggiani, A., Morris, D., & Siegford, J. and Juan P. Steibel. DEVELOPMENT AND ASSESSMENT OF PREDICTIVE MODELS FOR IMPROVED SWINE FARMING, 68.


6. Han, J., Siegford, J., Colbry, D., Lesiyon, R., Norton, T., Chen, C., & Steibel, J. P. (2022). 10 Deep Learning for Multi-Behavioral Video Classification of Interactive Behaviors of Pigs in Single-Spaced Automatic Feeders. Journal of Animal Science, 100(Supplement_2), 13-13.


7. Han, J., Siegford, J., de los Campos, G., Tempelman, R. J., Gondro, C., & Steibel, J. P. (2022). Analysis of social interactions in group-housed animals using dyadic linear models. Applied Animal Behaviour Science, 256, 105747.


8. Macon, A. P., Sharma, S., Vander Woude, E., Lee, B., Markvicka, E., Rohrer, G., & Brown-Brandl, T. (2022). Behavioral Time Budgets for Sows Before and After Farrowing. In 2022 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.


9. Norton, T., Brown Brandl, T., Panogakis, P., Cruz, V., Diefes-Dux, H., & Calvet, S. (2022). Educating for Precision Livestock Farming: The knowledge, skills and abilities to meet future industry and societal needs. Precision Livestock Farming'22, 186-192.


10. Pacheco, V. M., Brown-Brandl, T. M., Sharma, S., Sousa, R. V., Rohrer, G., Martello, L. S. Posture detection of sows housed in farrowing crates using composite image models. In 10th European Conference on Precision Livestock Farming ECPLF, 2022 Vienna, Austria p. 267-275.


11. Pacheco, V. M., Sousa, R. V., Sardinha, E. J., Rodrigues, A. V., Brown-Brandl, T. M., & Martello, L. S. (2022). Deep learning-based model classifies thermal conditions in dairy cows using infrared thermography. Biosystems Engineering, 221, 154-163.


12. Perttu, R. K., Peiter, M., Bresolin, T., Dórea, J. R. R., & Endres, M. I. (2022). Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States. Journal of Dairy Science, S0022-0302.


13. Ramirez, B. C., Hayes, M. D., Condotta, I. C., & Leonard, S. M. (2022). Impact of housing environment and management on pre-/post-weaning piglet productivity. Journal of animal science, 100(6), skac142.


14. Ramirez, B. C., Hoff, S. J., Hayes, M. D., Brown-Brandl, T. M., Harmon, J. D., & Rohrer, G. A. (2022). A review of swine heat production: 2003 to 2020. Frontiers in Animal Science. doi: 10.3389/fanim.2022.908434 Dept of Agriculture-NIFA


15. Sharma, S., Brown-Brandl, T., Rohrer, G., Rempel, L., Ostrand, L., & Mote, B. (2022). Lameness detection in sows using few-shot approach. In10th European Conference on Precision Livestock Farming, ECPLF 2022 Vienna, Vienna, Austria. p. 284-292.


16. Steibel J.P., J. Han, C. Chen, J. Siegford, T. Norton, D. (2022) Validation of computer vision algorithms for classifying video segments applied to behavioural phenotyping of pigs. Proceedings of the 12th World Congress on Genetics Applied to Livestock Production.


17. Steibel, J. P., T. Brown-Brandl, G. J. M. Rosa, J. M. Siegford, E. Psota, M.Benjamin, D. Morris, J. R. R. Dorea, T. Norton 2022 Coordinated innovation network for advancing Computer Vision in Precision Livestock Farming In 10th European Conference on Precision Livestock Farming, ECPLF 2022  Vienna, Austria p. 1030-1036.


18. Teixeira, V. A., Lana, A. M. Q., Bresolin, T., Tomich, T. R., Souza, G. M., Furlong, J., ... & Pereira, L. G. R. (2022). Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves. Journal of Dairy Science, 105(5), 4421-4433.


19. Trenhaile-Grannemann, M., Y. Xiong, W. Z. Liang, T. M. Brown-Brandl, K. Stalder, B. E. Mote, D. R. Obermier, S. G. Millburn 2022. Utilizing imaging methodologies to classify sow characteristics for optimized selection.  In 10th European Conference on Precision Livestock Farming, ECPLF 2022  Vienna, Austria p 409-416.


20. Wurtz, K., Norton, T., Siegford, J., & Steibel, J. (2022). Chapter 13: Assessment of open-source programs for automated tracking of individual pigs within a group. In Practical Precision Livestock Farming: Hands-on experiences with PLF technologies in commercial and R&D settings (pp. 213-230). Wageningen Academic Publishers.


21. Xiong, Y. , E. T. Psota, T. M. Brown-Brandl, B. Mote, T. B. Schmidt, G. E. Erickson. 2022. A prototype imaging method for feed estimation in beef cattle 2022. In 10th European Conference on Precision Livestock Farming, ECPLF 2022 Vienna, Vienna, Austria. p. 231-238.

12/12/2023

During the 2022-2023 period, members of NC1211 made significant contributions to the field of precision livestock farming through various publications. They produced 96 peer-reviewed articles in esteemed journals, showcasing research advancements in areas such as animal behavior, environmental impact on livestock, and precision farming technologies. Alongside these, the group presented 70 papers, posters, and presentations at key conferences, sharing their latest findings and innovations. They also engaged in extensive outreach and extension activities, evidenced by 12 presentations and workshops, along with 11 extension and trade publications. The group also published two new open-source datasets that will foster development of technology not only in the field of animal science but also machine learning and AI systems. This productive output highlights the group's active role in advancing and disseminating knowledge in animal science and precision agriculture.

 

Peer-Reviewed Publications, Abstracts, and Proceedings

1. Mei, W., X. Yang, Y. Zhao, X. Wang, X. Dai, K. Wang. 2023. Identification of aflatoxin-poisoned broilers based on accelerometer and machine learning. Biosystems Engineering, 227, 107-116.


2. Yang, X., Y. Zhao, H. Gan, S. Hawkins, L. Eckelkamp, M. Prado, R. Burns, J. Purswell, T. Tabler. 2023. Modeling gait score of broiler chicken via production and behavioral data. Animal, 17(1), 100692.


3. Nguyen, X., Y. Zhao, J.D. Evans, J. Lin, B. Voy, J.L. Purswell. 2022. Effect of ultraviolet radiation on reducing airborne Escherichia coli carried by poultry litter particles. Animals, 12(22), 3170.


4. Nguyen, X., Y. Zhao, J.D. Evans, J. Lin, L. Schneider, B. Voy, S. Hawkins, J.L. Purswell. 2022. Evaluation of bioaerosol samplers for collecting airborne Escherichia coli carried by dust particles from poultry litter. Transactions of the ASABE, 65(4), 825-833.


5. Li, G., X. Hui, Y. Zhao, W. Zhai, J.L. Purswell, Z. Porter, S. Poudel, L. Jia, B. Zhang, G.D. Chesser. 2022. Effects of ground robots on hen floor egg reduction, production performance, stress response, bone quality, and behavior. PLOS One, 17(4): e0267568. Nasiri, A., J. Yoder, Y. Zhao, S. Hawkins, M. Prado, H. Gan. 2022. Pose estimation-based lameness recognition in broiler using CNN-LSTM network. Computers and Electronics in Agriculture, 197, 106931.


