Bed, ridge and planting configurations influence crop performance in field-transplanted hybrid potato crops
Bibliographic record
Abstract
Current cultivation practices for field transplanted potato crops grown from nursery-raised hybrid potato seedlings are mostly borrowed from the tuber-based conventional system. Most studies on field performance of field transplanted seedling crops have largely reported the use of ridged rows and in exceptional cases, the use of beds. It is therefore critical to assess the feasibility of the use of alternative ridge or bed systems for cultivation of field-transplanted nursery-raised potato seedlings considering the differences in physiological behaviour of crops grown from different starting materials. This study assessed the effects of six systems which included bed and ridge systems of different dimensions and planting configurations for field transplanted seedling crops. Field crop establishment, canopy growth and development as well as yield and yield components were assessed. In general, systems that boasted high plant densities resulted in faster canopy development and higher number of tubers and tuber yield. Bed systems (raised and flat beds; 8.0 plants m −2 ) therefore gave the highest numbers of tubers and tuber yield across all treatments. These systems also produced the most tubers in all tuber size classes resulting in the highest yields in all classes. Standard ridge systems (full- and half ridges; 0.75 m row distance), had the lowest plant populations (5.3 plants m −2 ) which resulted almost always in fewer tubers and lower yield. Other ridge systems (0.9-m and 0.5-m ridge systems), although having higher plant densities than the standard ridge systems (8.9 and 8.0 plants m −2 , respectively) still performed poorer than the bed systems. The small and compact ridges in the 0.5-m ridge system and the compact arrangement of plants in the 0.9-m ridge system caused these effects. Conclusively, based on this study, productivity in field transplanting systems is highly influenced by plant density. Further, cultivation systems boasting higher planting densities should be recommended when the goal for production is to produce large quantities of seed tubers (> 35; ≤ 50 mm). • Higher planting densities favour faster canopy development in seedling-based crops • Bed systems give more tubers and higher tuber yield than ridge systems • Standard ridges are only suitable for tuber-based, not for seedling-based crops • High planting densities are required to produce large quantities of seed tubers
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".