SOLID SET CANOPY DELIVERY SYSTEM: DRIFT REDUCTION, PEST MANAGEMENT, AND 
FRUIT QUALITY OUTCOMES 

By 

Keith Daniel Koonter 

A THESIS 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements 
for the degree of 

Entomology—Master of Science 

2023 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

Advances in apple cultivation, aiming to maximize the efficiency of harvest and yield per 

hectare, have led to the development of high-density apple orchards. High density orchards are 

characterized by densely planted narrow fruiting rows, with a “hedgerow” appearance. Whilst 

modern apple production has led to changes in orchard architecture, the technology used to 

apply agrochemicals has remained largely the same. The most commonly used technology to 

apply agrochemicals in apple orchards is the axial-fan Airblast sprayer. Unfortunately, these 

large, fan assisted sprayers were designed to treat larger more voluminous canopies leading to 

off target drift. An alternative spraying technology, the solid set canopy delivery System, or the 

SSCDS is a series of spray emitters placed in or above the canopy, making applications without 

the assistance of a large fan. Research into the plausibility of the SSCDS indicates that these 

systems are able to reduce off target drift while providing pest management equivalent to the 

commonly used axial-fan Airblast sprayer. A majority of the solid set systems assessed place 

emitters in the canopy which increases the chances for damage or emitter occlusion. The above 

canopy configuration reduces these chances, placing emitters overhead. This research was to 

quantify losses to ground as well as vertical and downwind off target drift produced by an above 

and in canopy SSCDS configuration compared to an axial-fan Airblast sprayer. Additionally, my 

research assessed the coverage, and deposition provided by two SSCDS configurations as well 

as season-long pest management, and fruit quality in orchards treated by an in canopy and 

above canopy SSCDS as well as an axial-fan Airblast sprayer. Off target drift measures suggest 

that vertical as well as downwind off target drift are significantly lower compared to drift 

produced by an axial-fan Airblast sprayer. Deposition measures indicated that both SSCDS 

configurations provided equivalent deposition throughout the canopy. Coverage however was 

greater at the top of the canopy and adaxial surfaces in trees treated by the above canopy 

SSCDS while coverage was more evenly distributed through the canopy in trees treated by the 

in canopy SSCDS. Pest management evaluations conducted in 2019 and 2020 detected little 

arthropod or disease damage in apples or foliage, damages similar in all treatments. Fruit 

quality assessments, including: size, weight and fruit count did detect differences between the 

SSCDS and axial-fan Airblast applications. The SSCDS configurations failed to adequately thin, 

apples leading to higher fruit counts in SSCDS treated trees. However estimated yield was not 

affected with similar metric tonnage per hectare determined for each treatment. These results 

suggest both the above canopy and in canopy SSCDS configurations produce significantly less 

off target drift compared to an axial-fan Airblast sprayer while providing adequate, equivalent 

pest management when compared to an axial-fan Airblast sprayer.

ACKNOWLEDGEMENTS 

“If I have seen further, it is by standing on the shoulders of giants” -Isaac Newton  

I have had the privilege of being surrounded by giants. 

I would first like to thank Dr. Matt Grieshop for his ongoing support, mentorship, 

patience, and friendship. His attentiveness to mentorship, teaching, and ensuring his students 

were cared for has left a lasting impression. I would also like to thank Dr. John Wise 

(Entomology) and Dr. Rufus Issacs (Entomology). I thank Dr. Wise for all the advice, the 

support, and an opportunity to conduct independent research, as well as giving me a greater 

understanding of insect physiology and applied pest management research. Thank you, Dr. 

Issacs, for serving on my committee as well as the opportunity to take part in new research. I 

would also like to thank another one of my mentors, Mark Ledebuhr. Mark set me on the path to 

ag research. Mark provided training, mentorship, insight, and the resources needed to make this 

happen.    

I would also like to thank the staff of the Grieshop lab who made this research possible. 

Thanks to: Becky Schmidt, Bekki Reneker, Elizabeth Szczepanski, Maggie Foley, Robin Fisher, 

and Olivia Simaz. Thanks to Kyle Akred whose dependability and insight saved the day more 

than once. Kyle played a crucial role in the success of this research. I would also like to thank 

Dr. Yingxu Wang (Ocean) for assisting with fault detection and helping find interesting places to 

eat. Thanks to Ben Savage and Dr. Max Helmberger for assisting with the SSCDS and for the 

late-night taco bell and memes. I would also like to thank Dr. Jason Matlock for insights into 

navigating lab life, giving me a great opportunity, as well as making me feel welcome as I 

started in the lab. Also, thanks to Charlotte Schuttler for helping build the SSCDS, making sure I 

consumed more than energy drinks, and countless other things along the grad student path. I 

also owe thanks to my predecessor, Paul Owen-Smith, whose work helped pave the way for 

this research. 

The Staff of the Clarksville Research center were integral in making this research 

possible. Thanks to Jerry Skeltis for running the equipment used to build the SSCDS and 

making us all laugh. Thanks to Amy Scheil for fielding packages and constantly letting me 

borrow the spare key, because of course I forgot mine. Thanks to Corey Noyes for making 

equipment available when we needed it. Thanks to Dan Platte for helping with the drift study 

and all the assistance with orchard maintenance and so much more. Also, thanks to Chris 

Lattak for all the help with acquiring parts for the SSCDS.  

I would also like to thank Dr. Chris Difonzo, Dr. Henry Chung, Dr. David Mota-Sanchez, 

and Dr. Zsofia Szendrei for their wonderful courses and for being great instructors. I would also 

iii 

 
like to thank Heather Lenartson-Kluge, Linda Gallagher, as well as the entire entomology 

business office for all they do so we can be effective researchers. Also, thanks to Ryan 

Kimbirauskas who made me a better mentor and teacher. I would also like to thank the 

Michigan Apple committee and USDA SCRI for financial support.  

I would also like to thank Dr. Jeannine Scott for her support and guidance when I 

needed it most.  

Finally, I would like to thank my family as none of this would have been possible without 

them. My mother (Anita) and father (Troy) have supported and trusted me through every step of 

this journey, and I am forever grateful. Also, I would like to thank my wife Dani whose patience, 

love, and support through this process was unwavering. Thank you for all that you do.  

iv 

 
 
     
 
 
    
 
 
 
 
TABLE OF CONTENTS 

Chapter 1 - Literature Review 
Introduction 

1.1. 
1.2.  Horticultural and Pest Management Spray Operations in Michigan orchards 
1.3.  Agrochemical Applications in Orchards 
1.4.  Solid Set Canopy Delivery Systems 
1.5.  Contents of this Thesis 

Chapter 2 - Off-Target Drift Produced by the SSCDS vs. an Axial-Fan Airblast Sprayer 

Introduction 

2.1. 
2.2.  Solid Set Application Technology as a Drift Mitigation Technology 
2.3.  Materials and Methods 
2.4.  Results and Discussion 
2.5.  Conclusions 

Chapter 3 - SSCDS Pest Management and Fruit Quality Outcomes 

Introduction 

3.1. 
3.2.  Materials and Methods 
3.3.  Results and Discussion 
3.4.  Conclusions 

Chapter 4 - Conclusions and Future Directions 

4.1.  Future Outlook 

BIBLIOGRAPHY 

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v 

 
 
 
 
 
 
 
1.1.  Introduction 

Chapter 1 - Literature Review 

Pest management in Michigan’s apple (Rosaceae: Malus domestica L.) orchards 

presents a challenge as the climate is favorable to numerous diseases and arthropod pests. 

Effective pest management requires several plant protectant spray applications, up to twenty 

per year (Johnson, 2004; Holb et al. 2005; Jamar et al. 2010; Wise et al. 2015). The most 

commonly used technology to apply plant protectant agrochemicals such as insecticides and 

fungicides, is the axial-fan Airblast sprayer. Originally designed for treating large voluminous 

orchard canopies, the axial-fan Airblast sprayer remains largely unchanged since its inception 

(Fox, R., et al. 2008). Commercial apple orchards, however, have undergone significant 

change, from large voluminous canopies to short, narrow, high-density plantings. This has led to 

a mismatch between the axial-fan Airblast sprayer and the high-density apple orchards they 

serve, leading to spray materials passing through the canopy (Fox, R., et al. 2008; Kasnar et al. 

2018). A potential solution to this mismatch is an array of micro-emitters, fixed to trellis rows, 

that are optimized to operate in narrow row crops. This approach will be described in this 

thesis.  

Commercial apple production has been steadily moving towards high density orchards in 

order to maximize efficiency and ultimately, profitability. In order to maximize fruit production, 

canopies must receive the greatest amount of sunlight possible. Older growing methods that 

use larger canopied trees such as round crown shade the interior of the canopy (Wünsche & 

Lakso, 2000). Light levels within the first meter of the canopy can drop to 34% of full sunlight. 

Additionally, apples of high commercial quality do not appear on parts of trees exposed to less 

than 50% full daylight (Jackson, 1970). With the reduction of tree and canopy size, more light is 

allowed into the canopy, reducing the size of shaded areas and promoting higher yields 

(Robinson et al., 2013). The shorter trees and the less voluminous canopy also allow for easier 

pruning, as well as a more efficient and less laborious harvest. Apples can be harvested by 

hand from the ground and are much more concentrated in the growing space (Robinson, T., 

2008). The high-density orchard is structurally very different from previous growing systems, 

requiring evaluation of how best to manage its pests and diseases.  

1.2.  Horticultural and Pest Management Spray Operations in Michigan orchards 

Pest management in Michigan high density apple orchards requires controlling 

arthropod, fungal, and bacterial pests. Michigan's climate is favorable to several pest species 

requiring multiple pesticide applications per season (Wise et al., 2015; Jamar et al., 2010; 

Johnson, 2004). Several direct and indirect pests can damage apple orchards damaging both 

1 

 
trees and fruit, leading to negative economic impacts. Direct pests are those who predominantly 

damage the fruit, while indirect pests are those that predominantly damage the rest of the plant 

(Demchak et al., 2023).  

Direct arthropod pests in Michigan’s apple orchards include Apple maggot, Rhagoletis 

pomonella (Walsh); Plum curculio, Conotrachelus nenuphar, (Herbst); Tarnished plant bug, 

Lygus lineolaris, (Palisot de Beauvois); Brown marmorated stink bug (BMSB), Halyomorpha 

halys, Stål; Oriental fruit moth, Grapholita molesta, (Busck); Codling moth Cydia pomonella, (L.); 

and the Oblique banded leafroller, Choristoneura rosaceana, (Harris). These arthropods directly 

damage fruit, causing fruit rot, fruit disfigurement, and fruit drop leading to both crop loss and 

unsalable fruit (Howitt 1993). Treatments commonly consist of properly timed foliar applications 

of organophosphates, carbamates, and pyrethroids, as well as pheromone disruption for 

Tortricid moths (Tortricidae), among others (Wise et al. 2022). 

Indirect arthropod pests in Michigan’s apple orchards include: Japanese beetle, Popillia 

japonica, Walsh; White apple leafhopper, Typhlocyba pomaria, McAtee; mites 

(Arachnida:Trombidiformes); and San Jose scale, Quadraspidiotus perniciosus, Comstock. 

These arthropods damage the tree by either feeding on fluids from the trunk and foliage, or 

consuming foliage. Feeding causes reduced vigor and other complications such as lower yield, 

the spread of disease, and ultimately the untimely death of the tree (Howitt 1993). Treatment 

commonly consists of properly timed foliar applications of organophosphates, carbamates, or 

METI (Mitochondrial complex 1 Electron Transport Inhibitors) acaricides for mites, among others 

(Wise et al. 2022). 

Common diseases that affect Michigan’s apple orchards include: apple Scab, Venturia 

inaequalis Cooke (Wint.); Cedar apple rust, Gymnosporangium juniperi-virginianae, Schwein; 

powdery mildew, Podosphaera leucotricha, (E.S. Salmon); Sooty blotch/Flyspeck 

(fungal/bacterial complex). These diseases cause damage to foliage and fruit leading to 

defoliation, rot, reduced yield and disfigured fruit (Vaillancourt & Hartman, 2000; Strickland et al. 

2020; Marine, et al. 2010; Williamson et al. 2000). Treatment consists of properly timed foliar 

applications of Sterol biosynthesis inhibitor (SBI), Succinate dehydrogenase inhibitor (SDHI), 

Anilino-Pyrimidine (AP), and Quinone outside Inhibitor (Qol) based fungicides, amongst others 

(Wise et al. 2022).  

Necessary spray applications are not limited to pesticides; for instance, apple thinning 

compounds and foliar nutrition are also delivered by sprayer. Delivery of plant nutrients by way 

of foliar application is recommended as it poses the lowest risk of soil and groundwater 

contamination (Murtic et al., 2012). The addition of plant nutrition to an orchard treatment plan is 

2 

 
often necessary to prevent diseases that occur when plant resources are exhausted. One such 

disease is bitter pit which occurs in Honeycrisp apples. Bitter pit occurs when trees are deficient 

in calcium, which can occur when trees are overcropped. This causes apple tissue to become 

soft and discolored, leading to fruit decay (de Freitas et al., 2010). To treat and prevent bitter pit, 

foliar applications of calcium chloride as well as apple thinning are recommended (Biggs et al. 

2015). Fruit thinning reduces the number of apples per tree, preventing overcropping, which can 

lead to damaged limbs, low quality fruit, as well as bitter pit. Thinning can be achieved by 

applying plant growth regulators, as well as other compounds to the canopy, such as the 

combination of naphthalene acetic acid (NAA) and carbaryl (Dennis, 2000). 

1.3.  Agrochemical Applications in Orchards 

The most frequently utilized sprayer in high density apple orchards in the United States 

is the axial-fan Airblast sprayer. This technology is fan-assisted, with adjustable banks of 

pressure-driven emitters whose products are carried throughout the canopy during applications. 

The predecessor to the modern axial-fan Airblast sprayer made its debut in 1949, patented by 

G.W. Daughtry. This early technology was an improvement over the application of dry pesticide 

via aircraft propeller and was shown to successfully control pests in citrus fruit. Post World War 

II, labor shortage, the development of effective pesticides, and the demand for high-quality fruit 

marked the rapid adoption and adaptation of axial-fan Airblast sprayers (Fox, et al. 2008). The 

popularity of the technology was based on its ability to make use of more concentrated forms of 

agrochemicals, which allowed for faster coverage of a larger area before the tank was emptied. 

The axial-fan Airblast also provided better coverage compared to previous application methods 

(Potts,1958). While the axial-fan Airblast sprayer is the most commonly used sprayer in high 

density apple orchards, it may not be the most appropriate.  

The axial-fan Airblast sprayer is often affixed to or drawn by a large farm implement. 

When applying to orchard rows, the axial-fan Airblast sprayer is activated whilst being driven 

down multiple orchard rows. Repeated passage of large equipment can lead to damage to 

plants and infrastructure, as well as soil compaction (Becerra et al. 2010). Compaction can 

disrupt the rooting of plants, decrease oxygen diffusion in soil, and contribute to chemical runoff 

(Défossez et al. 2003). Additionally, the necessity of repeated passage also requires the burning 

of fossil fuels for each pass. Burning fossil fuels releases greenhouse gases such as carbon 

dioxide (CO₂) and methane (CH₄). These Gases are suspected to have already increased 

surface temperatures 1.1 °C above pre-industrial levels. Rising temperatures have led to 

extreme weather, including storms and flooding which displaced a record 7 million people in the 

first half of 2019 (Jackson et al., 2020). While spraying agrochemicals produces lower amounts 

3 

 
of carbon dioxide compared to other agricultural activities, improving the efficiency of all 

activities cannot be overemphasized (Lal, 2004).  

While orchards have undergone drastic changes from large voluminous canopies to 

narrow hedgerow structures, the axial-fan Airblast sprayer remains largely the same (Fox, et al. 

2008). This technology, which creates large plumes of spray, poorly targets the narrower 

orchard rows (Holownicki et al., 2000). Poor targeting may be attributed to the large powerful 

fan as well as the use of emitters originally designed for row crops. Emitters designed to treat 

row crops are made to spray downward onto a flat surface, rather than the complex, vertical 

structure of the high-density orchard canopy (Hoterman et al., 2017; Jones et al., 2000; Owen-

Smith, 2017).  

Common metrics used to assess spray technologies are coverage (percentage of 

surface covered) and deposition (quantity of material is deposited to a surface). These metrics 

are crucial for pest management, as pests can avoid contact with lethal doses of pesticide if an 

application results in inadequate coverage or deposition (Lewis et al., 2020). Axial-fan Airblast 

sprayers have been found to provide variable coverage and deposition: One study found an 

axial-fan sprayer had over-sprayed the lower portions of the canopy, while under-spraying the 

top (Brann, 1956). Greater deposition to the canopy can be achieved with higher wind speeds; 

this, however, leads to greater material ejected from the orchard (Jones et al. 2000). These 

materials ejected from the canopy contribute to off-target drift.   

Drift is the movement of agrochemical dust or droplets through the air to any site other 

than the intended area (EPA.gov). Pesticide drift can lead to both economic and human health 

impacts: a 2011 study found that 37% to 68% of pesticide exposure illnesses reported by US 

agricultural workers were attributed to off-target drift (Lee et al., 2011). In Washington state 

alone, between 2005 and 2017, 56% of agricultural workers that reported illness due to 

pesticide exposure were exposed via off-target drift (Ford et al., 2017). Pesticides have been 

linked to increased chances of mutagenesis via bioassay, correlating agricultural worker 

exposure to pesticide and cancer risk (Blair & Zahm, 1995). Off-target drift produced by a 

sprayer can occur via spraying in improper weather conditions, spraying with an improper boom 

height, or, in the case of the axial-fan Airblast sprayer, inappropriate canopy size in relation to 

the sprayer (Arvidsson et al. 2011; Herrington et al. 1981). Air-assisted sprayers assessed in 

vineyards, which share the hedgerow structures of the high-density orchard, were shown to 

produce increased levels of drift with higher fan speeds (Pergher et al. 1995). 

The axial-fan Airblast sprayer was designed for a different canopy: one taller, with 

greater volume compared to a high-density orchard (8m+ vs. < 4m tall). The combination of the 

4 

 
large fan and narrow hedgerows increases the chances of off-target drift, with higher deposits 

downwind when compared to field crops. Drift associated with the axial-fan sprayer is caused by 

the horizontal vectoring and smaller droplets produced by the large fan, which are more prone 

to off-target drift (Hoterman et al. 2017; Kasnar et al. 2018; Ferguson et al. 2015). An 

experiment quantifying spray coverage provided by an axial-fan Airblast sprayer in hedgerow 

apple trees determined that 35% of a typical low-volume spray had been lost to the ground or as 

drift (Herrington et al. 1981). Additionally, even with the installation of drift-reduction technology, 

the axial-fan Airblast sprayer still shows the potential to produce off-target drift (Zhu et al. 

2006).  

1.4.  Solid Set Canopy Delivery Systems 

A possible solution for the issues of soil compaction, fossil fuel usage, and off target drift 

discussed above is a fixed agrochemical delivery system. These systems were developed to 

improve the application of agrochemicals in high-density trellised fruit crops. They are a fixed 

emplacement consisting of micro-emitters distributed along a trellised row. Emitters are fed by a 

large holding tank via irrigation lines hung on trellis wire and driven by hydraulic pumps; liquid is 

returned from the lines via an air compressor.  

Early research into fixed agrochemical delivery systems began in the 1960s. These 

systems were composed of a network of impact sprinklers hung above the canopy of adult pear 

trees. The intent of the research was to compare the fixed agrochemical delivery system’s pest 

management capabilities to that of an axial-fan sprayer. This early system did not provide 

adequate pest management; however, the authors stated that with optimization the technology 

could be a competitive alternative (Lombard et al., 1966). Later, research into a similar system 

with micro emitters controlled by stop leak devices was proven to provide adequate pest 

management when compared to an axial-fan sprayer in an un-replicated experiment (Angello, 

A., & Landers, A. 2006). Building upon this design, a modern system was developed, known as 

the SSCDS (Solid Set Canopy Delivery System). SSCDS research continued in both high 

density apple orchards, vineyards, comparing the system’s efficacy to axial fan sprayers. 

Experimentation with the SSCDS included, but was not limited to, evaluations of coverage and 

deposition, off- target drift potential, fruit thinning, evaporative cooling, and pest management 

efficacy (Niemann et al. 2016; Sinha et al. 2019; Sinha et al. 2020; Owen-Smith et al. 2019; 

Sahni et al. 2022, Koonter 2023).  

The potential for soil compaction, a threat posed by the axial-fan Airblast sprayer, could 

be drastically reduced. The SSCDS mentioned in this thesis is operated remotely, outside of the 

orchard rows, without the need to pass by every row using a tractor. Reduced fossil fuel 

5 

 
requirements are also a strong probability as the SSCDS is stationary, and the system 

described in this thesis operates for less than 30 s. 

Use of an SSCDS optimized for the narrower rows of a high-density orchard could 

provide treatments better suited to the new hedge-like rows (Owen-Smith et al., 2019). SSCDS 

configurations similar to the in canopy SSCDS described in this thesis, have been shown to 

provide adequate coverage and deposition, as well as adequate pest management (Angello, A., 

& Landers, A. 2006; Sinha et al. 2019; Owen-Smith et al. 2019; Sanhi et al. 2022). 

Unfortunately, with several pieces of equipment littered through the canopy, this configuration 

lends itself to being occluded by foliage or damaged during trellis maintenance or harvest, 

requiring further optimization. Chapter 3 describes pest management and fruit quality outcomes 

in a high-density apple orchard treated by the in canopy and above canopy SSCDS and axial-

fan Airblast sprayer. This experiment compared the in canopy SSCDS with its commercially 

available emitters placed within the canopy, to an above canopy SSCDS with optimized static 

spreaders placed above the canopy along with the axial fan sprayer as a control.  

The SSCDS is a possible drift reducing technology (DRT). Compared to the axial-fan 

Airblast sprayer, the SSCDS does not require a fan as part of its delivery scheme. The absence 

of a fan limits horizontal vectoring and small droplets produced by the turbulence of a fan. The 

SSCDS has been shown to produce significantly less off-target drift both above ground and 

downwind compared to an axial-fan Airblast sprayer in both high density apple orchards and 

vineyards (Sinha et al. 2019; Koonter 2023). Off-target drift has been measured for both an 

axial-fan sprayer and the SSCDS in both high density orchard and vineyard (Sinha et al. 2019; 

Grella et al. 2017; & Pergher et al. 1995; Zhu et al. 2006). However, at the time of writing no 

published data have analyzed drift produced by the SSCDS farther than 4.5 m downwind or 

greater than 0.9 m above ground and directly compared it to an axial-fan sprayer. Chapter 2 of 

this thesis reports on an experiment measuring off-target drift losses above, downwind, and in 

the orchard row at distances up to 64 m downwind of, and up to 12 m above the canopy.  

6 

 
 
 
1.5.  Contents of this Thesis 

The goal of this thesis research was to further explore the feasibility of an alternative 

agrochemical delivery technology, the Solid Set Canopy Delivery System (SSCDS). The 

objectives were to: 

1) Compare the off-target drift potential of an in canopy SSCDS, an above canopy 

SSCDS, and an axial-fan Airblast sprayer (Chapter 2). 

2) Identify if differences exist between coverage and deposition provided by the in 

canopy and above canopy SSCDS (Chapter 3).  

3) Determine if the in canopy and above canopy SSCDS can provide season long pest 

management compared to an axial-fan Airblast sprayer (Chapter 3).  

4) Determine if there are differences between the in canopy and above canopy SSCDS 

and axial-fan Airblast sprayer pertaining to fruit quality (size, weight, fruit per tree) in high 

density orchard after a season of agrochemical applications (Chapter 3). 

7 

 
 
 
 
 
 
 
 
 
 
Chapter 2 - Off-Target Drift Produced by the SSCDS vs. an Axial-Fan Airblast Sprayer 

2.1.  Introduction 

Ideally, agrochemical delivery systems are designed for the crops they serve, optimized 

to deposit plant protection materials in the target area of the crop canopy and in the correct 

amount. When pesticide leaves the treatment area; as off target drift it can lead to a host of 

negative environmental, ecological, and economic impacts. Off target drift in particular carries 

with it the risk of negative environmental, commercial, as well as human health impacts. Drift is 

a significant factor when looking into human exposure and illness. For example, in Washington 

state between 2005 and 2017 56% of agricultural workers and 28% residential pesticide related 

illnesses were found to be the result of drift exposure (Ford, Dunn, Morrison, & Willis, 2017). Off 

target drift can be created from several situations not limited to: spraying in improper weather 

conditions, improper boom height, inappropriate spray implement use, or inappropriate canopy 

size in regards to a fan assisted sprayer (Arvidsson et al. 2011; Herrington et al. 1981).  

Commercial apple production has been steadily moving towards high density narrow 

spaced orchards in order to maximize usable product per hectare and increase the efficiency of 

harvest. The shorter trees and smaller canopy allows for easier pruning as well as a more 

efficient and less laborious harvest as the apples are much more concentrated in the growing 

space. (Robinson, T., 2008) The most common spray technology used in apple orchards are fan 

assisted, axial-fan sprayers. Axial-fan sprayers often have adjustable banks of emitters whose 

products are carried throughout the canopy by the axial-fan sprayer’s powerful fan. The sprayer 

most used in high density apple orchards in the United States is the axial-fan Airblast sprayer 

(Fox, et al. 2008). These sprayers were originally designed for large voluminous canopies in 

orchards populated by much taller trees (8m+ vs. <4m tall). The powerful fan of the axial-fan 

sprayer in the narrow rows of the high-density orchard can not only break sprays into clouds of 

finer particles (which linger minutes to hours in the air), but particles not captured by smaller 

canopies can be carried long distances by wind. Applications in fruit crops by orchard sprayers 

show significantly higher downwind deposits when compared to field crops. This higher 

downwind deposition is mostly caused by horizontally vectored sprays produced by common 

orchard sprayers (Hoterman et al., 2017; Kasnar et al., 2018)  Evaluations of drift, using air 

assisted implements in vineyards, which share the hedgerow like structure of the high-density 

orchard corroborate these findings of increased drift. Losses were shown to be upwards of 63% 

by Pergher et al. (1995) and up to 85% by Viret et al. (2003).   

8 

 
 
 
2.2.  Solid Set Application Technology as a Drift Mitigation Technology 

Drift reduction technology or DRT is technology engineered to reduce the off-target drift 

of applied agrochemicals. Droplet size is a contributor to off target drift, with smaller droplets 

being more prone to drift (Ferguson et al., 2015). Part of DRT involves the modification of 

equipment to increase droplet size without losing efficacy. Wind tunnel trials reveal that 

agrochemical sprays into wind currents resulted in smaller droplets at higher wind speeds 

(Hoffmann et al. 2011). The best practices handbook for axial-fan Airblast use, Airblast 101, 

recommends that to avoid drifting materials through and around the target canopy to slow the 

fan speed of the axial-fan Airblast sprayer. The small droplets influenced by the axial-fan Airblast 

sprayer’s fan are more prone to drift as their small settling velocities can cause them to be 

deflected off from or carried past the target entirely (Deveau 47). 

