Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

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.

The Partnerships for Data Innovations (PDI): Facilitating data stewardship and catalyzing research engagement in the digital age

“…Moreover, we posit that to fully harness the potential of data-driven transformation of agriculture, researchers and scientific institutions must shift away from the modus operandi that (a) data management "stops" after data collection and analysis, (b) data management is an afterthought or inconvenience, and (c) data ownership resides with the individual. Rather, a new paradigm is needed, a paradigm that views data stewardship as a fundamental scientific responsibility; that ensures data, especially when publicly funded, are shared for the broader common good (Kharel et al, 2020); and that embraces the multiplicative ability of shared data and technology to accelerate agricultural innovation.…”

“…Population growth, demographic shifts, accelerated eutrophication, and climate change persist as wicked problems threatening the security of the food-water-energy nexus (Chaubey et al, 2016;Harmel et al, 2020;United Nations, 2018;WWAP, 2014). Successful adaptation to, and mitigation of, these challenges will necessitate technology-driven innovations that harness and apply abundant data that were previously unfathomable in their current availability, precision, scale, and volume (Kharel et al, 2020). The fundamental importance of available and accurate data to solve complex problems is well established.…”

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

“…In a 2020 keyword citation burst analysis on ecological modernization approach by Rocchi et al, “sustainable agriculture” consistently shows very high citation burst in the past and current literature [ 6 ]. Due to its high popularity among researchers the more recently published bibliometrics studies conduct in the realm of sustainable agriculture explore the interactions between sustainable agri-food systems with the economy, society, and policy making [ 7 ], agriculture systems modernization approach [ 6 ], big data in sustainable agriculture [ 8 ]. In terms of Artificial Intelligence in agriculture, recent articles provide bibliometric analysis of the crossover between remote sensing technologies and agriculture [ 9 ], advanced information and communication technology in agriculture [ 10 ], global trends in precision agriculture technology [ 11 ].…”

Linking and Sharing Technology: Partnerships for Data Innovations for Management of Agricultural Big Data

“…Research now produces larger volumes of data, owing to progress in "omics", precision agriculture, and on-farm sensor advancements, which has resulted in a concurrent need for an advanced capacity to collect, process, and store these data. This has also resulted in more complex and integrated analyses, including multi-location, multi-discipline collaborations [1]. "Big Data", while not formally quantified, is generally described by the "volume, velocity, and variety of data collected being big and complex enough to make them difficult to process, manage, and handle using conventional analytical tools and techniques" [1,2].…”