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Virtual images of representative 3D digitized trees of three cultivars of apple (Malus domestica) subjected to two training systems. False colors were assigned to shoot types: red = bourses; blue = bourse shoots; and green = vegetative shoots. Crown volume was approximated by a set of voxels. Voxel size was 0.2 m and shows the scale of the images. Images were synthesized with VegeSTAR software. Gray bars indicate tree trunks.
Source publication
The effect of two training systems (Central Leader with branch pruning versus Centrifugal Training with minimal pruning, i.e., removal of fruiting laterals only) on canopy structure and light interception was analyzed in three architecturally contrasting apple (Malus domestica Borkh.) cultivars: 'Scarletspur Delicious' (Type II); 'Golden Delicious'...
Contexts in source publication
Context 1
... sequence of compu- tations was implemented in software written in FORTRAN, where inputs were the digitized tip coordinates of shoots and the parameters of the reconstruction rules. The reconstruction output was a collection of leaves with spatial coordinates, ori- entation angles and dimensions ( Figure 1). The method was previously shown suitable for computing light properties at the tree and shoot scales for peach trees ( Sonohat et al. 2006). ...
Context 2
... bounding box was then di- vided into cubic volume elements of 0.2 m called voxels. Crown volume was finally approximated as the cumulated vol- ume of vegetated voxels, i.e., voxels containing at least one leaf ( Figure 1). Mean leaf area density (LAD) was computed as the ratio of TLA to V. The relative variance of LAD (ξ)-which has previously been reported as a main contributor to foliage clumping ( Sinoquet et al. 2005)-was computed from values of LAD in voxels: ...
Context 3
... beams were sent to the canopy, and the target organ was visu- ally recorded as a spur or extension shoot leaf. The plant image processing method we used can be regarded as an extension of Wünsche's work, where each pixel in the image corresponds to a laser beam and target classification is automated by as- signing false colors to each shoot type (Figure 1) or individual shoot. The virtual computation allowed a large number of sampled beams (the image size used in VegeSTAR is 356 × 356 pixels, and one image is used for each of the 46 light direc- tions used to abstract the sky radiation) and fine light partition- ing among plant components. ...
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Citations
... Reports expressed that different pruning techniques account for various light interception (LI) levels and light distribution pattern across the plant canopy [65]. Stephan et al. [66] further pointed out that training and pruning have an impact on the growth, location, and crotch angle of branches, which affects their capacity to intercept light and, in turn, affects fruit quality and quantity as occurred in case of 30 cm pruning at the present experiment. However, excess or hard pruning is detrimental to producing optimum yield and sometimes can cause total death of the plants [32]. ...
Production of quality fruits in the dry and low humid October–May period has been a challenge in the tropics and sub-tropics having wide weather fluctuations throughout the year. Henceforth, the research aimed at investigating the seasonal variations in vegetative developments as well as flowering, fruiting, yield, and fruit quality of guava emphasizing the off-seasonality by pruning 0 cm (control), 15 cm, 30 cm, and 45 cm from shoot-tip, once a year at spring (early March), monsoon (early June) and autumn (early September) under such atmospheric implications. Yearly and quarterly documentation at wet (June–August and September–November) and dry (December–February and March–May) seasons revealed that pruning in spring and autumn exhibited statistical parity for higher yearly yield of 31.71 kg and 31.58 kg plant⁻¹, respectively. Moreover, spring pruning had maximum yield in the wet season (23.94 kg plant⁻¹), while autumn pruning governed the dry season production (18.11 kg plant⁻¹) having a notable wet period yield (13.47 kg plant⁻¹). Considering the yearly and quarterly in March–May and December–February harvests, autumn pruning exhibited statistical supremacy for total soluble solids, titratable acidity, total sugar, vitamin C, and specific gravity. However, pruning time didn't influence the fruit physiochemical traits at the June–August and September–November quarters producing fruits of inferior quality compared to those of March–May and December–February harvests. On the other hand, pruning lengths of 30 cm and 45 cm demonstrated statistical consistency for auspicious vegetative, reproductive and fruit biochemical properties. Meanwhile, 30 cm pruning produced maximum number of flowers (224.71 plant⁻¹) and fruits (155.89 plant⁻¹), consequently the highest yield (38.38 kg plant⁻¹). Treatment interactions too ascertained that off-season production of superior quality guava can be enhanced by 30 cm shoot-tip pruning in autumn without compromising the year-round harvests.
... For example, the leaf angle affects a plant's photosynthetic efficiency and yield [8], and the molecular mechanism of this factor has been widely reported in rice and maize, providing a possibility for the quantitative control of leaf angles at different canopy levels [9]. Other methods like density control, dwarfing rootstock [10], and pruning [11], have been studied in fruit tree management and used to manipulate shading to improve yield. Shading also affects plant metabolism. ...
