Performing Department
Chemistry & Physics
Non Technical Summary
Finding novel genetic targets to improve crop yields and maintain quality is increasingly important as plant breeders race to keep up increasingly unpredictable climates, rising costs of agricultural inputs, and the need to conserve natural resources. This project focuses on two themes towards accelerating plant breeding efforts: 1) development of new approaches for characterizing plant traits from high resolution drone imagery and 2) leveraging machine learning and artificial intelligence to identify genes important for improved crop performance under low input environments. Our proposed research will determine whether genome associations with multiple images, instead of just a single image, could help reveal novel targets for genetic improvement of rice, a critically important staple food. Rice production can have outsized environmental impacts due to water-intensive cultivation systems and greenhouse gas emissions, and so development of varieties that perform well under low-input conditions is a top priority. Additionally, we will determine whether genetic variants associated with improved nitrogen and water use efficiency could be determined from existing ecological and genomic datasets. Our model public-private partnership contributes to the National Artificial Intelligence Initiative through its focus on increasing crop yields while conserving natural resources, and contributes to the design and validation of novel methods for analysis and integration of big agricultural data, one of the top priorities for the Data Science for Food and Agricultural Systems Program Area.Development of rice varieties with improved performance under low water input management conditions will help to conserve freshwater resources in the face of increasing water scarcity. Additionally, reducing fertilizer inputs through development of nitrogen-efficient rice varieties and precision management tools will reduce nutrient pollution and decrease greenhouse gas emissions in water-limited rice production systems. Reduced input costs for irrigation and fertilizer will translate to improved economic viability for rice production, as costs for nitrogen and water inputs continue to rise. Beyond rice, our approach to identifying genes associated with reduced inputs in modern agricultural systems could be broadly applicable to other crops and systems.
Animal Health Component
20%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Goals / Objectives
The major goal of the proposed research is to accelerate development and deployment of improved crop varieties. To enable more rapid and accurate identification of genetic variants underlying agronomic traits of interest, we will develop improved computational frameworks for 1) characterizing high-dimensional plant phenotypes and 2) leveraging existing germplasm, genomic, and environmental data resources for crop landraces. As an example system, this project focuses specifically on nitrogen- and water-use efficiency in rice, a globally important crop that has outsized environmental impacts due to water-intensive cultivation systems and greenhouse gas emissions.First, we will develop a new analytical framework for characterizing dynamic phenotypes from multispectral, multitemporal imagery (Aim 1). Genome-wide association studies (GWAS) in breeding programs may consider individual trait measurements from images at specific timepoints (e.g. leaf area, plant height), excluding potentially useful information reflecting connections across several images over time. The objectives of Aim 1 include:Testing the use of manifold learning techniques for capturing phenotype trajectories, using drone images from two years of field experiments with rice,Comparing models trained on derived features vs. models trained directly on image embeddings,Evaluating phenotype trajectory offsets for nitrogen responsive vs. unresponsive rice cultivars as a way to capture variation in environmental tolerance, andComparing accuracy of trajectory-based models vs. models based on individual timepoints for predicting agronomic characteristics including yield, days to 50% heading, etc.Second, we will evaluate use of improved genotype-environment association (GEA) methods to identify genomic variants associated with rice landrace adaptation to water-limited environments, and determine whether these are useful targets for modern plant breeding efforts. Objectives for Aim 2 include:Developing a novel GEA method for identifying genetic variants associated with rice landrace adaptation to upland and rainfed lowland irrigation systems, both expected to impose strong selection for increased water use efficiency,Training a suite of genomic prediction models using data from published GWAS. We will train 'baseline' models representing the current state-of-the-art for genomic prediction and compare them with models trained using subsets of markers identified in (1),Identifying a subset of rice accessions from the USDA Mini-Core Collection with the largest differences in predicted phenotypes according to models in (2),Experimental testing of rice accessions identified in (3) to determine whether environment-associated genetic variants improve trait prediction under low input conditions.
Project Methods
General scientific methods for the project include data analysis/modeling techniques standard in the data science and machine learning communities as well as conducting greenhouse experiments with rice and collecting high-throughput plant phenotype data.Efforts to be used include scientific research conducted by the project team, hands-on experiential learning opportunities for the students and postdoctoral scientist, presentations to the scientific community and the public through formal and informal educational programs, and presentations and networking with breeders and growers at local and national conferences and events.For project evaluation, key milestones and indicators of success include 1) career development and success of trainees involved in the project, 2) submission/success of subsequent grant proposals based on the work supported by this Seed Grant by the project leaders, and 3) publication of two peer-reviewed manuscripts describing the outcomes of the supported research.