An alternative technology that does not make use of velocitized air is the Solid Set 

Canopy Delivery System (SSCDS). These systems are an in row, fixed emplacement shown to 

reduce off target drift when compared to axial fan spray applicators (Sinha, 2019). These 

systems also remove the need to drive heavy machinery into the treatment area to apply 

agrochemicals. This may mitigate soil compaction, equipment damage, and damage to the crop.  

Several of these in row, in canopy systems have been evaluated in high density orchards and 

vineyards. Agnello & Landers, (2006) developed an SSCDS with a mobile pumping system that 

showed equivalent pest management to an axial-fan sprayer when used in high density 

orchards. Similar equivalent pest management to an axial-fan sprayer in a high-density apple 

orchard was also determined by Owen-Smith (2019) and Sahni (2022). Carpenter, Reichard, & 

Wilson (1985) developed costs of infrastructure, data on nozzle selection, and analyzed 

economic feasibility of a proposed SSCDS, determining the technology could be feasibly 

implemented. Sinha (2019) determined the drift potential of an SSCDS and compared it to that 

of an axial-fan sprayer in a vineyard. Near field drift analysis (up to 4.5 m downwind) showed 

that depositions were higher downwind of the vineyard when using the axial fan sprayer. Sinha 

(2019) described the ability to automate an SSCDS removing the applicator from the field, 

reducing the probability of worker exposure. Thus, SSCD has the potential to reduce the 

probability of off-target exposure by removing personnel from the field and exposures caused by 

drifting agrochemicals. 

Exploration of different emitters and emitter placement would allow SSCDS technology 

to be adapted to various crops and growing systems. Differing emitters and their placement can 

impact the coverage and deposition provided by the SSCDS as shown by Ranjan et al. (2021).  

An SSCDS with emitters distributed throughout the canopy as described by Owen-smith (2019) 

9 

 
has the potential to be damaged by harvesting equipment or occluded by foliage. Variability in 

droplet sizes produced by emitters can increase the chances of drift with smaller droplet sizes 

more prone to becoming off target drift (Fox, R., et al., 2008; Ferguson et al. 2015). Evaluating 

an SSCDS with emitters overhead producing a larger droplet spectra may reduce both drift and 

potential equipment damage.  

The literature available provides data on the behavior of materials distributed by axial-

fan sprayers and SSCDS including off-target drift in vineyards and in high density apple 

orchards. (Sinha et al. 2019; Grella et al. 2017; & Pergher et al. 1995). However, data analyzing 

the behavior of drift farther than 4.5 m downwind and distances greater than 0.9 m above 

ground using an SSCDS have not been published. The evaluations of this study monitor in and 

near field drift as well as drift 12m above the ground and up to 64 meters downwind of the 

orchard along with losses to the ground in the drive row.  

I hypothesized that two SSCDS configurations, one that is placed within the canopy and 

has a smaller droplet spectrum, and a second with micro sprayers solely above the canopy with 

a larger droplet spectrum, would produce less off-target drift than a Rears PB533N axial-fan 

Airblast sprayer. I also hypothesized that the above canopy SSCDS will produce less downwind 

drift than both the axial-fan sprayer and the in canopy SSCDS. In-row losses to ground were 

hypothesized to show greater deposition for both the above canopy and in canopy SSCDS 

configurations. 

2.3.  Materials and Methods 

Two drift studies were conducted over the course of two years to determine the drift 

potential of an above canopy and in canopy SSCDS compared to a Rears PB533N axial-fan 

Airblast sprayer, referred to as Airblast in this document (Rears Manufacturing company, 

Coburg, OR, USA).  In 2017 off-target drift produced by a Rears axial-fan Airblast Sprayer was 

compared to an SSCDS composed of micro sprayers distributed throughout the canopy (in 

canopy SSCDS). This experiment makes use of elements from similar experiments assessing 

off target drift and deposition (Khot et al., 2012; Salyani et al., 2000; Zhu et al., 2006). 

In the 2018 experiment, the drift produced by an SSCDS with rotary emitters distributed 

above the canopy (above canopy SSCDS) was compared to the in canopy SSCDS and a Rears 

Airblast sprayer. The above canopy SSCDS was designed to explore the possibility of alternate 

emitter placements to reduce the probability of damage by removing emitters from within the 

canopy to above it, moving them out of the reach of equipment and contact with orchard 

personnel.  

10 

 
Experimental site. Drift evaluations took place at the Michigan State University Clarksville 

Research Center Clarksville, Michigan, United States (42.873933081589804, -

85.25857635397287) in a high-density apple orchard. The treated area consisted of four 60 m 

rows of densely planted Honeycrisp apple trees (Malus domestica) approximately 2.5 m tall with 

0.91 m spacing between trees and 3.66m row spacing. The tree rows were planted lengthwise, 

latitudinally (North/South) with the headland to the North side of the orchard. Upwind and 

downwind sides of the orchard facing East and West respectively. The area immediately 

downwind of the testing site, to the West, was mowed and field conditions were in accordance 

with ISO protocol (ISO, 2005).  

Weather Data. Weather data was recorded per ISO 22522 (ISO, 2007) using an Airmax 150X 

(Airmar, Milford, New Hampshire, USA) weather station measuring temperature, relative 

humidity, windspeed, and wind direction. 2017 and 2018 Weather data is as follows: 

Temperature °C, Relative Humidity, Wind Speed minimum, Wind Speed maximum, Wind 

direction, Wind speed average. (Table 2)  

General SSCDS design. The general SSCDS plumbing, and design used in these experiments 

were described in detail in Owen-Smith et al. (2019). The systems evaluated in my experiments 

consisted of 2.54cm diameter polyethylene irrigation lines (Toro, Bloomington, Minnesota, USA) 

hung at the top of each orchard row (2.5m), later called the application line, pressurized by a 

212cc 598 lpm water pump (Loncin Motor Company, Chongqing, China) fed from a 1987 liter 

reservoir tank (Norwesco, St. Bonifacius, Minnesota, USA). Each application line supplied a 

series of emitters controlled by a 220.63kpa leak prevention device (Jain irrigation, Jalgaon, 

Maharashtra, India). The 2.54cm lines at the top of the orchard terminated into a parallel 1.91cm 

diameter polyethylene irrigation tube (Toro, Bloomington, Minnesota, USA) hung from the 

bottom trellis (0.9m) of the orchard row, the return line. This polyethylene tube terminated at the 

reservoir tank and served as a return line for unused material remaining in the system. The 

system's pneumatic pressure was driven by a Doosan 185 SCFM commercial air compressor 

(Doosan, Seoul, South Korea). 

SSCDS application. The SSCDS operated in phases utilizing internal pressures to perform 

different actions. The first phase of this Solid Set Canopy Delivery System application was a 

low-pressure filling of the system (< 220.63 kpa) using two pumps, fed by a reservoir tank. The 

second phase was the application phase where the system was brought to high pressure 

(344.74 kpa), opening the leak prevention devices, and applying the spray treatment. 

Application rate was managed using time of application via stopwatch by which the number of 

emitters were active per hectare. Once desired application rate was reached the pumps were 

11 

 
deactivated bringing the system below 220.63kpa closing the leak prevention devices and 

stopping the application. Phase three: return, was the return of remaining spray material from 

the irrigation lines back to the reservoir tank. During pressurization and application, a closed 

valve separates the application line from the return line. The large construction grade air 

compressor was plumbed into the 2.54cm application line, the valve between the application 

and return line was opened and air was applied at <220.63 kpa keeping the leak prevention 

devices closed and returning unused spray material back to the reservoir tank.  

SSCDS and Airblast Systems. The in-canopy SSCDS emitters evaluated in the 2017 and 

2018 drift experiments were composed of a Jain 7110 bridge with Jain 3044 impactors (Jain 

irrigation, Jalgaon, Maharashtra, India) installed within the canopy with a flow rate of 0.660 l/m 

and were placed every 0.609m with one emitter on the top trellis and a pair mid canopy, 

approximately 2.5 m and 0.9 m above the orchard floor, respectively.  Each assembly of three 

emitters was controlled via a Jain leak prevention device. The above canopy SSCDS was 

composed of a Jain green fluid chamber fitted with a Jain gray rotary emitter attached to the 

same Jain leak prevention devices, one controlling each emitter. Each emitter had a flow rate of 

1.59 lpm and were placed every 0.94 m on the top trellis wire, 2.5 m above the orchard floor. 

The Airblast used Teejet DC23 hollow cone, ceramic discs and whirl plates (Teejet 

Technologies, Glendale Heights, IL, USA). Calibration of the Airblast was performed with 10 

nozzles at 69 kpa with a tractor speed of 6.1 km/h yielding an application rate of 458 l/ha. In 

2017 and 2018, the Airblast sprayer was used to apply test solution to the experimental plots 

consisting of four rows, with both nozzle banks active when between rows, switching off the 

downwind facing bank of nozzles when applying to the orchard edge row.  

The test solution consisted of water, 0.1% m/v Keystone pyranine 10G dye (Keystone 

Inc., Chicago, Illinois, USA), and 0.1% v/v commercially available non-ionic surfactant (Ragan & 

Massey Inc., Ponchatoula, Louisiana, USA). Treatment types (Airblast, in canopy SSCDS, and 

above canopy SSCDS) were applied randomly and independently from one another with 

samplers collected after each application.  The 2017 experiment applied test solution for the 

Airblast and in canopy SSCDS three and four times respectively over the course of two days 

June 27, 2017 and July 14, 2017. The 2018 experiment applied test solution from the Airblast 

three times, in canopy SSCDS four times, and above canopy SSCDS four times, also over the 

course of two days August 9, 2018 and August 14, 2018. In order to develop a calibration curve 

a 20ml sample of the dye mixture was taken from the tank. For 2017 only one sample was taken 

for the entire experiment. In 2018 a sample was collected after every spray application.  

12 

 
Measuring in-row losses to ground. The 2017 and 2018 in row losses to ground 

measurements were collected using five parallel rows of 15.24 cm diameter polystyrene Petri 

dish lids (VWR International, Randor, Pennsylvania, USA), opening facing skyward and 

distanced 6m apart tracking South, away from the headland. Dishes were placed 0.5m apart in 

seven positions spanning East and West from the center of the tree row immediately east of the 

most westerly row (Fig 1 & 2). Positions were at 0m (Center)(center of tree row), ±.5m (Up 1, 

Down 1)(East and West drip edge of tree row canopy), ±1m (Up 2, Down 2), and  ±1.5m(Up 

Center, Down Center)(the center of the drive rows East and West of the tree row) (Fig 4).  In 

2018 a 12.7 cm diameter piece of circular filter paper (Whatman, Maidstone, United Kingdom) 

was added to each Petri dish allowing for improved droplet collection during treatments. This 

paper impeded droplet reflection from the Petris dish’s smooth surface. Collection of the ground 

loss Petri dishes was performed with gloved hands. Plates were rejoined with their associated 

half, sealed with Parafilm ®, and placed in coolers containing dry ice and insulated with canvas 

for transport. Post transport, samples were stored at -20°C until processed. 

Vertical drift sampler collection. Vertical off target drift was measured by using polyester 

speargun string (Sgt. Knots, Statesville, North Carolina, USA) anchored to 8m and 12m tall 

telescopic poles (Jackite Inc., Virginia Beach, Virginia, USA) from 0m above the ground to either 

8m or 12 m above the ground. The 2017 vertical flux deposition was measured via three 8m tall 

poles spaced 30 m apart (North/South) 0m downwind (West) of the last downwind orchard row 

(Figure 2.1). Vertical drift in 2018 was measured from three 8m poles in the same positions as 

2017 with the addition of two 12m tall poles spaced 10m apart 8m downwind from the last 

downwind orchard row. (Figure 2.1 and 2.2) After each spray application, strings were carefully 

collected by two individuals with gloved hands. Strings were cut into 1 m segments and 

individually placed in 20 x 12 cm 6 mil plastic bags (US Plastics, Lima, Ohio, USA). Samples 

were placed in a cooler with dry ice, insulated by heavy canvas for transport until stored in 

freezers at -20°C. 

Downwind drift sampler collection. The 2017 and 2018 downwind drift evaluations were 

assessed with 5 parallel rows placed north/south; samplers spaced 6m apart (North/South). 

Samplers consisted of 10.2 × 10.2 cm × 0.025cm Duralar mylar cards (Grafix, Cleveland, Ohio, 

USA ) oriented parallel to the ground positioned incrementally downwind (West) of the orchard 

at distances of 0m, 1m, 2m, 4m, 8m, 16m, 32m and 64m as described in ISO standard 22866 

and ASABE S561.1 (Figures 2.1 and 2.2). Mylar cards were harvested by gloved hands and 

placed into individual 20 × 12 cm 6 mil plastic bags. Bagged mylar cards were then placed in a 

cooler of dry ice insulated by heavy canvas for transport, then stored at -20°C until processed.  

13 

 
Figure 2.1 - Diagram of row, sampler placement, and sampler type for the 2017 Drift 
assessment. Arrows denote the direction of Airblast sprayer travel.  

Figure 2.2 - Diagram of row, sampler placement, and sampler type for the 2018 Drift 
assessment. Arrows denote the direction of Airblast sprayer travel.  

14 

 
 
 
Figure 2.3 - Emitter placement in canopy and emitters. In canopy SSCDS, Jain 7110 
nozzle body and impactor (A) Above canopy SSCDS, Jain green spinning emitter and 
stop leak device (B).  

Figure 2.4 - Petri dish placement for collecting in row losses to ground.  

15 

 
 
 
 
 
 
 
Fluorometry Data Analysis. Fluorescence detection procedures are based on previous 

publications (Khot et al. 2012 & Salyani et al., 2000). A calibration curve was developed using 

tank samples collected during the experiments. The 2017 experiment made use of a single tank 

sample while the 2018 study used 11 (Four each for in canopy and above canopy SSCDS and 

three for the Airblast). Tank samples were diluted by a factor of 2 with 50% isopropyl alcohol 

water solution and then serially diluted by factors of 10, 100, 1000, 10,000 and 100,000 which 

corresponded to 0.5 g L−1, 0.05 g L−1, 0.005 g L−1, 0.0005 g L−1, 0.00005 g L−1 , respectively. 

Each one of these dilutions was added to three sequential chambers of a Trueline TR5002 24 

well plate then analyzed with the Biotek Synergy HT spectrophotometer, excitation 400/30 and 

emission 540/25. The three sequential wells relative fluorescence units were averaged and the 

resulting number equaling an average of relative fluorescence units. Fluorescence values from 

the tank samples were used to create a nonlinear equations for each application that were used 

to estimate the mass of application in g cm⁻². 

Table 2.1- Nonlinear equations generated from tank sample fluorescence, used to 
generate flux as a percent of the applied rate from deposition on samplers.  

Year  Run 

Treatment  

Equation  

2017 

2018 

In Canopy 

All 

SSCDS, 

(7 x 10⁻¹⁶X³) + (1 x 10⁻¹²X²) + (7 x 10-9X) + (7 x 10-8) 

Airblast  

Above Canopy 

SSCDS 

In Canopy 

SSCDS 

Airblast 

Airblast 

In Canopy 

SSCDS 

Airblast 

(2 x 10⁻¹⁵X³) + (9 x 10-13X2) + (1 x 10-8X) + (2 x 10-8) 

(4 x 10⁻¹⁵X³) - (6x10-13X2) + (3 x 10-8X) + (5 x 10-8) 

(5 x 10⁻¹⁶X³) + (4 x 10⁻¹²X²) + (1 x 10-8X) + (5 x 10-8) 

(5 x 10⁻¹⁶X³) + (4 x 10⁻¹²X²) + (1 x 10-8X) + (5 x 10-8) 

(2 x 10⁻¹⁵X³) - (4 x 10⁻¹³X²) + (2 x 10-8X) + (3 x 10-8) 

(4 x 10⁻¹⁵X³) - (1 x 10⁻¹¹X²) + (2 x 10-8X) + (3 x 10-8) 

Above Canopy 

SSCDS 

(-5 x 10⁻¹⁶X³) + (7 x 10⁻¹²X²) + (2 x 10-8) 

1 

2 

3 

4 

5 

7 

8 

16 

 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
Dye release from samplers. The samplers placed for in-row ground loss, vertical drift, and 

downwind drift, the dye was released from the petri-dishes, strings, and mylar cards 

respectively, brought into solution using 10% v/v of 90% Isopropyl alcohol mixture in distilled 

water. All samples were first thawed, and following thaw a 20 ml aliquot of the alcohol mixture 

was added to Petri dishes, and bags containing mylar cards and strings. Bags were agitated 

ensuring all exposed surfaces were rinsed with the alcohol mixture. Petri dishes from the 2017 

experiment were rotated ensuring all dye exposed surfaces were rinsed. The 2018 Petri dishes 

with the added filter paper were rotated exposing all surfaces to the solution and the filter paper 

was repeatedly folded and squeezed by a pair of sterilized forceps releasing trapped solution 

into the Petri dish.  

Fluorescence Detection. Detecting fluorescence from drift samplers used 6ml of the 

dye/release agent solution. This solution was removed from the string and mylar bags along 

with the Petri dishes in 2 ml aliquots and pipetted into three sequential chambers of a Trueline 

TR5002 24 well plate (Corning, New York, USA). Using the spectrophotometer methods 

described above for the tank samples, three sequential wells were used to generate an average 

of relative fluorescence units (RFU) per sample. Using the calibration curve produced by the 

fluorescent dilutions from each independent tank sample each drift sample was converted from 

RFU’s to grams per ml per meter squared using the two-dimensional area of the respective 

sampler. The grams per ml per meter squared detected on the sampler was then converted to a 

proportion of the applied rate by dividing the sampler deposition by the application rate in grams 

per meter squared at the known tank concentration.  

Deposition Statistical Analysis. Deposition data was analyzed using RStudio (1.4.1717 ver. 

3.4.0, R Foundation for Statistical Computing, Vienna, Austria) using a factorial analysis of 

variance (ANOVA) and post-hoc analysis using Tukey’s HSD (honest significant difference) with 

a significance level of α=0.05. Separate models were run for downwind drift, vertical drift, and 

in-row losses to ground for the response variable of proportion of applied rate. The proportion of 

the applied rate as flux was derived based on a protocol by Fritz et al. (2008), by dividing dye 

deposition to the sampler in grams per meter squared by the applied rate assuming 100% 

deposition to the sampler at the dye concentration of 0.1%. Arcsine transformations were 

carried out to ensure that data met assumptions of normality and heteroskedasticity. Treatments 

were in canopy SSCDS, above canopy SSCDS, and the Airblast sprayer. Distance from center 

of row (in row losses to ground), distance downwind from orchard (downwind drift), and distance 

above ground (vertical drift) were used as additional fixed factors. Akaike information criterion 

via the AICcmodavg R package was used to select the best fit between an interactive effect 

17 

 
model or an additive effect model. Additive models were selected for 2017 vertical drift and in-

row ground loss as well as 2018 8m and 12m vertical drift. Interactive models were selected for 

2017 downwind drift as well as 2018 in-row ground loss and downwind drift. 

2.4. Results and Discussion 

Table 2.2 - Mean temperature, relative humidity, windspeed, and wind direction during 
applications in 2017 and 2018.  

Application 

Treatment  

°C  %RH 

Windspeed ms-¹ 

Direction 

2017 

Min 

Max  Mean 

1 

2 

3 

4 

5 

6 

7 

In Canopy  

21.6 

45.0 

In Canopy  

21.3 

45.0 

Airblast 

21.7 

42.0 

Airblast 

21.8 

42.0 

In Canopy  

22.7 

50.0 

In Canopy  

23.4 

57.0 

Airblast 

22.8 

55.0 

Mean 

In Canopy  

22.3 

50.7 

Airblast 

22.1 

46.3 

2018 

1 

2 

3 

4 

5 

7 

8 

9 

10 

11 

12 

Above Canopy  26.1 

69.0 

In Canopy  

25.6 

70.0 

Airblast 

27.2 

65.0 

Airblast 

27.8 

63.0 

In Canopy  

26.1 

67.0 

Airblast 

29.4 

60.0 

Above Canopy  31.1 

55.0 

In Canopy  

30.0 

48.0 

In Canopy  

31.1 

48.0 

Above Canopy  30.0 

50.0 

Above Canopy  28.3 

50.0 

18 

3.6 

2.5 

2.0 

2.4 

1.1 

3.1 

1.3 

2.6 

1.9 

2.5 

1.1 

1.1 

1.3 

2.7 

0.4 

1.4 

1.8 

1.8 

2.7 

3.0 

4.9 

4.4 

5.8 

5.5 

2.2 

4.8 

3.8 

4.1 

5.1 

3.2 

2.8 

1.3 

1.3 

5.0 

0.7 

2.7 

4.0 

4.0 

5.1 

4.9 

4.2 

3.4 

3.9 

4.0 

1.7 

3.9 

2.6 

3.3 

3.5 

2.9 

1.9 

1.2 

1.3 

3.8 

0.6 

2.1 

2.9 

2.9 

3.9 

4.0 

WNW 

W 

WNW 

WNW 

WNW 

W 

WNW 

WSW 

WNW 

WSW 

W 

W-WSW 

WSW 

W-WNW 

WSW 

SW-WNW 

W 

W-WSW 

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2.2 (cont’d) 

Mean 

In Canopy  

28.2 

61.7 

Above Canopy  28.9 

56.0 

Airblast 

28.2 

62.7 

1.9 

2.4 

0.9 

3.9 

4.0 

1.1 

2.9 

3.2 

1.0 

2017 In row losses to ground.  A significant difference was detected for the treatment main 

effect (Airblast and in canopy SSCDS) (F1,234=22.38, p<.001), but not for the in row sampler 

position (F6,234=0.42, p=0.87) (Table 3). Mean flux for the Airblast treatment (x̅  =15.67% ±3.21%) 
did not statistically differ from the in canopy SSCDS (x̅  = 10.73% ± 6.22%). 

Figure 2.5 - 2017 Mean ± SEM flux as a percent of the applied rate as off target drift 
losses to ground. Differing letters signify statistically significant differences between 
treatments at α=0.05. 

19 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2.3 - 2017 Mean ± SEM flux as a percent of the applied rate as off target drift losses 
to ground by treatment and field location. Differing letters signify statistically significant 
differences between treatments and field position at α=0.05. 

Treatment 

Field Location 

Mean Flux (%) 

±SEM 

In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 

Center 
Down 1 
Down 2 
Down center 
Up 1 
Up 2 
Up Center 
Center 
Down 1 
Down 2 
Down center 
Up 1 
Up 2 
Up Center 

10.17% 
9.82% 
12.68% 
10.11% 
10.06% 
10.36% 
11.91% 
19.82% 
18.98% 
9.58% 
9.98% 
19.53% 
17.63% 
14.19% 

4.76% 
4.79% 
8.25% 
8.12% 
4.18% 
5.43% 
7.20% 
3.47% 
3.21% 
1.05% 
1.65% 
2.45% 
1.45% 
0.90% 

a 
a 
a 
a 
a 
a 
a 
b 
b 
b 
b 
b 
b 
b 

2018 In row losses to ground. Significant differences were detected in treatment (Airblast, in 

canopy SSCDS, above canopy SSCDS), field position, and interaction factor 

(F2,364=337.32,p<.001, F6,364=7.97, p<.001, and F12,364=7.60, p<.001 respectively). (Table 4)  

Average flux for the Airblast treatment was lower than the above canopy SSCDS for all 

positions. Comparisons of the upwind and downwind samplers treated by the Airblast 

determined that samplers upwind received on average lower flux than those downwind. The 

greatest average flux for the Airblast samplers was detected in the Down 1 position (x̅ = 24.17% 
± 5.34% flux). In contrast at this same position the above canopy SSCDS deposited (x̅ = 54.25% 
± 12.10% flux). 

Average flux was greatest for the in canopy SSCDS in the Up 1 position at (x̅ = 36.21% ± 

8.27% flux) where it was the greatest of all three treatments at that position. Overall, the 

average flux deposited to the in canopy SSCDS samplers did not significantly differ across all 

positions. The in canopy SSCDS shows similar flux averages to the above canopy at the 

extents of the testing area in the Up Center and Down Center positions.  

The above canopy SSCDS deposited the greatest amount of material of all the 

treatments, at the Center, Down 1, and Down 2 positions; these positions not only demonstrated 

20 

 
 
 
the greatest average flux for the above canopy samplers but of all treatments and positions. (𝑥= 

53.93% ± 8.51%, 𝑥= 54.25% ± 12.10%, 𝑥= 50.60% ± 14.45% respectively). 

Figure 2.6 - 2018 Means ± SEM flux as a percent of the applied rate as off target drift 
losses to ground collected in various positions within the last two downwind orchard 
rows.  Differing letters generated by Tukey's honest significant difference signify 
significant differences at α=0.05. 

Table 2.4 - 2018 Mean ± SEM flux as a percent of the applied rate as off target drift losses 
to ground by treatment and field location. Differing letters signify statistically significant 
differences between treatments and field location at α=0.05. 

Treatment 

Field Location  Mean Flux (%) 

±SEM 

Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 

Center 
Down 1 
Down 2 
Down Center 
Up 1 
Up 2 
Up Center 
Center 
Down 1 
Down 2 
Down Center 
Up 1 

8.51% 
12.10% 
14.45% 
12.84% 
6.54% 
8.72% 
6.81% 
8.71% 
6.30% 
8.27% 
15.12% 
8.27% 

a 
ab 
ab 
abc 
abc 
abc 
bcd 
cde 
cdef 
cdef 
cdef 
cdef 

53.93% 
54.25% 
50.60% 
31.54% 
16.09% 
11.97% 
35.41% 
24.89% 
18.73% 
23.59% 
27.00% 
36.21% 

21 

 
 
 
 
 
 
 
Table 2.4 (cont’d) 

In canopy 
In canopy 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 

Up 2 
Up Center 
Center 
Down 1 
Down 2 
Down Center 
Up 1 
Up 2 
Up Center 

31.88% 
26.71% 
23.03% 
24.17% 
18.99% 
6.49% 
8.14% 
5.90% 
9.87% 

12.51% 
15.10% 
6.97% 
5.34% 
4.32% 
1.88% 
3.75% 
2.38% 
3.27% 

cdef 
cdef 
cdef 
cdef 
def 
ef 
def 
ef 
f 

In row losses to ground comparisons. Overall mean flux values for 2017 were lower than 

those from 2018, this was likely due to the addition of the filter paper to the 2018 Petri dishes 

allowing for better droplet adhesion to the samplers. 2018 Airblast measures may also have 

been affected by windspeeds as they were lower on average compared to the SSCDS 

configurations. The 2017 Airblast and in canopy SSCDS mean flux did statistically differ 

between treatments however neither differed between sampler positions. The differences in 

2018’s experiment from 2017 allowed insight into the different deposition behaviors of the 

SSCDS configurations compared to the Airblast. In 2018, the Airblast was responsible for the 

lowest losses to ground, with average flux values downwind of the Center position with lower 

mean flux than both SSCDS systems. Mean windspeeds during axial-fan Airblast applications 

were lowest of the three treatments possibly effecting the percentage of flux detected. The 

Center position and the first two downwind positions show no statistically significant differences 

between both the in canopy SSCDS and the Airblast sprayer. By far the greatest flux via 

deposition to the ground was associated with the above canopy SSCDS. This is likely due to the 

greater flow rate and larger droplets produced by the emitters associated with the above canopy 

SSCDS. The greater mass of the droplets, thus their resistance to drift explain why the majority 

of the deposition remained in the row center and the first two downwind positions. The lower 

mean flux values of the in canopy SSCDS is hypothetically due to the lower flow rate and 

smaller droplet size, allowing for more material to distribute more evenly across the rows. While 

the mean depositions for the in canopy SSCDS are higher upwind they are not statistically 

significantly different than those immediately downwind. This pattern alluded to a somewhat 

more evenly distributed spray across the sampling area compared to the Above canopy 

SSCDS. Both SSCDS configurations had higher mean flux than the Airblast in upwind positions. 

