A sustainable option: Biochar addition can improve soil phosphorus retention and rice yield in a saline–alkaline soil
Bibliographic record
Abstract
Application of straw and its derived biochar has been confirmed to reduce resource waste and improve soil productivity, however, the impact of these amendments on soil phosphorus (P) retention and crop yield was unclear, especially in saline–alkaline soils. Here, we carried out a 3-year field and a 40-day laboratory incubation experiment to evaluate the impacts of soil amendments on P retention and crop yield in saline–alkalinesoil. In field experiment, on the basis of applying the same amount of chemical fertilizer, rice straw or biochar was added with 0, 1.8 and 3.6 Mg carbon ha −1, namely CK, Straw-L or -H and Biochar-L or -H, respectively. Results showed that the greatest rice yield was found in Biochar-H treatment, which was 9.1 Mg hm −2, followed by Straw-L (9.0 Mg hm −2), Biochar-L (8.6 Mg hm −2) and Straw-H (8.5 Mg hm −2). The available P content of biochar amendment was higher than that with straw addition, in which it was greater in Biochar-H compared to that in Biochar-L treatment. Both straw and biochar treatments reduced the degree of P saturation in 0–40 cm soil. Moreover, the cation exchange capacity in 0–20 cm soil was increased with by 58.8% and 107.6% in Biochar-L and Biochar-H treatments, respectively, while it was decreased in the straw treatments in all soil layers (except 0–20 cm soil). Incubation experiment indicated that the soil microbial biomass P content with straw addition was higher than that in CK and biochar treatments. However, the soil P retention capacity under saturated water condition was decreased in the straw treatments. Therefore, biochar had a potentially more positive impact on rice productivity and P retention than straw returning in saline–alkaline soil.
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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".