Big data analysis for sustainable agriculture

Authors: Delgado, Jorge; SHORT, NICHOLAS - Esri; Roberts, Daniel; Vandenberg, Bruce

Submitted to: Frontiers in Sustainable Food Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/27/2019
Publication Date: 7/16/2019
Citation: Delgado, J.A., Short, N.M., Roberts, D.P., Vandenberg, B.C. 2019. Big data analysis for sustainable agriculture. Frontiers in Sustainable Food Systems. https://doi.org/10.3389/fsufs.2019.00054.
DOI: https://doi.org/10.3389/fsufs.2019.00054

Interpretive Summary: Precision Agriculture emerged out of the 1980s because of the development of several key technologies as a way to improve margin through cost management of inputs while improving yield. Development of precision-conservation practices started in the early 2000s. New technologies like GPS, satellite imagery, and new methods of genetic modification in the green revolution have represented a disruption in agriculture not seen since the introduction of the first successful commercial tractor in the early 1900s and the green revolution that occurred between 1950 and the late 1960s. With the increasing impact of climate change, this paper has argued that the next revolution in precision agriculture will be driven by Sustainable Precision Agriculture and Environment (SPAE, also known as the 7Rs), which could leverage past technologies combined with Big Data analysis. Among other positive impacts, SPAE will contribute to increased yields and profits, increased adaptation to a changing climate, increased sustainability of agricultural systems, and increased sustainability outside of the field and across watersheds, reducing nutrient transport across watersheds and contributing to global sustainability. While the traditional definition of sustainable agriculture focused on incorporating new practices that deal with ecosystem services, this new, technology-focused sustainable agriculture transitions from a site-specific management focus to the notion of global sustainability. To accomplish this transition, we introduced the WebGIS framework as an organizing principle that connects local, site-specific data generators called smart farms to a regional and global view of agriculture that can support both the agriculture industry and policy makers in government. Automation and the use of AI, IoT, drones, robots, and Big Data serve as a basis for “Digital Twins”, which could allow for simulations of new ideas that can be tested virtually to determine environmental impact before implementation in the real world. In other words, constructing new practices in the virtual world will reduce the time to deploy new practices that lead to better environmental outcomes. If we are to feed 10 billion people by 2100 while preserving our environment, the next green revolution must incorporate the virtual world.

Technical Abstract: Humanity is confronted with the grand challenge of how to increase agricultural production to achieve food security during the 21st century and feed a population that is expected to grow to 10 billion people. This needs to be done while maintaining sustainable agricultural systems and simultaneously facing challenges such as a changing climate, depletion of water resources, and the potential for increased erosion and loss of productivity due to the occurrence of extreme weather events. Precision Agriculture emerged out of the advances in the 1980s because of the development of several key technologies like GPS and satellite imagery. This paper argues that with the increasing impact of climate change, the next revolution in precision agriculture and agriculture in general will be driven by Sustainable Precision Agriculture and Environment (SPAE, also known as the 7Rs), which could leverage past technologies combined with Big Data analysis. This new, technology-focused SPAE transitions from a site-specific management focus to the notion of global sustainability. To accomplish this transition, we introduced the WebGIS framework as an organizing principle that connects local, site-specific data generators called smart farms to a regional and global view of agriculture that can support both the agricultural industry and policymakers in government. This will help integrate databases located in networks of networks into a system of systems to achieve the needed SPAE management and connect field, watershed, national, and worldwide sustainability. Automation and the use of artificial intelligence (AI), internet of things (IoT), drones, robots, and Big Data serve as a basis for a global “Digital Twin”, which will contribute to the development of site-specific conservation and management practices that will increase incomes and global sustainability of agricultural systems.