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THESIS

1007/

LIBRARY
Michigan State
University

This is to certify that the

thesis entitled

PLANT-TO-STAND FIELD PERFORMANCE OF
SUGAR BEET PLANTERS

presented by

Andreas Ch. Smyrillis

has been accepted towards fulfillment
of the requirements for

 

M.S. degree in Agricultural Technology
and Systems Management

/17~:. Liam} w

Major professor \

 

Dr. Tim M. Harrigan

DateDEL“; 2°0‘

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6/01 cJClRC/DaieDuep65-p. 15

 

PLANT-TO-STAND FIELD PERFORMANCE OF SUGAR BEET PLANTERS

By

Andreas Ch. Smyrillis

A THESIS

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

MASTER OF SCIENCE

Department of Agricultural Engineering

2001

ABSTRACT

PLANT-TO-STAND FIELD PERFORMANCE OF SUGAR BEET PLANTERS

By

Andreas Ch. Smyrillis

An evaluation of the plant-to-stand field performance of two general purpose
planters (Monosem and John Deere) and two specialized sugar beet planters (Accord and
Stanhay) was conducted in two locations in 2000 and one location in 2001 at the Saginaw
Valley Bean and Beet Research Farm. A pelleted and a fasonated seed treatment were
evaluated at two planting speeds of 4.8 and 7.3 km/h (3.0 and 4.5 mph). Key measures of
planter performance were rate of seedling emergence, final plant population, seed spacing
uniformity, beet size uniformity and the standard measures of sugar beet yield and
quality. The rate of emergence varied among trials with soil moisture. When soil
moisture was adequate there was little difference among planters. In dry soil the Deere
and Monosem tended to provide more rapid emergence, likely due to improved seed-to-
soil contact. The Accord planter provided the best plant spacing uniformity while the
Deere and Monosem tended to provide the highest final plant stand. The 4.8 km/h
forward travel speed generally provided a more uniform plant spacing than when
traveling at 7.3 km/h. The three centimeter mode range measurement of plant spacing

uniformity was not a reliable predictor of beet size uniformity.

To my parents

ACKNOWLEDGMENTS

My thesis was a big challenge for me. I could not go through it without the
support and help of people who believed in me and my abilities to succeed.

I would like to thank my major professor, Dr. Tim Harri gan, who was always
there for me. As an advisor, he has always provided excellent guidance throughout all of
the stages of my thesis work. Through his sincere interest in my studies, he always
provided me with invaluable encouragement and support. I will never forget his patience
and trust on me.

I would like to thank the rest of my committee, Dr. Gary VanEe, Dr. Patrick Hart
and Dr. Steve Poindexter, for encouraging me throughout the thesis process, and for
providing me with invaluable input on the technical aspects and implications of my
research study. In addition, I would like to thank Paul Horny and Dennis Fleischman
from the Saginaw been and beet farm, for their help during the growing and harvesting
season.

I would also like to thank the various sources that have provided me with
financial support throughout my thesis study. These are, my parents (personal savings),
Dr. Tim Harrigan (research assistantship), and the Leventis foundation (scholarship).

I would also like to thank my fi'iends and roommates whose presence and
fiiendship empowered me to keep on. A special thanks to my fiiend Dr. Elena
Papanastasiou who has tolerated me with statistical questions, data collection and data
input.

Finally, I would like to thank my family. A special thanks goes to my

grandparents who I adore and respect and to my brothers Stelio and Mario for providing

me with moral support and company through out my study. Finally, I am very grateful for
my parents Pambo and Elli Smyrilli for their teachings and their support. They are the
ones who have instilled in me the value of education, and who have encouraged me

through out my student life. They are my Heroes and I love them very much.

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES ........................................................................................................... xi
CHAPTER 1. INTRODIUCTION ....................................................................................... 1
1.1 OBJECTIVES ........................................................................................................... 4
CHAPTER 2. LITERATURE REVIEW ............................................................................. 5
2.1 PLANTER DEVELOPMENT .................................................................................. 5
2.2 MECHANICAL SYSTEMS ..................................................................................... 7
2.2.1 Furrow Openers ................................................................................................ 7

2.2.2 Seed Metering ................................................................................................... 8

2.2.3 Seed Placement ................................................................................................. 9

2.2.4 Seed Covering ................................................................................................. 10

2.2.5 Press-Wheels ................................................................................................... 11

2.2.6 Gauge-Wheels ................................................................................................. 11

2.3 PLANTER PERFORMANCE ................................................................................ 11
2.3.1 Seed Spacing ................................................................................................... 12

2.4 PLANT SPACING COMPARISONS .................................................................... 13
2.5 THEORETICAL CONSIDERATIONS ................................................................. 14
2.5.1 Seed Metering ................................................................................................. 14

