the effects of mechanical pruning on yield, fruit quality and ...

THE EFFECTS OF MECHANICAL PRUNING ON YIELD, FRUIT QUALITY AND VEGETATIVE GROWTH

IN APPLE AND SWEET CHERRY

By

JACQUELINE ALEXANDRA GORDON NUNEZ

A thesis submitted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE IN HORTICULTURE

WASHINGTON STATE UNIVERSITY Department of Horticulture

MAY 2016

© Copyright by JACQUELINE ALEXANDRA GORDON NUNEZ, 2016 All Rights Reserved

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To the Faculty of Washington State University:

The members of the Committee appointed to examine the thesis of JACQUELINE

ALEXANDRA GORDON NUNEZ find it satisfactory and recommend that it be accepted.

______________________________ Matthew D. Whiting, Ph.D., Chair

_____________________________ Stefano Musacchi, Ph.D.

_____________________________

Karen Lewis, M.S.

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ACKNOWLEDGEMENT

First and foremost, I would like to thank God, for His countless blessings and for giving me the strength

to finish this work in spite of every obstacle.

I would never have been able to finish my thesis without the guidance of my committee members, help

from my friends, and support from my family and husband. I would like to express my deepest gratitude

to the following people who helped me and motivated me during this process:

My advisor, Dr. Matthew Whiting, for his continuous support and excellent mentoring with my studies

and research project. I have been fortunate to have an advisor who encouraged me to be an

independent researcher and gave me numerous opportunities to present my work and build

professional relationships. He always led our group as a mentor but also treated us like a family.

Dr. Stefano Musacchi, Karen Lewis and Sara Serra, for their effort in guiding me with my field work and

the development of this project. I appreciate their support and valuable suggestions for the elaboration

of this thesis.

Dr. Ines Hanrahan, for giving me the opportunity to learn from her and introducing me to the tree fruit

research in the Pacific Northwest. Her expertise, support and friendship since the moment I arrived to

Washington added considerably to my graduate experience.

Bernando Chavez, the WSU IAREC Stone Fruit Physiology crew and the Washington Tree Fruit Research

Commission crew, for their valuable help and support with statistical analyses, field work and data

collection.

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My beloved husband, Alejandro Sanchez, for his patience and selfless support. He has been by my side

on two years’ worth of effort, frustration and achievement; and has given up so much to make my

career a priority in our lives. Our daughter Emma and you motivated me every day to finish this work.

Finally, I would like to thank my family for their love and support through my entire life. My parents,

Franklin and Gladys; siblings, Jessy, Franklin, Paola, Stephany and Julio; grandmother, Emma; uncles and

aunts, Giomara, Nelson, Julio, Dora; and mother-in-law, Yolanda; who always believed in me, prayed for

my success, and helped me here and in Ecuador. They were my constant source of love, concern,

support and strength throughout these years.

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THE EFFECTS OF MECHANICAL PRUNING ON YIELD, FRUIT QUALITY AND VEGETATIVE GROWTH

IN APPLE AND SWEET CHERRY

Abstract

by Jacqueline Alexandra Gordon Nunez, M.S. Washington State University

May 2016

Chair: Matthew D. Whiting

Pruning is a labor- and time-demanding operation that generally represents the second greatest

annual expense for tree fruit growers worldwide. As orchardists adopt new planar orchard systems

there is improved potential for the mechanization of orchard operations, including pruning. This

research project compared standard hand pruning practices with mechanical pruning in sweet cherry

(Prunus avium L.) and apple (Malus domestica Borkh.) commercial orchards to better understand the

effects of mechanization on yield, fruit quality and vegetative growth. In ‘Tieton’/‘Gisela5’ sweet cherry,

treatments compared postharvest hand pruning with mechanical pruning and the combination of both

approaches over two years. Mechanical pruning required three passes per tree yet was 23 and 29 times

faster than hand pruning in 2014 and 2015, respectively (i.e., 16 and 15 s/tree). In 2015, the

combination of mechanical, followed by hand pruning was 66% more efficient (kg wood/min/tree) than

hand pruning alone. There was no effect of pruning treatment on yield. Current season shoot regrowth

from trees pruned mechanically was significantly higher (ca. +32%) than new extension growth from

hand pruned trees. A separate trial compared timing of tree topping, mimicking the operation of

mechanical pruning. Hand topping performed 2 and 3 months after full bloom removed approximately

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520% (i.e., 6 times) more wood than earlier pruning; however, the greatest new extension growth was

observed from trees pruned in the winter (ca. +95%). In ‘Fuji’/’Nic29’ apple, trees were hedged

mechanically or by hand at different timings (winter, summer 12-15 leaves, and summer 20 leaves).

Winter mechanical pruning was 2.3 times faster than hand pruning in 2014 (i.e., 8 s/tree). Trees pruned

mechanically exhibited ca. 53% higher return bloom compared to those pruned by hand. Yield from

trees pruned by hand in the winter and mechanically in the summer was ca. 32% and 41% lower than

those pruned only in the winter manually or with the machine, respectively. A preliminary economic

assessment indicates that mechanical pruning can reduce production costs and improve labor efficiency.

These results show promise for mechanizing pruning in apple and sweet cherry orchards trained to

vertical fruiting wall architectures.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENT .................................................................................................................................. iii

Abstract ......................................................................................................................................................... v

LIST OF TABLES ............................................................................................................................................. xi

LIST OF FIGURES .......................................................................................................................................... xiv

Dedication .................................................................................................................................................... xx

CHAPTER ONE ............................................................................................................................................... 1

INTRODUCTION ......................................................................................................................................... 1

Apple and sweet cherry growth habit .................................................................................................. 1

Pruning tree fruit................................................................................................................................... 5

Apple and sweet cherry training systems ........................................................................................... 12

Mechanical pruning of tree fruit ......................................................................................................... 17

Literature cited .................................................................................................................................... 23

CHAPTER TWO ............................................................................................................................................ 33

MECHANICAL PRUNING IS PROMISING IN VERTICAL FRUITING WALL SWEET CHERRY ORCHARDS ...... 33

Abstract ............................................................................................................................................... 33

Introduction ........................................................................................................................................ 34

Materials and Methods ....................................................................................................................... 37

Experimental design ........................................................................................................................ 37

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Pruning treatments 2014 ................................................................................................................ 37

Data collection 2014 ....................................................................................................................... 38


Data collection 2015 ....................................................................................................................... 39

Statistical analyses .......................................................................................................................... 40

Results and discussion ........................................................................................................................ 40

Pruning trials 2014 .......................................................................................................................... 40

Harvest 2015 ................................................................................................................................... 42

Pruning trials 2015 .......................................................................................................................... 44

Preliminary economic assessment .................................................................................................. 47

Conclusion ........................................................................................................................................... 51

Literature cited .................................................................................................................................... 53

CHAPTER THREEE ........................................................................................................................................ 82

PRUNING MECHANIZATION OF APPLE TREES ON FRUITING WALLS IN THE US PACIFIC NORTHWEST .. 82

Abstract ............................................................................................................................................... 82

Introduction ........................................................................................................................................ 83

Materials and methods ....................................................................................................................... 86

Experimental design ........................................................................................................................ 86

Pruning trials 2014 .......................................................................................................................... 87

Data collection 2014 ....................................................................................................................... 88

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Data collection 2015 ....................................................................................................................... 90

Statistical analysis ........................................................................................................................... 92

Results and discussion ........................................................................................................................ 92

Pruning trials 2014 .......................................................................................................................... 92

Pruning trials 2015 .......................................................................................................................... 98

Harvest 2015 ................................................................................................................................. 101

Economic assessment ................................................................................................................... 104

Conclusion ......................................................................................................................................... 106

Literature cited .................................................................................................................................. 107

CHAPTER FOUR ......................................................................................................................................... 149

TIMING OF PRUNING AFFECTS YIELD, FRUIT QUALITY AND VEGETATIVE REGROWTH IN SWEET CHERRY TREES ON FRUITING WALLS .................................................................................................................. 149

Abstract ............................................................................................................................................. 149

Introduction ...................................................................................................................................... 150

Materials and methods ..................................................................................................................... 153

Experimental design ...................................................................................................................... 153

Topping trials ................................................................................................................................ 154

Data collection .............................................................................................................................. 154

Statistical analyses ........................................................................................................................ 156

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Results and discussion ...................................................................................................................... 156

Topping trials ................................................................................................................................ 156

Harvest 2014-2015 ........................................................................................................................ 159

Regrowth 2014-2015 .................................................................................................................... 161

Conclusion ......................................................................................................................................... 163

Literature cited .................................................................................................................................. 164

CONCLUDING COMMENTS ....................................................................................................................... 189

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LIST OF TABLES

Table 2.1. Effects of mechanical and hand pruning on fruit quality parameters of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5………………………………….64 Table 2.2. Estimation of costs per acre to prune mechanically, by hand or with the combination of both methodologies ‘Tieton’/’Gisela5’ sweet cherry trees trained to the UFO system in 2015………………………65

Table 3.1. Treatment codification of pruning trials of ‘Fuji’/Nic29 in 2014 .............................................. 111 Table 3.2. Treatment codification of pruning trials of ‘Fuji’/Nic 29 in 2015 ............................................. 112 Table 3.3. Number of fruit removed per tree by mechanical hedging of ‘Fuji’/Nic29 with Gillison’s Center Mount topper and hedger during summer 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ....... 113 Table 3.4. Green thinning of ‘Fuji’/Nic29 after pruning trials performed in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. .............................................................................................................................................. 114 Table 3.5. Regrowth measurements of ‘Fuji’/Nic29 after pruning trials performed in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ....................................................................................................... 115 Table 3. 6. Mean time per tree (s), mean kg of wood removed per tree and mean kg of wood removed per cm2 TCSA of summer mechanical pruning of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ................................................................................................................................................. 116 Table 3.7. Mean kg of leaves removed per tree and mean kg of wood removed per tree by mechanical hedging of ‘Fuji’/Nic29 with LaGasse hedger during summer 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. .................................................................................................................................................................. 117 Table 3.8. Number of fruit removed per tree by mechanical hedging of ‘Fuji’/Nic29 with LaGasse hedger during summer 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. .............................................................. 118 Table 3.9. Mean number of leaves removed per tree, mean area per leaf and mean area of leaves removed per tree by mechanical hedging of ‘Fuji’/Nic29 with LaGasse hedger during summer 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ....................................................................................................... 119

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Table 3.10. Mean ratio of leaves and wood from dry matter determination from mechanical hedging of ‘Fuji’/Nic29 with LaGasse hedger during summer 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ....... 120 Table 3.11. Green thinning of ‘Fuji’/Nic29 after pruning trials performed in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. .............................................................................................................................................. 121 Table 3.12. Mean yield, yield efficiency, number of fruit per tree and kg per fruit of ‘Fuji’/Nic29 at harvest 2015 from pruning trials performed during 2014 and 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. .................................................................................................................................................................. 122 Table 3.13. Effects of pruning trials during 2014 and 2015 on fruit quality parameters of ‘Fuji’/Nic29 at harvest 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ...................................................................... 123 Table 3.14. Effects of pruning trials during 2014 and 2015 on fruit skin coloration, background color and sunburn of ‘Fuji’/Nic29 at harvest 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ................................. 124 Table 3.15. Regrowth measurements of ‘Fuji’/Nic29 after pruning trials performed in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. ....................................................................................................... 125 Table 3.16. Estimation of costs per acre to prune mechanically, by hand or with the combination of both methodologies ‘Tieton’/’Gisela5’ sweet cherry trees trained to the UFO system in 2015. ..................... 126

Table 4.1. Dates of hand topping ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2014 and 2015…………………………………………………………………………………………………………………………………………………….167

Table 4.2. Effects of timing of pruning on fruit quality parameters of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2014. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. .................................................. 168 Table 4.3. Effects of timing of pruning on fruit quality parameters of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2015. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. .................................................. 169 Table 4.4. New extension growth of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2014 expressed as number of current season shoots, current season shoot length per upright and current season shoot length per cm2 TCSA. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. .................................................. 170

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Table 4.5. New extension growth of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2015 expressed as number of current season shoots, current season shoot length per upright and current season shoot length per cm2 TCSA. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. .................................................. 171

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LIST OF FIGURES

Figure 1.1. The four classes of growth habit of apple trees (Maib et al., 1996) ......................................... 31 Figure 1.2. Pruning rules for sweet cherry on the Upright Fruiting Offshoot (UFO) system (Long et al., 2015) ........................................................................................................................................................... 32

Figure 2.1. Mean time/tree (min) of postharvest mechanical and hand pruning of ‘Tieton’/‘Gisela5’ sweet cherry in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ................. 66 Figure 2.2. Mean weight of fresh material removed/tree (kg) with postharvest mechanical and hand pruning of ‘Tieton’/‘Gisela5’ in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................................................................................................................... 67 Figure 2.3. Mean Kg of fresh material removed per cm2 TCSA with postharvest mechanical and hand pruning of ‘Tieton’/’Gisela5’ in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................................................................................................................... 68 Figure 2.4. Representative “dirty cuts” performed by the Gillison’t hedger compared to wood pruned by hand on ‘Tieton’/’Gisela5’ sweet cherry in 2014. ....................................................................................... 69 Figure 2.5. Mean yield (kg fruit/tree) from postharvest mechanical and hand pruning ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies (p < 0.05), n=5. Bars indicate standard error. .............................................................................................................................. 70 Figure 2.6. Mean yield efficiency (kg fruit/cm2 TCSA) from postharvest mechanical and hand pruning ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies (p < 0.05), n=5. Bars indicate standard error. .................................................................................................... 71 Figure 2.7. Mean time/tree (min) of postharvest mechanical and hand pruning of ‘Tieton’/’Gisela5’ sweet cherry in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ................. 72 Figure 2.8. Mean time/tree (min) of postharvest mechanical pruning, hand pruning and the combination of both approaches of ‘Tieton’/’Gisela5’ sweet cherry in 2014 and 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. .................................................................................................... 73 Figure 2.9. Mean Kg of fresh material removed/tree with postharvest mechanical and hand pruning of ‘Tieton’/’Gisela5’ in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ..... 74

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Figure 2.10. Mean Kg of fresh material removed per cm2 TCSA with postharvest mechanical and hand pruning of ‘Tieton’/’Gisela5’ in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................................................................................................................... 75 Figure 2.11. Mean weight of fresh material removed/tree (kg) with postharvest mechanical and hand pruning of ‘Tieton’/’Gisela5’ in 2014 and 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. .............................................................................................................................. 76 Figure 2.12. Healed “dirty cut” after postharvest mechanical pruning with the Gillison’s hedger on ‘Tieton’/’Gisela5’ sweet cherry in 2015. ..................................................................................................... 77 Figure 2.13. Mean pruning efficiency (kg of fresh material per min per tree) of postharvest mechanical pruning, hand pruning and the combination of both approaches of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................ 78 Figure 2.14. Mean current season wood length per tree measured during dormant season 2016 after postharvest mechanical, hand and mechanical + hand pruning of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. .......................................................... 79 Figure 2.15. Mean number of current season shoots per tree measured during dormant season 2016 after postharvest mechanical, hand and mechanical + hand pruning of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................ 80 Figure 2.16. Mean current season wood length per cm2 tree TCSA measured during dormant season 2016 after postharvest mechanical, hand and mechanical + hand pruning of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ......................... 81

Figure 3.1. Photograph of the Gillison’s Center Mount topper and hedger used for mechanical pruning of ‘Fuji’/Nic 29 apple in 2014……………………………………………………………………………………………………………........127 Figure 3.2. Photograph of the LaGasse Orchards Hedger used for mechanical pruning of ‘Fuji’/Nic29 apple in 2015............................................................................................................................................. 128 Figure 3.3. Agrofresh / Experico background color chart used for fruit quality analysis of ‘Fuji’/Nic29 apple from harvest after pruning trials in 2015 ........................................................................................ 129 Figure 3.4. Washington Tree Fruit Research Commission color classification chart (% of red) used for fruit quality analysis of ‘Fuji’/Nic 29 apple from harvest after pruning trials 2015. ........................................ 130

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Figure 3.5. Sunburn scale developed by Hanrahan (2012) based on Schrader and McFearson scale (2003), used to grade sunburn degree during fruit quality analysis of ‘Fuji’/Nic29 apple at harvest after pruning trials 2015. ................................................................................................................................... 131 Figure 3.6. Mean time/tree (s) of dormant mechanical and hand pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ........................................................ 132 Figure 3.7. Mean Kg of wood and leaves removed/tree with dormant mechanical and hand pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ... 133 Figure 3.8. Mean Kg of wood and leaves removed per cm2 TCSA with dormant mechanical and hand pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ......................................................................................................................................................... 134 Figure 3.9. ‘Fuji’/Nic29 apple trees pruned a) by hand and b) with the GIllison’s hedger during the dormant season 2014. .............................................................................................................................. 135 Figure 3.10. Photograph of “dirty cuts” of ‘Fuji’/Nic29 apple trees performed by the Gillison’s hedger compared to cleaner cuts performed by hand during dormant season 2014. ........................................ 136 Figure 3.11. Mean time/tree (s) of summer mechanical pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ........................................................ 137 Figure 3.12. Mean Kg of wood and leaves removed/tree with summer mechanical pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ....................... 138 Figure 3.13. Mean Kg of wood and leaves removed per cm2 TCSA with summer mechanical pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ... 139 Figure 3.14. Mean Kg of wood and leaves removed per cm2 TCSA from dormant and summer pruning trials of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ......................................................................................................................................................... 140 Figure 3.15. Mean pruning efficiency expressed as Kg of fresh material removed per second per tree from dormant and summer pruning trials of ‘Fuji’/Nic29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. .................................................................................................. 141

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Figure 3.16. Return bloom of ‘Fuji’/Nic29 apple expressed as number of flowers per cm2 TCSA after pruning trials 2014. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ....................... 142 Figure 3.17. Mean time per tree (s) from dormant pruning trials of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ........................................................ 143 Figure 3.18. Mean Kg of wood and leaves removed per tree from dormant pruning trials of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ....................... 144 Figure 3.19. Mean Kg of wood and leaves removed per cm2 TCSA from dormant pruning trials of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ... 145 Figure 3.20. Mean time per tree (s) from dormant and summer pruning trials of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ...................................... 146 Figure 3.21. Mean Kg of wood and leaves removed per cm2 TCSA from dormant and summer pruning trials of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. ......................................................................................................................................................... 147 Figure 3.22. Mean pruning efficiency expressed as Kg of fresh material removed per second per tree from dormant and summer pruning trials of ‘Fuji’/Nic29 apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3. Bars indicate standard error. .................................................................................................. 148 Figure 4.1. Dormant topping procedure of ‘Tieton’/’Gisela5’ sweet cherry in 2014. .............................. 172 Figure 4.2. Vegetative regrowth measurements of ‘Tieton’/’Gisela5’ sweet cherry trees during the dormant season 2015. .............................................................................................................................. 173 Figure 4.3. Mean caliper of wood removed by topping ‘Tieton’/’Gisela5’ at different timings during 2014. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................ 174 Figure 4.4. Mean caliper of wood removed by topping ‘Tieton’/’Gisela5’ at different timings during 2015. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................ 175 Figure 4.5. Mean length of wood removed by topping ‘Tienton’/’Gisela5’ at different timings during 2014. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................ 176

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Figure 4.6. Mean length of wood removed by topping ‘Tieton’/’Gisela5’ at different timings during 2015. Statistical comparisons are between pruning timings, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ........................................................ 177 Figure 4.7. Correlation between length of wood removed (cm) and caliper of wood removed (mm) by topping ‘Tieton’/’Gisela5’ at different timings during 2014. ................................................................... 178 Figure 4.8. Relationship between length of wood removed (cm) and caliper of wood removed (mm) by topping ‘Tieton’/’Gisela5’ at different timings during 2014. ................................................................... 179 Figure 4.9. Mean yield (kg fruit/upright) from topping ‘Tienton’/’Gisela5’ sweet cherry at different timings in 2014. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars indicate standard error. .......................................................................................................................................... 180 Figure 4.10. Mean yield efficiency (kg fruit/cm2 TCSA) from topping ‘Tienton’/’Gisela5’ sweet cherry at different timings in 2014. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars indicate standard error. ............................................................................................................................ 181 Figure 4.11. Mean yield (kg fruit/upright) from topping ‘Tienton’/’Gisela5’ sweet cherry at different timings in 2015. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars indicate standard error. .......................................................................................................................................... 182 Figure 4.12. Mean yield efficiency (kg fruit/cm2 TCSA) from topping ‘Tienton’/’Gisela5’ sweet cherry at different timings in 2014. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars indicate standard error. ............................................................................................................................ 183 Figure 4.13. Mean current season shoot length by canopy zone measured during dormant season 2015 after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2014. Statistical comparisons are between canopy zones, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ............................................................................................................................ 184 Figure 4.14. Mean number of current season shoots by canopy zone measured during dormant season 2015 after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2014. Statistical comparisons are between canopy zones, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ............................................................................................................................ 185 Figure 4.15. Photograph of new extension growth on zone 4 of the canopy from dormant topping of Tieton’/’Gisela5’ sweet cherry in 2014. .................................................................................................... 186 Figure 4.16. Mean current season shoot length by canopy zone measured during dormant season 2016 after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between canopy zones, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ............................................................................................................................ 187 Figure 4.17. Mean number of current season shoots by canopy zone measured during dormant season 2016 after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between

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canopy zones, treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error. ............................................................................................................................ 188

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Dedication

This thesis is dedicated to my husband and daughter, who have shared this amazing

journey with me. This work is also dedicated to my parents and siblings,

for their endless love, support and encouragement.

1

CHAPTER ONE

INTRODUCTION

Apple and sweet cherry growth habit

Apple

Apple (Malus domestica Borkh.) belongs to the Rosaceae family, subfamily Maloideae, genus

Malus (Ferree & Warrington, 2003; Jackson, 2003). This pome fruit is one the most important crops

cultivated in temperate zones or at high elevations in the tropics; little is known about wild apple

domestication but it began in Almaty (Kazakstan) (Robinson et al., 2001; Harris et al., 2002). The genus

Malus includes approximately 55 species, however 8 to 79 have been recognized and 15 are considered

primary species with 2 from Europe, 4 from North America and 9 from Asia (Westwood, 1993; Robinson

et al., 2001; Harris et al., 2002). In the 19th and 20th centuries Malus domestica cultivars found in Europe,

North America, Russia, New Zealand, Japan and Australia were spread around the world and now form

the basis of commercially produced apple (Ferree & Warrington, 2003).

Species from Maloideae are characterized for having a hypanthium and gynoecium fused to

form a fleshy fruit or pome (Ferree & Warrington, 2003). Apples grow on deciduous trees, rarely

evergreen or with spiny branches and the leaves are serrate or lobed (Westwood, 1993). This subfamily

has mixed flower buds comprised by both vegetative and reproductive parts. The flower cluster is a

cyme composed of up to 6 flowers (Ferree & Warrington, 2003) which are white to pink with 5 sepals, 5

suborbicular or obovate petals, 20 stamens in three whorls with yellow anters, and a pistil dividen into 5

styles fused at the base (Westwood, 1993; Ferree & Warrington, 2003; Jackson, 2003). The ovary is

inferior, has 5 locules each usually constitued by 2 ovules, therefore containing up to 10 seeds (Ferree &

Warrington, 2003; Jackson, 2003). Apple flowers can be initiated in terminal and axillary buds of spurs

2

and/or shoots, and in some cases they are borne on lateral buds of 1-year-old shoots; the flowering

habit varies depending of the cultivar (Westwood, 1993; Ferree & Warrington, 2003). Floral initiation for

next year’s crop starts in early summer (Westwood, 1993). Remaining vegetative growth forms from

lateral buds near to the flower clusters, which lead the development of one or two bourse shoots

(Ferree & Warrington, 2003).

Growth and fruiting habits vary depending on the species and cultivars as a result of apical

dominance differences, apple trees in their natural habit grow on a central leader trunk with the

exception of strongly basotonic varieties that have several laterals that reduce the predominance of this

leader (Lespinasse, 1983; Maib et al., 1996). Apple cultivars can be generally classified by their vigor,

their fruiting and their growth habits; vigor of the tree is measured by the trunk cross sectional area or

the volume gained by a tree in a certain period of time, growth habit refers to the growth pattern of the

tree, and fruiting habit includes the fruiting pattern of the cultivar, shoot length to flower production

relationship, number of spurs that fruit and location of the fruiting zone (Maib et al., 1996; Lauri et al.,

2011). While the vigor of a tree is mainly influenced by the rootstock, root system of the scion and soil

characteristics, growth and fruiting habits are influenced by the cultivar; understanding the natural

growth habit of the trees is essential for training and pruning correctly (Wilton, 1998; Lauri et al., 2011).

Apple cultivars are classified in four principal morphological or fruiting types (Figure 1.1). Type I

trees, the spur strains, exhibit a dominant shoot growth from the base of the tree (basitonic), they are

characterized by an architecture with a central leader which is not predominant for all cultivars, have a

tendency to alternate bearing, the scaffold branches are conical and most fruits are on short spurs

located on branches 2-years-old or older (Maib et al., 1996; Craig & Embree, 2006; Lauri, et al., 2011).

Type II trees, also known as ‘Reine des Reinettes’, have their trunk and branches forming a strong

structure with a dominant central leader, the main branches form wide angles, the fruiting zone does

3

not influence the tree form because most of the spurs are found on branches of 2 to 4-years-old, these

trees exhibit a basitonic tendency that is affected by the vigor of the rootstock (Lespinasse, 1983; Maib

et al., 1996; Craig & Embree, 2006). Type III trees are in between basitonic and acrotonic varieties, the

leader is strongly predominant, the main branches form wide angles and that bend due to the weight of

the fruit, fruiting is no longer developed on these branches but on younger lateral wood (Lespinasse,

1983; Maib et al., 1996; Craig & Embree, 2006). Finally, type IV varieties have dominant shoot growth

from the top of the tree (acrotonic), these are tip bearing trees which develop a weeping habit as they

age with overarching branches in the upper part of the tree, these cultivars exhibit a more regular

bearing pattern, and fruiting is located in the upper and outer zone of the tree on 1 to 2-years-old wood

in terminal or lateral position (Lespinasse, 1983; Maib et al., 1996; Wilton, 1998; Craig & Embree, 2006;

Lauri et al., 2011).

