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J.G. Carew, K. Mahmood, J. Darby, P. Hadley and N.H. Battey

The effects of temperature, photosynthetic photon flux density (PPFD) and photoperiod on vegetative growth and flowering of the raspberry (Rubus idaeus L.) `Autumn Bliss' were investigated. Increased temperature resulted in an increased rate of vegetative growth and a greater rate of progress to flowering. Optimum temperatures lay in the low to mid 20°C range. Above this the rate of plant development declined. Increased PPFD also advanced flowering. While photoperiod did not significantly affect the rate of vegetative growth, flowering occurred earliest at intermediate photoperiods and was delayed by extreme photoperiods. These responses suggest that there is potential for adjusting cropping times of raspberry grown under protection by manipulating the environment, especially temperature.

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Carlos Miranda Jiménez and J. Bernardo Royo Díaz

Spring frosts are usual in many of Spain's fruit-growing areas, so it is common to insure crops against frost damage. After a frost, crop loss must be evaluated, by comparing what crop is left with the amount that would have been obtained under normal conditions. Potential crop must be evaluated quickly through the use of measurements obtainable at the beginning of the tree's growth cycle. During the years 1997 through 1999 and in 86 commercial plots of peach and nectarine [Prunus persica (L.) Batsch], the following measurements were obtained: trunk cross-sectional area (TCA, cm2), trunk cross sectional area per hectare (TCA/ha), estimated total shoot length per trunk cross-sectional area (SLT, shoot m/cm2 TCA), crop density (CD, amount of fruit/cm2 TCA), fruit weight (FW, g), yield efficiency (YE, kg of fruit/cm2 TCA), yield per tree (Y, kg fruit/tree) and days between full bloom and harvest (BHP, days). CD and average FW were related to the rest of the variables through the use of multiple regression models. The models which provided the best fit were CD = SLT - TCA/ha and FW = SLT + BHP - CD. These models were significant, consistent, and appropriate for all three years. The models' predictive ability was evaluated for 32 different plots in 2001 and 2002. Statistical analysis showed the models to be valid for the forecast of orchards' potential yield efficiency, so that they represent a useful tool for early crop prediction and evaluation of losses due to late frosts.

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Carlos Miranda Jiménez and J. Bernardo Royo Díaz

Spring frosts are usual in many of Spain's fruit-growing areas, so it is common to insure crops against frost damage. After a frost, crop loss must be evaluated, by comparing what crop is left with the amount that would have been obtained under normal conditions. Potential crop must be evaluated quickly through the use of measurements obtainable at the beginning of the tree's growth cycle. During the years 1998 and 1999 and in 62 commercial plots of `Golden Delicious' and `Royal Gala' apple (Malus ×domestica Borkh.), the following measurements were obtained: trunk cross-sectional area (TCA, cm2), space allocated per tree (ST, m2) trunk cross-sectional area per hectare (TCA/ha), flower density (FD, number of flower buds/cm2 TCA), flower density per land area (FA, number of flower buds/m2 land area), cluster set (CS, number of fruit clusters/number of flower clusters, percent), crop density (CD, number of fruit/cm2 TCA), fruit clusters per trunk cross-sectional area (FCT, number of fruit clusters/cm2 TCA), fruit clusters per land area (FCA, number of fruit clusters/m2 land area), fruit number per cluster (FNC), average fruit weight (FW, g), average yield per fruit cluster (CY, g), yield efficiency (YE, fruit g·cm-2 TCA), and tree yield (Y, fruit kg/tree). FCT and average CY were related to the rest of the variables through the use of multiple regression models. The models which provided the best fit were FCT = FD - TCA/ha - FD and CY= -FCA - FCT. These models were significant, consistent, and appropriate for both years. Predicted yield per land area was obtained by multiplying TCA/ha × FCT × CY. The models' predictive ability was evaluated for 64 different plots in 2001 and 2002. Statistical analysis showed the models to be valid for the forecast of potential yields in apple, so that they represent a useful tool for early crop prediction and evaluation of losses due to late frosts.

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Carlos Miranda Jiménez and J. Bernardo Royo Díaz

Spring frosts are usual in many of Spain's fruit-growing areas, so it is common to insure crops against frost damage. After a frost, crop loss must be evaluated, by comparing what crop is left with the amount that would have been obtained under normal conditions. Potential crop must be evaluated quickly through the use of measurements obtainable at the beginning of the tree's growth cycle. During 1996 and 1997 and in 95 commercial plots of `Blanquilla' and `Conference' pear (Pyrus communis L.), the following measurements were obtained: trunk cross-sectional area (TCA, cm2), space allocated per tree (ST, m2), trunk cross-sectional area per hectare (TCA/ha), flower density (FD, number of flower buds/cm2 TCA), flower density per land area (FA, number of flower buds/m2 land area), cluster set (CS, number of fruit clusters/number of flower clusters, %), crop density (CD, number of fruit/cm2 TCA), fruit clusters per trunk cross-sectional area (FCT, number of fruit clusters/cm2 TCA), fruit clusters per land area (FCA, number of fruit clusters/m2 land area), fruit number per cluster (FNC), average fruit weight (FW, g), average yield per fruit cluster (CY, g), yield efficiency (YE, fruit g·cm-2 TCA), and tree yield (Y, fruit kg/tree). CS and average CY were related to the rest of the variables through the use of multiple regression models. The models that provided the best fit were CS = TCA/ha - FA and CY = -FA - FCT. These models were significant, consistent, and appropriate for both years. Predicted yield per land area was obtained by multiplying FA × CS × CY. The models' predictive ability was evaluated for 46 different plots in 2001 and 2002. Statistical analysis showed the models to be valid for the forecast of orchards' potential yield efficiency, so that they represent a useful tool for early crop prediction and evaluation of losses due to late frosts.

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Justin Morris, Gary Main, Renee Threlfall and Keith Striegler

in a uniform vineyard: 1) number of bearing vines per block, 2) clusters per vine, 3) cluster weight at lag phase of berry growth, and 4) cluster weight at harvest. Crop prediction can also be accomplished using yield predictor data, including berry