Location: Genomics and Bioinformatics Research
Project Number: 6066-21310-006-001-A
Project Type: Cooperative Agreement
Start Date: Sep 2, 2018
End Date: Sep 1, 2023
Objective:
Agriculture research has moved into more complex levels of analysis with the ability to add additional variables when looking to improve agriculture productivity. Historically, researchers have focused on genotype by environment interactions, but new technology is allowing for the overlay of management. Thus, the new paradigm is genotype by environment by management. This calls for the generation of input via different technologies and methods [unmanned aircraft systems (UAS)/unmanned aerial vehicles (UAV), microbiome, crop genetics, climate data, animal waste and other items related to crop productivity], creation of a Big Data paradigms, development of new analysis techniques (e.g. geospatial statistics, bioinformatics) and the need for high-performance computational system.
To fully utilize these technologies, computational analysis of Big Data of vast types is needed. In turn, the techniques need access to high-performance computing (HPC) for complex data analysis, modeling and machine learning. Also, computational tools need to run optimally on an HPC environment and this takes specialized development. All of this needs to be developed and put into place with training for proper analysis. Clear examples are development and use of new statistical and image analysis tools for geo-referenced agricultural data related to agricultural operations.
Approach:
The USDA, has extensive experience researching agriculture productivity (especially with its Long Term Agroecosystem Research network), microbiome research related to animal waste and field studies, crop genetics, unmanned aerial vehicles, and complex agricultural systems. Mississippi State University (MSU) has expertise in agriculture research and computational aspects related that research especially in the area of bioinformatics and geospatial statistics.
MSU also has extensive experience with the establishment and operations of high-performance computing (HPC) and developing/altering computational software to run in an HPC environment. In this project, ARS and MSU will work together to develop, implement, and utilize HPC and will work together to analyze Big Data generated by ARS to improve agriculture productivity. Types of analysis include geospatial statistics, bioinformatics, modeling and machine learning (also known as artificial intelligence). These types of analysis will require a non-standard HPC configuration of high memory nodes, normal nodes, graphical processing unit (GPU) nodes and data storage.
