RII Track-2 FEC: Building Field-Based Ecophysiological Genome-to-Phenome Prediction

Kansas State University, Manhattan KS

Investigators

Abstract

Nontechnical Description: It is widely agreed that agricultural crop production is not growing to meet the needs of the increasing human population. This project brings together researchers from the Kansas State University, Oklahoma State University, and Langston University (a Historically Black University), to develop a new way to model and predict important crop production traits in wheat. One of the greatest challenges of current crop trait prediction is that it falls in an underpopulated borderland between plant physiology, biological engineering, genetics, computational biology, mathematics, statistics, and computer science. Therefore, to bridge this gap, mathematical models will be produced that combine both observational data using Unmanned Aerial Vehicles and robots, and genetics data. These new models are expected to simplify crop modeling for farmers, and will aid in farm management, and can easily be applied to other crops and in other environments. Many additional benefits will also accrue. First, commonalities between these mathematical models will mean that results will readily transfer to many other crops. Moreover, the benefits of combining genetic and observational data in this way to predict crop traits will aid on-farm crop management, enhancing food security. Educational programs for undergraduates, graduate students, and faculty in these disciplines will enlarge a globally competitive workforce. Involvement of key corporate partners will also speed research transfer to the private sector both directly and by creating a market for project trainees. Finally, the sensing/measuring devices to be bought or built will enhance the ability of partners to conduct a wide range of related, data-intensive research. Technical Description: It is widely agreed that agricultural crop production is not on track to meet the production doubling needed by 2050 for humanity to avoid major food security disruption. Farmers need genetically-informed analytics to predict the outcomes of management options amongst which they may choose and apply in their unique field environments. This project brings together researchers from the sity of Kansas State University, Oklahoma State University, and Langston University (a Historically Black University), and presents new genetically- and physiologically-informed proof-of-concept wheat physiologically-based crop models (CMs). These CMs will link to state-of-the-art field monitoring technologies with genomic data, thus rebalancing direct monitoring vs. indirect model calculation. The data will include: (1) airborne imagery to extract morphological features, canopy temperatures, and light interception. (2) Multivariate soil profile data will be collected by robots at 2-30 cm (horizontal/vertical) and three-day temporal resolution. (3) Gene expression data on selected double haploid lines over 64 combinations of locations, dates, and years will aid in model building. (4) CM and quantitative genetics integration will also be aided by expanding the number of genotyped wheat lines within the Kansas and Oklahoma breeding programs. Such large data sets ordinarily pose computational challenges for models as complex as CMs. In contrast to extant CMs, the new models will efficiently combine differential equation solvers, maximum entropy and Bayesian methods, and high-performance computing. The results will be methods able to predict the traits of novel genotypes in novel environments not used to construct the models. Many additional benefits will also accrue. First, commonalities between CMs will mean that results will readily transfer to many other crops. Moreover, increased genome to phenome prediction accuracy will aid on-farm crop management, enhancing food security. Educational programs for undergraduates, graduate students, and faculty in these disciplines will create and enlarge a globally competitive workforce. Involving key corporate partners will also speed research transfer directly and by creating a market for project trainees. Finally, the sensing/measuring devices to be bought or built will enhance partner ability to conduct a wide range of related, data-intensive research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.