Combining Plant Growth with Nutrient Content Measurements as a Method to Compare Nutrient Use by Different Raspberry Cultivars
Bibliographic record
Abstract
Monthly above-ground destructive sampling and partitioning of the plant into distinct component parts for growth and nutrient element (N, P, K, Ca, Mg, Na, B, Cu, Zn, Fe and Mn) analyses were used to compare elemental compositions in two red raspberry (Rubus idaeus L.) cultivars (‘Willamette’ and ‘Haida’) grown in a uniformly managed field plot. Quantitative measurements showed that the two cultivars had different growth patterns. ‘Haida’ yielded greater fresh berry weight and dry matter content than ‘Willamette’ on a per floricane basis, but, because of fewer ‘Haida’ than ‘Willamette’ floricanes in the plot after standard commercial pruning practices, fresh berry yield was similar for both cultivars on a unit area basis. ‘Haida’ berries ripened slightly earlier than ‘Willamette’. Stem, lateral and leaf growth differed between the two cultivars. Although only whole above-ground accumulation (kg ha-1) of one (Ca) of the ten elements measured was different in the two cultivars, the amounts and patterns of all nutrients in the various plant components of floricanes and primocanes differed in significant ways. In some cases, element concentrations in a specific plant component differed between cultivars while dry matter accumulation differed in the opposite way resulting in the same total accumulation in the cane involved. In other cases, there was greater accumulation in one cane type (primocane vs. floricane) than the other such that there was similar accumulation of that element in the whole plant. For Ca, accumulation in primocanes was greater for ‘Willamette’ than ‘Haida’ whereas there was no difference of accumulation in floricanes which resulted in greater accumulation of Ca in the whole above-ground plant in ‘Willamette’. Maximum accumulation of the elements in floricanes occurred generally earlier in the growing season (July to September) than in primocanes (September to October), and these maxima were frequently at different times for the two cultivars, making comparisons of nutrient compositions in the two cultivars complex. Detailed sampling periodically over the growing season in combination with element analysis of these raspberry plant components as done in this study provided a better basis for comparing nutrients in raspberry cultivars than examining element concentrations of a specific plant component (e.g., leaves) sampled once during the growing season because patterns of nutrients over time in the cultivars differed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".