Sweet cherry

Sweet cherry (Prunus Avium L.) is member of the Rosaceae family, subfamily Prunoideae, genus

Prunus (Webster & Looney, 1996). Cherries are thought to have originated in the area between the

Caspian and Black seas of Asia Minor, birds might have carry them to Europe before human civilization

and their cultivation started in Greece by 300 B.C. (Webster & Looney, 1996; Marini, 2014). Sweet

cherry is indigenous to parts of Asia, Europe and came to the USA with English Colonists around 1629,

there is evidence that they were grown abundantly in Virginia in the 17th century and were moved in the

1800’s to their principal producers Washington, Oregon and California (Webster & Looney, 1996; Marini,

2014). There are less than 100 cultivars growing around the world, and in North America the principal

varieties are ‘Bing’, ‘Rainier’, ‘Napoleon’ and ‘Lambert’ (Westwood, 1993). Three main groups have been

identified for sweet cherries, including the Mazzards, composed of small fruits of different shapes and

4

colors; the Hearts or Geans, with soft-fleshed fruit; and the Bigarreaux, with hard-fleshed and light

colored fruit, shaped as heart (Webster & Looney, 1996).

Sweet cherry produces large deciduous trees that can reach almost 60 feet tall but are

maintained 12-15 feet tall (Webster & Looney, 1996; Marini, 2014). Trees have fewer but larger leaves

than tart cherries of about 4-6 inches long, they are elliptic, petioled with serrate and irregular margins

and strong veins (Westwood, 1993; Webster & Looney, 1996). Flowers are white with long pedicels,

borne in clusters composed of 2-4 simple buds which contain 2-5 flowers; they bear singly in the axils of

previous year’s wood or on long-lived spurs (10 – 12 years) on 2 year old wood and less frequently near

the base of 1-year-old wood (Westwood, 1993; Webster & Looney, 1996; Marini, 2014). Flowers consist

of outer sepals, petals, stamens with anthers and regularly have 1 pistil each but some can have 2 pistils

forming double fruits (Westwood, 1993; Webster & Looney, 1996). The pistil has an stigmatic surface,

the style and the ovary with 2 ovules, the primary ovule will form the seed and the secondary one aborts

very early (Webster & Looney, 1996). Fruits are round, small, have a deep stem cavity and their color

can vary from yellow to red and purplish black (Webster & Looney, 1996; Marini, 2014). Floral initiation

begins in the previous summer at the same time that new shoots grow, after the crop is harvested

(Westwood, 1993; Lang, 2001a). This process starts with the first alteration of a vegetative bud to a

floral bud, and the interruption of new shoot initial growth can increase flower bud formation (Lang,

2001a). Morphological development stops with the onset of dormancy; after the accumulation of

chilling requirements in late winter/early spring, buds swell and shoot budbreak occurs (Webster &

Looney, 1996; Lang, 2001a).

The sweet cherry tree grows as a central leader, with more upright growing and less branching

than tart cherry (Marini, 2014; Long et al., 2015). These trees are characterized for being non-

precocious, and exhibiting strong apical dominance and a tendency to branch below the terminal bud of

5

annual growth (Long et al., 2015). Sweet cherry buds only have leaves or flowers, the flower buds die

after flowering (Webster & Looney, 1996; Marini, 2014). All the buds on one-year-old shoots are

vegetative except for the basal ones, they may grow into spurs which are shoots of about 4 inches long

(Marini, 2014). Trees have strong vigorous growth, branches become less vigorous as they age and the

number of spurs decreases too (Marini, 2014; Long et al., 2015). Spur buds normally set better than

buds growing on 1-year-old shoots, the apical bud of vigorous shoots inhibits lateral buds growth due to

strong acrotonic behavior that characterize sweet cherry; these long vigorous shoots can be confused

with blind wood, but the dormancy of lateral buds can be released with pruning or bending (Webster &

Looney, 1996). Sweet cherry trees also have a tendency to branch with narrow crotch angles which are

prone to bark inclusion, bacterial canker infections can be produced more easily in this case (Long, et al.,

2015). Own-rooted trees take 5 to 6 years to produce fruit, which can be modified with pruning and

precocious rootstocks. Sweet cherry requires less pruning than other trees because fruiting occurs on

spur rather than on shoots (Westwood, 1993; Long et al., 2015).

Pruning tree fruit

Pruning and tree training are essential practices for optimizing tree growth and fruit quality.

Young trees need pruning for the establishment of a basic and strong structure and also to provide light

channels throughout the tree for adequate fruit ripening (LaRue & Johnson, 1989; Moulton & King,

2015). Tree training involves positioning branches for a permanent architecture and it determines, for

the most part, how the trees have to be pruned when they are mature (Forshey, 1976; Moulton & King,

2015). These two horticultural manipulations are very different but complement each other; training has

an effect on the tree form and development, while pruning affects primarily the tree function

(Crassweller, 2015). When a tree is appropriately trained to allow maximum light interception, pruning

6

becomes a maintenance practice instead of a corrective one (Ozkan et al., 2012).Proper pruning not only

influences tree architecture and light interception, but also removes unproductive wood (e.g. diseased,

dying and broken branches), improves air circulation, maintains optimum vigor of productive wood and

facilitates the penetration of foliar applications (Marini, 2001; WSU tree fruit extension, 2015).

Tree fruit growth is controlled by hormones; cytokinins are produced in the roots and move up

to the highest points of the trees, while auxins are manufactured in shoot apical meristems and move

downwards through gravity (Somerville, 1996; WSU tree fruit extension, 2015). Auxins from terminal

regions indirectly inhibit lateral bud growth below the dominant region promoted by cytokinins,

controlling growth rate and length of the terminal shoot; this is a phenomenon known as apical

dominance (Cline, 1991; Maib et al., 1996; WSU tree fruit extension, 2015). Apical dominance has an

effect on the number of shoots formed from lateral buds, the length of lateral shoots and the angle

between lateral shoots and the trunk; when a tree exhibits strong apical dominance, vegetative growth

is stimulated at expense of flowering development, this intensity varies between species and cultivars

(Westwood, 1993; Maib et al., 1996). Pruning and shoot orientation with training can alter these growth

patterns controlled by hormones (Marini, 2001). When vertical wood is bent to the horizontal or below

the horizontal, hormone movement is changed and auxin accumulates on the underside of the shoot;

apical dominance is lost temporarily, lateral shoot growth is no longer suppressed and the tree produces

vigorous water sprouts (Maib et al., 1996; Marini, 2001). Branches that grow straight up exhibit strong

vegetative growth, while limbs growing horizontally straight out of the trunk produce a lot of fruit but

little vegetative growth (Roper, 2005). An appropriate limb position creates a 40-60° angle between the

limb and the trunk (crotch angle), because it promotes new wood growth that supports future fruiting

and fruit production is stimulated (Marini, 2001; Roper, 2005). Limbs with wide crotch angles are also

7

stronger than upright branches with narrow angles are less susceptible to winter injury and the fruit

hanging from this type of branches tend to develop minimum limb rub (Marini, 2001; Roper, 2005).

Fruit quality is largely determined by the balance between vegetative and fruitful growth. Fruit

and woody tissues compete among themselves for the carbohydrates produced by the leaves; excessive

or little vegetative growth affects fruit size and quality, while adequate vegetative growth provides

functional leaf surface and encourages the development of new bearing wood (Forshey, 1976). Trees

exhibit different responses to pruning due to apical dominance modification; the balance between the

upper part of the tree and the roots is altered, overall dry matter accumulation is reduced, removal of

apical growing points stimulates lateral growth and fruit size is increased (Maib et al., 1996; Roper,

2005). Pruning severity also affects regrowth; this severity is measured by the number of apical points

removed during pruning and not by the amount of wood pruned (Forshey, 1976). Severe pruning

induces excessive growth that can reduce light interception; when large branches are pruned; more

reserves from the wood of the tree are removed causing a dwarfing effect, which results less stimulating

than the removal of many small branches with several growing points (Westwood, 1993; Craig &

Embree, 2006). Pruning stimulates growth near the cut, cytokinins accumulate in the growing points

that were removed below the cut and new shoots develop, which re-establishes apical dominance (Maib

et al., 1996). Tree age also has an influence on regrowth; in general, young trees respond better to light

pruning, while older trees can be pruned more severely (Stebbins, 1992). Old trees with low vigor can be

reinvigorated with small pruning cuts rather than with large ones, to encourage uniformity on growth

stimulation (Westwood, 1993). Severe pruning also causes a decrease in flower bud production, because

vegetative growth is made at expense of flower formation; this is especially important with young trees,

where delayed fruit set and production is a consequence of severe pruning (Maib, et al., 1996).

8

The two main categories of pruning cuts are heading and thinning. Heading cuts remove a part

of a branch or limb, thus removing apical dominance and altering the normal movement of hormones in

the portion of the branch that was left unpruned (Forshey, 1976; Stebbins, 1992; Maib et al., 1996).

Trees response to heading cuts with growth near the cut, the number of shoots and their length is

affected; typically this is an invigorating process, but the response depends on the age of the pruned

shoot, severity of pruning, shoot orientation and growth habit (Forshey, 1976; Crassweller, 2015). As

mentioned before, pruning young trees or shoots stimulates vigorous growth from the 3-4 buds below

the cut, and apical dominance is re-established (Maib et al., 1996; Crassweller, 2015). Marini et al.

(1993) found that heading back one third of 1 year old shoots on scaffold limbs of three year old

‘Redchief Delicious’/MM.111 apple reduced yield efficiency by 14% and cumulative gross returns by 9%

compared to unpruned trees, and confirmed previous research stating that heading should not be done

on young trees to avoid long term negative effects on orchard profitability. Flowering and fruiting habits

also determine the amount of growth and tree response to pruning; basipetal apple cultivars that are

headed back respond with less vigorous shoot growth than acropetal cultivars (Westwood, 1993;

Crassweller, 2015). Heading cuts are not always harmful to a tree, they stimulate branching and are

useful to stiffen branches and prevent fruit set at the end of the branches; when performed on fruits like

peaches with flower initiation on current shoots, the response is better than on those fruits that set

flowers on older spurs like cherries (Westwood, 1993). Differently, thinning cuts involve the removal of

an entire branch or shoot to its origin, apical dominance remains undisturbed and hormones movement

in the tree does not change (Forshey, 1976; Stebbins, 1992; Maib et al., 1996). Thinning cuts are not

invigorating, they improve light penetration in the canopy, remove competitive shoots, shorten limbs,

preserve more fruiting wood, and redirect limbs (Stebbins, 1992; Maib et al., 1996; Crassweller, 2015).

These types of cuts also increase flowering and improve fruit color due to better light distribution

9

(Crassweller, 2015). The bench cut, a type of thinning cut, develops water sprouts due to uprights

pruning to a horizontal limb; these cuts should be avoided because they increase the risk of sunburn and

winter injury (Maib et al., 1996).

Pruning is generally performed during the winter and/or the summer, and there are specific

effects on the trees depending on the timing they get pruned (Forshey, 1976). Dormant pruning can

result in vigorous regrowth, which is mainly influence by the severity of pruning (Parker, 2008; Wilson,

2009). The energy stored in the trunk and roots to support the upper part of a tree doesn’t change with

dormant pruning, but reduces the number of growing points. In the spring the tree responds with the

growth of vigorous wood that can influence adequate development with shading (Parker, 2008). Pruning

during the dormant season can be used as a cropload management technique when done properly and

help reducing the number of fruit that has to be hand or chemically thinned (WSU tree fruit extension,

2015). Dormant pruning can also be used to manage heavy croploads of small fruit like those produced

by trees on dwarfing rootstocks such us ‘Gisela 6’, which reduces the supply of photosyntates for the

fruits (Bennewitz et al., 2011). However, severe dormant pruning can have potentially detrimental

effects of fruit trees with the production of excessive vegetative growth and a lack of fruiting (Kappel &

Bouthillier, 1994; Parker, 2008). Bennewitz et al. (2011) observed that ‘Bing’/’Gisela 6’ sweet cherry

yield was reduced by 37% and 67% with moderate and severe dormant pruning, respectively;

additionally, all pruning treatments (soft, moderate and intense pruning) reduced fruit number per tree,

but only soft pruning (15% of fruiting wood removal) reduced cropload to an approximate ideal number

of fruit per tree established for ‘Bing’/’Gisela 5’in previous research. The negative effects on yield and

crop value caused by moderate and intense dormant pruning was attributed to the removal of excessive

fruiting wood and the possible limitation of assimilate supply to the fruit (Bennewitz et al., 2011).

Dormant pruning has to be performed as late in the winter as possible to avoid freezing damage,

10

extreme low temperatures and temperature swings must be avoided; furthermore, old trees should be

pruned first because young trees are more susceptible to winter injury (Forshey, 1976; Craig & Embree,

2006; Parker, 2008). Conversely, summer pruning is believed to be less invigorating than dormant

pruning and has been used in several crops to reduce tree size (Flore, 1992; Kappel et al., 1997).

Removal of active leaf surface during the growing season can influence the movement of assimilates to

the woody tissues, resulting in a reduction of reserves for shoots growth the following season (Forshey,

1976). Usenik et al. (2008) studied summer pruning effects on fruit quality and yield efficiency of

‘Kordia’ and ‘Regina’ on ‘Gisela 5’ for three consecutive years; they reported that unpruned trees

exhibited similar yield efficiency than pruned trees but lower fruit weight and quality, regrowth varied

depending on the cultivar and growth habit; however, unpruned trees had more blind wood than

pruned trees due to shading. Guimond et al. (1998) reported that summer pruning of 4 year old ‘Bing’

cherry trees increased flowering on current season wood compared to unpruned trees, pruning at 37 to

45 DAFB stimulated vegetative laterals growth more than earlier pruning, trees with no pruning

presented significantly lower regrowth probably due to undisturbed apical dominance. It has also been

observed that summer pruning in apple can delay senescence because trees are non-dormant and

pruning influences active interaction among shoots, roots and fruit (Saure, 1987).

As mentioned before, trees’ responses to pruning are different depending on the species,

cultivar and growth habits. Pome fruits such us apple have mixed buds that bear their crops primarily on

terminals of short spurs; thus, is important to prune these type of trees lightly to develop a spur system

(Westwood, 1993). Young apple trees must be trained and pruned right after they are planted,

emphasizing training rather than pruning during the first developmental years (Forshey, 1976). Pruning

on standard apple trees mainly consists of thinning dense areas and removing unproductive wood to

improve light distribution (Westwood, 1993). Robinson (2011) explained that removing whole limbs

11

from the top of trees trained as Central Leader systems was the best way to keep a balance between

vegetative and fruiting growth; while for trees in Tall Spindle systems, it is essential to maintain a conic

shape of the tree to ensure good light distribution by removing completely 1-2 large upper branches

annually with an angled cut. When the replacement branches are left unheaded they will bend down

with fruit, by repeating this procedure for 3-4 years the top of the tree will have young fruitful branches,

maintaining the tree with a conic shape (Robinson, 2011). The last practice has been successfully used

with most high density systems such as the Vertical Axis, Slender Spindle, Super Spindle and Y-trellis

(Robinson, 2011). Sweet cherry on the other hand, requires a lighter pruning than apple because it

needs greater growing points for a full crop (Westwood, 1993); however, similarly to apple, pruning

requirements and trees responses are influences by tree age, rootstock, training system, variety,

pruning severity, timing of pruning and orchard management (Webster & Looney, 1996). Sweet cherry

trees can be pruned in the dormant season and/or in the summer, yet dormant pruning can increase the

risk of damage by bacterial canker because shoots pruned in the winter don’t heal until the following

spring, which make them vulnerable to wood infection (Webster & Looney, 1996). The effects of

summer pruning on sweet cherry have been well documented (Wustenberghs et al., 1996; Kappel et al.,

1997; Guimond et al., 1998; Usenik et al., 2008). Results showed that light pruning is better than severe

pruning probably because of sweet cherry fruiting habit and that intense pruning can affect production

and allocation of assimilate reserves (Wustenberghs et al., 1996; Kappel et al., 1997; Usenik et al., 2008),

flower bud formation and lateral shoot growth were increased by summer pruning with a greater effect

from treatments performed late after full bloom (Wustenberghs et al., 1996; Guimond et al., 1998).

Roversi et al. (2008) investigated the effects of winter and summer pruning, and the combination of

both timings with 8-year-old trees of different varieties in Europe; they reported that although their

results were not significant, cumulative yields were higher with summer pruning than with winter

12

pruning, yet further investigation was suggested due to the variability in varietal production. On the

other hand, pruning techniques can greatly vary depending on the training system and the rootstock;

pruning trees on productive rootstocks like Gisela 6 or 12 must focus on increasing vigor and reducing

cropload, while trees on Mazzard require pruning practices that encourage precocity (Long, 2007). For

trees on productive rootstocks more aggressive pruning is needed than for standard rootstocks, with

thinning cuts to improve light distribution, stub cuts to reduce cropload and heading cuts to encourage

branching (Andersen et al., 1999; Long, 2007). Fruiting walls such us the Upright Fruiting Offshoots

(UFO) system simplifies the process of pruning and improves worker safety and efficiency because of its

design (Ampatzidis & Whiting, 2013). UFO mature pruning consists of a two-step process: renewal of 1-2

vigorous upright leaders per year with a stub cut (Figure 1.2), and removal of all lateral branches on

upright leaders with thinning cuts (Long, et al., 2015).

Apple and sweet cherry training systems

Traditionally, apple and sweet cherry (Prunus Avium L.) trees have been trained to an open

center multiple leader system on vigorous rootstocks and low tree densities (e.g. 200-400 trees per ha)

(Whiting, et al., 2005; Robinson et al., 2013). These architectures yield a significant production 4 to 6

years after planting the trees and achieve a maximum productivity from the 8th year with high

requirements in labor (Balmer, 2001; Whiting, et al., 2005). Increasing fruit demand and labor scarcity,

the need for early production with high fruit quality and yields, and the introduction of dwarfing

rootstocks contributed to the transition from traditional low density systems to more efficient orchard

canopies (Balmer, 2001; Weber, 2001; Whiting et al., 2005; Ampatzidis & Whiting, 2013; Radunic et al.,

2011).

13

Over the last five decades apple planting densities have been constantly increasing, growers

moved from 40 trees/acre to more than 3000 trees per acre with vigor controlling rootstocks and a

better use of available light (Ozkan et al., 2012; Robinson, et al., 2013). Light interception is one of the

most important factors that influence orchard productivity and it can be managed through canopy

display (Wunsche et al., 1996; Wunshe & Lakso, 2000). Light plays a key role in photosynthesis, leaves

and shoots development, flower bud initiation, fruit set and fruit quality and growth; total dry matter

production and yield are determined by light interception, and the design of an orchard can limit or

increase the efficiency of this interception (Robinson & Lakso, 1991; Rom, 1991). Shading can result in

reduced fruit mass and can affect fruit quality attributes such as color, soluble solids content and

metabolite concentrations; shoot photosynthesis and fruit temperature can also be influenced by shade

(Stephan, et al., 2008). Wunsche et al. (1996) examined yield variations from different apple production

systems in relation to different shoot types and found that fruit yield is related to spur leaf light

interception rather than extension shoot canopy interception; the results of this experiment emphasized

that canopy management should be directed to expose spur leaf area that intercept a high percentage

of light.

Rootstock can have an effect on fruit quality and yield, can improve fruit tolerance to

environmental stress and control tree vigor; this has been demonstrated on previous research with

apple and peach, and new rootstocks have also been developed for sweet cherry for their commercial

use (Whiting et al., 2005; Tworkosky & Miller, 2007). Controlling tree size is essential for a successful

high density orchard; environmental, genetic and cultural methods can be used for this purpose. Plant

material selection is considered as the most effective method; rootstocks not only control tree vigor but

also encourage early bearing on the scion variety (Heinicke, 1975). Tworkosky & Miller (2007) reported

the effect of rootstock on apple scions with different growth habits, concluding that size can be

14

controlled but apple tree architecture is not necessarily affected by this factor. MM.111 rootstock

produced the largest trees while M.9 tended to reduce tree growth; furthermore, a clear interaction

between rootstock and growth habit was observed, where scions influenced tree architecture the most

and rootstocks the growth rates (Tworkosky & Miller, 2007).

There are six high density systems leading apple production around the world: the Tall Spindle,

with 1000 to 1300 trees/ha is widely used in North America, it combines the characteristics of the

slender spindle, the vertical axis, the super spindle and solaxe systems, it uses fully dwarfing rootstocks

and feathered nursery trees (Robinson et al., 2006; Robinson, et al., 2013). The Super Spindle has

densities of about 2200 tree/acre; pruning is simple and yields high quality fruit. The vertical and V-

trellis systems are mostly used in western North America and require precise pruning and a calculation

of the bud number desired. The Solaxe system is used in Europe and Chile, it requires limb bending and

bud extinction to control of vegetative and fruiting growth. The Bi-axis has been extensively used in

Italy and recently implemented in North America; it has densities of 900 trees per acre but achieves

about 1800 leaders per acre due to a two-stem tree system. Finally, the Fruiting Wall system has been

adopted in some countries in Europe and it is originally from France, mechanical pruning is used with

this design in the summer (Robinson, et al., 2013). The Y-trellis system is an angled canopy similar to the

V-trellis, several studies have suggested that Y or V shapes intercept light better than the slender

spindles systems (Hampson, et al., 2004). Hampson et al. (2002) compared orchard performance

between angled canopies (Y-trellis and V-trellis) and slender spindles; they found that Y-trellis system

presented 11-14% higher cumulative yield than slender spindle or V-trellis system, but fruit quality,

growth or light interception did not differ significantly among systems. Tree density played a key role in

this experiment rather than training system, where high tree density increased light interception in the

young orchards producing early high yields per unit land area (Hampson et al., 2002).

15

Apple growers around the world started to transfer the techniques they used with their high

density orchards to their cherry plantings, given the profitable opportunities that this high value crop

offers. One of the main differences between apple and sweet cherry high density plantings was the lack

of a dwarfing rootstock like M.9 in apples; consequently, the transition to high density systems for sweet

cherry began with vigorous rootstocks such as Mazzard (Balmer, 2001; Robinson, 2005). The Zahn

system was developed with trees planted closer together than with traditional systems and resulted in

earlier production and high yields; however, the excessive vigor of this system did not facilitate orchard

management. Following this system, the Spanish bush was developed in Spain with the vigorous

rootstock maheleb and slightly lower densities (Robinson, 2005). This system with free-standing trees

was very successful in poor soils and dry climate, and it is still used in Spain and other countries around

the world; the trees reach a maximum height of 8 feet and can be harvested with small ladders as they

forms a semi-pedestrian orchard, it is easy to maintain and the mature trees are pruned with renovation

of horizontal fruiting wood on the permanent vertical scaffolds (Long et al., 2015; Robinson, 2005). The

Tatura trellis was developed as a V-shaped system in Australia with vigorous Mazzard, small in-row

spacing, high light interception and excellent yields made this system also very successful but expensive

to train (Robinson, 2005).

The potential to stimulate precocious fruiting and high productivity led to the development of

dwarfing and semi – dwarfing rootstocks for sweet cherry since the 1970s with groups like ‘Colt’ and in

the late 1980s with groups like the ‘Gisela’ series or Edabriz, which improved precocity, promoted

increased spur formation and controlled tree vigor (Lang, 2000; Lang, 2001a; Whiting et al., 2005; Rugini

et al., 2015). Propagation by grafting can influence tree size, growth habit and development through

interactions of scion-rootstock, which has been profoundly investigated (Gyeviki et al., 2012; Rugini et

al., 2015). The vigor in a tree can be controlled at different levels so they can be adapted to a variety of

16

training systems and soil characteristics, smaller trees can be planted closer together so growers can

have more trees per hectare and reach full production faster than with traditional systems (Lang, 2000;

Lang, 2001b). Smaller trees on precocious rootstocks also demand precision management (Lang, 2001a).

A simplified tree structure and less vigor requires a balance between leaf area and fruiting growth, frost

damage susceptibility can be increased as a higher proportion of the crop can be picked from the

ground, management decisions with young trees will determine crop production and fruit quality in the

4th and 5th years, and rootstock vigor must be considered in accordance to the soil type (Lang, 2000;

Lang, 2001b). Gyeviki et al. (2012) found that rootstocks can also influence the characteristics of sweet

cherry leaf population in high density orchards, noting than specific leaf weight was larger on leaves

from trees grafted on dwarfing rootstocks compared to those on vigorous rootstocks. Their

investigation led to the conclusion that rootstock vigor and site conditions can greatly impact the light

environment within an orchard, which will ultimately influence leaf area index and density within a tree

canopy (Gyeviki et al., 2012).

The development of precocious semi-dwarfing and dwarfing rootstocks allowed vigor control

with new training systems that are still used. The Vogel central leader has free-standing single leader

trees; it requires intensive labor for establishment which is lowered at maturity (Robinson, 2005; Long et

al., 2015). The Steep leader also has free-standing trees with a pyramidal architecture that allows good

light interception. The Super slender axe has free-standing trees that need a top-wire trellis; this very

high density system can be formed of up to 2000 trees per acre. The Tall spindle axe system has central

leader trees with spiraled whorl lateral branches that yield high quality fruit that can be picked from the

ground (Long, et al., 2015). The Upright fruiting offshoots (UFO) is a trellised system with a planar

architecture, trees are composed of one main horizontal scaffold with renewable vertical fruiting units;

the simplicity of the system facilitates tasks like pruning, harvest and thinning (Ampatzidis & Whiting,

17

2013; Long, et al., 2015). The Y-trellised UFO system has the fruiting units of the trees growing about 30

degrees off the vertical; due to this design there is improved light interception that results on high yields

(Long, et al., 2015).

The implementation of any training system requires knowledge of the vigor of the scion and the

rootstock, site and soil conditions, cultivar, orchard management and growing season. Careful

consideration is needed to make a good decision and comply with the characteristics of a modern high

density orchard which has to provide high yields, early production, low management costs and high

labor efficiency (Lang, 2001a; Weber, 2001; Long, et al., 2015).

Mechanical pruning of tree fruit

Training and pruning are necessary and powerful horticultural practices for tree fruit growers;

however, pruning represents a major component of production costs in terms of time and labor, and it is

also linked to high injury risk for workers due to the need of ladders for large trees (Forshey, 1976; Craig

& Embree, 2006). The evolution of canopy architecture to planar structures and training systems from

low to high density plantings with smaller trees, offers the opportunity to adopt mechanization and

reduce pruning costs (Forshey, 1976). Mechanical pruning systems are constituted by a series of blades

operated with a tractor that remove a portion of a branch and mimic traditional pruning practices to

obtain an optimal and continual crop production (Velazquez & Fernandez, 2010). Mechanized systems

perform a “surface” pruning that can involve topping or horizontal cuts of the trees and hedging or

vertical cuts of the sides of the trees. It is essential to plan ahead the timing; intensity and periodicity of

the mechanized pruning depending on the cultivar and characteristics of the tree, otherwise the trees

can response with undesired regrowth, fruit quality and yield (Sansavini, 1978; Stebbins, 1992).

18

Mechanical pruning is not as selective as hand pruning. Indiscriminate cuts can negatively

influence the growth habit of the tree and stimulate undesirable vigorous regrowth, which ultimately

will modify the shape of the tree and produce unfruitful branches (Forshey, 1976; Sansavini, 1978).