2.5.2 Seed Transport ................................................................................................ 17

2.5.3 Seed-to-Soil Contact ....................................................................................... 22

2.6 COMPARISON OF LABORATORY AND FIELD TESTS ................................. 22
2.7 SEED TREATMENT ............................................................................................. 25

vi

2.7.1 Seed Pelleting .................................................................................................. 26

2.7.2 Seed Coating ................................................................................................... 27
2.8 SEEDBED CULTIVATION .................................................................................. 28
2.9 PLANTING DATE ................................................................................................. 29

CHAPTER 3. METHODS AND MATERIALS ............................................................... 30
3.1 PLANTER ADJUSTMENTS ................................................................................. 34
3.2 DATA COLLECTION ........................................................................................... 34

3.2.1 Moisture .......................................................................................................... 34

3.2.2 Rate of Emergence .......................................................................................... 36

3.2.3 Plant Spacing .................................................................................................. 36
3.3 HARVEST PROCEDURE ..................................................................................... 37

3.3.1 Hand Harvest .................................................................................................. 37

3.3.2 Machine Harvest ............................................................................................. 38
3.4 STATISTICAL ANALYSIS .................................................................................. 39

CHAPTER 4. RESULTS AND DISCUSSION ................................................................. 41
4.1 RATE OF EMERGENCE AND FINAL STAND .................................................. 41

4.1.1 John Deere Planter .......................................................................................... 42

4.1.2 Monosem Planter ............................................................................................ 53

4.1.3 Stanhay Planter ............................................................................................... 54

4.1.4 Accord Planter ................................................................................................ 55
4.2 PLANT SPACING UNIFORMITY ....................................................................... 56

4.2.1 John Deere Planter .......................................................................................... 56

4.2.2 Monosem Planter ............................................................................................ 62

vii

4.2.3 Stanhay Planter ........................................................................................... 64

4.2.4 Accord Planter ............................................................................................ 65
4.3 BEET SIZE UNIFORMITY ............................................................................... 65
4.3.1 Descriptive Statistics ................................................................................... 66
4.3.2 Analysis of Variance and Mean Separation ................................................. 67

4.3.3 Three dimensional representation of plant spacing uniformity (CP3) and
beet size frequency (%) ........................................................................................ 68
4.3.4 Correlation Coefficients .............................................................................. 71
4.3.5 Multiple Linear Regression .................................................................... 72
4.3.6 Principal Component Analysis .................................................................... 74
4.4 STEPWISE REGRESSION ................................................................................ 78
4.4.1 Recoverable White Sugar per Acre (RWSA) ............................................... 79
4.4.2 3cm Mode Range (CP3) .............................................................................. 79
4.4.3 Rate of Emergence ...................................................................................... 80
CHAPTER 5. CONCLUSIONS .................................................................................... 81
CHAPTER 6 RECCOMENDATIONS .......................................................................... 83
APPENDIX A (DATED ACTIVITIES) ....................................................................... 84
APPENDIX B (PLANTER ADJUSTMENTS) ............................................................. 86
APPENDD( C (ANOVA TABLES) ............................................................................. 87
APPENDIX D (WEATHER DATA) .......................................................................... 100
BIBLIOGRAPHY ....................................................................................................... 104

viii

LIST OF TABLES

Table 1. Treatments for trials one and two in 2000 ........................................................... 31
Table 2. Treatments for trial three in 2001 ........................................................................ 31
Table 3. ANOVA table ...................................................................................................... 40
Table 4. Rate of emergence, final stand, 3cm mode range, sugar content clear juice purity
and sugar beet yield for trial 1, 2000 ................................................................................. 47
Table 5. Rate of emergence, final stand, 3cm mode range, sugar content clear juice purity
and sugar beet yield for trial 2, 2000 ................................................................................. 48
Table 6. Rate of emergence and 3cm mode range for trial 3, 2001 ................................... 49

Table 7. Rate of emergence, final stand, 3cm mode range, sugar content, clear juice
purity and yield for trials 1 and 2 over two locations ........................................................ 50

Table 8. Rate of emergence and 3cm mode range for the first 10 treatments of the three
trials .................................................................................................................................... 51

Table 9. Paired comparisons of the seed used by each planter (P<0.05) in terms of rate of
emergence and final stand for trials 1, 2, 3 and combination of the three trials ................ 52

Table 10. Paired comparisons for the 3cm mode range value (CP3) within and across
trials .................................................................................................................................... 58