6. Chai, L., Y. Zhao, H. Xin, B. Richardson. 2022. Heat treatment for disinfecting egg transport flats and pallets. Applied Engineering in Agriculture, 38(2): 343-350.


7. Nguyen X.D., Y. Zhao, J.D. Evans, J. Lin, J.L. Purswell. 2022. Survival of Escherichia coli in airborne and settled poultry litter particles. Animals, 12(3), 284.


8. Han J, Siegford J, de los Campos G, Tempelman RJ, Gondro C, Steibel JP. 2022. Analysis of social interactions in group-housed animals using dyadic linear models. Applied Animal Behaviour Science. 256:105747. doi: 10.1016/j.applanim.2022.105747.


9. Akinyemi BE, Vigors B, Turner SP, Akaichi F, Benjamin M, Johnson AK, Pairis-Garcia MD, Rozeboom DW, Steibel JP, Thompson DP, Zangaro C, Siegford JM. 2023. Precision Livestock Farming: a qualitative exploration of key swine industry stakeholders. Frontiers in Animal Science: Precision Livestock Farming. 4:1150528. doi: 10.3389/fanim.2023.1150528.


10. Han J, Siegford J, Colbry D, Lesiyon R, Bosgraaf A, Chen C, Norton T, Steibel JP. 2023. Evaluation of computer vision for detecting agonistic behavior of pigs in a single-space feeding stall through blocked cross-validation strategies. Computers and Electronics in Agriculture. 204:107520. doi: 10.1016/j.compag.2022.107520.


11. Guzhva O, Siegford J. 2022. Chapter 21: The unintended (and unconsidered) consequences of PLF: ethical and social considerations of PLF running the farm. In: Practical Precision Livestock Farming: Hands-on experiences with PLF technologies in commercial and R&D settings (eds. T. Bahhazi, V. Halas, & F. Maroto-Molina). Wageningen Academic Publishers. Pp. 383-396.


12. Wurtz K, Norton T, Siegford J, Steibel J. 2022. Chapter 13: Assessment of open-source programs for automated tracking of individual pigs within a group. In: Practical Precision Livestock Farming: Hands-on experiences with PLF technologies in commercial and R&D settings (eds. T. Bahhazi, V. Halas, & F. Maroto-Molina). Wageningen Academic Publishers. Pp. 213-230.


13. Siegford JM, Wurtz KE. 2022. Practical considerations for the use of precision livestock farming to improve animal welfare. In: Bridging Research Disciplines to Advance Animal Welfare Science: A Practical Guide (ed. I. Camerlink). CAB International. Pp. 241-265.


14. Akinyemi BE, Vigors B, Turner SP, Akaichi F, Benjamin ME, Johnson AK, Pairis-Garcia MD, Rozeboom DW, Steibel JP, Thompson DP, Zangaro C, Siegford JM. 2023. Swine industry stakeholder perceptions of precision livestock farming technology: A Q-methodology study. US Precision Livestock Farming 2023:Conference Proceedings of the 2nd US Precision Livestock Farming Conference, Knoxville, TN, May 21-24, 2023.


15. Steibel JP, Brown-Brandl T, Rosa GJM, Siegford JM, Psota E, Benjamin M, Morris D, Dorea JRR, Norton T. 2023. Progress report on the coordinated innovation network for advancing computer vision in precision livestock farming. US Precision Livestock Farming 2023:Conference Proceedings of the 2nd US Precision Livestock Farming Conference, Knoxville, TN, May 21-24, 2023. 2:146-150.


16. Han J, Dorea JR, Norton T, Parmiggiani A, Morris D, Siegford J, Steibel JP. 2023. US Precision Livestock Farming 2023:Conference Proceedings of the 2nd US Precision Livestock Farming Conference, Knoxville, TN, May 21-24, 2023. 2:618-625.


17. Steibel JP, Brown-Brandl T, Rosa GJM, Siegford JM, Psota E, Benjamin M, Morris D, Dorea JRR, Norton T. 2022. Coordinated Innovation Network for Advancing Computer Vision in Precision Livestock Farming. Precision Livestock Farming ’22: Papers Presented at the 10th European Conference on Precision Livestock Farming, Vienna Austria, August 29 – September 2, 2022.


18. Steibel JP, Han J, Chen C, Siegford J, Norton T, Colbry D. 2022. Validation of computer vision algorithms for classifying video segments applied to behavioural phenotyping of pigs. World Congress on Genetics Applied to Livestock Production. Rotterdam, The Netherlands, July 3-8, 2022.


19. Siegford J, Steibel J, Han J, Benjamin M, Brown-Brandl T, Dorea JRR, Morris D, Norton T, Psota E, Rosa GJ. 2022. The quest to develop automated systems for monitoring animal behaviour. Proceedings of the 55th Congress of the International Society for Applied Ethology. 55:3. (plenary talk)


20. Ferreira, R. E. P., T. Bresolin, G. J. M. Rosa, J. R. R. Dorea. 2022. Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms. Computer and Electronics in Agriculture. 201:107272.


21. Bresolin, T., R. E. P. Ferreira, F. Reyes, J. Van Os, J. R. R. Dorea. 2022. Feeding behavior of dairy heifers monitored through computer vision systems. Journal of Dairy Science. 106 (1), 664-675


22. Perttu, R. K., M. Peiter, T. Bresolin, J. R. R. Dorea, M. I. Endres. 2022. Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest. Journal of Dairy Science. https://doi.org/10.3168/jds.2022-22043


23. Caffarini, J. G., T. Bresolin, T., and J. R. R. Dorea. 2022. Predicting ribeye area and circularity in live calves through 3d image analyses of body surface. Journal of Animal Science, skac242. https://doi.org/10.1093/jas/skac24.


24. Reyes, F. S., A. R. Gimenez, K. M. Anderson, E. K. Miller-Cushon, J. R. R. Dorea and Jennifer C. Van Os. 2022. Impact ofStationary Brush Quantity on Brush Use in Group- Housed Dairy Heifers. Animals, 12, 972. https://doi.org/10.3390/ani12080972


25. Holdorf, H. T., J. Kendall, K. E. Ruh, M. J. Caputo, G. J. Combs, S. J. Henisz, W. E. Brown, T. Bresolin, R. E. P. Ferreira, J. R. R. Dorea, and H. M. White. 2023. Increasing the prepartum dose of rumen-protected choline: Effects on milk production and metabolism in high producing Holstein dairy cows. Journal of Dairy Science (accepted).


26. Vang, A. L., T. Bresolin, W. S. Frizzarini, G. L. Menezes, T. Cunha, G. J. M. Rosa, L. L. Hernandez, and J. R. R. Dorea. 2023. Longitudinal analysis of bovine mammary gland development. Journal of Mammary Gland Biology and Neoplasia (accepted).


27. Momen, M., Brounts, S. H., Binversie, E. E., Sample, S. J., Rosa, G. J. M., Davis, B. W. and Muir, P. Selection signature analyses and genome-wide association reveal genomic hotspot regions that reflect differences between breeds of horse with contrasting risk of degenerative suspensory ligament desmitis. G3: Genes, Genomes, Genetics 12(10): jkac179, 2022.


