Soil carbon dioxide and nitrous oxide emissions during the growing season from temperate maize‐soybean intercrops
Bibliographic record
Abstract
Abstract The Argentine Pampa is one of the major global regions for the production of maize (Zea mays L.) and soybean (Glycine max L. [Merr.]), but intense management practices have led to soil degradation and amplified greenhouse‐gas (GHG) emissions. This paper presents preliminary data on the effect of maize‐soybean intercrops compared with maize and soybean sole crops on the short‐term emission rates of CO2 and N2O and its relationship to soil moisture or temperature over two field seasons. Soil organic carbon (SOC) concentrations were significantly greater (p < 0.05) in the maize sole crop and intercrops, whereas soil bulk density was significantly lower in the intercrops. Soil CO2 emission rates were significantly greater in the maize sole crop but did not differ significantly for N2O emissions. Over two field seasons, both trace gases showed a general trend of greater emission rates in the maize sole crop followed by the soybean sole crop and were lowest in the intercrops. Linear regression between soil GHG (CO2 and N2O) emission rates and soil temperature or volumetric soil moisture were not significant except in the 1:2 intercrop where a significant relationship was observed between N2O emissions and soil temperature in the first field season and between N2O and volumetric soil moisture in the second field season. Our results demonstrated that intercropping in the Argentine Pampa may be a more sustainable agroecosystem land‐management practice with respect to GHG emissions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".