Age controls a tree’s responses to environmental cues and shading influences tree growth and physiology. These are basic principles of “Afforestation under canopy”, an approach that is widely used in the regeneration of Korean pine forests. Studying the underlying mechanism helps us to understand tree adaptation and utilize it in forest management. In this study, we investigated the transcriptomic changes in the uppermost main stems of the Korean pine tree (Pinus koraiensis, Sieb. et Zucc.) at different ages (5, 7, 10, 14, and 17 years) and in different growth conditions (open-grown and shade-grown trees) using RNA-Seq. In total, 434,005,837 reads were produced and assembled into 111,786 unigenes. After pairwise comparisons, 568 differentially expressed unigenes (DEUs) were identified. The greatest number of DEUs was identified in the comparison between 5-year-old open-grown trees and 17-year-old shade-grown trees, while no DEUs were identified in 15 pairwise comparisons. Among these 568 DEUs, 45 were assigned to gene ontology (GO) terms associated with response to environmental changes, including “response to stress” (26) and “response to light and temperature” (19); 12 were assigned to GO terms associated with sexual reproduction, such as “sexual reproduction”, “specification of floral organ identity”, “pollen tube guidance”, and “fruit ripening”; 15 were heat shock protein genes and showed decreased expression patterns with age; and one, annotated as Pinus tabuliformis DEFICIENS-AGAMOUS-LIKE 1, showed an increased expression pattern with age, independent of the reproductive state or growth conditions of Korean pine trees. Altogether, these findings not only demonstrate the molecular aspects of the developmental and physiological effects of age and shading on Korean pine trees, but also improve our understanding of the basic principles of “Afforestation under canopy”.
... Training systems affect development, position and angle of the branches, and thus light interception, which in turn affects yield and fruit quality [15]. Training systems simplify tree architecture, enabling efficient use of the orchard area, increased light interception and even distribution of that light over the entire canopy leaf area [16]. ...
Both ‘0900 Ziraat’ and ‘Regina’ grafted on ‘Krymsk 5’, or ‘Piku 1’ rootstocks were trained to either Upright Fruiting Offshoots (UFO), Super Slender Axe (SSA) or Kym Green Bush (KGB) training systems. Vegetative growth of the tree, determined by measuring trunk cross-sectional area (TCSA), canopy volume and leaf area, differed significantly, depending on the cultivar x rootstock x training system combination. In general, ‘Krymsk 5’ rootstock resulted in trees with significantly thicker trunks (TCSA: 37.75 cm²) and increased leaf area (up to 86.97 cm²). Fruit weight and fruit quality parameters including Hunter a*, firmness, TSS and acidity were variable between rootstocks and training systems and often not significantly different between treatments. In some years however, significant differences were highly dependent on the training system and rootstock interactions. Higher concentrations of bioactive phytochemical concentrations for total monomeric anthocyanin and antioxidant concentrations were mostly associated with the UFO training system in conjunction with the ‘Krymsk 5’ rootstock suggesting that these are linked to increased tree vigour and increased leaf surface area.
Keywords:
canopy management; Prunus avium; health
... Success in pruning systems may be changed according to cultivar, labor costs, fruit price and location. However, it was observed that pruning systems might change certain biological characteristics of cultivars, such as fertilization time, flowering density and fertilization percentage (Stephan et al. 2008). Pruning plays a crucial role in both decreasing the juvenile period and increasing the productivity and quality of fruit trees (Naira and Moieza 2014). ...
All details about our chapter are available here:
https://www.routledge.com/Handbook-of-Plum-Fruit-Production-Postharvest-Science-and-Processing/Gull-Nayik-Wani-Nanda/p/book/9781032062426#
... For instance, biomass partitioning among leaves and woody structure as well as leaf phyllotaxy will influence functions at annual shoot scale such as light capture and space exploration (Yagi and Kikuzawa 1999;Yagi et al. 2000;Lauri and Kelner 2001;Pearcy et al. 2005;Niinemets et al. 2004;Valladares and Niinemets 2007;Lauri and Normand 2017). Tree trimming also modify tree structure characteristics at different scales including the overall crown size and shape (Valladares and Niinemets 2007;Stephan et al. 2008;Leroy et al. 2009), branching pattern (e.g. bifurcation ratio, Kull et al. 1999;Kennedy 2010), branch motion between two points in time (Martin-Ducup et al. 2017), or initiation of traumatic reiterations (Millet and Bouchard 2003;Perrette et al. 2021) thereby influencing light interception and space exploration abilities. ...
Key message
Complementarities and/or synergies among different scales of tree architecture enable to achieve different functions simultaneously and/or optimize one function. After trimming, tree reaction occurred in all the scales.