Hand pruning complementing a mechanized pruning can yield better results than mechanical pruning

alone and costs can be greatly reduced, but it is important to consider factors such as tree size and

canopy structure (Forshey, 1976; Sansavini, 1978). Pruning machines are basically classified in two

groups: sickle bars and circular saws, which can be operated with tractors, fork lifts, and other

equipment or by their own (Sansavini, 1978). Sickle bars can be considered as more effective than

rotating blades because they can limit cuts and remove young wood, larger machines can perform

severe pruning and remove bearing surface (Forshey, 1976).

Research on the potential of mechanical pruning in citrus started in the 50s in USA, and the

variability in results due to pruning type and severity, species, tree age, varieties and other parameters,

led to further investigation that has continued up to the present time (Martin - Gorriz et al., 2014).

Kallsen (2005) reported that mechanical pruning did not affect yield and fruit size of mature

‘Frostnucellar’ navel orange compared to hand pruning; but degree of pruning did influence yield which

was reduced with severe topping and manual pruning. Yildirim et al. (2010) observed that mechanical

topping and hedging of thirteen-year-old ‘Star Ruby’ grapefruit trees in Turkey, resulted in significantly

higher yields compared to unpruned trees, and trees that were only hedged or topped during 3 years,

and no significant effects on fruit quality. They found that yield from trees that were topped and hedged

yielded a more consistent production over the 3 years of study, while unpruned trees exhibited large

variations from one year to another, which could be explained by the fact that pruning effects don’t

show entirely during the pruning year, but in the following years. Velazquez & Fernandez (2010) tested

the effects on yield and fruit quality of mechanical pruning vs. hand pruning and the combination of

19

both approaches on ‘Valencia Late’ orange; results indicated that mechanical pruning alone resulted in

low yields and excessive vegetative regrowth, whereas mechanical pruning combined with subsequent

manual pruning improved fruit production with similar costs of traditional pruning, thus increased

profits. Martin - Gorriz et al. (2014) likewise found a 22% reduction in yield of 20-year-old ‘Fortune’

mandarins with mechanical pruning alone over three years compared to hand pruning, mechanical and

hand pruning alternated during the years of the study did not show an effect on yield, and a pre-pruning

by machine reduced the time to perform hand pruning cleanup by 13%.

Pruning is also a labor intensive and costly task in olive production, and several studies have

been conducted to find a cheaper and efficient alternative that doesn’t affect yield and crop quality

(Dias et al., 2012). Giametta & Zimbalatti (1997) conducted field trials with 10-year-old ‘Leccino’ olive

trees in Italy testing mechanical pruning efficiency and the effect on yield compared to hand pruning

and mechanical pruning complemented with traditional pruning. Observations showed that mechanical

pruning was 32 times more efficient than hand pruning requiring 4 man/h to prune 100 trees, compared

to 128 man/h that were necessary with hand pruning, the combination of both methods was also highly

efficient with a labor utilization of 21 man/h for the same number of trees. There was no effect of

mechanical pruning on yield during the three years of research and no permanent damage was found on

the trees with the cuts performed with the machine. Similarly, pruning efficiency and yield were

evaluated by Dias et al. (2012) during 8 years comparing mechanical and hand pruning on an olive grove

in Portugal. Results also revealed a much higher efficiency of mechanical pruning compared to pruning

by hand, and fruit yield was significantly higher from trees mechanically pruned but there was no

difference with mechanical and hand pruning combined. Cherbiy-Hoffmann et al. (2012) on the other

hand, found that pruning methodology did not influence yield or fruit set in ‘Arbequina’ olive trees, but

20

the vegetative regrowth produced by mechanical pruning could have limited fruit weight and oil content

due to low light interception in the interior of the canopy.

Pruning mechanization on apple has been tested for decades with the steady transition from

traditional to high density systems and increasing labor shortage. Cutter bars and circular saws were

first used on apples with limited success due to the excessive peripheral growth caused by heading cuts

of large limbs from trees with vigorous rootstocks, which produced shading and low fruit quality (Ferree

& Short, 1972; Miranda Sazo & Robinson, 2013). Cain (1972) described slot pruning on ‘McIn-tosh’

apples in Geneva performed with a slotting saw; a continuous slot of about 2 feet wide was cut 24

inches into the side of the trees along the entire hedgerow following a similar slot above next year with

no additional pruning during 4-year long cycles. The shoots resulting from cutting one-fourth of the tree

each year were less vigorous than the ones produced by hedging the whole tree, which allowed good

light penetration, fruit color and fruiting spurs production. The slotting saw was compared with a cutter

bar during 3 years; results showed that slot pruning performed better producing three times more new

spurs, six times more bearing spurs and greater light penetration which improved fruit color and yields

(Cain, 1972). Ferree & Rhodus (1993) found that hedging did not influence tree height or average shoot

length of four different apple varieties on central leader or trellis systems, however it controlled tree

volume better than root pruning. They observed that yield from hedged or root pruned trees was

significantly lower than yield from trees with standard pruning; however, they noted that their results

were also influenced by training system, planting spacing and cultivar growth habit. Miranda Sazo &

Robinson (2013) tried summer mechanical hedging on several apple cultivars trained to the Tall Spindle

and Super Spindle systems. They reported that 70% of growing points remained on the trees after

pruning, light distribution in the lower part of the trees was improved by 10%, regrowth was short with

terminal buds that would likely become flower buds, and better fruit coloration compared to control

21

treatments. This study indicates that summer mechanical pruning could be a good approach to reduce

production costs without an effect on fruit quality and regrowth, but further investigation is required to

determine long term effects on tree growth and yield. Most recently, the lack of selectiveness of

mechanical pruning has led to the development of robotic dormant pruning, which shows promise on

high density systems with simple tree structures such as the Slender Spindle system and also simple

pruning rules (Miranda Sazo & Robinson, 2013). Karkee et al. (2014) developed a machine vision system

using a 3D camera to identify branches in apple trees on Tall spindle system, algorithms were used to

establish simple pruning rules and keep specified branch spacing and branch length; future work

includes identification of dead branches and large branches, and localization of pruning points in the

limbs. Likewise, Elfiky et al. (2015) were able to identify pruning points with 96% accuracy with Skeleton-

based Geometric features in 3D reconstruction of apple trees in the first phase of a research work that is

planned to automate dormant pruning of specialty crops.

Limited studies on mechanized pruning have been conducted on stone fruit and almost none on

sour and sweet cherry. Summer hedging has been regarded as an invigorating process with negative

trees’ responses if it is used repeatedly and uncoupled with dormant pruning (Webster & Looney, 1996).

On the other hand, it has been observed that sweet cherry hedging during the summer can improve

light penetration throughout the trees, and tipping can reduce shoot length and control tree size with

little effect on floral-bud numbers (Flore, 1992). Kesner et al. (1981) reported that hedging one-third to

one-half of current season shoots of ‘Montmorency’ sour cherry 40-47 days after petal fall reduced

shoot length, increased flower buds, number of spurs and fruit set, increased yield and fruit size

probably due to improved light interception or removal of competing sinks. Studies showed that tree

size can also be controlled on sour cherry by summer hedging when it is combined with dormant

pruning every 3-4 years to increase light distribution (Flore, 1992). Hedging timing is an important

22

consideration; when performed too early it can affect carbon accumulation and partitioning and if done

too late it can delay hardening due to growth stimulation, 45 days after full bloom has been identified as

a good timing for hedging since it promotes shoots and spur development which influences yield and

fruit quality (Kesner, 1990; Flore, 1992). Mechanical pruning showed great potential to reduce costs

when it was compared to hand pruning on 6-year-old ‘Selah’ sweet cherry on ‘Gisela 6’ trained to the

UFO system using rotary discs mounted on a tractor; results from this study revealed that mechanical

pruning took 8% of the time of hand pruning but removed 60% less wood, and the time to perform hand

pruning following a mechanical pre-pruning was 20% lower than the time to hand prune the trees and

removed similar amounts of wood, but exhibited lower yields (Whiting, unpublished).

Pruning is an essential practice that has a direct influence on yield and fruit quality. Over the last

few decades dramatic changes have occurred in the apple and sweet cherry industry, with a clear

evolution of training systems, the development of dwarfing rootstocks and intensive orchard

management strategies. Increasing competitiveness has forced the tree fruit industry to innovate and

transform traditional systems into futuristic orchards that allow the adoption of mechanization and

simplify tasks including thinning, pruning and harvest. Pruning accounts for a large percentage of

production costs and even though its omission may not affect crop production and quality immediately,

negative effects can be long lasting and irreversible. Growers around the world are ultimately interested

in finding a solution to increasing labor shortage and increasing demand, without compromising the

quality of their crops. Mechanical pruning shows the potential to reduce production costs, improve

workers’ safety and efficiency and increase long term sustainability of fruit production; however, there

is considerable work remaining to find the best conditions for an optimal mechanization of pruning.

23

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31

Figure 1.1. The four classes of growth habit of apple trees (Maib et al., 1996)

32

Figure 1.2. Pruning rules for sweet cherry on the Upright Fruiting Offshoot (UFO) system (Long et al.,

2015)

33

CHAPTER TWO

MECHANICAL PRUNING IS PROMISING IN VERTICAL FRUITING WALL SWEET CHERRY

ORCHARDS

Keywords: pruning, sweet cherry, efficiency, fruit quality, Tieton, sickle bar, hedging, yield efficiency.

Abstract

Pruning is a necessary annual intervention growers utilize to manage tree vigor, growth habit,

and improve canopy light distribution and fruit quality. Pruning is also a labor- and time-demanding

operation that generally represents the second greatest annual expense for tree fruit growers

worldwide. As orchardists adopt new fruiting wall, planar orchard systems, there is potential for

adopting mechanical pruning systems. The Upright Fruiting Offshoots (UFO) architecture is a high-

efficiency planar training system that simplifies pruning, is precocious, and yields high quality fruit. This

research evaluated the potential adopt mechanical pruning in a commercial UFO-trained

‘Tieton’/‘Gisela®5’ sweet cherry (Prunus avium L.) orchard to reduce costs and improve workers’ safety

and production efficiency. Pruning treatments (2014/2015) included 1) hand/hand; 2)

mechanical/mechanical; and 3) mechanical/mechanical+hand. All trees were pruned postharvest;

mechanical pruning was accomplished with a Gillison center-mount sickle bar system operated at ca. 2.0

kph. We studied treatment effects on fruit yield and quality, and vegetative regrowth. Mechanical

pruning required three passes per tree yet was 23 and 29 times faster than hand pruning in 2014 and

2015, respectively. Hand pruning removed twice as much vegetative growth (fresh weight) as

mechanical pruning, with an average of 7.7 kg and 6.5 kg removed per tree in 2014 and 2015,

respectively. In 2015, the combination of mechanical followed by hand pruning was 40% more efficient

34

(min/tree) than hand pruning alone. There was no effect of pruning treatment on yield, which varied

between 16.8 to 18.8 tons/ha. In 2015, trees that were pruned mechanically in 2014 exhibited fruit with

slightly lower diameter (4.5%) and weight than fruit from hand pruned trees. Current season shoot

regrowth from trees pruned mechanically was significantly higher (ca. +40%) than new extension growth

from hand pruned trees. A preliminary economic assessment indicates that mechanical pruning can

reduce production costs and improve labor efficiency with no effect on fruit yield. These results show

promise for adopting mechanical pruning in vertical fruiting wall sweet cherry orchard systems.

Introduction

Traditionally, sweet cherry has been trained to an open-center, multiple-leader architecture

based on vigorous seedling rootstocks and low tree densities (e.g. 200-400 trees per ha) (Whiting, et al.,

2005; Robinson et al., 2013). These production systems can take 5 to 7 years to start fruiting and 10 to

12 years to achieve full production and have high labor requirements at maturity due to the large tree

size. Adopting precocious rootstocks improved production economics for growers with the ability to

harvest fruit in the first 3 to 5 years after planting (Lang, 2001b; Whiting et al., 2005). Further, the

introduction of size-controlling rootstocks was fundamental to the transition from traditional systems to

higher density architectures that offer improved labor efficiency (Lang, 2001a; Rugini et al., 2015). The

modern training systems are relatively easy to maintain after their establishment, with appropriate

training and pruning methods; yields can be higher with early production of good fruit quality, foliar

applications are better accomplished due to increased penetration within the canopy, cultural practices

are performed efficiently because of minimal need for ladders, and the costs for labor are reduced

(Hansen, 2012). The Upright Fruiting Offshoots (UFO) training system for sweet cherry is a fully trellised

high density system that forms a compact fruiting wall architecture at maturity. Trees are planted at a

35

45-degree angle and trained slightly above the horizontal to form a permanent scaffold from which

upright fruiting leaders are trained at about 0.2 m apart (Long et al., 2015; Zhang et al., 2015). This

planar architecture allows the adoption of mechanization for tasks such as pruning, harvest and

thinning; improves labor efficiency, and simplifies cultural practices (Milkovich, 2011; Ampatzidis &

Whiting, 2013; Zhang et al., 2015). Tree fruit producers around the world are adopting this system

because of its precocity and precision canopy management. Growers in the Pacific Northwest of the

U.S. have reported higher yields with the UFO system compared to traditional systems, and that the

advantages of fruiting walls outweigh the initial investment required for its establishment (Milkovich,

2011; Letizia, 2012). Pruning a mature UFO orchard consists of a two-step process: renewal of 1-2 of the

most vigorous upright leaders per year with a stub cut, and removal of all lateral branches on upright

leaders with thinning cuts (Long, et al., 2015).

The scarcity of skilled orchard labor is a problem affecting agriculture worldwide and it

represents the greatest concern for the sweet cherry industry in the United States (Peterson & Wolford,

2001). Pruning and harvest are among the cultural practices that highly depend on manual and skilled

labor; improving worker’s safety and efficiency is also a priority in the tree fruit industry to increase

orchards productivity (Peterson & Wolford, 2001; Ampatzidis & Whiting, 2013). The adoption of

mechanized alternatives for harvest and pruning sweet cherry trees may be an effective solution to

these concerns (Peterson & Wolford, 2001), particularly in simplified architectures such as the UFO.

Mechanical pruning research is reported in citrus in the 1950s; the variability on yield and fruit quality

effects due to pruning type and severity, tree species, tree age, and other parameters, fueled further

investigation, that has continued up to the present time (Martin - Gorriz et al., 2014). Velazquez &

Fernandez (2010) tested the effects on yield and fruit quality of mechanical pruning vs. hand pruning

and the combination of both approaches on ‘Valencia Late’ orange; results indicated that mechanical

36

pruning alone produced low yields and excessive vegetative regrowth, whereas mechanical pruning

combined with subsequent manual pruning improved fruit production with similar costs of traditional

pruning.

Pruning mechanization on apple has been tested for decades with the steady transition from

traditional to high density systems and increasing labor shortage. Ferree & Rhodus (1993) found that

hedging did not influence tree height or average shoot length of four different apple varieties on central

leader or trellis systems, however it controlled tree volume better than root pruning. Robinson (2013)

documented that summer mechanical hedging on several apple cultivars trained to the Tall Spindle and

Super Spindle systems removed only 30% of the growing points of the trees after pruning, light

distribution in the lower part of the trees was improved by 10%, regrowth was short with terminal buds

that would likely become flower buds, and better fruit coloration was accomplished in comparison with

control treatments. Most recently, Elfiky et al. (2015) were able to identify pruning points with 96%

accuracy with Skeleton-based Geometric features in 3D reconstruction of apple trees in the first phase

of a research work that is planned to automate dormant pruning of specialty crops.

Limited studies on mechanized pruning have been conducted on stone fruit and almost none on

sour (P. cerasus L.) or sweet cherry (Webster & Looney, 1996). Mechanical pruning showed great

potential to reduce pruning time compared to hand pruning on 6-year-old ‘Selah’/‘Gisela 6’ sweet cherry

trained to the UFO system using rotary discs mounted on a tractor. Mechanical pruning took 8% of the

time that hand pruning required, but removed ca. 60% less wood (Whiting, unpublished). Further, the

time to perform hand pruning following a mechanical pre-pruning was 20% lower than the time for hand

pruning alone, removing similar amounts of wood (Whiting, unpublished). The objective of this research

was to compare standard hand pruning practices with mechanical pruning and the combination of both

approaches, and understand the trees’ responses in terms of yield, fruit quality and regrowth.

37

Materials and Methods

Experimental design

All research was conducted at a commercial orchard near Benton City, WA (46°18'34.7"N

119°33'58.6"W) during 2014 and 2015. Trials were set up as a complete randomized design with nine-

and ten-year old trees ‘Tieton’/’Gisela 5’ sweet cherry trees trained to the Upright Fruiting Offshoot

(UFO) architecture and spaced 2.4 x 3 m. Trees were drip-irrigated and overhead sprinklers were used

every third row for evaporative cooling if necessary after harvest. Standard management practices were

followed in the orchard both years for fertilization and pest control. Three pruning treatments were

established with five replicate blocks of 20 trees (i.e., 100 trees per treatment).

Pruning treatments 2014

Pruning treatments (2014/2015) consisted of a hand pruned (HP) control (i.e., HP/HP), full

mechanical pruning in both years (i.e., MP/MP) , and mechanical in 2014 followed by mechanical + hand

pruning follow-up in 2015 (i.e., MP/M+HP). All pruning was conducted postharvest. In 2014 pruning

was performed on the morning of 3 July. Hand pruning was performed by the commercial orchard crew

using 3.6 m ladders and loppers following the UFO pruning rules. The crew consisted of 4 people with 2

people pruning on each side of the tree (i.e., down one alleyway). Hand pruning consisted on the

removal of lateral branches from upright leaders with thinning cuts with stub cuts.

Mechanical pruning was accomplished with a hydraulic Gillison’s Center Mount Topper and

Hedger comprised of a 3.3 m sickle-type cutting head that could rotate 360°. Mechanical hedging was

performed at ca. 10 cm from the uprights at 2000 RPM engine speed, and both hand and mechanical

topping were done at 3.4 m.

38

Data collection 2014

For each pruning replicate, I recorded the time required to prune all trees, and weight of the

wood removed per plot. Time per plot for hand pruning included hedging and topping performed by the

crew of 4 people, and pruning efficiency is expressed as the time per person per tree. For each

mechanically pruned plot, I recorded the time required for 3 passes of the machine (hedging each side

of the row and topping). The wood pruned in each plot was collected immediately and weighed in the

field with an electronic scale Pelouze model 4010 (capacity: 68 kg x 0.1 kg).

Trunk cross-sectional area (TCSA) at 20 cm above the graft union was calculated after pruning

from measurements of trunk circumference. These data were used to estimate the amount of wood

removed per cm2 of TCSA.


Postharvest pruning was performed on 18 June, 2015 in the same plots as in 2014 using the

same commercial crew of 4 pruners following the same pruning rules as in 2014, and again using 3.6 m

ladders and loppers. One set of mechanically pruned plots were pruned again with the Gillison’s hedger

(i.e., MP/MP) and the other plots were pruned mechanically again followed by a hand pruning cleanup

(i.e., M+HP) with the same commercial pruning crew. Mechanical hedging was performed again at ca. 10

cm from the uprights, and both hand and mechanical topping were done at 3.4 m height. Pruning

efficiency was calculated with the data collected and expressed as kg of wood removed per minute per

tree.

39


Fruit were harvested at commercial maturity on 4 June, 2015 by a commercial picking crew and

the WSU-IAREC Stone Fruit Physiology crew, using 3.6 m ladders. Sampling units consisted of 3 trees

with similar characteristics randomly chosen from each plot (3 trees per repetition per treatment = 45

trees/treatment). Tree yield was determined by weighing all fruit per sample tree using an electronic

scale Pelouze model 4010 (capacity: 68 kg x 0.1 kg) in the field. A random sample of 25 cherries from

each sample tree was collected in labeled paper bags for fruit quality analyses.

Fruit quality was analyzed at the WSU-IAREC Stone Fruit Physiology Laboratory in Prosser, WA.

Fruit size was determined by measuring equatorial diameter (mm) of individual fruit, using a digital

caliper Johnson model # 1889-0600 (measurement range: 0-150 mm, accuracy: ± 0.02 mm). Fruit weight

(g) of 5-cherry groups was recorded with a digital scale Ohaus Adventurer Pro model AV212 (capacity:

210 x 0.001 g). Soluble solids content (SSC) was determined with % Brix from the combined juice of the

25 sampled cherries using an ATAG Pocket Refractometer (ATAGO U.S.A., Inc., Bellevue, WA). Fruit

firmness (g/mm) was recorded for individual fruit with a FirmTech 2 (Bioworks INC., Wamego, Kansas).

Time to prune each plot and weight of the wood removed per plot were recorded during the

postharvest pruning following the same protocol as in 2014. Trunk cross-sectional area (TCSA) at 20 cm

above the graft union was calculated after pruning from measurements of trunk circumference. The

results were used to estimate the amount of wood removed per cm2 of TCSA and yield efficiency. In

addition, a visual evaluation of the pruning cuts performed with the machine the previous year was

done to determine disease and insect impact.

Vegetative regrowth from pruning cuts was assessed by measuring total current season wood

length from the same sample trees in each plot used for yield determinations. Cumulative shoot length

40

(cm) of each tree was determined with flexible 7.5 m tape measure using the commercial orchard’s

platforms by the Washington Tree Fruit Research Commission (WTFRC) and the WSU-IAREC Stone Fruit

Physiology crews during the dormant season 2016.

Finally, a preliminary economic assessment was developed using data gathered from these trials

and the characteristics of the orchard where we conducted our study. The objective of this assessment

was to provide information comparing mechanical and hand pruning for sweet cherry trained in planar

systems.

Statistical analyses

The Statistical Analysis System (SAS, 9.3 version) was used to separate means for all data sets

(time of pruning, weight of wood pruned, pruning efficiency, yield, fruit quality, regrowth). Analysis of

variance (ANOVA) and Tukey’s procedure for pairwise comparisons with 95% confidence level was used

to evaluate the statistical significance of the time of pruning, weight of wood pruned and pruning

efficiency results. The significance of differences in yield, fruit quality and regrowth were analyzed with

General Linear Model (GLM) and Tukey’s test with 95% confidence level.

Results and discussion

Pruning trials 2014

In 2014, pruning efficiency (min/tree) was affected by pruning treatment. Mechanical pruning

was significantly faster than hand pruning; MP1 and MP2 were 24 and 22 times faster than hand pruning

respectively (Figure 2.1). Pruning took ca. 0.26 min/tree (16 s) with MP1 and 0.28 min/tree (17 s) with

MP2. Tractor speed was 1.6 km/h for MP1 and 1.5 km/h for MP2, at 2000 RPM in our experimental

conditions; the variation on tractor speed can be influenced by the species, cultivar, tree age, soil

41

topography, canopy architecture and planting density; these factor also have to be considered before

choosing a method of pruning (Sansavini, 1978). Wheel slippage can also cause a variation in the tractor

speed and influence the operation efficiency; 10-15% slip is considered optimal for tractors and tires, a

lower percentage can result in a waste of power and fuel to move the wheels, whereas as higher

percentage can produce excessive tire spin and loss in productivity (Grisso & Helsel, 2012). Pruning

efficiency differed between the mechanical pruning blocks because of the presence of over-tree

sprinklers that were higher than the topping height. This meant that at each sprinkler the tractor

operator had to stop and retract the blade, proceed beyond the sprinkler, and resume pruning. Hand

pruning with loppers and ladders took approximately 6.2 min (342 s) per tree per person. Whiting

(unpublished) reported similar results where mechanical pruning was 12 times faster than hand pruning

on sweet cherry trained to the UFO system during the dormant season and using a system with rotary

disc blades; hand pruning per tree was performed in 2.6 min (158 s) per person, which was 2.4 times

faster compared to our trial results. This difference is likely because the trees were younger (6-year-old)

and smaller in the study conducted by Whiting (unpublished). In addition, summer hand pruning could

take longer than dormant pruning because of the presence of leaves on the trees.

Hand pruning removed 85% and 147% more wood per tree than MP1 and MP2 respectively with

7.7 kg wood removed/tree (Figure 2.2). Similarly, hand pruning removed 101% and 156% more

wood/cm2 TCSA than MP1 and MP2 respectively (Figure 2.3). Hand pruning removed more wood than

mechanical pruning because it was selective, and most of the cuts performed by hand were thinning

cuts. The mechanical pruning process did not prune any wood that grew along the row, in a North-

South orientation. In addition, the machine was unable to prune as close to the upright leaders as

precise hand pruning could. Furthermore, it was observed that some of the branches were scraped

and/or peeled by the machine, (i.e., “dirty cuts”) compared to cleaner cuts made by hand (Figure 2.4).

42

These types of cuts expose the branches to disease infection than can be spread throughout the tree;

dying and diseased branches are considered unproductive wood that compete with the fruit for

carbohydrates and have to be removed with pruning (Westwood, 1993). Additionally, indiscriminate

pruning can have long term effects on the shape and behaviour of the trees, the fruiting zone can also

be affected and concentrate toward the periphery of the tree (Sansavini, 1978).

Harvest 2015

Pruning methodology had no effect on yield (kg fruit/tree) nor yield efficiency (kg fruit/cm2

TCSA). Yield ranged from 13.9 to 15.4 kg of fruit/tree which is equivalent to 7.5 - 8.4 tons/acre (Figure

2.5). Whiting (unpublished) recorded an 8% increase in yield per tree from mechanical pruning

compared to hand pruning. This was attributed to the mechanically-pruned trees having slightly greater

bearing surface due to the machine removing less wood than manual pruning. Kesner et al. (1981) also

reported an increase in yield when hedging one third to one half of current season’s wood on

‘Montmorency’ sour cherry 40-47 days after full bloom. This effect was purported to be due to an

increase in leaf photosynthesis due to greater light penetration and/or the removal of competing sinks.

Yield efficiency from our experiment ranged from 0.05 to 0.06 kg fruit/cm2 TCSA (Figure 2.6) and was

not affected by pruning treatment. Kappel et al. (1997) reported that heavily-pruned ‘Sweetheart’ sweet

cherry trees (i.e., 2/3 current season wood) had 42% lower yields than trees pruned less severely (i.e.,

1/3 current season wood) and suggested that excessive removal of current season wood could cause a

reduction in spurs development and consequently, lower fruit production. In the current trial, HP

removed 85% more wood than MP1 which could be considered as a more severe pruning that produced

lower yields.

Fruit firmness was not affected by pruning treatment – it varied between 302 to 313 g/mm

(Table 2.1), which is considered acceptable for fresh market quality (Long et al., 2005). Likewise, there

43

was no treatment effect on soluble solids content (SSC), which ranged from 15.7 to 16.1%. Kappel et al.

