Red Clover–Potato Cultivar Combinations for Improved Potato Yield
Bibliographic record
Abstract
One of the challenges to potato (Solanum tuberosum L.) production systems is to reduce N applications without incurring any marketable yield penalty. From previous studies, it was established that red clover (Trifolium pratense L.) can encourage the development of beneficial rhizobacterial communities that promote potato growth and development, in essence making agricultural soils as or more productive for specific quality attributes. In the present 2‐yr study, we examined the influence of the red clover cultivars AC Charlie, AC Endure, AC Kingston, Atlas, Marino, and Prosper on the potato cultivars Kennebec, Russet Burbank, and Shepody, grown in the following season. We found that the preceding clover cultivar had no influence on either Kennebec or Russet Burbank. However, Shepody potato following AC Kingston showed a significant yield advantage (P = 0.05), in tonnes per hectare, over other clover cultivars (except Atlas) in the Size 2 category of tubers (tubers >51 mm in diam. and <280 g), the grade for which growers are characteristically paid the most. We encourage breeding programs to examine the ability of any given line to manipulate its root zone microflora with respect to its own needs and to those of subsequent crops. While the complexities of plant–soil–microbial interactions are great, the beneficial biological interactions that stimulate crop yields and improve plant health can be evaluated relatively simply, and general management strategies can be devised accordingly for any given set of crop combinations and growing environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".