NC1211: Precision Management of Animals for Improved Care, Health, and Welfare of Livestock and Poultry

(Multistate Research Project)

Status: Active

Project Menu

SAES-422 Reports

Annual/Termination Reports:

[03/22/2023] [12/12/2023]

Date of Annual Report: 03/22/2023

Report Information

Annual Meeting Dates: 09/07/2022 - 09/08/2022
Period the Report Covers: 05/16/2021 - 09/07/2022

Participants

Fitzgerald Robert robert.fitzgerald@genusplc.com
Steibel Juan steibelj@msu.edu
Regmi Prafulla pregmi@uga.edu
Dorea Joao joao.dorea@wisc.edu
Davis Jeremiah j.davis@auburn.edu
Li Lingjuan lwang5@ncsu.edu
Byrd Christopher christopher.byrd@ndsu.edu
Labrecque Jacquelin jacquelin.labrecque@ro-main.com
Zhao Yang yzhao95@utk.edu
Rosa Guilherme grosa@wisc.edu
Abbelfattah Essam eabdelfattah@ucdavis.edu
Benjamin Madonna gemus@msu.edu
McGee Marcus mm283@msstate.edu
Paudyal Sushil sushil.paudyal@ag.tamu.edu

Brief Summary of Minutes

NC1211 had our first formal meeting this spring. A poll was distributed among participants ahead of the meeting to decide on face to face or virtual meeting and all the potential attendant indicated that they could only participate online.

Thus, the meeting was held virtually on September 7^th from 12 noon to 4:30 PM. A total of 14 participants attended (listed above) and the agenda was the following:

12:00 PM Introduction - Archie Clutter (Advisor; UNL); Christina Hamilton (NCRA)

12:15 PM Presentation 1 – Juan Steibel (Iowa State Univ.)

12:35 PM Presentation 2 – Essam Abdelfattah (UC-Davis)

12:55 PM Presentation 3 – Madonna Benjamin (Michigan State Univ.)

1:15 PM Presentation 4 – Joao Dorea (Univ. of Wisconsin-Madison)

1:35 PM Presentation 5 – Jeremiah Davis (Auburn University)

1:55 PM Presentation 6 – Guilherme Rosa (Univ. of Wisconsin-Madison)

2:15 PM Break

2:45 PM Business Meeting

4:30 PM Close

The goals of our meeting were to 1) introduce new members, 2) update on progress by participating stations, 3) conduct business meetings with continuation plans.

We started the meeting with brief individual introductions, four new participants were introduced. Six participants from five stations presented updates.

Official Actions:

Juan Steibel was elected chair, Joao Dorea was elected secretary. The 2023 meeting will be held during the USPLF meeting in Knoxville Tennessee, Sunday May21st.

Accomplishments

Activities:  Members of the NC1211 have worked together to create the second USPLF conference to be held Knoxville, TX May 22-25.  This will allow increase visability of precision animal management research and highlight the needs to producers and current technology through the producer and industry panels. In addition, several members have teamed up to secure grant funding: University of Nebraska-Lincoln/University of Illinois Shi, Condotta, Brown-Brandl FACT-AI: Cyberinformatic Tools for Exploring and Validating Sow Posture and Piglet Activity University of Michigan, University of Nebraska-Lincoln, University of Wisconsin, Katholic University of Leuven Steibel, J., Brown-Brandl, T., Rosa, Siegford, Psota, Dorea, Morris, Benjamin, and Norton FACT-CIN: A Coordinated Innovation Network for Advancing Computer Vision in Precision Livestock Farming Iowa State University, University of Nebraska-Lincoln, University of Kentucky, Ramirez, Hoff, Harmon, Brown-Brandl, T., Hayes, Rohrer CARE: Modern pigs urgently need facilities with modern ventilation: Updating swine ventilation standards/guidelines Morris, D., Benjamin M., Brown-Brandl, T. Sharma, S.R. and Rohrer, G., FACT-CIN: Swine health and growth monitoring   Milestones: (2022): Recruit members from experiment stations to participate in the objective and tasks delineated to ensure deep expertise. Complete inventory of past, ongoing, and future work in each objective; identify commonalities and areas for synergy.  Membership went up from 10 members in the foundational group of NCDC 235 to 47 current members in this first year of NC1211. Moreover, a total of 25 institutions are represented including LGU experimental stations and industry. Current collaborative projects between members of NC1211 include animal scientists, engineers and computer scientists. Thus, we consider that this milestone has been accomplished.