(1996) suggest a minimum SSC of 15% for sweet cherries; therefore, the values exhibited by the

analyzed fruit are considered acceptable for the fresh market. Pedicel-fruit retention force (PFRF) was

not different among pruning methodologies and all the treatments showed fruit with an average PFRF of

1.7 kg. In contrast, there was a pruning effect on fruit diameter and fruit weight. Cherries from MP1

and MP2 had slightly lower fruit size compared to fruit from hand pruned trees. Fruit diameter from

MP1 and MP2 was 0.9 and 0.7 mm less compared to the fruit diameter from HP trees, respectively. The

practical significance of such small differences in fruit diameter may be negligible however since all fruit

were classified as 9-row (an industry sales designation based on fruit diameter), which is considered as

very large fruit (Kappel et al., 1996). Average fruit weight from MP1 was 0.8 g lower than from HP, and

fruit weight from MP2 did not differ from HP fruit. Weight varied between 11.3 to 12.1 g which is within

the range of an ideal red sweet cherry for the North American market (Kappel et al., 1996). We

hypothesize that the reduction in fruit size could be related to cropload and ratio of fruit number to leaf

area (F:LA). Whiting & Lang (2004) reported that heavily cropped trees can exhibit source-limiting

conditions during stage III of fruit development, which could explain the reduced fruit size in our trial.

Furthermore, limbs with high croploads may not export photosynthates to support fruit growth, and a

deficit of storage reserves in the trees could affect fruit quality as well. Sweet cherry on precocious

rootstocks as ‘Gisela 5’ can produce high yields of small fruit, resulting in high F:LA ratios that affect fruit

quality (Usenik et al., 2008). Although fruit SSC was uneffected by our treatments, past studies have

shown that systems with high pruning requirements generally have lower yields than those needing low

amounts of pruning, but the fruit was sweeter and larger (Lang, 2001b). Usenik et al. (2008) also found

that removing major branches with thinning cuts after harvest had a greater influence on fruit weight

than with heading cuts. Moreover, summer pruning in apple has shown inconsistent results on fruit SSC

44

and firmness due to the influence of several factors including the environment, the characteristics of the

trees and pruning procedures (Saure, 1987). Overall, it seems that hand pruning achived a better F:LA

balance than mechanical pruning which resulted in slightly larger fruit; however, since the rest of the

fruit quality traits analyzed were not affected by pruning method, more investigation is needed to

understad long-term pruning effects.

Pruning trials 2015

Similarly to 2014, pruning treatments had a dramatic effect on pruning efficiency. MP/MP was

29 times faster than HP/HP at a tractor speed of 1.8 km/h at 2000 RPM and 17 times faster than

MP/M+HP with the tractor traveling at an average of 1.7 km/h at 2000 RPM. The pruning time per tree

was approximately in 0.23 min (15 s) and 4.1 min (246 s) (Figure 2.7) for MP/MP and MP/M+HP,

respectively. In 2015, tractor speed was also influenced by experimental and orchard conditions. In

addition, MP+HP/HP also was 1.6 times faster than hand pruning alone, each tree pruned by hand

required approximately 6.8 min per person (408 s). Previous studies of mechanical pruning sweet cherry

trained to the UFO system also showed that mechanical pruning followed by a hand pruning cleanup

was 20% faster than hand pruning alone, in the dormant season. In that study, hand pruning took 2.6

min per tree(158 s) and the combination of mechanical and hand pruning 2.1 min/tree (127 s) (Whiting,

unpublished). In the current study, hand pruning from MP/M+HP only took 56% of the time of HP/HP.

This improvement in pruning efficiency may be attributed improved worker visibility within the canopy

so that pruning locations were more easily identified. Mechanical pruning in 2015 was 23% faster than

in 2014 (Figure 2.8), improved efficiency was directly related to the tractor speed. We also evaluated

pruning mechanization with the Gillison’s hedger on apple in 2014. An initial speed of 2.9 km/h was

tested yielding negative results. At this speed, the sickle bar tended to hit the branches instead of

45

cutting them, causing damages to the structure of the tree. The motor of the bar was positioned at the

top to avoid damaging the lower parts of the trees, which generated some wobbling of the bar and

some branches were missed during pruning. A fast speed of the tractor affected the stability of the bar

and increased the wobbling. An ideal tractor speed is yet to be evaluated in sweet cherry; however, the

preliminary results observed with apple indicate that it is necessary to consider some factors such as

training system, the age of the trees and soil topography to define an ideal speed.

We also studied the effect of pruning treatment on the amount of wood that was removed. In

2015, hand pruning alone removed 6.5 kg wood/tree. This was 160% and 22% more wood per tree than

MP/MP and MP+HP/HP, respectively (Figure 2.9). Similarly, 172% and 15% more wood per cm2 TCSA

was removed by hand pruning alone, with 0.03 kg of wood pruned/ cm2 TCSA (Figure 2.10). The weight

of wood pruned in 2014 was 18% and 64% higher for control and MP/MP plots, respectively, than that

removed in 2015 (Figure 2.11). This discrepancy is likely due to the renewal pruning done in the

2014/2015 dormant season when the most vigorous uprights in each tree (1-2) were removed just prior

to full bloom. It is recommended that uprights in the UFO system do not exceed 6 or 7 years old to

maintain good yields (Long et al., 2015). Full mechanical pruning was 11 times more efficient than hand-

pruned control (kg wood/min/tree) and 8.2 times more efficient than MP/M+HP (Figure 2.13). MP/MP

removed approximately 10.9 kg of fresh weight /min/tree, while HP/HP pruned 0.95 kg fresh

material/min/tree and MP/M+HP 1.3 kg/min/tree. MP/M+HP was also 66% more efficient than hand

pruning alone, due to improved canopy visibility achieved with a mechanical pre-pruning. Although

there was no data collected on the use of ladders, we think that there is also a potential for reduced

manipulation of ladders by reducing the canopy height with mechanical topping and reducing the

canopy volume.

46

“Dirty cuts” were observed again in 2015; however, a visual evaluation of such pruning cuts

from the previous year showed no negative impact with pests or disease caused by the type of cuts

performed in our trial. This suggests that wounds from mechanical pruning cuts were healed sufficiently

and that a living cambium was formed as a protection against insects and diseases with the production

of antimicrobial sustances that promoted rapid would healing (Westwood, 1993) (Figure 2.12).

Pruning treatments in 2015 affected the amount of vegetative regrowth in the same season.

Trees that were pruned mechanically had significantly greater current season shoot growth per tree

compared to hand pruned trees and pruned with the machine followed by hand (Figure 2.14). Trees

from MP/MP had 32% and 60% more regrowth than HP/HP and MP/M+HP, respectively, which were

similar. There was no effect of pruning treatment on the number of current season shoots (Figure 2.15)

which ranged from 164 to 193 shoots. Likewise, mean current season shoot length per cm2 tree TCSA

from MP/MP was 39% and 52% greater than that on trees from HP/HP and MP/M+HP respectively

(Figure 2.16). Selective summer pruning and hedging are different processes that reduce dry matter

production in the tree, stimulate new shoots growth and improve light penetration in the center of the

tree (Flore, 1992). Hand pruning is more selective than mechanical pruning and removes major branches

after harvest, stimulating less regrowth at the site of the cut in the interior of the tree (Flore, 1992;

Usenik et al., 2008). Hand pruning was mainly done with thinning cuts; thus, apical dominance remained

undisturbed and hormones movement in the tree did not change (Forshey, 1976; Stebbins, 1992; Maib

et al., 1996). It is believed that mechanical hedging stimulated more regrowth than hand pruning

because the heading cuts performed by the sickle bar removed apical dominance and altered the

normal movement of hormones in the portion of the branches that were left unpruned (Forshey, 1976;

Stebbins, 1992; Maib et al., 1996). As a result, trees respond with growth near the cut and the number

of shoots and their length is affected, normally as an invigorating process (Forshey, 1976; Crassweller,

47

2015). The amount of new extension growth is also influenced by the timing of pruning; the earlier the

pruning is performed, the greater is the effect (Flore, 1992). Summer pruning is believed to be less

invigorating than dormant pruning and has been used in several crops to reduce tree size (Flore, 1992;

Kappel et al., 1997). Removal of active leaf surface during the growing season can stimulate the

movement of assimilates to the woody tissues, resulting in a reduction of reserves for shoots growth the

following season (Forshey, 1976). Guimond et al. (1998) reported that summer pruning of 4 year old

‘Bing’ sweet cherry trees at 37 to 45 DAFB stimulated vegetative laterals growth more than earlier

pruning, and that trees with no pruning presented significantly lower regrowth probably due to

undisturbed apical dominance. Dry matter production on the trees is related to light interception, and

shading can affect vegetative and reproductive growth. Shoots result larger and thinner due to shading,

leaf area can also be decreased as well as photosynthetic rates (Webster & Looney, 1996). Excessive

vegetative growth produced after postharvest pruning should be removed during the dormant season to

improve light distribution (Flore, 1992). Regardless of the approach used when pruning, new regrowth

of sweet cherry trees on precocious rootstocks has to be managed to stimulate vigor; this is necessary

because the leaves from current season wood and 1 year-old shoots are essential for fruit development

of spurs from 2-year-old shoots (Usenik, et al., 2008).

Preliminary economic assessment

The results of this study were used to assess the cost of pruning by hand, mechanically or with

the combination of both methodologies an acre of a sweet cherry orchard trained to the UFO system,

with an in-row spacing of 2.4 m and between-row spacing of 3 m, with 9-year-old ‘Tieton’/‘Gisela5’

sweet cherry trees at a density of 545 trees per acre. To construct the economic assessment I used the

efficiency data gathered in the 2015 pruning trials. In this case study, hand pruning was assumed to be

48

performed by 1 person using 1 ladder and a pair of loppers, with 8 hours of work per day and an hourly

wage of $12/h as it is normally managed in this commercial orchard (Keith Oliver, Orchard Manager,

pers. comm.). The postharvest pruning method followed the UFO pruning rules – removal of lateral

branches from uprights and no renewal of leaders. Mechanical pruning was performed with the

Gillison’s hedger mounted on a tractor driven by 1 person with hourly wage of $12/h. Estimated

purchase price of the machine was $25,000 with a 15-year estimated lifespan (Karen Lewis, pers.

comm.). Equipment maintenance costs were calculated at 5% of cost of the machine, salvage due 10%

of purchase price, fuel usage approximately 6 gallons/day and depreciation calculated as the

relationship between the cost of the equipment minus salvage due over lifetime of the equipment (K.

Gallardo, pers. comm.). Calculations made with these assumptions and the efficiency data collected in

2015 are specified in table 2.2. Given the assumptions, the total postharvest hand pruning cost for an

acre of 8-year-old sweet cherry trees trained to the UFO is estimated at $741.15. This is ca. 4 times

higher than cost of pruning the same acre with the Gillison’s hedger and 1.25 times higher than the cost

of mechanical and hand pruning combined.

Our estimated costs for hand pruning are supported by a previous study conducted in 2009 for

sweet cherry on planar systems, where hand pruning and green fruit thinning costs in a year were

assessed at $911.20/acre (Galinato et al., 2009). Pruning represents approximately 50% of labor costs

and 10-15% of growing costs. High density plantings have simplified this horticultural practice; however,

pruning is still considered the second greatest annual expense for tree fruit growers around the world.

Ommission of pruning to save money can have long-term effects on fruit quality and yield, as well as on

vegetative growth; thus, pruning is an essencial component of the production budget (Forshey, 1976).

On the other hand, labor accounts for up to 78% of variable costs for cherries with manual production

tasks including harvest, green fruit thinning, tree training and pruning (Lewis, 2015). Dormant and

49

summer pruning require the use of ladders, which implies a risk of injury that accounts for up to 30% of

the cost of insurance claims; these injuries can result expensive for both the employer and the employee

(Lewis, 2015; Filippi, 2016). Morover, setting, climbing and re-setting the ladders each time the workers

move them represent a reduction on efficiency and increase of per unit cost. It has been observed that

there is an efficiency gain of 25-40% over the use of ladders while pruning, which would result in

reduced production costs. (Filippi, 2016). The alternative of mechanizing pruning operations can result

in savings of 80-100 labor hours/ha/year as well as increased efficiency. Mechanical pruning on tree fruit

orchards are basically carried out with sickle bars or rotating blades. The cost of pruning mechanization

depends of several factors including the cost of the machine, the acreage to be pruned, the pruner’s

working capacity and the length of the season over which the machine can be used. (Sansavini, 1978).

Machines’ prices vary depending on their design and capacity; we found with our research that sickle

bars’ prices range from $16.000 to $25.000. The working capacity for each machine is also influenced by

the species to be pruned, the cultivar, training system and age of the trees. The vigor of the trees affects

the amount of pruning and machine’s efficiency as well, trees on weak rootstocks don’t require as much

pruning as those on vigorous rootstocks (Perry et al., 1997).

Mechanical pruning alone is advantageous over hand pruning as it can improve efficiency by at

least 10-15 times, which means an increase in profits. (Sansavini, 1978). Miranda Sazo & Robinson

(2013) documented encouraging results from summer mechanical pruning of several apple varieties

with a sickle bar; they indicated that costs for summer shearing represented only 5% of hand pruning

costs, as well as an improvement on fruit color. Further research was required to observe the effects on

return bloom and vegetative regrowth, in order to confirm mechanical pruning impact on orchard

profitability. Velazquez & Fernandez (2010) used rotating blades to prune citrus trees and reported that

mechanical pruning combined with hand pruning improved production with similar costs of hand

50

pruning, which increased profits. Mechanical pruning alone substantially reduced costs, but vegetative

growth evaluation was necessary for more years to determine efficiency.

The results from our economic assessment demonstrate than mechanical pruning has potential

to improve operation efficiency by reducing pruning costs and labor requirements without affecting

yields. The use of the hedger in combination with hand pruning should also reduce pruning costs and

improve labor efficiency. These estimated improvements in efficiency and pruning costs are promising

considering the increasing skilled labor shortage. For example, within 3 weeks, our data indicate that,

with 8 hours work days, and 5 working days per week, a mechanical pruner could prune ca. 60 acres of a

UFO-trained sweet cherry orchard. The costs that were calculated for our study would vary if other

machine was used. We observed that the cost of a simple hedger can be approximately 51% lower than

the cost of a topper and hedger, which would reduce pruning costs further. On the other hand, when

complementary hand pruning is desired, a topper would be more useful to increase efficiency and

profitability by avoiding hand topping. It is also important to consider the training system for the

adoption of pruning mechanization and costs reduction. For example, the cost of the equipment would

not be justified if it was used on angled canopies, since the hedger can prune only outside the system.If

a commercial orchard were to use a piece-rate wage, the costs of hand pruning an acre of a similar

orchard would be $272.5 at $0.50/tree (K. Oliver, pers. comm.), only 1.6 times higher than the

estimated costs for mechanical pruning. One would also expect improved worker productivity (i.e.,

faster pruning) with a change to a piece-rate wage. Normally, commercial pruning crews workers are

paid by a piece-rate; though this varies among orchards. In the current research, the hand pruning crew

was paid an hourly wage, and this may affect their productivity and pruning efficiency. Lazear (2000)

reported a 44% increase in productivity when a glass company moved from hourly wages to piece-rate

regime because there is an incentive effect, more able workers and other factors. In this case the

51

combination of mechanical and hand pruning would be more efficient but more expensive, although we

believe that when these methodologies are combined, hand pruning should be paid by the hour and not

by the piece since it is a cleanup performed after a pre-pruning with the machine.

Conclusion

This trial was set up to observe the potential of pruning mechanization on sweet cherry orchards

trained as fruiting walls, as well as the alternative of partial mechanization. There is a potential for

complete postharvest pruning mechanization due to increased pruning efficiency, reduced pruning

costs, improvement of worker’s safety and efficiency with no effect on yield and a minimal effect on

fruit quality. Cost savings depend on several factors, however mechanical pruning accounts for a small

fraction of the traditional pruning costs. We hypothesize that part of the efficiency gains from

mechanical pruning include a reduced use of ladders due to topping performed with the sickle bar and

the reduction of canopy volume as well. Moving, climbing and setting of ladders represent a lost on

efficiency and possible expenses related to insurance claims as a result of potential injuries. A dramatic

difference in pruning efficiency and similar yields between the mechanized and the traditional pruning

alternative means an increase in orchard profitability. We observed with our trial that the lack of

selectiveness from mechanized pruning can stimulate vigorous regrowth, which could have an effect on

light interception in the interior of the canopy and have repercussions on fruit quality the following year

when the regrowth is not removed. As an alternative, we also observed the effects of a pre-pruning with

the sickle bar followed by a hand pruning cleanup. This scenario resulted more efficient than traditional

pruning as well, due to improved visibility for the workers that prune the trees by hand. The

combination of both approaches is promising due to similar productions and new extension growth to

manual pruning, plus an increased efficiency and reduced costs. Furthermore, a postharvest mechanical

52

pruning could be complemented by a dormant pruning clean up that is limited to the removal of upright,

diseased, unproductive and dead branches. Pruning costs are significantly reduced when hourly wages

are used at commercial orchards. Overall, pruning mechanization offers a solution to increasing fruit

demand and labor shortage, and reduces pruning costs even when complemented with hand pruning.

Further investigation is suggested to determine long-term effects of mechanical pruning on regrowth,

return bloom, yield and fruit quality, as well as the possibility of using a mechanized option on alternate

years, and/or the combination of mechanical pruning and growth regulators instead of hand pruning

cleanup.

53

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Table 2.1.Effects of mechanical and hand pruning on fruit quality parameters of ‘Tieton’/’Gisela5’ sweet

cherry trained to the UFO system in 2015. Statistical comparisons are between pruning methodologies,

treatment means with different letters are significantly different (p < 0.05), n=5.

Treatment Weight

(g)

Firmness

(g/mm)

SSC

(%)

PFRF

(Kg)

Diameter

(mm)

Row size

equivalence

HP 12.1 a 313 16.1 1.7 29.2 a 9

MP1 11.3 b 302 15.7 1.7 28.3 b 9

MP2 11.6 ab 310 16.0 1.7 28.5 b 9

p-value 0.042 0.223 0.503 0.557 0.006

65

Table 2.2.Estimation of costs per acre to prune mechanically, by hand or with the combination of both

methodologies ‘Tieton’/’Gisela5’ sweet cherry trees trained to the UFO system in 2015.

Variables HP/HP MP/MP MP/M+HP

Pruning efficiency

(min/tree/person)

6.8 0.23 4.1

Pruning efficiency

(h/acre/person)

61.7 2.1 36.3

Salary

($/person)

740.4 25.1 445.9

Fuel

($/day)

- 23.4 23.4

Maintenance machine

($/year)

- 1250 1250

Depreciation

($)

- 1500 1500

Total cost

($/acre/year)

741.15 168.25 590.02

66

Figure 2.1.Mean time/tree (min) of postharvest mechanical and hand pruning of ‘Tieton’/‘Gisela5’ sweet

cherry in 2014. Statistical comparisons are between pruning methodologies, treatment means with

different letters are significantly different (p < 0.05), n=5. Bars indicate standard error.

a

b b

0

1

2

3

4

5

6

7

HP MP1 MP2

Tim

e/t

ree

(min

)

Pruning treatments

67

Figure 2.2. Mean weight of fresh material removed/tree (kg) with postharvest mechanical and hand

pruning of ‘Tieton’/‘Gisela5’ in 2014. Statistical comparisons are between pruning methodologies,

treatment means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard

error.

a

b

b

0

1

2

3

4

5

6

7

8

9

HP MP1 MP2

Kg

fre

sh m

ate

rial

rem

ove

d /

tre

e

Pruning treatments

68

Figure 2.3. Mean Kg of fresh material removed per cm2 TCSA with postharvest mechanical and hand

pruning of ‘Tieton’/’Gisela5’ in 2014. Statistical comparisons are between pruning methodologies,


error.

a

b

b

0.00

0.01

0.01

0.02

0.02

0.03

0.03

0.04

0.04

0.05

0.05

HP MP1 MP2

Kg

fre

sh m

ate

rial

rem

ove

d /

cm

2 T

CSA

Pruning treatments

69

Figure 2.4. Representative “dirty cuts” performed by the Gillison’t hedger compared to wood pruned by

hand on ‘Tieton’/’Gisela5’ sweet cherry in 2014.

70

Figure 2.5. Mean yield (kg fruit/tree) from postharvest mechanical and hand pruning ‘Tieton’/’Gisela5’

sweet cherry in 2015. Statistical comparisons are between pruning methodologies (p < 0.05), n=5. Bars

indicate standard error.

0

2

4

6

8

10

12

14

16

18

20

HP MP1 MP2

Kg

fru

it/t

ree

Pruning treatments

71

Figure 2.6. Mean yield efficiency (kg fruit/cm2 TCSA) from postharvest mechanical and hand pruning

‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between pruning methodologies (p <

0.05), n=5. Bars indicate standard error.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

HP MP1 MP2

Yie

ld e

ffic

ien

cy (

Kg

fru

it/c

m2 T

CSA

)

Pruning treatments

72

Figure 2.7. Mean time/tree (min) of postharvest mechanical and hand pruning of ‘Tieton’/’Gisela5’

sweet cherry in 2014. Statistical comparisons are between pruning methodologies, treatment means

with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error.

a

c

b

0

1

2

3

4

5

6

7

8

HP/HP MP/MP MP/M+HP

Tim

e/t

ree

(min

)

Pruning treatments

73

Figure 2.8. Mean time/tree (min) of postharvest mechanical pruning, hand pruning and the combination

of both approaches of ‘Tieton’/’Gisela5’ sweet cherry in 2014 and 2015. Statistical comparisons are

between pruning methodologies, treatment means with different letters are significantly different (p <


0

1

2

3

4

5

6

7

8

HP/HP MP/MP MP/M+HP

Tim

e/t

ree

(min

)

Pruning treatments

2014

2015

74

Figure 2.9. Mean Kg of fresh material removed/tree with postharvest mechanical and hand pruning of

‘Tieton’/’Gisela5’ in 2015. Statistical comparisons are between pruning methodologies, treatment

means with different letters are significantly different (p < 0.05), n=5. Bars indicate standard error.

a

c

b

0

1

2

3

4

5

6

7

8

HP/HP MP/MP MP/M+HP

Kg

fre

sh m

ate

rial

rem

ove

d/

tree

Pruning treatments

75

Figure 2.10. Mean Kg of fresh material removed per cm2 TCSA with postharvest mechanical and hand

pruning of ‘Tieton’/’Gisela5’ in 2015. Statistical comparisons are between pruning methodologies,


error.

a

c

b

0.00

0.01

0.01

0.02

0.02

0.03

0.03

0.04

HP/HP MP/MP MP/M+HP

Kg

fre

sh m

ate

rial

rem

ove

d /

cm

2 T

CSA

Pruning treatments

76

Figure 2.11. Mean weight of fresh material removed/tree (kg) with postharvest mechanical and hand

pruning of ‘Tieton’/’Gisela5’ in 2014 and 2015. Statistical comparisons are between pruning

methodologies, treatment means with different letters are significantly different (p < 0.05), n=5. Bars


0

1

2

3

4

5

6

7

8

9

HP/HP MP/MP MP/M+HP

Kg

fre

sh m

ate

rial

rem

ove

d/t

ree

Pruning treatments

2014

2015

77

Figure 2.12. Healed “dirty cut” after postharvest mechanical pruning with the Gillison’s hedger on

‘Tieton’/’Gisela5’ sweet cherry in 2015.

78

Figure 2.13. Mean pruning efficiency (kg of fresh material per min per tree) of postharvest mechanical

pruning, hand pruning and the combination of both approaches of ‘Tieton’/’Gisela5’ sweet cherry in

2015. Statistical comparisons are between pruning methodologies, treatment means with different

letters are significantly different (p < 0.05), n=5. Bars indicate standard error.

b

a

b

0

2

4

6

8

10

12

HP/HP MP/MP MP/M+HP

Kg

fre

sh m

ate

rial

/min

/tre

e

Pruning treatments

79

Figure 2.14. Mean current season wood length per tree measured during dormant season 2016 after

postharvest mechanical, hand and mechanical + hand pruning of ‘Tieton’/’Gisela5’ sweet cherry in 2015.

Statistical comparisons are between pruning methodologies, treatment means with different letters are

significantly different (p < 0.05), n=5. Bars indicate standard error.

b

a

b

0

1000

2000

3000

4000

5000

6000

HP/HP MP/MP MP/M+HP

Cu

rren

t se

aso

n s

ho

ot

len

gth

/tre

e (c

m)

Pruning treatments

80

Figure 2.15. Mean number of current season shoots per tree measured during dormant season 2016

after postharvest mechanical, hand and mechanical + hand pruning of ‘Tieton’/’Gisela5’ sweet cherry in



0

50

100

150

200

250

HP/HP MP/MP MP/M+HP

Nu

mb

er o

r cu

rren

t se

aso

n s

ho

ots

/tre

e

Pruning treatments

81

Figure 2.16. Mean current season wood length per cm2 tree TCSA measured during dormant season

2016 after postharvest mechanical, hand and mechanical + hand pruning of ‘Tieton’/’Gisela5’ sweet

cherry in 2015. Statistical comparisons are between pruning methodologies, treatment means with


b

a

b

0

5

10

15

20

25

30

35

HP/HP MP/MP MP/M+HP

Cu

rren

t se

aso

n s

ho

ot

len

gth

/cm

2 T

CSA

Pruning treatments

82

CHAPTER THREEE

PRUNING MECHANIZATION OF APPLE TREES ON FRUITING WALLS IN THE US PACIFIC

NORTHWEST

Keywords: mechanical pruning, slender spindle, return bloom, Fuji, hedger, summer pruning, dormant

pruning, fruit quality, yield

Abstract

Over the last 10 years apple (Malus domestica Borkh.) growers around the world have rapidly

adopted orchard mechanization as a necessary solution to labor shortage and greater fruit demand.

Pruning has been regarded as a labor intensive operation that represents a major component of

production costs. However, pruning is an important horticultural tool that establishes a balance

between vegetative and reproductive activities in the trees. Mechanical pruning represents an

opportunity to set a block up for year round mechanization, to reduce pruning costs and increase apple

orchards productivity. In this trial we compared hand pruning vs mechanical pruning in a 5-years-old

orchard of ‘Fuji’/Nic 29 trained as a Slender Spindle system during 2014 and 2015. Trees in the test plots

were hedged mechanically or pruned by hand at different timings (winter, summer 12-15 leaves, and

summer 20 leaves) and the trees responses were evaluated over the two experimental years.

