Brief Summary of Minutes of Annual Meeting

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

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:

1. University of Tennessee:

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:

• Precision Livestock Farming: Investing for Future Success. UT AgResearch, $200,000, 2022-2023
• Automated welfare and behavior detection via vision-based PLF system in broilers, USDA-NIFA, $1,000,000, 2022-2026
• Improving health and well-being in broiler chickens through environmental management, USDA Cooperative Agreement, $164,900, 2020-2022.
• Computer vision characterization of respiration as an indicator of cattle health. USDA-NIFA, $300,000, 2022 - 2023.
• A Greenfeed System for Quantifying Emissions and Improving Metabolic Efficiency in Beef Cattle. UT AgResearch, $112,410, 2022.
• 1/1/2022 – _12/31/2022. Addition of Two Feed Bins to Increase Research Capacity of Automatic Milking System. UT AgResearch, $44,000, 2022.
• Empowering U.S. broiler production for transformation and sustainability. USDA-NIFA. $249,000, 2019-2024.

2. Michigan State University:

Activities and projects:

1. 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.
2. Morris led work on a computer vision approach to detect eggs laid in the litter.

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. https://www.canr.msu.edu/precision-agriculture/Precision-Livestock-Farming-Webinar-Series/

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

• 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. 
• 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. 

One new federal grant with collaborators from other stations.

• 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/ 

Two new internal grants with MSU personnel involved in NC1211

• 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.
• 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.

3. University of Wisconsin-Madison:

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:

• NIH (PI: Rosa). Gordon Research Conference and Seminar on Quantitative Genetics and Genomics. $10,000. 2023-2023
• 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
• USDA-AFRI (PI: Rosa). Gordon Research Conference and Seminar on Quantitative Genetics and Genomics. $35,050. 2023-2023
• USDA AFRI (PI: Xin). 2022 Conference on Applied Statistics in Agriculture and Natural Resources. $22,575. 2022-2022
• USDA-IDEAS (PI: Rosa). Integrating Enviromics, Genomics, and Machine Learning for Precision Breeding of Resilient Beef Cattle. $1,000,000. 2023-2027
• WARF-Accelerator Program (PI: Dorea). Digital Solutions for Optimal Management of Beef Cattle Feedyards. $146,824. 2022-2024
• AG2PI (PI: Dorea). Democratizing the access to artificial intelligence solutions for underrepresented and non-expert communities. $17,606. 2022-2023
• 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
• 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
• 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
• Dairy Innovation Hub Capital Equipment (PI: Van Os). Multi-barn high-resolution camera system for behavioral monitoring of dairy heifers. $89,670. 2022-2023
• 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

4. Mississippi State University:

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

5. University of Kentucky:

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:

• Iowa State, UNL, UKY USDA NIFA CARE Swine ventilation
• University of Vermont, UKY- Delaval shower system

6. North Carolina State University:

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:

• The project was funded by NIFA AFRI Award No. 2017-67021-26329. $499.998

7. University of Florida:

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

8. University of Nebraska-Lincoln:

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               

• FACT-AI: Cyberinformatic Tools for Exploring and Validating Sow Posture and Piglet Activity. $500,000

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.

• FACT-CIN: A Coordinated Innovation Network for Advancing Computer Vision in Precision Livestock Farming$1,000,000

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. $299,850

Morris, D., Benjamin M., Brown-Brandl, T. Sharma, S.R. and Rohrer, G.,

• FACT-CIN: Swine health and growth monitoring

9. University of Illinois at Urbana-Champaign:

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: 

• 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

• 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

10. University of Vermont:

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

11. Texas A&M University:

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:

• 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
• 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
• 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
• USDA AFRI (PI: Daigle, Co-PI: Paudyal). PARTNERSHIP: Optimizing dairy cattle welfare and productivity in a thermally challenging climate. $799,185. 2023-2027
• 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

12. North Dakota State University:

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

13. Purdue University

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.

• USDA-IDEAS (PI: Karcher). Agent Based Modeling to improve cage-free housing systems: What does the hen see? $1,000,000. 2022-2026
• USDA-AFRI (PI: Erasmus). Environmental and Genetic Strategies for Improving Bone Health and Walking Ability of Commercial Turkeys. $795,000. 2022-2025

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.
10. 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.
11. Thornton, T., and T. Tabler. 2023. Rethinking lighting for broiler chickens. UT Institute of Agriculture Publ. No. W 1146.