The Airblast’s fan, able to move large volumes of air containing velocitized droplets may be the 

reason for the lower deposits upwind, as the spray material influenced by the Airblast is more 

22 

 
 
prone to downwind drift. This could also explain the lower overall deposition for the Airblast 

sprayer, as the material was dispersed and vectored away from the orchard rows.  

2017 Vertical off target drift. Significant differences were detected for treatment (Airblast, in 

canopy SSCDS), and height above ground main effects. (F1,135=148.73, p<.001, F7,135=7.53, 

p<.001 respectively) (Table 2.5).  

The Airblast deposited higher mean flux at every height sampled compared to the in 

canopy SSCDS. Mean Airblast flux peaked at 4m above ground at (x̅ = 25.11% ± 4.67%) with the 
Airblast minimum mean flux value at 8m above ground ( x̅ = 3.46% ± 1.31%).  

In canopy SSCDS mean flux declined with height, with similar mean values when 

comparing each height. (Table 5). In canopy SSCDS mean flux was greatest for the first two 

meters above ground, with nearly equivalent mean values (x̅ = 3.74 ± 1.44% and x̅ = 3.73 ± 
0.95% respectively) that were approximately eight times lower than the mean flux determined 

for Airblast at these heights (x̅ = 23.15 ± 7.47% and x̅ = 21.05% ± 6.52% respectively).   

Figure 2.7 - 2017 Mean ± SEM flux as a percent of the applied rate as off target drift 
produced by the in canopy SSCDS and axial-fan Airblast sprayer from 1 m to 8 m above 
the orchard floor. 

23 

 
 
 
 
Table 2.5 - Mean ± SEM flux as a percent of the applied rate as off target drift from 1 m to 
8 m above the orchard floor. Differing letters signify statistically significant differences 
between treatments and heights sampled at α=0.05.  

Treatment 

Height (m) 

Mean %Flux 

±SEM 

In Canopy  
In Canopy  
In Canopy 
In Canopy  
In Canopy  
In Canopy  
In Canopy  
In Canopy  
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 

1 
2 
3 
4 
5 
6 
7 
8 
1 
2 
3 
4 
5 
6 
7 
8 

3.74% 
3.73% 
3.11% 
2.12% 
1.54% 
1.28% 
0.85% 
0.50% 
23.15% 
21.05% 
20.28% 
25.11% 
14.86% 
9.78% 
5.13% 
3.46% 

1.44% 
0.95% 
0.61% 
0.75% 
0.65% 
0.53% 
0.46% 
0.37% 
7.47% 
6.52% 
6.79% 
4.67% 
7.72% 
3.11% 
1.57% 
1.31% 

cde 
cde 
cde 
de 
e 
e 
e 
e 
a 
ab 
ab 
a 
abc 
abcd 
bcde 
cde 

2018 Vertical Off target drift: 8m collection poles. Significant differences were detected for 

treatment (Airblast, in canopy SSCDS, above canopy SSCDS) and height main effects (1m-8m) 

(F2,238=176.4, p<.001, F7,238=4.42, p<.001 respectively.) (Table 6). 

Airblast mean flux peaked at 3m (𝑥= 65.67% ± 19.72%), this value is approximately thirty 

times and fourteen times respectively, larger than the mean values for the in canopy and above 

canopy SSCDS at this height (x̅ =5.12% ± 1.57% and x̅ =2.03% ± 1.32% respectively). Airblast 
showed greater mean flux when compared to the above canopy and in canopy SSCDS at all 

heights. 

In canopy SSCDS mean flux values were inversely related to height with the greatest 

mean flux value at 1m above ground, (x̅ =10.88% ± 4.75%) and the lowest at 8m above ground 
(x̅ =0.34% ± 0.16%). In contrast, at 1m the Airblast’s mean deposition value was nearly four 
times that of the in canopy SSCDS (x̅ =44.95% ± 13.49%). The in canopy SSCDS average flux 
reduced with height dropping below 1% average flux at 6m and above.  

Similar to the in canopy SSCDS, the above canopy SSCDS mean flux was lower at each 

height sampled compared to the Airblast. The greatest mean flux for the above canopy SSCDS 

occurred at 1m above ground (x̅ =13.35% ± 7.34%) where it was approximately 3% larger than 

24 

 
 
 
the in canopy SSCDS and approximately 32% lower than the mean flux of the Airblast at this 

height (x̅  =10.88 ± 4.75% and x̅ =44.95% ± 13.49% respectively). The above canopy SSCDS 
minimum mean flux was detected at 8m above ground with the lowest mean flux of the three 

treatments (x̅ =0.06% ± 0.05%). In contrast, at the same height of 8m the average flux for the 
Airblast was (x̅ =29.57% ± 19.70%). 

Figure 2.8 - 2018 Mean ± SEM flux as a percent of the applied rate as off target drift 
produced by the Above and in canopy SSCDS as well as Airblast sprayer from 1m to 8m 
above the orchard floor. 

Table 2.6 - 2018 Mean ± SEM flux as a percent of the applied rate as vertical off target drift 
by height from ground from 1 m to 8 m above the orchard floor. Differing letters signify 
statistically significant differences between treatments at heights at α=0.05.  

Treatment 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 

Height (m)  Mean %Flux 

1 
2 
3 
4 
5 
6 
7 
8 

13.35% 
7.12% 
2.03% 
0.68% 
1.80% 
0.29% 
0.33% 
0.06% 

±SEM 
7.34% 
4.84% 
1.32% 
0.36% 
2.67% 
0.38% 
0.44% 
0.05% 

cde 
de 
de 
e 
e 
e 
e 
e 

25 

 
 
 
 
 
 
 
Table 2.6 (cont’d) 

In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 

1 
2 
3 
4 
5 
6 
7 
8 
1 
2 
3 
4 
5 
6 
7 
8 

10.88% 
9.85% 
5.12% 
2.65% 
1.69% 
0.94% 
0.49% 
0.34% 
44.95% 
59.15% 
65.67% 
61.28% 
55.08% 
45.64% 
40.92% 
29.57% 

4.75% 
4.88% 
1.57% 
0.92% 
0.75% 
0.60% 
0.26% 
0.16% 
13.49% 
18.28% 
19.72% 
21.39% 
22.44% 
23.45% 
24.06% 
19.70% 

cde 
cde 
de 
de 
e 
e 
e 
e 
ab 
ab 
a 
a 
ab 
ab 
abc 
bcd 

2018 Vertical Off target drift: 12m collection poles. Statistically significant differences were 

detected in treatment (Airblast, in canopy SSCDS, above canopy SSCDS), and height from 

ground (1m-12m) (F2,250=196.75, p<.001, F11,250=6.70, p<.001 respectively).  

Mean flux values for the Airblast were greater than both SSCDS configurations at all 

sampled heights. Mean flux detected for the Airblast peaked at 4m and was lowest at 12m 

(𝑥=30.74% ± 13.90% and 𝑥=3.57% ± 3.02% respectively)(Table 7). Airblast flux values did not 

significantly differ from 7m to 12m above ground. 

Mean flux values associated with the in canopy SSCDS peaked at two meters (𝑥=3.55% 

± 1.07%), a significantly lower value than the Airblast at this height (𝑥=28.36% ± 8.32%). Mean 

flux values associated with the in canopy SSCDS decreased with ascending height dropping to 

zero at 10m above ground. The in canopy SSCDS was determined to have no significant 

differences between flux values at all sampled heights. (Table 7) The in canopy SSCDS and the 

above canopy SSCDS samples neared values close to zero at 9m and 8m above ground 

respectively.   

Above canopy SSCDS average flux values were lower for all heights when compared to 

average flux values associated with the Airblast and in canopy SSCDS up to 9m where both 

SSCDS systems average flux neared zero. Above canopy SSCDS flux values were highest 

nearest the ground at 1m. Neither the in canopy or above canopy SSCDS statistically differed at 

any of the heights sampled. Contrasted with the Airblast, the Above canopy SSCDS mean flux 

26 

 
 
peaked at 1m above ground at (𝑥=0.96% ± 0.55%) with the Airblast mean flux equal to 

(𝑥=23.92% ± 6.13%) at the same height (Table 7). 

Figure 2.9 - 2018 Mean flux as a percent of the applied rate as off target drift losses from 
1m to 12m above the orchard floor. 

Table 2.7- 2018 Mean ± SEM flux as a percent of the applied rate as off target drift by 
height from ground using 12 m poles. Differing letters signify statistically significant 
differences between treatments at heights sampled at α=0.05. 

Treatment 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
In canopy 
In canopy 
In canopy 

±SEM 
0.56% 
0.44% 
0.40% 
0.28% 
0.13% 
0.10% 
0.10% 
0.07% 
0.00% 
0.00% 
0.00% 
0.00% 
0.92% 
1.07% 
0.89% 

ef 
ef 
ef 
f 
f 
f 
f 
f 
f 
f 
f 
f 
def 
def 
def 

Height (m)  Mean % Flux 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
2 
3 

0.93% 
0.74% 
0.70% 
0.54% 
0.23% 
0.10% 
0.10% 
0.07% 
0.00% 
0.00% 
0.00% 
0.00% 
3.22% 
3.55% 
2.90% 

27 

 
 
 
 
 
 
Table 2.7 (cont’d) 

In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 

4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 

1.93% 
1.17% 
0.79% 
0.56% 
0.34% 
0.06% 
0.00% 
0.00% 
0.00% 
23.92% 
28.36% 
29.83% 
30.74% 
29.39% 
29.73% 
25.64% 
20.79% 
13.48% 
7.60% 
4.78% 
3.57% 

0.59% 
0.68% 
0.34% 
0.21% 
0.19% 
0.08% 
0.00% 
0.00% 
0.00% 
6.13% 
8.32% 
11.58% 
13.90% 
15.86% 
19.31% 
17.09% 
13.97% 
8.61% 
4.98% 
3.89% 
3.02% 

ef 
ef 
ef 
ef 
f 
f 
f 
f 
f 
ab 
a 
a 
a 
a 
ab 
abc 
abcd 
abcde 
bcdef 
cdef 
def 

Vertical Drift Conclusions. Mean flux was inversely related to sampler height on the 8m poles 

for both SSCDS configurations in both 2017 and 2018, as well as the 12m pole in 2018. The 

Airblast trends for both years and string sampler lengths reveal an increase in flux for the first 

few meters then decreasing with every meter after peak mean flux. SSCDS average flux values 

for both in canopy and above canopy did not statistically differ in both 2017 and 2018 for the 8m 

and 12m poles. Overall, the Airblast was responsible for the highest average flux at all heights 

for both years and both 8m and 12m sampling poles.  

2017 Downwind off target drift. Significant differences were detected for treatment (Airblast 

and in canopy SSCDS), distance downwind, and interaction factor. (F1,256=100, p<.001, 

F7,256=47.49, p<.001, F7,256=4.89, p<.001 respectively) (Table 8).   

 Mean flux was similar for both the Airblast and in canopy SSCDS at the 0m sampler. (x̅ = 

14.49% ± 2.13% and x̅  =16.09% ± 7.59% respectively) (Table 8).  Samplers greater than 0m 
downwind recorded greater average flux deposited by the Airblast at every sampling distance. 

Both the Airblast and in canopy SSCDS deposited less than one percent flux at 64m downwind 

of the orchard. 

28 

 
 
The 2017 in canopy SSCDS mean flux peaked at 0m downwind (x̅ = 16.09% ± 7.59%) 
with reduced average flux for every sampler farther downwind than 0m. Average percent flux 

reduced with distance reaching below one at 16m downwind of the orchard (x̅ = 0.51% ± 0.12%). 
In contrast the average flux deposited by the Airblast at this distance was (x̅ = 5.29% ± 0.94%) 
and did not drop below 1% average flux until 64m downwind.  

Figure 2.10 - 2017 Mean flux as a percent of the applied rate as off target drift downwind 
of the last orchard row. 

Table 2.8 - 2017 Mean ± SEM flux as a percent of the applied rate as off target drift by 
distance downwind. Differing letters signify statistically significant differences between 
treatments at distances measured downwind at α=0.05. 

Treatment  Distance Downwind (m)  % Flux 
16.09% 
In canopy 
11.75% 
In canopy 
7.89% 
In canopy 
3.22% 
In canopy 
1.21% 
In canopy 
0.51% 
In canopy 
0.32% 
In canopy 
0.26% 
In canopy 
14.49% 
Airblast 
18.66% 
Airblast 
15.99% 
Airblast 
14.29% 
Airblast 

0 
1 
2 
4 
8 
16 
32 
64 
0 
1 
2 
4 

± SEM 
7.59% 
6.35% 
3.93% 
1.37% 
0.42% 
0.12% 
0.04% 
0.03% 
2.93% 
3.55% 
2.19% 
2.13% 

cd 
de 
de 
e 
e 
e 
e 
e 
a 
ab 
ab 
ab 

29 

 
 
 
 
 
 
Table 2.8 (cont’d) 

Airblast 
Airblast 
Airblast 
Airblast 

8 
16 
32 
64 

9.51% 
5.29% 
1.67% 
0.62% 

1.23% 
0.94% 
0.20% 
0.08% 

ab 
abc 
bc 
cd 

2018 Downwind off target drift. Significant differences were detected in treatment (Airblast, in 

canopy SSCDS, above canopy SSCDS), distance downwind, and interaction factor 

(F2,411=204.24, p<.001, F7,411=368.48, p<.001, F14,411=30.36, p<.001 respectively.)(Table 9) 

Average flux associated with the Airblast was greatest 2m downwind (x̅ = 24.43% ± 

4.98%), similar to the mean flux detected for the above canopy SSCDS at the same distance 

(x̅ = 10.57% ± 4.65%). In contrast, mean deposition for the in canopy SSCDS at 2m downwind 
was lowest of the three treatments (x̅ = 3.84% ± 1.59 ng cm-²). Airblast mean flux was greater 
than the in canopy SSCDS at all distances and was greater than than the above canopy 

SSCDS farther than 2m downwind.   

In canopy SSCDS mean flux was lower than the Airblast for all distances sampled, with 

the greatest disparity at 4m downwind with the in canopy SSCDS mean flux approximately ten 

times lower than the Airblast (x̅ = 1.84% ± 0.69%, x̅ = 18.88% ± 4.09% respectively). At 4m 
downwind average flux for both the in canopy and above canopy SSCDS were near equivalent 

(x̅ = 1.84% ± 0.69%, x̅ = 1.52% ± 0.84% respectively.), remaining so with increasing distance, 
both dropping under 1% at 8m downwind.  

The above canopy SSCDS’s highest mean flux was at 0m (x̅ = 24.47% ± 5.49%). This 

value is approximately two times greater than the in canopy SSCDS (x̅ = 10.20% ± 3.04%) and 
is similar to the Airblast (x̅ = 27.74% ± 4.80%) at this distance. As distance downwind increases, 
average flux values for the above canopy SSCDS reduce, equalizing with the in canopy SSCDS 

at 4m as described above. At 4m downwind the above canopy SSCDS mean flux (x̅ = 1.52% ± 
0.84%) is approximately twenty times less than its mean flux at 0m downwind (x̅ = 24.47% ± 
5.49%).  

30 

 
 
 
Figure 2.11 - 2018 Mean flux as a percent of the applied rate as off target drift downwind 
of the last orchard row. 

Table 2.9 - 2018 Mean ± SEM flux as a percent of the applied rate as off target drift by 
distance downwind. Differing letters signify statistically significant differences between 
treatments at distances measured downwind at α=0.05. 

Treatment 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
Above canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
In canopy 
Airblast 
Airblast 

Distance (m)  % Flux 
24.47% 
18.99% 
10.57% 
1.52% 
0.28% 
0.11% 
0.03% 
0.01% 
10.20% 
5.49% 
3.84% 
1.84% 
0.75% 
0.18% 
0.05% 
0.02% 
27.74% 
23.63% 

0 
1 
2 
4 
8 
16 
32 
64 
0 
1 
2 
4 
8 
16 
32 
64 
0 
1 

±SEM 
5.49% 
5.74% 
4.65% 
0.84% 
0.19% 
0.07% 
0.01% 
0.01% 
3.04% 
1.95% 
1.59% 
0.69% 
0.28% 
0.07% 
0.02% 
0.01% 
4.80% 
3.85% 

CLD 
a 
ab 
ab 
ab 
abc 
bc 
cd 
de 
hij 
hij 
hij 
ij 
ij 
ij 
j 
j 
de 
def 

31 

 
 
 
 
 
Table 2.9 (cont’d) 

Airblast 
Airblast 
Airblast 
Airblast 
Airblast 
Airblast 

2 
4 
8 
16 
32 
64 

24.43% 
18.88% 
12.16% 
6.13% 
1.31% 
0.21% 

4.98% 
4.09% 
3.02% 
1.74% 
0.28% 
0.06% 

ef 
fg 
gh 
ghi 
ghij 
hij 

Downwind off target drift observations. The 2017 and 2018 downwind off target drift 

measurements revealed an overall pattern of reduced mean flux the greater the distance 

downwind of the orchard for both SSCDS configurations and the Airblast. The only departure 

from this pattern is for the 2017 Airblast, in which flux increased from 0 m to 1 m downwind but 

declined with distance greater than 1 m downwind. For both 2017 and 2018 a majority of 

average flux occurred nearest the orchard with the highest flux within the first 4m downwind. 

The Airblast’s mean flux, while declining with distance downwind from the orchard, remained 

greater than both the in canopy and above canopy SSCDS overall. For 2017, distances greater 

than 1m and for 2018 distances greater than 2 m show the Airblast with greater mean flux. 

Disparities occurring 2 m - 16 m downwind, range anywhere from near equal to several times 

greater mean flux produced by the Airblast, compared to the SSCDS configurations for both 

years.  

Greater flux was detected downwind and above the orchard when the Airblast made 

applications and was most likely due to the Airblast’s powerful fan used for delivering 

agrochemicals. The fan vectored materials away from the sprayer moving large volumes of air 

during applications which, in mis-matched canopy, are more prone to downwind drift 

(Herrington, et al., 1981; Triloff, 2015).  The in canopy SSCDS’s delivery in contrast did not 

make use of a fan and materials were vectored mostly downward. Hypothetically, downward 

vectoring and lack of velocitized air in a mismatched canopy is why a majority of the average 

flux deposited by the in canopy SSCDS is nearest the orchard and was lower than the Airblast 

overall. This may also explain the higher flux depositions detected on the ground when using 

the SSCDS configurations. Similar findings of reduced downwind deposition were published by 

Washington State University comparing the SSCDS to an Airblast sprayer in vineyard (Sinha et. 

al., 2019). 

The greatest average flux occurred in 2018 using the above canopy SSCDS which had 

significantly greater deposition than both the Airblast and in canopy SSCDS for the 0 m and 1 m 

downwind samplers. The above canopy system includes spinning emitters with greater output 

32 

 
 
volume per emitter than the in canopy SSCDS and whose droplet spectra is coarser. The higher 

volume emission and greater mass, drift resistant droplets are a likely explanation for the 

greater deposition nearest the orchard and lower mean deposits downwind when comparing the 

above canopy SSCDS with the Airblast.  

These findings as well as findings by (Balsari et al., 2014) as well as assessments into 

near field drift by (Sinha et. al., 2019) suggest that the large volume of air disturbed by axial-fan 

Airblast sprayers velocitizes spray material, and in smaller canopies increases the potential for 

material to travel downwind from the orchard, creating downwind off target drift. Overall, 

determining the amount of material leaving the orchard as non-target downwind drift, both 

SSCDS configurations displayed lower downwind mean depositions. 

Off target Drift. These experiments indicate the delivery of agrochemicals via the axial-fan 

Airblast sprayer is more prone to vertical and downwind off-target drift compared to the two 

SSCDS systems evaluated. Axial-fan Airblast sprayers were designed for spray applications in 

large and voluminous canopies, not the short, narrow canopies typical of modern high-density 

apple orchards (Hoterman et al., 2017).  Fan-assisted technology, like the axial-fan Airblast 

make use of the disturbance of large volumes of air to deliver its agrochemical payload. The 

SSCDS, lacking the high velocity vectoring and wind shear, appear to contain spray applications 

closer to the targeted orchard rows. Further evidence for the reduction of drift away from the 

orchard may be explained by the higher mean deposition values present using the SSCDS 

configurations in the losses to ground measurements. Findings by (Owen-Smith 2019) suggest 

that higher overall canopy depositions may exist for the SSCDS when compared to an axial-fan 

Airblast sprayer. Similar data published by Washington State University suggests that compared 

to an axial-fan Airblast sprayer, the SSCDS lacking the fan assisted delivery, is less prone to 

downwind drift (Sinha et. al. 2019). 

2.5.  Conclusions 

This study determined spray deposition in the form of off target drift produced by two 

configurations of the Solid Set Canopy Delivery System (in canopy and above canopy) vs. a 

axial-fan Airblast sprayer. Losses to ground were higher when applying with the SSCDS 

compared to the axial-fan Airblast sprayer. Vertical drift flux measurements suggested that 

overall drift above the orchard floor when using the Airblast sprayer is greater than that of both 

SSCDS configurations. The SSCDS showed significant reduction of downwind and vertical drift 

produced by the Solid Set Canopy Delivery Systems compared to the Airblast. With lower 

depositions downwind and above the canopy and higher depositions to the ground using the 

SSCDS, suggests that more material is leaving the orchard as off-target downwind drift when 

33 

 
using an Airblast sprayer compared to either SSCDS configuration. The results of this study are 

in support of the further development of the SSCDS as an effective, drift reducing agrochemical 

delivery system in high density apples.  

34 

 
 
Chapter 3 - SSCDS Pest Management and Fruit Quality Outcomes 

3.1.  Introduction 

The Solid Set Canopy Delivery System (SSCDS) was developed as an alternative 

application method to improve the delivery of agrochemicals in high-density trellised fruit crops. 

The SSCDS is a fixed emplacement consisting of micro-emitters distributed along a trellised 

row. Emitters are fed by irrigation lines hung on trellis wire and driven by hydraulic pumps. This 

technology operates without heavy tractor-driven equipment or personnel needing to access the 

application area. Repeated passage of farm equipment leads to the compaction of soils, 

especially in places that receive elevated levels of rainfall. Compaction can hinder the rooting of 

plants, decrease oxygen diffusion, and lead to chemical runoff (Défossez et al., 2003). The 

SSCDS’s network of emitters can be operated from a single point, delivering agrochemicals 

without requiring equipment to make a series of passes within an orchard, reducing soil 

compaction, time, and the fuel used to make multiple trips.  

This technology also reduces the risk of worker exposure to agrochemicals by removing 

personnel from the treated area as well as reducing the amount of material lost as off target 

drift. Pesticide drift is defined as the movement of dust or droplets that move through the air to 

any site other than the area intended (EPA.gov). SSCDS systems have been shown to reduce 

off target drift when compared to axial fan sprayers in both grapevines and high-density apple 

orchards (Sinha et al., 2019; Koonter, 2023).  

Similar solid set systems have previously been explored. Beginning in the 1960’s at the 

Southern Oregon Experiment Station an overhead system was developed that placed sprinkler 

heads above the canopy of pear trees. An experiment was conducted comparing the system's 

ability to provide pest management to an axial-fan sprayer. Due to technical limitations of the 

time, the system was not able to provide adequate pest management. The author states that 

improvements to this type of application method could make it a competitive alternative 

(Lombard et al., 1966). Later experiments would look to improve upon the design and cost of 

the system, exploring the integration of check valves, positive shut off valves, anti-siphon 

valves, micro emitters and chemical resistant parts preventing contamination caused by leaking 

or backflow of agrochemical into water sources (Threadgill, 1985; Carpenter et al.,1985, Wilson, 

1983; Sawyer & Oswalt, 1983).   

In 2005 a network of micro-emitters controlled by stop leak devices, distributed through 

the canopy of a high-density orchard in New York was able to provide adequate pest 

management comparable to a commonly used axial fan sprayer (Angello, A., & Landers, A. 

2006). More recent research has built upon this with installations of solid set canopy delivery 

35 

 
 
systems installed in both  high-density apple orchards and grape vineyards in Michigan and 

Wanshington State. Evaluations of these systems describe coverage, deposition, decreased off 

target drift potential, and adequate pest management provided by the SSCDS (Sinha et al., 

2019; Sinha et al. 2020; Owen-Smith et al., 2019; Sahni et al., 2022). Evaluations of alternative 

emitters, and emitter placement have also been explored. Various emitter types have been 

assessed both in and above the canopy attempting to optimize treatment provided by the 

SSCDS (Ranjan et al., 2021). High density apple orchards for instance have tall narrow fruiting 

walls, so adjusting emitter spray patterns and placement to treat within a limited area is required 

to maximize coverage and deposition and reduce product losses.  

In apple production, the move towards high density fruiting walls is based on maximizing 

yield by planting as many trees as possible in a space without incurring negative effects caused 

by overcrowding. This method also allows for easier pruning and less laborious harvest 

(Robinson et al., 2013). These narrower, thinner canopies are a mismatch for the technology 

most commonly used to apply agrochemicals to them, the fan assisted axial sprayer. These 

sprayers were developed to treat orchard systems with taller more voluminous canopies. This 

mismatch leads to the off-target drift of applied agrochemicals (Fox R., et al. 2008; Holterman et 

al., 2017; Kasner et al., 2018). The SSCDS is a possible solution for a more optimized delivery 

of agrochemicals is the Solid Set Canopy Delivery System. 

Solid Set Canopy Delivery Systems produce dramatically less drift than axial fan 

sprayers because they do not require active air movement as part of the application process. It 

has been shown that when compared to an axial fan sprayer the SSCDS can reduce drift both 

above and downwind of the application area. (Sinha et al. 2019; Koonter 2023). Experiments 

conducted in a high-density apple orchard over a two-year period, determined that the in canopy 

SSCDS produced significantly less off target downwind drift compared to an axial fan sprayer. 

This study monitored drift deposits up to 64m downwind of the orchard and found that both 

SSCDS configurations’ mean deposition reached zero at approximately 16m downwind and the 

axial fan sprayer mean deposition was still detectable at up to 64m downwind of the orchard 

(Koonter 2023). While the technology does have the ability to reduce off target drift when 

compared to an axial fan sprayer, the SSCDS must also provide pest management with better 

or equal efficacy if it is to be adopted.  

