Managing agricultural greenhouse gases : coordinated agricultural research through GRACEnet to address our changing climate
Bibliographic record
Abstract
Section One: Agricultural Research for a Carbon-Constrained World Agriculture and climate change: Mitigation opportunities and adaptation imperatives (Mark A. Liebig, Alan J. Franzluebbers, and Ron F. Follett) GRACEnet: Addressing policy needs through coordinated cross-location research (Charles L. Walthall, Steven R. Shafer, and Michael D. Jawson) Section Two: Agricultural Management and Soil Carbon Dynamics Cropland management in the eastern United States for improved soil organic C sequestration (Curtis J. Dell and Jeffrey M. Novak) Soil carbon sequestration in central USA agroecosystems (Cynthia A. Cambardella, Jane M. F. Johnson, and Gary E. Varvel Agricultural management and soil carbon dynamics: Western U.S. croplands (Harold P. Collins, Maysoon M. Mikha, Tabitha T. Brown, Jeffrey L. Smith, David Huggins, and Upendra M. Sainju) Soil carbon dynamics and rangeland management (Justin D. Derner and Virginia L. Jin) Soil organic carbon under pasture management (Alan J. Franzluebbers, Lloyd B. Owens, Gilbert C. Sigua, Cynthia A. Cambardella, and Richard L. Haney) Sustainable bioenergy feedstock production systems: Integrating C dynamics, erosion, water quality and greenhouse gas production (Jane M. F. Johnson and Jeffrey M. Novak) Section Three: Agricultural Management and Greenhouse Gas Flux Cropland management contributions to GHG flux: Central and eastern U.S. (Michel A. Cavigelli and Timothy B. Parkin) Management to reduce greenhouse gas emissions in western U.S. croplands (Ardell D. Halvorson, Kerri L. Steenwerth, Emma C. Suddick, Mark A. Liebig, Jeffery L. Smith, Kevin F. Bronson, and Harold P. Collins) Greenhouse gas flux from managed grasslands in the U.S. (Mark A. Liebig, Xuejun Dong, Jean E.T. McLain, and Curtis J. Dell) Mitigation opportunities for life cycle greenhouse gas emissions during feedstock production across heterogeneous landscapes (Paul R. Adler, Stephen J. Del Grosso, Daniel Inman, Robin E. Jenkins, Sabrina Spatari, and Yimin Zhang) Greenhouse gas fluxes of drained organic and flooded mineral agricultural soils in the United States (Leon Hartwell Allen, Jr.) Section Four: Model Simulations for Estimating Soil Carbon Dynamics and Greenhouse Gas Flux from Agricultural Production Systems DayCent model simulations for estimating soil carbon dynamics and greenhouse gas fluxes from agricultural production systems (Stephen J. Del Grosso, William J. Parton, Paul R. Adler, Sarah C. Davis, Cindy Keough, and Ernest Marx) COMET2.0 - Decision support system for agricultural greenhouse gas accounting (Keith Paustian, Jill Schuler, Kendrick Killian, Adam Chambers, Steven DelGrosso, Mark Easter, Jorge Alvaro-Fuentes, Ram Gurung, Greg Johnson, Miles Merwin, Stephen Ogle, Carolyn Olson, Amy Swan, Steve Williams, and Roel Vining) CQESTR simulations of soil organic carbon dynamics (H.T. Gollany, R. F. Follett, and Y. Liang) Development and application of the EPIC model for carbon cycle, greenhouse-gas mitigation, and biofuel studies (R.C. Izaurralde, W.B. McGill, and J.R. Williams) The general ensemble biogeochemical modeling system (GEMS) and its applications to agricultural systems in the United States (Shuguang Liu, Zhengxi Tan, Mingshi Chen, Jinxun Liu, Anne Wein, Zhengpeng Li, Shengli Huang, Jennifer Oeding, Claudia Young, Shashi B. Verma, Andrew E. Suyker, Stephen Faulkner, and Gregory W. McCarty) Section Five: Measurements and Monitoring: Improving Estimates of Soil Carbon Dynamics and Greenhouse Gas Flux Quantifying biases in non-steady state chamber measurements of soil-atmosphere gas exchange (Rodney T. Venterea and Timothy B. Parkin) Advances in spectroscopic methods for quantifying soil carbon (James B Reeves, III, Gregory W. McCarty, Francisco Calderon, and W. Dean Hively) Micrometeorological methods for assessing greenhouse gas flux (R. Howard Skinner and Claudia Wagner-Riddle) Remote sensing of soil carbon and greenhouse gas dynamics across agricultural landscapes (C.S.T. Daughtry, E.R. Hunt Jr., P.C. Beeson, S. Milak, M.W. Lang, G. Serbin, J.G. Alfieri, G.W. McCarty, and A.M. Sadeghi) Section Six: Economic and Policy Considerations Associated with Reducing Net Greenhouse Gas Emissions from Agriculture Economic outcomes of greenhouse gas mitigation options (David W. Archer and Lyubov A. Kurkalova) Agricultural greenhouse gas trading markets in North America (D.C. Reicosky, T. Goddard, D. Enerson, A.S.K. Chan, and M.A. Liebig) Eligibility criteria affecting landowner participation in greenhouse gas programs (Robert Johansson, Greg Latta, Eric White, Jan Lewandrowski, and Ralph Alig) Section Seven: Looking Ahead: Opportunities for Future Research and Collaboration Potential GRACEnet linkages with other greenhouse gas and soil carbon research and monitoring programs (John M. Baker and Ronald F. Follett) Elevated CO2 and warming effects on soil carbon sequestration and greenhouse gas exchange in agroecosystems: A review (Feike A. Dijkstra and Jack A. Morgan) Mitigation opportunities from land management practices in a warming world: Increasing potential sinks (J.L. Hatfield, T.B. Parkin, T.J. Sauer, and J.H. Prueger) Beyond mitigation: Adaptation of agricultural strategies to overcome projected climate change (Ronald F. Follett)
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".