Modeling soil organic carbon in corn ( <i>Zea mays</i> L.)-based systems in Ohio under climate change
Bibliographic record
Abstract
Soil organic carbon (SOC) is a key indicator of soil quality. Knowledge of the effects of land management and climate change on SOC stocks is of vital importance in creating future sustainable land use systems. This study presents both the promise and current challenges of modeling SOC in mineral soils under climate change. Soils data from two long-term agricultural research sites in Ohio under no-till (NT) and plow-till (PT) management, the RothC soil C model, and climate data from the Canadian Regional Climate Model were used to project future SOC content in agricultural soils using low-emissions (LE) and high-emissions (HE) climate change scenarios. It was hypothesized that from 2015 to 2070, SOC levels in soils under NT management in Ohio will show increasing trends under the LE scenario, decreasing trends in NT under the HE scenario, and decreasing trends in PT under both scenarios, with lower levels of SOC for both treatments under the HE scenario. The results of this study projected total SOC content in the topsoil layers (0 to 25 cm [0 to 10 in] at Wooster and 0 to 23 cm [0 to 9 in] at Hoytville) to decrease at all sites and under all management and climate projections, with the exception of NT at Wooster and Hoytville and PT at Wooster under the LE scenario. Starting at 32.4 Mg C ha−1 (14.5 tn C ac−1) in 1962 at Wooster, by 2070, soil under NT management is projected to have 45.4 and 32.1 Mg C ha−1 (20.3 and 14.3 tn C ac−1) for LE and HE scenarios, respectively, while PT management starting at 31.5 Mg C ha−1 (14.1 tn C ac−1) would have 29.4 and 21 Mg C ha−1 (13.1 and 9.4 tn C ac−1) for LE and HE scenarios, respectively. Starting at 65.2 Mg C ha−1 (29.1 tn C ac−1) in 1963 at Hoytville, by 2070, soil under NT management would have 65.9 and 51 Mg C ha−1 (29.4 and 22.8 tn C ac−1) for LE and HE scenarios, respectively, and PT starting at 63.5 Mg C ha−1 (28.3 tn C ac−1) would have 36.9 and 28.7 Mg C ha−1 (16.5 and 12.8 tn C ac−1) for LE and HE scenarios, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".