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 & EngineeringGregory Lang, Michigan State University and Robert J. Lang, Origami Art & Engineering

RationaleNew 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.

Visit our website: www.hrt.msu.edu/faculty/langg.htm

MSU Tree Fruit Research

ObjectiveTo 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

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)

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

Financial support from the International Fruit Tree Association, Gisela Inc., California Cherry Advisory Board, and Michigan Agricultural Experiment Station is gratefully acknowledged.

2005 growth2005 growth2006 growth2006 growth

2007 growth2007 growth

New growth leaves New growth leaves (1/node)(1/node)

Non-fruiting spur leaves Non-fruiting spur leaves (6-8/node) (6-8/node)

Fruiting spur leaves Fruiting spur leaves (7-9/node) (7-9/node)

Fruit density increases Fruit density increases terminallyterminally A few nonspur fruitA few nonspur fruit

Year 1 Growth (Nursery)

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

Year 2 Growth (Orchard)

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.

A 4th Leaf Virtual Cherry Tree Trained as a Whorled Axe

A 4th Leaf Sweet Cherry Tree Trained as a Whorled Axe

Whorled Axe

LA:F 150 cm2/frt

55% FSp, 45% NSp

~ 2800 fruit

Solaxe

LA:F 157 cm2/frt

~ 2700 fruit

67% FSp, 33% NSp

Steep Leader

LA:F 211 cm2/frt

~ 1500 fruit

54% FSp, 46% NSp

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.