Table 11. Paired comparisons for the 3cm mode range value (CP3) and rate of emergence
on the Monosem planter with the press-wheels adjusted for sugar beet (1.25 in., narrow)

and the press-wheels adjusted for corn (2.25 in., wide) in trial 3 ...................................... 64
Table 12. Treatments selected for beet size sampling ....................................................... 66
Table 13. Descriptive statistics for trial 1 and 2 ................................................................ 67
Table 14 Beet weight frequencies as a percent of the beets collected for trial 1 ............... 67
Table 15 Beet weight frequencies as a percent of the beet collected for trial 2 ................ 68
Table 16 Correlations between CP3 and each of the beet size categories for trial 1 ......... 71
Table 17 Correlations between CP3 and the beet size categories for trial 2 ...................... 72
Table 18 ANOVA and multiple linear regression coefficients for trial 1 .......................... 73

Table 19 ANOVA and multiple linear regression coefficients for trial 2 .......................... 74

Table 20 Correlation (Pearson) coefficients for trial 1 ...................................................... 75
Table 21 Factor pattern matrix and communalities for trial 1 ........................................... 75
Table 22 Rotated factor loadings and communalities (Varimax rotation) ......................... 76
Table 23 Correlation (Pearson) coefficients for trial 2 ...................................................... 77
Table 24 Factor pattern matrix and communalities for trial 2 ........................................... 77
Table 25 Rotated factor loadings and communalities (Varimax rotation) ......................... 78

Table 26 Stepwise regression for the dependent variables RWSA, CP3, stanle, stand20
and stand30 for trials 1, 2 and 3. ........................................................................................ 79

LIST OF FIGURES

Figure l. The design and dimensions of the field plot ....................................................... 33
Figure 2. The selected treatments (shaded) for soil moisture testing ................................ 35
Figure 3. The selected treatments (shaded) for hand harvest ............................................. 38
Figure 4. Rate of emergence for Trial 1, year 2000 ........................................................... 44
Figure 5. Rate of emergence for Trial 2, year 2000 ........................................................... 45
Figure 6. Rate of emergence for Trial 3, year 2001 ........................................................... 46
Figure 7. Plant spacing uniformity, Trial 1, 2000 .............................................................. 59
Figure 8. Plant spacing uniformity, Trial 2, 2000 .............................................................. 60
Figure 9. Plant spacing uniformity, Trial 3, 2001 .............................................................. 61
Figure 10. 3D chart of beet size frequencies vs. beet size categories vs. CP3 values for

trial 1 .................................................................................................................................. 69
Figure 11. Top view of beet size categories vs. CP3 values for trial 1 .............................. 69

Figure 12. 3D chart of beet size frequencies vs. beet size categories vs. CP3 values for
trial 2 .................................................................................................................................. 70

Figure 13. Top view of beet size categories vs. CP3 values for trial 2 .............................. 70

xi

CHAPTER 1

1. INTRODUCTION

Agriculture plays an important role in Michigan’s economy. In 1999 the total cash
receipts from crops, livestock and livestock products was $3.47 billion (MASS, 1999).
Michigan’s sugar beet industry was valued at $129.7 million and was ranked fourth
nationally with 10.6% of the US production. In 1999, 194,000acres were planted and
190,000 acres were harvested. Yields averaged 18.6 tons per acre. Continued
improvement in the productivity of the sugar beet industry is required in order to stay
competitive in the world market.

Currently there are two types of planters used: specialized and general purpose
planters. Both types of planters have either a mechanical or pneumatic seed delivery
system. The fimctions of the planter are the same for each type of planter: 1) open a seed
furrow, 2) meter the seed, 3) place the seed, 4) close the furrow, and 5) firm the soil over
the seed. Seed spacing is important because it helps provide a uniform root size which in
turn reduces harvest loss and leads to a potential increase in sugar yield (Lan et al.,1999).
Seed spacing may be influenced by the type of seed, planter speed, and seedbed
preparation.

Until the 1970’s nearly all sugar beet crops were planted in a high plant
population and thinned to a final stand (Panning et al.,1997). This technique, plant-and-
thin, was used to manage seed spacing since the planters which were used were not
precise in terms of seed spacing uniformity. The idea was to plant excess seed and thin

the plants to a desired plant spacing. This technique was inefficient in terms of cost and

time. The cost increased because of the unnecessary seeds sown, the purchase of thinning
equipment, and the extra labor for thinning.

An alternative to plant-and-thin is plant-to-stand. Plant-to-stand is a method
which places seed at the targeted spacing. In general, plant-to-stand has not been as
rapidly accepted in the United States as in Europe. In certain geographic regions of the
United States it is estimated that plant-to-stand is used on as much as 90% of the crop,
while in other regions as little as 20% of the crop is planted-to-stand. However, plant-to-
stand is increasing and is expected to eventually become the predominate practice (Smith
et al., 1991b).