28. Li, M., Rosa, G. J. M., Reed, K. F and Cabrera, V. E. Investigating the impact of temporal, geographic, and management factors on US Holstein lactation curve parameters. Journal of Dairy Science 105: 7525-7538, 2022.


29. Momen, M., Kranis, A., Rosa, G. J. M., Muir, P. and Gianola, D. Predictive assessment of single-step BLUP with linear and non-linear similarity RKHS kernels: A case study in chickens. Journal of Animal Breeding and Genetics 139: 247-258, 2022.


30. Alves, A. A., Costa, R. M., Fonseca, L. S., Carvalheiro, R., Ventura, R., Rosa, G. J. M. and Albuquerque, L. G. A Random Forest-based genome-wide scan reveals fertility-related candidate genes and potential inter-chromosomal epistatic regions associated with age at first calving in Nellore cattle. Genet., 13: 834724, 2022.


31. Mora, M., David, I., Gilbert, H., Rosa, G. J. M., Sánchez, J. P. and Piles, M. Analysis of the causal structure of traits involved in sow lactation feed efficiency. Genet Sel Evol 54:53, 2022.


32. Amalfitano, N., Mota, L. F. M., Rosa, G. J. M., Cecchinato, A. and Bittante, G. Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile. Journal of Dairy Science 105: 6001-6020, 2022.


33. Souza, F. M., Lopes, F. B., Rosa, G. J. M., Fernandes, R. S, Magnabosco, V. S. and Magnabosco, C. U. Genetic selection of Nellore cattle raised in tropical areas: Economic indexes and breeding decisions risks. Livestock Science 265: 105098, 2022.


34. Bresollin, T., Passafaro, T. L., Braz, C. U., Alves, A. A. C., Carvalheiro, R., Chardulo, L. A. L., Rosa, G. J. M. and Albuquerque, L. G. Investigating potential causal relationships among carcass and meat quality traits using structural equation model in Nellore cattle. Meat Science 187: 108771, 2022.


35. Souza, F. M., Lopes, F. B., Rosa, G. J. M. and Magnabosco, C. U. Economic values of reproductive, growth, feed efficiency and carcass traits in Nellore cattle. Journal of Animal Breeding and Genetics 139: 170-180, 2022.


36. Lopes, F. B., Rosa, G. J. M., Pinedo, P., Santos, J. E. P., Chebel, R. C., Galvao, K. N., Schueneman, G. M., Bicalho, R. C., Gilbert, R. O., Rodriguez-Zas, S., Seabury, C. M., Rezende, F. and Thatcher, W. Investigating functional relationships among health and fertility traits in dairy cows. Livestock Science 266: 105122, 2022.


37. Ramirez, B. Hoff, S.J. Hayes, M., Brown-Brandl, T., Harmon, J., and Rohrer, G. (2022). A review of swine heat production: 2003 to 2020.  Anim. Sci.3:908434. doi: 10.3389/fanim.2022.908434

 

38. Mazon, G., Montgomery, P., Hayes, M., Jackson, J.J., and Costa, J. (2021). Development and Validation of an Autonomous Radio-Frequency Identification Controlled Soaking System for Dairy Cattle. Applied Engineering in Ag. 37(5): 831-837. doi: 10.13031/aea.14344


39. Akter, S. B. Cheng, D. West, Y. Liu, Y. Qian, X. Zou, J. Classen, H. Cordova, E. Oviedo, L. Wang-Li* . 2022. Impact of Air Velocity Treatments under Summer Conditions: Part I-Heavy Broiler Surface Temperature Response Animals. 2022, 12, 328. https://doi.org/10.3390/ani12030328.


40. Akter, S. Y. Liu, B. Cheng, J. Classen, E. Oviedo, L. Wang-Li* . 2022. Impact of Air Velocity Treatments under Summer Conditions: Part II-Heavy Broilers' Behavior Responses. Animals. 2022,12,1050. https://doi.org/10.3390/ani12091050


41. West, D., B. Cheng, S. Akter, Y. Liu, Y. Qian, X. Zou, J. Classen, H. Cordova, E. Oviedo-Rondon, N. Nelson, L. Wang-Li. Impacts of Air Velocity Treatments under Summer Condition: Part III-Litter Characteristics, Ammonia Emissions, and Broiler Leg Health. (in review)


42. Akter, S., B. Cheng, D. West, Y. Qian, J. Classen, C. Saydi, E. Oviedo-Rondon, L. Wang-Li. Impacts of Air Velocity Treatments under Summer Condition: Part IV-Heavy Broiler Heat and Moisture Production. (in review)


43. Oviedo-Rondon, E.O., H.A. Cordova, V. San Martin, G. Quintana, C. Alfaro, I. Cardenas, I. Camilo Ospina, M. Chico, B. Cheng, Y. Zhao, D. West, and L. Wang-Li. Impacts of Air Velocity Treatments under Summer Condition: Part V-Broiler Live Performance, Meat Yield And Breast Meat Quality. (In preparation)


44. Akter, S., J. Classen, C, Saydi, E. Oviedo-Rondon, L. Wang-Li. Design of a Retractable Baffle to Increase Wind Chill Effects for Mitigation of Heavy Broiler Heat Stress: CFD Modeling (in review)


45. Conference Papers, Posters, and Presentations


46. Akter, S. L. Wang-Li, J. Classen, E. Oviedo, C. Sayde. 2023. Heat and Moisture Production of Heavy Broilers under Hot Summer Condition. An ASABE Annual International Meeting (AIM) Presentation 250. Presented at 2023 ASABE AIM, July 9- 12. Omaha, Nebraska


47. Akter, S. L. Wang-Li, J. Classen, E. Oviedo, C. Sayde. 2022. Retractable Baffle Design to Promote Wind Chill Effects for Heavy Broiler Heat Stress Mitigation: CFD Modeling. An ASABE Annual International Meeting (AIM) Presentation 2200584. Presented at 2022 ASABE AIM, July 17-20, Houston, TX.


48. Wang-Li, L, S. Akter, D. West, B. Cheng, YY. Liu, YY. Qian, Z. Zou, E. Oviedo-Rondon. 2021. Responses of litter quality, broiler surface temperature, leg health and broiler behavior to air velocity treatments under summer conditions. Virtually presented at 2017 International Symposium on Animal Environment and Welfare (2021 ISAEW). October 20-20. Chongqing, China.


49. Akter, S., Y. Liu, B. Cheng, D. West, J. Classen, L. Wang-Li, E. Oviedo-Rondon, H.A. Cordova, V. San Martin. 2021. Broilers' Behavioral Responses to Air Velocity Treatments under Hot Summer Condition. An ASABE Annual International Meeting (AIM) Presentation 2100637. Presented at 2021 ASABE Virtual AIM, July 11-15, 2021.


