Photosynthetic Responses to Heat Treatments at Different Temperatures and following Recovery in Grapevine (Vitis amurensis L.) Leaves

BACKGROUND: The electron transport chain, Rubisco and stomatal conductance are important in photosynthesis. Little is known about their combined responses to heat treatment at different temperatures and following recovery in grapevines (Vitis spp.) which are often grown in climates with high temperatures. METHODOLOGY/FINDINGS: The electron transport function of photosystem II, the activation state of Rubisco and the influence of stomatal behavior were investigated in grapevine leaves during heat treatments and following recovery. High temperature treatments included 35, 40 and 45°C, with 25°C as the control and recovery temperature. Heat treatment at 35°C did not significantly (P>0.05) inhibit net photosynthetic rate (P(n)). However, with treatments at 40 and 45°C, P(n) was decreased, accompanied by an increase in substomatal CO(2) concentration (C(i)), decreases in stomatal conductance (g(s)) and the activation state of Rubisco, and inhibition of the donor side and the reaction center of PSII. The acceptor side of PSII was inhibited at 45°C but not at 40°C. When grape leaves recovered following heat treatment, P(n), g(s) and the activation state of Rubisco also increased, and the donor side and the reaction center of PSII recovered. The increase in P(n) during the recovery period following the second 45°C stress was slower than that following the 40°C stress, and these increases corresponded to the donor side of PSII and the activation state of Rubisco. CONCLUSIONS: Heat treatment at 35°C did not significantly (P>0.05) influence photosynthesis. The decrease of P(n) in grape leaves exposed to more severe heat stress (40 or 45°C) was mainly attributed to three factors: the activation state of Rubisco, the donor side and the reaction center of PSII. However, the increase of P(n) in grape leaves following heat stress was also associated with a stomatal response. The acceptor side of PSII in grape leaves was responsive but less sensitive to heat stress

Increasing the source/sink ratio in Vitis vinifera (cv Sangiovese) induces extensive transcriptome reprogramming and modifies berry ripening

<p>Abstract</p> <p>Background</p> <p>Cluster thinning is an agronomic practice in which a proportion of berry clusters are removed from the vine to increase the source/sink ratio and improve the quality of the remaining berries. Until now no transcriptomic data have been reported describing the mechanisms that underlie the agronomic and biochemical effects of thinning.</p> <p>Results</p> <p>We profiled the transcriptome of <it>Vitis vinifera </it>cv. Sangiovese berries before and after thinning at veraison using a genome-wide microarray representing all grapevine genes listed in the latest V1 gene prediction. Thinning increased the source/sink ratio from 0.6 to 1.2 m²leaf area per kg of berries and boosted the sugar and anthocyanin content at harvest. Extensive transcriptome remodeling was observed in thinned vines 2 weeks after thinning and at ripening. This included the enhanced modulation of genes that are normally regulated during berry development and the induction of a large set of genes that are not usually expressed.</p> <p>Conclusion</p> <p>Cluster thinning has a profound effect on several important cellular processes and metabolic pathways including carbohydrate metabolism and the synthesis and transport of secondary products. The integrated agronomic, biochemical and transcriptomic data revealed that the positive impact of cluster thinning on final berry composition reflects a much more complex outcome than simply enhancing the normal ripening process.</p

Yield prediction in apple orchards based on image processing

It has been suggested that apple ( Malus * domestica Borkh) flowering distribution maps can be used for site-specific management decisions. The objectives of this study were (i) to study the flower density variability in an apple orchard using image analysis and (ii) to model the correlation between flower density as determined from image analysis and fruit yield. The research was carried out in a commercial apple orchard in Central Greece. In April 2007, when the trees were at full bloom, photos of the trees were taken following a systematic uniform random sampling procedure. In September 2007, yield mapping was carried out measuring yield per ten trees and recording the position of the centre of the ten trees. Using this data (the measured yield of the trees and the pictures samples, representing the flower distribution), an image processing-based algorithm was developed that predicts tree yield by analyzing the picture of the tree at full bloom. For the evaluation of the algorithm, a case study scenario is presented where the error of the predicted yield was set at 18%. These results indicated that potential yield could be predicted early in the season from flowering distribution maps and could be used for orchard management during the growing season