Abstract
The grand challenge confronting agriculture is the development of technologies for sustainable intensification of crop production systems to feed the estimated future human population of 9 to 10 billion. It is thought that crop production must be increased by 60 to 100% by the year 2050 to meet these nutritional needs. Crop production systems that yield more food of higher nutritional content are needed that at the same time have a diminished impact on the environment. Prior agricultural intensification was through substantial use of fertilizer, pesticides, and irrigation all at significant environmental cost. As a part of Sustainable Agriculture, next-generation, on cropping systems that couple biologically based technologies (plant-beneficial microbes, cover crops) and precision agriculture need to be developed to decrease fertilizer, pesticide, and water inputs. Over the past two decades, Information Technology has been the disruptive force in industries by driving out market inefficiencies through automation and better decision support tools that require the inclusion of both the citizens and consumers in the process. Like all industries, Agriculture has not been immune to the constant disruptions over the past century. However, recent advances in the computing infrastructure, sensor technology, big data and advanced algorithms ( suggest that a major disruption or paradigm shift is on the horizon, leading to opportunities for sustainable agriculture entering the mainstream. Specifically, solutions based on these new technologies will be needed for mass transfer of genomic and other genetic data for development of these advanced crop cultivars, and for the management of agronomic data for the development of these next-generation production systems. Geospatial solutions based on imagery, IoT, AI, mobile data collection, etc. will be critical for the operation of precision agriculture systems where intelligent application of resource inputs are applied at precise geo-specific field locations based on crop need. Finally, solutions will be needed to allow immediate feedback from digital farm communities regarding the performance of these new cropping systems; speeding their development. Here we describe a “system of systems” approach to building a scientific network that integrates the scientific and farming communities, based on a Geoinformatics cloud framework.
Highlights
The 21st century presents formidable challenges to sustainability that humanity will have to confront
Big Data Analysis for Sustainable Agriculture are occurring at a faster rate than humanity is addressing them and that humanity will be impacted by sea level rise and more extreme weather (United Nations, 2018)
The rest of this paper argues that big data analytics is at the core of combining precision and sustainability into an earlier notion of Sustainable Agriculture (Berry et al, 2003, 2005; Bongiovanni and Lowenberg-DeBoer, 2004; Delgado and Berry, 2008)
Summary
The 21st century presents formidable challenges to sustainability that humanity will have to confront. Acting in the role of an early adopter of technology (Moore, 1991), today’s farmer will have to quickly learn how these new technologies can be used to help make decisions about how to increase profits by increasing yields or implementing precision management and conservation practices that could produce sustainability benefits that could potentially be traded in ecosystem service markets. The use of this new technology for SPAE will be dominated in the future by analytic techniques and AI to help provide solutions to complex problems and decisions. From a SPAE perspective, simulation models like the aforementioned DNDC, COMET-Farm, CEEOT, and/or the NTT models are among some of the available tools that could be used to form the basis of ensuring the least impact on environment without driving up costs
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