50. Akter, S., D. West, B. Cheng, J. Classen, L. Wang-Li, E. Oviedo-Rondon, H.A. Cordova, V. San Martin. 2020. Heavey Broiler Surface Temperature responses to Air Velocity Treatments under heat Stress. 2020 ASABE Paper No. 2000604. Presented at 2020ASABE Virtual Annual International Meeting, July 12-16, 2020


51. Liu, Y., B. Cheng, D. West, S. Akter, L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa. 2019. Image Analysis of Heavy Broiler Behavior under Summer Heat Stress Condition. Presented at 2019 International Symposium on Animal Environment and Welfare (2019 ISAEW). October 21-24. Chongqing, China.


52. Liu, Y., D. West, S. Akter, B. Cheng, J. Classen L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa. 2019. Behavior Responses of heavy broilers to air velocity treatments under hot summer conditions. ASABE Paper No. 1900570. Presented at 2019ASABE Annual International Meeting, July 8-10. Boston, MA.


53. Akter, S., Y. Qian, Y. Liu, B. Cheng, D. West, J. Classen L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa, V. San Martin-Diaz. 2019. Heat and moisture production of heavy broilers under hot summer conditions. ASABE Paper No. 1900590. Presented at 2019ASABE Annual International Meeting, July 8-10. Boston, MA.


54. West, D., B. Cheng, S. Akter, Y. Liu, J. Classen L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa. 2019. Responses of broiler live performance and welfare parameters to air velocity under summer conditions. ASABE Paper No. 1901359. Presented at 2019ASABE Annual International Meeting, July 8-10. Boston, MA.


55. Oviedo-Rondon, E.O., H.A. Cordova, V. San Martin, G. Quintana, C. Alfaro, I. Cardenas, I. Camilo Ospina, M. Chico, B. Cheng, Y. Zhao, D. West, and L. Wang-Li. 2019. Air velocity under heat stress affects heavy broiler live performance and breast meat yield without changing meat quality or welfare parameters. International Poultry Scientific Meeting, Atlanta, GA, Feb 11- 12.


56. Qian, Y., B. Cheng, D. West, Y. Zhao, X. Zou, S. Liu, F. Hu, L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa, V. San Martin-Diaz. 2018. Heat and moisture production of heavy broilers: a chamber study. ASABE Paper No. 1800895. Presented at 2018ASABE Annual International Meeting, July 29-August 1Detroit, Michigan.


57. West, D., B. Cheng, Y. Zhao, X. Zou, Y. Qian, F. Hu, L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa, V. San Martin-Diaz. 2018. Ammonia emission as impacted by air velocity treatment for heavy broiler: a chamber study. ASABE Paper No. 1801179. Presented at 2018 ASABE Annual International Meeting, July 29-August 1. Detroit, Michigan.


58. Cheng, B., D. West, Y. Zhao, X. Zou, Y. Qian, F. Hu, L. Wang-Li, E. Oviedo-Rondón, H. Cordova-Noboa, V. San Martin-Diaz. 2018. Emission and characteristics of particulate matter as impacted by air velocity treatment for heavy broiler: a chamber study. ASABE Paper No. 1800872. Presented at 2018ASABE Annual International Meeting, July 29-August 1. Detroit, Michigan.


59. Cifuentes, J., E. Oviedo-Rondón, H. Cordova-Noboa, A. Sarsour, V. San Martin-Diaz, S. Alvarez-Muñoz, I. MartinezˇRojas, F. Tovar, C. Florez-Leguizamon, L. Wang-Li, B. Cheng, and Y. Zhao. 2018. Effect of air velocity on broiler live performance, meat yield and breast meat quality up to 61 d. Abstract M116, p. 32. International Poultry Scientific Meeting, Atlanta, GA, Jan 29- 30, 2018.


60. Wang-Li, L, E. Oviedo-Rondon, J.J. Classen. 2017. Mitigating environmental stress for enhanced broiler production performance & welfare. Presented at 2017 International Symposium on Animal Environment and Welfare (2017 ISAEW). October 23-26. Chongqing, China


61. Wang, J., Xiang, L., Morota, G., Wickens, C. L., Miller-Cushon, E. K., Brooks, S. A., & Yu, H. (2023). ShinyAnimalCV: Interactive web application for object detection and three-dimensional visualization of animals using computer vision. 2023 ASAS-CSAS-WSASAS Annual Meeting. Albuquerque, New Mexico. July 16-20, 2023. Conference Papers, Posters, and Presentations


62. Wang, J., Xiang, L., Morota, G., Wickens, C. L., Miller-Cushon, E. K., Brooks, S. A., & Yu, H. (2023). ShinyAnimalCV: Interactive web application for object detection and three-dimensional visualization of animals using computer vision [Poster Presentation]. Future of Food Forum - Transforming Food Systems with Artificial Intelligence, Gainesville, Florida.


63. Dong, Y., Bonde, A., Codling, J. R., Bannis, A., Cao, J., Macon, A., ... & Noh, H. Y. (2023). PigSense: Structural Vibration-based Activity and Health Monitoring System for Pigs. ACM Transactions on Sensor Networks.


64. Brown-Brandl, T. M., Hayes, M. D., Rohrer, G. A., & Eigenberg, R. A. (2023). Thermal comfort evaluation of three genetic lines of nursery pigs using thermal images. Biosystems Engineering, 225, 1-12.


65. Brown, B., Fudolig, M., Brown-Brandl, T. M., & Keshwani, D. R. (2023). Impacts on Teamwork Performance for an Engineering Capstone in Emergency Remote Teaching. Journal of the ASABE, (Accepted Jan. 2023)


66. Siegford, J. M., Steibel, J. P., Han, J., Benjamin, M., Brown-Brandl, T., Dórea, J. R., ... & Rosa, G. J. (2023). The quest to develop automated systems for monitoring animal behavior. Applied Animal Behaviour Science, 106000.


67. Xiong, Y., Condotta, I. C., Musgrave, J. A., Brown-Brandl, T. M., & Mulliniks, J. T. (2023). Estimating Body Weight and Body Condition Score of Mature Beef Cows using Depth Images. Translational Animal Science, txad085. https://doi.org/10.1093/tas/txad085


68. Brown-Brandl, T. M., and Condotta, I. S.C. (2022). Depth cameras for animal monitoring Chapter in Encyclopedia of Smart Agriculture Technologies. Splinger. https://doi.org/10.1007/978-3-030-89123-7


69. Dotto, J., Xiong, Y., Pitla, S. K., & Gates, R. S. (2023). A web-based interface for automatic pollutant emission estimations in poultry facilities. In 2023 ASABE Annual International Meeting(p. 1). American Society of Agricultural and Biological Engineers.


70. Xiong, Y., Li, G., Willard, N. C., Ellis, M., & Gates, R. S. (2023). Modeling neonatal piglet rectal temperature with thermography and machine learning.

