Critical irrigation threshold and cranberry yield components

Pelletier, Vincent; Gallichand, Jacques; Caron, Jean; Jutras, Sylvain; Marchand, Sébastien

doi:10.1016/j.agwat.2014.09.025

Cited by 15 publications

(27 citation statements)

References 19 publications

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“…Water limitation was also previously associated with a significant reduction of the number of berries per upright and fruit set leading to a significant reduction of final yield [4]. When root water uptake is less than the potential evapotranspiration, which is caused by insufficient water fluxes, cranberries transpiration is affected and photosynthesis is reduced [6].…”

Section: Yield Componentsmentioning

confidence: 99%

“…Despite its high sugar content, the high concentration in titratable acidity causes the bitter taste of cranberry. The most important yield components related to final yield are the number of marketable berries per area, the number of fruiting uprights per area, the number of marketable berries per upright, and the fruit set [4]. Flower buds are formed during the summer of the previous year while berries formed in early July grow until harvest in October.…”

Section: Introductionmentioning

confidence: 99%

“…For maximizing yield, yield components, and water productivity, there is evidence that the pump should be turned on when soil water tension (SWT) at 10 cm depth in the root zone reaches 7.5 kPa [4][5][6]. Cranberry evapotranspiration ranges between 0.5-4.0 mm day −1 in Washington [7] whereas the maximum value was found to be 5.0 mm day −1 in Wisconsin [8].…”

Section: Introductionmentioning

confidence: 99%

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Water Table Control for Increasing Yield and Saving Water in Cranberry Production

et al. 2015

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Abstract:Water table control has been successfully tested to improve the sustainability of water management in cranberry production. In the province of Québec (Canada), three sites were investigated to determine the optimum water table depth below soil surface (WTD) using three criteria: (1) increasing yield without decreasing fruit quality; (2) minimizing the amount of water needed by the sprinkler system; and (3) avoiding hypoxic stresses in the rhizosphere. Our results show that the final yield, the berry sugar content, the total number of berries, the number of berries per upright, and the fruit set were maximized when the WTD was 60 cm. Sprinkler water savings of 77% were obtained where the WTD was shallower than 66 cm. In order to avoid hypoxic conditions due to poor drainage, the water level in the canals surrounding the beds should be lowered to 80 cm when a rainfall or a frost protection irrigation is anticipated. All sides of a block of beds must be surrounded by canals to ensure a uniform WTD and to avoid lateral hydraulic gradients that could cause peripheral seepage losses.

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Section: Yield Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Water Table Control for Increasing Yield and Saving Water in Cranberry Production

et al. 2015

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“…It is generally recognized that fruit set and the number of fruiting uprights are the main components of yield responsible for its variation (Eaton andKyte 1978, Baumann andEaton 1986). The major contribution to explain yield variability by NU/ m 2 was found earlier by Pelletier et al (2015). The authors explained this phenomenon as yield determination by bud initiation during the year prior to sampling.…”

Section: Discussionmentioning

confidence: 71%

“…Various statistical and biometrical methods were used to explain variation in cranberry yield, i.e. sequential yield component analysis on logarithmic data (Eaton and Kyte 1978), simple regression (Pelletier et al 2015) and multiple regression (DeVetter et al 2015). The objective of the presented work was to evaluate the possibility of cranberry cultivar cultivation in a non-flooding system on mineral soils of different pH.…”

Section: Introductionmentioning

confidence: 99%

Initial growth and yield structure of selected cultivars of cranberry (Vaccinium macrocarpon Ait.) cultivated on mineral soils

Szwonek¹,

Maciorowski²,

Koziński³

et al. 2016

Folia Horticulturae

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A study was conducted to evaluate the possibility of cranberry cultivation on mineral soils and to assess the influence of vegetative biomass development, generative growth and yield components on the yielding of three cranberry cultivars originating in the USA (Stevens, Pilgrim and Ben Lear) at two locations in Poland. The key biometrical traits involved in yield formation were taken into account, and the soil and plant chemical conditions were evaluated. All of the measured biometrical characteristics were strongly influenced by the location and the year of cultivation, and varietal differences were also noted. The most important determinants that explained yield variation were: the number of uprights per square meter, floral induction and berry set. However, the participation of each component in yield variation was strongly affected by the location, age of plantation and to a minor extent by the cultivar. The study confirmed the possibility of cranberry cultivation on mineral soils with a low pH. The biggest average yield of the three years was collected from cv. Stevens as cultivated on sandy soil in contrast to the same cultivar grown on sandy loam soil. In the case of sandy loam soil after acidification, cv. Pilgrim appeared to be a relatively better yielding cultivar.

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Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

Brédy

Gallichand

Celicourt

et al. 2020

Agricultural Water Management

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Critical irrigation threshold and cranberry yield components

Cited by 15 publications

References 19 publications

Water Table Control for Increasing Yield and Saving Water in Cranberry Production

Water Table Control for Increasing Yield and Saving Water in Cranberry Production

Initial growth and yield structure of selected cultivars of cranberry (Vaccinium macrocarpon Ait.) cultivated on mineral soils

Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

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