Nitrogen and phosphorus control carbon sequestration in agricultural ecosystems: modelling carbon, nitrogen, and phosphorus balances at the Breton Plots with <i>ecosys</i> under historical and future climates
Bibliographic record
Abstract
Perennial legumes in crop rotations increase soil C sequestration from greater productivity with N2 fixation. Here, we corroborated increases in soil organic carbon (SOC) and harvests modelled in 5 yr wheat–oats–barley–alfalfa/brome–alfalfa/brome (5Y) vs. 2 yr wheat–fallow (WF) rotations with those measured from 1929 to 2018. Harvest and SOC gains of 100–150 g C m−2 yr−1 and 15–25 g C m−2 yr−1 were modelled and measured in 5Y vs. WF rotations with different fertilizer and manure amendments. Modelled gains were closely related to annualized rates of N2 fixation by alfalfa of 8–10 g N m−2 yr−1. However, N2 fixation also drove increases in modelled N2O emissions of ca. 0.06 g N m−2 yr−1, which partially offset gains in SOC. Gains in harvest, SOC, and N2O emissions of 60–90 g C m−2 yr−1, 15 g C m−2 yr−1, and 0.05 g N m−2 yr−1 were modelled and measured in both rotations with amendments of N + P relative to unamended treatments. Harvest and SOC gains were smaller, and leaching and N2O losses larger, with amendments of N without P. After 100 yr of RCP 8.5 climate change, harvests in WF changed little from those in baseline runs, whereas those in 5Y rose with N + P because of increased N2 fixation. SOC declined in WF with all amendments and could only be raised in 5Y with N + P amendments. These model findings indicated the importance of N2 fixation and P amendments in determining responses of agroecosystem productivity and C sequestration to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".