Carbon, Nitrogen, and Greenhouse Gas Balances in an 18‐Year Cropping System Study on the Northern Great Plains
Bibliographic record
Abstract
Agroecosystems provide a range of benefits that are strongly influenced by cropping practice. Crop productivity and C, N, and greenhouse gas (GHG) balances were evaluated in an 18‐yr cropping system study on an Aridic Haplustoll in the northern Great Plains. Application of synthetic fertilizers consistently increased crop yield and soil organic carbon (SOC), with greatest impact in perennial grass and continuous wheat ( Triticum aestivum L.) rotations and least impact in rotations with fallow or annual legumes. Based on N balance, N inputs other than fertilizer were 16 to 30 kg N ha −1 yr −1 in rotations without legumes and 62 kg N ha −1 yr −1 in a legume‐wheat (LW) rotation, while losses of synthetic fertilizer N were 32% in annual crop rotations and 3% in perennial grass. Due to large gains in SOC, perennial grass reduced atmospheric GHG by 20 to 29 Mg CO 2 equivalent (eq.) ha −1 during the 18 yr of this study. For annual crop rotations, seed yield ranged from 1.2 to 2.5 Mg ha −1 yr −1 , protein yield from 0.20 to 0.41 Mg ha −1 yr −1 , and GHG intensity from 0 to 0.5 Mg CO 2 eq. Mg −1 seed. Fertilized continuous wheat had the highest crop productivity and lowest net GHG intensity, while an annual LW rotation had the highest protein productivity and among the lowest GHG intensities (0.2 Mg CO 2 eq. Mg −1 seed). Further evaluation at broader temporal and spatial scales is necessary to account for future changes in SOC and differences in use of crop products.
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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".