Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/11/2020
Publication Date: 6/24/2020
Citation: Kharel, T.P., Ashworth, A.J., Buser, M.D., Owens, P.R. 2020. Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions. Agronomy Journal. 112:4498-4510. https://doi.org/10.1002/agj2.20285.
Interpretive Summary: Development and use of new technologies in farming systems is generating intensive volumes of data. These data are diverse in terms of their coverage (spatial and temporal representation) as well as their structure (e.g., frequency). Diversity and volume of these data requires appropriate methods for data formatting, handling, and processing in order to develop meaningful information. There is a growing need to effectively integrate, process, store, and analyze "Big Data" in a common platform so that real-time decisions can be made. We reviewed the literature on the current status of Big Data issues in agricultural systems. This review revealed that necessary software, tools, and infrastructure are available to integrate diverse datasets for web-based real-time decision making and output delivery. Adequate training and education for agricultural scientists on Big Data principles and data management is recommended for best use of the growing volume of agricultural data.
Technical Abstract: Big Data in agriculture is growing by advancements in metagenomics, increasing use of on-farm sensor technologies, as well as the increasing 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 increase in data synthesis across temporal and spatial scales. Therefore, in our data-rich world, what are lacking is a data management system across various spatial and temporal resolutions including workflows, interpretation methodology, and a delivery system 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 sources and offer potential solutions towards developing a platform for bridging component parts, encompassing multiple scales and disciplines to analyze system functionality for greater resiliency, which will provide a mechanism to understand alternatives and manage risk. Two datasets (for systematic literature reviews) were generated and reviewed using a bibliometrics method. Result shows that 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, Rackspace, and Qubole). Cloud-based computing platforms provide opportunities to extrapolate agricultural research results to a larger extent. Training and education of agricultural scientist on Big Data principles, database management, improved data visualization, as well as incentives for data sharing is recommended for the best use of Big Data in systems agriculture as these new research innovations emerge.