Evaluation of Ground and Surface Water Hydrochemistry for Irrigation Suitability in Borneo: Insights from Brunei Darussalam
Bibliographic record
Abstract
Water for irrigation use plays a critical role in agriculture via supporting crop growth and maintaining food production worldwide. Irrigation water quality evaluations provide useful information for sustainable water practices in many agricultural regions. In Brunei Darussalam, the quality of irrigation water is still poorly understood. The present study aims to investigate the hydrochemical characteristics of water resources in Brunei Darussalam and evaluate their quality and suitability for irrigation use. A total of fifteen sampling locations were chosen from selected ground and surface water sources found in all four Brunei districts. The water samples’ physicochemical properties, including pH, EC, and major cations and anions, were measured and compared with FAO standards. Hydrochemical classification based on the Piper diagram revealed that water mainly belongs to the calcium-, magnesium-, and bicarbonate-type, or Type IV water class. The evaluation of heavy metals (Fe, Zn, Cu, Cr, As, and Cd) showed concentrations within the FAO’s permissible limits. In this regard, iron showed the highest concentration among the investigated metals. Established water quality indices such as SAR, Na%, RSC, MAR, KR, PS, and IWQI were used to evaluate and classify the water’s suitability for irrigation use. Overall, our findings revealed that almost all of the analysed water samples in the study area have low salinity and sodicity risks. However, only 27% of the samples passed the magnesium hazard assessment, and one sample showed a very poor IWQI result. Thus, additional testing and treatment are recommended for these cases. This study provides valuable insights on water quality for present and future utilisation, aiming to contribute to the protection of water resources in Brunei Darussalam.
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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 | 0.001 | 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) | 0.001 | 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 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".