An evaluation of estimating and indexing methods to simplify the determination of management treatment effects on raspberry yields
Notice bibliographique
Résumé
The effectiveness of using several proposals to estimate or index yield and size of raspberries as an alternative to picking berries as they ripen was examined in two field plot trials over two seasons at two locations in south coast British Columbia. The evaluation included examination of general correlations of the proposed estimate and index values with fresh picked yield, comparison of the significant nutrient and inter-row management treatment effects on proposed method values with effects on fresh picked yield values, influence of individual cane variability to distinguish significant treatment effects, and the effect of N on plant components used to derive the estimate and index method. Correlation coefficients for all yield estimate and index method values with fresh picked yields were generally good. Crop management treatment effects determined by the estimate and index values, however, were not the same as determined by harvesting the berries as they ripened. This showed that the estimate and index method values were biased relative to picked yield. Cane-to-cane variability within individual treatment plots was sufficiently large that differences between treatments had to be greater than 10 to 15% to be significant at P < 0.05 when five canes were randomly sampled for index component measurements to represent the plants in the plot. The five canes sampled for each plot were 5 to 10% of all the floricanes in the plots of this study. The concentration and biomass N measurements that were possible on the floricane components that were sampled for the index methods showed that management treatments of the two trials of the study could have influenced berry development, and hence contributed to the bias of the estimate and index method values relative to fresh picked yield. Although the estimate and index methods were generally quite well correlated with fresh picked yield, caution is advised when they are used directly as alternatives to fresh picking to evaluate crop management treatment effects on berry yield. Further knowledge about the physiological changes that occur during berry ripening may provide opportunities to improve the estimate and index measurements. Key words: Raspberry, Rubus idaeus L., yield estimate, yield index, nutrient effects, nitrogen effects
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».