Publications

<ol> <li>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.</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol>

Impact Statements

Date of Annual Report: 12/12/2023

Report Information

Annual Meeting Dates: 05/21/2023 - 05/21/2023
Period the Report Covers: 09/07/2022 - 09/07/2023

Participants

Zhao, Yang - yzhao@utk.edu
Angelica Van Goor - Angelica.Van.Goor@usda.gov
Siegford, Janice M - siegford@msu.edu
Ferket, Peter R - Peter_Ferket@ncsu.edu
Brown-Brandl, Tami M. - tami.brownbrandl@unl.edu
Ramirez, Brett - bramirez@iastate.edu
Ho, Alphina - ajho@pvamu.edu
Rosa, Guilherme - grosa@wisc.edu
Daigle, Courtney - cdaigle@tamu.edu
Benjamin, Madonna - gemus@msu.edu
Xiong, Yijie - yijie.xiong@unl.edu
Li, Lingjuan Wang - lwang5@ncsu.edu
Koltes, James E - jekoltes@iastate.edu
Johnson, Anna K - johnsona@iastate.edu
Dorea, Joao - joao.dorea@wisc.edu
Leonard, Suzanne - smleona4@ncsu.edu
McGee, Marcus - marcus.mcgee@msstate.edu
Steibel, Juan P. - jsteibel@iastate.edu
Yu, Haipeng - haipengyu@ufl.edu
Zhou, Jianfeng - zhoujianf@missouri.edu
Burns, Robert - rburns@utk.edu
Condotta, Isabella - icfsc@illinois.edu
Kukekova, Anna - avk@illinois.edu
Costa, Joao HC - joao.costa@uvm.edu
Morris, Daniel – dmorris@msu.edu
Hector Santiago, hsantiago@unl.edu

Brief Summary of Minutes

Brief Summary of Minutes of Annual Meeting

NC1211 had our second formal meeting this spring. The meeting was held hybrid on May 21^st from 9:00 AM to 2:15 PM. The goals of our meeting were to 1) introduce new members, 2) update on progress by participating stations, 3) conduct business meetings with continuation plans. We started the meeting with brief individual introductions. A total of 26 participants attended (listed above) and the agenda and minutes were the following:

9:00-9:15 Introduction and Welcome

9:15 – 11:30 Station reports

Angelica Vangoor: Presented NIFA update.

Tami Brown-Brandl-UNL: Using CV combined with RFID to predict weights, CV for farrowing sow welfare,

Joao Dorea-UWMad: Computer vision in several species. Animal ID. Research infrastructure for implementing CV in dairy barns.

Steibel-Koltes ISU: Phenomics and PLF

Yang Zhao: PLF in UT, broiler activity, and welfare image analyses. Robotized poultry barns.

11:30-1:30 Lunch and collaboration discussions

1:30 – 2:00 Discussion of milestones

Increase membership and network:

Encourage other joining, open to everyone, LGU can work membership with their reps, non-LGU can contact NC1211 chairs and they will work with our adviser, Hector Santiago to enroll other members.

Find synergies with other NC and NRSP8 groups

Data Sharing:

Identify existing resources:

UMN,

OSF such as that of the CV-CIN, contact Juan Steibel,

AgBioData: Data reuse, Contact James Koltes from ISU.

Consider barriers to data sharing: Privacy concerns, IP, Rules of use. Societal and ethical implications of sharing data

Outreach:

How to disseminate activities to industry?

Education: Short courses on data sharing, FAIR data principles, computer vision tasks. Which courses would you like to have in your institutions.

Focus on cross-training. CV/image processing to ANS, Animal Behavior to ENG, CS, and EE.

Create a space to share educational resources.

Put this as a component of integrated grants

2:30-3:00 Discussion of future grant opportunities

Two most relevant programs @ NIFA: 1) IDEAS, 2) DFASAS.

3:00-4:00 Business Meeting.

Where to meet on USLPF years: pre-meeting for USPLF. Make it ½ a day long and combine with IDEAS PD.

2024 meeting will be in Bologna and Hybrid Sep 9-12.

Official Actions:

Next Chair and Secretary:

Chair per by laws: Joao Dorea

Secretary by Vote:

Yang Zhao (UT): 8

Marcus McGee (Umiss): 5

Isabella Condotta (UIUC): 5

Adjurn: 2:15

Accomplishments

Members of NC1211, representing 13 experimental stations, have submitted their annual reports for the 2022-2023 period, detailing their activities and achievements, as well as information about conference grants obtained during this time. The collective reports indicate that the group was successful in securing a total of 39 research grants, both intramural and extramural, amounting to $17,380,898. These funds have been instrumental in supporting collaborative research between the group members and the experimental stations, focusing on the two primary objectives outlined by the NC1211 group. It is noted that some stations did not disclose their funding amounts, suggesting that the actual total funds secured may be even higher. The successful acquisition of these grants represents a significant MILESTONE for the year, as it aligns perfectly with the group's dedication to advancing the field of precision livestock farming. This achievement not only underscores the group's commitment to these objectives but also highlights the progress made in securing substantial funding to develop this crucial area. The following details the specific activities, outcomes, and grants associated with each research station:   <ol> <li>University of Tennessee:</li> </ol> Activities and projects: In the past year of the project, we have focused on working with colleagues on developing vision-based PLF system and artificial intelligence algorithms to monitor behavioral animal-based measures (ABMs) in poultry; understanding the ABMs affected by management factors, including stocking density and light intensity; and modeling long-distance airborne transmission of poultry-related pathogen among farms using Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT). We also develop producer and researcher surveys to understand their perspectives on barriers implementation of PLF at commercial farms. We established a PLF model in Master Commercial Poultry Producer Extension Program and presented it to producers and county agencies. Lead by Burns, the UT PLF workgroup has been planning and organizing 2nd US Precision Livestock Farming Conference (USPLF2023) at Knoxville, Tennessee, which is being offered in a hybrid format and has over 200 participants (180 in-person and 27 virtual) and over 120 paper submissions. The USPLF2023 Conference will be held from May 21-24, 2023 in Knoxville, TN. Outcomes and accomplishments: A vision-based system to detect the feeding and drinking behaviors of broiler chicken has been developed. Effects of growth rate and stocking density on broiler feed conversion ratio, feather condition, gait score, bone breaking strength, and activity were investigated and a manuscript has been submitted to Poultry Science journal. In our modeling study, we also found that long-distance airborne transmission was possible among farms, however, validation of this model is a huge challenge and requires further efforts.  Funding: Collaborative grants between stations and members: <ul> <li>Precision Livestock Farming: Investing for Future Success. UT AgResearch, $200,000, 2022-2023</li> <li>Automated welfare and behavior detection via vision-based PLF system in broilers, USDA-NIFA, $1,000,000, 2022-2026</li> <li>Improving health and well-being in broiler chickens through environmental management, USDA Cooperative Agreement, $164,900, 2020-2022.</li> <li>Computer vision characterization of respiration as an indicator of cattle health. USDA-NIFA, $300,000, 2022 - 2023.</li> <li>A Greenfeed System for Quantifying Emissions and Improving Metabolic Efficiency in Beef Cattle. UT AgResearch, $112,410, 2022.</li> <li>1/1/2022 – _12/31/2022. Addition of Two Feed Bins to Increase Research Capacity of Automatic Milking System. UT AgResearch, $44,000, 2022.</li> <li>Empowering U.S. broiler production for transformation and sustainability. USDA-NIFA. $249,000, 2019-2024.</li> </ul>    <ol start="2"> <li>Michigan State University:</li> </ol> Activities and projects: <ol> <li style="font-weight: 400;">Akinyemi conducted interviews of key stakeholders in the swine industry were conducted to learn their views on PLF. The USDA IDEAS project team conducted surveys of swine producers and veterinarians in Michigan, North Carolina and Iowa were conducted to understand their perception of PLF. The USDA IDEAS stakeholder advisory panel and grant team met in December and additional data were collected from the advisory board using Q methodology.</li> <li style="font-weight: 400;">Morris led work on a computer vision approach to detect eggs laid in the litter.</li> </ol> Benjamin and Steibel lead a FACT CIN webinar series in fall 2022 and spring 2023, which included suggesting speakers and overseeing webinars by introducing speakers and moderating question and answer sessions. <a href="https://www.canr.msu.edu/precision-agriculture/Precision-Livestock-Farming-Webinar-Series/">https://www.canr.msu.edu/precision-agriculture/Precision-Livestock-Farming-Webinar-Series/</a> Outcomes and accomplishments: Swine industry stakeholders hold a range of views related to the potential of PLF to improve pig production, health and welfare. Though most view the technology as promising, others worry about logistical challenges or ethical issues related to further intensification of swine farms. Funding: Collaborative grants between stations and members:  Two ongoing federal grants with collaborators from other stations <ul> <li>USDA NIFA Agriculture and Food Research Initiative – Inter-Disciplinary Engagement in Animal Systems. Siegford JM, Turner SP, Akaichi F, Benjamin M, Butters-Johnson AK, Pairis-Garcia MD, Rozeboom D, Steibel JP, Vigors B, Thompson DP, Zangaro C. $1,000,000. Understanding precision livestock farming adoption in the U.S. swine industry: examining needs, perceptions, and willingness to pay of users and consumers. 