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Enregistrement W2591182099 · doi:10.17660/actahortic.2013.1000.53

THE QUALITY INDEX - A NEW TOOL FOR INTEGRATING QUANTITATIVE MEASUREMENTS TO ASSESS QUALITY OF YOUNG FLORICULTURE PLANTS

2013· article· en· W2591182099 sur OpenAlexaboutno aff
Christopher J. Currey, Ariana P. Torres, Roberto G. López, Douglass F. Jacobs

Notice bibliographique

RevueActa Horticulturae · 2013
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFlowering Plant Growth and Cultivation
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Food and AgricultureFred C. Gloeckner FoundationPurdue University
Mots-clésFloricultureIndex (typography)Quality (philosophy)Computer scienceBiologyHorticultureWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Floriculture crops are an important sector of ornamental horticulture, with an estimated wholesale value in the United States (U.S.) of US$ 4.13 billion in 2010. Furthermore, the value of propagative materials for these crops is US$ 376 million. The majority of floriculture crop producers utilize young plants including seedlings (plugs) or rooted stem-tip cuttings (liners) produced by propagation specialists and shipped to producers for finishing. Research has focused on improving the efficiency and quality of young plants production, since the advent of the “plug revolution” and increasing popularity of annuals produced from stem-tip cuttings has provided many challenges and opportunities in young plant production. Numerous quantitative measurements of seedlings and rooted cuttings are taken to measure the effects of environmental and/or cultural treatments during propagation. However, of greater importance to young plant producers is the cumulative effect of quantitative parameters on young plant quality. To resolve this, a subjective rating based on perceived visual quality is sometimes used. Here we present the Quality Index (QI), a tool integrating several quantitative measurements and indices to provide an objective assessment of young plant quality. For example, we found that when the daily light integral is increased during propagation of seeds and cuttings of new and current floriculture crops, the QI of young plants increased by up to 858%. We will introduce this concept and discuss the applications, opportunities, and limitations of using the QI for assessing the effects of environmental and/or cultural conditions during propagation of young plant quality in floriculture production. INTRODUCTION Rooted cuttings (liners) and seedlings (plugs) comprise a valuable sector of the global floriculture market. The use of young plants (plugs and liners) allows growers the ability to increase the consistency of their crop and minimize post-transplant finishing time. An increase in the popularity and use of young plants in the past few decades has resulted in more research regarding the effects of environmental conditions and cultural practices on the growth, development, and performance of plugs and liners. For example, research on cutting propagation of herbaceous annual bedding plants has focused on the effects of temperature and duration during shipping on cuttings (Lopez and Runkle, 2008) and environmental and cultural conditions during propagation, including substrate composition (Giselrod, 1983), misting (Graves and Zhang, 1996; Wilkerson et al., 2005a), substrate temperature (Wilkerson et al., 2005b), and mineral nutrition (Santos et al., 2008, 2009). In these studies morphological data were collected and, while the quality of rooted cuttings was not measured, many of the data reported reflect the quality of young plants. For example, important characteristics of seedlings and rooted cuttings include overall growth or biomass accumulation during propagation, the relative amount of root and shoot biomass (R:S ratio), and thickness of stems for easier handling and transplanting (Lopez and Runkle, 2008; Pramuk and Runkle, 2005). These types of data influence the quality and performance of young plants. Proc. VIIth IS on New Floricultural Crops Eds.: G. Facciuto and M.I. Sanchez Acta Hort. 1000, ISHS 2013 386 We may look to the reforestation and afforestation literature from greenhouse and nursery production of tree seedlings for a more comprehensive and integrated morphological assessment of young plant quality (Ritchie, 1984, 2010; Mattsson, 1996; Thompson, 1985). Forest nursery scientists have developed quantitative indices that integrate morphological traits correlated to outplanting success, including the “Dickson Quality Index” (Dickson et al., 1960). The quality index (QI) was originally designed for assessing the quality of Picea abies (L.) H. Karst. and Pinus strobus L. seedlings (Dickson et al., 1960), and is a product of the total dry mass (TDM) divided by the sum of the shoot: root dry mass ratio (S:R) and ratio of stem caliper to stem length (sturdiness quotient; SQ), or QI = TDM/(S:R + SQ). Because these data are frequently collected in young floriculture crop research, an integrated index such as QI provides a tool to easily and objectively measure young plant quality. We have found no reports using any indices such as the QI to assess the quality of young herbaceous floriculture propagules. Therefore, our objective with this research was to evaluate the potential of the QI as an integrated, quantitative measurement of seedling and rooted cutting quality. MATERIALS AND METHODS Experiment 1 Seeds of Tecoma stans (L.) Juss. ex Kunth ‘Mayan Gold’ (Pan American Seed, West Chicago, IL, USA) were sown on 13 Feb. 2009, 23 June 2009, and 15 Jan. 2010 in 72-cell plug trays (44-mL individual cell volume; Dillen Products, Middlefield, OH, USA) filled with a commercial soilless medium composed of (v/v) 70% Canadian sphagnum peat moss and 30% perlite (Super Fine Germinating Mix; Conrad Fafard, Anderson, SC, USA). Seeds were covered with a thin layer of vermiculite (Sunshine; SunGro Horticulture, Bellevue, WA, USA) to maintain moisture and were irrigated as necessary with acidified water supplemented with water-soluble fertilizer (Peters Excel© Cal-Mag© 15N–2.2P–12.5K; Scotts Co., Marysville, OH, USA) to provide 100 mg·L-1 N with every watering beginning at sowing. The greenhouse air temperature set point was a constant 23°C. A 16-h photoperiod (5 am to 9 pm) was maintained with natural day lengths and day-extension lighting provided from high-pressure sodium (HPS) lamps (e-system HID; PARsource, Petaluma, CA, USA). An automatic woven shade curtain was retracted when the outdoor light intensity reached 1000 μmol·m-2·s-1 (OLS 50; Ludvig Svensson Inc., Charlotte, NC, USA) throughout the study to prevent leaf scorch. Immediately after sowing, seeds were placed under daily light integral (DLI) treatments created in the propagation environment with the combination of supplemental light provided by HPS lamps and fixed woven shade cloths placed above individual propagation compartments that reduced light by 30, 50, or 70% (DeWitt Company, Sikeston, MO, USA) or no shade. Five weeks after sowing, fifteen seedlings per DLI treatment were randomly harvested for data collection. Stem caliper above the lowest leaf and stem length from the surface of the substrate to the stem tip were measured with a digital caliper (digiMax; Wiha, Schonach, Germany). Roots were excised and roots and shoots were dried separately in an oven at 70°C for 3 d then weighed. Total dry mass (TDM; shoot dry mass + root dry mass), root: shoot dry mass ratio (R:S; root dry mass/stem dry mass), a modified version of the sturdiness quotient (SQ; shoot length/stem caliper) (Thompson, 1985), and a modified version of the Quality Index [QI = TDM × (R:S + SQ)] (Dickson, 1960) were calculated. Data were analyzed using regression analysis (SPSS 17.0; SPSS, Inc., Chicago, IL, USA) with DLI as the independent variable. Experiment 2 Cuttings of Angelonia angustifolia Benth. ‘AngelMist White Cloud’, Argyranthemum frutescens (L.) Sch. Bip. ‘Madeira Cherry Red’, Diascia barberae Hook. f.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,006
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,005
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

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.

Tête enseignante Opus0,142
Tête enseignante GPT0,333
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations13
Publié2013
Routes d'admission1
Résumé présentoui

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