1. Casella, E., Cantor, M. C., Setser, M. M. W., Silvestri, S., and Costa, J. H. C.^† 2023. A Machine Learning and Optimization Framework for the Early Diagnosis of Bovine Respiratory Disease. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3291348


2. Grinter, L. N. Mazon, G., and Costa, J. H. C.^† 2023.  Voluntary heat stress abatement system for dairy cows: does it mitigate the effects of heat stress on physiology and behavior? J. Dairy Sci. https://doi.org/ 10.3168/jds.2022-21802


3. Cantor, M. C., Casella, E., Silvestri, S., Renaud, D. L. and Costa, J. H. C.^† 2022.  Using machine learning and precision livestock farming technology for early indication of Bovine Respiratory Disease status in preweaned dairy calves. Front. Ani. Sci. https://doi.org/10.3389/fanim.2022.852359


4. Creutzinger, K.C., Broadfoot, K., Goetz, H. M., Proudfoot, K. L., Costa, J. H. C., Meagher, R. Truman, C. R., Campler, M. R., Costa, J. H. C. ^† 2022. Body Condition Score Change throughout Lactation Utilizing an Automated BCS System: A Descriptive Study. Animals. https://doi.org/10.3390/ani12050601


5. Abreu, M. B., Cunha, C. S., Costa, J. H. C., Miller-Cushon, E., Rotta, P. P., Machado, A. F., Moraes, V. C. L., and Marcondes, M. I. 2022. Performance and feeding behavior of Holstein and Holstein × Gyr crossbred heifers grazing temperate forages. Tropic. Anim. Health. https://doi.org/10.1007/s11250-022-03106-w


6. Cantor, M. C. ^‡, and Costa, J. H. C.^† 2022. Daily feeding and activity behavioral patterns collected by precision technology are associated with Bovine Respiratory Disease in preweaned dairy calves. J. Dairy Sci. https://doi.org/10.3168/jds.2021-20798


7. Cantor, M. C. ^‡, Renaud, D. L., Neave, H.W., and Costa, J. H. C.^† 2022. Feeding behavior and activity levels are associated with recovery status in dairy calves treated with antimicrobials for Bovine Respiratory Disease. Sci. Rep. https://doi.org/10.1038/s41598-022-08131-1


8. Morrison, J., Winder, C. B., Medrano-Galarza, C., Denis, P., Haley, D., LeBlanc, S., Costa, J. H. C., Steele, M. A., and Renaud, D. L. 2022. Case-control study of behavior data from automated milk feeders in healthy or diseased dairy calves. Tranl. AS. https://doi.org/10.3168/jdsc.2021-0153


9. Conboy, M. H., Winder, C. B., Cantor, M. C., Costa, J. H. C., Steele, M.A., Medrano-Galarza, C., von Konigslow, T. E., Kerr, A., and Renaud, D. L. 2022. Associations between feeding behaviors collected from an automated milk feeder and neonatal calf diarrhea in group housed dairy calves: a case-control study. Animals. https://doi.org/10.3390/ani12020170

80. Sommer, D. M., Young, J. M.*, Sun, X., Lopez-Martinez, G., Byrd. C. J. (2023) Are infrared thermography, feeding behavior, and heart rate variability measures capable of characterizing group-housed sow social hierarchies? Journal of Animal Science https://doi.org/10.1093/jas/skad143


81. Li, J., Green-Miller, A.R., Hu, X., Lucic, A., Mohan, M.M., Dilger, R.N., Condotta, I.C.F.S., Aldridge, B., Hart, J.M. and Ahuja, N., 2022. Barriers to computer vision applications in pig production facilities. Computers and Electronics in Agriculture, 200, p.107227.


82. Ramirez, B.C., Hayes, M.D., Condotta, I.C.F.S. and Leonard, S.M., 2022. Impact of housing environment and management on pre-/post-weaning piglet productivity. Journal of animal science, 100(6), p.142.


83. Brown-Brandl, T. M.; Condotta, I. C. F. S. (2023). Depth Cameras for animal monitoring. In: Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham.


84. Bushman, J.; Condotta, I. C. F. S.; Knox, R.; Caesar, M.; Green-Miller, A. (May 2023). I-SEEDS: Illinois System for Electronic Estrus Detection and Stimulation. In: Proceedings of 2nd US-PLF Conference (Poster Presentation).


85. Condotta, I.C. F. S.; Lima, I. B. G.; Rahman, M.; Dunning, N. M.; Dilger, R. N. (May 2023). I-PICS: Illinois Pig Identification through Computer vision System. In: Proceedings of 2nd US-PLF Conference (Oral Presentation).


86. Condotta, I.C. F. S.; Lima, I. B. G.; Rahman, M.; Dunning, N. M.; Dilger, R. N. (July 2023). Insights on the feasibility of pig identification through computer vision. ASABE Annual International Meeting(Oral Presentation).


87. Benicio, L.; Condotta, I. C. F. S.; Cardoso, F. (May 2023). IDetection of feeding activity of dairy cows through depth image processing. In: Proceedings of 2nd US-PLF Conference (Poster Presentation).


88. McCan, J.; Dawson, C.; Shike, D.; Condotta, I. C. F. S. (May 2023). Hind leg angle and step length measured by 3-D imaging account for variance of locomotion score and growth performance of cattle in slatted feeding facilities. In: Proceedings of 2nd US-PLF Conference (Poster Presentation).


89. Li, J.; Green-Miller, A.;Senthil, P.; Williams, T.; Lucic, A.; Hu, X.; Aldridge, B.; Hart, J.; Dilger, R.; Condotta, I. C. F. S. (May 2023). PigLife: an open-source image and video dataset for pig identification and behavior for benchmarking computer vision and learning model applications. In: Proceedings of 2nd US-PLF Conference (Poster Presentation).


90. Lozada, Claudia Carolina, Rachel M. Park, and Courtney L. Daigle. "Evaluating accurate and efficient sampling strategies designed to measure social behavior and brush use in drylot housed cattle." Plos one 18.1 (2023): e0278233.


91. Daigle, C. L., Ridge, E. E., Caddiell, R. M., & Jennings, J. S. (2023). Effect of Dietary Corn Stalk Inclusion on the Performance of Non-Nutritive Oral Behaviors of Drylot-Housed Beef Steers. Journal of Applied Animal Welfare Science, 1-8.


92. Daigle, C. L., Sawyer, J. E., Cooke, R. F., & Jennings, J. S. (2023). Consider the Source: The Impact of Social Mixing on Drylot Housed Steer Behavior and Productivity. Animals, 13(18), 2981.


93. Paudyal, S., Maunsell, F., Melendez, P. and Pinedo, P., 2023. Milk component ratios for monitoring of health during early lactation of Holstein cows. Applied Animal Science, 39(4), pp.191-201.


94. Paudyal, S., Piñeiro, J. and Papinchak, L., 2023. Associations of Eliminating Free-Stall Head Lock-Up during Transition Period with Milk Yield, Health, and Reproductive Performance in Multiparous Dairy Cows: A Case Report. Dairy, 4(1), pp.215-221.


95. Papinchak, L., S. Paudyal, and J. Pineiro. 2022. Effects of prolonged lock-up time on milk production and health of dairy cattle. Veterinary Quarterly, 42, pp175-182.


96. Manríquez, D., Zúñiga, S., Paudyal, S., Solano, G. and Pinedo, P.J., 2022. Waiting time in the pre-milking holding pen and subsequent lying and walking behaviors of Holstein cows. JDS communications, 3(4), pp.280-284.

 

 

Conference Papers, Posters, and Presentations

1. Nguyen, X., Y. Zhao, J.D. Evans, J. Lin, B. Voy, J.L. Purswell. 2022. The use of ultraviolet radiation to reduce airborne Escherichia coli. In: 2022 ASABE Annual International Meeting, Houston, TX, USA.


