Developing indices for agricultural water quality
Bibliographic record
Abstract
Irrigated crops and livestock depend on the provision of water of a specific quality to thrive. However, water is rarely pure and may contain contaminants from a variety of natural and anthropogenic sources. Every variety of plant or breed of animal can tolerate a different concentration of these contaminants. Water used for agriculture can be tested to ensure these water quality thresholds are not being exceeded, thus ensuring it is fit-for-purpose. It can become complicated to determine this fit-for-purpose as more water quality parameters are considered along with a larger number of crops and stock. An Agricultural use Water Quality Index (AgWQI) is a multi-criteria mathematical method that can be applied in these situations to help simplify the assessment and decision making process. The aim of this research is to identify, describe and assess global examples of AgWQI as well as include a case study applied in the Fitzroy Basin, in Central Queensland, Australia. In this research, global surface water AgWQI methods were identified and assessed using the systematic quantitative literature review technique leading to the identification of 42 relevant AgWQI studies published across 19 countries. This research determined that two underlying WQI methods were adopted in these studies, including the National Sanitation Foundation Water Quality Index (NSF WQI) methodology and the Council of Ministers of the Environment Water Canadian Quality Index (CCME WQI). Studies that utilised the NSF WQI, adopted one of three strategies for parameter weightings (Wi) and eight functions for developing water quality ratings (Qi). One barrier identified in the systematic review was variability in the terms and operators used to describe methods in the 42 different studies. To overcome this barrier, an AgWQI common key was created to facilitate the normalisation of equations presented in the studies reviewed, which in turn enabled a more comprehensive comparative assessment of the various functions used to calculate AgWQI quality ratings. As a result of this research, a 10-step guide has also been developed that can be used by practitioners as a roadmap in AgWQI methodology development and associated reporting. This research also described an AgWQI prepared for the Fitzroy Basin, which adopted the NSF WQI methodology and employed a report card grading system. This AgWQI relied on expert opinion to set parameter Wi and employed two types of Qi ratings depending upon the number of available threshold values relevant to the parameter of interest. The Fitzroy AgWQI reporting confirmed that surface waters are generally suitable for irrigated cropping and stock watering purposes across the 11 catchments, with occasional exceedances. A well-known issue with WQI is eclipsing, which occurs when reporting on multiple indicators leads to parameter exceedances being hidden. Eclipsing was addressed in the Fitzroy AgWQI case study via exception reporting presented alongside the main report card results. Adoption of a tiered reporting structure for this report also helped to alleviate the issue of eclipsing by facilitating deeper exploration of the Fitzroy AgWQI report card results. In the context of increasing water scarcity, the results provide agricultural enterprises, government agencies and agricultural fund managers with an additional stream of data analytics to inform on-farm, policy, and investment decisions.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".