A possible contribution of plant-to-stand establishment is better seed quality and
seed treatment. Today, pelleted and coated seed are both used to improve plantability.
Both types of seed contain fungicides which are used to prevent seed and soil diseases
and increase the chance for better germination. However, when comparing pelleted
versus coated seed, it is not clear which is handled better by precision planters and which
gives the most uniform spacing, and if improved spacing justifies the higher cost.
Pelleted seed is an improvement from the standpoint of planting due to its spherical
shape. This allows the round seed to slide with little friction through the internal surface
of the planter’s parts and improve uniformity of spacing.

The forward travel speed of the planter also affects seed placement. The delivery
of the seed from the metering mechanism to the furrow follows a trajectory which is
affected by a combination of the horizontal forward speed of the planter and the vertical
gravitational force that is exerted on the seed while dropping. Hence, the seed hits the

ground with a certain speed which causes the seed to bounce in the furrow and change its

location. The drop distance from the metering mechanism to the furrow varies from
planter to planter. The higher the point from which the seed is released, the higher the
vertical speed of the seed and bounce due to acceleration. Moreover, since the seed
follows a certain trajectory during delivery, the path it follows in the drop tube may not
be straight downward due to seed bounce on the walls of the drop tube. Seed bounce in
the drop tube may delay seed drop to the fiirrow. A delayed seed drop will alter the seed
spacing.

The question of whether to buy a specialized planter for sowing sugarbeets is
being asked by many growers. General purpose planters can sow corn and beans as well
as sugar beet. On the other hand, some specialized planters can only sow sugar beet seed
(Smith et al., 1991b). There is a need to evaluate the performance of planters, seed
treatments, and planter forward travel speed in order to improve stands and sugar

recovery.

1.1 OBJECTIVES

The purpose of this study was to compare the field performance of general
purpose and precision seeders in non-irrigated plant—to-stand establishment of sugar beet.
Specific objectives were to:

1. Compare the rate of emergence, uniformity of plant spacing and final plant
stand for a pelleted and fasonated seed treatment using general purpose
and precision seeders.

2. Evaluate the effect of forward travel speed on the uniformity of seed
spacing.

3. Evaluate the effect of plant stand uniformity on beet size uniformity.

4. Compare the effects of planter performance on beet yield, percent sugar,
percent clear juice purity (CJ P), recoverable white sugar per ton (RWST),

and recoverable white sugar per acre (RWSA).

CHAPTER 2

2. LITERATURE REVIEW
2.1 PLANTER DEVELOPMENT

The history of sowing and planter development dates back to 1300B.C. One of the
first methods of sowing, broadcast-by-hand, was first performed by the people of ancient
Babylon (McClelland, 1997). This method was inefficient because the seeds were not
distributed evenly and many were not covered by soil. Emergence and yields were often
low. The need to mechanize for better seed distribution was recognized from the time of
ancient Egyptians but mechanizing the sowing operation was very difficult.

In 1733, Jethro Tull published Horse-Hoeing Husbandry in England, where he
described his revolutionary drill (McClelland, 1997). His drill was based on a device used
to open furrows that was originally used by the Babylonians. Behind a furrow-creating
device Tull added a funnel to transfer the seed from the hopper to the furrow. The seed
covering device was a harrow which had wooden tines.

Americans employed a different planter development strategy in] 815. Rather than
trying to develop a complex planter, they developed a simple mechanical broadcast
seeder. A long, triangular-shaped seed box was mounted on a one-wheel chassis. Known
as Bennett’s sowing machine, the seed box contained a set of revolving brushes that
threw seed against openings at the back of the box. The machine was inexpensive to build
and easy to maintain. The Bennett sowing machine lead to the replacement of hand labor
with horse power (McClelland, 1997).

In 1860, a two row planter in which the operator tripped the seed drop mechanism

by hand when passing a specific mark was developed. In 1875, the first automatic check-

row planter using a knotted cord to trip the seed drop mechanism was introduced. By
1890 planter development had moved to the introduction of a single kernel, cumulative-
drop planter, and in 1900, fertilizer attachments were added. In 1923 the tractor mounted
planters were introduced (Deere and Company, 1992).

From 1923 until today, planter development has focused on three types of
planters; the broadcast seeder, the drill seeder, and the precision planter (Srivastava et.
al., 1994). Broadcast seeders were used for small grains such as oats, wheat, barley and
grass or legume seed. The seeds were metered from a hopper through a variable orifice
(Srivastava et. al., 1994). An agitator was used on top of the orifice to prevent bridging of
the seed over the gate and to assure continuous feeding. The metered seed dropped onto a
spinning disk which accelerated and threw it, usually horizontally. The width of coverage
depended upon the size, shape, and density of the seeds. The seeding rate was controlled
by the size of the gate opening, the speed of travel and the width of coverage. Afier
broadcast seeding, a secondary tillage operation was used to cover the seed with soil.