2. Yang, X., Y. Zhao. 2022. Applications of precision livestock farming technologies in broiler production. In: Proceedings of 10^thEuropean Conference on Precision Livestock Farming 2022. Vienna, Austria.


3. Tabler, T., S. Hawkins, Y. Zhao. 2022. Litter management. Proceedings of Midwest Poultry Federation Convention. Minneapolis, MN, USA.


4. Moon, J., T. Tabler, J. Dubien, R. Ramachandran, Y. Liang, and S. Dridi. 2022. Sprinkling broilers maintains performance and improves poultry industry sustainability. World’s Poultry Congress. Paris, France. August 7-11. Poster presentation.


5. Siegford J. Invited speaker. Food animal management through innovation and technology. At: Spring 2023 Animal Welfare Assessment Contest, American Veterinary Medical Association, April 22, 2023


6. Siegford J. Invited speaker. Understanding adoption of precision livestock farming by the US swine industry. At: Application of PLF and AI Technology in Animal Production symposium. Adaptation and Physiology Group, Wageningen University & Research, Wageningen, The Netherlands, October 4, 2022


7. Siegford J. Invited speaker. Animal behavior and welfare: what can technology tell us? At: Automated Phenotyping. IMAGEN & Breed4Food Individual Tracking symposium, Wageningen, The Netherlands, September 29, 2022


8. Benjamin ME. Invited speaker. Merging Precision Livestock Farming, Sustainability and Welfare in Livestock Production. 2022 MiVetCon (MVC). DeVos Place in Grand Rapids, Michigan. October 8, 2022. 16 veterinarians in attendance.


9. Benjamin ME. Invited speaker. Application of Animal Welfare Practice and Digital Technologies on Swine Farms. Virtual presentation through an online and onsite mode with translator to 1st International China-U.S. Swine Veterinarian Conference. April 28-30, 2022. Hangzhou, China. Co-organized by Chinese Veterinary Medical Association, Western Institute for Food Safety & Security-UC Davis, U.S.-China Center for Animal Health-K State and Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management-China, hosted by Zhejiang A&F University, 6000 veterinarians in attendance. 


10. Dorea, J. R. R. Artificial intelligence for livestock systems. 2022. Dairy Sci. Vol. 105, Suppl. 1.


11. Bresolin, T., A. Wick-Lambert, R. E.P. Ferreira, A. Vang, D. Oliveira, G. J. M. Rosa, L. Hernandez, and J. R. R. Dorea. 2022. Phenotyping udder and mammary gland of dairy cows using computer vision systems. J. Dairy Sci. Vol. 105, Suppl. 1.


12. Ferreira, R. E. P., T. Bresolin, H. T. Holdorf, H. M. White, and J. R.R. Dorea. 2022. Integrating animal-level data for early detection of subclinical ketosis in dairy cows using machine learning algorithms. J. Dairy Sci. Vol. 105, Suppl. 1.


13. Dado-Senn, B., V. Ouellet, V. Lantigua, J. Van Os, J. R. R. Dorea, and J. Laporta. Heat stress detection and prevention in Midwestern outdoor hutch-housed dairy calves. 2022. J. Dairy Sci. Vol. 105, Suppl. 1.


14. Negreiro, A., T. Bresolin, R. Ferreira, S. I. Arriola Apelo, and J. R. R. Dorea. 2022. Leveraging computer vision systems to better understand feeding behavior patterns in dairy cows. J. Dairy Sci. Vol. 105, Suppl. 1.


15. Ferreira, R. E. P., J. C. F. Silva, and J. R. R. Dorea. 2022. Using computer vision and mixed reality to detect compliance with standard milking procedures in real time. J. Dairy Sci. Vol. 105, Suppl. 1.


16. Silva, J. C. F., R. E. P. Ferreira, J. R. R. Dorea. 2022. Using computer vision for animal identification in dairy barns using isometric view images. J. Dairy Sci. Vol. 105, Suppl. 1.


17. Perttu, R., M. Peiter, T. Bresolin, J. R. R. Dorea, and M. Endres. 2022.The associations between feeding behaviors collected from automatic milk feeders and disease in group-housed preweaned dairy calves. J. Dairy Sci. Vol. 105, Suppl. 1.


18. Caffarini, J., T. Bresolin, and J. R. R. Dorea. 2022. Predicting ribeye area and shape of live calves through 3-dimensional image analyses of body surface. J. Dairy Sci. Vol. 105, Suppl. 1.


19. Holdorf, H. T., K. E. Ruh, S. J. Erb, S. J. Henisz, G. J. Combs, T. Bresolin, R. E. P. Ferreira, W. E. Brown, S. M. Edwards, J. C. Rule, F. P. Zhou, M. J. Martin, K. E. Estes, J. R. R. Dorea, H. M. White. 2022. Increasing dose of prepartum rumen-protected choline: Effects on energy and nitrogen metabolism in Holstein dairy cows. J. Dairy Sci. Vol. 105, Suppl. 1.


20. Pereira, L. G. R., R. R. Silvi, C. A. V. Paiva, T. R. Tomich, M. M. Campos, F. S. Machado, and J. R. R. Dorea. 2022. Adoption of automatic milk systems by Brazilian dairy farms. J. Dairy Sci. Vol. 105, Suppl. 1.


21. Souza, G. M., M. A. C. Danés, V. A. Teixeira, T. Bresolin, T. R. Tomich, J. P. P. Rodrigues, S. G. Coelho, J. E. F. Filho, M. M. Campos, L. G. R. Pereira, and J. R. R. Dorea. 2022. Anaplasmosis prediction using microchip with a thermal sensor or clinical rectal thermometer. J. Dairy Sci. Vol. 105, Suppl. 1.


22. Negreiro, A., T. Bresolin, R. Ferreira, B. Dado-Senn, J. Laporta, J. Van Os, and J. R. R. Dorea. 2022. Monitoring heat stress behavior in dairy calves through computer vision systems. J. Dairy Sci. Vol. 105, Suppl. 1.


23. Ribeiro, L. A. C., T. Bresolin, S. I. A. Apelo, and J. R. R. Dorea. 2022. Using Fourier-transform infrared spectroscopy to predict urinary allantoin and creatinine from urine and milk samples. J. Dairy Sci. Vol. 105, Suppl. 1.


24. Dorea, J. R. R. and G. J. M. Rosa. 2022. Computer vision systems to advance high-throughput phenotyping in livestock. World Congress on Genetics Applied to Livestock Production. Rotterdam, Netherlands.


25. Moreira, L. C., T. Bresolin, J. R. R. Dorea, G. J. M. Rosa. Estimating Body Condition Score of Dairy Cows from Color Depth Images Using a Mobile Device.


