Effects of Arsenic, Iron and Fertilizers in Soil on Rice in Cambodia
Bibliographic record
Abstract
BACKGROUND: In parts of Cambodia, irrigation with groundwater results in arsenic accumulation in soils and rice, leading to health concerns associated with rice consumption. A high concentration of iron in groundwater can precipitate arsenic and reduce its bioavailability, however high concentrations of arsenic and iron can also reduce rice production. Furthermore, concerns have been raised about chemical contamination from inorganic fertilizers used to grow rice. The relationship between soil geochemistry and arsenic concentrations in rice is not yet fully understood. OBJECTIVES: The primary objective of this project was to investigate the relationship between arsenic concentrations in irrigation water, soil and rice collected from different sites in Cambodia. A secondary objective was to explore arsenic and phosphorus levels in fertilizer samples obtained from the study area in Cambodia. METHODS: The present study collected 61 well water samples, 105 rice samples, 70 soil samples, 11 inorganic fertilizer samples and conducted interviews with 44 families along the Mekong River in Cambodia. Analyses for metals, total arsenic, and arsenic species in the water and rice were conducted in Canada by inductively coupled plasma mass spectrometry. Analyses for metals, total arsenic and phosphorus in soils and inorganic fertilizers were conducted in Cambodia and Singapore by X-ray fluorescence. RESULTS: The concentration of arsenic in rice paddy soils was highly variable and as much as 20 times higher near the irrigation wells than in more distal areas of the paddy. Two farmers in Preak Russey had integrated soil samples with arsenic levels above the concentration associated with toxicity to rice in Taiwan (40 mg/kg) and above the Dutch concentration requiring intervention or remediation (55 mg/kg). The highest total arsenic measured in soil was 95 mg/kg. In Preak Russey, the loading of arsenic from irrigation water was 3710 times greater than the loading of arsenic from inorganic fertilizers. Half of the commercial inorganic fertilizers had less than 50% of the labelled content of phosphorus. CONCLUSIONS: Emphasis should be placed on improving the management of irrigation water, not on inactivation of arsenic in soil. The high levels of iron in groundwater mitigate arsenic toxicity, but the accumulation of iron could later result in lower rice productivity. Irrigation of rice with groundwater is not likely sustainable. To improve rice productivity, the content of phosphorus in local inorganic fertilizers must be improved to world standards. X-ray fluorescence analysis can quickly identify poor quality fertilizers. INFORMED CONSENT: Obtained. COMPETING INTERESTS: The authors declare no competing financial interests.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".