6/21-5/26. </li> <li>USDA NIFA Agriculture and Food Research Initiative Competitive Grants Program–Food and Agriculture Cyberinformatics and Tools. Steibel JP, Brown-Brandl T, Rosa GJ, Siegford JM, Psota E, Dorea J, Benjamin M, Morris D, Norton T. $1,000,000. FACT-CIN: A Coordinated Innovation Network For Advancing Computer Vision In Precision Livestock Farming. 4/21-3/26. </li> </ul> One new federal grant with collaborators from other stations. <ul> <li>United States Department of Agriculture. Morris D, Benjamin M, Brown-Brandl T, Rohrer G, Steibel J, Sharma S, and J. Siegford. $598,000. An Automated Swine Phenotyping Tool to Advance Management, Research and Genetics. 4/23-3/ </li> </ul> Two new internal grants with MSU personnel involved in NC1211 <ul> <li>Michigan Alliance for Animal Agriculture. Morris D, Siegford J, Ali A. $150,000. Automated targeted interventions to reduce eggs laid in the litter. 6/22-5/24.</li> <li>Michigan Alliance for Animal Agriculture. Akinyemi B, Siegford J. $136,924. Understanding the public’s attitudinal acceptance of precision livestock farming in the swine industry: a longitudinal survey in 3 US states. 6/22-5/24.</li> </ul>   <ol start="3"> <li>University of Wisconsin-Madison:</li> </ol> Activities and projects: In the 2022-2023 reporting year, the research station's participants made considerable academic contributions. They published 17 peer-reviewed articles, delivered 16 invited talks, held 7 research seminars, presented 19 abstracts in conference proceedings, and successfully secured a total of $3,105,629 US dollars in funding. Outcomes and accomplishments: We investigated the use of computer vision for animal identification and behavior monitoring. The station’s participant studied different machine learning methods for phenotype prediction of livestock species. The research grants awarded for the reporting period will address relevant PLF aspects such as the use of an enviromics approach to investigate genetics by environment interaction in beef cattle across the US, and the development of economically viable computer vision system to monitor lactating dairy cows for early health detection. Funding: Collaborative grants between stations and members: <ul> <li>NIH (PI: Rosa). Gordon Research Conference and Seminar on Quantitative Genetics and Genomics. $10,000. 2023-2023</li> <li>USDA-AFRI. (PI: Weigel). The Resilient Cow: Next-Generation Selection Strategies using High-Frequency Phenotypes to Achieve Predictable Performance under Unpredictable Conditions. $650,000. 2023-2027</li> <li>USDA-AFRI (PI: Rosa). Gordon Research Conference and Seminar on Quantitative Genetics and Genomics. $35,050. 2023-2023</li> <li>USDA AFRI (PI: Xin). 2022 Conference on Applied Statistics in Agriculture and Natural Resources. $22,575. 2022-2022</li> <li>USDA-IDEAS (PI: Rosa). Integrating Enviromics, Genomics, and Machine Learning for Precision Breeding of Resilient Beef Cattle. $1,000,000. 2023-2027</li> <li>WARF-Accelerator Program (PI: Dorea). Digital Solutions for Optimal Management of Beef Cattle Feedyards. $146,824. 2022-2024</li> <li>AG2PI (PI: Dorea). Democratizing the access to artificial intelligence solutions for underrepresented and non-expert communities. $17,606. 2022-2023</li> <li>Dairy Innovation Hub: Short-term High Impacts (PI: Rosa). DairyTrader®: An Instantaneous Cull Dairy Cow Price Estimation to Help Farmers Decision. $39,754. 2022-2023</li> <li>Dairy Innovation Hub: Short-term High Impacts (PI: Laporta). Innovative methods to detect and protect against heat stress in Wisconsin dairy calves in a hutch environment. $45,095. 2022-2023</li> <li>Dairy Innovation Hub: Short-term High Impacts (PI: Dorea). Predicting meat cuts and carcass traits of beef-on dairy calves through 3D images of live animals. $49,055. 2022-2023</li> <li>Dairy Innovation Hub Capital Equipment (PI: Van Os). Multi-barn high-resolution camera system for behavioral monitoring of dairy heifers. $89,670. 2022-2023</li> <li>USDA-IDEAS (PI: Dorea). Development And Implementation Of An Economically Viable Computer Vision System To Monitor Metabolic Disorders In Dairy Cows. $1,000,000. 2023-2027</li> </ul>    <ol start="4"> <li>Mississippi State University:</li> </ol> Activities and projects: Use of Environmental Enrichment Devices on dairy cattle welfare and production. A Agri-Comfort brush was placed in a pen of 10 freestalled housed dairy cows for a 1-month period. Milk and saliva was collected 3 times per week at milking for cortisol analysis. Production records were recorded using our computing system to note changes. In progress (2 of 3 trials complete) Use of uncrewed ground vehicles to herd dairy cattle. A ClearPath warthog outfitted with a custom sensor suite was used to remotely move dairy calves in a randomized layout within a pastured. Robot was human controlled during trial to gain preliminary data towards autonomous real-time movement. This is a pilot study for a USDA NIFA grant. Three popular press publications have been published including MSSTATE, ClearPath, and Beef Magazine. Outcomes and accomplishments: All projects are in progress with analysis being performed this summer. Briefly, dairy cows used the brush multiple hours per day and had some displacement events. Cortisol measures will add physiological backing towards welfare aspects. Dairy calves were easily moved using the ground robot into various corners of the pasture with a limiting proximity measure of 15ft from fence line. Cattle were undisturbed with the presence of the robot in the pasture and often did not scurry away during movement events. Video analysis of movement patterns and distance will be calculated this summer. Funding: Collaborative grants between stations and members: Not Reported   <ol start="5"> <li>University of Kentucky:</li> </ol> Activities and projects: Not Reported Outcomes and accomplishments: Initial testing on the RFID dairy shower was completed and funding was found to complete further testing on a commercial farm.  