Improved Targeting Technique and Parsimonious Optimization as Synergistic Combination for Nitrate Hot Spots Identification and Best Management Practices Implementation in a watershed of the Midwestern USA
Bibliographic record
Abstract
The contamination of rivers with nitrate from agricultural diffuse sources is not just a risk for ecosystems and their services, but also a health risk for water users. The Great Lakes (USA and Canada) are suffering from eutrophication problems. The Midwest is one of the richest farming land and one of the most productive areas on the planet. Thus, agriculture is one of the biggest drivers of local economies, accounting for billions of dollars of exports and thousands of jobs. The Midwest encompasses the Corn Belt region, a specialised system in corn production. Many of its agricultural basins drain into the Great Lakes. Corn requires a heavy amount of fertilizer to keep the best-yielding varieties. Some of the soils also require artificial drainage due to their low permeability, and to enable agriculture. The Cedar Creek watershed (CCW) in northeastern Indiana in the Corn Belt region is used as a case study area in this dissertation. Intensive agriculture in the CCW is characterised mainly by corn and soybean production. Tile drains are used, ejecting nitrate directly into the water. To find hotspots of nitrate is, then, crucial to avoid water quality deterioration. Identification of critical source areas of nitrate (CSAs) impairing waters is challenging. There are, mainly, two methodologies to identify hotspots of nitrate for the implementation of Best Management Practices (BMP): the targeting technique and the optimization approach. The targeting technique tends to identify hotspots based on loads of nitrate, omitting geomorphological watershed characteristics, costs for BMP implementation, and their spatial interactions. On the other hand, the parsimonious strategy does contemplate the trade-off of the economic and environmental contribution but requires sophisticated computational resources and it is more data-intense. This research presents a new framework based on the synergistic combination of both methodologies for the identification of CSAs in agricultural watersheds. Changes in watershed response due to alternative BMP applications were assessed using the model Soil and Water Assessment Tool (SWAT). Outputs in SWAT (nitrate export rates and nitrate concentration at the subbasin level) were used to evaluate the changes in water quality for the CCW. The newly developed targeting technique (HosNIT) considers SWAT outputs and intrinsic watershed parameters such as stream order, crop distance to the draining stream, and downstream nitrate enrichment/dilution effects within the river network. HosNIT establishes a workflow, based on a threshold system for the parameters considered, in order to spatially identify priority areas from where nitrate is reaching water. The more precise hotspots of nitrate are identified, the more improved the allocation of limited resources for conservation practices will be. HosNIT allows for a more spatially accurate CSAs identification, which enables a parsimonious optimization for BMP implementation. This parsimonious strategy will test BMP’s performance based on the environmental contribution and cost at the hotspots determined by HosNIT. The optimised solution for the CCW comes from the environmental contribution (decrease percentage of nitrate concentration at outlets) per dollar spent. For this case study means a year average of 3.7% of nitrate reduction with the optimised selection of scenarios for the studied period.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".