The world population is growing and climate is changing. Agricultural systems are challenged to scale and adapt to meet the increasing demands for food, feed, fiber, and fuel produced on shrinking arable land and with diminishing water supplies.
In the Predictive Plant Phenomics (P3) Program, we have the right people and the right physical and cultural environment necessary to foster the interdisciplinary research needed to solve these looming problems. Groups of plant biologists, engineers, and computational scientists are performing research focused in predictive phenomics, looking at massive amounts of data to create systems and models to predict plant yield. Iowa State engineers are designing robots, sensors, and other advanced equipment that monitor plants in the field and the lab. Data scientists are crunching and analyzing the data collected. These collaborations are developing the agricultural products and computational solutions to meet the emerging needs.
P3 students and faculty share their research through many avenues: conferences held on campus, national association conferences, international association conferences, symposia, and through peer-reviewed journal articles and other publications.
Here's a selection of recent talks and articles from P3 students and faculty (highlighted in bold):
- Ou, S., J. Liu, K.M. Chougule, A. Fungtammasan, A.S. Seetharam, J. Stein, V. Llaca, N. Manchanda, A.M. Gilbert, X. Wei, C. Chin, D.E. Hufnagel, S. Pedersen, S. Snodgrass, K. Fengler, M. Woodhouse, B.P. Walenz, S. Koren, A.M. Phillippy, B. Hannigan, R.K. Dawe, C.N. Hirsch, M.B. Hufford, Doreen Ware. 2020. Effect of sequence depth and length in long-read assembly of the maize inbred NC358. Nature Communications In Press https://doi.org/10.1038/s41467-020-16037-7
- Schuyler D. Smith, P Colgan, F Yang, EL Rieke, ML Soupir, TB Moorman, HK Allen, A Howe. 2019. Investigating the dispersal of antibiotic resistance associated genes from manure application to soil and drainage waters in simulated agricultural farmland systems. PLOS One. journal.pone.0222470
- Bao, Y., Zarecor, S., Shah, D., Tuel, T., Campbell, D., Chapman, A.V.E., Imberti, D., Keikhaefer, D., Imberti, H., Lübberstedt, T., Yin, Y., Nettleton, D., Lawrence-Dill, C.J., Whitham, S., Tang, L., and Howell, S.H. 2019. Assessing Plant Performance in the Enviratron. Plant Methods, 15: 117. https://doi.org/10.1186/s13007-019-0504-y
- Carolyn J Lawrence-Dill, Patrick S. Schnable, and N. M. Springer. 2019. Idea Factory: the Maize Genomes to Fields Initiative. Crop Science. 59:1406-1410. doi:10.2135/cropsci2019.02.0071
- Cassandra Winn. "Crop Growth Model Calibration and Simulation of 12 Maize Hybrids". Corn Breeding Research Annual Meeting, St. Louis, MO. 13 March 2019
- Clayton Carley, "The Soybean Nodule Acquisition Program: Solving a Phenomics Challenge in a SNAP using Machine Learning Solutions." 5th International Plant Phenotyping Symposium, Adelaide, Australia. October 2-5, 2018 [click here to view a short clip of his talk]
- Patrick Schnable, “The Potential of Predictive Plant Phenotyping to Address (some of) the Challenges Facing Crop Production.” International Maize and Wheat Improvement Center (CIMMYT). Mexico. 6 September 2018
- Ian Braun, J Balhoff, TZ Berardini, L Cooper, G Gkoutos, L Harper, E Huala, P Jaiswal, T Kazic, H Lapp, JA Macklin, CD Specht, T Vision, RL Walls, CJ Lawrence-Dill (2018). 'Computable' phenotypes enable comparative and predictive phenomics among plant species across domains of life. Application of Semantic Technologies in Biodiversity Science. Studies on the Semantic Web 33. Thessen, AE. IOS Press/AKA Verlag. ISBN: 978-1-61499-853-2.
- Carolyn J. Lawrence-Dill, Ted J Heindel, Patrick S Schnable, SJ Strong, J Wittrock, ME Losch, Julie A Dickerson (2018). Transdisciplinary Graduate Training in Predictive Plant Phenomics. Agronomy. 8 (5), 73. DOI: 10.3390/agronomy8050073
- James P McNellie, J Chen, X Li & Jianming Yu. (2018). Genetic Mapping of Foliar and Tassel Heat Stress Tolerance in Maize. Crop Science, 58(6), 2484-2493.