The drill seeder used a fluted wheel to meter the seed as it passed through an
adjustable gate (Srivastava et. al., 1994). The seed then passed through a tube to a furrow
which had been opened by a disk. A common method for covering the seeds was to pull a
small drag chain behind each furrow opener.

The evolution of the precision planters began with the conventional horse-drawn,
horizontal plate planter (Shearer et. al., 1991). Cells, located around the plate, were
directed through a seed mass. These cells were filled by seeds which were forced to the
cavities by the help of the weight of the seed mass. As the cells left the mass, a small

metal edge prevented additional seeds from exiting the metering device. These horizontal

plate planters remained popular until the mid 1960’s when the finger pickup, vertical
plate planters were introduced (Kepner, 1978; Srivastava et. al., 1994). The finger-pickup
metering mechanism was developed to eliminate the need to change plates every time the
seed size changed. The system had 12 spring-loaded fingers that were opened and closed
by a cam as they rotated. The seed was fed into the reservoir by gravity. As the fingers
rotated in the reservoir, they closed and trapped the seed between the finger and the
stationary plate (Srivastava et. al., 1994). This method was not particularly sensitive to
seed shape and size, but at high rotational speeds over-planting occurred (Shearer et al.,
1991)

In the late 1960’s, the first successful pneumatic metering device was produced
(Kepner, 1978). Dimples with holes in the center were located in rows around the
periphery of a drum. As the drum rotated, the seeds tumbled into the dimples and were
held in place by a pressure differential created by pressurizing the inside of the drum
(Shearer et al., 1991). In the early 1970’s, several equipment manufacturers incorporated
the pressure differential concept into vertical plate planters.

2.2 MECHANICAL SYSTEMS

The five main functions of modern planters are to: 1) open a furrow, 2) meter the
seed, 3) deliver the seed to the furrow, 4) cover the seed, and 5) firm the soil around the
seed (Panning et. al., 1997).

2.2.1 Furrow Openers

The major function of the furrow opener is to open a groove in the soil where the

seed can be placed at the proper depth. There are five types of furrow openers: 1) V-

trench opener, 2) disk opener, 3) runner opener, 4) combination runner and disk opener

(Deere and Company, 1992). The V-trench furrow opener is effective in moist soil
conditions, and is popular because it can be used for conventional or conservation tillage
systems. The two angled disks and close hugging gauge wheels are used to make a v-
shaped planting trench. The wheels not only gauge the depth but they also firm the soil
around the trench.

Disk openers are popular in minimum tillage systems. These furrow openers have
the ability to cut through corn stalks and grain stubble and maintain a uniform planting
depth. The double or single disk openers are placed in such an angle as to cut through the
soil and push it aside.

Runner openers are used when planting crops such as corn and soybeans in
ground that has been conventionally tilled. The runner type opener tends to push residue
ahead and then ride over it causing irregular planting depths. A combination of the runner
and double-disk opener, provide the advantage of both types of openers. The disk cuts
through the trash while the runner holds the soil apart long enough to allow the seed to
come to the seed groove before the loose soil can fall onto the seed. This method
provides for better seed to soil contact and a more uniform depth of planting (Deere and
Company, 1992).

2.2.2 Seed Metering

Seed metering systems select the seed from the hopper either individually for
crops such as corn or sugar beet, or randomly for crops such as soybeans, or cotton, and
deliver the seed to the seed placing mechanism at a selected rate (Deere and Company,
1992). There are three types of seed metering systems: 1) seed plate, 2) air devices and 3)

volume devices.

The seed plate metering system has openings or cells at the edge of the plate and
rotates at the bottom of the seed hopper. As the seed plate turns, each of the plate cells
collect one seed on each rotation. A spring loaded cutoff pawl keeps seed other than the
one in the seed plate cell from dropping from the hopper into the discharge tube. When a
cell containing a seed passes over the discharge hole in the hopper bottom, a spring
loaded knockout pawl ejects the seed through this opening to the seed placement device.

Air-type seed metering consist of a pressurized metering disk or a vacuum
metering disk. This system can be used to meter various kinds of seed. To go from one
type of seed to another there is a need to match the size openings of the wheel or drum to
the seed and to adjust the pressure differential accordingly.

Volume seed metering can handle a number of crops such as soybeans, peanuts,
cotton, wheat and oats. These devices are used on row-crop planters, grain drills and
broadcast seeders. The four most common volume devices used on row-crop planters are:
the feed cup, the picker wheel, the adjustable hole and the adjustable cutoff plate (Deere
and Company, 1992).