Very clear differences in how cows interact with the shower. Proof of concept was published. Funding: Collaborative grants between stations and members: <ul> <li>Iowa State, UNL, UKY USDA NIFA CARE Swine ventilation</li> <li>University of Vermont, UKY- Delaval shower system</li> </ul>     <ol start="6"> <li>North Carolina State University:</li> </ol> Activities and projects: Five flocks of live broiler heat stress experiments were conducted under 2 dynamic air velocity (AV) treatments at NC State University in summers before pandemic (2017-2019) to investigate the effectiveness of wind chill application to mitigate heat stress. Data analysis and modeling work continued to 2022. In each flock, a total of 400 broilers were hatched and raised under similar conditions and then 264 birds uniform in body weight (BW) without leg defect were selected randomly to be placed in the 6 experimental chambers with 44 birds per chamber and 3 chambers per treatment at 28d. Broilers were fed grower, finisher, and withdrawal diets in pellet until 61d. The final stocking density was ≤40 kg/m2. AV of each chamber were automatically adjusted to target design according to environmental conditions and age. In Flocks 1-4, the core chamber thermal conditions were monitored by thermocouples and HOBO T/RH sensors, broiler behavior were monitored by GoPro cameras. In Flock 5, a fiber optic distributed temperature sensing system was installed to monitor inside and outside surface areas and the inlet and outlet air temperatures of the core chambers with high spatial (0.1m) and temporal (1s) resolution. Three surveillance cameras were added to each of the core chambers to collect videos for analysis of broiler behavioral responses under the heat stress. Broiler behavior and performance responses to the treatment were quantitatively analyzed, heat and moisture production rate of broilers from age 35-61 were calculated, litter quality, air emission and broiler leg health were investigated and retractable baffle design were modeled based upon the experimental data. Outcomes and accomplishments: This project advanced our knowledge about broilers’ response to air velocity treatment under heat stress condition; it also filled knowledge gaps in heat and moisture production of heavy broilers in 35-61d. Moreover, through a CFD modeling, the project identified the optimal retractable baffle design parameters that may be applied in existing broiler grow-out houses to enhance air velocities at birds’ height without compromising ventilation rates. The project was conducted by a multidisciplinary team with expertise in broiler production and welfare, housing environmental control, air quality, and waste management. This project produced (1) new knowledge on heat and moisture production of heavy broilers under different T/RH/AV combinations; (2) a cost-effective engineering solution for mitigating heat stress and air quality to improve heavy broiler performance and welfare. Knowledge obtained will help guide the poultry industry to improve engineering design of housing ventilation system for welfare friendly and sustainable production systems.  Funding: Collaborative grants between stations and members: <ul> <li>The project was funded by NIFA AFRI Award No. 2017-67021-26329. $499.998</li> </ul>   <ol start="7"> <li>University of Florida:</li> </ol>   Activities and projects: Our project focused on developing data analytic software for precision livestock farming systems using artificial intelligence to enhance animal management and health. We have created a user-friendly graphical user interface open-source software for processing and analyzing image data of pigs and cattle using computer vision and machine learning. This image analytic software utilizes the artificial intelligence (AI) resources provided by the AI partnership between the University of Florida and NVIDIA to effectively process high-dimensional image data and extract useful information for optimizing management decisions, therefore, improving animal management and health.   Outcomes and accomplishments: We built an online web application that allows users to perform object detection, segmentation, and three-dimensional visualization using image data to extract useful biometric measurements in a user-friendly manner. A manuscript introducing this web application is under preparation. The web application is deployed on the supercomputer (HiPerGator) at the University of Florida and will be made available to the public alongside the publication.   Funding: Collaborative grants between stations and members. Not Reported    <ol start="8"> <li>University of Nebraska-Lincoln:</li> </ol> Activities and projects: Not Reported Outcomes and accomplishments: Not Reported Funding: Collaborative grants between stations and members: University of Nebraska-Lincoln/University of Illinois Shi, Condotta, Brown-Brandl                <ul> <li>FACT-AI: Cyberinformatic Tools for Exploring and Validating Sow Posture and Piglet Activity. $500,000</li> </ul> Michigan State University, University of Nebraska-Lincoln, University of Wisconsin, Katholic University of Leuven Steibel, J., Brown-Brandl, T., Rosa, Siegford, Psota, Dorea, Morris, Benjamin, and Norton. <ul> <li>FACT-CIN: A Coordinated Innovation Network for Advancing Computer Vision in Precision Livestock Farming$1,000,000</li> </ul> Iowa State University, University of Nebraska-Lincoln, University of Kentucky, Ramirez, Hoff, Harmon, Brown-Brandl, T., Hayes, Rohrer <ul> <li>CARE: Modern pigs urgently need facilities with modern ventilation: Updating swine ventilation standards/guidelines. $299,850</li> </ul> Morris, D., Benjamin M., Brown-Brandl, T. Sharma, S.R. and Rohrer, G., <ul> <li>FACT-CIN: Swine health and growth monitoring</li> </ul>    <ol start="9"> <li>University of Illinois at Urbana-Champaign:</li> </ol> Activities and projects: In the 2022-2023 reporting year, the