A decision support system for soil and water conservation measures on agricultural watersheds
Bibliographic record
Abstract
Abstract Integrated watershed management (IWM) is vital in achieving agricultural sustainability in terms of both production and environmental protection. A decision support system (DSS) is useful in generating alternative decision scenarios for management of natural resources, facilitating the implementation of IWM concepts in an interactive and holistic way. The decision to implement an appropriate land use coupled with suitable soil and water conservation techniques not only enhances watershed health but also prevents sediment losses. Besides reducing basin fertility, such losses decrease the storage capacity of downstream reservoirs through silt deposition, which can, in turn, give rise to low biomass production and poorer flood control. In order to facilitate IWM, an effort was made to develop, in the Visual Basic programming language, a soil and water conservation DSS which considered both structural and cropping practices for arresting sediment loss. Input parameters to the DSS for a given tract of land included: mean slope; sediment loss; soil type; and land capability class (LCC). Outputs included decision criteria to choose among alternative structural measures and suggested cropping systems to serve as biological measures to reduce soil loss and conserve water. Structural watershed management measures included a variety of soil and water conservation structures widely adopted by farming communities throughout the world. The DSS is capable of providing sediment control solutions not only for small watersheds but also for larger drainage basins, by dividing the basin into smaller watersheds. The DSS was validated for a watershed on the Caribbean island of St Lucia and used to suggest measures for a 10° slope under specific soil type, sediment loss and LCC conditions. The measures proposed included bench terraces, graded contour bunds, conservation ditches, concrete chute spillways, diversion dams and conservation cropping systems. The measures actually adopted on‐site were conservation ditches, graded contour bunds and conservation cropping systems, a close parallel to the DSS's proposed measures. On slopes ranging from 5–55°, implementation of the suggested control measures resulted in a 34–37 per cent reduction in soil loss on the watershed. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".