• Energy Research

In the current context of global warming, an analysis is required of spatially-extensive and long-term blooming data in fruit trees to make up for insufficient information on regional-scale blooming changes and determinisms that are key to the phenological adaptation of these species. We therefore analysed blooming dates over long periods at climate-contrasted sites in Western Europe, focusing mainly on the Golden Delicious apple that is grown worldwide. On average, blooming advances were more pronounced in northern continental (10 days) than in western oceanic (6-7 days) regions, while the shortest advance was found on the Mediterranean coastline. Temporal trends toward blooming phase shortenings were also observed in continental regions. These regional differences in temporal variability across Western Europe resulted in a decrease in spatial variability, i.e. shorter time intervals between blooming dates in contrasted regions (8-10-day decrease for full bloom between Mediterranean and continental regions). Fitted sequential models were used to reproduce phenological changes. Marked trends toward shorter simulated durations of forcing period (bud growth from dormancy release to blooming) and high positive correlations between these durations and observed blooming dates support the notion that blooming advances and shortenings are mainly due to faster satisfaction of the heating requirement. However, trends toward later dormancy releases were also noted in oceanic and Mediterranean regions. This could tend toward blooming delays and explain the shorter advances in these regions despite similar or greater warming. The regional differences in simulated chilling and forcing periods were consistent with the regional differences in temperature increases.

In the current context of global warming, an analysis is required of spatially-extensive and long-term blooming data in fruit trees to make up for insufficient information on regional-scale blooming changes and determinisms that are key to the phenological adaptation of these species. We therefore analysed blooming dates over long periods at climate-contrasted sites in Western Europe, focusing mainly on the Golden Delicious apple that is grown worldwide. On average, blooming advances were more pronounced in northern continental (10 days) than in western oceanic (6-7 days) regions, while the shortest advance was found on the Mediterranean coastline. Temporal trends toward blooming phase shortenings were also observed in continental regions. These regional differences in temporal variability across Western Europe resulted in a decrease in spatial variability, i.e. shorter time intervals between blooming dates in contrasted regions (8-10-day decrease for full bloom between Mediterranean and continental regions). Fitted sequential models were used to reproduce phenological changes. Marked trends toward shorter simulated durations of forcing period (bud growth from dormancy release to blooming) and high positive correlations between these durations and observed blooming dates support the notion that blooming advances and shortenings are mainly due to faster satisfaction of the heating requirement. However, trends toward later dormancy releases were also noted in oceanic and Mediterranean regions. This could tend toward blooming delays and explain the shorter advances in these regions despite similar or greater warming. The regional differences in simulated chilling and forcing periods were consistent with the regional differences in temperature increases.

Climate change is responsible for major alterations in the phenology of fruit trees in Germany. Observations over the past 70 years at 3 study sites in Germany have shown the flowering of apple trees and subsequently, fruit development to set is continuously earlier, with serious implications for fruit quality as well as storability. Calcium (Ca) plays an important role in stabilizing cell walls in all fruit including apple; sufficient calcium supply is, therefore, essential for good storability and for controlling postharvest disorders. However, as Ca is phloem immobile, the transport of the mineral into the fruit occurs solely with the transpiration stream through the xylem. Ca accumulates in the first weeks after flowering, in which the fruit shows the highest transpiration per unit (surface or mass) and subsequently the greatest transport of water. With an earlier flower onset, Ca uptake may suffer as the evapotranspiration and therefore the transport of Ca is reduced due to fewer sunlight hours in a shorter photoperiod and a smaller thermal amplitude early in the season. However, Ca deficiency and an unfavorable relation to its antagonist potassium can increase the risk of the accelerated loss of firmness or the emergence of disorders such as bitter pit or flesh browning. On account of this significant role of Ca for long-term fruit storability, a project was launched with the objective to assess the long-term effects of climate change on the Ca nutrition of apples cultivated in Germany. Climate data of the past 60 years will be analyzed and related to the recordings of apple tree phenology development stages in the respective years, in order to estimate the evapotranspiration potential during the main period of Ca uptake. Historic Ca data of ‘Golden Delicious’ apples, recorded starting in the 1970s at the Lake of Constance Research Center for Fruit Cultivation (KOB), will be analyzed to investigate trends in the Ca nutrition of apple fruit. Ultimately, the project aims to develop a multivariate model for the prediction of Ca content in apples at harvest, that incorporates factors such as the weather during the season, soil type, tree age, cultivar and cultivation practices. In the long term, this is intended to support fruit production in adaption to challenges presented by climate change.

Climate change is responsible for major alterations in the phenology of fruit trees in Germany. Observations over the past 70 years at 3 study sites in Germany have shown the flowering of apple trees and subsequently, fruit development to set is continuously earlier, with serious implications for fruit quality as well as storability. Calcium (Ca) plays an important role in stabilizing cell walls in all fruit including apple; sufficient calcium supply is, therefore, essential for good storability and for controlling postharvest disorders. However, as Ca is phloem immobile, the transport of the mineral into the fruit occurs solely with the transpiration stream through the xylem. Ca accumulates in the first weeks after flowering, in which the fruit shows the highest transpiration per unit (surface or mass) and subsequently the greatest transport of water. With an earlier flower onset, Ca uptake may suffer as the evapotranspiration and therefore the transport of Ca is reduced due to fewer sunlight hours in a shorter photoperiod and a smaller thermal amplitude early in the season. However, Ca deficiency and an unfavorable relation to its antagonist potassium can increase the risk of the accelerated loss of firmness or the emergence of disorders such as bitter pit or flesh browning. On account of this significant role of Ca for long-term fruit storability, a project was launched with the objective to assess the long-term effects of climate change on the Ca nutrition of apples cultivated in Germany. Climate data of the past 60 years will be analyzed and related to the recordings of apple tree phenology development stages in the respective years, in order to estimate the evapotranspiration potential during the main period of Ca uptake. Historic Ca data of ‘Golden Delicious’ apples, recorded starting in the 1970s at the Lake of Constance Research Center for Fruit Cultivation (KOB), will be analyzed to investigate trends in the Ca nutrition of apple fruit. Ultimately, the project aims to develop a multivariate model for the prediction of Ca content in apples at harvest, that incorporates factors such as the weather during the season, soil type, tree age, cultivar and cultivation practices. In the long term, this is intended to support fruit production in adaption to challenges presented by climate change.

(1) Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3) Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. “Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4) Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

(1) Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3) Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. “Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4) Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.