research station's participants made considerable academic contributions. They published 3 peer-reviewed articles, delivered 5 invited talks, presented 6 abstracts in conference proceedings, and successfully secured a total of $3,986,000 US dollars in funding. Outcomes and accomplishments: We investigated the use of computer vision for animal identification, behavior monitoring, estrus detection, Dry Matter Intake prediction, and social interactions. Furthermore, we studied an array of wearable and non-wearable sensors for the prediction of biomass intake for grazing animals. The station’s participants studied different artificial intelligence methods for phenotype prediction of livestock species (swine, beef cattle, and dairy cattle). The research grants awarded for the reporting period will address relevant PLF aspects such as automated grazing management and improved feed management, and behavior monitoring of beef and dairy cattle. Funding: Collaborative grants between stations and members:  <ul> <li>PI: G. Chowdhary. Co-PIs: O. Bolden-Tiller; I. Condotta; K. Guan; M. Khanna; D. Vasisht. Illinois Farming and Regenerative Management (I-FARM) testbed. $3,936,000. 2022-2025</li> </ul> <ul> <li>ANSC Matchstick. PI: F. Cardoso. Co-PI: I. Condotta. Development of precision dairy cow management models for feed intake and water usage. $50,000. 2023-2027</li> </ul>   <ol start="10"> <li>University of Vermont:</li> </ol> Activities and projects: Develop, validate, and integrate precision technology tools to improve the sustainability of livestock production. Also, to develop sustainable management systems based on reliable data. Develop on-farm decision and farm management based on data. Outcomes and accomplishments: This research done in the last period provides new insight into the use of precision dairy technologies, the examination of feeding behavior and activity development of calves, and the potential for compost bedded pack barns. Funding: Collaborative grants between stations and members:  Not Reported   <ol start="11"> <li>Texas A&M University:</li> </ol> Activities and projects: In the 2022-2023 reporting year, the research station's participants made considerable academic contributions. They published 7 peer-reviewed articles, presented 8 abstracts in conference proceedings, and successfully secured a total of $2,239,185 US dollars in funding. Outcomes and accomplishments: Our aim is to utilize PLF to improve health, welfare, and well-being in livestock (dairy and beef cattle). This calendar year, we investigated the social behavior and social mixing in beef cows using precision tools. We also investigated the use of machine learning models for the identification of lameness in dairy cows. We are developing databases designed to identify cows that are thermotolerant by linking weather data with productivity data. Funding: Collaborative grants between stations and members: <ul> <li>USDA-HSI (PI: Huang, Co-PI: Paudyal). Cross Training Future Workforce on Data-Driven Decision Support Tools for Digital Agriculture. $1,000,000. 2023-2027</li> <li>SSARE (PI: Gurrero, CO PI: Paudyal) Labor Demands of Southern Cattle-Dairy Farmers under H-2A Program’s Current Guidelines and Proposed Modifications. $300,000. 2023-2026</li> <li>United Sorghum checkoff. (PI: Pineiro, CoPI: Paudyal) Reducing leachate and preventing undesired fermentation of whole plant sorghum silage harvested with low dry matter content. $90,000. 2022-2024</li> <li>USDA AFRI (PI: Daigle, Co-PI: Paudyal). PARTNERSHIP: Optimizing dairy cattle welfare and productivity in a thermally challenging climate. $799,185. 2023-2027</li> <li>USDA AG2PI (PI: Daigle, Co-PI: Daigle. Creation of a database designed to promote dairy cow welfare using non-invasive phenotypic indicators of heat stress. $50,000. 2021-2023</li> </ul>   <ol start="12"> <li>North Dakota State University:</li> </ol>   Activities and projects: In the 2022-2023 reporting year, the research station's participants published one peer-reviewed publication and one conference presentation related to NC1211 objectives. Outcomes and accomplishments: The study’s objective was to determine whether technologies currently in use- or in various stages of development for use in swine production could characterize the social hierarchy within sow gestation group housing systems. We evaluated the use of infrared thermography, electronic sow feeder, and heart rate variability technologies. Both electronic sow feeding and heart rate variability technologies were able to characterize certain aspects of the sow social hierarchy. However, further work is needed to confirm these results. This study satisfies the first NC1211 objective (“Develop, validate, and evaluate sensor, instrumentation, controls and related hardware systems applied to livestock and poultry production”).  Funding: Collaborative grants between stations and members: Not Reported    <ol start="13"> <li>Purdue University </li> </ol> Activities and projects: The Erasmus lab at Purdue University is currently conducting research to assess activity levels of chickens and turkeys using accelerometers and exploring the use of artificial intelligence and computer-based models to track individual chickens within two cage-free housing systems.   Outcomes and accomplishments: The team is currently collecting data and has not published data yet. Cameras have been installed in two laying hen housing systems. A computer model has been developed to track chickens’ locations within each housing system. Custom maps of chickens’ housing systems have been developed. Funding: Collaborative grants between stations and members. <ul> <li>USDA-IDEAS (PI: Karcher). Agent Based Modeling to improve cage-free housing systems: What does the hen see? $1,000,000. 2022-2026</li> <li>USDA-AFRI (PI: Erasmus). Environmental and Genetic Strategies for Improving Bone Health and Walking Ability of Commercial Turkeys. $795,000. 2022-2025</li> </ul>

