VCHERRY – The Virtual Cherry Tree Program for: - Developing and Testing Pruning and Training Decisions

1. Whorled Axe Solaxe Steep Leader Fruit density increases terminally A few nonspur fruit LA:F 150 cm2/frt LA:F 157 cm2/frt LA:F 211 cm2/frt ~ 2800 fruit ~ 2700 fruit ~ 1500 fruit Non-fruiting spur leaves (6-8/node) Fruiting spur leaves (7-9/node) New growth leaves (1/node) 67% FSp, 33% NSp 54% FSp, 46% NSp 55% FSp, 45% NSp 2005 growth 2006 growth 2007 growth VCHERRY – The Virtual Cherry Tree Program for: - Developing and Testing Pruning and Training Decisions - Evaluating Their Impact on Sweet Cherry Yield and Fruit Quality Gregory Lang, Michigan State University and Robert J. Lang, Origami Art & Engineering MSU Tree Fruit Research Visit our website: www.hrt.msu.edu/faculty/langg.htm Rationale New precocious, dwarfing rootstocks such as the Gisela® series can alter sweet cherry growth and cropping patterns significantly. Orchard management strategies to balance sufficient leaf area with a modest crop load is challenging, yet critical for optimized yields of large and high quality fruit to achieve premium market returns. • The Selectable Orchard Parameters Window • Environmental Inputs for Orchard Location Genetic Inputs for Orchard Production • Soil type:  fertile (deep, loamy) - Cultivar:  Bing or Ulster •  poor (shallow, sandy)  Rainier •  Lapins • Regional climate:  Great Lakes  Regina •  Pacific Northwest  Sweetheart •  California • - Rootstock:  vigorous (e.g., Mazzard or Mahaleb) • These choices affect growing season duration,  semi-vigorous (e.g., Gisela® 6 or 12) • daily light integral, annual growth vigor, etc.  dwarfing (e.g., Gisela®5) • These choices affect fruit size potential, fruit color, ripening time, branch angle, tree vigor, etc. • Objective • To integrate emerging and fundamental genetic and physiological knowledge of sweet cherry tree growth, canopy architecture, cropping, rootstocks, and varieties into an interactive computer model for growers to: • - simulate multi-season tree develop-ment to facilitate testing and teaching of orchard pruning and management strategies with new rootstocks • predict the short- and long-term effects of management decisions on future yields and fruit quality • Visualization of Tree Growth Window • The virtual tree grows on a week-by-week basis • Leaves expand, flowers open and become growing fruit, shoots elongate, fruit ripen, leaves turn yellow and fall, buds go dormant • - The orange cone (above) is the marker to identify individual buds or spurs for orchard management actions Year 1 Growth (Nursery) Year 2 Growth (Orchard) Sweet Cherry Growth and Flowering Habit -» Fruiting is primarily on 2-year- and older spurs -» The fruit at the base of the previous year’s new shoots is generally of the highest quality due to high (localized) leaf area-to-fruit (LA:F) ratios -» Pruning decisions have both long- and short-term consequences for development of canopy leaf populations, LA:F ratios, and therefore sustainable production of high quality fruit Typical Tree Training Techniques include: Bud manipulation: removal, activation (real and virtual examples above) Branch bending, shoot pruning (heading or thinning cuts), sucker removal Tipping of new shoots, fruit spur thinning, flower cluster thinning A 4th Leaf Virtual Cherry Tree Trained as a Whorled Axe A 4th Leaf Sweet Cherry Tree Trained as a Whorled Axe The Virtual Cherry Computer Program The Virtual Cherry Tree grows bud-by-bud, leaf-by-leaf, shoot-by-shoot, with upper shoots inhibiting the outgrowth of lower buds (“apical dominance”). There are a variety of pruning and training commands available to alter the natural growth and cropping patterns. There are 9 different computer screen windows in VCHERRY: √ Visualization of Tree Growth (the main tree-growing window) √ Selectable Orchard Parameters (to “customize” the virtual orchard to represent the site, rootstock, and variety to be simulated) √ Quantitative Tree Growth Information (to track the development of, and management effects on, leaf area and crop load) √ Interactive View Controls (to virtually “walk” 360º around the tree) √ Visual Resolution Settings (to speed up simulated growth sessions) √ Interactive Marker to Select Meristems (to pick specific buds for pruning or thinning or activation) √ Quantitative Data Plots (to graph out changing leaf area and crop loads over the current season or over several years) √ Growth Session Command Log/Script (to record every step of each orchard management session for later use or editing) √ Keyboard Command List (an on-screen reference guide for which keyboard strokes are used for each training command) Analysis of Tree Training Effects on Cropping Simulation sessions can be initiated, before the real orchard is even planted, to envision years of training and crop load management decisions to compare training systems, predicted yields, labor inputs for pruning, and fruit quality. Or, simulation sessions can be conducted prior to each spring to test potential pruning strategies for optimizing canopy development, crop load balancing, fruiting wood renewal, etc. Visual and Quantitative Outputs The VCHERRY trees and outputs (figures to the left) compare the predicted tree architectures, crop loads, and LA:F ratios for 4-year-old ‘Bing’ / Gisela®5 trees trained to 3 different systems. VCHERRY can remove and replace leaves at any time to reveal where the crop is being borne in the canopy. The VCHERRY analysis reveals similar crop loads and LA:F ratios for the Whorled Axe and Solaxe trees, but a higher proportion of non-spur (NSp) fruit borne on the Whorled Axe trees; these are more likely to be of the highest quality. The Steep Leader tree has a smaller crop load and thus a better LA:F ratio, along with a well-balanced proportion of non-spur and spur (FSp) fruit, suggesting that while yield will be lower, fruit quality will be higher throughout the canopy. Financial support from the International Fruit Tree Association, Gisela Inc., California Cherry Advisory Board, and Michigan Agricultural Experiment Station is gratefully acknowledged.