The role of crop rotations in determining soil structure and crop growth conditions
Bibliographic record
Abstract
Increasing concern about the need to provide high-quality food with minimum environmental impact has led to a new interest in crop rotations as a tool to maintain sustainable crop production. We review the role of rotations in the development and preservation of soil structure. After first introducing the types of rotations in current practice and their impact on yield, we assess how soil and crop management in rotations determines soil structure, and in turn how soil structure influences crop growth and yield. We also briefly consider how soil structure might contribute to other beneficial effects of rotations, namely nutrient cycling and disease suppression. Emphasis is given to the influence of crop choice and, where relevant, interaction with tillage system and avoidance of compaction in the improvement and maintenance of soil structure. Crop rotations profoundly modify the soil environment. The sequence of crops in rotation not only influences the removal of nutrients from a soil, but also the return of crop residues, the development and distribution of biopores and the dynamics of microbial communities. These processes contribute to the development of soil structure. We have identified areas where further research is needed to enable the potential benefits of rotations in the management of soil structure to be fully exploited. These include: improved quantitative linkages between soil structure and crop growth, the consequences to soil structure and nutrient cycling of crop residue incorporation, developing natural disease suppression, amelioration of subsoils by crop roots, the fate of carbon deposited by plant roots in soil and the fate of organic nitrogen in soil. Key words: Organic farming, microbial activity, nutrient cycling, compaction, disease suppression, soil structure
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".