Soil nutrients and plant uptake parameters as related to greenhouse gas emissions
Bibliographic record
Abstract
Abstract Fertilizer and water management practices have short‐ and long‐term effects on soil chemical and physical properties and, in turn, greenhouse gas (GHG) emissions. The goal of this 4‐yr field study was to establish the relationships between soil properties, agronomic practices, and GHG (CO 2 and N 2 O) emissions under different fertilizer and water table management practices. There were two fertilizer treatments: inorganic fertilizer (IF) and a mix of solid cattle manure and inorganic fertilizer (SCM), combined with tile drainage(DR) and controlled drainage with subirrigation(CDS). The cropping system was a maize ( Zea mays L.)–soybean [ Glycine max (L.) Merr.] rotation. Nitrogen in biomass (BMN) and N in grain (GRN) were measured and used to calculate other plant N parameters. Nitrous oxide and CO 2 fluxes were collected weekly, and their respective cumulative emissions were calculated. The results show that soil organic matter (OM), soil total carbon (C), and soil total nitrogen (N) were greater in SCM than IF by 23.7, 35.2, and 24.4%, respectively. Water table management did not significantly affect soil N and C. Increased CO 2 emissions were witnessed under higher soil OM, soil total C, and total N. Plant N uptake parameters were negatively correlated with N 2 O and CO 2 emissions. Higher plant N uptake can reduce environmental pollution by limiting N 2 O and CO 2 emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".