2.2.3 Seed Placement

Seed placement mechanisms can be classified either as a gravity or as a power
drop mechanism (Deere and Company, 1992). The fiinction of the seed placement
mechanism is to accept seed from the metering device and deliver it to the furrow so that
the seeds are properly spaced.

Gravity drop seed placement is the simplest and least expensive. The most
common gravity drop seed placement device is the drop tube. It has the disadvantage

though, of not placing small seed (sugar beet seed and vegetable seed) uniformly in the

row (Deere and Company, 1992). A seed dropped to the soil has momentum due to the
forward travel planter.

The four most common power systems are: a) seed conveyor belt drop, b) rotary-
valve drop, c) chain drop and d) air drop. Since the gravity drop devices were not
accurate in terms of plant spacing within the row, the power drop devices were built to
improve the accuracy of seed placement in the row (Deere and Company, 1992). The
seed conveyor belt is used with the finger pickup metering devices. It allows the seed to
eject into the seed belt and be carried down to the furrow. This method eliminates seed
bounce in the fiirrow (Deere and Company, 1992). The rotary valve seed drop is used
with plate type metering systems. The seeds are carried down by the rotor. The valve
which is located at the bottom of the planter unit, insures that the seed is in contact with
the rotor lug before is ejected. The chain seed drop picks up the seed at the bottom of the
seed metering mechanism and carries it to a point above the soil. The air seed drop
transports the seeds from the meter to the soil by air velocity (Deere and Company,
1992).

2.2.4 Seed Covering

Once the seed is in the furrow it has to be covered by soil. Four common seed
covering devices are: l) shovel, 2) knife, 3) disk and 4) chain. The shovel is used in
sticky soil conditions. The knife coverer is the least expensive and works well in
conventional tilled soils. However, it tends to plug in trashy conditions. The disk coverer
will either cut through surface residue or ride over it. Reduced and no-tillage systems
require the use of disk coverers to obtain enough loose soil to cover the seed. The chain

coverer is the simplest of all. It is attached at the very end of the furrow opener and

10

dragged over the top of the furrow which pushes the soil into the furrow and covers the
seed (Deere and Company, 1992).
2.2.5 Press-Wheels

Good seed-to—soil contact is necessary for emergence and germination of the seed.
Press wheels firm the soil around the seed. There are three types of press wheels: 1) seed
firming wheel, 2) press wheel and 3) seed packer wheel. The seed-firming wheels close
the seed furrow and firm the seedbed. The surface of the soil directly over the covered
seed is not packed to prevent crusting and aid in seeding emergence. Press wheels are
used in soil conditions where good seed-to-soil contact is not a problem. The press wheel
firms the soil after the seed has been covered. In addition, the same wheel serves as a
gauge wheel for depth control. The seed packer presses the seed into the bottom of the
groove by the firming wheel before the seed is covered. This operation ensures that the
seed will stay in place (Deere and Company, 1992).
2.2.6 Gauge-Wheels

Gauge wheels are used to control the depth at which seed is placed. Gauge wheels
can be found in several different locations on the planting unit. Gauge wheels may be
mounted in the front or the rear of the furrow opener, or in both front and rear. When the
gauge wheels are located in front of the furrow opener they may also be used to prepare
the seedbed for reduced tillage systems (Deere and Company, 1992).
2.3 PLANTER PERFORMANCE

Vacuum and air planters have the advantage of reduced seed damage and
improved accuracy. However, many of these pneumatic systems give multiple seed

drops, show decreased drop rates with increased planting rates, and are susceptible to

11

orifice clogging with dirt and seed treatment residues (Shafii and Holmes, 1990). Sugar
beet plant populations are often determined by the placement of the seed during planting
(Giles et al., 1990). By evaluating seed spacing, the performance of the planters can be
studied. Factors which affect planter performance are divided into internal and external
effects. Internal effects are dependent on planter adjustments and their components
(Smith et al., 1991b). Depending on the planter that is used, components such as seed
metering mechanisms, drop tubes and furrow openers can affect the planter’s
performance. External factors that directly affect the performance of planters are seed
treatment, the seedbed cultivation and planting date.
2.3.1 Seed Spacing

Seed spacing uniformity has been shown to be a significant factor in production
cost and yield (Lan et. al., 1999). Uniform seed spacing ensures a uniform root size
which reduces harvest loss, leading to a potential increased sugar yield (J aggard, 1990;
Lan et al., 1999). In England, the sugar beet industry gives a lot of attention to uniform
plant spacing because it is believed to give higher yields and reduce weed and insect
problems (Jaggard, 1990). Fomstrom and Miller (1989) suggested that sugar beet yield
was not affected by small variations in plant spacing if the plant population was
maintained.

Plant-to-stand is a practice that eliminates planting excess seed and thinning
(Smith et. al., 1991a). Adopting the plant—to—stand practice provides reduced costs from

thinning the population and from using less seed (Smith et. al., 1991a).

