Trade-offs in organic nutrient management strategies across mixed vegetable farms in Southwest British Columbia
Bibliographic record
Abstract
Balancing economic and environmental objectives can present trade-offs for organic farmers maximizing crop yields while maintaining core principles of ecology and health. A primary challenge for achieving this balance is nitrogen (N) and phosphorus (P) management. Meeting crop N requirements with compost can build soil carbon (C) and soil health but often over-applies P and increases soil P and associated environmental risks. Alternatively, high-N organic fertilizers can provide N without surplus P but can be expensive and lack C inputs that composts supply. We evaluated these potential trade-offs in 2-year field trials on 20 mixed vegetable farms across three regions of Southwest British Columbia, Canada, capturing a range of climatic-edaphic conditions and organic amendments. Three nutrient management strategies were evaluated: High Compost : compost applied to meet crop N removal, Low Compost + N : compost applied to meet crop P removal plus an organic fertilizer to meet crop N removal, and Typical : varying combinations of composts and/or organic fertilizers (“typical” nutrient application on the farm). Nutrient strategies were evaluated in terms of yield, input costs, and soil properties [permanganate oxidizable C (labile C responsive to soil management), and post-season available N and P]. Soil P was 21% higher with High Compost than Low Compost + N . In one region characterized by inexpensive but nutrient-rich composts and soils high in P, input costs were lowest with Typical , but in the second year, High Compost outperformed Typical in crop yield. Principal component analysis showed a divergence in post-season <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msubsup><mml:mrow><mml:mtext>NO</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>-</mml:mo></mml:mrow></mml:msubsup></mml:math> between nutrient strategies in relation to compost and soil properties: High Compost using high-N composts increased post-season <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msubsup><mml:mrow><mml:mtext>NO</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>-</mml:mo></mml:mrow></mml:msubsup></mml:math> (0–30 cm), whereas relative yields in High Compost tended to be higher on farms with lower soil C and lower C:N composts, while yields with Typical were higher under opposite conditions but associated with higher post-season <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msubsup><mml:mrow><mml:mtext>NO</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>-</mml:mo></mml:mrow></mml:msubsup></mml:math> . Combining input types (e.g., Low Compost + N ) can meet environmental objectives in reducing surplus soil P without short-term yield or cost trade-offs compared to High Compost . However, maintaining soil C needs to be investigated to achieve effective ecological nutrient management in organic vegetable production with improved nutrient balances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".