Research conducted by Owen-Smith (2019) demonstrated that an axial-fan sprayer 

provides greater coverage to the underside of leaves than the in-canopy SSCDS, but coverage 

is near equivalent for the topside of the leaves comparing both the in-canopy SSCDS and the 

Axial fan sprayer. Overall greater deposition to the canopy exists for the in-canopy SSCDS 

36 

 
when comparing it to an axial-fan sprayer. This relationship was also described by Washington 

State University evaluating an SSCDS in a vineyard (Sinha et al., 2020). While disparities exist 

for coverage and deposition between the in-canopy SSCDS and the axial-fan sprayer, this does 

not seem to influence pest efficacy as both were shown to provide equivalent pest management 

(Owen-Smith et al., 2019; Sahni et al., 2022). 

Pest management in Michigan apple orchards requires management of a variety of 

arthropod, fungal and bacterial pests. Temperate climates like Michigan’s, are favorable to 

several pest species requiring fifteen to twenty plant protectant sprays per year, oftentimes 

weekly from April to June (Johnson, 2004; Holb et al., 2005; Jamar et al., 2010; Wise et al., 

2015).  

One of the most common diseases in Michigan apples is apple scab, which is caused by 

Venturia inaequalis, a fungal pathogen. This pathogen overwinters in fallen leaf tissues in its 

sexually reproductive form and after reproduction disperses to young apple tissues under wet 

conditions (Vaillancourt & Hartman, 2000). Seventy-five percent of agrochemical treatments 

from April to June are applied to control apple scab (Johnson, 2004). Control of apple scab 

requires frequent application of fungicides, oftentimes after rainfall. (Koetter 2019). Other 

diseases affecting Michigan apples include: Cedar apple rust (Juniperus virginiana), powdery 

mildew (Podosphaera leucotricha), and sooty blotch/flyspec (microbial complex) and disorders 

like bitter pit.  

Cedar apple rust is a fungus that infects apple foliage and fruit but requires an alternate 

host to reproduce, like trees of the Juniperus genus. When cedar apple rust spores spread from 

alternate hosts, they begin their infection of young apple tissues. One to two weeks later, red-

orange lesions begin to form on the surfaces of the leaves and fruit. Recommended control is 

the removal of host plants from near the orchard, planting of resistant cultivars, and treatment 

with fungicide (Strickland et al., 2020).  

Powdery mildew is a fungal pathogen that infects both apple leaves and fruit. Colonized 

young tissues of terminal buds have a silver-gray appearance and the infection leads to 

defoliation, stunted growth, and die back. Infections of the blossom or developing fruit cause a 

net like russeting and disfiguring of the fruit. Infections lead to smaller, distorted fruit leading to 

reduction of fruit yield and quality. Fruit trees with heavy levels of infection become weakened 

and are more prone to infections from other pathogens. Control recommendations are the 

planting of resistant cultivars, removal of infected plant tissues from the orchard to avoid 

reinoculation, and foliar fungicide applications. Susceptible cultivars may require as many as 

eighteen sprays per season for adequate control (Marine, S.C. et al., 2010). 

37 

 
Sooty blotch/Flyspeck is a fungal community that colonizes the waxy layers of apples. 

This colonization does not damage the tissues below but causes the fruit to develop unsightly 

blemishes which leads to the downgrading of the fruit, and economic loss. Sooty blotch/flyspeck 

infections can cause a loss of valuation of up to ninety percent in high value cultivars in eastern 

North America. (Gleason et al., 2011). To control Sooty blotch/Flyspeck it is recommended to 

prune the canopy, which lowers the humidity making it less hospitable for the growth of fungi. 

Biological and chemical anti-fungal foliar applications are also recommended, as well as post-

harvest dips to stop the growth of sooty blotch/flyspeck in storage (Williamson et al., 2000). 

Bitter pit is a disease characterized by dark, corky, depressed spots on the surface of the 

apple due to calcium deficiency. Cellular structure and function are reliant on calcium, the 

disorder leads to water-soaked symptoms caused by the breakdown of the plasma membrane 

followed by dehydration and tissue disintegration (de Freitas et al., 2010). Causative conditions 

of bitter pit can be low soil pH, boron deficiency, drought, and/or excessive tree vigor and fruit 

size (Rosenberger et al., 2004). To control bitter pit, it is recommended that high levels of 

calcium chloride are applied throughout the summer, however these applications alone may be 

ineffective if crop load, fruit size, or thinning have predisposed fruit to bitter pit conditions (Biggs 

et al., 2015).  

Treating common arthropod pests in Michigan high density apple orchards requires 

dealing with direct and indirect arthropod pests that cause damage to both fruit and foliage. 

Common arthropod pests include: Aphids (Hemiptera: Aphididae), apple maggot, Rhagoletis 

pomonella (Walsh), Japanese beetle, Popillia japonica, Walsh, leafhoppers (Hemiptera: 

Cicadellidae), Latreille, mites (Arachnida:Trombidiformes), plum curculio, Conotrachelus 

nenuphar, (Herbst) , San jose scale, Quadraspidiotus perniciosus, Comstock, sucking 

hemipterans (Hemiptera:Heteroptera), and tortricid moths (Lepidoptera:Tortricidae).  

Common aphid pests in Michigan apple orchards include green apple aphid, Aphis pomi 

(de Geer), rosy apple aphid, Dysaphis plantaginea (Passerini), and wooly apple aphid, 

Eriosoma lanigerum (Hausmann). Both green and rosy apple aphids overwinter in the egg and 

hatch around the same time in the spring. Wooly apple aphids overwinter as eggs or as nymphs 

underground on apple roots if elm trees are not available. All three species feed on sap within 

plant tissues causing leaves to curl and can also lead to the malformation of fruit, making it 

unsalable. Aphid excrement known as “honeydew”, which is a byproduct of sap feeding, 

accumulates with large aphid populations and promotes the growth of fungal pathogens. Wooly 

apple aphid differs from rosy and green apple aphid as they cover themselves with a waxy 

fibrous covering as well as feed below ground. Wooly apple aphids do the majority of their 

38 

 
damage underground, just above the root which can lead to root decay. Increased aphid 

populations can be attributed to cool wet springs limiting natural predators, and untrimmed 

trees, which provide safe haven and hinder control methods. Control methods include pruning 

the canopy as well as the use of contact or systemic aphicides beginning at pre blossom. Early 

applications are most effective as they allow for compounds to reach aphids before they have 

the ability to take refuge in curled leaves. Pesticide usage can lead to increased aphid numbers 

as it also eliminates insects that predate on aphids. (Howitt, 1993). Some recommended 

treatments include but are not limited to applications of Carbamates, Sulfoximines, 

Neonicotinoids, Diamides, as well as alternative, softer chemistries like potassium salts of fatty 

acids, etc. (Wise et al., 2022).   

Apple maggot is a species of fly, whose larva tunnel through apple flesh, softening the 

fruit leading to its decay. Females puncture immature fruit and lay eggs, the eggs hatch and 

develop into larvae who burrow through the apple. The puncture site often becomes a sunken 

dimple as damaged tissues do not grow with the rest of the apple. Mature larvae leave the fruit 

and enter the soil where they enter the pupal stage. Adults emerge the following summer from 

late June to early September. Successful control is based primarily on killing the flies before the 

females have a chance to oviposit. This 8-to-10-day window is known as the pre-oviposition 

period which takes place after adults emerge from the soil. During this pre oviposition period 

adult females and males rest and feed with little to no interest in fruit. Monitoring with yellow 

sticky traps and baited red plastic balls for oviposition activity allows for the proper timing of an 

insecticidal spray (Howitt, 1993). Recommended control of apple maggot includes but is not 

limited to applications of organophosphates, neonicotinoids, spinosyns, diamides, and bacterial 

based biopesticides etc. (Wise et al., 2022).  

Japanese beetles are most active from June to September in mating clusters on host 

plants post emergence. Control can be a challenge as the beetles are strong flyers and can 

feed on more than 300 species of plant (Wise et al. 2007). Females lay eggs in the soil with the 

larva overwintering until their emergence in the summer as adults. When temperatures reach 

~70°F beetles are most active and will consume foliage or fruit, with a preference for ripening or 

diseased fruit. Protection of fruit and foliage can be achieved by hanging aggregation 

pheromones and traps for detection and spraying insecticides when beetles first appear. 

(Howitt, 1993). Applications of organophosphates, carbamates, and neonicotinoids are 

recommended to control Japanese beetle infestations ect. (Wise et al., 2022).  

White apple leafhopper, Typhlocyba pomaria, McAtee feeds on both the foliage and fruit 

of the apple tree. The first generation overwinters as eggs beneath the bark and the second as 

39 

 
eggs laid on the underside of leaves. The first-generation nymphs feed on chlorophyll leaving 

white streaks or spotting on leaf surfaces, heavy feeding can cause the entire tree to appear this 

way. This damage can affect fruit quality and the formation of buds if feeding occurs early 

enough in the season. Spotting and streaking on fruit is caused by an accumulation of 

leafhopper waste produced by the second generation. Control of leafhoppers can be difficult 

due to their tendency to occupy the underside of leaves, shielded from spray applications and 

quick development of pesticide resistance. Thorough Coverage of both top and bottom of 

foliage with effective chemistry is essential for control (Howitt, 1993). Applications of pyrethroids 

and neonicotinoids are recommended to control leafhoppers, as well as oxadiazines 

(indoxacarb) and chitin biosynthesis inhibitors (buprofezin) etc. (Wise et al., 2022).  

European red mites, Panonychus ulmi, (Koch), Apple rust mites, Aculus schlechtendali, 

(Nalepa) and two spotted spider mites, Tetranychus urticae, (Koch) are pests of apples in 

Michigan orchards. All three species feed by inserting their mouth parts into plant cells and 

consuming their contents. This feeding gives the underside of leaves a brown or bronzed color. 

European red mite eggs overwinter with the condition of the tree at the end of the season 

determining the number of eggs left to overwinter. Trees heavily damaged in midsummer by a 

large red mite population will leave few eggs behind as resources were expended and high 

numbers of predators have reduced the population. The opposite is true for trees with lower 

population levels and the following year will have larger infestations earlier in the season. 

Serious infections by the two spotted spider mite leaves similar signs of damage to foliage as 

the European red mite with the difference being a more grayish tone to affected leaves. Also, a 

silken web may be spun over infested leaves. The apple rust mite is rarely seen as a pest and is 

actually known to be beneficial as it feeds mite predators early in the season before more 

damaging mite populations develop. Predacious mites can effectively suppress pest mites, as 

long as they are not harmed by treatments of broad-spectrum insecticides. Applications of 

miticides are recommended for controlling mite infestations (Howitt, 1993). Treatments to control 

European red mite are recommended to begin at tight cluster with applications of superior oil 

and mite growth inhibitors affecting chitin synthase 1 (Clofentezine, Diflovidazin, Hexythiazox), 

ect. Treatments to control apple rust mite are recommended to begin at first cover with 

applications of mite growth inhibitors affecting chitin synthase 1 (Clofentezine, Diflovidazin, 

Hexythiazox), METI acaricides (e.g., Fenazaquin, Pyridabin), and inhibitors of Acetyl COA 

carboxylase (tetroinic and tetramic acid derivatives), etc. Treatment of two spotted spider mite is 

recommended to begin at third cover with applications of Beta-ketonitrile derivatives (e.g., 

Cyenopyrafen), pyrethrins, pyrethroids, carbamates, etc. (Wise et al., 2022).  

40 

 
Plum curculio is a beetle that causes damage to apples at multiple points in its lifecycle. 

First an adult female cuts a cavity under the skin of a developing apple, then proceeds to cut a 

crescent shaped slit under the cavity, sliding the egg into the cavity. This crescent shaped scar 

remaining at harvest is a sign that the egg did not hatch and develop into larva. If the larva 

hatches it will burrow through the fruit, to its core, feeding until maturity with a majority of the 

infected fruit dropping in June. After apple drop larvae make their way into the soil, construct a 

pupal cell, and in approximately thirty days time emerge as an adult. As an adult it will continue 

damaging apples as it feeds, until cold weather where it will seek shelter for hibernation. 

Monitoring for plum curculio is performed by beating tray, by dislodging adults from the foliage 

during the early morning of petal fall. Assessing fruit for adult feeding and oviposition injury can 

also signal the presence of plum curculio in the orchard. To control plum curculio at petal-fall, 

first cover (7-10 days after petal fall), and second cover (7-10 days after first cover) sprays of an 

insecticide are recommended, intended to disrupt adult oviposition. This pest is considered 

difficult to control and full dosages of effective pesticide are recommended (Howitt 1993). 

Treatments to control plum curculio are recommended to begin at petal fall with applications of 

pyrethrins, pyrethroids, diamides, organophosphates, oxadiazines, neonicotinoids, etc. (Wise et 

al., 2022). 

San Jose scale feeds on the sap of the host plant it has infested. The pest is so prolific 

that just a few pairs can be responsible for millions of progeny in a season or two, this leading to 

complete coverage of the host by San Jose Scale, a single female can produce more than 300 

million per year. As this is an invasive species it lacks adequate predation to control its numbers, 

becoming so prolific it is able to kill a shrub in just a few years, even older trees cannot 

withstand the prolific feeding of so many individuals. Feeding is not limited to the bark but will 

also infest leaves and fruit, sign of scale on fruit is a red spot, with infestations making the fruit 

unmarketable. Crawlers, younger forms of the insect can be spread by wind, wildlife, farm 

implements, and the clothes of those who've made contact with an infected tree. Scale may go 

undetected until it has infested fruit due to its small size. In order to detect scale pheromone 

traps can be placed to capture winged males during bloom and petal fall, along with sticky traps 

to detect the crawling phase of the insects life cycle. Control can be achieved by the application 

of superior oil during pre bloom. Insecticide applied at early petal fall will control males before 

they are able to mate, as well as crawlers with adequate coverage (Howitt 1993). Applications to 

control San Jose scale, are recommended to begin at tight cluster, applying organophosphates, 

butenolides, superior oil, juvenile hormone mimics (Pyriproxyfen), etc. Insecticide applications 

41 

 
are recommended to begin at pink and crawler stage using pyriproxyfen, butenolids, 

benzoylureas, ect. (Wise et al., 2022).       

Sucking hemipterans like Tarnished Plant Bug, Lygus lineolaris, (Palisot de Beauvois), 

and Brown Marmorated Stink Bug (BMSB), Halyomorpha halys, Stål both feed on plant sap 

inserting feeding mouthparts into plant tissues. Results from feeding in apples range from 

discoloration to disfiguration, making fruit unmarketable. In spring tarnished plant bugs lay eggs 

in blossom buds, causing damage near the calyx. Control of tarnished plant bug is considered 

difficult as it migrates from outside of the orchard and should be controlled by spraying at the 

pink stage (Howett 1993). Brown marmorated stink bugs lay eggs on the bottom side of leaves, 

of a chosen host plant. As the nymphs reach adulthood in early to late August, they become a 

greater threat to orchards as large populations begin aggregating. Apples are listed in the 

moderate to high-risk group for Michigan with nymphs appearing as early as June. Both adults 

and nymphs can cause damage to apples but may not be detectable until months later. Damage 

from BMSB can appear similar to tarnished plant bug. Orchards near woodlots are considered 

at highest risk for a Brown marmorated stink bug invasion. Traps should be set to confirm the 

presence of BMSB in the orchard. Insecticidal sprays are recommended for control (Wilson et 

al., 2020). Treatments to control tarnished plant bug are recommended to begin at pink stage 

with applications of carbamates, pyrethroids, pyrethrins, and flonicamid (Wise et al., 2022). 

Treatments to control BMSB are recommended to begin at sixth cover with applications of 

carbamates, pyrethroids, pyrethrins, neonicotinoids, etc. (Wise et al., 2022).        

Tortricid moths like the Oriental fruit moth, Grapholita molesta, (Busck) and the Codling 

moth Cydia pomonella, (L.) are internal feeders, their larva developing inside growing fruit. 

Moths like the Oblique banded leafroller, Choristoneura rosaceana, (Harris) damage the surface 

of the fruit. Both oriental fruit moth and codling moth are similar in the way they cause damage 

to apples. Eggs are laid near the developing apple, the larva makes its way into the apple where 

it feeds, damaging apple tissues. Two types of damage are caused by larva, stings and deep 

entries. Stings are shallow holes bored where the larva died and failed at entry into the fruit. 

Deep entries are where the larva have penetrated the fruit, from either the side or calyx end, 

leading to significant damage. Successful larvae will feed inside the apple for about three weeks 

at which point it will cocoon itself on the trunk or branches of the tree. After 14 to 21 days 

depending on temperature and rainfall an adult moth will emerge. While their damage and larva 

look similar, the way to differentiate oriental fruit moth larva from codling moth larva is the 

presence of an anal comb on the posterior end of the oriental fruit moth larva. Pheromone-

baited traps are recommended to confirm the presence of codling moth, or oriental fruit moth in 

42 

 
the orchard, as effective treatment requires larva to be exposed to pesticide before making entry 

into the apple (Howitt 1993). To control oriental fruit moth, use of mating disruption pheromones 

are recommended beginning at pink stage. Insecticides applications are recommended to begin 

at petal fall using carbamates, organophosphates, pyrethrins, pyrethroids, diamides, spinosyns, 

etc. To control codling moth mating disruption pheromones are recommended beginning at 

bloom.       

Obliquebanded leafroller feeds on both foliage and fruit. They begin hatching out in bud 

clusters where they feed on parts of the flower and developing leaf tissue. After petal fall they 

begin feeding on fruit and growing leaves. At this point they begin to feed on the surface of 

apples leaving deep gouges. Pupation occurs at the site of feeding, lasting 10 to 12 days, moths 

emerging mid-June to mid-July. Broad spectrum insecticides applied at pink or petal fall are 

recommended to control damage from overwintering larvae. Summer sprays are recommended 

to be timed with the presence of leaf rollers in traps (Howitt 1993). Treatments to control 

obliquebanded leafroller are recommended to begin at pink stage with applications of 

pyrethroids, diamides, IGRs, spinosyns, etc. (Wise et al., 2022). 

Other agrochemicals are applied to the orchard aside from those used to control pests. 

For example, nutritional and thinning agents are often applied as foliar treatments. Foliar 

nutrients are often sprayed to maintain plant health, prevent disorders, and improve fruit quality. 

Foliar applications are optimal for delivering all sources of plant nutrients and pose the lowest 

risk of soil and groundwater contamination (Murtic et al., 2012). Fruit thinning is the practice of 

reducing the number of fruit per tree. The reduction of the number of apples per tree protects 

tree limbs from breaking due to fruit load, prevents losses of tree reserves, and allows for larger 

higher quality apples. Chemical thinning is achieved by applying compounds that disrupt the 

formation of, or prevent apples from reaching maturity. For example, naphthalene acetic acid 

(NAA) is a plant bioregulator that when applied after bloom reduces fruit set. The insecticide 

carbaryl (1-naphthyl-N-methylcarbamate) is regarded as a mild thinning agent and is 

recommended to be used with NAA in difficult to thin varieties (Dennis, 2000).  

Previous experiments involving Solid Set Canopy Delivery Systems demonstrate not 

only the technology’s ability to reduce off-target drift but to also provide comparable coverage to 

the leaf surface, produce higher deposition, and comparable year-long pest management when 

compared to a fan-assisted orchard sprayer (Angello, & Landers, 2006; Sinha et al. 2019; 

Owen-Smith et al. 2019; Sanhi et al. 2022). What has yet to be determined is to combine 

coverage, deposition, pest management, and fruit quality measurements across multiple 

43 

 
varieties of apple treated by the SSCDS. This experiment also includes a new emitter 

configuration, the above canopy SSCDS, that removes emitters from within the canopy. 

I hypothesized in canopy SSCDS coverage and deposition values will be similar in 

differing strata within the canopy. I hypothesized that above canopy SSCDS deposition and 

coverage values will be highest at the top of the canopy and coverage will be highest on the 

adaxial face of spraycards. I also hypothesized that the above canopy SSCDS, in canopy 

SSCDS, and an Airblast axial-fan sprayer will provide equivalent pest management. Along with 

pest management I hypothesized that fruit quality will be equivalent among the above canopy 

SSCDS, in canopy SSCDS, and Airblast.  

3.2.  Materials and Methods 

To meet the experimental objectives, two years of pest management efficacy evaluations 

were conducted. The experiment was composed of three treatments, above canopy SSCDS, in 

canopy SSCDS and a Rears PB533N Radial Airblast sprayer, known as the Airblast (Rears 

Manufacturing company, Coburg, OR, USA) in a high density apple orchard. In canopy and 

above canopy SSCDS coverage and deposition were evaluated August of 2019, as were the 

incidence of clean fruit and foliage and insect and disease damaged fruit and foliage. In August 

of 2020 I measured incidence of clean fruit and foliage, insect disease damaged fruit and foliage 

as well as fruit quality measures of apple weight, apple size, apples per tree, and apples per 

hectare.  

Experiment Site. Coverage, deposition, pest management, and fruit quality evaluations were 

conducted in a high-density orchard at the Michigan State University Clarksville Research 

Center Clarksville, Michigan, United States (42.873933081589804, -85.25857635397287). The 

treated area consisted of 6 0.20 hectare plots; each plot contained fifteen 38.1 meter long rows 

of densely planted Gala, Honeycrisp, and Fuji varieties of apple trees; each variety occupied 

five rows each. Apple cultivars were Buckeye Gala on bud 9 rootstock, Royal Red Honeycrisp 

on M9-337 rootstock and Aztec Fuji on M9-377 rootstock. Trees were approximately 2.5 m tall 

with 0.91m spacing between trees, with forty-five trees per row and 3.66 m row spacing. Tree 

rows were planted lengthwise, latitudinally (North/South) in Spring of 2017. 

Experimental design. This experiment was conducted as a complete block design, with three 

plots dedicated to treatment from the in canopy and above canopy SSCDS and three plots 

adjacent treated by the Airblast sprayer. The SSCDS treated blocks were split perpendicular to 

the orchard rows with half of the plot treated by the in-canopy SSCDS and half the plot by the 

above-canopy SSCDS. The SSCDS half plots were also split East/West with eight rows to the 

east controlled separately from the seven to the west this allowed for greater pressure control 

44 

 
(Figure 3.2). Splitting the orchard plots created 60 m orchard rows placing an in canopy and 

above canopy SSCDS in each apple variety, with a 5 m buffer in the center with no emitters, 

preventing areas from receiving overlapping treatment from both systems (Figure 3.1). The 

remainder of the orchard plots were treated by the Airblast sprayer, serving as a positive control.  

Past experiments in the surrounding orchard blocks provided background pest pressure 

so an untreated control was not assessed during this experiment. Untreated controls assessed 

by Owen-Smith (2019) detected ~80% scab damaged fruit along with ~30% of the fruit 

damaged by external feeding arthropods and ~20% of the fruit damaged by internally feeding 

arthropods. Similar findings in the same orchard by Grieshop (2019) detected > 90% of the 

apples treated conventionally free of arthropod and disease damage. In the same orchard a 

failed treatment, detected no pest management with the percentage of clean fruit at highest 

~50%.  

Figure 3.1- Experimental site plots treated by both SSCDS configurations and Airblast. 
Six 0.2 ha orchard plots with three apple varieties, five rows each. Three plots were 
treated by Rears Airblast and three split in the center one half treated by the above 
canopy and the other the in canopy SSCDS. 

SSCDS Design. The SSCDS designs used in these experiments were based on a design 

described in Owen-Smith et al. (2019). The basic form of an SSCDS used a hydraulic pump fed 

from a holding tank driving liquid through irrigation lines plumbed to micro-emitters. The systems 

evaluated in my experiments consisted of 2.54 cm diameter polyethylene irrigation tube (Toro, 

Bloomington, Minnesota, USA) hung at the top (2.5 m) of each orchard row, referred to as the 

application line (Figure 3.2 A). Micro-emitters were plumbed into the application lines via 220.63 

kPa leak prevention devices (Jain irrigation, Jalgaon, Maharashtra, India). My system, differing 

45 

 
 
 
from (Owen-Smith et al.) 2019 was constructed with 5.08 cm diameter “Tigerflex” spa tube 

(Kuriyama Inc., Schaumburg, IL, USA) which fed the application lines, referred to as a feed line 

(Figure 3.2 B. These feed lines were attached to a Honda GX 160 water pump (Honda, Minato 

City, Tokyo, Japan) and ran perpendicular to the orchard rows and attached to every other 

application line. Application lines not attached to the feed line were attached to a 2.54 cm 

diameter polyethylene irrigation tube known as the return line (Figure 3.2 C) that returned 

material back to a 956.4 L reservoir tank (Norwesco, St. Bonifacius, Minnesota, USA). 

Figure 3.2 - SSCDS plot configuration. Plots were split between the above canopy and in 
canopy SSCDS. Application lines carried agrochemical to the emitters and attached to 
both the feed lines and return lines (A.). Solid lines in the center of the plot were “feed 
lines” pushing agrochemical from tank to the application lines (B.) Stripped lines are 
return lines and carried agrochemical from the application lines back to the holding tank 
(C.). 

SSCDS Configurations. The in-canopy SSCDS configuration that was used for this experiment 

was the same as described in Koonter (2023). In canopy SSCDS emitters consisted of a Jain 

7110 bridge with Jain 3044 impactors (Jain irrigation, Jalgaon, Maharashtra, India) with a flow 

rate of 0.56 lpm with one emitter at the top of the canopy (2.5 m above ground) and a pair mid 

46 

 
 
 
 
canopy (0.9 m above ground). Each triplet of emitters was controlled by a Jain 220.63 kPa leak 

prevention device plumbed into the delivery line.  

Each above canopy SSCDS emitter consisted of a Jain 7110 bridge and Jain green 

nozzle with a flowrate of 1.14 lpm. These emitters were fitted with 3D printed impactors as 

described by Ranjan (2021). These impactors were designed with fifty-degree deflection angles 

and produced a wetted diameter of 3 m when emitters were located 2.5 m above ground. 

Emitters were placed above the canopy 2.5 m above ground and placed every 0.94 m with 

every two emitters controlled by the same Jain leak prevention device as the in canopy SSCDS, 

placed every 1.83 m. 

Figure 3.3 - In canopy and above canopy SSCDS emitter placement. In canopy SSCDS 
(A.) 7110 body 3044 impactor, single emitter at top of canopy and pair in the center of the 
canopy. Above canopy SSCDS (B.) 7110 body custom 50-degree impactor, single emitters 
above the canopy.  

Agrochemical Application (SSCDS and Airblast). The SSCDS system operated in three 

phases. The first phase was the low pressure (<220.63 kPa) filling phase where the entire 

system was filled from the holding tank. The second phase was the application phase, where 

the system pressure was increased to ~ 344.74 kPa opening the leak prevention devices and 

47 

 
 
 
 
passing agrochemical to the emitters for application. The third phase was the return phase 

where pressurized air from an industrial air compressor was fed into the system at a lower 

pressure than the opening pressure of the leak prevention devices (<220.63 kPa) pushing all 

liquid remaining in the lines back to the holding tank. 

The target application rate for the in-canopy SSCDS, above-canopy SSCDS and Airblast  

sprayer for determining coverage, deposition and pest management applications was 467.7 

l/ha. The application rate for the SSCDS is controlled by time of application. In order to achieve 

this application rate, the in-canopy SSCDS was activated for 11 seconds and the above-canopy 

SSCDS was operated for 8 seconds. The Rears PB533N Airblast sprayer was fitted with Teejet 

DC23 hollow cone, ceramic discs and whirl plates (Teejet Technologies, Glendale Heights, IL, 

USA) and applied to every row at the same field rate as the SSCDS configurations (467.7 l/ha). 