26. Bresolin, T., R. E. P. Ferreira, J. R. R. Dorea. 2022. Effect of Camera Exposure Time on Image Segmentation and Body Weight Prediction. Journal of Animal Science https://doi.org/10.1093/jas/skac247.586


27. Menezes, G. L. Oliveira, A. L. Gonçalves, L. Ferraretto, J. R. R. Dorea, D. Jayme. 2022. Efficacy of Formic Acid as a Silage Preservative on Dairy Cattle Performance. Journal of Animal Science. https://doi.org/10.1093/jas/skac247.692


28. Freitas, L., R. E. P. Ferreira, R. Savegnago, J. R R. Dorea, G. J. M. Rosa, C. Paz. 2022. Computer Vision System to Predict Famacha© Degree in Sheep from Ocular Conjunctiva Images. Journal of Animal Science. https://doi.org/10.1093/jas/skac247.452


29. Rosa, G. J. M. Big Data and Genomics for Improvement of Beef Cattle Production. 10^th Goiás Genética, Goiania - Brazil, Aug 30-Sept 03, 2022.


30. Rosa, G. J. M. Leveraging on High-throughput Phenotyping Technologies to Optimize Genetic Improvement in Livestock. 30^thConference on Intelligent Systems for Molecular Biology (ISMB), Madison - WI, July 10-14, 2022.


31. Rosa, G. J. M. Levering Big Data and Digital Tools to Improve the Efficiency and Sustainability of the Pig Production. PorciForum, Lleida - Spain, March 23-24, 2022.


32. Rosa, G. J. M. Can Deep Neural Networks Improve Genome-Enabled Prediction of Complex Traits? Conference on Applied Statistics in Agriculture and Natural Resources, Logan – UT, May 16-19, 2022.


33. Rosa, G. J. M. Precision Livestock Farming Methods for Animal Health and Welfare. 18^th International Conference on Production Diseases in Farm Animals (ICPD), Madison-WI, June 15-17, 2022.


34. Gianola, D., Crossa, J., Gonzalez-Recio, O. and Rosa, G. J. M. Machine learning and genetic improvement of animals and plants: where are we? World Congress on Genetics Applied to Livestock Production, Rotterdam, The Netherlands, July 3-8, 2022.


35. Dorea, J. R. R. and Rosa, G. J. M. Computer vision systems to advance high-throughput phenotyping in livestock. World Congress on Genetics Applied to Livestock Production, Rotterdam, The Netherlands, July 3-8, 2022.


36. Dorea, J. R. R. Leveraging Artificial Intelligence in Livestock Systems. 2022 American Society of Animal Science - Midwest Section, Omaha-NE,March 15^th, 2022


37. Dorea, J. R. R. Harnessing the Power of High-Throughput Phenotyping Technologies to Improve Farm Management. Plant and Animal Genomics (PAG/ transferred to NRSP8: National Animal Genome Research Program/ Cattle and Swine), San Diego-CA, April 3^rd, 2022.


38. Dorea, J. R. R. Machine Learning and Computer Vision for Agriculture. REDTalk. School of Computer, Data & Information Sciences. Madison-WI, April 21^st , 2022.


39. Dorea, J. R. R. Artificial intelligence for Livestock Systems. 2022 American Dairy Science Association annual meeting, Kansas City-MO, June 24^th, 2022.


40. Dorea, J. R. R. Machine Learning and Computer Vision for Livestock. ML+X Forum. American Family Insurance Data Science Institute, Madison-WI, October 4^th, 2022.


41. Dorea, J. R. R. Machine Learning and Computer Vision for Agriculture. Microsoft Research Summit. AI for Digital Agriculture. (online), November 9^th, 2022.


42. Dorea, J. R. R. Leveraging Artificial Intelligence in Livestock Systems. Dairy Cattle Reproduction Council (DCRC), Middleton-WI, November 15^th, 2022.


43. Dorea, J. R. R. Harnessing the Power of High-Throughput Phenotyping Technologies to Improve Dairy Management. GPS Dairy Conference. Minneapolis-MN, December 7^th, 2022.


44. Dorea, J. R. R. Leveraging Artificial Intelligence in Livestock Systems. Fundacao Getulio Vargas. Summer School on Data Science, Rio de Janeiro, Brazil, Jan 24^th, 2023.


45. J, M. Rosa. Statistical Modeling in Animal Breeding and Genetics. Sao Paulo State University (UNESP) - Jaboticabal, Brazil. November 07-11, 2022.


46. Dorea, J. R. R. and G. J, M. Rosa. Big Data and Digital Tools Applied to Livestock Production. University of Padova, Padova - Italy. Sept 26 - 30, 2022.


47. J, M. Rosa. Regression and Classification Applied to Precision Agriculture, Brazilian Agricultural Research Corporation (EMBRAPA), Goiania - Brazil, Sept 05, 2022.


48. J, M. Rosa. Quantitative Genetics (co-taught with Dr. Bruce Walsh) at the 27th Summer Institute in Statistical Genetics, University of Washington, Seattle - WA. July 18-20, 2022. (online course)


49. J, M. Rosa. Mixed Models in Quantitative Genetics (co-taught with Dr. Bruce Walsh) at the 27th Summer Institute in Statistical Genetics, University of Washington, Seattle - WA. July 20-22, 2022. (online course)


50. J, M. Rosa. Regression and Classification Applied to Precision Agriculture, Utah State University, Logan - UT, May 16-19, 2022.


51. McGill, M. Hayes, J. Jackson, and R. Coleman. 2023 Spatial and Temporal Analysis of Air Speeds in Equine Indoor Arenas. 2^nd US PLF


52. Mazon, B. Farmer, J. Jackson, M. Hayes, and J. H.C. Costa. 2023. Evaluating the effects of a voluntary soaking system on the behavior, physiology, and production of dairy cows milked in voluntary milking systems. 2^nd US PLF


53. Sommer, D. M.*, Young, J. M.*, Byrd, C. J. (2023) Can nonlinear heart rate variability analysis be used to characterize the sow social hierarchy within group-housed gestation systems? Accepted for oral presentation at the U.S. Precision Livestock Farming Conference (May 21-24, 2023; Knoxville, TN) .


54. Condotta, I. C. F. S. ASAS Annual International Meeting, Oklahoma City, OK, June 28th, 2022 – Title: “Precision Management of Animals: Computer Vision Applications, Challenges, and Opportunities”


55. Condotta, I. C. F. S. Midwest Swine Nutrition Conference, Danville-IN, September 8th, 2022– Title: “Technology to make nutrition implementation easier”


56. Condotta, I.C. F. S. NSIF Annual Meeting, Kansas City-MO, November 1st, 2022 – Title: “Precision Livestock farming: Challenges and Opportunities.”


57. Condotta, I.C. F. S. SAFER AG Workshop, Urbana-IL, November 3rd,2022 – Title: “Precision Management of Animals: the Future of Farming?”


58. Condotta, I. C. F. S. XI Jornadas Internacionais de Suinicultura, University of Trás-os-Montes and Alto Douro, Portugal (virtual), March 11th, 2022 – Title: “Precision Swine Management: Challenges and Opportunities”


59. Condotta, I. C. F. S. 2. 1st International Congress on Veterinary and Animal Science: Under One Health Concept, University of Trás-os-Montes and Alto Douro, Portugal (virtual), December 7th, 2022 - Title: “Animal welfare in production systems: precision livestock farming role”


60. Condotta, I. C. F. S. UIUC International Agronomy Day, August 8^th, 2023 - Title: Precision Management of Animals.


61. Condotta, I. C. F. S. ASAS Annual International Meeting. July 2023 – Title: Swine precision nutrition: how computer vision can help?


