Water Quality in Organic Systems
Bibliographic record
Abstract
Non-point source contamination is a major water quality concern in the upper Midwestern USA, where plant nutrients, especially NO3-N, are susceptible to leaching due to extensive subsurface draining of the highly productive, but poorly drained, soils found in this region. Environmental impacts associated with intensive mineral fertilization in conventional production have encouraged producers to investigate organic methods. The USDA-ARS Organic Water Quality (OWQ) experiment, established in 2011, compares organic (C-S-O/A-A) and conventional (C-S) crop rotations and an organic pasture (bromegrass, fescue, alfalfa, white clover) system. Thirty fully-instrumented, subsurface-drained plots (30.5 m × 30.5 m) laid out in a randomized block design with 5 field replications, isolate subsurface drainage from each plot and permit comparison of treatment effects on subsurface drainage water flow and nutrient concentrations. Objectives for this study were to quantify growing season subsurface drainage water flow, NO3-N concentrations, and NO3-N loads for conventional and organic grain cropping systems from 2012-2014. Temporal patterns of subsurface drainage water flux were similar for all cropping systems for all years, except for the pasture system in 2012 and subsurface drainage water N concentrations were highest in the conventional C-S system except for the early spring 2012. Subsurface drainage water N loading loss for the entire 3-year period from the conventional C-S system (79.2 kgN ha-1) was nearly twice as much as the N loss from the organic C-S-O/A-A system (39.9 kgN ha-1); the pasture system (16.5 kgN ha-1) lost the least amount of N over the 3 years. Results of this study suggest that organic farming practices, such as the application of composted animal manure and the use of forage legumes and green manures within extended cropping rotations, can improve water quality in Midwestern subsurface-drained landscapes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".