12

2.4 PLANT SPACING COMPARISONS

Planter performance has been evaluated in many ways. Hofrrran (1988) and Jasa
and Dickey (1982), developed a seed spacing index for the comparison of planter seed
spacing uniformity for sunflower and corn seeds respectively. Brooks and Church (1987),
Hollewell (1982), and Thomson (1986) examined the variability in plant spacing with the
use of histograms of distance between plants. Kachman and Smith (1995) compared
alternative measures such as mean, standard deviation, and the 1984 ISO-7256/1
standards (quality of feed index, multiples index, miss index, and precision). They
concluded that the measures based on theoretical spacing appeared to do well in
summarizing distributions of plant spacing for single seed planters, while the sample
mean and sample standard deviation were not appropriate. The mean is the average of all
the spacing. This average may include not only the targeted spacing, but also wide and
narrow spacing. The mean may look artificially good, but in reality is not good because
there are wide and narrow spacing, which are undesirable.

Smith et. al. (1991b) proposed a parameter for plant spacing comparisons based
on the percentage of seed spacing that occurred within a 3cm range centered about the
mode. Higher values for the 3cm mode range represent higher uniformity of seed spacing
near the mode. The 3cm mode range is a representation of the ability of a planter to space
seeds near the true planter spacing setting. Centering the range on the mode (target
spacing) rather than selecting an arbitrary range helps remove the bias of operator
adjustment or available planter components or settings (Smith et al., 1991b). An example
of the 3cm mode range is as follows: the targeted spacing for planting a study is 15 cm.

Hence the 3cm mode range is from 13.5cm to 16.5cm. 10 of the spacing collected from a

13

treatment consisted of 5cm, 13.5cm, 11cm, 18.5cm, 24cm, 8cm, 12.5cm, 16.5cm, 15cm,
14cm. As a result the amount of numbers that are included in the 3cm mode range is 4
(13.5, 16.5, 15, 14). 4, is the frequency of occurrence in the 3cm mode range and is
divided by the total number of spacing collected (in this case is 10), and then is
multiplied by 100 to get a percentage. Consequently, the 3cm mode range for this
treatment is 40%.
2.5 THEORETICAL CONSIDERATIONS
2.5.1 Seed Metering

The seed metering mechanism for row-crop planters meters one seed at a time.
The theoretical seeding rate for the row-crop planters is the ideal number of seeds per
hectare. The theoretical seeding rate can be calculated as (Srivastava et. al., 1994):

_ 10,000

.. — W * X. (1)
Where:

R5, is theoretical seeding rate (seeds/ha)

W is row width (m)

X8 is seed spacing along the row (m).
The actual seeding rate will equal the theoretical seeding rate if the row-crop planter
works perfectly and meters one seed at a time, all the time. The seed spacing can be
calculated as (Srivastava et al., 1994):
X5: fl (2)

Ac * n

14

where:

X, is seed spacing

V is travel speed of planter (m/s)

1c is number of seeds delivered per revolution of the metering device

n is the rotational speed of the metering device (rev/min).

Kocher et al. ( 1998) reported that seed spacing was affected by the seed drop,
variability in planter metering, seed trajectory, and seed bounce in the furrow. Based on
variations from the uniformity of seed spacing, the International Organization for
Standardization (1984), defined a number of indices based on the theoretical spacing for
the planter. These measures include the occurrence of multiples, misses, quality of feed,
and precision.

A theoretical spacing is the distance between seedlings assuming there are no
skips, multiples or variability and is based on the manufacturer’s specifications. The
theoretical spacing, Xref, forms the basis for obtaining the multiples index, miss index,
quality of feed index, and precision. It is used to divide the observed spacing into regions.

These regions are:

ni= Zn, (X, e (0 to 0.5)) multiple seed region

n'z = in, (X, 6 (>05 to <1 .5)) single seed region
rig: in, (X, e (>1.5 to <2.5)) single skip region
122; = Zn, (X, 6 (>25 to <3.5) double skip region
n3 = in, (X, 6 (>35 to + 00) triple skip region.

15

where:

 

X, = ' rs variable allocated in the segment (drvrsrons which equal to
xref

0.1 *Xref distributed on either side of Xref)
Xref is theoretical seed spacing
x, is median of the segment
n, is number of times that each value of X, has been plotted.
The multiples index, D, is the percentage of spacing that are less than or equal to

half of the theoretical spacing. That is:
D = 1’1, * 100 (3)
N
where:
n2: "1 is number of multiples
N= nf + n'z + n3 + n; + n3 is number of seeds recorded during the test

N'= n3 + Zn; + 3n; + 4213 is number of intervals.

