Wheat straw and biochar effect on soil carbon fractions, enzyme activities, and nutrients in a tobacco field
Bibliographic record
Abstract
Annual removal of tobacco residues and insufficient input of organic materials have exacerbated total organic carbon (TOC) depletion and soil degradation in a tobacco field in the Huanghuai area. Straw residue and biochar application may be effective ways to increase TOC accumulation and improve soil fertility. In this field experiment, wheat straw (WS) and wheat-straw-derived biochar (BC) with mineral fertilizer were compared with mineral fertilizer alone (CK), and we assessed their effects on soil organic carbon fractions, enzyme activities, and nutrients in Shandong Province, China, during 2016 and 2017. At 0–20 cm depth, the WS treatment had a greater overall effect on the measured soil properties. Compared with the control, the WS treatment significantly increased the concentrations of microbial biomass carbon (MBC), hot-water-extractable carbon (HWC), and permanganate-oxidizable carbon concentrations (POXC; by 252.41%, 107.02%, and 65.53%, respectively); the activities of sucrase, urease, and phosphatase (by 112.52%, 7.81%, and 34.33%, respectively); and the contents of alkaline hydrolysable nitrogen, available phosphorus, and available potassium (by 92.22%, 100.78%, and 10.57%, respectively). Compared with the control, the BC treatment significantly increased TOC content, MBC content, light fraction organic carbon (LFOC), and potassium (TK) concentration (by 74.93%, 86.24%, 153.73%, and 21.92%, respectively). Most soil enzyme activity and nutrient parameters were significantly correlated with MBC. Thus, straw application improved soil fertility by increasing the concentrations of high labile organic carbon fractions (HWC, MBC, and POXC), stimulating soil enzyme activities, and releasing more soil available nutrients, and BC addition contributed to the accumulation of TOC, MBC, LFOC, and TK.
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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 | 0.001 |
| 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) | 0.000 | 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".