Assessing organic potato cropping systems at the end of the first cycle of four-year rotations using principal component analysis
Bibliographic record
Abstract
A single or a few variables may not be sufficient to evaluate management practice effects in a complicated cropping system, so six plant and 13 soil variables were integrated using principal component analysis (PCA) to examine nine 4-yr organic potato rotations. The rotations were combinations of three forage levels (0, 1, and 2 yr of forages) with three soil amendments (monogastric compost, ruminant compost, and alfalfa meal). Quantities of amendments were estimated by soil test recommendations and amendment nutrient availabilities. In the 4th potato year, one half of each original plot was not amended ("the 4th year unamended plots"), while the other half received soil amendments ("4th year amended plots"). The first three principal components explained 67 and 63% of the overall variation for the 4th-yr amended and unamended plots, respectively. PCA ordination plots indicated that, overall, the type of soil amendments had larger effects on soil and plant variables, but forage frequencies were influential for the amendments showing weaker effects. PCA loading plots indicated that plant nutrient uptake and potato total tuber weight would be the best single variables for characterizing the current cropping systems. Plant variables, except for potato petiole nitrate, were closely displayed, but they were not strongly correlated with soil variables, which may reflect the high background fertility of this site. Applications of soil amendments in the 4th yr affected the relationships among variables, most notably the strength of relationships between soil pH and soil N variables. The results suggest that PCA provides an effective way to compare complex cropping systems, especially in situations with high site heterogeneity. Key words: Principal component analysis, soil amendment, livestock system, forage, potato, organic crop rotation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".