Emerging nutrient management databases and networks of networks will have broad applicability in future machine learning and artificial intelligence applications in soil and water conservation

Delgado, Jorge A.; Vandenberg, Bruce; Neer, Donna; D'Adamo, Robert

doi:10.2489/jswc.74.6.113a

Cited by 10 publications

(9 citation statements)

References 38 publications

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“…Hatfield and Walthall (2015) suggested a similar database for understanding productivity factors and reducing the yield gap. The Agricultural Collaborative Research Outcome System (AgCROS) is an ongoing effort by the USDA‐Agricultural Research Service (ARS) for an integrated data repository that connects diverse databases that are currently available, such as: Long‐Term Agroecosystem Research (LTAR), Greenhouse gas Reduction through Agricultural Carbon Enhancement Network (GRACEnet), Nutrient Uptake and Outcome Network (NUOnet), as well as capacity to add and streamline future systems‐level data integration (Delgado, Vandenberg, Neer, & D'Adamo, 2019a, 2019b). AgCROS is a SQL database that resides on a Microsoft Azure cloud platform and uses automated quality assurance/quality control to process data and directly interface with Esri's ArcGIS Enterprise for viewing and retrieval of data (Figure 4).…”

Section: Resultsmentioning

confidence: 99%

“…Deriving insights from agricultural Big Data is possible through combining extensive results conducted in diverse biophysical environments in platforms such as AgCROS. Delgado et al (2019a) explain the potential of AgCROS to reduce N 2 O emission by choosing right conservation practices (with their coefficients to reduce N losses or N 2 O emissions) per field, thereby reducing total N 2 O emissions from fertilizer/manure in the United States by 30-40%. Combining these data into a central searchable and linked repository will require researchers' willingness to share data, knowledge, and skillsets to work with Big Data infrastructure, as well as time and effort to make necessary changes in data structure of data already collected.…”

Section: Issues and Prospective Solutions For Handling Big Datamentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Issues and Prospective Solutions For Handling Big Datamentioning

confidence: 99%

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

et al. 2020

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“…Pathak et al [185] reviewed some of the decision support systems in use in Asian countries and pointed out how their widespread adoption will lead to improved fertilizer management and reduced water pollution. No doubt with the rapid progress of even more sophisticated machine learning and artificial intelligence technologies, as developed in the USA, their application to precision farming will make major contributions towards the same goal [186].…”

Section: Key Issues For Researchmentioning

confidence: 99%

Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem

2021

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Nitrate pollution of ground and surface water bodies all over the world is generally linked with continually increasing global fertilizer nitrogen (N) use. But after 1990, with more fertilizer N consumption in developing countries especially in East and South Asia than in the industrialized nations in North America and Europe, nitrate pollution of freshwaters is now increasingly becoming a pervasive global problem. In this review it has been attempted to review the research information generated during the last two decades from all over the world on different aspects of nitrate pollution of natural water bodies. It is now evident that not more than 50% of the fertilizer N is directly used by the crops to which it is applied. While a small portion may directly leach down and may reach ground and surface water bodies, a large proportion ends up in the soil organic N pool from where N is mineralized and is taken up by plants and/or lost via leaching during several decades. Present trends of nitrate pollution of freshwaters, therefore, reflect legacies of current and past applications of fertilizers and manures. Tools such as simulation models and the natural variation in the stable isotopes of N and oxygen are now being extensively used to study the contribution of fertilizers and other sources to nitrate enrichment of freshwaters. Impacts of agricultural stewardship measures are being assessed and nitrate enrichment of water bodies is being managed using modern digital models and frameworks. Improved water and fertilizer management in agroecosystems can reduce the contribution of fertilizers to nitrate pollution of water bodies but a host of factors determine the magnitude. Future research needs are also considered.

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“…In the past and current research environment, much of the agricultural research data and analysis output collected across large geographic areas and over multiple years has been located within desktops and file cabinets. Consolidation and aggregation of such data and visualization in real time can aid research focused on complex climate-soil-plant interactions by extrapolation of past research results for improved sustainable production, mitigation of climate stress, and natural resource management [9,10]. The AgCROS is connected with dashboards (Figure 1) to help users quickly interrogate and visualize data to find answers to their questions.…”

Section: Decision Support Informatics Platformmentioning

confidence: 99%

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

Kharel

Ashworth

Owens

2022

Data

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Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

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Emerging nutrient management databases and networks of networks will have broad applicability in future machine learning and artificial intelligence applications in soil and water conservation

Cited by 10 publications

References 38 publications

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

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

Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem

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

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