Recycled Household Ash in Rice Paddies of Bangladesh for Sustainable Production of Rice Without Altering Grain Arsenic and Cadmium
Why this work is in the frame
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Bibliographic record
Abstract
In Bangladesh most agronomic biomass (straw, husk, dried dung) is burnt for domestic cooking use. Consequently, the soil is continuously stripped of mineral nutrients and carbon (C) substrate. Here we investigate if recycling of household ash (ash) as fertilizer can sustainably improve soil fertility as well as minimise accumulation of toxic elements (As, Cd) in rice grain. Large scale field trials across two geographic regions (Barind, Madhupur) and two seasons (wet, dry) and with application of 3 fertiliser treatments (NPKS, ash, NPKS + ash) were conducted. At the end of each season, the impact of region*season*treatment on soil microbial comunities, rice yield, and grain quality (As, Cd, nutrient elements) was assessed. When compared to conventional field application rates of NPKS (control), application of ash boosted rice yield by circa. 20% in both regions during wet and dry season, with no effect on rice grain carcinogenic inorganic arsenic (iAs), dimethylarsonic acid (DMA) or cadmium (Cd), but with potential to increase zinc (Zn). For soil microbial communities, a significant region and season effect as well as correlation with elements in rice grain was observed, amongst these Cd, Zn, iAs and DMA. This study illustrates that application of ash can reduce the requirement for expensive chemical fertiliser, whilst at the same time increasing rice yield and maintaining grain quality, making farming in Bangladesh more sustainable and productive. The study also implies that the combined impact of region, season, and soil microbes determines accumulation of elements in rice grain. Supplementary Information: The online version contains supplementary material available at 10.1007/s12403-023-00539-y.
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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 | 0.001 | 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 it