Mechanical pruning was performed with a Gillison center mount sickle bar system in 2014 and with a

LaGasse orchard hedger in 2015. Winter mechanical pruning was 2.3 times (132%) faster than hand

pruning in 2014 with an average speed of 8.8 s/tree, however the latter removed 156% more wood and

leaves with approximately 0.96 kg fresh material per cm2 TCSA. During green thinning, we observed a

maximum of 6.5% of damaged fruit which would be easily recognized by a thinning crew. Pruning

83

methodology had an effect over the vigor of the tree; mechanical winter and mechanical pruning

exhibited 56% lower current season shoot length per tree than winter mechanical pruning alone. Trees

pruned mechanically exhibited ca. 54% higher return bloom compared to those pruned by hand in 2014.

Mechanical dormant pruning followed by a hand cleanup was 68% faster than hand pruning alone

during 2015 with the LaGasse hedger. Yield per tree from dormant hand and summer mechanical

pruning was 32% lower than that from winter hand or mechanical pruning alone; however, yield

efficiency was not different from control. Fruit quality parameters were not affected by mechanical

pruning except for sunburn, which was 84% higher on fruit from trees pruned mechanically both in the

winter and in the summer. A preliminary economic assessment shows that pruning mechanization can

reduce pruning costs and improve labor efficiency. These results are promising but further investigation

is needed to understand long-term effects of pruning methodology and timing on fruit yield, quality,

vegetative regrowth and return bloom.

Introduction

Apple (Malus domestica Borkh) is the top agricultural commodity grown in Washington State.

Between 2014 and 2015, approximately 76.5 million metric tons of apples were produced worldwide

with China being the largest producer; United States ranked second with 6.5% of the world’s production

followed by Turkey, Poland and Italy. In the U.S., apples are grown in 36 states with Washington State

producing about 70% of the total national production. New York, Michigan, California and Pennsylvania

are also important producers; although unlike Washington’s their production is mainly directed for local

and processing markets (Foreign Agricultural Service/USDA, 2015).

Tree training and pruning are essential practices for optimizing tree growth and apple fruit

quality. Pruning is necessary for the establishment of basic tree structure and also to improve light

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distibution throughout the tree (Moulton & King, 2015). Tree training involves positioning branches for

a permanent architecture and it determines, for the most part, how the trees have to be pruned when

they are mature (Forshey, 1976; Moulton & King, 2015). When a tree is appropriately trained to allow

maximum light interception, pruning becomes a maintenance practice instead of a corrective one; this

affects both fruit quality and yield (Ozkan et al., 2012). Fruit quality is largely determined by the balance

between vegetative and fruitful growth (Forshey, 1976). When there is an excessive vegetative growth,

the trees yield small fruit due to the use of carbohydrates by woody tissues (Forshey, 1976; Parker,

2008), a dense top canopy with poor structure shades the lower part of the tree and the productive fruit

becomes inaccessible (Parker, 2008). Severe pruning can induce vegetative growth that leads to a

reduction of light interception and also interferes with the correct development and growth of fruit

(Cherbiy-Hoffmann et al., 2012).

Light interception and availability is one of the most important factors that influence orchard

productivity and it can be managed through canopy design (Wunsche et al., 1996; Wunshe & Lakso,

2000). Light plays a key role in photosynthesis, leaves and shoots development, flower bud initiation,

fruit set and fruit quality and growth; therefore, pruning must be intended to maximize light distribution

and penetration within the canopy (Craig & Embree, 2006; Robinson & Lakso, 1991). Old apple systems

have large trees with globular shape where 60 to 100% of the light is directed to the upper part of the

tree. Fruits are difficult to harvest and only 29% of the light is distributed in the lower portion of the tree

(Craig & Embree, 2006). Shading from the top zone of the trees can result in reduced fruit mass and can

affect fruit quality attributes such as color, soluble solids content and metabolite concentrations; shoot

photosynthesis and fruit temperature can also be influenced by shade (Stephan, et al., 2008). Over the

last five decades apple planting densities have been constantly increasing, growers moved from 40

trees/acre to more than 3000 trees per acre with vigor controlling rootstocks and a better use of

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available light (Ozkan et al., 2012; Robinson, et al., 2013). High density systems with planar architectures

intercept more light and allow a better light distribution. Yield is high and trees are early bearing for a

quicker return of initial investments. Mechanization can be adopted and cultural practices such us

harvest and pruning became more simply (Robinson et al., 2013). The Slender Spindle is among the

leading planting systems for apple around the world; it is a high density system with narrow canopies,

trees have a conical shape and it is a modified miniature central leader with limbs trained to a

horizontal position to control tree vigor and stimulate early production (Parker, 1998; Craig & Embree,

2006). Pruning mature slender spindle trees should remove branches larger than 30% of the leader’s

diameter, remove old unproductive wood, eliminate upright shoots and thin out fruiting branches

maintaining a conical shape of the tree (Craig & Embree, 2006).

The increasing costs of skilled labor and labor shortage have encouraged apple growers to adopt

mechanization into their high density orchards, including platforms and hedgers (Robinson et al., 2013).

Orchard mechanization started more than 50 years ago in Europe and it has expanded around the world

in an attempt to reduce production costs and improve labor efficiency (Miranda et al., 2010). The use of

platforms has shown a reduce pruning costs by about 30% and an increase of efficiency as well

(Robinson, 2011). Pruning mechanization on apple has also been tested for decades; cutter bars and

circular saws were first used with limited success due to the excessive regrowth caused by heading cuts

of large limbs from trees with vigorous rootstocks, which produced shading and low fruit quality (Ferree

& Short, 1972; Miranda Sazo & Robinson, 2013). Cain (1972) observed that slot pruning on ‘McIn-tosh’

apples in Geneva performed with a slotting saw performed better than a cutter bar by producing three

times more new spurs, six times more bearing spurs and greater light penetration which improved fruit

color and yields. Miranda Sazo & Robinson (2013) reported their results from a summer mechanical

hedging trial with several apple cultivars trained to the Tall Spindle and Super Spindle systems. They

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observed that the machine only removed 30% of the growing points from the trees, improved light

exposure in the lower portion of the trees, and little to no regrowth with early summer pruning.

Furthermore, it has been found that mechanical pruning at different timings can induce different

benefits; winter pruning could be efficient in moderate-growing orchards to shape trees, early summer

hedging could increase flower differentiation and reduce regrowth and mechanical pruning before

harvest can improve fruit color (Robinson, et al., 2013). Most recently, the lack of selectiveness of

mechanical pruning has led to the development of robotic dormant pruning. Karkee et al. (2014)

developed a machine vision system using a 3D camera to identify branches in apple trees on Tall spindle

system, algorithms were used to establish simple pruning rules and keep specified branch spacing and

branch length. Similarly, Elfiky et al. (2015) were able to identify pruning points with 96% accuracy with

Skeleton-based Geometric features in 3D reconstruction of apple trees in a study that is planned to

automate dormant pruning of specialty crops. This chapter reports on a series of experiments

conducted over two years to understand the requirements in the orchard and from the equipment

when mechanizing pruning of ‘Fuji’ apple in planar systems. The goals of our study are to verify the

possibility to prune mechanically an existing orchard to convert it to a fruit wall system and check the

effects of mechanical pruning on fruit quality.

Materials and methods

Experimental design

Experiments were carried out in a 5-years old orchard of Fuji grafted on Nic29’ at the

Whispering Rock Orchard (Grant, WA, USA; 46°45'49.4"N 119°52'28.3"W) Trees were trained as Slender

Spindle with a planting distance of3m x 0.90m. Trees were drip irrigated, micro-sprinklers installed

underneath the trees and overhead sprinklers were also used, the latter for evaporative cooling

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purposes. This study was carried over during 2014 and 2015; standard management practices were

followed in the orchard both years. Five treatments were assigned as a Complete Randomized Block

Design with 3 repetitions; each repetition consisted of 2 plots with 24-26 trees total (i.e. 72-78 trees per

treatment). Treatments were distributed along 5 rows from west to east with two of them treated as

guard rows.

Pruning trials 2014

Trees in the test plots were hedged mechanically or by hand at different timings (1) dormant

hand pruning control, (2) mechanical dormant pruning, (3) dormant hand pruning plus mechanical

summer pruning at 12-15 leaves stage, (4) mechanical dormant pruning plus mechanical summer

pruning at 12-15 leaves stage, and (5) dormant hand pruning plus mechanical dormant pruning at 20

leaves stage. Codification for the different pruning treatments are detailed in table 3.1. Dormant

pruning was performed in March to avoid freezing injury, during the morning. Hand pruning (control)

was conducted with a pair of loppers and mainly with thinning cuts. Mechanical pruning was carried out

with the Gillison’s Center Mount Topper and Hedger (Figure 3.1) that has a hydraulic system, a cutter

head that can rotate 360° to position the side bar with alternating movement of blades completely

horizontal or completely vertical, side shift 1.1 m on either side of the tractor, height adjustment of 1.1

m to 6.1 m and cutting capability of 38.1 mm diameter limbs (Craig Gillison, 2016 pers. comm.).

Mechanical hedging on the west and east side of the trees was done with the sickle bar at 15-20 cm

from the trees’ trunk. A preliminary evaluation on growth stage of the trees was performed to carry out

our summer pruning trials at 12 and 20 leaves stage, which were conducted in June, with the Gillison’s

hedger on the plots that were assigned to this treatment. No hand pruning was performed during the

summer in our trial plots.

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During our dormant pruning trials, the time to prune each plot and the weight of the wood

removed per plot were recorded. Wood pruned in each plot was weighted with an electronic scale

Pelouze model 4010 (capacity: 68 kg x 0.1 kg) and with a digital hanging scale Rapala Model RGSDS 15

(capacity: 25 kg).

Trunk cross-sectional area (TCSA) of all the trees in each treatment was calculated by measuring

the trunk circumference at 10 cm above the graft union. The results were used to elaborate a

distribution graph because of the variability that exists in the Fuji block. Due to the site conditions and

especially the type of soil, some trees are not uniform and self-rooted. We identified that between 9 to

11 cm2 of TCSA there was the least variability in the orchard, and we chose trees in this range to

evaluate yield, fruit quality, return bloom and regrowth.

On our summer pruning trials, time to prune each plot and the weight of the wood removed per plot

were also recorded. Wood pruned in each plot was weighted with an electronic scale Cas PB-150

(capacity: 75 kg x 0.1 kg). No data on harvest has been recorded.

Green thinning was performed on 24 June, 2014 by the commercial crew on our experimental block.

Fruit was thinned from each plot in the three middle rows and weight with an electronic scale Pelouze

model 4010 (capacity: 68 kg x 0.1 kg). Thinned fruit was also counted as well as any damaged fruit from

our mechanical pruning trials. The results were used to express weight of thinned fruit per tree and per

cm2 TCSA, number of thinned fruit per tree and per cm2 TCSA, and percentage of damaged fruit.

Regrowth was estimated by measuring total current season wood length from 3 sample trees

per repetition. Sample trees with similar vigor characteristics were randomly chosen from a range

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between 9 to 11 cm2 TCSA. Cumulative shoot length (cm) of each tree was determined during February

2015. Results were expressed as cumulative current season shoot length per tree.

Return bloom was evaluated on March 2015 from the same 3 sample trees per repetition used

to measure regrowth. Blossom clusters were counted on entire trees and the trunk circumference was

measured again to calculate TCSA. The number of flowers per cluster was later counted on randomly

chosen trees to estimate a normal average of flowers for Fuji cultivar. Return bloom was expressed as

number of flowers per tree per cm2 TCSA.


Pruning during the dormant and summer seasons were replicated on the same plots pruned in

2014, with the exception of treatment T5-14 that on the basis of the 2014 results was not replicated.

Pruning treatments and they codification are detailed in table 3.2. Dormant pruning was performed in

March, only our dormant mechanical pruning plots were hedged again mechanically, the rest of the

treatments were cleaned up by hand. Trees from all the treatments were hedged as well as topped;

topping was performed by the commercial orchard’s crew with platforms. Plots that were pruned by

hand in the dormant season of 2014 were pruned again by hand in 2015 by one person with a pair of

loppers and mainly with thinning cuts. Mechanical pruning was carried out with the LaGasse Orchard

Hedger (Figure 3.2), which is designed to be mounted to a three-point hitch of a tractor, it has a single

vertical cutter bar mounted 1.12 m to the right of the centerline of the tractor, the sickle bar is

approximately 3.35 m long with a maximum cutting capability of 25 mm caliper limbs (Dan LaGasse,

2016 pers. comm.). Mechanical hedging on the west and east side of the trees was done with the sickle

bar at 10 cm from the trees’ trunk. A preliminary evaluation on growth stage of the trees was performed

to carry out our summer pruning trial at 12, which was conducted in June during the morning as well,

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with the LaGasse hedger on the plots that were assigned to this treatment. No hand pruning was

performed during the summer in our trial plots. Pictures were taken before and after pruning for each

treatment.


Time to prune each plot and weight of the wood removed per plot were recorded during the

dormant and summer seasons following the same protocol as in 2014. Trunk cross-sectional area (TCSA)

at 10 cm above the graft union was calculated from measurements of trunk circumference. The results

were used to estimate the amount of wood removed per cm2 of TCSA and yield efficiency. During our

summer pruning trial, one plot per repetition was randomly chosen and the wood removed per tree was

collected and taken to the WSU-IAREC Stone Fruit Physiology lab. Wood and leaves were separated and

weighted with an electronic scale Pelouze model 4010 (capacity: 68 kg x 0.1 kg). A subsample

corresponding to the 10% of the total leaves’ weight was separated from each repetition, and

cumulative leaf area of the sample was measured with a leaf area meter Li-Cor model 3100C (resolution

0.1 or 1 mm2). Total number of leaves per sample was also counted. The results were used to calculate

leaf area removed per tree and per cm2 TCSA. A subsample corresponding to the 10% of the total wood

weight was also separated for each plot. Leaves and wood subsamples were placed in a Precision

laboratory oven Thelco model 130 D (250 °C ± 0.3 °C) at 70°C until they reached a constant weight

(approximately 15 days) to determine dry matter removed.

Green thinning was carried out on 22 June, 2015 following the same protocol as in 2014. Results

were expressed as weight of thinned fruit per tree and per cm2 TCSA, number of thinned fruit per tree

and per cm2 TCSA, and percentage of damaged fruit.

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Fruit was picked at commercial harvest on 1 September, 2015 on our experimental block.

Sampling units consisted of 5 trees per repetition in a range between 9-11 cm2 TCSA for a total of 15

trees per treatment (i.e. 75 trees total). All the fruit from each tree was counted and weighted with

electronic scales Pelouze model 4010 (capacity: 68 kg x 0.1 kg). Damaged fruit per tree was also counted

and weighted. 3 representative trees from the sample units with similar characteristics were chosen and

the fruit was sized with an apple linear sizer Turoni model 53311 (55 to 90 mm), and 30 fruit per

repetition size 80 to 85 mm (i.e. 90 fruit per treatment, total 450 apples) were selected and placed in

apple trays size 80 and cardboard boxes for fruit quality analysis at the Washington Tree Fruit Research

Commission Laboratory (Wenatchee, WA). Quality analyses were completed within 48 hours of harvest.

Individual fruit weight (g) was measured with a digital scale Pelouze model SP5 DC5108 (capacity: 2.2

kg). Fruit firmness (lb) was determined by peeling 2 sides of each fruit and placing them under a Fruit

Texture Analyzer model GS-15-643 (Norfolk, WA). Soluble solids content (% brix) was assessed from a

collective juice from 5 apples per sampled tree with a pallet refractometer Atago model PR-32 alpha

(Bellevue, WA). For titratable acidity (TA - % malic acid) measurement the juice from 5 apples per

sampled tree was used and estimated with a titrator 815 Robotic USM Sample Processor XL and

Tritrando 888 Metrohm (Riverview, FL). Starch degradation was determined by spraying one half of each

apple with a solution of iodine-potassium iodide and results were obtained using a scale (1-8) with 1

being most immature and 8 most mature. Disorders evaluation of individual fruit included splits, russet,

watercore and internal browning (%). Background color of each apple was measured on a scale from

0.5-6 (0.5, green; 6, creamy yellow) (Figure 3.3) and red coloration (% red) with a similar scale from 1-4

(1, no red coloration; 4, red coloration) (Figure 3.4). Sunburn was determined visually on individual fruit

according to the Schrader-McFerson scale (Figure 3.5).

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Regrowth was estimated by measuring total current season wood length from 3 sample trees

per repetition. Sample trees with similar vigor characteristics were randomly chosen from a range

between 9 to 11 cm2 TCSA. Cumulative shoot length (cm) of each tree was determined with 7.5 m tape

measure using the commercial orchard’s 3.6 m ladders by the Washington Tree Fruit Research

Commission (WTFRC) and the WSU-IAREC Stone Fruit Physiology crews during December 2015. Results

were expressed as cumulative current season shoot length per tree and per cm2 TCSA.

Finally, an economic assessment was estimated using the data we gathered from our trial and

the characteristics of the orchard where we conducted our study. The objective of this assessment was

to provide information comparing mechanical and hand pruning for apple trained in planar systems.

Statistical analysis

The Statistical Analysis System (SAS, 9.3 version) was used to separate means for all data sets.

The significance of differences in time of pruning, weight of wood pruned, pruning efficiency, yield, fruit

quality, regrowth and return bloom were analyzed with General Linear Model (GLM) and Tukey’s test

with 95% confidence level.


Pruning trials 2014

Our data from 2014 showed an effect of pruning methodology on efficiency. During the

dormant season, mechanical pruning was 2.3 times faster than hand pruning (Figure 3.6) at a tractor

speed of 0.8 km/h, at 2000 RPM with the characteristics of our field trials plots. Pruning speed is

subjected to the training system, cultivar, soil topography, tree age, planting density and wheel slippage

(see Chapter 2); these factors have to be considered in a pruning plan ahead of time to define a speed

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(Sansavini, 1978). The hedger pruned each tree in approximately 8.8 s (0.15 min) with two passes of the

machine. Sansavini (1978) observed that winter mechanical pruning on 12-years-old Golden Delicious

trained as “palmetta” hedgrow was done in 1.5 min per tree combining topping and hedging, which is 10

times slower than the results from our experiment. This difference is likely because of the vigorous

rootstock and the age of the trees which were older than the ones in our trial, and were not topped with

the machine either. Nonetheless, Sansavini (1978) also found that mechanical topping and hedging were

16 times faster than hand pruning alone, each tree was pruned by hand in 24 min. Conversely, our

results showed that hand pruning with loppers took 20.4 s per tree per person (0.34 min), this big

increase in hand pruning efficiency is likely due to the low vigor situation of our trial. T3-14 and T5-14

were also pruned by hand only; the latter was 1.3 faster probably due to differences in the vigor of the

trees.

The amount of wood and leaves pruned per tree ranged between 0.27 to 1.19 kg. T1-14

removed 156% and 259% more material per tree than T2-14 and T4-14 respectively, with approximately

1 kg of fresh material removed. There was no difference among plots mechanically hedged (Figure 3.7).

It was observed 58% more wood and leaves removed per tree by T5-14 compared to T3-15, both hand

winter pruning, which confirms the hypothesis made earlier regarding differences in pruning speed. Kg

of material removed per cm2 TCSA ranged from 0.027 to 0.116 kg (Figure 3.8). Similarly, there was no

difference among mechanically pruned plots, and T1-14 removed 201% and 256% more wood and

leaves per cm2 TCSA than T2-14 and T4-14 respectively. Ferree (1984) also documented a higher

amount of fresh material cut with hand pruning compared to dormant hedging with Golden

Delicious/M26 trained as central leaders. He found that winter hand pruning removed 99.9% more

wood and leaves than dormant hedging with approximately 10.7 kg per tree, and similar results were

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obtained on the subsequent years of his study. Hand pruning always removed more material than

dormant hedging because corrective hand thinning cuts were necessary to maintain fruit quality.

We found in our experiment that hand pruning was more selective than mechanical pruning

(Figure 3.9). Overall, most of the cuts performed by hand were thinning cuts; differently, the hedger

mainly made heading cuts and did not remove upright limbs and water sprouts. This could explain the

higher pruning weights observed from hand pruned treatments compared to those mechanically

pruned. It was also noticed that some branches were scrapped and/or peeled by the machine, which

was identified as “dirty cuts” compared to cleaner cuts made by hand (Figure 3.10). We believe that the

sickle bar was not as effective as expected because the machine was used for the first time with this trial

in the dormant season. General observations on the performance of the machine allowed

troubleshooting some issues and improving the performance of the hedger for the summer pruning

trials. The motor of the sickle bar was initially positioned at the bottom, causing damage to the lateral

branches and trees. When shifting the motor to the top of the bar, the hedger performed better but it

was less stable. The weight of the motor generates some wobbling of the bar; therefore, some branches

were missed during pruning. An initial tractor speed of 2.9 km/h was tested yielding negative results, at

this speed the sickle bar hit the branches instead of cutting them, causing damages in the structure of

the tree.

Summer hedging was performed in approximately 3.7 s per tree at a tractor speed of 1.8 km/h

and 2000 RPM with our experimental conditions; there was no difference among treatments (i.e.

summer pruning timing) (Figure 3.11). Summer pruning weights were significantly higher at 20 leaves

stage than at 12 leaves; T5-14 removed 102% and 80% more fresh material per tree than T4-14 and T3-

14 respectively, with 0.34 kg of wood and leaves (Figure 3.12). Likewise, T5-14 removed 94% more

material per cm2 TCSA than T4-14 and T3-14, with 0.03 of kg wood and leaves (Figure 3.13). It is

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reasonable to assume that higher pruning weights at a later stage are due to terminal shoot and leaf

growth that continues until mid-August (Flore et al., 1983). T5-14 detached 67% and 113% more fruit

per tree than T3-14 and T4-14 with an average of 6 apples removed per tree (Table 3.3). Summer

pruning at 12 leaves stage removed a maximum of 5 fruits per tree, while at 20 leaves there were a

maximum of 9 apples detached per tree. Miranda Sazo & Robinson (2013) also noted that an average of

8 fruits per tree were detached with summer hedging of ‘Fuji’/M.9 trained to the tall spindle system,

supporting the results we obtained. The number of fruit that was removed is considered acceptable and

not higher than the amount of fruit that would be dropped by hand thinning.

Regarding green thinning, we found that the greatest amount of fruit thinned per tree came

from T2-14 plots with approximately 1.1 kg of fruit thinned, which was ca. 40% higher than in the rest of

the treatments. There was no difference across the other treatments (Table 3.4). Additionally, the

number of fruit thinned per tree from T2-14 plots was not different from that on T1-14 and T4-14. The

lowest number of fruit thinned per tree was observed from T3-14, which was 50% lower than T2-14.

Furthermore, the number of damaged fruit than was thinned per tree did not differ among treatments

with an average of 0.4 damaged fruit/tree and there was a maximum of 6.5% of damaged fruit/tree. We

expect that a thinning crew could easily recognize this type of damaged fruit and remove it during green

fruit thinning.

When comparing dormant and summer hedging it is noticeable than the latter takes half of the

time of dormant pruning; suggesting that the use of sickle bars is more manageable during the summer

and can be more efficient because cutting young shoos is easier (Sansavini, 1978). The combined

amount of material pruned in the winter and summer of 2014 varied among treatments (Figure 3.14).

T5-14 (hand + mechanical) exhibited the highest pruning weight per cm2 TCSA, which was 55% and 368%

greater than T1-24 (hand only) and T2-14 (mechanical only) respectively. T2-14 and T4-14 weights

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(mechanical) did not differ from each other, approximately 0.013 extra kg were removed with T4-14

after winter hedging which would correspond to the shoot growth since the dormant season. T1-14 and

T3-14 (hand + mechanical) were not different among each other either; however, the amount of wood

and leaves removed per cm2 TCSA was significantly higher than that removed from T2-14 and T4-14.

Furthermore, T4-14 pruned 118% less fresh material than T1-14. When analyzing at the combined

amount of wood removed during 2014 by T3-14 we noticed that there were 0.026 extra kg that were

pruned with summer hedging after hand dormant pruning, which was 50% more shoot regrowth to

remove compared to regrowth caused by mechanical dormant pruning. With the results from both the

dormant and summer seasons in 2014, we determined pruning efficiency for each treatment for the

whole year, expressed as kg of wood removed per second per tree (Figure 3.15). T5-14 resulted 57%

more efficient than T4-14 due to the higher amount of wood removed per tree. There was no difference

among the rest of the treatments.

Current season shoot growth measurements during the dormant season 2015 showed that

mechanical pruning had an effect over the vigor of the tree. T4-14 (mechanical winter + summer)

exhibited 56% less regrowth than T2-14 (winter mechanical only) and was not different from the rest of

the treatments (Table 3.5). Our results are supported by literature that reports that summer pruning is

considered advantageous over winter pruning because it avoids excessive shoot regrowth (Sansavini,

1978). Removal of active leaf surface during the growing season can stimulate the movement of

assimilates to the woody tissues, resulting in a reduction of reserves for shoots growth the following

season (Forshey, 1976). Even though there was no difference in current season shoot length bewteen

T2-14 and T3-14, the number of current season shoots from T2-14 was significantly greater than the

number of shoots from T3-14, with the latter presenting 33% less current season shoots per tree than

T2-14. Greater regrowth resulted from winter pruning could be due to an increased production of

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cytokinins, auxins and gibberellins from the trees above the ground (Jackson, 2003). T2-14 also showed

28% more shoots per tree than T5-14. (Saure, 1987) also documented that new extension growth after

summer pruning was limited and reduce the canopy. No differences were observed in terms of current

season shoot length per cm2 TCSA. On the other hand, Maib et al. (1996) reported that heading cuts can

produce higher number of shoots that regrow after pruning compared to thinning cuts. The results from

our trials didn’t show significant differences in shoot length or number of shoots among hand winter

pruning, where most of the cuts are thinning cuts, and mechanical winter pruning, that only performs

heading cuts.

We also found that mechanical pruning in 2014 influenced return bloom. T1-14 (hand winter)

showed 54% and 52% lower return bloom than T4-14 (mechanical winter + mechanical summer) and T5-

14 (hand winter + mechanical summer), respectively (Figure 3.16). T1-14 and T2-14 did not differ from

each other. We hypothezise that these results are related to timing of pruning. Summer pruning usually

stimulates spurs production instead of shoot regrowth; when performed early in the season, summer

pruning increases flower bud initiation in apical buds of spurs when trees are not vigorous, like the ones

in our trials (Saure, 1987; Jackson, 2003). Related studies conducted in Italy have also found that early

mechanical summer pruning at 8-12 leaves maximized flower bud differentiation (Miranda Sazo et al.,

2010). Additionally, our results might also be related to pruning severity, considering that hand winter

pruning removed 50% or more material than our mechanical pruning treatments. Previous studies have

documented that as pruning severity increases, so does vegetative growth at expense of flower buds

formation. Excessive pruning has been found to delay the onset of fruit production and reduce flower

development (Maib et al., 1996).

