Exploring a Dynamic Soil Information System Proceedings of a Workshop (2021) / Chapter Skim
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7 Breakout Sessions
7 Breakout Sessions
Pages 55-66
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From page 55... ...
On the workshop's third day, the first session was devoted to summary reports from the breakout groups delivered by the moderators for each of the three topics. Charles Rice from Kansas State University synthesized the discussions on measurements, sampling, and archiving; Ranveer Chandra from Microsoft Azure Global summarized the session on collection and curation; and Bruno Basso from Michigan State University described the session on data analysis and models.
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From page 56... ...
Other physical characteristics were aggregate stability or some measure of soil structure, bulk density, water content, and color and aeration status. Rice observed that, unlike other physical characteristics that can be measured from archived soil samples, bulk density has to be measured on site.
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From page 57... ...
A participant suggested taking advantage of the N ational Ecological Observatory Network, the Long-Term Ecological Research, and the Long-Term Agroecosystem Research sites, and perhaps also have each land grant university in the United States dedicate a few acres to a management practice or a land use practice to serve as a reference site. Participants on Slack noted that existing rangeland and forest monitoring networks could be used as well.
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From page 58... ...
A second topic was how to best find the data for a dynamic soil information system, given the many different potential data sources. One approach might be to leverage the semantic web, using knowledge graphs to link similar data, so that having one source of data leads to many others.
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From page 59... ...
'" -- and that the AI methods are applied ethically. DATA ANALYSIS AND MODELS The breakout group discussions on data analysis and models considered the promise of the current machine learning and AI methods for working with dynamic soil information systems.
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From page 60... ...
Modeling soil organic carbon, nutrient, and water dynamics requires the proper simulation of crop yields, biomass, roots partitioning, uptake, water balance, and other factors, he said. The variability in crop history from yield maps and remote sensing is captured via yield stability maps and thermal stability maps.
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From page 61... ...
• The resolution of data needs to be improved at both the spatial and temporal scales. DISCUSSION A lengthy discussion followed the reports from the breakout sessions.
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From page 62... ...
Chandra agreed that more work is needed in the area of causal inference in machine learning, but that as it develops, there could be interest in applications for soil data. Colin Averill of ETH Zürich commented on the importance of learning more about the biology of the systems.
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From page 63... ...
Mark Bradford asked how the field should interpret microbial indicators and enzymes. For example, in agricultural soils, an increase in enzymes is perceived as good because that indicates increased microbial activity.
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From page 64... ...
"So even if we go all in on a dynamic soil system that tells us fine-scale detail about how these things change," he said, "it's not going to be hugely practical and useful until we can say what those changes mean." Melissa Ho from the World Wildlife Fund built on Wood's comments, saying that it is crucial to first understand the objectives of a study and then have those objectives determine what is being measured and the appropriate scale. The objectives will depend on the needs of clients and users, the cost-effectiveness of various approaches, and so on, and will likely differ by stakeholder (e.g., policy maker, market-driven actor)
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From page 65... ...
The vision is the use of phones to measure some soil properties, but as was discussed by the collection and curation breakout group, challenges with data fidelity and data calibration remain. However, the increase in the amount of data collected helps to lower measurement costs.
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