62. Erasmus. 2023. Center for Food Animal Wellbeing, University of Arkansas. Title: Gaitway to sustainability: how the environment shapes the walking ability and welfare of meat poultry.


63. Ceja, G., Paudyal, S., Spencer, J., Piñeiro, J. M., & Daigle, C. L. (2023). 149 Case Study: The Impact of a Fogging System on Dairy Cow Comfort in Cows Housed in a Barn with Tunnel Ventilation and an Automatic Milking System. Journal of Animal Science, 101(Supplement_1), 94-95.


64. Rahmel, Logan W., Genevieve M. D’Souza, Juan C. Llarena, Libby S. Durst, Brandi B. Karisch, Courtney L. Daigle, Jason R. Russel, and Kelsey M. Harvey. "133 Feeding Behavior of High-Risk Steers Newly Received for Backgrounding." Journal of Animal Science 101, no. Supplement_1 (2023): 85-86.


65. Shrestha B., J. Pineiro, S. Paudyal. 2023. Evaluating the potential of a thermal imaging system to identify subclinical mastitis in dairy cattle. ASAS Annual meeting 2023.


66. Neupane, R., S. Paudyal, A. Aryal and P. Pinedo. 2023. Evaluating machine learning algorithms to use accelerometer data for identification of lameness in dairy cows. J. Dairy Sci. Vol. 106, Suppl. 1; Page 148


67. Paudyal, S., K. Kaniyamattam, J. Piñeiro, J. Spencer, B. W. Jones, and E. Kim. 2023. The availability of local tech support is the most important factor for dairy farmers when choosing precision dairy technologies in Texas. J. Dairy Sci. Vol. 106, Suppl. 1; Page 271-272


68. Duhatschek, B. Newcomer, G. M. Schuenemann, B. T. Menichetti, S. Paudyal, V. N. Gouvêa, and J. M. Piñeiro. 2023 Effect of supplementing one or 2 calcium boluses at calving on serum pH and minerals, performance, rumination, and activity of multiparous dairy cows. J. Dairy Sci. Vol. 106, Suppl. 1; Page 228


69. Neupane, R., S. Paudyal, A. Aryal and P. Pinedo. 2023. Evaluating machine learning algorithms to predict locomotion scoring in dairy cattle. Dairy Sci. Vol. 106, Suppl. 1; Page 400.


70. Sushil Paudyal, Mahendra Bhandari, Lucy Huang, 79 Cross-Training Future Workforce on Data Handling and Interpretation for Precision Agriculture Systems, Journal of Animal Science, Volume 101, Issue Supplement_1, May 2023, Pages 113–114,

  

Datasets, Databases, Software

1. Transcripts from interviews with 12 swine industry stakeholders related to PLF perceptions and perceived uses and resulting Q method data.


2. Contributors: Rafael Ehrich Pontes Ferreira, Joao R. R. Dorea. Date created: 2023-01-30 04:54 PM | Last Updated: 2023-02-01 09:04 PM. Identifier: DOI 10.17605/OSF.IO/VYH5J. Description: Dataset used in the study [Using pseudo-labeling to improve performance of deep neural networks for animal identification. Link: https://osf.io/vyh5j/

  

Extension and Outreach Presentations, Workshops, etc.

1. Master Backyard Flock Program for UT/TSU Extension Agents. January 25th and 26th 2022


2. Offered Engineering Elements: Broiler PLF Extension Agent In-Service August 4th, 2022 Tabler, T., J. Moon, and Y. Liang. 2022. Sprinkler technology improves broiler production and water conservation efforts. Invited presentation to Poultry Science Research Day, University of Arkansas, Fayetteville, AR. May 25.


3. Tabler, T., J. Moon, S. Dridi, and Y. Liang. 2022. Sprinkler use improves flock performance and water use conservation. Invited virtual presentation to Poulina Holding Group. Tunisia, North Africa. September 8.


4. Tabler, T. 2022. Troubleshooting LED lamp and light dimmer issues. Invited presentation to The Poultry Federation Symposium. Rogers, AR. August 25.


5. Tabler, T. 2023. Litter management: Moisture removal and ventilation. Invited presentation to Minnesota Turkey Growers Association Poultry Health and Management School. East Lansing, MI. May 16.


6. Recordings of FACT CIN webinars are available online at MSU at https://www.canr.msu.edu/precision-agriculture/Precision-Livestock-Farming-Webinar-Series/index


7. Dorea, J. R. R. UW-Science Expeditions: Artificial Intelligence for Animal Farming, 2022 (850 participants)


8. Dorea, J. R. R. UW-Science Expeditions: Artificial Intelligence for Animal Farming, 2023 (1,218 participants)


9. Dorea, J. R. R. Wisconsin Science Festival Expeditions: Artificial Intelligence for Animal Farming, 2022 (512 participants)


10. Dorea, J. R. R. FFA Students. Artificial Intelligence for Livestock – Wisconsin Youth Program, 2022 (140 participants).


11. Condotta, I. C. F. S. REU Summer Program: minority undergraduate student mentoring.


12. Paudyal, S. Southwest Dairy Day: Precision tools in Dairy farms, 2023 (450 participants)

 

Extension Publications, Trade Publications, Outreach Activities, etc.

1. Nguyen, X. D., and T. Tabler. 2023. Broiler litter management. University of Tennessee Institute of Agriculture Publ. No. W 1135.


2. Tabler, T. 2022. The benefits of sprinklers in poultry barns. Canadian Poultry. February 3. Available at: https://www.canadianpoultrymag.com/the-benefits-of-sprinklers-in-poultry-barns/.


3. Tabler, T., S. Hawkins, Y. Zhao, and P. Maharjan. 2022. Litter management. Proceedings 2022 Midwest Poultry Federation Convention. Minneapolis. March 22-24.


4. Tabler, T., S. Hawkins, Y. Zhao, P. Maharjan, and J. Moon. 2022. Litter management key to broiler performance. UT Department of Animal Science Publ. No. D 163.


5. Tabler, T., M. L. Khaitsa, A. Odoi, S. Hawkins, P. Maharjan, and J. Wells. 2022. Antibiotic alternatives in poultry production: An African viewpoint. UT Department of Animal Science Publ. No. D 165.


6. Tabler, T., V. Ayers, Y. Liang, P. Maharjan, J. Moon, and J. Wells. 2022. Manage litter quality for better paw quality. UT Department of Animal Science Publ. No. D 173.


7. Tabler, T., M. L. Khaitsa, J. N. Jeckoniah, A. Odoi, S. Hawkins, P. Maharjan, and J. Wells. 2022. Poultry production and food security in East Africa: Impact of personnel, technology and genetics. UT Department of Animal Science Publ. No. D 176.


8. Tabler, T., P. Maharjan, Y. Liang, J. Wells, and J. Moon. 2022. Alternatives to antibiotic growth promoters in broiler production. UT Department of Animal Science Publ. No. D 187.


9. Tabler, T., Y. Liang, J. Moon, V. Ayres, P. Maharjan, and J. Wells. 2023. Poultry production going forward: Where will the water come from? UT Department of Animal Science Extension Publ. No. D 203.


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