Recycled Household Ash in Rice Paddies of Bangladesh for Sustainable Production of Rice Without Altering Grain Arsenic and Cadmium
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".