Biochar-Based Amendments in Chromite Mine Tailing Debris-Contaminated Soil - Influence on Physiological Traits of Tomato and Soil Properties
Bibliographic record
Abstract
Mining activities contaminate agricultural soils in the mining area with heavy metals, which ultimately enter the food chain, posing serious threat to human health. Chromite mining activities have been carried out since decades in Muslim Bagh, Balochistan, Pakistan. Farmers transport soils from far-flung areas to avoid possible negative influence of mining activities on crops. Nevertheless, the effect of chromite contamination from mining activities in freshly transported (non-contaminated) agricultural soil on vegetable crops needs to be evaluated. Furthermore, the amendment of organic wastes as fertilizers in contaminated soil from mining activities needs to be tested for their possible attenuation potential against the negative effect of contamination on crops. In this study, top soil layer was contaminated with chromite mine tailing debris (thorough mixing with soil up to 5 cm of soil depth) at 1 t ha−1 yr−1 rate for two years. Manures from small ruminant (SG), poultry (PM), and farm yard (FYM) were combined at 1:1:1 ratio, followed by their further mixing with wood- and manure-derived biochars at 1:1 biochar:manure mixture ratio. Biochars were also combined with synthetic NPK fertilizer. Wood-derived biochar was also co-composted with the above-mentioned manure mixture, as well as with SG, PM and FYM manures. Two tomato fruit varieties were taken into account for this study. Three experiments were conducted; in experiment 1, biochar+manure mixtures were mixed in contaminated soil for two years at 5, 10, and 20 t ha−1 rates and large tomato fruit variety was grown for two years. Experiment 2 was the same as experiment 1 except that small tomato fruit variety was selected as a crop. In experiment 3, the co-composted biochar fertilizers were amended in contaminated soil at 10 and 20 t ha−1 rates for one year and large tomato fruit variety was selected as a crop. Results showed that none of the fertilizer treatments increase the yield of large tomato fruit variety. Some treatments especially manure-derived biochar+manure mixture, amended at 10 and 20 t ha−1 rates improved yield of small tomato fruit variety compared to control. Tomato crops were not negatively impacted by the chromite contamination in terms of yield. Fertilizers did not lower the concentration of Pb in tomato fruits, except for few fertilizer treatments, which reduced the concentration of Pb in the fruits of small tomato fruit variety of second year crop (p < .05). Cadmium was not detected; whereas, chromium was detected in only two tomato fruit samples from two different treatments. This experiment suggests that crops may not take heavy metals in high concentrations for at least 5 years, following the deposition of metal(loids) from mining activities into the transported soils of agricultural lands of the region Muslim Bagh, Balochistan, Pakistan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".