Project Impacts

By aligning the strengths of Mississippi State University and the USDA-Agricultural Research Service, the AAR-HPC project made a significant impact on some of today’s most urgent agriculture and food security challenges. Through expanded high-performance computing resources, expert-led training, and collaborative research, the project accelerated innovation in agricultural science, enhanced data-driven decision-making, and strengthened the capacity of both institutions to address complex issues affecting global food systems.

Project Impacts:

• Expanded high-performance computing capabilities at USDA-ARS through the installation of a new supercomputer, Atlas housed at Mississippi State University’s High-Performance Computing Collaboratory. Atlas a powerful Cray CS500 Linux cluster with 23,040 logical cores, 101 terabytes of RAM, and 8 NVIDIA V100 GPUs. With peak performance reaching 565 TeraFLOPS, Atlas enables researchers to process complex, data-intensive models at unprecedented speed and scale—pushing the frontiers of agricultural and environmental science. To learn more about Atlas visit: Atlas.


• Facilitated knowledge exchange by delivering a series of training sessions and workshops led by MSU faculty and staff in areas including geospatial technology, epidemiology, data science, and UAS. Since January 2021, over 700 USDA-ARS scientists and collaborators nationwide have participated in training sessions strengthening technical skills and fostering multi-disciplinary partnerships that support collaborative research and innovation in agricultural science. (insert training map/graphic below this statement (you can find them my scratch folder titled (NACA-HPC) – the multicolored box graphic should be smaller than the map, but neither have to be very big – just use your best judgement).


• Advanced collaborative research by enabling MSU faculty and USDA-ARS scientists to collaborate on projects that analyze large, complex datasets using high-performance computing. Numerous joint projects were conducted, resulting in peer-reviewed journal articles, oral and poster presentations, and symposium publications, highlighting the impact of this project on scientific discovery and innovation. Current Peer Reviewed Journal Articles include:

A G Firth, J P Brooks, M A Locke, D J Morin, A Brown, B H Baker, Soil bacterial community dynamics in plots managed with cover crops and no-till farming in the Lower Mississippi Alluvial Valley, USA, Journal of Applied Microbiology, Volume 134, Issue 2, February 2023, lxac051, https://doi.org/10.1093/jambio/lxac051

Firth, A.G.; Brooks, J.P.; Locke, M.A.; Morin, D.J.; Brown, A.; Baker, B.H. Dynamics of Soil Organic Carbon and CO2 Flux under Cover Crop and No-Till Management in Soybean Cropping Systems of the Mid-South (USA). Environments 2022, 9, 109. https://doi.org/10.3390/environments9090109

Ayoola, M.B.; Pillai, N.; Nanduri, B.; Rothrock, M.J.; Ramkumar, M. Preharvest Environmental and Management Drivers of Multidrug Resistance in Major Bacterial Zoonotic Pathogens in Pastured Poultry Flocks. Microorganisms 2022, 10, 1703. https://doi.org/10.3390/microorganisms10091703

Pillai, N.; Ramkumar, M.; Nanduri, B. Artificial Intelligence Models for Zoonotic Pathogens: A Survey. Microorganisms 2022, 10, 1911. https://doi.org/10.3390/microorganisms10101911

Ayoola, M.B., Pillai, N., Nanduri, B. et al. Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach. anim microbiome 5, 57 (2023). https://doi.org/10.1186/s42523-023-00260-w

Ayoola, M.B.; Das, A.R.; Krishnan, B.S.; Smith, D.R.; Nanduri, B.; Ramkumar, M. Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility. Microorganisms 2024, 12, 134. https://doi.org/10.3390/microorganisms12010134

Ram Das, A.; Pillai, N.; Nanduri, B.; Rothrock, M.J., Jr.; Ramkumar, M. Exploring Pathogen Presence Prediction in Pastured Poultry Farms through Transformer-Based Models and Attention Mechanism Explainability. Microorganisms 2024, 12, 1274. https://doi.org/10.3390/microorganisms12071274

Schultz, E. A., Ellison Neary, N., Boudreau, M. R., Street, G. M., Jones, L. R., Evans, K. O., & Iglay, R. B. (2024). On the move: Influence of animal movements on count error during drone surveys. Ecology and Evolution, 14(10), e70287. https://doi.org/10.1002/ece3.70287

Reynolds, J. H., Lewis, S., Johnson, M., & Thompson, P. (2025). Biologger attachment and retention in channel catfish (Ictalurus punctatus). Marine and Freshwater Behaviour and Physiology. Advance online publication. https://doi.org/10.1016/S0044-8486(25)00167-X

Jablonowski, D. P., Gonzalez-Meler, M. A., Rozema, A. D., Anderson, D., & White, E. P. (2024). In search of an optimal bio-logger epoch and device combination for assessing cattle behaviors in extensive rangeland environments. Methods in Ecology and Evolution, 9(100646). https://doi.org/10.1016/S2772-3755(24)00251-X

Ferreira, L. B., Martins, V. S., Venâncio Aires, U. R., Wijewardane, N., & Samiappan, S. (2025). FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10 m Sentinel 2 imagery. Computers and Electronics in Agriculture, 232, 110086. https://doi.org/10.1016/j.compag.2025.110086

Jablonowski, D. P., Gonzalez-Meler, M. A., Rozema, A. D., Anderson, D., & White, E. P. (2024). Combining animal interactions and habitat selection into models of movement: A PDE and step-selection framework. Wildlife Biology, e01211. https://doi.org/10.1002/wlb3.01211

Ellison, N., Potts, J. R., Boudreau, M. R., Börger, L., Strickland, B. K., & Street, G. M. (2024). Social interactions and habitat structure in understanding the dynamic space use of invasive wild pigs (Sus scrofa). Wildlife Biology, 2024(5), e01247. https://doi.org/10.1002/wlb3.01247