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Pruning trials 2015

Type of pruning also had an effect on efficiency in 2015. Mechanical dormant pruning followed

by a hand clean up (T2-15) was 1.7 times faster than hand pruning alone (T1-15) at a tractor speed of 1.1

km/h, at 2000 RPM with the characteristics of our field trials plots (Figure 3.17). Each tree was pruned in

approximately 20.6 s per tree (0.34 min) with T2-15. Hand clean up of our plots that were pruned

mechanically in the summer the previous year (T3-15 and T4-15) only took 32-37% of the time of T2-15,

due to the low amount of current season wood that regrew. Sansavini (1978) also documented that

dormant mechanical pruning (topping and hedging) followed by hand clean up fo 12-years-old ‘Golden

Delicious’ was 1.6 times faster than hand pruning alone with an average of 14.8 min per tree. The data

reported by Sansavini are similar to our trial results in terms on pruning efficiency; however; the time to

prune each tree by hand and with the machine is much higher for traditional systems with vigorous

rootstocks than with high density systems and dwarfing rootstocks. Mechanized pruning was tried in

the past with little success due to excessive regrowth resulting from vigorous rootstocks and severe

pruning; the introduction of dwarfing rootstocks and planar systems has allowed the adoption of

mechanization with better results (Miranda Sazo & Robinson, 2013).

Mechanical pruning followed by a hand clean up removed similar amounts of fresh material

compared to hand pruning control during the dormant season, and there was significantly less wood

and leaves removed from T3-15 and T4-15 (Figure 3.18). T3-15 exhibited the least wood and leaves

volume pruned per tree, which was 423% lower than the amount of material removed with T1-15. T4-14

also removed 86% and 106% less wood and leaves than T1-15 and T2-15 respectively, with 0.092 kg per

tree. These data demonstrate again the regrowth differences across all treatments, with T3-15 and T4-

15 plots having the lowest length of current season wood to be removed by dormant pruning. Similar

results were observed in Kg of wood and leaves removed per cm2 TCSA, which ranged from 0.008 to

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0.0037 kg (Figure 3.19). Hand pruning following a pre-pruning with the hedger is considered beneficial

to perform a more detailed work by removing upright or pendant branches, after the indiscriminate cuts

performed with the machine (Forshey, 1976). Combined hand and mechanical pruning can also maintain

a good shape of the canopy and yield uniform production and fruit quality (Sansavini, 1978). On the

other hand, we believe that the LaGasse hedger was more stable than the Gillison’s hedger, due to the

lack of a motor in the sickle bar that generates wobbling; therefore, the tractor was driven at a higher

speed without causing damages to the structure of the tree.

In the summer 2015, each tree was pruned with the hedger at an average of 2.7 s (0.045 min)

per tree with a tractor speed of 2.4 km/h at 2000 RPM in our experimental conditions (Table 3.6). There

was no difference among T3-15 and T4-15. Likewise, the amount of fresh material per tree (Table 3.6)

did not differ between treatments with an average of 0.43 kg (67% leaves, 33% wood) (Table 3.7) and

0.04 kg per cm2 TCSA. “Dirty cuts” were also done with this machine, and they were cleaned up with

hand pruning. A visual assessment of the pruning cuts from the previous year showed no diseases pests

caused by the type of cuts performed in our trial. Furthermore, the growers didn’t report any problem in

the trial plots (data not reported). These results imply that pruning cuts were adequate indepently of

the methodology used and that a living cambium was formed as a protection against insects and

diseases with the production of antimicrobial sustances that promoted rapid would healing (Westwood,

1993). No difference was found on the number of fruit removed per tree by T3-15 or T4-15 (Table 3.8).

Plots from T3-15 had a maximum of 10 apples dettached per tree and plots from T4-15 a maximum of 8

fruits removed per tree. Similarly to our 2014 results, the total number of apples removed by summer

mechanical pruning is considered acceptable and an amount of fruit that would be normally removed by

hand thinning. The number of leaves and the leaf area removed by summer pruning was not different

between treatments (Table 3.9) with and average of 289 leaves and 5609 cm2 removed per tree, each

100

leaf had an approximate area of 20 cm2. Dry matter determination showed no difference among

treatments on leaf and wood ratios, with an average of 0.40 for leaves and 0.44 for wood (Table 3.10).

Leaves dry matter ranged from 38 to 40% and wood dry matter from 42 to 45%; research previously

conducted also found leaves dry matter content ranging from 37 to 41.5% for Golden Delicious/M9

trees (Verheij, 1972). The amount of leaves removed by summer pruning has to be considered in order

to improve the light penetration into the canopy and to avoid an excessive loss of carbohydrate supply

produced by a decrease of photosynthetically active leaf surface (Saure, 1987). Previos studies have

indicated that shading due to excessive vegetative growth can cause fruit abssicion and reduce

photosynthesis, which could also affect fruit growth (Lakso & Corelli, 1992). Furthermore, Wunsche et

al. (1996) reported that in healthy apple orchards, fruit yield is related to spur leaf light interception.

Orchard management, including training and pruning should emphasize the exposure of spur leaf area

rather than extension shoot leaf area (Wunsche et al., 1996; Li & Lakso, 2004).

Green thinning in 2015 showed that the highest amount of thinned fruit per tree corresponded

to T1-15 and T2-15 treatments with approximately 1.1 kg of thinned fruit/tree, similarly as in 2014. Kg of

thinned fruit per tree from T2-15 was 98% and 68% higher than from T3-15 and T4-15, respectively

(Table 3.11). Summer pruning treatments did not differ from each other. Likewise, T1-15 and T2-15

exhibited the greatest number of thinned fruit per tree. T2-15 plots had 83% and 67% more fruit thinned

per tree than T3-15 and T4-15 respectively. No difference was found among summer treatments on the

number of damaged fruit per tree. As in 2014, a thinning crew should easily recognize this type of fruit

and remove it during green fruit thinning.

We also analyzed our pruning trials during the whole year including winter and summer pruning

during 2015; and we observed that the time spent pruning each tree with T3-15 and T4-15 was 148%

and 119% lower than T1-15 and 48% and 30% lower than T2-15, respectively (Figure 3.20),

101

demonstrating again that hedging is more manageable and faster in the summer compared to winter

pruning. Time of pruning from T1-15 also differed significantly from T2-15 with an average of 34.63 s per

tree. Pruning weights per cm2 TCSA from winter and summer hedging in 2015 varied among treatments

(Figure 3.21). T1-15 and T2-15 removed 52% less wood and leaves per cm2 TCSA than T4-15 with an

average of 0.04 kg. T3-15 was not different from T4-15. During this research year we also determined

pruning efficiency as kg of wood removed per secod per tree (Figure 3.22). T1-15 was the least efficient

treatment, it was 83% and 214% less efficient than T2-15 and T4-15, respectively. T4-15 resulted highly

efficient compared to the rest of the treatments, due to a fast clean up that was performed during the

winter and the use of the machine in the summer. T3-15 was not different from T4-15 or T2-15.

Harvest 2015

Pruning methodology influenced yield and yield efficiency as observed during harvest 2015.

Total kg of fruit per tree from treatment T3-15 were 32% and 41% lower than those from T1-15 and T2-

15 respectively (Table 3.12). There was no difference among T1-15 and T4-15, as well as T3-15 and T4-

15. Regarding yield efficiency only T2-15 and T3-15 were significantly different, with T3-15 being 33%

lower than T2-15. No difference was found across the other treatments. The yield reduction in T3-15

can be mainly due to the leaves removed with the summer prunning treatment. Hayden & Emerson

(1976) also documented reduced yield from Golden Delicious trees pruned in the winter by hand and

mechanically in the summer compared to dormant hedging or summer hedging only, assuming that the

result was due to a restriction of leaf surface wich had an effect on yield. Ferree & Lakso (1979) also

reported low yields on Golden Delicious/M26 trees when combining summer hedging and winter hand

pruning annually, production was 12% lower compared to trees pruned every year only by hand. When

combining hedging and hand pruning bienally, yields were not different from hand pruning alone.

102

Similarly to yield efficiency, the number of fruit per tree only differed among T2-15 and T3-15, with T3-

15 resulting in 45% less fruit per tree than T2-15. Additionally, it was observed that the heaviest fruit

came from T1-15, total kg per fruit were 19% higher than the rest of the treatments. In the mechanical

winter ( T2-15; 213 g) and summer pruning plots (T3-15 and T4-15; 223 g and 219 g, respectively) the

fruit weight was reduced compared to control (259 g) . Previous research has documented reduced fruit

size and weight with summer pruning; however, results were not always consistent since they can vary

due to timing and severity of pruning during the summer, cultivar and tree vigor (Saure, 1987).

Mechanical pruning trials performed in France also showed dimished fruit size compared to hand

pruning (Miranda Sazo & Robinson, 2013). We hypothesize that these results were due to excessive

regrowth caused by mechanical winter pruning which competed with the fruit for photosynthates, and

leaf area removal by summer hedging which affected the production of assimilates to support fruit

growth (Whiting & Lang, 2004). The number of damaged fruit per tree caused by mechanical pruning

was not different among treatments with approximately 4 damaged apples per tree with summer

treatments (data not shown).

Pruning methodology and timing only had an effect on fruit skin red coloration and sunburn;

fruit diameter, weight, firmness, starch, SSC, TA and background color were not different across all

treatments (Table 3.13). Fruit from T3-15 had 17% more red coloration than fruit from T4-15 (Table

3.14). The former can be explained due to the fact that summer pruning, differently from winter

pruning, removes leaf surface and improves light penetration into the canopy which enhances fruit

coloration. We believe that fruit from T3-15 were more colored than fruit from T4-15 because hand

pruning removed water sprouts and upright branches that could have been producing shade in the

canopy and to the fruit, while mechanical pruning could not remove those types of branches. It has been

documented that summer pruning has a positive influence on apple skin red coloration, which depends

103

of the amount and quality of light reaching the fruit directly (Saure, 1987). The number of apples with

some degree of sunburn was only different among T4-15 and T2-15 (table 3.14). A greater number of

apples with sunburn from T4-15 was due to removal of leaf surface and fruit exposure to direct sunlight,

while winter pruning did not remove leaves. Interestingly, fruit from T1-15 was not different from fruit

from summer pruning treatments. This result suggests again that the removal of more fresh weight with

hand pruning compared to mechanical pruning, resulted on more fruit exposed to the sunlight. Our

experiment was conducted with low vigor trees, which is a factor that could have contributed to an

increase in sunburn incidence. Practices like netting or the application of sunburn protectants could be

beneficial in this case. Average fruit diameter was 26 mm corresponding to size 80 (number of apples

per 38-pound box) during fruit quality analysis. Fruit circumference at harvest ranged between 55-90

mm, but the majority of apples had a circumference of 75-80 mm. Fruit soluble solids content ranged

from 12.43% to 13.74% which was above the value of 10.8%, considered as desirable for good eating

quality (Corrigan et al., 1997). Average fruit firmness was 13.7 lb which coincides with the results

obtained with a previos study that evaluated sensory characteristics of Fuji apple (Corrigan et al., 1997).

Starch was not different across treatments and ranged between 5.7 and 6.2. Titratable acidity of the

samples chosen for analysis was approximately 0.3% (malic acid), which is also a similar value to

research conducted previously with Fuji (Corrigan et al., 1997). Trees responses to pruning timing and

methodology have been inconsistent as observed in past studies; this variation is influenced by the

environment, pruning severity, cultivar, tree vigor and soil characteristics (Saure, 1987). Our results

indicate a short-term response to pruning procedures and timing, the effects of our experiment could be

shown throughout several seasons as pruning itself can have long-term influence on trees response.

104

Regrowth measurements performed during the dormant season in 2016 also showed that

pruning methodology had an influence over the vigor of the tree. T1-15 (hand pruning) exhibited the

greatest regrowth among pruning treatments with approximately 75% greater current season shoot

length per tree than the rest of the treatments, which were not different from each other (Table 3.15).

There was no significant variation on the number of current season shoots per tree, and the results from

regrowth measurements per cm2 TCSA also indicated greater length from T1-15 compared to the other

treatments. These data are supported by a previous study conducted by Miranda Sazo & Robinson

(2013), they also found that mechanical pruning performed during the summer did not induce vigorous

regrowth on Fuji/M9 trees. We also hypothesize than hand pruning increased shading, which yields

large and thin shoots (Webster & Looney, 1996). Winter pruning resulted in more regrowth than

summer pruning in our trial; however, T2-15 (mechanical winter pruning) was not different from T3-15

and T4-15. Our results suggest again that heading cuts performed with the sickle bar do not produce

more vigorous regrowth than thinning cuts done by hand. Trees’ vigorous response to hand pruning

could be associated with pruning severity, which removed more wood per tree than mechanical pruning

(Forshey, 1976).

Economic assessment

The data obtained from our pruning trials were used to perform an economic assessment

comparing mechanical and hand pruning of Fuji/Nic29 with the conditions of the orchard were we

conducted our study. The results were calculated based on an acre of Fuji over Nic29 rootstock with a

spacing of 3 m x 0.91 m and a density of 1452 trees per acre. The efficiency data from our 2014 pruning

trials were used; hand winter pruning was assumed to be performed by 1 person using a pair of loppers

and a ladder, with 8 hours of work per day and an hourly wage of $12.42 (Orchard manager 2015, pers.

105

comm.). Summer hand pruning was assumed to be done by 1 person with a pair of loppers and without

a ladder to avoid hitting the fruit, as a regular practice at the experimental orchard (Orchard manager

2015, pers. comm.). Mechanical pruning for this particular case was performed with the Gillison’s

hedger mounted on a tractor that was driven by 1 person with an hourly wage of $12.42. The hedger

cost $25000 with 15-years lifespan (Karen Lewis 2016, pers. comm.), 5% of the cost of the machine was

estimated for equipment maintenance, 10% of purchase price was taken into account as salvage due,

machine depreciation was calculated as the relationship between the cost of the equipment minus

salvage due over lifetime of the equipment (Karina Gallardo, 2016 pers. comm.) and fuel usage was

estimated at 6 gallones per day. A summary of the calculations made with the efficiency data is shown in

table 3.16. With the assumptions made for this study we obtained a yearly cost of $194.1 to hand prune

1 acre of a 5-year-old Fuji/Nic29 in the winter and in the summer, which results 25% higher than the

cost calculated to prune the same acre mechanically with the Gillison’s hedger. We observed an 74%

cost reduction during summer pruning with the machine; similar results were found by Miranda Sazo &

Robinson (2013), who reported a 95% reduction of the cost of summer pruning for several apple

cultivars. They concluded that reduced pruning costs, improved fruit color and no negative effects on

return bloom from mechanical summer pruning, would have a significant impact on orchard

profitability. The cost of pruning can be affected by several factors including the cultivar, tree age,

canopy architecture, tree vigor, acreage, length of the season and machine working capacity (Sansavini,

1978). The working capacity for each machine is also influenced by the species to be pruned, the

cultivar, training system and age of the trees. The vigor of the trees affects the amount of pruning and

machine’s efficiency as well, trees on weak rootstocks don’t require as much pruning as those on

vigorous rootstocks (Perry et al., 1997). Low vigor trees in our trial orchard allowed hand pruning to be

performed relatively fast as well as the trees’ age, thus the economic advantage of mechanicanization

106

over hand pruning seems minimal. Our results showed that mechanical pruning can potentially save

money while increasing efficiency; however, pruning costs could be further reduced when the trees are

appropiately trained to be mechanically pruned.

Conclusion

The data collected during 2014 and 2015 suggest that pruning mechanization could prove to be

effective on reducing costs and labor requirements, as well as increase workers’ efficiency on apple

orchards on fruiting walls. Mechanical pruning alone and followed by a hand clean up were more

efficient than traditional pruning alone; however, the latter removes greater fresh material. Pruning

efficiency is directly related to tractor speed; when the tractor is driven at a fast speed, the structure of

the trees results damaged. Heading cuts performed with the machine compared to thinning cuts done

by hand did not show and effect on the amount of new extension growth following our pruning

treatmens; higher vegetative regrowth observed in 2016 from the hand pruned plots suggest that the

removal of greater amount of pruning weights by hand could be related with severe pruning. The

LaGasse hedger resulted more suitable for apples than the Gillison’s hedger, mainly because the latter

had the motor attached to the sickle bar which cause wobbling and missed a few branches when

operating. Winter hand pruning followed by mechanical summer pruning resulted in reduced yield and

yield efficiency, which suggests that that hand dormant pruning should be limited to the removal of

diseased, unproductive and dead branches and avoid a severe pruning. Our short-term results are

encouraging in that mechanical pruning yielded lower or similar regrowth, higher return bloom,

improved fruit coloration, no effect on fruit quality, reduced costs and improved efficiency compared to

standard hand pruning. Further investigation could help understanding long-term effects of pruning

mechanization on trees’ response and to determine the best timing to use the machine.

107

Literature cited


food and life sciences bulleting, Volume Extension bulleting No. 15, pp. 1-4.




Corrigan, V. K., Hurst, P. L. & Boulton, G., 1997. Sensory characteristics and consumer acceptability of

'Pink Lady' and other late-season apple cultivars. New Zealand Journal of Crop and Horicultural

Science, 25(4), pp. 375-383.


extension, Volume Publication ACC 1208, pp. 1-18.




foundation.org/openaccess/content_cvpr_workshops_2015/W05/papers/Elfiky_Automation_of_

Dormant_2015_CVPR_paper.pdf


Ferree, D. C., 1984. Influence of various times of summer hedging on yield and growth of apple trees.

Fruit Crops 1984: a summary of research, Ohio Ag. Res. Dev. Cen.(Res. Circ. 283), pp. 33-37.

Ferree, D. C. & Lakso, A. N., 1979. Effect of selected dormant pruning techniques in a hedgerow apple

orchard. J. Amer. Soc. Hort. Sci. , 104(6), pp. 736-739.

Ferree, D. C. & Short, T. H., 1972. A slotting saw for mechanical pruning. Fruit crops research, Volume

Research summary 60, pp. 1-2.

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Flore, J. A., De Grandt-Hoffman, G. & Perry, R. L., 1983. Apple tree growth & development. In: Biological

monitoring in apple orchards: an instruction manual. Michigan: Michigan State University, pp. 21-

35.

Foreign Agricultural Service/USDA, 2015. Fresh deciduous fruit: world markets and trades (apples,

grapes & pears). [Online]

Available at: http://apps.fas.usda.gov/psdonline/circulars/fruit.pdf



Hayden , R. A. & Emerson, F. H., 1976. It pays to hedge. American Fruit Grower, November, pp. 11-13.



apple trees for automated pruning. Computers and elentronics in agriculture, Volume 103, pp.

127-135.

Lakso, A. N. & Corelli, G. L., 1992. Implications of pruning and training practices to carbon partitioning

and fruit development in apple. Acta Horticulturae, Volume 322, pp. 231-239.

Li, K.-T. & Lakso, A. N., 2004. Photosynthetic characteristics of apple spur leaves after summer pruning

to improve exposure to light. HortScience, 39(5), pp. 969-972.



Miranda Sazo, M., De Marree, A. & Robinson, T., 2010. The platform factor - Labor positioning machines

producing good results for NY apple industry. New York Fruit Quarterly, 18(2), pp. 5-9.



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Sci. (Prague), 39(4), pp. 158-163.

Parker, M., 1998. High-density apple orchard management techniques. [Online]

Available at: http://content.ces.ncsu.edu/high-density-apple-orchard-management-techniques







21(3), pp. 11-16.





253-282.



Verheij, E. W. M., 1972. Competition in apple as influenced by alar sprays, fruiting, pruning and tree

spacing. Mededelingen Landbouwhogeschool Wageningen, 72-4(54), pp. 1-54.

110


Press.





111

Table 3.1. Treatment codification of pruning trials of ‘Fuji’/Nic29 in 2014

Treatment code Dormant pruning 2014 Summer pruning 2014

T1-14 Hand control -

T2-14 Mechanical -

T3-14 Hand Mechanical 12-15 leaves

T4-14 Mechanical Mechanical 12-15 leaves

T5-14 Hand Mechanical 20 leaves

112

Table 3.2. Treatment codification of pruning trials of ‘Fuji’/Nic 29 in 2015

Treatment code

2014 2015

Dormant pruning

Summer pruning

Dormant pruning Summer pruning

T1-15 Hand - Hand (hedging and topping) -

T2-15 Mechanical - Mechanical + hand cleanup

(hedging and topping)

-

T3-15 Hand Mech. 12-15

leaves

Hand cleanup (hedging and

topping)

Mech. 12-15

leaves

T4-15 Mechanical Mech. 12-15

leaves

Hand cleanup (hedging and

topping)

Mech. 12-15

leaves

113

Table 3.3. Number of fruit removed per tree by mechanical hedging of ‘Fuji’/Nic29 with Gillison’s Center

Mount topper and hedger during summer 2014. Statistical comparisons are between pruning

methodologies, treatment means with different letters are significantly different (p < 0.05), n=3.

Treatments Number fruit removed/tree

T3-14 3.69 b

T4-14 2.90 b

T5-14 6.20 a

p-value 0.007

`

114

Table 3.4. Green thinning of ‘Fuji’/Nic29 after pruning trials performed in 2014. Statistical comparisons

are between pruning methodologies, treatment means with different letters are significantly different (p

< 0.05), n=3.

Treatments Number thinned fruit/tree

Kg thinned fruit/tree

Number damaged fruit/tree

T1-14 14.0 ab 0.79 b 0.00

T2-14 18.7 a 1.11 a 0.00

T3-14 12.5 b 0.68 b 0.28

T4-14 14.5 ab 0.79 b 0.40

T5-14 12.5 b 0.72 b 0.39

p-value 0.005 0.002 0.675

115

Table 3.5. Regrowth measurements of ‘Fuji’/Nic29 after pruning trials performed in 2014. Statistical

comparisons are between pruning methodologies, treatment means with different letters are

significantly different (p < 0.05), n=3.

Treatments Current season

shoot length/tree (cm)

Number current season

shoots/tree

Current season shoot length/

cm2 TCSA

T1-14 1481.00 ab 92.33 ab 136.67

T2-14 1637.33 a 96.33 a 143.51

T3-14 1176.78 ab 72.33 b 107.31

T4-14 1051.11 b 77.22 ab 97.36

T5-14 1223.11 ab 75.22 b 119.25

p-value 0.027

0.011

0.1458

116

Table 3. 6. Mean time per tree (s), mean kg of wood removed per tree and mean kg of wood removed

per cm2 TCSA of summer mechanical pruning of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are


0.05), n=3.

Treatment Time/tree (s) Kg wood

removed/tree Kg wood

removed/cm2 TCSA

T3-15 2.72 0.430 0.04

T4-15 2.72 0.433 0.04

p-value 0.000 0.045 0.007

117

Table 3.7. Mean kg of leaves removed per tree and mean kg of wood removed per tree by mechanical

hedging of ‘Fuji’/Nic29 with LaGasse hedger during summer 2015. Statistical comparisons are between

pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3.

Treatment Kg leaves

removed/tree Kg wood

removed/tree

T3-15 0.14 0.07

T4-15 0.14 0.06

p-value 0.545 0.834

118

Table 3.8. Number of fruit removed per tree by mechanical hedging of ‘Fuji’/Nic29 with LaGasse hedger

during summer 2015. Statistical comparisons are between pruning methodologies, treatment means

with different letters are significantly different (p < 0.05), n=3.

Treatment Number fruit removed/tree

T3-15 7.41

T4-15 6.56

p-value 0.584

119

Table 3.9. Mean number of leaves removed per tree, mean area per leaf and mean area of leaves

removed per tree by mechanical hedging of ‘Fuji’/Nic29 with LaGasse hedger during summer 2015.



Treatment Number leaves removed/tree

Area leaves removed/tree (cm2)

Area/leaf (cm2)

T3-15 303.08 5613.31 19.68

T4-15 275.38 5604.35 20.69

p-value 0.685 0.212 0.718

120

Table 3.10. Mean ratio of leaves and wood from dry matter determination from mechanical hedging of

‘Fuji’/Nic29 with LaGasse hedger during summer 2015. Statistical comparisons are between pruning

methodologies, treatment means with different letters are significantly different (p < 0.05), n=3.

Treatment Ratio leaves Ratio wood

T3-15 0.40 0.44

T4-15 0.41 0.44

p-value 0.323 0.677

121

Table 3.11. Green thinning of ‘Fuji’/Nic29 after pruning trials performed in 2014. Statistical comparisons

are between pruning methodologies, treatment means with different letters are significantly different (p

< 0.05), n=3.

Treatment Number thinned

fruit/tree

Kg thinned fruit/tree

Number damaged fruit/tree

T1-15 19.2 ab 1.06 a 0.00

T2-15 24.6 a 1.25 a 0.00

T3-15 13.4 b 0.63 b 0.12

T4-15 14.8 b 0.74 b 0.17

p-value 0.008 0.002 0.291

122

Table 3.12. Mean yield, yield efficiency, number of fruit per tree and kg per fruit of ‘Fuji’/Nic29 at

harvest 2015 from pruning trials performed during 2014 and 2015. Statistical comparisons are between

pruning methodologies, treatment means with different letters are significantly different (p < 0.05), n=3.

Treatment Number

fruit/tree Total

kg/tree Yield eff. (kg/cm2)

Fruit weight (Kg)

T1-15 54.47 ab 14.09 a 1.14 ab 0.26 a

T2-15 70.27 a 15.01 a 1.27 a 0.22 b

T3-15 48.47 b 10.63 b 0.95 b 0.22 b

T4-15 58.67 ab 12.90 ab 1.25 ab 0.22 b

p-value 0.048 0.004 0.017 0.009

123

Table 3.13. Effects of pruning trials during 2014 and 2015 on fruit quality parameters of ‘Fuji’/Nic29 at

harvest 2015. Statistical comparisons are between pruning methodologies, treatment means with

different letters are significantly different (p < 0.05), n=3.

Treatments Diameter

(mm) Weight

(g) Firmness

(lb) Starch (1-8)

SSC (% Brix)

Titratable acidity

T1-15 26.05 229.74 13.62 5.76 12.43 0.32

T2-15 25.99 231.47 14.01 6.22 13.23 0.31

T3-15 25.93 230.03 13.66 6.09 13.74 0.30

T4-15 25.80 226.00 13.49 5.91 13.24 0.29

p-value 0.281 0.209 0.183 0.410 0.055 0.142

124

Table 3.14. Effects of pruning trials during 2014 and 2015 on fruit skin coloration, background color and

sunburn of ‘Fuji’/Nic29 at harvest 2015. Red coloration is classified as 1 (0-25%), 2 (26-50%), 3 (51-75%)

and 4 (76-100%). Statistical comparisons are between pruning methodologies, treatment means with

different letters are significantly different (p < 0.05), n=3.

