A statistical evaluation of the relationship between cranberry yield in New Jersey and meteorological factors☆

DeGaetano, Arthur T.; Shulman, Mark D.

doi:10.1016/0168-1923(87)90045-1

Cited by 14 publications

(11 citation statements)

References 13 publications

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“…These comparisons similarly revealed no strong nor consistent relationship between accumulated GDD and PAR on yield between genetically confirmed populations of 'Stevens'. These findings are contrary to previous work that demonstrates the importance of climactic variables, particularly temperature and light, on cranberry yield (Degaetano and Shulman, 1987;Franklin and Cross, 1948). Warm temperatures during the months of May, June, Oct., and Nov. preceding harvest are associated with increased yields in New Jersey (Degaetano and Shulman, 1987).…”

Section: Site Predicted Yield (G) a Residualcontrasting

confidence: 85%

“…Variable selection was based on previous yield component studies and studies that identified important factors influencing yield (Degaetano and Shulman, 1987;Eaton and Kyte, 1978;Eaton and MacPherson, 1978;Franklin and Cross, 1948). Initial evaluation of measured predictor variables was performed using simple linear regression, whereby yield was regressed on each variable within single and combined growing cycles, as well as within and across grower sites for a given cultivar.…”

Section: Methodsmentioning

confidence: 99%

“…A second model that included berry number and accumulated GDD from bud initiation to 1 July of harvest year was also selected to test the influence of GDD on predicted yield. The accumulation of GDD from bud set to 1 July of harvest year was selected specifically because of its potential value for earlier crop forecasting without compromising significant R 2a adj values, which has been indirectly evaluated by Degaetano and Shulman (1987) and Franklin and Cross (1948). Because no method of GDD calculation emerged as being a superior predictor variable, the Perry method (Perry et al, 1986) was selected for subsequent tests of model performance.…”

Section: Model Generation and Validationmentioning

confidence: 99%

“…The North American cranberry industry performs annual crop estimates and these estimates are used to assist the establishment of crop prices potential for large genotype by environment (G × E) interactions that can also challenge yield prediction in cranberry. The potential for these interactions is poorly understood and has been largely overlooked in previous yield component and prediction studies in cranberry (Degaetano and Shulman, 1987;Eaton and Kyte, 1978;Eaton and MacPherson, 1978;Franklin and Cross, 1948;Morzuch et al, 1983).…”

Section: Introductionmentioning

confidence: 96%

See 3 more Smart Citations

Yield estimation in commercial cranberry systems using physiological, environmental, and genetic variables

DeVetter

Colquhoun

Zalapa

et al. 2015

Scientia Horticulturae

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Section: Site Predicted Yield (G) a Residualcontrasting

confidence: 85%

Section: Methodsmentioning

confidence: 99%

Section: Model Generation and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Yield estimation in commercial cranberry systems using physiological, environmental, and genetic variables

DeVetter

Colquhoun

Zalapa

et al. 2015

Scientia Horticulturae

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“…Studies suggest that high temperatures are a major limiting factor in cranberry production (Degaetano and Shulman 1987;DeMoranville et al 1996). Water table control may result in higher canopy temperatures than sprinkler irrigation, since in the latter case water is applied directly to the leaves, buds, and fruits, and is likely cooler than midday canopy temperatures.…”

Section: Avoiding Heat Stressmentioning

confidence: 99%

Irrigation and drainage management strategies to enhance cranberry production and optimize water use in North America

Caron¹,

Pelletier²,

Kennedy³

et al. 2017

Can. J. Soil. Sci.

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Recent research funding, as well as technological and management changes, has led to important scientific discoveries on irrigation and drainage of cranberry that could significantly impact on plant yield and water use. This paper integrates all this information into new proposed guidelines for irrigation and drainage management of cranberry. It explains the interaction of the different concepts, with the most recent ones published in this special issue. Cranberry yield is very sensitive to wet anaerobic conditions (soil matric potential >−4 kPa) or dry bed conditions (<−7 kPa) limiting capillary rise. It also appears that important water savings can be achieving by irrigating by a combination of overhead and subirrigation maintaining the top 15 cm of the bed within those soil matric potential limits and to meet an evapotranspiration demand up to 7.5 mm d −1 , provide frost and heat protection, and avoid salt accumulation, as this crop also appears sensitive to salinity stress. Finally, following plantings, soil properties appear to evolve dynamically and should be followed through profile observations, and combination of soil water potential and ground penetrating radar data, to identify potential yield limitations.Key words: cranberry, irrigation, drainage, aeration, frost protection, heat stress.Résumé : Le financement récent de recherches ainsi que des changements sur les plans de la technologie et de la conduite culturale ont donné lieu à d'importantes découvertes scientifiques sur l'irrigation et le drainage de la canneberge. Ces découvertes pourraient avoir un impact notable sur le rendement de la culture et l'utilisation de l'eau. L'article que voici regroupe ces informations pour proposer de nouvelles lignes directrices sur l'irrigation et le drainage des cultures de canneberge. On y explique l'interaction des différents concepts, dont les plus récents apparaissent dans ce numéro spécial. Le rendement de la canneberge est très sensible à l'humidité dans des conditions anaérobiques (>−4 kPa) et quand le lit de culture s'assèche (<−7 kPa), ce qui restreint l'ascension capillaire. Apparemment, on peut aussi réaliser d'importantes économies d'eau en irrigant la culture à la fois par aspersion et par apport souterrain, afin que les premiers 15 cm du lit de culture demeurent dans cette plage potentiel matriciel, de compenser une évapotranspiration de jusqu'à 7,5 mm par jour et de protéger la culture contre le gel, la chaleur et le sel, car elle paraît sensible à la salinité. Enfin, après la plantation, les propriétés du sol semblent connaître une évolution dynamique qu'on devrait suivre par observation du profil ainsi qu'au moyen des données sur le potentiel hydrique du sol et des relevés de géoradar, de manière à identifier les limites éventuelles du rendement. [Traduit par la Rédaction]

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A mathematical model for prediction of time course of vegetable supply based on meteorological factors

Yang

Chen

1989

Agricultural and Forest Meteorology

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A statistical evaluation of the relationship between cranberry yield in New Jersey and meteorological factors☆

Cited by 14 publications

References 13 publications

Yield estimation in commercial cranberry systems using physiological, environmental, and genetic variables

Yield estimation in commercial cranberry systems using physiological, environmental, and genetic variables

Irrigation and drainage management strategies to enhance cranberry production and optimize water use in North America

A mathematical model for prediction of time course of vegetable supply based on meteorological factors

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