Abstract
Trees are modular organisms within which each scale has attributes enabling them to optimize different functions. Among these functions, space exploration and light interception are strong determinants of tree productivity and survival. By modifying the crown shape, including foliage dispersion in the 3D space and carbon allocation, tree trimming (i.e. the removal of the higher part of the crown) may largely influence how trees fulfill the space exploration and light interception functions. In this study, we used field measurements of tree annual shoots combined with tree reconstruction from Terrestrial Laser Scanning data to analyze how different scales of the tree architecture complement each other to fulfill different functions simultaneously or combine to fulfill one single function more efficiently. We used morphological and functional variables measured at the annual shoot, axis, and tree scales to compare the architectural and functional strategies of two Fraxinus pennsylvanica cultivars and observe how trimming modifies these strategies. Results show that the two cultivars exhibit different strategies. One cultivar tended to optimize the space exploration function at both the annual shoot and axis scales and to optimize the light interception by displaying a large leaf area at the tree scale. The other cultivar tended to optimize light interception efficiency at both axis and tree levels. In both cultivars, trimming improved light interception efficiency and increased investments into space exploration. Nevertheless, after trimming, the two cultivars maintained their main architectural and functional strategy.
... For instance, biomass partitioning among leaves and woody structure as well as leaf phyllotaxy will influence functions at annual shoot scale such as light capture and space exploration (Yagi and Kikuzawa 1999;Yagi et al. 2000;Lauri and Kelner 2001;Pearcy et al. 2005;Niinemets et al. 2004;Valladares and Niinemets 2007;Lauri and Normand 2017). Tree trimming also modify tree structure characteristics at different scales including the overall crown size and shape (Valladares and Niinemets 2007;Stephan et al. 2008;Leroy et al. 2009), branching pattern (e.g. bifurcation ratio, Kull et al. 1999;Kennedy 2010), branch motion between two points in time (Martin-Ducup et al. 2017), or initiation of traumatic reiterations (Millet and Bouchard 2003;Perrette et al. 2021) thereby influencing light interception and space exploration abilities. ...
... The young flushes had the greatest proportion of their leaves under sunlit conditions. Manipulation of tree architecture has been shown to effect light interception, growth and productivity (Willaume et al., 2004;Stephan et al., 2008). In tree crops pruning usually increases the penetration of sunlight to the lower levels of the canopy and increases photosynthesis. ...
... This includes fruit position in the canopy and tree architecture (Layne and Quamme, 1975;Faragher and Brohier, 1984;Palmer et al., 1997;Fideghelli, 2007;Serra et al., 2016;Wu et al., 2018). The resulting variable light within the canopy has been linked to effects on fruit quality (Lakso, 1980;Ramos et al., 1994;Warrington et al., 1996;Awad et al., 2001;Stephan et al., 2008;Zhang et al., 2016). Indeed, large canopy trees (that have a more variable in-canopy environment) produce fruit crops with greater variability in fruit quality compared to planar-canopy trees (Musacchi, 2008;Zhang et al., 2016;Serra et al., 2018). ...
Estimating maturity in pome fruits is a critical task that directs virtually all postharvest supply chain decisions. This is especially important for European pear (Pyrus communis) cultivars because losses due to spoilage and senescence must be minimized while ensuring proper ripening capacity is achieved (in part by satisfying a fruit chilling requirement). Reliable methods are lacking for accurate estimation of pear fruit maturity, and because ripening is maturity dependent it makes predicting ripening capacity a challenge. In this study of the European pear cultivar ‘d’Anjou’, we sorted fruit at harvest based upon on-tree fruit position to build contrasts of maturity. Our sorting scheme showed clear contrasts of maturity between canopy positions, yet there was substantial overlap in the distribution of values for the index of absorbance difference (IAD), a non-destructive spectroscopic measurement that has been used as a proxy for pome fruit maturity. This presented an opportunity to explore a contrast of maturity that was more subtle than IAD could differentiate, and thus guided our subsequent transcriptome analysis of tissue samples taken at harvest and during storage. Using a novel approach that tests for condition-specific differences of co-expressed genes, we discovered genes with a phased character that mirrored our sorting scheme. The expression patterns of these genes are associated with fruit quality and ripening differences across the experiment. Functional profiles of these co-expressed genes are concordant with previous findings, and also offer new clues, and thus hypotheses, about genes involved in pear fruit quality, maturity, and ripening. This work may lead to new tools for enhanced postharvest management based on activity of gene co-expression modules, rather than individual genes. Further, our results indicate that modules may have utility within specific windows of time during postharvest management of ‘d’Anjou’ pear.
... Success in pruning systems may be changed according to cultivar, labor costs, fruit price and location. However, it was observed that pruning systems might change certain biological characteristics of cultivars, such as fertilization time, flowering density and fertilization percentage (Stephan et al. 2008). Pruning plays a crucial role in both decreasing the juvenile period and increasing the productivity and quality of fruit trees (Naira and Moieza 2014). ...
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... Quantitative variables of ASs could also be derived, such as the number of internodes, the number of leaves, or the leaf area, through the application of allometric relations [39], making their application possible on large reconstructed tree structures. This method could also be used in plant phenotyping, which often consists of retrieving and quantifying the abundance of different structural elements [62] such as specialized axes [63,64]. However, quantifying the abundance of different axes types trough manual digitization is time consuming [65] and TLS has been mostly used to derive coarse variables of the tree canopy (e.g., crown volume and leaf area [66]). ...
The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.
