Optimising Cropping Techniques for Nutrient and Environmental Management in Organic Agriculture
Bibliographic record
Abstract
Depth and architecture of root systems play a prominent role in crop productivity under conditions of low water and nutrient availability. The subsoil contains high amounts of nutrients that may potentially serve for nutrient uptake by crops including finite resources such as phosphorus that have to be used in moderation to delay their exhaustion. Biopores are tubular shaped continuous soil pores formed by plant roots and earthworms. Taproot systems especially those of perennial legumes can make soil nutrients plant available from the solid phase and increase the density of vertical biopores in the subsoil thus making subsoil layers more accessible for succeeding crops. Density of larger sized biopores is further enhanced by increased abundance and activity of anecic earthworms resulting from soil rest and amount of provided feed. Nutrient rich drilospheres can provide a favorable environment for roots and nutrient uptake of subsequent crops. Future efficient nutrient management and crop rotation design in organic agriculture should entail these strategies of soil fertility building and biopore services in subsoil layers site specifically. Elements of these concepts are suggested to be used also in mainstream agriculture headlands, e.g. as ‘Ecological Focus Areas’, in order to improve soil structure as well as to establish a web of biodiversity while avoiding constraints for agricultural production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".