Location: National Programs
Project Number: 0500-00093-001-001-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 29, 2019
End Date: Sep 28, 2024
Objective:
It is necessary to increase the productivity of USA agriculture in novel ways that improves the quality of the product, improves food safety, and increases output while also trying to reduce inputs. Since the development of the modern desktop computer, many agricultural research efforts have taken advantage of these relatively inexpensive and nearby computational workhorses. However, agricultural research has moved into the era of “big data” and there is a rapidly growing need for computational power, a reeducated workforce, and streamlined methods to properly handle this wave of data in an integrated way across multiple disciplines. Data generation is rapidly increasing due to changes in DNA sequencing, unmanned aircraft systems (UAS)/unmanned aerial vehicles (UAV), and other changes in the fields of microbiome, crop genetics, climate data, animal waste, and other areas related to agricultural productivity and food safety.
Agricultural research must move into more complex levels of analysis with the ability to synthesize and integrate multiple variables or data types when looking to improve agricultural productivity. Thus, a paradigm shift in agricultural research and analysis techniques is needed.
Approach:
ARS and Iowa State University (ISU), have extensive experience in various aspects of agricultural research. ARS has the unique ability to generate a broad array of agricultural data relevant to most agricultural systems across the USA, such as its Long-Term Agroecosystem Research network. ISU has an extensive experience with high performance computing (HPC) research and with managing an HPC environment. In this project, ARS and ISU will work together to develop, implement, and utilize analysis procedures in an HPC environment. ISU and ARS will work together to analyze Big Data maintained and generated by ARS to improve agricultural productivity. Types of analysis include geospatial statistics, bioinformatics, modeling, and AI/machine and deep learning. ISU will join in ARS’ Ag100Pest project to generate the genomes for 100 pests. Both organizations will work together to develop SOP work books to help agricultural researchers operate efficiently in an HPC environment. This will be one form of a training method to help in the development of workshops and working groups related to the improvement of agricultural research in new analysis methods. ISU will support ARS’ efforts to better utilize HPC resources by helping with education and training, development of efficient software scripts, and general guidance of HPC operations. The two organizations will work together to improve agricultural research by utilizing and improving HPC-related analytical methods that will be made available to a broad range of ARS researchers.