Coverage and Deposition. Weather data was recorded via the Enviroweather system located 

at the Clarksville Research Center. Weather conditions recorded were as follows: Temperature 

ºC, Relative Humidity, Wind Speed minimum, Wind Speed maximum, Wind speed average. 

(Table 3.3) 

Coverage and deposition provided by the in-canopy and above-canopy SSCDS was 

measured using of 10.16 cm × 2.54 cm water sensitive paper (Teejet Technologies, Glendale 

Heights, IL, USA) and 10.16 cm × 10.16 cm mylar cards (Grafix, Cleveland, Ohio, USA) 

respectively. To determine both coverage and deposition at the time of application a mixture of 

0.1% m/v Keystone pyranine 10G dye (Keystone Inc., Chicago, Illinois, USA), 0.1% v/v 

commercially available non-ionic surfactant (Ragan & Massey Inc., Ponchatoula, Louisiana, 

USA) and water was applied to the orchard. At each application a 20 ml tank sample was 

collected for the development of a calibration curve. This pyranine mixture acted as an 

agrochemical surrogate for quantification of deposition made to the mylar cards and also 

reacted with the water sensitive paper, allowing for the quantifying of coverage.  

Water sensitive paper and mylar cards were placed in the top, middle, and bottom third 

of the orchard canopy in the east and west sides of three trees located in the center row of each 

variety (Gala, Honeycrisp, Fuji) for each treatment (above canopy, in canopy) in each of the 

three blocks used. At each water sensitive paper location, two pieces of water sensitive paper 

were placed, one facing upward (adaxial) and one facing the ground (abaxial) simulating the 

upper and underside of leaves.  

Individual water sensitive papers and mylar cards were collected with gloved hands and 

placed in 20 × 12 cm 6 mil plastic bags (US Plastics, Lima, Ohio, USA) after each treatment. 

Both water sensitive paper and mylar cards were stored in a cooler of dry ice buffered by heavy 

48 

 
canvas. Water sensitive paper was stored in a dark, cool, and dry place until processing. Mylar 

cards were stored in a –20ºC freezer until time of processing.  

Coverage: Water Sensitive Paper. Water sensitive papers were scanned at 4800 dots per inch 

using a Canon photo quality flatbed scanner (Canon Inc., Arlington, VA, USA). The imaging 

software ImageJ (Fiji Software) first thresholded images of the water sensitive paper then 

analyzed them for percent coverage based on the color difference displayed by the surfaces of 

the cards with exposure to moisture. 

Deposition: Tank sample curve. Detection procedures were based on previous publications 

(Khot et al. 2012 & Salyani et al. 2000). A calibration curve was developed by tank samples 

collected for each application. Tank samples were initially diluted by 50% with a mixture of 

10%v/v isopropyl alcohol/distilled water solution and then serially diluted by factors of 10, 100, 

1000, 10,000 and 100,000 which corresponded to 0.5 g L⁻¹, 0.05 g L⁻¹, 0.005 g L⁻¹, 0.0005 g 

L⁻¹, 0.00005 g L⁻¹ , respectively. Each one of these dilutions was added to three sequential 

chambers of a Trueline TR5002 24 well plate (Corning Inc., Corning, NY, USA), then analyzed 

with the Biotek Synergy HT spectrophotometer, excitation 400/30 nm and emission 540/25 nm 

(BIOTEK, Winooski, VT, USA). The three sequential wells relative fluorescence units were 

averaged, and the resulting number equaled an average of relative fluorescence units. 

Fluorescence values from the tank samples were used to create a nonlinear equation for each 

spray application that were then used to calculate the mass of pyranine deposition to mylar 

cards in grams per centimeter squared.  

Deposition: Dye release. To release the dye from the mylar cards, the cards in their bags were 

thawed and 10 ml of a 10% v/v of 90% isopropyl alcohol mixture in distilled water was added to 

the bags. Bags were agitated, and all surfaces of the card were exposed to the mixture to 

ensure the release of the dye from the mylar card.  

Deposition: Dye detection. To detect the fluorescence of the pyranine deposited to the mylar 

cards, 6 ml of the dye and release agent in the bags was pipetted in 2 ml aliquots into three 

sequential chambers of a Trueline TR5002 24 well plate. Using the spectrophotometer methods 

described above for the tank samples, three sequential wells were used to generate an average 

of relative fluorescence units or RFU per sample. Using the calibration curve produced by the 

fluorescent dye dilutions from each independent tank sample, each drift sample was converted 

from RFU’s (reflectance units) to grams per centimeter squared. This value was converted to 

percent flux as a proportion of the applied rate assuming 100% deposition based on Fritz et al. 

(2008).  

49 

 
Pest management. Pest management for 2019 and 2020 was provided by the above canopy 

SSCDS, in canopy SSCDS and Rears Airblast sprayer. In 2019, the above-canopy and in-

canopy SSCDS began treatments on 6/20/2019 once system installation was complete. From 

4/10/2019 to 6/12/2019 the Airblast provided agrochemical applications to the entire orchard 

including the SSCDS blocks. The number of applications made by the above canopy SSCDS, in 

canopy SSCDS, and Airblast were equivalent in 2020. Applications were made by all three 

treatments on the same day, using the same products, and at the same rate. SSCDS 

application rates were controlled by time, the above canopy SSCDS emitters applied at a rate of 

0.56 l/m and were active for 11 s, the above canopy SSCDS emitters applied at a rate of 1.14 

l/m and were active for 8 s. SSCDS applications were mixed with water in a 956.4 L tank. All 

treatments were (above canopy SSCDS, in canopy SSCDS, Airblast) applied at a rate of 467.70 

l/ha.  

Table 3.1 - 2019 Applications of agrochemical to the orchard. Date, product name, active 
ingredient, type, Formulation, and amount per hectare of applications made to the 
orchard by above canopy SSCDS, in canopy SSCDS, and Airblast in 2019.  

Active Ingredient 
copper oxychloride 
mancozeb 
cyprodinil 
benzovindiflupyr 

Type 
Fungicide 
Fungicide  WDG 
Fungicide  WDG 
Fungicide 

EC 

Formulation  Rate/ha 
DF 

4.4 kg 
3.36 kg 
0.35 kg 
501.31 ml 

flupyradifurone 

Insecticide  EC 

667.93 ml 

Date  Product 
4/10  COC DF 
4/19  Roper 
4/19  Vangard 
4/25  Aprovia 
Sivanto 
Prime L 

4/25 

5/2  Roper 

5/2  Merivon 

5/10  Roper 
5/10  Vangard 
5/10  Anarchy 
5/17  Omega 
5/17  Sonata 

mancozeb 
fluxapyroxad + 
pyraclostrobin 
mancozeb 
cyprodinil 
acetamiprid 
fluazinam 
Bacillus pumilis 

5/17  FireWall 

streptomycin sulfate 

5/17  FireLine 

oxytetracycline 

Fungicide  WDG 

3.36 kg 

Fungicide 

SC 

Fungicide  WDG 
Fungicide  WDG 
Insecticide  SG 
Fungicide 
Fungicide 
Fungicide/ 
Bactericide 
Fungicide/ 
Bactericide 

F 
SC 

WP 

WP 

292.31 ml 

3.36 kg 
0.35 kg 
0.16 kg 
935.40 ml 
6664.69 ml 

1.12 kg 

1.12 kg 

50 

 
 
 
 
Table 3.1 (cont’d) 

5/24  Fortuna 
5/24 

Indar 

mancozeb 
fenbuconazole 

5/24  Kasumin 

kasugamycin 

5/29  Belay 

5/29  Merivon 

5/31  PoMaxa 

clothianidin 
fluxapyroxad + 
pyraclostrobin 
1-Naphthalene 
Acetic Acid 

5/31 

Sevin XLR 
Plus 

carbaryl 

6/7  PoMaxa 

1-Naphthalene 
Acetic Acid 

6/7 

Sevin XLR 
Plus 

carbaryl 

6/12  Omega 
6/12  Delegate 
6/20  Omega 
6/20  Movento 

6/27 

6/27 

Voliam 
Flexi 
Luna 
Sensation 

7/23 

7/4  Movento 
Indar 
8/9  Anarchy 
Incognito 
8/9 

8/23  Assail 
8/23 

Incognito 

9/4  Exirel 
9/4 

Incognito 

fluazinam 
spinetoram 
fluazinam 
spirotetramat 
chlorantraniliprole + 
thiamethoxam 
fluopyram + 
trifloxystrobin 
spirotetramat 
fenbuconazole 
acetamiprid 
thiophanate-methyl 
acetamiprid 
thiophanate-methyl 
cyantraniliprole 
thiophanate-methyl 

F 

Fungicide  WDG 
Fungicide 
Fungicide/ 
Bactericide 
Insecticide  SL 

L 

Fungicide 

SC 

L 

L 

F 

fruit 
thinning 
fruit 
thinning 
fruit 
thinning 
fruit 
thinning 
Fungicide 
Insecticide  WDG 
Fungicide 
Insecticide  SC 

F 

F 

F 

6.73 kg 
438.47 ml 

4676.98 ml 

438.47 ml 

350.77 ml 

146.16 ml 

1169.24 ml 

456.74 ml 

584.62 ml 

935.40 ml 
0.42 kg 
935.40 ml 
657.7 ml 

Insecticide  WDG 

0.47 kg 

Fungicide 

SC 

F 

Insecticide  SC 
Fungicide 
Insecticide  SG 
Fungicide  WDG 
Insecticide  SG 
Fungicide  WDG 
Insecticide  SC 
Fungicide  WDG 

365.39 ml 

657.7 ml 
584.62 ml 
0.56 kg 
0.77 kg 
0.56 kg 
0.77 kg 
1039.17 ml 
0.77 kg 

Table 3.2- 2020 Applications of agrochemical to the orchard. Date, product name, active 
ingredient, type, formulation, and amount per hectare of applications made to the 
orchard by above canopy SSCDS, in canopy SSCDS, and Airblast in 2020.  

Date   Product 
4/22 
fortuna  
4/22  approvia 
4/22  zinc 7% 
4/30  penncozeb  mancozeb 

Active Ingredient  Type 
mancozeb 
Benzovindiflupyr 
zinc 

Fungicide  WDG 
EC 
Fungicide 
Fungicide  WP 
DF 
Fungicide 

Formulation  Rate/ha 
3.36 kg 
438.47 ml 
4676.98 ml 
3.36 kg 

4/30 

inspire 
super  

5/8  omega  

Difenoconazole + 
Cyprodinil  
fluazinam 

Fungicide 

EW 

0.84 kg 

Fungicide 

EC 

935.40 ml 

51 

 
 
Table 3.2 (cont’d) 

5/13  mervion  

Fluxapyroxad + 
pyraclostrobin  

Fungicide 

EC 

365.39 ml 

5/23 

flint extra  

Trifloxystrobin 

Fungicide 

5/23  omega  

fluazinam 

5/23  kasumin 2L   kasugamycin 

5/25  kasumin 2L   kasugamycin 

5/27 

agri-mycin 
17 

streptomycin 

5/27  mycoshield   oxytetracycline 

Fungicide 
Fungicide/B
actericide 
Fungicide/B
actericide 
Fungicide/B
actericide 
Fungicide/B
actericide 

EC 

EC 

L 

L 

WP 

WP 

Fluazinam 

Fungicide 

EC 

Clothianidin 
Benzovindiflupyr 

Insecticide   EC 
EC 
Fungicide 

166.62 ml 

935.40 ml 

4676.98 ml 

4676.98 ml 

1.12 kg 

1.12 kg 

935.40 ml 

438.47 ml 
467.70 ml 

5/28 

omega 
500F  
5/28  belay  
5/28  aprovia 

6/1 

Sevin XLR 
plus  

6/1  PoMaxa 

6/8  mervion  

6/8 

6/26 

6/26 

voliam 
Flexi  
voliam 
Flexi  
Luna 
sensation  

carbaryl 

Thinning 

EC 

2338.50 ml 

1-Naphthalene 
Acetic Acid 
Fluxapyroxad + 
pyraclostrobin 
Thiamethoxam + 
Chlorantraniliprole 
Thiamethoxam+ 
Chlorantraniliprole 
Fluopyram + 
Trifloxystrobin 

Thinning  

L 

292.31 ml 

Fungicide 

EC 

365.39 ml 

Insecticide   WDG 

935.40 ml 

Insecticide   WDG 

935.40 ml 

Fungicide 

SC 

730.78 ml 

7/6  Endivor  

Spirodiclofen 

7/6  movento  

7/6 

inspire 
super  

Spirotetramat 
Difenoconazole + 
Cyprodinil 

Acaricide, 
Insecticide 
Insecticide   SC 

SC 

Fungicide 

EW 

1315.40 ml 

657.7 ml 

876.93 ml 

7/6  prey 

Imidacloprid 

Insecticide   EC 

292.31 ml 

7/6  Rainier 

Polyoxyethylene 
polyol fatty acid 
ester, butyl lactate, 
alcohol ethoxylate 
phosphate ester 

Surfactant 

EC 

2338.50 ml 

52 

 
 
 
Table 3.2 (cont’d) 

7/17  anarchy 

Acetamiprid 

Insecticide   WSG 

0.56 kg 

7/17  Rainier 

Polyoxyethylene 
polyol fatty acid 
ester, butyl lactate, 
alcohol ethoxylate 
phosphate ester 

Surfactant 

EC 

935.40 ml 

7/31  anarchy 

Acetamiprid 

Insecticide   WSG 

7/31 

incognito  

8/19  belay  
Indar 
8/19 

9/3 

incognito  

Thiophanate-
methyl 
Clothianidin 
fenbuconazole 
Thiophanate-
methyl 

Fungicide 

L 

Insecticide   EC 
EC 
Fungicide 

Fungicide  WDG 

0.56 kg 

0.56 kg 

438.47 ml 
0.56 kg 

0.897 kg 

Foliar and fruit damage assessment. Fruit and foliar damage were evaluated in summer and 

fall in 2019 and 2020. Mid-season assessments occurred on July 25th and 26th in 2019 and 

June 26th in 2020. Fall assessments occurred on September 6th and 7th in 2019. For 2020, 

assessments occurred at apple harvest for Gala on September 9th, Honeycrisp on September 

16th, and Fuji on October 15th. Ten trees were randomly selected in the central three rows of 

each apple variety, in each experimental plot avoiding the 5 m meters nearest the center where 

the separation between the above canopy and in canopy SSCDS was located. Twenty-one 

terminals and apples were sampled per tree with samples evenly distributed across the east 

and west sides and from the top, middle and lower third of the canopy yielding 210 terminals 

and fruit per variety per plot. Foliage, counts were made of the number of terminal leaves with 

clean leaves, incidence of apple scab (Venturia inaequalis), leaf hopper (Typhlocyba pomaria) 

feeding, (cedar apple rust (Gymnosporangium juniperi-virginianae), powdery mildew 

(Podosphaera leucotricha), insect defoliation (multiple sources), mite bronzing (Tetranychidae), 

and aphids (Aphididae). Fruit included the number of clean fruit, the number of fruit with apple 

scab (Venturia inaequalis), powdery mildew (Podosphaera leucotricha), cedar apple rust 

(Gymnosporangium juniperi-virginianae Schwein), sooty blotch and flyspeck (microbial 

complex), rotted fruit, tortricid moth sting and entry (Tortricidae), plum curculio sting and feeding 

(Conotrachelus nenuphar (Herbst), and sucking insect damage (Heteroptera). Incidence of bitter 

pit disorder in Honeycrisp apples was also recorded.  

Fruit quality and yield assessment. Apple yield was calculated by apple counts carried out for 

each tree undergoing damage assessment. Fruit quality was assessed at harvest for each 

variety in 2020 with the collection of apples of each variety for each treatment at harvest. These 

53 

 
 
apples were chosen randomly, selecting ten per tree, in 10 trees with samples evenly distributed 

across the east and west sides and from the top, middle and lower third of the canopy yielding 

one-hundred apples per variety per treatment. Apple weight was measured using a lab scale 

weighing each individual apple. Size was assessed measuring the diameter of the apple with a 

digital caliper (Clockwise tools Inc., Valencia, CA, USA). Yield in metric tons per hectare was 

approximated using mean apple weight and mean apple counts extrapolated using the number 

of apples per tree and the number of trees per hectare. 

Statistical Analysis Software. All statistical analyses were carried out using RStudio (1.4.1717 

ver. 3.4.0, R Foundation for Statistical Computing, Vienna, Austria).  

Coverage Analysis. Percent coverage data was analyzed using a factorial analysis of variance 

(ANOVA) and post-hoc analysis using Tukey’s HSD (honest significant difference) with a 

significance level of α=0.05. An arcsine transformation was carried out to ensure that data met 

assumptions of normality and heteroskedasticity. After detection of no significant differences 

between varieties, a single interactive model was used with treatment, canopy strata, and card 

face as fixed factors and percent coverage as a response variable.  

Deposition Analysis. Deposition was analyzed using a factorial analysis of variance (ANOVA) 

and post-hoc analysis using Tukey’s HSD (honest significant difference) with a significance level 

of α=0.05. A quarter root transformation was carried out to ensure that data met assumptions of 

normality and heteroskedasticity. Separate models were carried out for each variety with fixed 

factors of treatment, and canopy strata, flux as a percent of the applied rate was the response 

variable.  

Damage evaluations: Foliar.  Damage evaluations were analyzed using Kruskall-Wallace 

analysis of variance at an alpha level of 0.05 followed by a post-hoc, pairwise Conover-Iman 

test, in which the null hypothesis was rejected if p<ɑ/2, ɑ=0.05. Kruskall-Wallis analysis of 

variance was chosen as the data did not meet normality assumptions required of a one way 

ANOVA. Separate models were generated for response variables, percentage of clean foliage, 

percentage of arthropod damage foliage, and percentage of disease damaged foliage. The 

percentage of arthropod damaged foliage combined counts of damage caused by insect 

defoliation, mite bronzing, and aphids divided by total number of leaves assessed. Disease 

damage combined damage caused by apple scab, powdery mildew, and cedar apple rust, 

divided by the number of leaves assessed. For each of these response variables models were 

generated separating years assessed (2019, 2020), seasons assessed (summer, fall), and 

variety (Gala, Honeycrisp, Fuji), with the fixed factor of treatment (above canopy SSCDS, in 

canopy SSCDS, Airblast).  

54 

 
Damage evaluations: Fruit: Fruit damage analysis was performed in the same manner as 

foliar damage. Response variables were clean fruit, arthropod damage, and disease. Arthropod 

damage combined damage caused by tortricid moths, plum curculio, and damage caused by 

sucking insects. Disease counts combined apple scab, cedar apple rust, sooty blotch and 

flyspeck, as well as rotted fruit. 

Bitter pit Analysis. Analysis of bitter pit disease in Honeycrisp apples was analyzed using 

Kruskall-Wallace analysis of variance followed by a post-hoc, pairwise Conover-Iman test. The 

response variable was the percentage of fruit damaged by bitter pit with a fixed factor of 

treatment.  

Fruit Quality Analysis. Analysis of fruit diameter, fruit count, and fruit weight measurements 

were analyzed using a factorial analysis of variance (ANOVA) and post-hoc analysis using 

Tukey’s HSD (honest significant difference) with a significance level of α=0.05. Separate models 

were carried out for each variety for each respective response variable of fruit diameter, fruit 

count per tree, and fruit weight. Treatment was a fixed factor for each.  

Harvest Yield Analysis. Harvest yield was the product of mean fruit counts per tree and mean 

weights of individual apples (grams). Average fruit per tree and average weight per apple were 

assessed in each treatment within each variety and multiplied giving mean weight in apples per 

tree. Dividing the length of a hectare (100 m) by row spacing in the orchard (3.35 m) equaled 

the number of rows per hectare (30 rows/ha). Dividing the width of a hectare (100 m) by tree 

spacing (.914 m) equals the number of trees per row (109 trees/row). Multiplication of the 

number of trees per row by the number of rows per hectare equaled the number of trees per 

hectare (3270 trees/ha). Weight per tree (g) was then multiplied by the number of trees per 

hectare equaling apple weight in grams per hectare. This value was then converted from g/ha to 

mt/ha (metric ton/hectare) by multiplying g/ha by 1 x 10⁶.   

Harvest yield was analyzed using a factorial analysis of variance (ANOVA) and post-hoc 

analysis using Tukey’s HSD (honest significant difference) with a significance level of α=0.05. 

Separate models were carried out for each variety with a fixed factor of treatment.  

55 

 
3.3.  Results and Discussion 

Table 3.3 Weather conditions recorded during coverage and deposition evaluations. 
Mean temperature, Mean % relative humidity, and wind speed during coverage and 
deposition evaluations. Winds predominantly out of the NNW during applications.  

Treatment 

Mean 
temp. 
°C 

Mean % 
RH 

Wind speed ms⁻¹ 

Above Canopy SSCDS 
In Canopy SSCDS 

26.4 
25.9 

63.5 
65.4 

Min 
0.3 
0.6 

Max 
1.6 
2.0 

Mean 
1.2 
1.4 

Coverage. No significant differences among varieties (Gala, Honeycrisp, Fuji) or treatments 

(above canopy SSCDS, in canopy SSCDS) (F2,102=0.90, p=0.41 and F1,102=2.21, p=0.14) were 

detected for coverage. Significant differences were detected between canopy strata (Top, 

Middle, Bottom) (F2,102=5.03, p=0.008), water sensing paper face (Adaxial, Abaxial) 

(F1,102=100.96, p<0.001), and interaction between treatment and canopy strata (F2,102=13.35, 

p<0.001).  

The greatest coverage was in the top of the canopy in the above canopy SSCDS in the 

adaxial face of the samplers, coverage decreased descending with strata (Table 3.4) (Figure 3.1 

A). Abaxial mean coverage for the top and middle stratas of the canopy were nearly equivalent 

x̅ = 21.02% ± 7.32%, x̅ = 21.64% ± 8.78% respectively) with mean abaxial coverage at the 
bottom of the canopy being the lowest of the three ( x̅ = 3.97% ± 3.41%).  

The in canopy SSCDS mean coverage for adaxial surfaces was greatest for the top and 

bottom strata of the canopy (x̅ = 38.54% ± 6.76%, x̅ =45.95% ± 11.47% respectively). Coverage 
for abaxial surfaces were not significantly different with means at top, middle, and bottom strata 

with similar values (x̅ = 9.15% ± 6.10%, x̅ = 10.11% ± 6.73%, x̅ = 12.05% ± 6.70% respectively).  

Coverage was greatest on the adaxial surfaces for both treatments (above canopy 

SSCDS, in canopy SSCDS) (Table 3.4). The above canopy and in canopy SSCDS mean 

coverage values were significantly different at the top of the canopy at both the adaxial and 

abaxial faces. Coverage from the above canopy SSCDS at the top strata in the adaxial and 

abaxial positions (x̅ = 54.10% ± 12.28%, x̅ = 21.02% ± 7.32% respectively) were significantly 
greater than those positions treated by the in canopy SSCDS (x̅ = 38.54% ± 6.76%, x̅ = 9.15% ± 
6.10% respectively). This was likely due to emitter placement and greater flow rate associated 

with the above canopy SSCDS whose emitters were only present above the canopy. Abaxial 

56 

 
 
 
 
 
 
 
 
 
 
 
 
surfaces for all strata treated by the in canopy SSCDS did not statistically differ, this was also 

true for the adaxial surfaces (Table 3.4) (Figure 3.7B). This was not the case for the above 

canopy SSCDS, whose coverage was significantly different for the top of the canopy in the 

abaxial face and the middle strata of the abaxial and adaxial faces (Table 3.4). These 

differences suggest that the in canopy SSCDS provided a more uniform coverage to the canopy.  

Figure 3.4- A,B – Mean coverage ± SEM in above canopy and in canopy SSCDS. 
Coverage from treatment by the Above canopy SSCDS (A.) and in canopy SSCDS (B.) by 
orchard strata (top, middle, bottom of the canopy) and sampler face: Adaxial (upward 
facing), Abaxial (downward facing). Differing letters denote significant differences 
between sampler surface and canopy placement at α=0.05.  

57 

 
 
 
 
 
Table 3.4- Mean ±SEM percent coverage at strata, height and face. Percent coverage 
provided by treatments at stratus (Top ⅓, Middle ⅓, Bottom ⅓ ) within the canopy, and 
face (Adaxial: upward facing, Abaxial: downward facing) of water sensitive paper. 
Differing letters in Tukey’s HSD column signify significant differences between face and 
strata at α=0.05. 

Treatment 

Strata 

Face 

Above 
Canopy 

Top 

Middle 

Bottom 

Top 

In Canopy 

Middle 

Bottom 

Abaxial 
Adaxial 
Abaxial 
Adaxial 
Abaxial 
Adaxial 
Abaxial 
Adaxial 
Abaxial 
Adaxial 
Abaxial 
Adaxial 

Mean % 
coverage 
21.02 
54.10 
21.64 
42.93 
3.97 
22.74 
9.15 
38.54 
10.11 
28.71 
12.05 
45.95 

±SEM 

7.32 
12.28 
8.78 
5.62 
3.42 
5.52 
6.10 
6.76 
6.73 
7.64 
6.70 
11.47 

a 
ab 
bcd 
bcd 
abc 
abc 
cd 
cde 
de 
e 
de 
de 

Deposition. Analysis of deposition data detected significant differences between apple varieties 

(Gala, Honeycrisp, Fuji), interaction between variety and treatments (above canopy SSCDS, in 

canopy SSCDS), as well as interactions between treatments and canopy strata (Top ⅓, Middle 

⅓, Bottom ⅓) (F2,300=4.36, p=0.01, F2,300=4.96, p<0.008, F2,300=4.00, p=0.02 respectively).  

Gala deposition. No significant differences were detected between treatments, canopy strata, 

or interaction factor (F1,102=0.032, p=0.86, F2,102=0.62, p=0.54, F2,102=2.76, p=0.07 respectively.) 

No significant differences were detected between means for treatments or canopy strata. Mean 

above canopy SSCDS Gala deposition followed a similar pattern to coverage with higher means 

associated with positions higher in the canopy. The deposition mean for the in canopy SSCDS 

was greatest in the bottom of the canopy with middle and top means similar in value, differing 

from the above canopy SSCDS (Table 3.5) (Figure 3.2).  

58 

 
 
 
 
Figure 3.5 - Percent flux as deposition by canopy strata for Gala. Deposition by treatment 
at strata (Top ⅓, Middle ⅓, Bottom ⅓) within the canopy. Differing letters signify 
significant differences between treatments in sampler face and strata at α=0.05. No 
significant differences were detected between treatments in strata for either in canopy or 
above canopy SSCDS.  

Honeycrisp Deposition. No significant differences were detected between canopy strata or 

treatment’s interaction factor between canopy strata (F2,96=1.31, p=0.27, F2,96=0.69, p=0.51 

respectively). A significant difference was detected between the treatments (above canopy 

SSCDS, in canopy SSCDS) (F1,96=6.24, p=0.01). Mean deposition was near equivalent for the in 

canopy SSCDS across all strata (Bottom, Middle, Top) (x̅ = 16.56% ± 15.46%, x̅ = 31.38% ± 
18.22%, x̅ = 20.28% ± 14.95% respectively). Above canopy SSCDS means follow a similar 
pattern as observed with the Gala variety, a lower mean deposition, lower in the canopy (Figure 

3.5).  