Publications

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 <ol> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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)</li> <li>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.</li> <li>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</li> <li>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. <a href="https://doi.org/10.3168/jds.2022-22043">https://doi.org/10.3168/jds.2022-22043</a></li> <li>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. <a href="https://doi.org/10.1093/jas/skac24">https://doi.org/10.1093/jas/skac24</a>.</li> <li>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. <a href="https://doi.org/10.3390/ani12080972">https://doi.org/10.3390/ani12080972</a></li> <li>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).</li> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> </ol>   <ol start="38"> <li>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</li> <li>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.</li> <li>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</li> <li>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)</li> <li>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)</li> <li>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)</li> <li>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)</li> <li>Conference Papers, Posters, and Presentations</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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. 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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).</li> <li>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).</li> <li>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).</li> <li>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).</li> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol>     Conference Papers, Posters, and Presentations <ol> <li>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.</li> <li>Yang, X., Y. Zhao. 2022. Applications of precision livestock farming technologies in broiler production. In: Proceedings of 10thEuropean Conference on Precision Livestock Farming 2022. Vienna, Austria.</li> <li>Tabler, T., S. Hawkins, Y. Zhao. 2022. Litter management. Proceedings of Midwest Poultry Federation Convention. Minneapolis, MN, USA.</li> <li>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.</li> <li>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</li> <li>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</li> <li>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</li> <li>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.</li> <li>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. </li> <li>Dorea, J. R. R. Artificial intelligence for livestock systems. 2022. Dairy Sci. Vol. 105, Suppl. 1.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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. <a href="https://doi.org/10.1093/jas/skac247.692">https://doi.org/10.1093/jas/skac247.692</a></li> <li>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. <a href="https://doi.org/10.1093/jas/skac247.452">https://doi.org/10.1093/jas/skac247.452</a></li> <li>Rosa, G. J. M. Big Data and Genomics for Improvement of Beef Cattle Production. 10th Goiás Genética, Goiania - Brazil, Aug 30-Sept 03, 2022.</li> <li>Rosa, G. J. M. Leveraging on High-throughput Phenotyping Technologies to Optimize Genetic Improvement in Livestock. 30thConference on Intelligent Systems for Molecular Biology (ISMB), Madison - WI, July 10-14, 2022.</li> <li>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.</li> <li>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.</li> <li>Rosa, G. J. M. Precision Livestock Farming Methods for Animal Health and Welfare. 18th International Conference on Production Diseases in Farm Animals (ICPD), Madison-WI, June 15-17, 2022.</li> <li>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.</li> <li>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.</li> <li>Dorea, J. R. R. Leveraging Artificial Intelligence in Livestock Systems. 2022 American Society of Animal Science - Midwest Section, Omaha-NE,March 15th, 2022</li> <li>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 3rd, 2022.</li> <li>Dorea, J. R. R. Machine Learning and Computer Vision for Agriculture. REDTalk. School of Computer, Data & Information Sciences. Madison-WI, April 21st , 2022.</li> <li>Dorea, J. R. R. Artificial intelligence for Livestock Systems. 2022 American Dairy Science Association annual meeting, Kansas City-MO, June 24th, 2022.</li> <li>Dorea, J. R. R. Machine Learning and Computer Vision for Livestock. ML+X Forum. American Family Insurance Data Science Institute, Madison-WI, October 4th, 2022.</li> <li>Dorea, J. R. R. Machine Learning and Computer Vision for Agriculture. Microsoft Research Summit. AI for Digital Agriculture. (online), November 9th, 2022.</li> <li>Dorea, J. R. R. Leveraging Artificial Intelligence in Livestock Systems. Dairy Cattle Reproduction Council (DCRC), Middleton-WI, November 15th, 2022.