Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions. (24th June 2020)

• Record Type: Journal Article
• Authors: Kharel, Tulsi P.; Ashworth, Amanda J.; Owens, Phillip R.; Buser, Michael
• Is Part Of: Agronomy Journal. Volume 112:Number 5(2020)
• Page Start: 4498
• Page End: 4510
• Publication Date: 2020-06-24
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.1002/agj2.20285
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract: Big Data in agriculture is growing rapidly through advancements in metagenomics, precision agriculture, and on‐farm sensor technologies, as well as through increased capacity to collect, process, and store these data. Concurrent with 60% increases in food production demands by 2050 and the need for sustainable intensification, is the increased need for data synthesis across temporal and spatial scales. Therefore, in our data‐rich world, what is lacking is a data management system across spatial and temporal resolutions including workflows, interpretation methodology, and a delivery structure for identifying optimal systems for sustainable intensification or diversification. Consequently, the objective of this paper is to explore the current state of handling spatially and temporally disparate data and offer solutions for developing a platform for bridging component parts (encompassing multiple scales and disciplines) to analyze system functionality for greater resiliency, which may help manage risk. Two datasets were generated using bibliometrics (research articles from systematic literature reviews) evaluated trends and historical Big Data applications in agronomy. Results indicate research and industry progress is advancing towards web‐based real‐time output delivery systems using several well‐established Big Data handling platforms (e.g., Amazon Web Service, Google Cloud, Microsoft Azure), which are not yet widely used by or designed for agronomic researchers. Cloud‐based computing may provide opportunities to extrapolate agricultural research results across larger scales. Authors suggest training and educating agricultural practitioners on Big Data principles, database management, improved data visualization, as well as incentives for data sharing for optimizing Big Data in systems agriculture as these research innovations emerge.