Evidence of the Interaction Between Crop Species and Organic Amendments: Modelling of the Differential Grain Yield Response of Wheat, Soybean, and Canola to Organic Amendments
Bibliographic record
Abstract
Knowledge on the interaction between plants and organic amendments is critical for the basic understanding of agroecosystems sustainability. Organic amendments are of great interest in agriculture by virtue of their ability to restore lost soil organic carbon in eroded or conventionally cultivated soils. The major objective of this study was to demonstrate and model the differential response of crop species to organic amendments. Despite the potential of such an interaction to improve crop production, it has never been formally demonstrated in a planned experiment. A two-year greenhouse experiment set as 3×3×5 factorial in a strip-split plot design was conducted. The effects of crop species, type of organic amendment, and application rates on grain yield of soybean, canola, and wheat were evaluated. To account for the asymmetry of the concave responses of soybean, mathematical transcendental models were fitted, for the first time, to yield data. The interaction between crop species and amended soils was highly significant. Soybean displayed concave transcendental yield responses whereas canola and wheat exhibited negative exponential responses, irrespective of the type of amendment. Turkey compost outperformed turkey litter and beef manure by 30% and 52%, respectively, with respect to soybean production; whereas turkey litter outperformed turkey compost and beef manure by 144% and 264%, respectively, with respect to canola and wheat production. It is concluded that in greenhouse settings and perhaps field conditions, growth and development of crop species can be enhanced by matching the specific characteristics of organic amendments to the specific nutrients demand of crop species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".