Developing a Strategic Network to Address the Global Challenges of Agricultural Land and Soil Management to Adapt to Climate Change
Bibliographic record
Abstract
Agricultural land and soil (ALS) systems face complex and interacting socio-ecological challenges and tradeoffs. These systems play a critical role in food and water security and climate change adaptation and mitigation. Improving the resilience of complex ALS systems is a societal grand challenge that requires cross-disciplinary research collaboration and cross-sectoral stakeholder engagement. We have established a network of researchers and practitioners who have been meeting for the past two years to discuss ways to leverage research and educational resources on food and water security to address challenges associated with adapting climate change mitigation strategies in ALS systems. Within the U.S., our network includes universities that are geographically distributed along the climate gradient, with each location addressing unique local land and soil management challenges. These locations include University of Wisconsin-Madison, Texas A&M University, Prairie View A&M University, Mississippi State University, University of Nebraska-Lincoln, Kansas State University, University of California-Davis, and University of Alaska Fairbanks. In August 2021, we conducted a virtual workshop, The Role of Agriculture, Land, and Soil Management in Climate Change Reversal: Partnership Building and Proposal Co-creation. The overarching goal of the workshop was to develop a common platform for knowledge, data, information and experience sharing to determine how soil management strategies could improve productivity, sustainability, and resilience in land and agricultural systems that could potentially reverse climate change. One outcome of the workshop and the follow-up discussions was an expanded network that now includes individuals affiliated with UN FAO, CGIAR-IWMI, NEMEDUSSA, LTAR-USDA, Global EverGreening Alliance, German Agricultural Society, Global Landscape Forum, Soil and Water Conservation Society, Circumpolar Agricultural Association, Soil Health Institute, Indian Council for Agricultural Research, US Biochar Initiative, and institutions in Canada, Finland, Germany, Sri Lanka, Lebanon, Benin, Russia, Ethiopia, India, Costa Rica, and Kenya. During the workshop, we identified existing knowledge gaps that such a network can address; assessed the state of the science, existing barriers, and opportunities under each topic; and discussed potential mechanisms for building the network and collaboration among networks on the identified topics. We will present the outcome of the workshop and our vision to develop a network of networks that will support broader strategic collaboration and team building among U.S. research networks, complimentary global networks, professional society networks, and social networks to develop strategic plans to address the challenges associated with adapting climate change mitigation strategies in ALS systems. This partnership will cross institutional boundaries and engage diverse stakeholders. It will include consumers and industry partners, in both developed and developing nations, in the scientific discovery and implementation processes to build a plan for putting knowledge into action. We will integrate biophysical, social, economic, and behavioral sciences that will enhance the knowledge to action plan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".