Soil surface greenhouse gas emissions and hydro-physical properties as impacted by prairie cordgrass intercropped with kura clover
Bibliographic record
Abstract
Prairie cordgrass (PCG) is a perennial crop which has the potential for biofuel production under marginal lands. The intercropping of a perennial legume, kura clover (KC) with PCG can reduce the use of chemical fertilizer while maintaining the soil hydro-physical conditions. The objective of this study was to compare the soil hydro-physical properties and greenhouse gas (GHG) fluxes under PCG intercropped with KC (PCG–KC), and PCG fertilized with graded levels of N (0, 75, 150, and 225 N kg ha−1). During the summer of 2021, soil samples (0–10 cm) were collected. Additionally, gas samples were collected weekly from April through September of the same year. Soil water retention, saturated hydraulic conductivity ( Ksat), thermal conductivity (λ), soil organic carbon (SOC), and total N (TN) concentrations were measured. Soil pore characteristics were measured using X-ray computed tomography. The PCG–KC had 1.42 g kg−1 TN and 24 g kg−1 SOC at 0–10 cm, non-significant to PCG-75, 150, and 225 N. Nonetheless, TN significantly increased in both PCG–KC and other fertilized treatments compared to the control. Intercropping boosted macroporosity (0.024 cm3 cm−3), Ksat (+50%), and lowered λ (−1%), compared to the N fertilized treatments. Soil cumulative CO2 under PCG–KC (1012.67 kg C ha−1) was similar to PCG-75, 150 N, but lower than PCG-225 N (1418.66 kg C ha−1). Overall, this study showed that PCG–KC can be a sustainable option over the use of N fertilizers since they had similar levels of hydro-physical characteristics and had a comparable ability to mitigate 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".