59 

 
  
 
 
Figure 3.6 - Percent flux as deposition by canopy strata for Honeycrisp. Deposition by 
treatment at strata (Top ⅓, Middle ⅓, Bottom ⅓) within the canopy. Differing letters 
signify significant differences between treatments in sampler face and strata at α=0.05. 
No significant differences were detected between treatments in strata for either the 
above canopy or in canopy SSCDS. 

Fuji Deposition. No significant differences were detected between treatments, canopy strata, 

or interaction factor (F1,102=2.40, p=0.13, F2,102=0.36, p=0.70, F2,102=1.41, p=0.25 respectively.) 

Above canopy SSCDS mean deposition was greatest at the top of the canopy and lowest at the 

bottom, similar to the other varieties. Mean deposition values for the in canopy SSCDS were 

similar for all strata, similar to the other two varieties (Figure 3.4). 

60 

 
 
 
 
Figure 3.7-Percent flux as deposition by canopy strata for Fuji. Deposition by treatment 
at strata (Top ⅓, Middle ⅓, Bottom ⅓) within the canopy. Differing letters signify 
significant differences between treatments in sampler face and strata at α=0.05. No 
significant differences were detected between treatments in strata for either above 
canopy or in canopy SSCDS.   

Table 3.5 Mean percentage of flux ± SEM as deposition. Flux as a percent of the applied 
rate by treatment and strata (Top ⅓, Middle ⅓, Bottom ⅓) of the canopy. Differing letters 
signify significant differences between treatments within strata for each variety at α=0.05.  

Variety 

Gala 

Honeycrisp 

In Canopy 

Above 
Canopy 

Treatment  Strata  %Flux/deposition  ±SEM 
13.89% 
18.87% 
15.24% 
18.25% 
13.51% 
15.84% 
15.63% 
26.91% 
25.41% 
15.46% 
18.31% 
14.95% 

26.89% 
34.67% 
39.28% 
45.22% 
25.28% 
29.78% 
20.88% 
31.38% 
45.50% 
16.56% 
18.22% 
20.28% 

Bottom 
Middle 
Top 
Bottom 
Middle 
Top 
Bottom 
Middle 
Top 
Bottom 
Middle 
Top 

Above 
Canopy 

In Canopy 

a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 

61 

 
 
 
 
 
Table 3.5 (cont’d) 

Fuji 

Above 
Canopy 

In Canopy 

Bottom 
Middle 
Top 
Bottom 
Middle 
Top 

14.61% 
28.22% 
39.94% 
39.94% 
38.11% 
33.83% 

10.98% 
18.56% 
23.44% 
20.44% 
25.01% 
20.65% 

a 
a 
a 
a 
a 
a 

Coverage and Deposition Conclusions. Coverage patterns for above canopy and in canopy 

SSCDS differed with the highest coverage means located higher in the canopy for the above 

canopy while means were more evenly distributed for the in canopy SSCDS. The in canopy 

SSCDS mean coverage means were most similar at the top and the bottom strata in the adaxial 

faces, with all three abaxial faces being near equivalent. These patterns were most likely due to 

the distribution of the emitters. The above canopy SSCDS with emitters located only at the top 

of the canopy had the highest coverage at the top of the canopy. This was clearest when 

comparing adaxial and abaxial faces at the top strata and the bottom strata with the lowest 

coverage being the abaxial face of the bottom strata. Similar coverage patterns for an in canopy 

SSCDS were reported by Owen-Smith (2019) with the SSCDS providing similar coverage to 

adaxial faces at all strata (Figure 3.1B) (Table 3.4). 

Deposition means were significantly different between apple varieties. These differences 

may be attributed to physiology with differing canopy densities between varieties or in the 

location of the varieties themselves in the plots. Deposition for the in canopy SSCDS was 

highest at the top and bottom strata in Gala and Fuji (Table 3.5). Analysis of deposition in 

Honeycrisp did detect a significant difference between the above canopy and in canopy 

SSCDS. Honeycrisp deposition measurements for the in canopy SSCDS were similar at each 

strata and lower than the above canopy at each strata (Figure 3.6) (Table 3.5). Deposition in the 

above canopy SSCDS decreased with greater distance from the top of the canopy. Fuji 

deposition was determined to have no significant difference between treatments or strata, its 

mean deposition was similar to gala but with greater similarity in mean deposition associated 

with the in canopy SSCDS (Tables 3.4 & 3.5).  

Damage Evaluations: 2019 Clean Fruit. The percentage of clean fruit analyzed in the summer 

of 2019 detected no significant differences between treatments for Gala, Honeycrisp, or Fuji 

(𝜒²=2.66, df= 2, p=0.27, 𝜒²=1.03, df= 2, p=0.60, and 𝜒²=1.03, df= 2, p=0.59 respectively). A 

majority of the fruit assessed was determined to be clean for all varieties and treatments, with all 

means near one hundred percent (Table 3.6).  

62 

 
 
Analysis of the percentage of clean fruit in the fall of 2019 detected statistically 

significant differences between treatments in Gala and Fuji (𝜒²=38.09, df= 2, p=<0.001, 

𝜒²=12.49, df= 2, p=<0.001 respectively). In Gala, significant differences were detected between 

the in canopy SSCDS and the Airblast and the above canopy SSCDS and the Airblast with no 

significant differences detected between above canopy and in canopy SSCDS 

(p<0.001,p<0.001, and p=0.50 respectively). In Gala, the percentage of clean fruit was highest 

for fruit treated by the above canopy and in canopy SSCDS compared to the Airblast (x̅ = 
95.71% ± 4.53%, x̅ = 95.24 ± 5.67%, x̅ = 86.83 ± 7.64% respectively). In Fuji, a significant 
difference was detected between the above canopy SSCDS and Airblast treatments (p<0.001) 

with no significant differences detected between the above and in canopy SSCDS or the in 

canopy SSCDS and the Airblast (p=0.05, p=0.03 respectively). In Fuji, the percentage of clean 

fruit treated by the Airblast was greater than the percentage of clean fruit treated by the above 

canopy SSCDS (x̅ = 95.71% ± 4.53%, x̅ = 95.24 ± 5.67% respectively).  
Damage Evaluations: 2019 Clean Foliage Analysis of the percentage of clean foliage 

assessed in the summer of 2019 detected no significant differences between treatments in 

Gala, Honeycrisp, or Fuji (𝜒²=5.30, df= 2, p=0.07, 𝜒²=0.392, df= 2, p=0.822, and 𝜒²=0.88, df= 2, 

p=0.64 respectively). A majority of the foliage assessed in the summer of 2019 was free of 

damage, percentages of clean foliage in all varieties and treatments were near ninety percent 

(Table 3.7).  

Analysis of the percentage of clean foliage in the fall of 2019 detected no significant 

differences between treatments in Gala or Honeycrisp (𝜒²=3.85, df= 2, p=0.15, 𝜒²=2.87, df= 2, 

p=0.24 respectively) with means all approximately ninety percent clean foliage in all treatments. 

Significant differences were detected between treatments in Fuji (𝜒²=16.29, df= 2, p<0.001) 

between the above canopy SSCDS and the Airblast as well as the in canopy SSCDS and the 

Airblast (p<0.001, p<0.001 respectively). No significant differences were detected between the 

in canopy and above canopy SSCDS (p=0.47). The percentages of clean foliage for both the 

above canopy SSCDS and in canopy SSCDS were greater than the percentage of clean foliage 

treated by the Airblast (x̅ = 92.62% ± 5.95%, x̅ = 91.62 ± 7.72%, x̅ = 84.60% ± 9.16% 
respectively). 

Damage Evaluations: 2020 Clean Fruit. Analysis of the clean fruit assessed in the summer of 

2020 detected no statistically significant differences between treatments in Gala or Honeycrisp 

(𝜒²=2.66, df= 2, p=0.27, 𝜒²=0.15, df= 2, p=0.93 respectively). Percentages of clean fruit for all 

treatments in both Gala and Honeycrisp varieties were approximately 100%. Significant 

differences between treatments were detected for Fuji (𝜒²=12.38, df= 2, p=0.002) between the 

63 

 
above canopy SSCDS and the Airblast, and the in canopy SSCDS and the Airblast (p=0.001, 

p=0.008 respectively). No significant differences were detected between the in canopy and 

above canopy SSCDS (p=0.20). The percentage of clean fruit treated by the Airblast in Fuji was 

slightly higher than the percentage of clean fruit treated by both the above canopy and in 

canopy SSCDS (𝑥= 99.44% ± 1.50%, x̅ = 96.67 ± 3.87%, x̅ = 97.22% ± 3.87% respectively) 

(Table 3.6). 

Analysis of the clean fruit assessed in the fall of 2020 detected no significant differences 

between treatments for Gala, Honeycrisp, or Fuji varieties (𝜒²=4.30, df= 2, p=0.12, 𝜒²=1.45, df= 

2, p=0.48, 𝜒²=2.73, df= 2, p=0.25 respectively). A majority of the fruit assessed in the fall of 

2020 were free of damage with the percentage of clean fruit detected near 100%. (Table 3.6)  

Damage Evaluations: 2020 Clean Foliage. The analysis of clean foliage in the summer of 

2020 detected no statistically significant differences between treatments in Gala, Honeycrisp, or 

Fuji (𝜒²=3.24, df= 2, p=0.20, 𝜒²=0.98, df= 2, p=0.61, 𝜒²=2.36, df= 2, p=0.31 respectively). A 

majority of the foliage assessed in the summer of 2020 was free of damage, the proportion of 

clean foliage detected in all varieties and treatments was greater than ninety percent (Table 

3.7).  

The percentage of clean foliage analyzed for the fall of 2020 detected no significant 

differences between treatments in Fuji (𝜒²=1.80, df= 2, p=0.41) with means near eighty percent 

clean foliage (Table 3.7). Significant differences were detected between treatments for Gala and 

Honeycrisp (𝜒²=9.18, df= 2, p=0.01, 𝜒²=6.96, df= 2, p=0.03 respectively). Significant differences 

were detected between treatments in Honeycrisp using omnibus testing, however post hoc 

testing did not detect significant differences between treatments (p>.025). The mean percent 

clean foliage for treatments in Honeycrisp ranged from approximately eighty-four to ninety 

percent. In Gala no significant differences were detected between the above canopy SSCDS 

and Airblast or in canopy SSCDS and Airblast (p=0.11, p=0.21 respectively). Significant 

differences were detected between the above canopy SSCDS and in canopy SSCDS (p=0.01) 

with the percentage of clean foliage higher in trees treated by the above canopy SSCDS (x̅ = 
92.26% ± 5.44%, x̅  = 87.65 ± 11.45% respectively). Overall, a majority of the foliage assessed in 
the fall of 2020 was free of pest damage with some differences between SSCDS configurations 

detected in Gala.  

64 

 
 
 
Table 3.6 Mean ± SEM percentage of clean fruit detected in 2019 and 2020. Clean fruit by 
variety, treatment, season. Differing letters signify significant differences between 
treatments within year, variety, and season at α=0.025. 

Variety 

Season 

Gala 

Honeycrisp 

Fuji 

Gala 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

2019 

Treatment 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

2020 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

% Clean Fruit  ± SEM 
4.38% 
5.04% 
5.84% 
4.53% 
5.67% 
7.64% 
3.01% 
4.42% 
3.92% 
5.45% 
3.75% 
4.59% 
4.41% 
4.98% 
3.60% 
10.14% 
10.92% 
6.08% 

96.25% 
95.56% 
93.96% 
95.71% 
95.24% 
86.83% 
97.78% 
96.83% 
96.67% 
95.71% 
97.14% 
96.83% 
96.61% 
95.60% 
96.98% 
83.33% 
86.03% 
92.38% 

98.75% 
98.61% 
99.31% 
93.59% 
91.42% 
94.48% 

2.43% 
2.28% 
1.98% 
5.22% 
5.66% 
4.16% 

a 
a 
a 
 a  
a 
 b  
a 
a 
 a  
a 
a 
 a  
a 
a 
 a  
a 
a 
 b  

a 
a 
 a  
a 
a 
 a  

65 

 
 
 
 
Table 3.6 (cont’d) 

Honeycrisp 

Summer 

Fall 

Summer  

Fuji 

Fall 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

99.44% 
99.31% 
99.31% 
98.31% 
98.99% 
98.69% 
96.67% 
97.22% 
99.44% 
87.73% 
91.39% 
91.67% 

1.50% 
1.98% 
1.66% 
2.50% 
2.03% 
2.45% 
3.87% 
3.87% 
1.50% 
7.95% 
5.99% 
5.19% 

a 
a 
 a  
a 
a 
 a  
a 
a 
 b  
a 
a 
 a  

Table 3.7 Mean ± SEM percentage of clean foliage detected in 2019 and 2020. Percentage 
of clean foliage detected by variety, treatment, season. Differing letters signify significant 
differences between treatments within year, variety, and season at α=0.025. 

Variety 

Season 

Gala 

Honeycrisp 

Fuji 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

2019 

Treatment 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

% Clean Foliage  ± SEM 

8.84% 
7.05% 
7.92% 
10.07% 
7.90% 
8.96% 
8.21% 
6.97% 
6.83% 
10.38% 
11.34% 
9.82% 
7.22% 
5.41% 
6.76% 
5.95% 
7.72% 
9.16% 

a 
a 
a 
a 
a 
b 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
b 

87.61% 
89.32% 
91.84% 
86.38% 
87.34% 
83.51% 
89.13% 
89.26% 
90.28% 
84.73% 
83.98% 
81.64% 
91.38% 
93.24% 
91.14% 
92.62% 
91.62% 
84.60% 

66 

 
 
 
 
 
Table 3.7 (cont’d) 

Gala 

Honeycrisp 

Fuji 

Summer 

Fall 

Summer 

Fall 

Summer  

Fall 

2020 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

94.75% 
93.35% 
95.00% 
92.26% 
87.65% 
89.78% 

96.25% 
96.00% 
96.92% 
86.87% 
89.31% 
84.17% 
95.28% 
96.94% 
95.28% 
75.14% 
76.96% 
82.31% 

5.79% 
5.53% 
5.39% 
5.44% 
6.61% 
5.75% 

5.13% 
4.60% 
4.77% 
7.01% 
6.80% 
8.15% 
5.36% 
4.09% 
4.79% 
13.52% 
12.56% 
7.15% 

a 
a 
a 
a 
a 
a 

a 
a 
a 
a 
a 
a 
a 
a 
b 
a 
a 
a 

Damage Evaluations: 2019 Arthropod Damaged Fruit. Analysis of arthropod damaged fruit 

assessed in the summer 2019 detected no significant differences between treatments in 

Honeycrisp or Fuji (𝜒²=2.45, df= 2, p=0.29, 𝜒²=2.03, df= 2, p=0.60 respectively). The percentage 

of damaged fruit detected in both varieties for all treatments was under 1%. Significant 

differences were detected between treatments in Gala (𝜒²=14.22, df= 2, p<0.001) between the 

above canopy SSCDS and the in canopy SSCDS as well as between the above canopy SSCDS 

and the Airblast (p<0.001, p=0.001 respectively). No significant differences were detected 

between the in canopy SSCDS and the Airblast (p=0.49). The percentage of damaged apples 

treated by the above canopy SSCDS was greater than the percentage of damaged apples 

treated by either the in canopy SSCDS or Airblast (x̅ = 2.02% ± 3.58%, x̅ = 0.16 ± 0.87%, 
x̅ =0.14% ± 0.76% respectively) (Table 3.8). 

Analysis of arthropod damaged fruit from the fall of 2019 detected no statistically 

significant differences between treatments for Gala or Fuji (𝜒²=4.01, df= 2, p=0.13, 𝜒²=1.00, df= 

2, p=0.61 respectively). Omnibus testing detected significant differences between treatments for 

Honeycrisp (𝜒²=6.45, df= 2, p=0.04), however, post-hoc analysis detected no significant 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
differences between treatments (p>.025). A majority of the fruit assessed were free of pest 

damage with means near or below one percent damaged fruit (Table 3.8).  

Damage Evaluations: Arthropod Damaged Foliage. Analysis of foliar arthropod damage from 

the summer 2019 detected no statistical differences between treatments for Honeycrisp or Fuji 

varieties (𝜒²=0.39, df= 2, p=0.82, 𝜒²=0.88, df= 2, p=0.64 respectively) with the percentages of 

damaged foliage ranging from ~ 3% - 6% (Table 3.9).  

Significant differences between treatments were detected in Gala (𝜒²=13.88, df= 2, 

p<0.001) between the above canopy SSCDS and Airblast as well as the in canopy SSCDS and 

Airblast. No statistical differences were detected between above canopy SSCDS and in canopy 

SSCDS (p<0.001, p=0.001, p=0.11 respectively). The percentage of foliar damage was higher in 

trees treated by both the above canopy and in canopy SSCDS than those treated by the Airblast 

(x̅  = 5.62% ± 6.13%, x̅  = 3.88 ± 4.69%, x̅ = 1.43% ± 3.31% respectively). 

Analysis of foliar arthropod damage assessed in the fall of 2019 detected no statistically 

significant differences between treatments for Gala (𝜒²=2.65, df= 2, p=0.27) with the 

percentages of damaged foliage ranging from ~3% - 4%. Statistically significant differences 

were detected between treatments for Honeycrisp and Fuji (𝜒²=8.19, df= 2, p=0.02, 𝜒²=23.10, 

df= 2, p<0.001 respectively). In Honeycrisp significant differences were detected between the 

above canopy SSCDS and Airblast (p=0.02) and no significant differences were detected 

between the above canopy and in canopy SSCDS or the in canopy SSCDS and Airblast 

(p=0.07, p=0.42 respectively). The percentage of arthropod damaged foliage in Honeycrisp was 

greater in foliage treated by the Airblast when compared to the above canopy SSCDS (𝑥= 

14.40% ± 9.36%, x̅ = 7.91 ± 7.24% respectively). Analysis of foliar damage in Fuji detected 
significant differences between above canopy SSCDS and Airblast as well as in canopy SSCDS 

and Airblast ( p<0.001, p<0.001 respectively) with no significant differences detected between 

above and in canopy SSCDS treatments (t=–0.74, p=0.23). Foliage treated by both the above 

canopy and in canopy SSCDS had less arthropod damage than foliage treated by the Airblast 

(x̅ = 2.20% ± 3.47%, x̅ = 3.30 ± 4.78%, x̅  = 8.38% ± 6.90% respectively).  
Damage Evaluations: 2020 Arthropod Damaged Fruit. Analysis of the percentage of fruit 

damaged by arthropods in the summer of 2020 detected no significant differences between 

treatments in Honeycrisp (𝜒²=0, df= 2, p=0.91) with means below 1% for all treatments. No 

statistical analysis was performed for Gala or Fuji as no damage was detected for Gala in any 

treatment and no damage was present for the above canopy SSCDS or Airblast treatments for 

Fuji.  

68 

 
Analysis of the percentage of fruit damaged by arthropods in the fall of 2020 detected no 

significant differences between treatments in Honeycrisp (𝜒²=2.02, df= 2, p=0.36) with 

percentages of damaged fruit well below 1% (Table 3.8). For Gala and Fuji no statistical 

analysis was performed due to no arthropod damage detected in any treatment for Gala and no 

damage detected in either SSCDS system in Fuji.  

Damage Evaluations: 2020 Arthropod Damaged Foliage. Analysis of arthropod damaged 

foliage for summer of 2020 detected no significant differences between treatments for Gala, 

Honeycrisp, or Fuji (𝜒²=3.24, df= 2, p=0.20, 𝜒²=0.76, df= 2, p=0.68, 𝜒²=0.97, df= 2, p=0.61 

respectively) with means ranging from 1.39%-3.46% damaged foliage.  

Analysis of arthropod damaged foliage from the fall of 2020 detected no significant 

differences between treatments in Fuji (𝜒²=4.27, df= 2, p=0.12) with means ranging from 2.48%-

4.29% damaged foliage. Significant differences were detected between treatments in Gala and 

Honeycrisp (𝜒²=7.31, df= 2, p=0.03, 𝜒²=7.43, df= 2, p=0.02 respectively). Post-hoc analysis of 

Gala detected no significant differences between treatments, means ranged from 0.14%-1.23% 

damaged foliage. Analysis of Honeycrisp detected significant differences between the in canopy 

SSCDS and Airblast (p=0.021) with a greater percentage of damaged foliage treated by the 

Airblast compared to the in canopy SSCDS (x̅  = 15.83 ± 8.15%, x̅  = 10.56% ± 6.75%).  

Table 3.8 Mean ± SEM percentage of arthropod damaged fruit detected in 2019 and 2020. 
Percentages of arthropod damaged fruit detected by variety, treatment, season. Differing 
letters signify significant differences between treatments within year, variety, and season 
at α=0.025. 

Variety 

Season 

Gala 

Honeycrisp 

Summer 

Fall 

Summer 

Fall 

2019 

Treatment 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

% Arth. Damage 
2.02% 
0.16% 
0.14% 
0.32% 
0.00% 
0.00% 
0.95% 
0.32% 
0.48% 
1.27% 
0.16% 
0.32% 

±SEM 
3.58% 
0.87% 
0.76% 
1.22% 
0.00% 
0.00% 
2.07% 
1.22% 
1.49% 
2.66% 
0.87% 
1.22% 

a 
a 
b 
a 
a 
a 
a 
a 
a 
a 
b 
b 

69 

 
 
 
Table 3.8 (cont’d) 

Fuji 

Gala 

Honeycrisp 

Fuji 

Summer 

Fall 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

2020 

Summer 

Fall 

Summer 

Fall 

Summer  

Fall 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

0.53% 
0.14% 
0.00% 
0.16% 
0.00% 
0.16% 

0.00% 
0.00% 
0.00% 
0.00% 
0.00% 
0.00% 
0.14% 
0.14% 
0.14% 
0.14% 
0.30% 
0.00% 
0.00% 
0.14% 
0.00% 
0.00% 
0.00% 
0.28% 

2.20% 
0.76% 
0.00% 
0.87% 
0.00% 
0.87% 

0.00% 
0.00% 
0.00% 
0.00% 
0.00% 
0.00% 
0.76% 
0.76% 
0.76% 
0.76% 
1.15% 
0.00% 
0.00% 
0.76% 
0.00% 
0.00% 
0.00% 
1.07% 

a 
a 
a 
a 
a 
a 

a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 

a 
a 
a 

Table 3.9 Mean ± SEM percentage of arthropod damaged foliage detected in 2019 and 
2020. Arthropod damaged foliage detected by variety, treatment, and season. Differing 
letters signify significant differences between treatments within year, variety, and season 
at α=0.025. 

Variety 

Season 

Gala 

Summer 

Fall 

2019 

Treatment 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

% Arth. Damage  ±SEM  CLD 

5.62% 
3.88% 
1.43% 
2.85% 
3.93% 
4.10% 

6.13% 
4.69% 
3.31% 
6.58% 
5.38% 
4.99% 

a 
a 
b 
a 
a 
a 

70 

 
 
  
 
  
 
 
 
Table 3.9 (cont’d) 

Honeycrisp 

Fuji 

Gala 

Honeycrisp 

Fuji 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer  

Fall 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

2020 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

5.30% 
5.81% 
3.93% 
7.91% 
12.87% 
14.40% 
3.54% 
2.50% 
2.98% 
2.20% 
3.30% 
8.38% 

2.76% 
3.46% 
1.39% 
0.56% 
0.14% 
1.23% 
1.94% 
2.27% 
1.94% 
13.13% 
10.56% 
15.83% 
2.78% 
1.67% 
1.81% 
2.48% 
2.97% 
4.29% 

4.81% 
5.37% 
4.46% 
7.24% 
9.86% 
9.36% 
4.19% 
3.36% 
3.78% 
3.47% 
4.78% 
6.90% 

3.86% 
4.45% 
2.75% 
1.50% 
0.76% 
2.15% 
3.23% 
3.23% 
3.89% 
7.01% 
6.75% 
8.15% 
4.69% 
3.46% 
2.97% 
3.27% 
3.53% 
4.06% 

a 
a 
a 
a 
ab 
b 
a 
a 
a 
a 
a 
b 

a 
a 
a 
ab 
a 
b 
a 
a 
a 
ab 
a 
b 
a 
a 
a 
a 
a 
a 

Damage Evaluations: 2019 Disease Damaged Fruit. Analysis of disease damaged fruit from 

the summer of 2019 detected no significant differences between treatments for Honeycrisp or 

Fuji varieties. (𝜒²=3.83, df= 2, p=0.60, 𝜒²=1.64, df= 2, p=0.44 respectively) The percentages of 

damaged fruit ranged from approximately 1%-3% in Honeycrisp and 3%-4% in Fuji. Significant 

differences between treatments were detected for Gala (𝜒²=9.78, df= 2, p<0.001) between 

above canopy and Airblast treatments (t=–3.14, p=0.003). No significant differences were 

detected between above and in canopy SSCDS or between in canopy SSCDS and Airblast 

71 

 
 
 
treatments (p=0.04, p=0.11 respectively). In Gala a greater percentage of disease damaged 

apples were detected when treated by Airblast compared to the in canopy SSCDS (x̅ = 5.90 ± 
5.84%, x̅ =4.29% ± 5.01% respectively).  
Analysis of disease damaged fruit from the fall of 2019 detected no significant differences 

between treatments for Honeycrisp (𝜒²=0.11, df= 2, p=0.95) with means near 3% damaged fruit. 

Significant differences between treatments were detected for Gala and Fuji (𝜒²=40.24, df= 2, 

p<0.001, 𝜒²=12.60, df= 2, p=0.002, respectively). In Gala, significant differences were detected 

between treatments above canopy SSCDS and Airblast and also between in canopy SSCDS 

and Airblast (p<0.001, p<0.001 respectively). No significant differences were detected between 

above canopy and in canopy SSCDS (p=0.39). Gala trees treated by Airblast were found to 

have greater percentages of damaged fruit compared to those treated by either the above 

canopy or in canopy SSCDS (x̅ = 13.17 ± 7.64%, x̅ =3.97% ± 4.46%, x̅  =4.76% ± 5.67% 
respectively). In Fuji, a significant difference was detected between treatments above canopy 

SSCDS and Airblast (p<0.001). No significant differences were detected between above and in 

canopy SSCDS or in canopy SSCDS and Airblast (t=1.65, p=0.05 and t=–1.97, p=0.04 

respectively). Fuji trees treated by the above canopy SSCDS were found to have a greater 

percentage of damaged fruit than those treated by the Airblast (x̅  = 16.51 ± 10.11%, x̅  =7.62% ± 
6.08% respectively).  

Damage Evaluations: 2019 Disease Damaged Foliage. Analysis of the percentage of disease 

damaged foliage from the summer of 2019 detected no significant differences between any 

treatments for Gala, Honeycrisp, or Fuji (𝜒²=0.40, df= 2, p=0.82, 𝜒²=0.31, df= 2, p=0.86, 

𝜒²=2.18, df= 2, p=0.34 respectively). The percentage of damaged foliage in all varieties and 

treatments ranged from ~ 7%-10%.  

