Soil Organic Carbon Stock and Crop Yields in Huang-Huai-Hai Plains, China
Bibliographic record
Abstract
The Huang-Huai-Hai-plains (HHH) is the main wheat (Triticum aestivum)-maize(Zea mays) production area of China. Therefore, adoption of appropriate fertilizer management strategies of improving soil organic carbon (SOC) and crop yields is an important option in HHH. These studies included a total of 6 land use and management treatments including: (i) no fertilizer(CK); (ii) chemical nitrogen(N), phosphorus(P) and potassium(K) fertilizers separately(UF); (?) combined application of chemical fertilizer N,P and K(CF); (?) wheat and maize straw retention or manures including that from soybean (Glycine max) cake, chicken, horse and cow dung or manures only (O); (?) combined application N, P and K and organic fertilizers (CFO); (?) combined application of chemical fertilizer N,P or K separately and organic fertilizers (UFO). The data indicated the following: (i) The baseline SOC stock of arable land was 18.9±1.8 Mg ha-1 and the corresponding crop yield was 4.4±1.5 Mg ha-1; the highest SOC stock was 24.6±1.8 Mg ha-1 for CFO and the corresponding crop yield was 9.7±3.2 Mg ha-1; (ii) The rate of increase of SOC stock was in the order of CFO>UFO>CF>O>UF, while that of increase in crop yield was in the order of CFO>CF>UFO>UF>O; (?) Crop yield increased (Mg ha-1 yr-1) by 0.114 in UF and CF, by 0.039 in treatment O,CFO and UFO, and by 0.033 in CK by increase in SOC stock by 1 Mg ha-1; (?) Yield increased (Mg ha-1 yr-1) by 0.298, 0.119,0.065, and 0.022 by over 5, 10, 15, and over 25 years by increase in SOC stock by 1 Mg ha-1. Therefore, the combined application of chemical and organic fertilizers is the best choice for the developing countries to adapt to and mitigate climate change while advancing food security.
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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 | 0.001 | 0.001 |
| 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".