</li> <li>Dorea, J. R. R. Harnessing the Power of High-Throughput Phenotyping Technologies to Improve Dairy Management. GPS Dairy Conference. Minneapolis-MN, December 7th, 2022.</li> <li>Dorea, J. R. R. Leveraging Artificial Intelligence in Livestock Systems. Fundacao Getulio Vargas. Summer School on Data Science, Rio de Janeiro, Brazil, Jan 24th, 2023.</li> <li>J, M. Rosa. Statistical Modeling in Animal Breeding and Genetics. Sao Paulo State University (UNESP) - Jaboticabal, Brazil. November 07-11, 2022.</li> <li>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.</li> <li>J, M. Rosa. Regression and Classification Applied to Precision Agriculture, Brazilian Agricultural Research Corporation (EMBRAPA), Goiania - Brazil, Sept 05, 2022.</li> <li>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)</li> <li>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)</li> <li>J, M. Rosa. Regression and Classification Applied to Precision Agriculture, Utah State University, Logan - UT, May 16-19, 2022.</li> <li>McGill, M. Hayes, J. Jackson, and R. Coleman. 2023 Spatial and Temporal Analysis of Air Speeds in Equine Indoor Arenas. 2nd US PLF</li> <li>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. 2nd US PLF</li> <li>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) .</li> <li>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”</li> <li>Condotta, I. C. F. S. Midwest Swine Nutrition Conference, Danville-IN, September 8th, 2022– Title: “Technology to make nutrition implementation easier”</li> <li>Condotta, I.C. F. S. NSIF Annual Meeting, Kansas City-MO, November 1st, 2022 – Title: “Precision Livestock farming: Challenges and Opportunities.”</li> <li>Condotta, I.C. F. S. SAFER AG Workshop, Urbana-IL, November 3rd,2022 – Title: “Precision Management of Animals: the Future of Farming?”</li> <li>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”</li> <li>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”</li> <li>Condotta, I. C. F. S. UIUC International Agronomy Day, August 8th, 2023 - Title: Precision Management of Animals.</li> <li>Condotta, I. C. F. S. ASAS Annual International Meeting. July 2023 – Title: Swine precision nutrition: how computer vision can help?</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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</li> <li>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</li> <li>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.</li> <li>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,</li> </ol>    Datasets, Databases, Software <ol> <li>Transcripts from interviews with 12 swine industry stakeholders related to PLF perceptions and perceived uses and resulting Q method data.</li> <li>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: <a href="https://osf.io/vyh5j/">https://osf.io/vyh5j/</a></li> </ol>    Extension and Outreach Presentations, Workshops, etc. <ol> <li>Master Backyard Flock Program for UT/TSU Extension Agents. January 25th and 26th 2022</li> <li>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.</li> <li>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.</li> <li>Tabler, T. 2022. Troubleshooting LED lamp and light dimmer issues. Invited presentation to The Poultry Federation Symposium. Rogers, AR. August 25.</li> <li>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.</li> <li>Recordings of FACT CIN webinars are available online at MSU at <a href="https://www.canr.msu.edu/precision-agriculture/Precision-Livestock-Farming-Webinar-Series/index">https://www.canr.msu.edu/precision-agriculture/Precision-Livestock-Farming-Webinar-Series/index</a></li> <li>Dorea, J. R. R. UW-Science Expeditions: Artificial Intelligence for Animal Farming, 2022 (850 participants)</li> <li>Dorea, J. R. R. UW-Science Expeditions: Artificial Intelligence for Animal Farming, 2023 (1,218 participants)</li> <li>Dorea, J. R. R. Wisconsin Science Festival Expeditions: Artificial Intelligence for Animal Farming, 2022 (512 participants)</li> <li>Dorea, J. R. R. FFA Students. Artificial Intelligence for Livestock – Wisconsin Youth Program, 2022 (140 participants).</li> <li>Condotta, I. C. F. S. REU Summer Program: minority undergraduate student mentoring.</li> <li>Paudyal, S. Southwest Dairy Day: Precision tools in Dairy farms, 2023 (450 participants)</li> </ol>   Extension Publications, Trade Publications, Outreach Activities, etc. <ol> <li>Nguyen, X. D., and T. Tabler. 2023. Broiler litter management. University of Tennessee Institute of Agriculture Publ. No. W 1135.</li> <li>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/.</li> <li>Tabler, T., S. Hawkins, Y. Zhao, and P. Maharjan. 2022. Litter management. Proceedings 2022 Midwest Poultry Federation Convention. Minneapolis. March 22-24.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Tabler, T., M. L. Khaitsa, J. N. Jeckoniah, A. Odoi, and J. Wells. 2023. Agricultural technologies offer sustainable smallholder chicken production efficiency. UT Department of Animal Science Extension Publ. No. D 205.</li> <li>Thornton, T., and T. Tabler. 2023. Rethinking lighting for broiler chickens. UT Institute of Agriculture Publ. No. W 1146.</li> </ol>