Analysis of the percentage of disease damaged foliage from the fall of 2019 detected no 

significant differences between any treatments for Gala, Honeycrisp, or Fuji (𝜒²=4.62, df= 2, 

p=0.99, 𝜒²=3.27, df= 2, p=0.19, 𝜒²=1.10, df= 2, p=0.59 respectively). Means ranged from ~ 6%-

19% damaged foliage.  

Damage Evaluations: 2020 Disease Damaged Fruit . Analysis of the percentage of disease 

damage fruit from the summer of 2020 detected no significant differences between treatments 

for Gala or Honeycrisp (𝜒²=2.66, df= 2, p=0.27, 𝜒²=0.20, df= 2, p=0.91 respectively) with means 

near 1% damaged fruit across all treatments. Significant differences were detected between 

treatments for Fuji (𝜒²=12.21, df= 2, p=0.002) between the above canopy and Airblast 

treatments as well as the in canopy and Airblast treatments (p<0.001 and p=0.01 respectively). 

No significant differences were detected between the in canopy and above canopy SSCDS 

72 

 
treatments (p=0.14). The percentage of disease damaged fruit in Fuji was greater in trees 

treated by the above and in canopy SSCDS when compared to the Airblast (𝑥= 3.33 ± 3.87%, 

𝑥=2.64% ± 3.82%, 𝑥=0.56% ± 1.50% respectively).  

Analysis of the percentage of disease damaged fruit from the fall of 2020 detected no 

significant differences between treatments for Gala, Honeycrisp, or Fuji (𝜒²=4.30, df= 2, p=0.12, 

𝜒²=2.33, df= 2, p=0.31, 𝜒²=3.12, df= 2, p=0.21 respectively). The mean percentage of damaged 

fruit ranged from ~6%-9% for Gala, ~1%-2% for Honeycrisp, and ~9%-13% for Fuji (Table 3.10).  

Damage Evaluations: 2020 Disease Damaged Foliage. Analysis of the percentage of disease 

damaged foliage from the summer of 2020 detected no significant differences between 

treatments in Gala, Honeycrisp, or Fuji varieties (𝜒²=1.56, df= 2, p=0.46, 𝜒²=0.88, df= 2, p=0.65, 

𝜒²=2.49, df= 2, p=0.29 respectively). The mean percentage of damaged fruit ranged from ~2%-

4% for Gala, ~ 1%-2% for Honeycrisp, and ~1%-3% for Fuji (Table 3.11).  

Analysis of the percentage of disease damaged foliage from the fall of 2020 detected no 

significant differences between the treatments in Fuji (𝜒²=3.39, df= 2, p=0.18), means ranged 

from ~13%-22%. No statistical analysis was performed on Honeycrisp due to no damage 

detected in foliage treated by the above canopy SSCDS and Airblast treatments. Mean damage 

for foliage treated by the in canopy SSCDS was less than 1%. Significant differences in Gala 

were detected (𝜒²=10.65, df= 2, p=0.005) between the treatments above canopy and in canopy 

SSCDS (p=0.002). No significant differences were detected between the above canopy SSCDS 

and Airblast or in canopy SSCDS and Airblast (p=0.15, p=0.09 respectively). The percentage of 

damaged foliage in Gala treated by the in canopy SSCDS was greater than foliage treated by 

the above canopy SSCDS (x̅  = 12.21 ± 6.65%, x̅  =7.18% ± 5.26% respectively).  

73 

 
 
 
 
Table 3.10- Mean ± SEM percentage of disease fruit detected from 2019 and 2020. 
Percentage of disease damaged fruit detected by variety, treatment, and season. 
Differing letters signify significant differences between treatments within year, variety, 
and season at α=0.025. 

Variety 

Season 

Treatment 

2019 

Gala 

Honeycrisp 

Fuji 

Gala 

Honeycrisp 

Fuji 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

2020 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

±SEM 

2.69% 
5.01% 
5.84% 
4.46% 
5.67% 
7.64% 
2.36% 
4.32% 
3.54% 
5.02% 
3.69% 
4.49% 
3.95% 
4.85% 
3.60% 
10.11% 
10.88% 
6.08% 

2.43% 
2.28% 
1.98% 
5.22% 
5.66% 
4.16% 
1.30% 
1.85% 
1.50% 
2.41% 
1.71% 
2.45% 
3.87% 
3.82% 
1.50% 
7.95% 
5.99% 
5.22% 

a 
ab 
b 
a 
bc 
c 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
b 

a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 

% Dis. 
Damage 
1.73% 
4.29% 
5.90% 
3.97% 
4.76% 
13.17% 
1.27% 
2.86% 
2.86% 
3.02% 
2.70% 
2.86% 
2.86% 
4.27% 
3.02% 
16.51% 
13.81% 
7.62% 

1.25% 
1.39% 
0.69% 
6.41% 
8.58% 
5.52% 
0.42% 
0.56% 
0.56% 
1.55% 
0.71% 
1.31% 
3.33% 
2.64% 
0.56% 
12.27% 
8.61% 
8.06% 

74 

 
 
 
 
Table 3.11- Mean ± SEM percentages of disease damaged foliage from 2019 and 2020. 
Disease damaged foliage detected by variety, treatment, and season. Differing letters 
signify significant differences between treatments within year, variety, and season at 
α=0.025. 

Variety 

Season 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Fall 

Summer 

Gala 

Honeycrisp 

Fuji 

Gala 

Honeycrisp 

2019 

Treatment 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

2020 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

Above Canopy 

Fall 

In Canopy 

Airblast 

a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 
a 

a 
a 
a 
a 
b 
a 
a 
a 
a 

±SEM 
9.62% 
8.54% 
30.85% 
14.62% 
10.20% 
14.35% 
11.28% 
6.75% 
8.02% 
32.45% 
12.05% 
13.18% 
8.77% 
6.96% 
7.45% 
7.99% 
8.97% 
11.56% 

3.61% 
3.35% 
4.87% 
5.26% 
6.65% 
5.29% 
2.77% 
3.00% 
2.10% 

0.00% 

0.76% 

0.00% 

% Dis. Damage 
10.11% 
9.18% 
13.33% 
16.50% 
12.17% 
18.84% 
8.74% 
6.58% 
7.90% 
15.97% 
6.32% 
6.76% 
7.23% 
5.60% 
7.90% 
6.99% 
7.42% 
10.86% 

2.48% 
3.19% 
3.61% 
7.18% 
12.21% 
8.99% 
1.81% 
1.73% 
1.13% 

0.00% 

0.14% 

0.00% 

75 

 
 
  
 
  
 
 
Table 3.11 (cont’d) 

Fuji 

Summer 

Fall 

Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

1.94% 
1.39% 
2.92% 
22.37% 
20.07% 
13.40% 

2.84% 
2.52% 
3.91% 
13.89% 
12.60% 
6.84% 

a 
a 
a 
a 
a 
a 

Damage Evaluations: Bitter Pit Disorder in Honeycrisp Apples. Analysis of bitter pit 

diseased Honeycrisp apples in the Fall of 2020 detected significant differences between the 

treatments (𝜒²=65.39, df= 2, p<0.001) above canopy SSCDS and the Airblast as well as the in 

canopy SSCDS and the Airblast (p<0.001, p<0.001 respectively.) No significant differences were 

detected between the above canopy and in canopy SSCDS treatments (p =0.3). Bitter pit 

incidence was greatest for apples treated with the Airblast sprayer.  

Table 3.12 Percentage of bitter pit diseased Honeycrisp apples. Percentage of 
Honeycrisp apples with bitter pit detected in each treatment. Differing letter denotes 
significant difference at ɑ=0.025. A significantly greater amount of bitter pit was detected 
in apples treated by the Airblast. 

Treatment 

% Bitter pit 

Above Canopy 

In Canopy 

Airblast 

6.93% 

6.94% 

27.29% 

±SEM 

12.31% 

8.91% 

19.06% 

a 

a 

b 

Pest Management and Disease Conclusions: Clean fruit. A majority of the fruit assessed in 

both the summer and fall of 2019 and 2020 were free of damage. Treatments in all varieties for 

the summer of 2019 and the fall of 2020 did not statistically differ, with means near 100% clean 

fruit. Analysis of the clean fruit data from the fall of 2019 detected significant differences in 

treatments for both Gala and Fuji. In Gala, the percentage of clean apples was greater in trees 

treated by the SSCDS configurations when compared to the Airblast. In Fuji, the opposite: trees 

treated by the Airblast had a higher percentage of clean fruit. In the summer of 2020, significant 

differences were detected in Fuji, with the Airblast treated trees having a higher percentage of 

clean fruit and means across all treatments were near 100% clean fruit. While differences were 

detected a majority of the fruit assessed in all treatments was free of damage (Table 3.6).  

As with fruit, a majority of the foliage assessed in both summer and fall of 2019 and 

2020 were free of damage in all treatments. Analysis of clean foliage from the summers of both 

76 

 
 
 
 
 
2019 and 2020 detected no significant differences between treatments in any varieties with the 

percentages of clean foliage near ninety percent in 2019 and near 100% in 2020. Significant 

differences between treatments were detected in the fall of both 2019 and 2020. Airblast treated 

Fuji foliage from 2019 had lower percentages of clean foliage compared to the SSCDS 

configurations with Airblast means ~ 6% lower. Gala trees treated by the above canopy SSCDS 

in the fall of 2020 had ~5% greater clean foliage compared to the in canopy SSCDS (Table 3.7). 

Comparing summer and fall for two years, no findings suggest a specific treatment or 

treatment in variety that provides superior pest management over another. Overall findings 

suggested adequate pest management from both SSCDS configurations, similar to the Airblast. 

Owen-Smith et al. (2019) and Owen-Smith (2017) described similar findings with low incidences 

of apple scab and arthropod damage in apple plots treated by Airblast and in canopy SSCDS.  

Pest Management and Disease Conclusions: Arthropod damage. Very little arthropod 

damaged fruit was detected in summer or fall for both 2019 and 2020. The percentage of 

damaged fruit assessed in the summer of 2019 was under one percent in all but Gala. In Gala, 

significant differences were detected between the above canopy SSCDS and both the Airblast 

and in canopy SSCDS. Trees treated by the above canopy SSCDS had a greater percentage of 

arthropod damaged fruit than either the Airblast or in canopy SSCDS. For the summer of 2020, 

little to no damage was detected for any treatments in all three varieties. Little to no damage 

was detected in the fall of 2019 with all damage <1% aside from the above canopy treatment in 

Honeycrisp near 1%. Fall of 2020 was similar, with no damage detected in Gala for any 

treatments, and all other treatments in Honeycrisp and Fuji had less than 1% damage detected. 

A greater amount of damage was detected in trees treated by the above canopy SSCDS in both 

summer and fall of 2019 in Gala and Honeycrisp. However, these differences were quite small 

and the majority of the fruit assessed in the two years of the study were clean for all treatments 

in all three varieties (Table 3.8). 

As with fruit, arthropod damaged foliage assessed in the summer was low, most 

percentages were < 6%. Analysis of damaged foliage in the summer of 2019 detected no 

significant differences between treatments in Honeycrisp and Fuji with damages ranging from ~ 

3%-6%. Significant differences were detected between treatments in Gala with trees treated by 

the Airblast having a lower percentage of damaged foliage when compared to either the above 

canopy or in canopy SSCDS. Analysis of damaged foliage from the summer of 2020 detected 

no significant differences between treatments in any variety with damages ranging from ~1%-

3%. Analysis of foliar damage from the fall of 2019 detected no significant differences between 

treatments in Gala with damages ranging from ~3%-4%. In Honeycrisp the percentage of 

77 

 
damaged foliage was greater in trees treated by the Airblast compared to the above canopy 

SSCDS. Fuji trees treated by the Airblast had a greater amount of foliar damage than those 

treated by either SSCDS configuration. Analysis of foliage from the fall of 2020 detected no 

significant differences between treatments in Gala or Fuji with damage ~1% for Gala and ~2%-

4% for Fuji (Table 3.9).  

Analysis of arthropod damaged fruit for the summer and fall of both years detected little 

to no damage. The only significant difference between treatments was detected in the summer 

of 2019, in Gala, otherwise all other treatments in respective years and varieties were similar 

(Table 3.8). Similarly, a majority of the foliage assessed for arthropod damage was also clean. 

Analysis of foliar damage from 2019 detected at least one significant difference between 

treatments in each variety. These differences do show a pattern of greater foliar damage in trees 

treated by the Airblast when compared to the above canopy SSCDS. However, foliar damage 

assessed in the fall of 2020 detected no significant differences between treatments from 

summer and a significant difference between the SSCDS configurations in Honeycrisp for the 

fall (Table 3.9).  These findings, like the findings from the analysis of clean fruit, suggest that no 

single treatment provides overall better protection from damage in either fruit or foliage.  

Pest Management and Disease Conclusions: Disease Damage. The percentage of disease 

damaged fruit from the summer of 2019 and 2020 ranged from <1% to ~6% across all 

treatments in all varieties. Analysis of disease damaged fruit in Gala from the summer of 2019, 

significant differences were detected between the above canopy SSCDS and Airblast, with less 

damaged fruit in trees treated by the above canopy SSCDS. No significant differences were 

detected between treatments in Honeycrisp or Fuji. Analysis of disease damaged fruit in the 

summer of 2020 detected significant differences between treatments in Fuji with less disease 

damaged fruit in trees treated by the Airblast when compared to either SSCDS configuration. 

Analysis of damages from the fall of 2019 detected significant differences between treatments in 

Gala and Fuji. In Gala, trees treated by the Airblast had a greater percentage of damaged fruit 

when compared to either SSCDS configuration. In Fuji, trees treated by the above canopy 

SSCDS had a greater percentage of damaged fruit compared to the Airblast. Analysis of 

damages from the fall of 2020 detected no significant differences between treatments in any 

variety (Table 3.10).  

Analysis of diseased foliage for the summer and fall of 2019, as well as the summer of 

2020 detected no significant differences between treatments in any variety. Analysis of disease 

damage from the fall of 2020 detected no significant differences between treatments in 

Honeycrisp or Fuji. In Gala, significant differences were detected between the above and in 

78 

 
canopy SSCDS. Trees treated by the in canopy SSCDS had a greater percentage of diseased 

foliage when compared to the above canopy SSCDS (Table 3.11).  

A majority of the fruit and foliage assessed from 2019 and 2020 were free of disease. 

Analysis of fruit damage detected significant differences between treatments in both Gala and 

Fuji in both 2019 and 2020 (Table 3.10). Analysis of foliar damage had similar results with the 

only significant difference occurring in Gala in the fall of 2020 between the SSCDS 

configurations, otherwise all other treatments in all varieties were similar (Table 3.11). 

Comparing treatments from significantly different pairs in each variety, no treatment appeared to 

provide overall better disease management.  

Pest Management and Disease Conclusions: Bitter Pit Disorder in Honeycrisp. Analysis of 

bitter pit incidence in Honeycrisp apples detected significant differences between treatments 

with the Airblast significantly higher than the SSCDS configurations (Table 3.12). Chances of 

bitter pit disorder increase with conditions of excessive tree vigor and fruit size (Rosenberger et 

al. 2004). The percentage of bitter pit affected apples for above canopy and in canopy SSCDS 

were similar, with values that are approximately four times lower than the Airblast treated apples 

(Table 3.12). This disorder being correlated with the Airblast treatment was most likely due to a 

lower than effective concentration of apple thinning agents applied by the SSCDS 

configurations.  

Fruit Quality: Gala Apple Diameter: Analysis of Gala apple diameter detected significant 

differences (F2,943=33.35, p<0.001) between above canopy SSCDS and in canopy SSCDS, 

above canopy SSCDS and Airblast, as well as in canopy SSCDS and Airblast (p=0.01, p<0.001, 

p<0.001 respectively). Fruit from Above canopy and Airblast treated trees were significantly 

larger than apples from trees treated by the in canopy SSCDS (x̅  = 66.10 mm ± 3.21 mm, x̅  = 
68.40 mm ± 3.40 mm, x̅ = 64.77 mm ± 3.20 mm respectively) (Table 3.13) (Figure 3.5) 
Fruit Quality: Honeycrisp Apple Diameter: Analysis of Honeycrisp apple diameter detected 

significant differences (F2,940=172.1, p<0.001) between above canopy SSCDS and Airblast as 

well as in canopy SSCDS and Airblast (p<0.001, p<0.001). No significant differences were 

detected between in canopy SSCDS and above canopy SSCDS (p=0.42). Trees treated by the 

Airblast had significantly larger apples compared to both the above canopy and in canopy 

SSCDS (x̅  = 83.0 mm ± 3.46 mm, x̅  = 73.61 mm ± 4.49 mm, x̅  = 74.32 mm ± 4.24 mm 
respectively) (Table 3.13) (Figure 3.5). 

Fruit Quality: Fuji apple diameter. Analysis of Fuji apple diameter detected significant 

differences (F2,942= 22.05, p<0.001) between above canopy SSCDS and Airblast as well as in 

canopy SSCDS and Airblast. (p<0.001, p<0.001) No significant differences were detected 

79 

 
between the in canopy SSCDS and above canopy SSCDS (p=0.99). Trees treated by the 

Airblast had significantly larger apples compared to both the above canopy and in canopy 

SSCDS (x̅  = 68.68 mm ± 3.32 mm, x̅  = 66.04 mm ± 3.54 mm, x̅  = 66.0 mm ± 3.21 mm 
respectively) (Table 3.13) (Figure. 3.5) 

Figure 3.8 - Mean ± SEM apple diameter (mm) by variety and treatment. Apple diameter 
measured by caliper for each treatment within respective variety. Differing letters signify 
significant differences between treatments within variety at α=0.05. 

Table 3.13 Mean ± SEM apple diameter (mm) Apple diameter in (mm) by variety and 
treatment. Differing letters signify significant differences between treatments within 
variety at α=0.05. 

Variety 

Treatment 

Diameter 
(mm) 

±SEM 

Gala 

Honeycrisp 

Fuji 

Above Canopy 
Airblast 
In Canopy 
Above Canopy 
Airblast 
In Canopy 
Above Canopy 
Airblast 
In Canopy 

66.09 
68.40 
64.77 
73.61 
83.01 
74.32 
66.04 
68.68 
66.00 

3.21 
3.40 
3.20 
4.49 
3.46 
4.24 
3.54 
3.32 
3.21 

a 
b 
c 
b 
a 
b 
b 
a 
b 

80 

 
 
 
 
 
 
Fruit Quality: Gala Apple Weight. Analysis of Gala apple weights detected significant 

differences (F2,943= 48.08, p<0.001) between the above canopy SSCDS and in canopy SSCDS, 

above canopy SSCDS and Airblast, as well as in canopy SSCDS and Airblast (p=0.02, p<0.001, 

p<0.001 respectively). Trees treated by the Airblast had the heaviest apples compared to either 

the above canopy or in canopy SSCDS (x̅  = 151.85 g ± 19.53 g, x̅  = 135.03 g ± 17.69 g, x̅  = 
128.20 g ± 16.76 g respectively) (Table 3.14) 

Fruit Quality Honeycrisp Apple Weight: Analysis of Honeycrisp apple weights detected 

significant differences (F2,940= 223.10, p<0.001) between the above canopy SSCDS and Airblast 

as well as the in canopy SSCDS and Airblast, no significant differences were detected between 

the above canopy and in canopy SSCDS (p<0.001, p<0.001, p=0.66 respectively). Trees 

treated by the Airblast had significantly larger apples compared to the above and in canopy 

SSCDS (x̅ = 241.39 g ± 28.42 g, x̅  = 170.71 g ± 24.92 g, x̅  = 174.0 g ± 28.56 g respectively) 
(Table 3.14) (Figure 3.6). 

Fruit Quality: Fuji Apple Weight. Analysis of Fuji apple weights detected significant 

differences (F2,942= 24.47, p<0.001) between the above canopy SSCDS and Airblast as well as 

in canopy SSCDS and Airblast, no significant differences were detected between the above 

canopy and in canopy SSCDS (p<0.001, p<0.001, p=0.99 respectively). Trees treated by the 

Airblast had significantly larger apples compared to the above and in canopy SSCDS (x̅ = 
148.74 g ± 20.18 g, x̅ = 132.19 g ± 20.53 g, x̅ = 132.34 g ± 18.34 g respectively) (Table 3.14) 
(Figure 3.6). 

81 

 
 
 
 
Figure 3.9- Mean ± SEM apple weight (g) by variety and treatment. Differing letters signify 
significant differences between treatments within variety at α=0.05. Apple weights were 
significantly greater in varieties treated by Airblast.  

Table 3.14- Mean ± SEM apple weight. Apple weight in (g) by variety and treatment. 
Differing letters signify significant differences between treatments in variety at α=0.05. 

Variety 

Gala 

Honeycrisp 

Fuji 

Treatment 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 
Above Canopy 
In Canopy 
Airblast 

Weight (g) 
135.03 
128.20 
151.85 
170.71 
173.99 
241.39 
132.19 
132.34 
148.74 

±SEM 
17.69 
16.76 
19.53 
24.92 
28.56 
28.42 
20.53 
18.34 
20.18 

a 
b 
c 
a 
a 
b 
a 
a 
b 

Fruit Quality: Gala Fruit Count. Analysis of fruit counts in Gala detected significant differences 

(F2,87= 1.05, p<0.001) between the above canopy SSCDS and Airblast as well as the in canopy 

SSCDS and Airblast (p<0.001, p<0.001). A significantly lower number of apples were counted in 

82 

 
 
 
 
 
trees treated by the Airblast compared to the above and in canopy SSCDS (x̅ = 61.93 ± 8.10, x̅ = 
79.97 ± 11.49, x̅ = 86.87 ± 14.48 respectively).  
Fruit Quality: Honeycrisp Fruit Count. Analysis of fruit counts in Honeycrisp detected 

significant differences (F2,87= 32.64, p<0.001) between the above canopy SSCDS and Airblast 

as well as the in canopy SSCDS and Airblast (p<0.001, p<0.001). Trees treated by the Airblast 

had a significantly lower number of apples compared to the above and in canopy SSCDS (x̅ = 
59.67 ± 10.26, x̅ = 105.67 ± 15.03, x̅ = 103.87 ± 18.89 respectively). 
Fruit Quality: Fuji Fruit Count. Analysis of fruit counts in Fuji detected significant differences 

(F4,87= 3.49, p=0.035) between the above canopy SSCDS and the Airblast (p=0.03). Trees 

treated by the above canopy SSCDS had a greater number of apples compared to the in 

canopy SSCDS and Airblast (x̅ = 59.67 ± 10.26, x̅ = 105.67 ± 15.03, x̅ = 103.87 ± 18.89 
respectively).  

Table 3.15 Mean ± SEM fruit count. Mean number of apples per tree by variety and 
treatment. Differing letters signify significant differences between treatments in 
respective variety at α=0.05. 

Variety 

Gala 

Honeycrisp 

Fuji 

Treatment 

In Canopy 
Airblast 

Count 
Above canopy  79.97 
86.87 
61.93 
Above canopy  105.67 
103.87 
59.67 
Above canopy  120.83 
111.83 
103.43 

In Canopy 
Airblast 

In Canopy 
Airblast 

SEM 
11.49 
14.48 
8.10 
15.03 
18.89 
10.26 
15.2 
12.88 
15.34 

a 
a 
b 
a 
a 
b 
a 
ab 
b 

Fruit Quality: Harvest yield. Analysis of the estimated metric tons of fruit harvested in Gala, 

Honeycrisp, and Fuji detected no significant differences between treatments in any variety (F2,6= 

0.045, p<0.66, F2,6= 2.14, p<0.20, F2,6= 1.5, p<0.30 respectively).     

83 

 
 
  
  
 
 
 
Table 3.16 Theoretical mean yield ± SEM metric tons per hectare harvested from 
treatment in variety. Mean Differing letters signify significant differences between 
treatments in respective variety at α=0.05. 

Variety 

Gala 

Honeycrisp 

Fuji 

Treatment 
Above canopy 
In Canopy 
Airblast 
Above canopy 
In Canopy 
Airblast 
Above canopy 
In Canopy 
Airblast 

MT/Ha 
35.45 
36.63 
30.8 
58.59 
58.38 
49.86 
51.87 
48.08 
50.28 

±SEM 
4.37 
6.35 
2.03 
1.5 
1.98 
5.51 
2.31 
0.74 
1.16 

a 
a 
a 
a 
a 
a 
a 
a 
a 

Fruit Quality Conclusions: Fruit sizes in trees treated by the above canopy and in canopy 

SSCDS were similar in Fuji and Honeycrisp. All treatments were significantly different in Gala. 

Fruit sizes in trees treated by the above canopy and in canopy SSCDS are significantly different 

from the Airblast in all three varieties. Trees treated by the Airblast had the largest fruit in all 

three varieties (Table 3.14).  

Fruit weights were greatest in trees treated by the Airblast. Significant differences were 

detected in all varieties between the SSCDS configurations and the Airblast, with apple weights 

heavier in Airblast treated trees. Airblast treated Honeycrisp apples had the heaviest weight of 

all combinations of treatments and varieties, approximately seventy grams heavier than weights 

associated with the above canopy SSCDS and in canopy SSCDS (x̅ = 241.01 g ± 3.46 g, (x̅ = 
170.71 g ± 24.92 g, x̅ = 173.99 g ± 28.56 g respectively) (Table 3.14).  

Fruit counts were greatest in trees treated by the above and in canopy SSCDS for Gala 

and Honeycrisp. In Fuji, trees treated by the above canopy SSCDS had greater fruit counts 

compared to the Airblast. In Fuji, the in canopy SSCDS did not statistically differ from either the 

above canopy SSCDS or Airblast. Honeycrisp mean apple counts in rows treated by the above 

canopy and in canopy SSCDS were almost double that of those treated by the Airblast (x̅ = 
105.67 ± 15.03, x̅ = 103.87 ± 18.90, x̅ = 59.67 g ± 10.27 respectively) (Table 3.15).  

Fruit size, weight, and count, in all varieties, differed significantly between the Airblast 

and at least 1 SSCDS configuration. Trees treated by the Airblast produced fewer, but larger, 

heavier fruit when compared to the SSCDS configurations. The larger, less numerous fruit 

identified in Airblast treatments and the smaller more numerous fruit in the SSCDS 

configurations suggests thinning applications were not as effective using either the above or in 

canopy SSCDS.  

84 

 
  
 
Both fruit counts and fruit weights were used to estimate the metric tons per hectare 

harvested in the fall of 2020. Even with the observed issues with thinning analysis of each 

variety detected no significant differences between treatments. Largest mean yields came from 

the Honeycrisp and Fuji varieties (Table 3.16). While the estimated weight of harvested fruit was 

greater this does not take into consideration saleable fruit. Pest damage and bitter pit disorder in 

Honeycrisp affect salability of harvested fruit. Honeycrisp apples, which are prone to bitter pit 

disorder, did have a higher incidence of bitter pit in trees treated by the Airblast, however this did 

not seem to have an effect on the estimated mt/ha of apples at harvest. 