Treatments Red color

(1-4) Background color

(1-6) Apples with

sunburn

T1-15 3.59 ab 4.50 5.3 ab

T2-15 3.39 b 4.64 4.1 b

T3-15 3.76 a 4.74 5.1 ab

T4-15 3.19 b 4.71 7.6 a

p-value 0.006 0.51 0.034

125

Table 3.15. Regrowth measurements of ‘Fuji’/Nic29 after pruning trials performed in 2015. Statistical



Treatment Current season shoot length/tree (cm)

Number of current season shoots/tree

Current season shoot length/cm2 TCSA (cm)

T1-15 2043.0 a 105.7 177.7 a

T2-15 1468.1 b 98.4 122.7 b

T3-15 1099.0 b 91.8 102.7 b

T4-15 1167.3 b 91.8 106.1 b

p-value 0.0003 0.271 0.003

126

Table 3.16. Estimation of costs per acre to prune mechanically, by hand or with the combination of both

methodologies ‘Tieton’/’Gisela5’ sweet cherry trees trained to the UFO system in 2015.

Variables Hand pruning Mechanical pruning

Pruning efficiency winter

(h/acre/person)

7.15 3.5

Pruning efficiency summer

(h/acre/person)

8.47 1.5

Salary winter ($/person)

88.9 44.1

Salary summer ($/person)

105.2 18.6

Fuel ($/day)

- 46.8

Maintenance machine ($/year)

- 1250

Depreciation ($)

- 1500

Total cost ($/acre/year)

194.1 145.5

127

Figure 3.1. Photograph of the Gillison’s Center Mount topper and hedger used for mechanical pruning of

‘Fuji’/Nic 29 apple in 2014.

128

Figure 3.2. Photograph of the LaGasse Orchards Hedger used for mechanical pruning of ‘Fuji’/Nic29

apple in 2015.

129

Figure 3.3. Agrofresh / Experico background color chart used for fruit quality analysis of ‘Fuji’/Nic29

apple from harvest after pruning trials in 2015

130

Figure 3.4. Washington Tree Fruit Research Commission color classification chart (% of red) used for fruit

quality analysis of ‘Fuji’/Nic 29 apple from harvest after pruning trials 2015.

131

Figure 3.5. Sunburn scale developed by Hanrahan (2012) based on Schrader and McFearson scale

(2003), used to grade sunburn degree during fruit quality analysis of ‘Fuji’/Nic29 apple at harvest after

pruning trials 2015.

132

Figure 3.6. Mean time/tree (s) of dormant mechanical and hand pruning of ‘Fuji’/Nic 29 apple in 2014.



ab b

c

a

0

5

10

15

20

25

30

T1-14 T3-14 T4-14 T5-14

Tim

e/t

ree

(s)

Pruning treatments

133

Figure 3.7. Mean Kg of wood and leaves removed/tree with dormant mechanical and hand pruning of

‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment


ab

c

b

c

a

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

T1-14 T2-14 T3-14 T4-14 T5-14

Kg

wo

od

an

d le

aves

rem

ove

d/t

ree

Pruning treatments

134

Figure 3.8. Mean Kg of wood and leaves removed per cm2 TCSA with dormant mechanical and hand

pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies,


error.

ab

c

b

c

a

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

T1-14 T2-14 T3-14 T4-14 T5-14

Kg

wo

od

an

d le

ave

s re

mo

ved

/cm

2 T

CSA

Pruning treatments

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Figure 3.9. ‘Fuji’/Nic29 apple trees pruned a) by hand and b) with the GIllison’s hedger during the

dormant season 2014.

a b

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Figure 3.10. Photograph of “dirty cuts” of ‘Fuji’/Nic29 apple trees performed by the Gillison’s hedger

compared to cleaner cuts performed by hand during dormant season 2014.

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Figure 3.11. Mean time/tree (s) of summer mechanical pruning of ‘Fuji’/Nic 29 apple in 2014. Statistical

comparisons are between pruning methodologies, (p < 0.05), n=3. Bars indicate standard error.

3.6

3.6

3.6

3.6

3.7

3.7

3.7

3.7

3.7

3.8

T3-14 T4-14 T5-14

Tim

e/t

ree

(s)

Pruning treatments

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Figure 3.12. Mean Kg of wood and leaves removed/tree with summer mechanical pruning of ‘Fuji’/Nic

29 apple in 2014. Statistical comparisons are between pruning methodologies, treatment means with


b b

a

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

T3-14 T4-14 T5-14

Kg

wo

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an

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aves

rem

ove

d/t

ree

Pruning treatments

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Figure 3.13. Mean Kg of wood and leaves removed per cm2 TCSA with summer mechanical pruning of



b

b

a

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

T3-14 T4-14 T5-14

Kg

wo

od

an

d le

aves

rem

ove

d/c

m2

TC

SA)

Pruning treatments

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Figure 3.14. Mean Kg of wood and leaves removed per cm2 TCSA from dormant and summer pruning

trials of ‘Fuji’/Nic 29 apple in 2014. Statistical comparisons are between pruning methodologies,


error.

b

c

b

c

a

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

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T1-14 T2-14 T3-14 T4-14 T5-14

Wei

ght

wo

od

rem

ove

d/T

CSA

(K

g/cm

2)

Pruning treatments

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Figure 3.15. Mean pruning efficiency expressed as Kg of fresh material removed per second per tree

from dormant and summer pruning trials of ‘Fuji’/Nic29 apple in 2014. Statistical comparisons are



ab ab

b

a

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

T1-14 T3-14 T4-14 T5-14

Kg

fre

sh m

ate

rial

rem

ove

d/s

/tre

e

Pruning treatments

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Figure 3.16. Return bloom of ‘Fuji’/Nic29 apple expressed as number of flowers per cm2 TCSA after

pruning trials 2014. Statistical comparisons are between pruning methodologies, treatment means with


b

ab ab

a a

0

10

20

30

40

50

60

70

80

T1-14 T2-14 T3-14 T4-14 T5-14

Flo

wer

s/cm

2 T

CSA

Pruning treatments

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Figure 3.17. Mean time per tree (s) from dormant pruning trials of ‘Fuji’/Nic 29 apple in 2015. Statistical



a

b

c c

0

5

10

15

20

25

30

35

40

T1-15 T2-15 T3-15 T4-15

Tim

e/t

ree

(s)

Pruning treatments

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Figure 3.18. Mean Kg of wood and leaves removed per tree from dormant pruning trials of ‘Fuji’/Nic 29

apple in 2015. Statistical comparisons are between pruning methodologies, treatment means with


a

a

c

b

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

T1-15 T2-15 T3-15 T4-15

Kg

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an

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rem

ove

d/t

ree

Pruning treatments

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Figure 3.19. Mean Kg of wood and leaves removed per cm2 TCSA from dormant pruning trials of



a a

c

b

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

T1-15 T2-15 T3-15 T4-15

Kg

wo

od

an

d le

aves

rem

ove

d/c

m2

TC

SA

Pruning treatments

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Figure 3.20. Mean time per tree (s) from dormant and summer pruning trials of ‘Fuji’/Nic 29 apple in



a

b

c c

0

5

10

15

20

25

30

35

40

T1-15 T2-15 T3-15 T4-15

Tim

e/t

ree

(s)

Pruning treatments

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Figure 3.21. Mean Kg of wood and leaves removed per cm2 TCSA from dormant and summer pruning

trials of ‘Fuji’/Nic 29 apple in 2015. Statistical comparisons are between pruning methodologies,


error.

b b

ab

a

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

T1-15 T2-15 T3-15 T4-15

Kg

wo

od

an

d le

ave

s re

mo

ved

/cm

2 T

CSA

Pruning treatments

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Figure 3.22. Mean pruning efficiency expressed as Kg of fresh material removed per second per tree

from dormant and summer pruning trials of ‘Fuji’/Nic29 apple in 2015. Statistical comparisons are



c

b

ab

a

0

0.01

0.02

0.03

0.04

0.05

0.06

T1-15 T2-15 T3-15 T4-15

Kg

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Pruning treatments

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CHAPTER FOUR

TIMING OF PRUNING AFFECTS YIELD, FRUIT QUALITY AND VEGETATIVE REGROWTH IN SWEET

CHERRY TREES ON FRUITING WALLS

Keywords: UFO, topping, full bloom, Tieton, dormant, summer, upright, vegetative growth, yield

efficiency.

Abstract

Physiological responses to pruning in tree fruit are influenced by a number of factors including

genotype, vigor, pruning type, severity, and timing. The objective of the current study was to evaluate

the effect of pruning timing on vegetative regrowth and fruit yield and quality in a sweet cherry (Prunus

avium L.) orchard trained to the vertically planar Upright Fruiting Offshoots architecture. Trials were

established in an 8-year-old ‘Tieton’/‘Gisela 5’ orchard near Prosser, Washington. Trees were manually

topped during the dormant season (DP), at full bloom (FB), and ca. 1, 2 or 3 months after full bloom

(FB1, FB2, FB3) with a single cut at 3.35 m height. In 2014, FB2 removed ca. 29% to 520% more wood

than all other treatments, with an average of 882.3 cm. Pruning at DP and FB removed the least wood

in both years. There was a weak positive correlation between limb caliper at the point of pruning and

length of pruning weight removed. Yield per upright varied between 0.93 and 1.2 kg in 2014 and was

similar across treatments, while yield efficiency was highest from unpruned and FB1 treatments. In

2014, fruit from unpruned uprights had 6% lower weight than fruit from FB1 and 8% lower firmness

than fruit from DP. Fruit DP uprights had 8% and 6% higher soluble solids content (SSC) than fruit from

FB and FB1, respectively. In 2015, yield efficiency was similar among treatments but yield varied

between 1.3 to 1.9 kg, and fruit from DP had 35% lower yield than unpruned uprights. Timing of pruning

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in 2015 affected fruit SSC. Fruit pruned at FB1 had the lowest SSC (14.5%), 4-7% lower than fruit SSC

from the other timings. In both years, current season shoot growth in response to pruning was about

85% greater from DP and FB pruning compared with other timings. Additionally, the greatest vegetative

regrowth was distributed on the last zone of the canopy (zone 4) immediately below the pruning cut.

Our results suggest that dormant and full bloom pruning might affect yield negatively the most. Fruit

quality had variable results, thus further studies are necessary to understand the long-term effect of

timing of pruning on quality traits. Growers should consider that a pruning plan must take into account

the response desired from the trees as well as tree vigor and bearing habit.

Introduction

Fruit quality, yield, precocity and productivity are parameters that can be manipulated by

management decisions which include pruning, tree training and orchard spacing (Webster & Looney,

1996). Pruning is intended to maximize fruit production while avoiding excessive growth of

unproductive wood that will use the photosynthates produced by the leaves. There are basic principles

of pruning that will influence the tree’s response which include types of cuts, timing, methodology and

severity (Maib et al., 1996). Timing of pruning affects the growth response and whole-tree source-sink

relations. Pruning is generally performed during the winter and/or the summer, and there are specific

effects on the trees depending on the timing they get pruned (Forshey, 1976).

Dormant pruning removes wood before active new growth starts and it can result in vigorous

regrowth, which is mainly influence by the severity of pruning (Parker, 2008; Wilson, 2009). The energy

stored in the trunk and roots during the fall results unchanged when a portion of the tree is removed

during the dormant season; the removal of growing points produces vigorous growth of uprights or

water sprouts that can shade the fruit (Parker, 2008). The best time to prune a tree during the winter is

before the buds swell, pruning too early in the winter or too late in the fall greatly increases the risk of

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freezing damage (Marini, 2001; Wilson, 2009). After a cut is made during the dormant season, the

wounds don’t start healing until the following spring; therefore, the shoots are susceptible to disease

infection (Webster & Looney, 1996). Pruning during the dormant season can be used as a cropload

management technique when done properly and help reducing the number of fruit that has to be hand

or chemically thinned (WSU tree fruit extension, 2015). Dormant pruning can also be used to manage

heavy croploads of small fruit like those produced by trees on dwarfing rootstocks such us ‘Gisela 6’,

which reduces the supply of photosyntates for the fruits (Bennewitz et al., 2011). Dormant pruning can

be limited to the removal of unproductive, diseased and dead branches to avoid excessive vegetative

growth that can affect fruit production (Parker, 2008). Moderate and severe pruning during the winter

have reduced ‘Bing’/’Gisela 6’ sweet cherry yield by 37% and 67%, respectively; possibly due to

assimilate supply limitation to the fruit (Bennewitz, et al. 2011).

Differently, summer pruning removes shoots during the growing season and a portion that

produces energy for the tree, which results in reduced regrowth (Webster & Looney, 1996; Parker,

2008). Pruning during the summer has been long used as a management method to control tree growth,

it is considered as a dwarfing process due to the removal of leaf surface that reduces the accumulation

of assimilates on woody tissues (Forshey, 1976; Parker, 2008; Ikinci, 2014). It has also been associated

with better light interception, flowering stimulation and fruit quality improvement (Westwood, 1993).

Summer pruning can be started as soon as the buds start to grow; the greatest dwarfing effect has been

observed with June or July pruning, while midsummer pruning produces little to no regrowth (Parker,

2008; Wilson, 2009). The effects of summer pruning on sweet cherry have been well documented

(Wustenberghs et al., 1996; Kappel et al., 1997; Guimond et al., 1998; Usenik et al., 2008). Summer

pruning of 4 year old ‘Bing’ cherry trees showed an increase in flowering on current season shoots at 37

to 45 DAFB, treatments of 31 to 34 DAFB were less effective but also influenced flowering. Pruning

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severity and cut location were also examined along with different timings; however, there was no

difference among treatments and all of them increased number of flower buds (Guimond et al., 1998).

Roversi et al. (2008) reported that winter and severe pruning had an unfavorable effect on 8-years-old

sweet cherry trees of different varieties, while postharvest pruning alone on alternate years had a

positive influence on yields. Usenik et al. (2008) studied summer pruning effects on fruit quality and

yield efficiency of ‘Kordia’ and ‘Regina’ on ‘Gisela 5’ for three consecutive years; they reported that

unpruned trees exhibited similar yield efficiency than pruned trees but lower fruit weight and quality,

regrowth varied depending on the cultivar and growth habit; however, unpruned trees had more blind

wood than pruned trees due to shading. When testing different treatments that varied on their severity

combined with dormant and summer pruning on sweet cherry, Crane (1931) found that light dormant

pruning resulted on the highest yield and bloom among treatments; timing did not significantly influece

productivity but severity did, with the most severe pruning producing the most negative effects.

In the past sweet cherry trees used to get very little to no pruning, which combined with the

upright growth habit of this crop, trees reached heights of 25-40 ft. As a result, the upper part of the

trees was the most productive while the lower part was shaded and yielded low production of poor

quality fruit (Micke et al., 1968). High density systems with dwarf and size controlled trees facilitate

cultural practices including pruning, thinning, harvesting and spraying yielding high production at

reduced production costs (Ikinci, 2014). The Upright fruiting offshoots (UFO) is a trellised system with a

planar architecture, trees are composed of one main horizontal scaffold with renewable vertical fruiting

units, this is a highly efficient system that is precocious and yields high quality fruit (Ampatzidis &

Whiting, 2013). There are two fundamental pruning rules for the UFO system: 1) remove all lateral

shoots, and 2) renew the most vigorous uprights. Previous research has shown that leaving a short (i.e.,

ca. 5 cm) stub when pruning off lateral, current season shoots improves yield because there are several

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fruiting nodes at the base of current season shoots (Whiting, unpublished). In addition, Labbe and

Whiting (unpublished) showed that renewing uprights is best accomplished near full bloom by leaving 1-

2 live spurs for points of regrowth. However, there has been no research into pruning strategies for

maintaining canopy height in the UFO system. Micke et al. (1968) reported that topping sweet cherry

trees as a single treatment lack of selectiveness, and required additional hand pruning to remove

unproductive wood. However, topping combined with thinning out of unfruitful branches resulted in

shorter trees with better light interception. Furthermore, dormant topping caused excessive vegetative

growth compared to midsummer pruning. Sansavini (1978) noted that summer topping of ‘Stark Spur

Golden Delicious’ apple resulted in 19% more fruit buds per tree compared to no pruning, while topping

and hedging yielded 37% more fruit buds than topping alone. He also documented similar results with

‘Butirra Precoce Moretinni’ pears, with topping producing the highest amount of fruit buds (ca. + 26%)

compared to no pruning. Yildirim et al. (2010) found the lowest yields from topping or hedging 'Star

Ruby’ grapefruit compared to no pruning and topping and hedging combined. They concluded that

topping the trees for one year and hedging them the following year might have better results than

topping and hedging on the same year. Furthermore, topping yielded the greatest fruit size among

treatments.The objective of this research was to determine the effects of pruning timing to the top of

uprights of UFO-trained sweet cherry trees on yield, fruit quality, and vegetative regrowth. Trees were

manually headed at a fixed height to mimic the operation of a tree-topping mechanical pruner.

Materials and methods

Experimental design

Topping trials were set up in a complete randomized design in an 8th and 9th leaf commercial

orchard of ‘Tieton’/‘Gisela 5’ near Benton City, WA (46°18'34.7"N 119°33'58.6"W) during 2014 and

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2015. Trees were trained to the Upright Fruiting Offshoot (UFO) architecture and spaced 2.4 m x 3 m.

Plots were drip irrigated with overhead sprinklers were used every third row for evaporative cooling.

Standard management practices were followed for irrigation, pest and disease management in both

years. The experimental design consisted on five treatments with five repetitions and 3 trees per

repetition.

Topping trials

Trials were initiated in 2014. Individual uprights from each tree were topped by hand at 3.35 m

height using loppers or a pruning saw when necessary, with level cuts to mimic the operation of the

Gillison topper and hedger (i.e., only those uprights that exceeded 3.35 m were pruned). Hand pruning

was performed by the WSU-IAREC Stone Fruit Physiology crew using a 3.6 m ladder (Figure 4.1). Pruning

treatments were conducted on different dates (Table 4.1) to reveal the effect of timing of pruning: (1)

dormant season (DP), (2) full bloom (FB), (3) full bloom + 1 month (FB1), (4) full bloom + 2 months (FB2),

and (5) full bloom + 3 months (FB3). Pruning was always performed during the morning; dormant

pruning was done on March 19th and full bloom topping on April 14th. The trial was replicated in 2015

to different trees in the same orchard. Dormant pruning in 2015 was carried out on March 17th and full

bloom topping on April 3rd.

Data collection

Each topped upright was labeled distinctly at both eye level and at the cut site for subsequent

data collection. Limb diameter at the cut site and cumulative length of wood removed were recorded at

the time of pruning. The diameter was measured using a digital caliper Johnson model # 1889-0600

(measurement range: 0-150 mm, accuracy: ± 0.02 mm) and the length was determined with a 7.5 m

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flexible tape measure. Trunk cross-sectional area (TCSA) at 10 cm above the point of origin of each

upright was calculated 13-15 days after harvest from measurements of upright circumference. The

results were used to estimate yield efficiency and regrowth per cm2 of TCSA.

Fruit were harvested at commercial maturity both years using the grower’s commercial crew

and the WSU-IAREC Stone Fruit Physiology crew, using 3.6 m ladders. All fruit from every pruned upright

were harvested in a single strip-pick. Upright yield was determined by weighing all harvested fruit per

upright in the field using an electronic scale Pelouze model 4010 (capacity: 68 kg x 0.1 kg). A random

sample of 25 cherries from each pruned upright was collected in labeled paper bags for fruit quality

analysis. All fruit were collected when branches had fewer than 25 cherries. Fruit quality was evaluated

at the WSU-IAREC Stone Fruit Physiology Laboratory in Prosser, WA by the laboratory crew. Fruit size

was determined by measuring equatorial diameter (mm) of individual fruit, using a digital caliper

Johnson model # 1889-0600 (measurement range: 0-150 mm, accuracy: ± 0.02 mm). Fruit weight (g) of

5-fruit groups was recorded with a digital scale Ohaus Adventurer Pro model AV212 (capacity: 210 x

0.001 g). Soluble solids content (SSC) was determined with % Brix from the juice of the 25 sampled

cherries using an ATAG Pocket Refractometer (ATAGO U.S.A., Inc., Bellevue, WA). Fruit firmness (g/mm)

was recorded for all individual fruit with a FirmTech 2 (Bioworks INC., Wamego, Kansas).

Vegetative regrowth at the pruning site, and the entire upright, was determined by measuring

total current season wood length from the topped uprights in each plot. Regrowth from 2014 pruning

trials were evaluated in March 2015, and in January 2016 from the 2015 trials. Cumulative current

season shoot length (cm) of each upright was determined with 7.5 m tape measure using the

commercial orchard’s platforms by the Washington Tree Fruit Research Commission (WTFRC) and the

WSU-IAREC Stone Fruit Physiology crews (Figure 4.2). Vegetative growth measurement was assessed in

distinct canopy zones; 1) between the lowest wire and the second wire of the trellis system (0.5-1.1 m),

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2) between the second and the third wires (1.1-1.8 m), 3) between the third and the fourth wire (1.8-2.6

m), and 4) any regrowth observed above the uppermost wire of the system (2.6-3.4 m ). Trunk cross

sectional area previously calculated was used to express current season shoots length per cm2 TCSA.

Statistical analyses

The Statistical Analysis System (SAS, 9.3 version) was used to separate means for all data sets

(length of wood removed, yield, yield efficiency, fruit quality parameters, and regrowth). General Linear

Model (GLM) and Tukey’s procedure for pairwise comparisons with 95% confidence level was used to

evaluate the statistical significance of all the parameters evaluated per topped upright.


Topping trials

In 2014 caliper at the pruning site and length of the wood removed by topping varied with the timing of

pruning. Basal caliper ranged from 25.4 mm to 41.3 mm depending on pruning date (Figure 4.3).

Uprights pruned 3 months after full bloom (FB3) had the greatest caliper, and there was no variation

among the other timings of pruning. Caliper of uprights at FB3 were 62% greater than those from DP,

and 20% greater than uprights topped at FB2. Carbohydrates partitioning varies among tree organs as

well as the timing of growth; vegetative growth is usually strongest earlier in the season and gradually

declines in the midseason. It has been observed that shoot growth in apple declines in the midseason;

whereas in stone fruit, the diameter of shoots and trunk continue to increase even after harvest

(Baugher & Singha, 2003). In 2015 there were no significant differences among any pruning treatments,

basal caliper ranged from 31.4 mm to 35.1 mm (Figure 4.4). Usenik et al. (2008) also reported no

differences in basal diameter from pruning 1/3 or 2/3 of new shoots in ‘Regina’ and ‘Kordia’ sweet

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cherry trees grafted on ‘Gisela 5’ rootstock. Previous research on Golden Delicious/M9 apple trees

documented that secondary growth is mainly affected by the age of the branch, with decreasing RDAI

(radial daily area increase) from current season shoots to older branches. The growing season had a

lower effect on secondary growth with 3 distinct phases starting at budburst through mid September,

with low, maximum and intermidiate RDAI values, respectively. Morover, secondary growth was

considered to have a median position among all potential sinks; however, this ranking is conceived as a

dynamic process, where the sink strength of secondary growth shifts from low at the beginning of the

season to high during phases II and III (Lauri et al., 2010). On the contrary, Whiting & Lang (2004)

reported that cambial meristems are weak sinks in sweet cherry trees, and that radial expansion was

inhibited during maximum sink demand from fruit and vegetative growth. Additionally, they found that

trunk expansion increased after harvest suggesting a strong competition among sinks.

The length of wood removed by topping in 2014 varied among treatments and ranged from 142

cm to 882 cm (Figure 4.5). The greatest amount of wood removed with pruning was observed from FB2,

and the least from DP and FB pruning, which were similar. FB2 removed 520% and 430% more wood

than DP and FB, respectively. This is likely due to the removal of only previous season wood with DP and

FB whereas FB2 removed also new current season shoots. Interestingly, mean length pruned was 29%

higher from FB2 compared to FB3, which might be due to a higher number of uprights with similar

cumulative length from FB3 than FB2 resulting in a smaller average value. Uprights from FB1 had 82%

and 56% more wood removed than those from DP and FB also due to current season shoots removed at

FB1.

Similar results were observed in 2015, length removed from FB2 and FB3 timings was the

greatest, while DP and FB the least among treatments (Figure 4.6). Cumulative length removed at FB3

was 207% and 261% higher than DP and FB, respectively due to the removal of leaves and current

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season shoots. Additionally, length from FB2 and FB3 was 111% higher than than from FB1. In 2014 and

2015, FB2 and FB3 were performed after fruit harvest, which would explain the greater length of wood

removed by these 2 treatments. At the early stage of development, fruits and shoots compete for

photosynthates; and fruit sink strength is greatest during final swell of fruit growth (i.e., stage III). The

removal of fruit would eliminate the sink competition and allow greater shoot growth. Costes et al.

(2000) also reported that the prescence of fruit affected primary and secondary growth of ‘Fantasme’

apricot; and that secondary growth would compete with primary growth for reserves and ultimately be

resposible of primary growth cessation. Their results suggested that sink demand from secondary

growth as well as the competition with primary growth should be taken into account for the economy of

the tree. In both of our study years we observed a positive correlation between basal caliper and the

length of wood removed per upright (Figure 4.7, Figure 4.8) which was best explained as a linear

regression. Length was increasingly greater in correlation to bigger upright calipers; however, the

correlation was weak. The coefficient of determination (r2) for 2014 was 0.43 meaning that only 43% of

the variance in length of wood removed can be explained by its linear relationship with basal caliper.

The coefficient of determination in 2015 was much lower than in 2014, with a value of ca. 0.14 that

could be explained due to a higher variation in the parameters measured. Previous studies have showed

a positive relationship between basal caliper and leaves development, as well as scaffold branches.

These correlations are taken into account in the ‘pipe model’, which considers stems and branches as a

set of unit pipes that support certain amount of photosynthetic organs. It has also been observed that

the relationship between the branch caliper and the leaves it bears varies depending on the age of the

branch. The correlation has shown compatibility with the pipe model in old branches and not on current

season shoots, which behavior may be subjected to the light they intercept (Lauri, et al., 2010).

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Harvest 2014-2015

In 2014 there was no effect of pruning treatment on yield per upright, which ranged from 0.93 kg to

1.19 kg fruit per upright (Figure 4.9). In contrast, yield efficiency was significantly different among

treatments and ranged from 0.05 kg to 0.1 kg/cm2 TCSA (Figure 4.10). Control and FB1 had the highest

yield efficiency. Uprights pruned at FB had 62% and 81% lower yield efficiency than unpruned uprights

and FB1, respectively. This is likely due to the removal of more productive wood with spurs located at

the upper region of the uprights (Sansavini, 1978), which caused a production decrease. There was no

difference on uprights topped during the dormant season, compared to those pruned closer to harvest.

