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Physiological storage disorders cause significant economic losses in a number of commercially important pome fruit varieties worldwide. Under the same storage conditions with the same cultivar, the incidence and frequency of disorders may vary in different years in a manner that must be explained by an interaction of pre-harvest and post-harvest factors. Major factors known to influence disorder incidence include annual weather pattern, management in the orchard, such as leaf-fruit ratio, calcium content of the fruit, harvest date and storage conditions. So far, the occurrence of the disorders cannot be predicted with certainty, and thus to adapt the management in the orchard or in the storage facility accordingly. Analysis of large amounts of data using Artificial Intelligence (AI) offers new opportunities to link large data sets into a meaningful context. The aim of the present work is the small-scale and non-destructive monitoring of fruit development in orchards under the above mentioned management practices. AI prediction models were created for the cultivar 'Braeburn'. 'Braeburn' is most susceptible to physiological storage disorders which cause browning in the fruit tissue below the skin. With tent-like constructions over the fruit trees, in which the temperature was controlled during two fruit physiologically important periods (petal drop to T-stage, three weeks before harvest), the temperature influence on disorder incidence was investigated. To the best of our knowledge, this was the first time that different temperature profiles could be generated in the orchard for mature 'Braeburn' trees. Thereby a positive influence on the reduction of internal browning caused by warm night temperatures (>10°C) before harvest was observed. Overall, each trial year showed different occurrences of physiological disorders. Orchard management and weather conditions resulted in significantly different fruit growth patterns and optical non-destructive point spectroscopy. The bi-weekly spectroscopy measurements on the same fruit and subsequent use in Partial Least Square Regression (PLSR) models were tested for their informative value. Additionally, the future fruit state was predicted in models based on weather data. The statistical evaluation of three years of data showed that the number of destructive soluble solids content (SSC) samples required to be collected each year for the {PLSR} models with an acceptable error rate was 100. These samples in particular needed to include the range of low and high SSC values. An unbalanced laboratory error of 0.1-1.0 °Brix had no influence on the modeled SSC values. Compared to destructive SSC determination in the laboratory, SSC could be non-destructively determined with an average deviation of 0.5 °Brix. Field measurements after a rain event had no influence on visible spectral indices. However, orchard covers such as rain protection over the fruit trees led to lower water absorption values at 975 nm. The separation of data on a tree-sector level in this work was performed manually, but initial steps were taken to display fruit growth and spectroscopy data via GPS signal location within a map. The acquisition of small-scale data at the tree-sector level revealed significant differences for SSC, chlorophyll and anthocyanin indices as well as significant differences in the incidence of physiological disorders. In future research and modeling approaches the tree sector information should be taken into account, even if this is not yet feasible to implement in a completely automated system under the given circumstances. The prediction models for the development of physiological disorders (core browning, cavities) were able to predict with two years of data the development of the disorders in the storage and the fruit flesh firmness at harvest 90 % correctly. Further research using non-destructive fruit measurements in orchards and the influence of weather conditions will provide further insights into apple quality improvement.
Apple (Malus x domestica Borkh.) is the major tree fruit in Germany, but there still exist major uncertainties in commercial apple production. Thinning has been practiced for many years and is an essential part for successful apple production, but is still an unpredictable part with large variations from year to year and even within years. Another uncertainty for apple growers is the need of apple trees for adequate water supply. Due to the advancing climate change apple growers are uncertain whether additional irrigation will be necessary during the establishment phase of young apple trees.
Therefore, to facilitate these decisions in future, the motivation of this study was to acquire a universal apple model to be used in Germany and Central-Europe. The apple carbon-balance model MaluSim which has been developed in the USA was chosen as a basis. At first, the model has been parameterized for German growing conditions and first simulation runs were conducted. In comparison to the original model, the seasonal assimilate production for German standard trees was about half of the one for standard trees in the USA, while standard trees in Germany were only about one third of the size of US ones.
Subsequently, the previously untested fruit growth and abscission submodel has been modified and tested. Simulated final fruit numbers were compared to counted fruit numbers of unthinned trials in Zornheim (GER), Jork (GER), Lindau (CH), Güttingen (CH) and Wädenswil (CH). The modification ‘G4-4’ showed good simulation results with very low deviations to actual fruit numbers. Average deviation to fruit numbers was only 3.7 %. Results indicate that the model is able to very adequately calculate final fruit numbers of natural fruit drop of Central-European apple trees and that it could be a tool used for thinning advices.
Based on published data, a new water submodel has been included into the MaluSim framework to additionally improve the model. Midday stem water potential has been used as an indicator for water status of the tree. A water stress effect on long shoot growth, respiration, and photosynthesis based on midday stem water potential was used in the water submodel. First simulation runs using the new water submodel indicate that general behavior is realistic. Modeling results generally corresponded to findings of published experimental results. The submodel is able to simulate variable intensities of water deficit effects at variable times.
To study the effects of additional irrigation and water deficits on young apple orchards a field experiment was conducted during 2012 and 2013 in Geisenheim, Germany. Trees of the cultivars ‘Fresco’ (‘Wellant®’), ‘Jugala’, and ‘AW 106’ (‘Sapora®’) were planted in autumn 2011 in a randomized plot design. In 2012, three irrigation treatments, CT (control treatment, only rain fed), NT (normal treatment, irrigated with 2 L/tree/day), and ET (evapotranspiration treatment, irrigation based on calculated water balance), were applied and effects on tree growth and physiology were recorded in 2012 and 2013. Besides soil moisture, water status of the trees has been recorded using predawn and midday stem water potential measurements.
In both years, amounts of precipitation were higher than the 30-year average for the site, with 2013 being wetter than 2012. Between treatments, statistically significant differences developed in both seasons, but were less profound in 2013. In 2012, vegetative growth recorded in NT and ET was statistically significantly higher than in CT for all cultivars. In the wetter season 2013, similar tendencies evolved. But only in ‘Jugala’ and ‘Fresco’ long shoot growth and wood surface area was significantly higher, while for ‘AW 106’ differences in long shoot growth were not statistically significant. Similar results were obtained for the first harvest in 2013.
Light interception and calculated leaf area, measured in ‘Fresco’, was significantly lower in CT trees (2012: 17.4 %; 2013: 24.9 %) compared to ET (2012: 26.3 %; 2013: 42.1 %) and NT (2012: 24.2 %; 2013: 38.3 %), in both years. Additionally, differences between CT and the two irrigated treatments were found for leaf gas exchange rates during the seasons. A relationship between midday stem water potential and leaf gas exchange has been confirmed. Grouping of photosynthesis measurements according to midday stem water potentials led to statistically significant differences between groups. Photosynthesis started to decrease between -1 and -1.5 MPa.
In conclusion, the results show that although both years have even been wetter than usual, the additional irrigation was highly beneficial for tree growth and had a positive effect on orchard establishment. This effect is supposed to be considerably higher in drier years.