The Effect of Land Management Changes and Nutrient Runoff Capture on Water Quality and Farm and Watershed Economics
Bibliographic record
Abstract
Beneficial management practices (BMPs) are designed to minimize environmental impacts and provide on- and off-farm benefits such as improved farm economics and enhanced water quality for domestic consumption, recreation and healthier aquatic ecosystems. Adoption of BMPs depends on the willingness of producers to implement them and on producer capacity to finance the investment. The Watershed Evaluation of Beneficial Management Practices (WEBs) project was initiated in 2004 under Agriculture and Agri-Food Canada (AAFC) to investigate water quality issues and economics related to agricultural production. The project selected nine watersheds across the country where BMPs were implemented to determine the effect on water quality in terms of nutrients, pathogens and sediment and on-farm economics. The South Tobacco Creek (STC) watershed, located in southern Manitoba, was one of the nine watersheds selected. Several BMPs were investigated in STC, including land management changes, the use of small dams and retention ponds to control the runoff from livestock yards from entering the water ways. The objective of the WEBs STC economics component was to assess on-farm economic costs and to identify and assess potential on-farm and off-farm benefits of applying the selected BMPs. The STC economic results have shown that some of the BMPs tested contribute positively to improved farm economics and financial returns but there are some BMPs whose revenues will not fully offset BMP costs. Certain off-farm benefits resulting from BMP implementation have also been identified. Reduced tillage BMP was shown to have significant benefits to producers based on combined experimental and model results. For the small dam/reservoir BMP the net present value based on only flood damage control was shown that the financial payback period can be less than 35 years. Significant additional potential benefits in terms of irrigation, sediment and nutrient entrapment, and recreational activities have also been determined. For land conversion to forage BMP, the cost saving of inclusion of forage to annual crop rotation was not enough to compensate the loss of opportunity through the loss of net income in annual crops. Similarly, the farm benefit of the holding pond which built to capture run-off from an upstream winter cattle containment area was not enough to justify its instalment cost, although nutrients, sediment and pathogens export (off-farm benefits) were reduced significantly. The STC research provided producers, governments, and watershed groups with credible information that can be used to promote adopting and maintaining BMPs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".