Biochar and manure influences tomato fruit yield, heavy metal accumulation and concentration of soil nutrients under wastewater irrigation in arid climatic conditions
Bibliographic record
Abstract
Balochistan produces more than 40% of the total tomato production of Pakistan. The climate of this province is mostly arid, and agriculture around urban areas commonly depends on wastewater irrigation. This study evaluated under groundwater and wastewater irrigation the influence of wood-derived biochar, cow manure and their co-amendment (as 1:1 biochar:manure ratio) at 0.5 kg m−2 (or 5 t ha−1) and 1 kg m−2 (or 10 t ha−1) rate on fruit yield production of tomato, concentration of heavy metals (lead (Pb), copper (Cu), zinc (Zn), nickel (Ni) and chromium (Cr)), nutrient use efficiency (NUE) of heavy metals (calculated as fruit yield/concentration of a given heavy metal in fruits) and the pH, concentration of mineral nitrogen (N) and soluble inorganic phosphorus (P) of tomato-grown soil. As compared to groundwater irrigation, the biomass and yield production was higher under wastewater irrigation. Organic amendments significantly improved yield production and tended to increase soil pH than control under both irrigation treatments. Wood-derived biochar applied at 1 kg m−2 caused the highest yield under both irrigation treatments. Organic amendments tended to reduce the concentration of Pb, Cu and Cr and increased the NUE of tomato fruits, indicating that fruits require less acquisition of heavy metals per unit yield production. Organic amendments increased the concentration of soluble inorganic P under wastewater irrigation. Our findings suggest that amendment of biochar, manure and their mixture promoted tomato fruit yield under both irrigation treatments, increased NUE of fruits for heavy metals and increased the concentration of soluble P under wastewater irrigation.
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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) | 0.000 | 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".