Transcript Document

VCHERRY – The Virtual Cherry Tree Program for:
- Developing and Testing Pruning and Training Decisions
- Evaluating Their Impact on Sweet Cherry Yield and Fruit Quality
MSU Tree Fruit
Research
Visit our website:
www.hrt.msu.edu/faculty/langg.htm
Gregory Lang, Michigan State University and Robert J. Lang, Origami Art & Engineering
The Selectable Orchard Parameters Window
Rationale
New precocious, dwarfing rootstocks such as the Gisela® series can
Environmental Inputs for Orchard Location
Genetic Inputs for Orchard Production
alter sweet cherry growth and cropping patterns significantly. Orchard
- Soil type:
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.
 fertile (deep, loamy)
 poor (shallow, sandy)
- Regional climate:
Objective
- Cultivar:
 Great Lakes
 Pacific Northwest
 California
These choices affect growing season duration,
daily light integral, annual growth vigor, etc.
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:
 Bing or Ulster
 Rainier
 Lapins
 Regina
 Sweetheart
- Rootstock:  vigorous (e.g., Mazzard or Mahaleb)
 semi-vigorous (e.g., Gisela® 6 or 12)
 dwarfing (e.g., Gisela® 5)
These choices affect fruit size potential, fruit color,
ripening time, branch angle, tree vigor, etc.
- simulate multi-season tree development 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
2005 growth
Fruit density increases
terminally
- Leaves expand, flowers open and become
2006 growth
2007 growth
A few nonspur fruit
growing fruit, shoots elongate, fruit ripen, leaves
turn yellow and fall, buds go dormant
- The orange cone (above) is the marker to
Year 1 Growth (Nursery)
Year 2 Growth (Orchard)
identify individual buds or spurs for orchard
management actions
Fruiting spur leaves
(7-9/node)
Non-fruiting spur leaves
(6-8/node)
New growth leaves
(1/node)
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
The Virtual Cherry Computer Program
The Virtual Cherry Tree grows bud-by-bud, leaf-by-leaf, shoot-byshoot, 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.
A 4th Leaf Virtual Cherry Tree
Trained as a Whorled Axe
Tipping of new shoots, fruit spur thinning, flower cluster thinning
Whorled Axe
Solaxe
Steep Leader
√ Visualization of Tree Growth (the main tree-growing window)
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.
√ Selectable Orchard Parameters (to “customize” the virtual orchard to
represent the site, rootstock, and variety to be simulated)
LA:F 150 cm2/frt
LA:F 157 cm2/frt
LA:F 211 cm2/frt
√ Interactive View Controls (to virtually “walk” 360º around the tree)
Visual and Quantitative Outputs
√ Visual Resolution Settings (to speed up simulated growth sessions)
√ Interactive Marker to Select Meristems (to pick specific buds for
pruning or thinning or activation)
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.
There are 9 different computer screen windows in VCHERRY:
√ Quantitative Tree Growth Information (to track the development of,
and management effects on, leaf area and crop load)
A 4th Leaf Sweet Cherry Tree
Trained as a Whorled Axe
~ 2800 fruit
~ 2700 fruit
~ 1500 fruit
55% FSp, 45% NSp
67% FSp, 33% NSp
54% FSp, 46% NSp
√ 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)
The VCHERRY trees and outputs (figures to the left) compare the
predicted tree architectures, crop loads, and LA:F ratios for 4-yearold ‘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.