Apple thinning treatments are used to reduce the number of apples per tree in order to 

prevent crop overload and increase the abundance of larger, more marketable apples (Dennis 

2020). Both SSCDS treatments had smaller apples for at least one variety compared to the 

Airblast treatment, suggesting poor thinning.  One possible explanation for this is that the 

thinning agents were diluted or neutralized by residual liquids remaining in the SSCDS lines 

from a previous application. Since the dosage of the thinning agent was low in volume, its 

dilution by residual liquid may have altered its efficacy. Further evidence of inadequate thinning 

lies in the incidence of bitter pit disorder in Honeycrisp (Table 3.12). The thinning of apples to 

produce larger higher quality fruit also creates the conditions for bitter pit disorder (Rosenberger 

et al. 2004). Bitter pit disorder is at much lower incidence in fruit treated by both the above and 

in canopy SSCDS compared to the Airblast, a further indication of inadequate thinning in 

SSCDS treated trees. Future SSCDS projects should therefore evaluate thinning operations. 

If the SSCDS is to be an adoptable technology, even with its other benefits, it must 

provide equivalent or better pest management efficacy compared to the current dominant 

technology. The above-canopy and in-canopy SSCDS differ in the coverage and deposition 

provided by each configuration with more homogeneous coverage and deposition across strata 

associated with the in canopy SSCDS and greater amounts of coverage and deposition higher 

in the canopy associated with the above-canopy SSCDS. Even with the differences in coverage 

and deposition the clean fruit and foliage assessed for the above canopy and in canopy SSCDS 

are near equivalent, with the Airblast sprayer. A vast majority of both fruit and foliage treated by 

the above and in canopy SSCDS as well as the Airblast were determined to be clean. 

Differences were detected in arthropod and disease damage, however these differences have 

more to do with varietal physiology like apple scab resistance and insect feeding preferences 

than treatments. Cultivars that are more susceptible to specific pests or diseases may require 

specialized emitters and emitter placement to properly treat pests with maximum efficacy. As 

stated by Owen-Smith (2019) There is concern regarding coverage to the underside of leaves 

85 

 
treated by the SSCDS allowing for pathogens to go untreated. A possible solution for this would 

be to select chemistries that allow for the uptake and translaminar motion of pesticides (Klittich 

et al., 2013; Bostainan et al., 2012). Fruit quality by way of size, weight, and fruit count revealed 

that the SSCDS configurations require more optimization in the delivery of low volume active 

ingredients like thinning agents.  

3.4.  Conclusions 

Moving forward, the SSCDS requires optimizations to ready it for consumer adoption. 

Optimization of chemical thinning application, operating pressure and volume, mass producible 

components, and effectiveness in multiple crops should be assessed. The system currently 

uses large volumes of liquid to bring lines to pressure and large air compressors to return liquids 

back to the tank. The addition of valves, reducing air requirements and reducing the amount of 

liquid required to run the system is a potential solution that requires exploration. Optimization of 

valving will also allow for investigation into injection molded or 3D printed parts allowing for 

easier installation in the field, as well as the production of affordable, fast, and easily 

replaceable parts should the need arise. As efficiencies in the usage of air and liquid 

distributions are explored re-evaluation of the application of thinners should be assessed as the 

small volume of thinning agents may have been affected by materials in the current system. 

Fundamental system specifications will most likely remain the same as the technology is 

adapted to other crops, however in order to move into other crops assessments should be made 

to optimize the SSCDS for the crops it is intended to serve. These changes will most likely be in 

the form of crop specific emitters and emitter placement.  

The above-canopy and in-canopy SSCDS were able to provide equivalent season long 

pest management when compared to a fan assisted orchard sprayer. Fruit quality shows 

differences in fruit weight and size for Honeycrisp, with the Airblast treated rows producing 

larger fruit. This is due to higher apple counts in rows treated by the SSCDS configurations and 

is most likely due to ineffective thinning treatment. Bitter pit is present in blocks treated by the 

Airblast but not in the blocks treated by the SSCDS configurations again most likely associated 

with the ineffective thinning treatment leading to greater apple load. Adaxial and abaxial 

coverage provided by the in-canopy SSCDS was more homogeneous across the tree strata 

than the above-canopy SSCDS with the majority of the coverage on the adaxial surface for both 

above-canopy and in-canopy SSCDS, however data indicated this did not have significant 

effects on pest management efficacy. This research supports the idea that the SSCDS is a 

technology capable of effective year-long pest management of high-density apples.  

86 

 
 
 
 
Chapter 4 - Conclusions and Future Directions 

The Solid Set Canopy Delivery System (SSCDS) is an alternative agrochemical spray 

system designed for trellised crops, such as high-density apple orchards and vineyards. High 

density training systems have become increasingly common, due to higher yields and because 

they are more efficient for pruning and harvesting. The axial-fan Airblast sprayer, developed for 

larger canopies, has remained the dominant agrochemical delivery system. This has led to the 

problem of off target drift occurring during agrochemical applications (Robinson et al. 2013; 

Holownicki et al. 2000). The SSCDS, optimized to treat the narrower orchard rows may be the 

solution to reduce off target drift as well as deposit more spray into the canopy. The goal of this 

thesis was to: Evaluate the SSCDS’s potential to reduce off target drift compared to an axial-fan 

sprayer and to evaluate an over the canopy SSCDS’s ability to provide agrochemical coverage 

and deposition, and adequate yearlong fruit quality and pest management. 

Chapter 2 describes two off-target drift experiments comparing the SSCDS and axial fan 

Airblast sprayer applications. In 2017 I measured the off-target drift produced by an axial-fan 

Airblast sprayer and an in canopy SSCDS described by Owen-Smith (2019), adding an above 

canopy style SSCDS in 2018. My objectives were to determine the amount of off target drift 

produced by each technology, and where that off target drift was deposited. For these 

experiments, in row losses to ground, drift losses up to 12 m above the orchard, and drift losses 

up to 64 m downwind of the orchard were measured. I hypothesized that both the in canopy and 

above canopy SSCDS would produce the least amount of downwind and above ground off-

target drift compared to a Rears PB533N Airblast sprayer. I also hypothesized that the off-target 

drift deposited to ground would be greater for the above canopy SSCDS and in canopy SSCDS 

compared to the Rears PB533N Airblast sprayer.  

Losses to ground measured for both 2017 and 2018 were numerically similar between 

the in canopy SSCDS and axial-fan Airblast sprayer. In 2017 significant differences were 

detected between the in canopy SSCDS and axial-fan Airblast sprayer, however no significant 

differences were detected between sampler positions within either treatment (Figure 2.5, Table 

2.3). This suggests that the position on the ground did not affect the amount of flux recorded in 

either treatment. In 2018’s experiment the same methods of ground detection were used with 

the addition of a piece of filter paper added to the petri dish. This addition led to a greater 

collection of material and led to the detection of differences between treatments and positions 

(Figure 2.6, Table 2.4). In 2018 ground losses from the axial-fan Airblast and in canopy SSCDS 

had numerically similar deposition, the above canopy SSCDS depositing the greatest of the 

three. The larger droplets and higher flow rate per emitter associated with the above canopy 

87 

 
SSCDS were most likely the cause of this (Figure 2.3 B). The axial-fan Airblast and In canopy 

SSCDS had similar mean deposition values at every position, suggesting similar amounts of 

material were distributed below the canopy. However, it should be noted that the mean wind 

speed during axial-fan Airblast sprayer applications was considerably lower than booth SSCDS 

configurations (Table 2.2). The larger droplets provided by the above canopy SSCDS are likely 

more resistant to drift, making them more likely to stay near the orchard rows (Ferguson et al., 

2015). For both SSCDS configurations deposition was numerically higher in upwind positions 

when compared to the Airblast sprayer, this may indicate the influence the fan has on particle 

movement with particles tending to move more downwind, or more readily influenced by wind.  

Significantly higher vertical off target drift was produced by the axial-fan Airblast sprayer 

in both 2017 and 2018. All treatments followed the same pattern of greater drift near the ground 

and decreased with height. In 2017, percent flux as vertical drift peaked at 5% at 4 m for the in 

canopy SSCDS but was >25% at the same height for the Airblast sprayer (Figure 2.8, Table 

2.5). This relationship was similar in the 2018 experiment with both SSCDS configurations 

greatest vertical off target drift at 1 m above ground (~10%) while the peak vertical off target flux 

for the axial-fan Airblast sprayer was ~66% at the same height (Figure 2.9, Table 2.6). Flux 

detected for both SSCDS configurations did not statistically differ and were <5% at all heights, 

nearing 0% at heights greater than 9 m. In contrast the axial-fan Airblast sprayer was near 30% 

flux for a majority of the heights sampled (Figure 2.10, Table 2.7). The pattern of off target drift 

ejected above ground suggests that the powerful fan of the Airblast sprayer is responsible for 

ejecting more material out of the orchard when compared to both SSCDS configurations.   

Downwind off target drift was similar to vertical off target drift, SSCDS configurations 

produced significantly lower flux than the axial-fan Airblast sprayer. For 2017 and 2018 

downwind drift deposited by the axial-fan Airblast sprayer was greater than both the in canopy 

and above canopy SSCDS for a majority of the distances sampled. In 2017 both the axial-fan 

Airblast sprayer and in canopy SSCDS peaked within the first 4 m downwind, the axial-fan 

Airblast sprayer at ~ 19% flux and the in canopy SSCDS at ~12% flux (Table 2.8). At 16 m 

downwind the percentage of flux deposited by the in canopy SSCDS dropped below 1%. At this 

same distance the percent flux deposited by the axial-fan Airblast sprayer was above 5%, and 

did not drop below 1% until 64 m downwind (Figure 2.11, Table 2.8). Downwind off target drift 

from 2018 was similar to 2017, also with a majority of the downwind drift produced by the axial-

fan Airblast sprayer. Both the above canopy SSCDS and axial-fan Airblast sprayer produced 

~20%-~30% flux as downwind drift at 0 m and 1 m downwind, the in canopy at these heights 

was between 5% and 10% flux. Flux measured for the above canopy SSCDS greatly decreased 

88 

 
from 2 m to 4m downwind becoming near equivalent to the in canopy SSCDS, at greater than 2 

m. In 2018’s experiment flux measurements for both the above canopy and in canopy SSCDS 

were ~0% at greater than 8 m downwind. In contrast the axial-fan Airblast sprayer produced 

between ~20% to ~30% flux for the first 4m downwind, only dropping below 1% flux after 64 m 

downwind (Figure 2.12, Table 2.9).  

The greatest flux produced by the above canopy SSCDS was measured nearest the 

downwind orchard row, the percentage of flux much greater than the in canopy SSCDS at that 

distance. The above canopy SSCDS produced much larger droplets compared to those of the 

axial-fan Airblast sprayer and in canopy SSCDS. These droplets with short trajectories made 

landfall not far from the orchard row. These larger droplets are most likely the reason for greater 

deposition in the first two meters downwind and why deposition further downwind was much 

lower, with similar values to the in canopy SSCDS. These larger droplets are less prone to 

drifting downwind and most likely made landfall within a few meters of emission. This is 

corroborated by ground loss, as deposition produced by the above canopy SSCDS was 

significantly greater than the in canopy SSCDS and axial-fan Airblast sprayer at the center and 

first two downwind positions, and numerically greater at a majority of the positions sampled 

(Figure 2.6, Table 2.4). In 2018 both the in canopy and above canopy SSCDS deposited their 

greatest flux near the orchard row, flux decreasing substantially within the first 4 m. This was not 

the case with downwind flux produced by the axial-fan Airblast sprayer, as flux values remained 

numerically similar for the first 4 m and much greater than those of the SSCDS configurations 

before gradually declining (Figure 2.12, Table 2.9). Similar patterns were present in the analysis 

of vertical off target drift for both years, with the flux produced by the SSCDS configurations 

greatest near the ground and steadily decreasing with height. The axial-fan Airblast sprayer 

again differs with much greater deposition at all heights and an initial increase in flux with height 

before a gradual decline.  

Vertical and downwind drift data strongly suggests the axial-fan Airblast sprayer vectors 

spray droplets more prone to drift in comparison to the SSCDS. The axial-fan Airblast sprayer 

was responsible for greater vertical and downwind off-target drift both above and downwind of 

the orchard. This is further supported by ground loss data in which ground deposition produced 

by the Airblast sprayer was lowest, even with the lowest average wind speeds in 2018 further 

supporting that a greater amount of material is leaving the treatment area (Table 2.2). These 

findings are similar to Sinha (2019), in that, compared to an SSCDS, an axial fan sprayer was 

responsible for greater off target drift. Overall, the SSCDS produced much less off target drift 

89 

 
that the axial-fan Airblast sprayer as it lacks the powerful fan shearing spray into fine droplets 

and vectoring them both above and downwind of the orchard.  

Chapter 3 describes the comparisons of the overall horticultural performance of an 

above canopy SSCDS with an established, in canopy SSCDS and an axial-fan Airblast sprayer. 

Coverage and deposition were evaluated for the two SSCDS configurations, while pest 

management efficacy and fruit size and yield were evaluated for all three treatments. Coverage 

and deposition comparisons were made between the above and in canopy SSCDS while pest 

management efficacy and fruit quality assessment comparisons were made between the above 

canopy SSCDS, in canopy SSCDS, and axial-fan Airblast sprayer. The study objectives were to: 

1) Assess coverage and deposition provided by the above and in canopy SSCDS. 2) Determine 

if the above and in canopy SSCDS could provide adequate pest management equivalent to an 

axial-fan Airblast sprayer 3) Determine if foliar agrochemical treatments provided by the above 

canopy SSCDS, in canopy SSCDS, or a axial-fan Airblast sprayer had differing effects on fruit 

count, fruit weight, or fruit size. I hypothesized that coverage and deposition values would differ 

by canopy strata, with the above canopy SSCDS responsible for greater coverage and 

deposition near the top of the canopy and the in canopy SSCDS providing more even coverage 

and deposition through the canopy. I also hypothesized that both the above and in canopy 

SSCDS would provide adequate season-long pest management equivalent to the axial-fan 

Airblast sprayer, with no differences in fruit quality between the treatments.  

Spray coverage and deposition varied between the in canopy and above canopy 

SSCDS. Coverage differed by treatment, with the above canopy SSCDS providing greater 

coverage to the top third of the canopy while the in canopy SSCDS provided greater coverage 

to the bottom third of the canopy. In the top third of the canopy, both adaxial (top) and abaxial 

(bottom) surface coverage was greater in orchard canopy treated by the above canopy SSCDS, 

compared to the in canopy SSCDS. However, coverage in the bottom third of the canopy on 

both adaxial and abaxial surfaces was greater in trees treated by the in canopy SSCDS. This 

difference between the above canopy SSCDS and in canopy SSCDS is likely due to the 

positioning of the emitters. The above canopy SSCDS with emitters only present above the 

canopy and lacking the second emitter located within the canopy is likely why the top of the 

canopy and the adaxial face of samplers had greater coverage compared to the in canopy 

SSCDS. Analysis of deposition measurements indicated that applications by both the above and 

in canopy SSCDS did not significantly differ in any variety (Figures 3.5, 3.6, 3.7). Similar 

deposition across strata in high density apple orchards provided by the in canopy SSCDS were 

described by Owen-Smith (2019). Similar patterns of uneven coverage have been observed in 

90 

 
previous studies (Owen-Smith et al. 2019; Owen-Smith et al. 2019; Sinha et al. 2020; Ranjan et 

al. 2021). This uneven coverage raised questions as to the SSCDS’s ability to provide season 

long pest management. However, configurations similar to the in canopy SSCDS system, 

including this research have shown that an SSCDS can provide adequate pest management 

(Angello, & Landers, 2006; Owen-Smith et al. 2019; Owen-Smith et al. 2019; Sanhi et al. 2022; 

Koonter 2023).  

A majority of the apples surveyed in all treatments and varieties were free of insect or 

disease damage, regardless of treatment for both years. A majority of the percentages of clean 

fruit detected in all treatments in all varieties were ~95% or > (Table 3.6). These findings are 

corroborated by the analysis of both arthropod and disease damaged fruit. In apples surveyed 

for arthropod damage a majority of the apples assessed were free of damage. A vast majority of 

the percentages of arthropod damaged apples in all treatments, in all varieties were below 1% 

(Table 3.8). The low percentages of diseased apples detected also suggests a majority of the 

fruit in all treatments and varieties were clean as most percentages of disease damaged fruit 

were < 5% (Table 3.10). 

Foliar damage surveys suggest that a majority of the leaves in all treatments and 

varieties were free of arthropod or disease damage. A majority of the percentages of clean 

foliage detected in all treatments and varieties were found to be ~85% or > (Table 3.7). The 

surveys of both arthropod and disease damaged foliage showed that a majority of the foliage in 

all treatments and varieties were free of damage. The amount of foliage damaged by arthropods 

was low in comparison to the amount of clean foliage with a majority of the percentages of 

arthropod damages in all treatments and varieties < 5% (Table 3.9). The survey of disease 

damaged foliage also showed low levels of damage with a majority of the percentages of 

damage detected in all treatments and varieties < 10% (Table 3.11).  

Overall, very little arthropod or disease damage was detected in either fruit or foliage. 

While differences were detected between treatments, no patterns emerged to suggest one 

treatment had a greater effect on pest management compared to another. A majority of the 

treatments in all varieties describe adequate pest management, and equivalency to an axial-fan 

Airblast sprayer. Similar findings are discussed in previous SSCDS research (Angello, A., & 

Landers, A. 2006; Owen-Smith et al. 2019; Owen-Smith et al. 2019).  

Fruit diameter, weight, and fruit per tree were heavily influenced by treatment, with 

differences detected between the axial-fan Airblast sprayer and the SSCDS configurations. Fruit 

diameters measured in all varieties differed by treatment type, with significant differences 

detected between treatments in all varieties. In Gala and Fuji, apples treated by the axial-fan 

91 

 
Airblast sprayer were largest. Honeycrisp apples treated by the axial-fan Airblast sprayer and in 

canopy SSCDS were largest. (Table 3.13). Fruit weights were similar to fruit diameter in that 

weight was dependent on treatment. Significant differences between treatments for all varieties 

were detected, with apples treated by the axial-fan Airblast sprayer being the largest. 

Honeycrisp apples, treated by the axial-fan Airblast sprayer, were twice as heavy, ~70 g larger 

than apples treated by the SSCDS configurations (Table 3.14, Figure 3.9). Fruit counts also 

appear to have been affected by treatment. Significant differences between treatments in all 

varieties were detected. Gala and Honeycrisp trees treated by the axial-fan Airblast sprayer had 

a significantly greater number of apples in comparison to the SSCDS configurations (Table 

3.15). Honeycrisp trees treated by the SSCDS configurations had twice as many apples as 

those treated by the axial-fan Airblast sprayer. Higher fruit counts, smaller diameter fruit, and 

lower fruit weights in trees treated by the SSCDS configurations indicate ineffective apple 

thinning provided by the SSCDS. 

Bitter pit disorder was found exclusively in Honeycrisp apples; its incidence in axial-fan 

Airblast sprayer treated trees was four times higher than in those treated by the SSCDS 

configurations (Table 3.12). Bitter pit can be brought on by vigorous tree growth or excessive 

fruit size (Biggs et al. 2015). Higher incidence of bitter pit along with fewer, larger apples in trees 

treated by the axial-fan Airblast sprayer is further evidence of ineffective thinning provided by 

the SSCDS configurations. Trees treated by the SSCDS produced significantly more, and 

smaller apples, this correlates with reduced incidence of bitter pit in SSCDS treated trees 

compared to the effective treatment provided by the axial-fan Airblast sprayer. The smaller, 

more numerous apples did not seem to have an effect on the theoretical harvest tonnage, as no 

statistically significant differences were detected between treatments in all varieties (Table 

3.16). However, this does not take into account apple quality.  

Overall, my research demonstrates that the SSCDS produces significantly less 

downwind and vertical off target drift when compared to an axial-fan Airblast sprayer in a high-

density apple orchard. Additionally, two configurations of the SSCDS, the above and in canopy, 

were able to provide season-long pest management equivalent to a axial-fan Airblast sprayer. 

This is a significant finding because the above canopy SSCDS configuration removes emitters 

from the canopy decreasing the chances of emitter damage or occlusion due to tree growth, 

pruning, and training. This research provides further evidence that the SSCDS is an effective 

alternative spray technology better tailored to high density cropping systems compared to large 

fan driven implements. 

92 

 
The SSCDS has the potential for use in multiple crops, especially trellised row crops. 

The SSCDS was developed using technology intended for greenhouse irrigation, and with the 

narrower spaces present in a greenhouse, further research into greenhouse applications would 

be a logical next step (Owen-Smith, 2017). Cropping systems similar to a grape vineyard or 

high-density apple orchard may also benefit from an SSCDS, with research concentrating on 

optimizing emitters and emitter placement in order to maximize coverage and deposition in each 

crop. Due to the large volume of air needed to purge the system and agrochemical required to 

pressurize the system, research into control valving to reduce the required air and reduce waste 

agrochemical will be a necessary next step. Further research into the potential benefit of 

reduced fossil fuel use and reduced soil compaction compared to large tractor drawn 

implements should also be conducted. 

4.1.  Future Outlook 

The fully optimized SSCDS will not only provide reduced off target drift and pest 

management but will be utilized as a fully automated system. The fully optimized system will 

provide the benefits that come with drift reduction such as worker safety and environmental 

protection. Automation will also provide worker safety and reduce labor by removing the 

operator from the treatment area. Automation may also provide the benefit of reduced soil 

compaction. 

To reach this optimized system, research is required into some of the fundamental 

functions of the SSCDS. Calibration of fruit thinning and fruit retention treatments will be 

required as inefficiencies were detected in this research. Fault detection will also be paramount 

in the technology moving forward as thousands of emitters will be in use. The SSCDS needs 

excess liquid and large volumes of air to operate, removing waste and developing a more 

efficient system is required. Finally, research into the SSCDS’s full automation would be a 

logical next step, allowing for numerous benefits.  

 As a drift reducing technology, the SSCDS could be a powerful tool in the future. As 

shown from this research and other work reviewed herein, the SSCDS reduces off-target drift 

which may allow it to fall under a category of equipment known as Drift Reducing Technology 

(DRT). As pesticides come under greater scrutiny, any off-target exposures may lead to the 

banning or further restrictions on usage. Future regulations may ban certain chemistries from 

certain application equipment. However, with its marked drift reduction, an SSCDS could serve 

as an alternative, or even the only option for some products.  

Aside from pest management applications, the SSCDS must also be able to provide 

chemical fruit thinning and fruit retention. Thinning was attempted using the SSCDS utilizing a 

93 

 
protocol developed for the axial-fan Airblast sprayer, however this failed. Due to the structure of 

the SSCDS evaluated in this research, it is possible that the thinning agents used were in such 

low volume that residual chemical remaining from a previous spray either diluted or interfered 

with the actives in the thinners. Chemical thinning is difficult, and success depends on the right 

mixtures of thinning agents and can differ by apple variety, rootstock, weather, tree condition, 

bee activity, temperature, etc. (WSU 2023). For the SSCDS to succeed at thinning, it will require 

testing to develop a thinning program specific to both the apples and the SSCDS treating them.  

The development of a fault detection procedure is one of the most important pieces of 

research remaining. Orchards spanning hundreds of hectares could require the deployment of 

tens of thousands of emitters. Each emitter has the potential to be dislodged (spilling more than 

the prescribed amount of material) or occluded (no material emitted). This could lead to the 

overdosing or underdosing of agrochemical leading to wasted agrochemical or the proliferation 

of pests.  

Fault detection could come in the form of pressure sensory or thermal detection. 

Pressure sensory has the limitations of needing to place several pieces of equipment into the 

field and needing to detect minute pressure changes within the system. Upon evaluation of this, 

off the shelf components were not able to detect changes based on single occluded or removed 

emitter. It is also very possible that if sensory equipment sensitive enough to detect a 

malfunctioning emitter is used, that pressures incurred by the heat of the day will interfere. 

The thermal approach used thermal imagery taken from above the orchard via drone. 

Both dislodged and occluded emitters were successfully detected during an application of water. 

Fault detection in the above canopy SSCDS was much more successful than attempts with an 

in canopy SSCDS. The emitters within the canopy were not detected from above. Future 

research into thermal fault detection would make use of an above canopy SSCDS and 

numerous automated drones. A calibration standard for thermal imaging would need to be 

developed as the detectable differences between ambient leaf temperatures and applied water 

temperatures will change with crop type and environment.  

Research to eliminate the need for wasted agrochemical and the large air requirements 

of the SSCDS will also need to be conducted. The SSCDS utilized in this thesis requires a filling 

stage where all the lines are filled at low pressure. Not all of the liquid in this filling stage is 

emitted and is lost as waste. Ultimately, air is forced through the system driving excess waste 

liquid back to the tank. Due to the compressibility of air and the large volume of the SSCDS, the 

amount of compressed air required to achieve clearing is often provided by an industrial size air 

94 

 
compressor. This large air compressor may be required to run for several minutes depending on 

the size of the SSCDS.  

Current solutions for reducing waste and the need for large volumes of air, segment the 

system into smaller chambers. Two of the systems explored are the “canister” as described by 

Ranjan (2021) and an inline segmentation being explored by Application Insight LLC and MSU 

Technology. The inline system uses a series of valves to allow liquid to escape through emitters 

during the application of compressed air, but seals when no liquid is present reducing the 

consumption of air. Further research into this particular set of optimizations is best suited for 

engineers specializing in both spray technology and hydraulics/pneumatics. 

The fully realized SSCDS is an ecosystem of automated components. This system 

would be a combination of sensors and programming controlling spray application timing, 

dosages, products, and thermal fault detection. The system would either be operated from a 

central location where the pump, compressor, and agrochemical tank are directly attached to 

the entire SSCDS installation, similar to Sinha (2020). Or the system will be operated by a 

mobile automated compressor, pump and agrochemical tank which drives up to multiple sets of 

the SSCDS in the orchard. 

 Sensory equipment could time sprays around rain events as well as indicate faults 

during automatic thermal scans. This equipment could also mix the appropriate agrochemicals 

and apply them all based on preprogrammed scheduling, reactions to the environment, or 

commands from the user. This system will most likely be operated by all three.  

The benefits of an automated system are safety, reduced labor, as well as reduced soil 

compaction. Automation removes the user from the area being sprayed. The removal of the 

operator from the field not only reduces the chances for pesticide exposure but reduces labor. 

For climates like Michigan’s, numerous pesticide applications are required, automation removes 

the need for a laborer to drive an implement into the orchard. This also removes the need to 

drive a heavy farm implement several times into the orchard rows, thus reducing the chances of 

soil compaction. 

The fully optimized SSCDS has a range of benefits, including drift reduction, and pest 

management but also the technology can readily be integrated into an automated sensor array. 

This automated system would provide greater worker safety, reduce labor, as well as reduce 

soil compaction. As research continues this promising technology may yet find its way into 

several new crops and cropping systems.  

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BIBLIOGRAPHY 

Agnello, Arthur, and Andrew Landers. Current Progress in Development of a Fixed-Spray 
Pesticide Application System for High-Density Apple Plantings. no. 4, 2006, p. 5. 

Althoff, Emily R., and Kevin B. Rice. “Japanese Beetle (Coleoptera: Scarabaeidae) Invasion of 

North America: History, Ecology, and Management.” Journal of Integrated Pest 
Management, vol. 13, no. 1, Jan. 2022, p. 2. Silverchair, 
https://doi.org/10.1093/jipm/pmab043. 

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