In contrast to our results, Crane (1931) found that winter pruned Montmorency sour cherry trees had

higher yields compared to summer pruned trees, concluding that leaf area removal affected the

photosynthates production for the trees, consequently reducing yields. Usenik et al. (2008) found higher

yields of lower weight fruit on Kordia and Regina trees on ‘Gisela 5’ trained to the Vogel spindle system

pruned in the summer, compared to the resuls from our trial. Yield efficiency per branch on their study

ranged from 0.64 kg to 0.78 kg/cm2 TCSA for Kordia and 1.92 kg to 2.09 kg/cm2 TCSA for Regina. We

hypothesize that lower yields in the current trials were due to the characteristics of Tieton sweet cherry,

which has been regarded as a low productivity variety and producer of very large cherries (Long et al.,

2005; Long & Kaiser, 2010).

Fruit quality analyses showed an effect of timing of topping on every quality trait but diameter.

Weight was different only among control treatments and FB1; fruit from unpruned uprights had 6%

higher weight than fruit from FB1 with an average of 11.1 g, but exhibited 8% lower firmness than fruit

from DP (Table 4.1). We hypothesize that diminished fruit weight at FB1 was likely due to new

vegetative extension growth after our pruning treatment that was done at expense of the fruit (Whiting

& Lang, 2004). Fruit SSC ranged from 16.1% to 17.5%; fruit from DP had SSC values 8% and 6% higher

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than fruit from FB and FB1, respectively. It is possible that the removal of leaf surface limited the

production of assimilates which affected fruit SSC. It has been observed that summer pruning can

reduce fruit size and sugar levels due to leaves removal (Marini, 2014). PFRF from control uprights was

10% and 12% lower than FB1 and FB, respectively. Diameter ranged from 27.8 mm to 28.3 mm and did

not differ among treatments. In contrast, harvest in 2015 showed a difference in yield per upright but no

difference on yield efficiency. Yield per upright varied between 1.3 kg to 1.9 kg of fruit (Figure 4.11). The

only difference was observed between control uprights and those from DP, the latter had 53% lower

yield. We believe that dormant pruning produced excessive vegetative regrowth that acted as a

dominant above-ground sink and competed with the fruit for assimilates (Whiting & Lang, 2004). Micke

et al. (1968) also found a reduction in yield caused by topping, suggesting that the removal of fruiting

wood caused a reduction of bearing area and fruiting. They also observed that this reduction was

proportional to the amount of wood removed by topping. Yield efficiency ranged from 0.12 kg to 0.18 kg

fruit/cm2 TCSA (Figure 4.12). Only fruit SSC and PFRF varied among treatments from our 2015 trial.

Similar to the 2014 results, fruit pruned at FB1 exhibited the lowest SSC value with an average of 14.5%,

which is below the SSC of 15% suggested as the minimum content necessary to consider sweet cherry

fruit acceptable for fresh market (Kappel et al., 1996). This value was 4-7% lower compared to fruit SSC

from the rest of the treatments (Table 4.2). Similarly to 2014, fruit sugar content was probably reduced

due to leaf removal. Past studies have shown that to maximize fruit quality, sweet cherry fruit requires

not only the products from leaves of fruiting spurs but also additional photosynthates, principally those

from acropetal sources including leaves on one-year-old shoots and spurs (Whiting & Lang, 2004).

Unpruned uprights had fruit with 9% lower PFRF than fruit from FB1, and no difference was found with

the other treatments. Fruit weight was higher in 2015 than 2014 and varied from 11.3 g to 11.6 g;

161

however, fruit had lower firmness in 2015 with values ranging from 230 g/mm to 312 g/mm. Diameter

was also greater compared to 2014 and all the fruit was in average row size 9.

Regrowth 2014-2015

Pruning timing influenced vegetative regrowth during 2014 and 2015. The number of current

season shoots per upright did not vary significantly in 2014; but current season shoot length per upright

and per cm2 TCSA was different among treatments (Table 4.3). Uprights topped at DP and FB had

greater new extension growth than those pruned at FB2 and FB3. Cumulative current season shoot

length from DP was 80% and 124% greater than that from FB2 and FB3, respectively; while current

season shoot length from FB was 83% and 128% greater than FB2 and FB3, respectively. Similar results

were observed on cumulative shoot length/ cm2 TCSA, where regrowth from DP was 56% and 71%

higher than FB2 and FB3, respectively; and shoot length/ cm2 TCSA from FB was 72% and 89% higher

than FB2 and FB3, respectively. These results suggest that dormant pruning resulted in vigorous

regrowth because it removes wood and growing points in the trees before active new growth starts

(Parker, 2008; Wilson, 2009). Micke et al. (1968) found that delayed dormant topping around bud swell

of sweet cherry trees was more invigorating than summer pruning, when trees responded with shorter

shoots that regrew from pruning. Mika & Piatkowski (1989) also indicated that winter topping

estimulates the growth of vigorous watersprouts that can shade the tree and reduce fruit quality due to

poor light interception. Additionally, they documented that summer pruning can keep the canopy small

and secure good yields, and recommended that this practice should not be performed too early as it can

promote much secondary growth after pruning.

There was a significant difference in vegetative regrowth among all zones in the canopy. Zone 1

exhibited the lowest regrowth while zone 4 had the greatest new extension growth. Regrowth from

162

zone 4 (between the 2 last wires) was 1134% greater than regrowth from zone 1, 286% higher than zone

2 and 90% higher than zone 3 (Figure 4.13, Figure 4.14). It is reasonable to expect greater regrowth from

zone four where the cut was performed during topping (Figure 4.15). Micke et al. (1968) also reported

significantly greater shoot growht just beneath the cuts from dormant pruning of sweet cherry trees.

The removal of terminal buds and developing leaves altered the hormonal balance of the trees, which

stimulated the growth of new shoots directly below the cut (Crassweller, 2015). Additionally, heading

cuts performed on upright shoots produce very narrow angled shoots immediately below the cut, which

are also very vigorous (Forshey, 1976;Crassweller, 2015). The location of the pruning cut might also

influece the amount of regrowth. Past studies reported that pruning performed above a node yielded

significantly greater regrowth than pruning below a node. Ethylene or other senescence associated

metabolites produced by the stub may inhibit the development of vegetative meristems, which would

explain this regrowth results (Guimond, et al. 1998). Likewise, in 2015 greater current season shoots

growth was noticeable from trees pruned at DP and FB compared to those topped at FB2 and FB3. Trees

from DP had 85% and 95% greater current season shoot length than FB2 and FB3, respectively (Table

4.4). Trees pruned at FB1 and FB3 also exhibited significantly lower number of current season shoots

than those pruned at DP and FB. Our results of reduced regrowth from postharvest topping are

supported by literature that indicates that delaying pruning until after harvest can reduce the

accumulation of reserves to support subsequent season new extension growth (Usenik et al., 2008).

Finally, regrowth was also significantly greater from zone 4 in the canopy, with 654%, 318% and 87%

greater new extension growth than zones 1, 2 and 3 respectively (Figure 4.16, Figure 4.17).

163

Conclusion

Combined, the results of this research indicate that timing of topping uprights in the UFO system affects

sweet cherry yield, fruit quality and tree vigor. Topping 2 or 3 months after full bloom removes

significantly more wood than dormant pruning. Yield and yield efficiency were inconsistent over the

two years of our trial; however, dormant and full bloom pruning seem to affect yield negatively the

most. Fruit quality also had variable results, further studies are necessary to understand the long-term

effect of timing of pruning on quality traits. Dormant and full bloom pruning produced the highest

vegetative regrowth immediately below the cut, which could ultimately affect productivity and fruit

quality. Leaves from fruiting spurs do not produce enough photosynthates to support fruit growth, and

leaves from current season and 1-year-old shoots are required to obtain good quality fruit; thus, new

regrowth on sweet cherry trees on precocious rootstocks must be managed to promote vigor. Our data

also suggest that excessive leaves removal prior to harvest might affect negatively fruit quality especially

sugar content and weight. On the other hand, excessive new extension growth not only shades the

lower portion of the tree but also acts as a strong sink that competes with fruit for photosynthates, thus

affecting fruit growth. Our results show that postharvest pruning of sweet cherry trees positively

influences vegetative growth which could improve light penetration within the canopy. It is also

important to consider that a pruning plan must take into account the response desired from the trees as

well as tree vigor and bearing habit. Future research is required to better understand how to achieve a

balance between vegetative and reproductive growth through adequate pruning and tree training.

164

Literature cited





agraria, 38(3), pp. 339-344.

Crane, H. L., 1931. Effects of pruning on the growth and yield of cherry trees.. West Virginia Agricultural

Experimental Station Bulletin 240.

Crassweller, R. 2015. Pruning basics. [Online]



[Accessed 1 January 2016].Forshey, C., 1976. Training and pruning apple trees. Cornell Coop.

Extension Info Bul. 112.



Ikinci, A., 2014. Influence of pre- and postharvest summer pruning on the growth, yield, fruit quality, and

carbohydrate content of early season peach cultivars. The Scientific World Journal, Volume 2014, pp.

1-8.

Long , L. E. & Kaiser, C., 2010. Sweet cherry rootstocks. Pacific Northwest Extension Publication. , PNW

619, pp. 1-8.

Long, L. E., Nunez-Elisea, R. & Cahn, H., 2005. Five most important attributes of sweet cherries and the

varieties that fill these needs. [Online]

165

Available at:

http://extension.oregonstate.edu/wasco/sites/default/files/horticulture/Cultivars/documents/IDFTAVar

ietypaper2005_000.pdf




Marini, R. P., 2001. Training and pruning apple trees. Virginia Cooperative Extension, Publication 422-

021.

Micke, W. C., Ryugo, K., Alderman, D. C. & Yeager, J. T., 1968. Pruning bearing. Californial Agriculture,

May, pp. 6-7.

Mika, A. & Piatkowski, M., 1989. Controlling tree size in dense plantings by winter and summer pruning.

Acta Horticulturae, Volume 243, pp. 95-102.








Hort. Sci., 73(2), pp. 62-68.


Cab International.

166


Press.

Whiting, M. & Lang, G., 2004. Effects of leaf area removal on sweet cherry vegetative growth and fruit

quality. Acta Horticulturae, Volume 636, pp. 467-472.

Wilson, K., 2009. Pruning fruit trees. Ministry of agriculture, food and rural affairs Ontario, Extension

fact sheet 210/24.

167

Table 4.1. Dates of hand topping ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2014 and

2015

Timing of pruning Treatment code

Date of pruning 2014

Date of pruning 2015

Dormant DP 3/19/2014 3/17/2015

Full bloom FB 4/14/2014 4/3/2015

Full bloom + 1 month FB1 5/16/2014 5/8/2015

Full bloom + 2 months FB2 6/16/2014 6/11/2015

Full bloom + 3 months FB3 7/16/2014 7/16/2015

168

Table 4.2. Effects of timing of pruning on fruit quality parameters of ‘Tieton’/’Gisela5’ sweet cherry

trained to the UFO system in 2014. Statistical comparisons are between pruning timings, treatment

means with different letters are significantly different (p < 0.05), n=5.

Treatments Weight (g)

Firmness (g/mm)

SSC (%)

PFRF (Kg)

Diameter (mm)

Row size equivalence

Unpruned 11.08 a 314 b 17.02 ab 1.33 b 28.29 9

DP 10.82 ab 340 a 17.53 a 1.41 ab 28.23 9

FB 10.64 ab 324 ab 16.10 b 1.49 a 27.83 9.5

FB+1 10.41 b 333 ab 16.47 b 1.47 a 27.76 9.5

p-value 0.022 0.018 0.0074 0.0004 0.2168

169

Table 4.3. Effects of timing of pruning on fruit quality parameters of ‘Tieton’/’Gisela5’ sweet cherry

trained to the UFO system in 2015. Statistical comparisons are between pruning timings, treatment


Treatments Weight (g)

Firmness (g/mm)

SSC (%)

PFRF (Kg)

Diameter (mm)

Row size equivalence

Unpruned 11.3 301 15.54 a 1.47 b 28.17 9

DP 11.5 312 15.16 a 1.55 ab 28.52 9

FB 11.6 300 15.27 a 1.55 ab 28.68 9

FB+1 11.4 310 14.49 b 1.59 a 28.34 9

p-value 0.196 0.295 0.003 0.022 0.095

170

Table 4.4. New extension growth of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2014

expressed as number of current season shoots, current season shoot length per upright and current

season shoot length per cm2 TCSA. Statistical comparisons are between pruning timings, treatment


Treatments Number of current season shoots

Current season shoot length

(cm)

Current season shoot length/cm2 TCSA

(cm)

DP 34.7 1444.8 a 74.64 a

FB 30.5 1469.5 a 82.13 a

FB1 26.1 1074.8 ab 63.86 ab

FB2 25.8 802.5 b 47.70 b

FB3 23.5 643.1 b 43.41 b

p-value 0.1861 0.0018 0.0017

171

Table 4.5. New extension growth of ‘Tieton’/’Gisela5’ sweet cherry trained to the UFO system in 2015

expressed as number of current season shoots, current season shoot length per upright and current

season shoot length per cm2 TCSA. Statistical comparisons are between pruning timings, treatment


Treatments Number of current season shoots

Current season shoot length

(cm)

Current season shoot length/cm2 TCSA

(cm)

DP 29.7 a 1318.9 a 91.4 a

FB 29.4 a 1331.9 a 90.1 a

FB1 24.3 b 991.9 b 69.8 b

FB2 25.9 ab 711.5 c 52.8 b

FB3 24.7 b 676.4 c 51.2 b

p-value 0.0017 0.0000 0.0000

172

Figure 4.1. Dormant topping procedure of ‘Tieton’/’Gisela5’ sweet cherry in 2014.

173

Figure 4.2. Vegetative regrowth measurements of ‘Tieton’/’Gisela5’ sweet cherry trees during the

dormant season 2015.

174

Figure 4.3. Mean caliper of wood removed by topping ‘Tieton’/’Gisela5’ at different timings during

2014. Statistical comparisons are between pruning timings, treatment means with different letters are


b

b b

b

a

0

5

10

15

20

25

30

35

40

45

DP FB FB1 FB2 FB3

Cal

iper

wo

od

rem

ove

d (

mm

)

Timing of topping

175

Figure 4.4. Mean caliper of wood removed by topping ‘Tieton’/’Gisela5’ at different timings during



0

5

10

15

20

25

30

35

40

45

DP FB FB1 FB2 FB3

Cal

iper

wo

od

rem

ove

d (

mm

)

Timing of topping

176

Figure 4.5. Mean length of wood removed by topping ‘Tienton’/’Gisela5’ at different timings during



d d

c

a

b

0

100

200

300

400

500

600

700

800

900

1000

DP FB FB1 FB2 FB3

Len

gth

wo

od

rem

ove

d (

cm)

Timing of topping

177

Figure 4.6. Mean length of wood removed by topping ‘Tieton’/’Gisela5’ at different timings during



c c

b

a a

0

100

200

300

400

500

600

700

800

900

1000

DP FB FB1 FB2 FB3

Len

gth

wo

od

rem

ove

d (

cm)

Timing of topping

178

Figure 4.7. Correlation between length of wood removed (cm) and caliper of wood removed (mm) by

topping ‘Tieton’/’Gisela5’ at different timings during 2014.

y = 7.5798x - 32.16 R² = 0.4307

0

100

200

300

400

500

600

700

800

0 10 20 30 40 50 60 70 80 90

Len

gth

wo

od

rem

ove

d (

cm)

Caliper wood removed (mm)

179

Figure 4.8. Relationship between length of wood removed (cm) and caliper of wood removed (mm) by

topping ‘Tieton’/’Gisela5’ at different timings during 2014.

y = 9.6048x + 91.468 R² = 0.136

0

200

400

600

800

1000

1200

1400

1600

1800

0 10 20 30 40 50 60 70

Len

gth

wo

od

rem

ove

d (

cm)

Caliper wood removed (mm)

180

Figure 4.9. Mean yield (kg fruit/upright) from topping ‘Tieton’/’Gisela5’ sweet cherry at different timings

in 2014. Bars indicate standard error, (p < 0.05), n=5.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Unpruned DP FB FB1

Kg

fru

it/u

pri

ght

Timing of topping

181

Figure 4.10. Mean yield efficiency (kg fruit/cm2 TCSA) from topping ‘Tieton’/’Gisela5’ sweet cherry at

different timings in 2014. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars


a

ab b

a

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Unpruned DP FB FB1

Yie

ld e

ffic

ien

cy (

Kg/

cm2

TC

SA)

Timing of topping

182

Figure 4.11. Mean yield (kg fruit/upright) from topping ‘Tieton’/’Gisela5’ sweet cherry at different

timings in 2015. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars indicate

standard error.

a

b

ab ab

0

0.5

1

1.5

2

2.5

Unpruned DP FB FB1

Kg

fru

it/u

pri

ght

Timing of topping

183

Figure 4.12. Mean yield efficiency (kg fruit/cm2 TCSA) from topping ‘Tieton’/’Gisela5’ sweet cherry at

different timings in 2015. Statistical comparisons are between pruning timings (p < 0.05), n=5. Bars


0

0.05

0.1

0.15

0.2

0.25

Unpruned DP FB FB1

Yie

ld e

ffic

ien

cy K

g/cm

2 T

CSA

Timing of topping

184

Figure 4.13. Mean current season shoot length by canopy zone measured during dormant season 2015

after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2014. Statistical comparisons are between

canopy zones, treatment means with different letters are significantly different (p < 0.05), n=5. Bars


d

c

b

a

0

100

200

300

400

500

600

700

800

1 2 3 4

Cu

rren

t se

aso

n s

ho

ot

len

gth

(cm

)

Canopy zones

185

Figure 4.14. Mean number of current season shoots by canopy zone measured during dormant season

2015 after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2014. Statistical comparisons are between



d

c

b

a

0

2

4

6

8

10

12

14

16

18

1 2 3 4

Nu

mb

er o

f cu

rren

t se

aso

n s

ho

ots

Canopy zones

186

Figure 4.15. Photograph of new extension growth on zone 4 of the canopy from dormant topping of

Tieton’/’Gisela5’ sweet cherry in 2014.

187

Figure 4.16. Mean current season shoot length by canopy zone measured during dormant season 2016

after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between



d

c

b

a

0

100

200

300

400

500

600

700

1 2 3 4

Cu

rren

t se

aso

n s

ho

ot

len

gth

(cm

)

Canopy zones

188

Figure 4.17. Mean number of current season shoots by canopy zone measured during dormant season

2016 after hand topping of ‘Tieton’/’Gisela5’ sweet cherry in 2015. Statistical comparisons are between



d

c

b

a

0

2

4

6

8

10

12

14

16

1 2 3 4

Nu

mb

er o

f cu

rren

t se

aso

n s

ho

ots

Canopy zones

189

CONCLUDING COMMENTS

Fruit quality is largely determined by the balance between vegetative and fruitful growth.

Pruning and tree training are essential practices for optimizing tree growth and fruit quality. These two

horticultural manipulations are very different but complement each other; training has an effect on the

tree form and development, while pruning affects primarily the tree function. Pruning is intended to

maximize fruit production while avoiding excessive growth of unproductive wood that will use the

photosynthates produced by the leaves.

Traditionally, apple and sweet cherry trees have been trained to an open center multiple leader

system on vigorous rootstocks and low tree densities. These architectures yield a significant production

4 to 6 years after planting the trees and achieve a maximum productivity from the 8th year with high

requirements in labor. The need for early production with high fruit quality and yields, and the

introduction of dwarfing rootstocks contributed to the transition from traditional low density systems to

more efficient fruiting walls.

The scarcity of skilled orchard labor is a problem affecting agriculture worldwide and it

represents the greatest concern for the sweet cherry and apple industries in the United States. Pruning

is also a labor intensive and costly task, and it is also linked to high injury risk for workers due to the

need of ladders for large trees. The evolution of canopy architecture to planar structures and training

systems from low to high density plantings with smaller trees, offers the opportunity to adopt

mechanization and reduce pruning costs. There is a global understanding that successful adoption of

orchard mechanization is tied closely to tree architecture and the ability to employ mechanization

across all operations. Mechanical pruning is the practice that sets the orchard up for improved efficiency

including platform operations, mechanical thinning, and targeted delivery of chemicals.

190

This research evaluated the potential to adopt mechanical pruning in a commercial

‘Tieton’/‘Gisela®5’ sweet cherry and a ‘Fuji’/’Nic29’ orchards on fruiting walls, to reduce costs and

improve workers’ safety and production efficiency. Additionally, this project was established to better

understand the effects of mechanization on yield, fruit quality and vegetative growth.

Complete postharvest mechanization of sweet cherry on fruiting walls is promising. Pruning

efficiency was dramatically increased with no effect on yield and minimal effect on fruit quality. The

efficiency of mechanical pruning was directly related to the tractor speed, thus it is important to

consider the species, cultivar, tree age, soil topography, canopy architecture and planting density to

establish a tractor speed. We hypothesized that part of the efficiency gains from mechanical pruning

include a reduced use of ladders due to topping performed with the sickle bar and the reduction of

canopy volume as well. Moving, climbing and setting of ladders represent a lost in efficiency and

possible expenses related to insurance claims as a result of potential injuries. Traditional pruning

removed more wood than the hedger, mainly due to its selectiveness and the possibility to prune

upright branches out by hand. We also observed that the motor of the sickle bar has to be positioned at

the top to avoid damaging the lower parts of the trees; however, the branches that were scraped and/or

peeled by the machine did not have a negative impact with pests or diseases. Mechanical pruning had a

minimal effect on fruit size, with a small reduction observed from the plots that were hedged. The

practical significance of such small differences in fruit diameter may be negligible since all fruit were

classified as 9-row which is considered as very large fruit in the industry. Moreover, the lack of

selectiveness from mechanized pruning can stimulate vigorous regrowth, which could have an effect on

light interception in the interior of the canopy and have repercussions on fruit quality the following year

when the regrowth is not removed. As an alternative, we also examined the effects of a pre-pruning

191

with the sickle bar followed by a hand pruning cleanup. Our results suggest that this scenario is more

efficient than hand pruning as well, due to improved visibility for the workers that prune the trees by

hand. The combination of both approaches is promising as it yields similar production and extension

growth as manual pruning, plus an increased efficiency and reduced costs, which means an increase in

orchard profitability. We believe that when both methodologies are combined, hand pruning should be

paid by the hour and not by the piece since it is a cleanup performed after a pre-pruning with the

machine. Furthermore, a postharvest mechanical pruning could be complemented by a dormant pruning

clean up that is limited to the removal of upright, diseased, unproductive and dead branches.

The data from our trial developed with apple trained to the Slender Spindle system during 2014

and 2015 indicates that pruning mechanization could prove to be effective on reducing costs and labor

requirements. Similarly as with our sweet cherry trial, the efficiency of mechanical pruning was directly

related to the tractor speed; thus, same considerations must be taken into account to decide on a

tractor speed. We observed that at a fast tractor speed the sickle bar tended to hit the branches instead

of cutting them, causing damages to the structure of the tree. It also affected the stability of the bar and

increased the wobbling. An ideal tractor speed is yet to be evaluated in apple and sweet cherry;

however, the preliminary results observed with apple indicate that it is necessary to consider some

factors such as training system, the age of the trees and soil topography to define an ideal speed. Hand

pruning also removed more wood than mechanical pruning and performed cleaner cuts; our

observations showed again that “dirty cuts” by the machine did not cause diseases on the hedged trees.

The LaGasse hedger resulted more suitable for apples than the Gillison’s hedger, mainly because the

latter had the motor attached to the sickle bar which caused wobbling and missed a few branches when

operating. Yield and yield efficiency were only affected when hand pruning was performed in the winter

192

and the sickle bar was used in the summer, which suggests that hand dormant pruning should be limited

to the removal of diseased, unproductive and dead branches and avoid a severe pruning. Fruit size was

decreased by all treatments compared to control, which is supported by previous research that found

smaller fruit from mechanical pruning. Starch, fruit SSC, TA and background color were not affected by

the hedger. Our data showed increased red skin coloration with dormant hand pruning complemented

with summer mechanical pruning. We believe that the removal of watersprouts that could probably

shade in the center of the canopy had an effect on improved fruit skin color. A greater number of apples

with sunburn from summer mechanically pruned trees was due to removal of leaf surface and fruit

exposure to direct sunlight. Our experiment was conducted with low vigor trees, which is a factor that

could have contributed to an increase in sunburn incidence. Practices like netting or the application of

sunburn protectants could be beneficial in this case. Mechanical winter and summer pruning presented

lower regrowth than mechanical winter pruning in 2015, while trees pruned by hand exhibited

significantly higher regrowth compared to the rest of the treatments in 2016. Return bloom was also

reduced on trees pruned by hand, whereas those pruned with the machine had higher number of

flowers per cm2 TCSA. Our short-term results are encouraging in that mechanical pruning yielded lower

or similar regrowth, higher return bloom, improved fruit coloration, improved effiency, no effect on fruit

quality and reduced costs compared to standard hand pruning.

Finally, the data collected from our trial that evaluated different topping timings for sweet

cherry showed that there was an effect on yield, fruit quality and tree vigor. Topping 2 or 3 months after

full bloom removed significantly more wood than dormant pruning. Yield and yield efficiency were

inconsistent over the two years of our trial; however, dormant and full bloom pruning seemed to affect

yield negatively the most. Fruit quality also had variable results. Dormant and full bloom pruning

193

produced the highest vegetative regrowth immediately below the cut, which could ultimately affect

productivity and fruit quality. Our results also suggest that excessive leaves removal prior to harvest

might affect negatively fruit quality especially sugar content and weight. Our results show that

postharvest pruning of sweet cherry trees positively influences vegetative growth which could improve

light penetration within the canopy. We believe that it is important to consider a pruning plan that takes

into account the response desired from the trees as well as tree vigor and bearing habit.

Future work on pruning mechanization should include long-term studies of the use of the

hedger on fruiting walls to determine if there is a consistent effect on yield, fruit quality and new

extension growth. Further exploration on the combination of hand and mechanical pruning is suggested,

as well as the possibility of using a mechanized option on alternate years, and/or the combination of

mechanical pruning and growth regulators instead of hand pruning cleanup. It would also be important

to examine pruning severity among treatments, to verify if any negative effect observed on yield or fruit

quality is related to a severe pruning. Light interception plays an essential role on fruit production and

trees growth; thus, further research investigating this factor should provide a better understanding of

the effects of mechanical and hand pruning on trees’ response. Finally, the performance of the machine

could be evaluated considering different speeds of the tractor and the blades of the sickle bar to

establish an ideal speed; as well as examine the use of different pruning machines with apple and sweet

cherry to determine the most suitable option for each crop.

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