The miss index, M, is the percentage of spacing greater than 1.5 times the

theoretical spacing. That is:
M = 19; *100 (4)
N

where:
no: n3 + 2n; + 3n; is number of misses.
The quality of feed index, A, is the percentage of spacing that are more than 1.5

times the theoretical spacing. It is a measure of how often the spacing were close to the

theoretical spacing. That is:

16

=1"—*100 5
N’ ( )

where:

n1=N-2n2 is number of seed normally sown.
Possible causes for a low quality of feed index could be a large number of multiples or
misses, and a large amount of variability around the drop site.

The precision, C, is a measure of the variability in spacing between plants afier
accounting for variability due to both multiples and skips. It is the coefficient of variation

of the spacing that are classified as singles. That is:

 

o= _—_z” *X‘ZJZ

‘ (6)
"2
where:
o is standard deviation
— zni * X ° . . .
X = ——,—'— rs average spacrng of normally sown seed at the region
"2
(Xie (>05 to <1.5)).
Hence, the coefficient of variation, C, is expressed as:
c = o *100 (7)

2.5.2 Seed Transport

Since the metering mechanism is positioned above ground, there is a fixed height
from which the seed is released. This height gives seed an acceleration due to gravity.
Increasing drop distance increases transport time. Row-crop planters use drop tubes to
transport the seed from the metering mechanism to the furrow. The path that the seed

follows along the drop tube though varies since the seed bounces in the tube due to the

17

horizontal travel speed and the vertical acceleration. Seed bounce varies by the drop
distance and time, hence affecting the seed spacing.

Giles et al. (1990) reported that the higher the ground speed of the planter, the
more error in seed spacing. Increased travel speed increased percent skips and decreased
seedling percentage. Seedling emergence, root yield, and recoverable sugar lbs/acre,
(kg/ha) were all affected by planter speed (Giles et al., 1990). Recommendations for
ground speed have not exceeded 7.3 km/h (4.5 mph) with any of the planter types (Giles
and Cattanach, 1998). In England, growers maintain planter field speed below 6km/h (3.7
mph). Growers are encouraged to add more planter row units to their planter tool bar to
increase field capacity without increasing field speed (Smith et al., 1991a). Increasing the
travel speed from 3.2km/h to 8.0km/h, decreased the uniformity of seed spacing (Panning
et al., 1997). Assuming the drop tubes are straight and there is no fiiction between the
seed and the internal surface of the drop tube, the drop time and vertical velocity at the

exit point can be calculated as (Goering et al., 1972; Pitt et al., 1982):

E=g—Cl*z*\/Iiz+22 (8)

where:
z is vertical direction, m, positive downward
'z' is vertical acceleration

z' is vertical seed velocity in the drop tube

Ii is horizontal seed velocity in the drop tube
Cl= 0.5*CD*pa*Ap/m is constant
CD is drag coefficient

Ap is projected frontol area of particle (m)

18

m is mass of particle (kg)
pa is mass density of air (kg/m3 = 29*Pb/(8.314®a)
O, is ambient air temperature (°K = °C + 273)
Pb is barometric pressure (kPa)
g is acceleration of gravity (m/sz).
The drag coefficient, CD, varies with the Reynolds number.

24
C = for Nregl 9
D N ( )

re

 

CD: (26.38 * Nr"e0'845 + 0.49) for N. 21 (10)
The equation for Reynolds number is given below:

*V *d
N..=p" p p (11)
M.

 

where:
Nre is dimensionless Reynold’s number
Vp is velocity ofparticle (m/s = (Ii2 + 2'2 )0'5)
tip is effective diameter of particle (m)
ha is dynamic viscosity of air (N .s/mz).
Over a wide range of barometric pressures, the air viscosity is a function only of air
temperature, ie.:
“a: 4.79.] 045... eO.678+0.00270(1 (12)
Another method for calculating the trajectory of a particle was reported by Pitt et

al. (1982) who assumed that the initial vertical velocity of a particle is zero and the CD is

19

constant. The approximate transit time in the tube can be calculated by the following

equation:

t— ln(Arg+ Arg2 —1)

 

 

2 * C1 * C2 (13)

where:

t is time of particle to fall distance 2.

Arg = 2"'e(2‘.ICl *1) -1

Z is length of the tube (m)

C2 = (g/C1)0'5 is constant.
The horizontal distance traveled during that fall can be calculated by:
h: 1n(C,*I&O*1+1) (14)

C1

where:

h is horizontal distance (m)

t is time for particle to fall distance 2
110 is initial velocity in the horizontal direction (m/s).

In addition, by solving ‘t’, for ‘z’ and differentiating with respect to time, the following

equation can be obtained for seed velocity in the drop tube:

 

z,_ C2 *sinh*(2*C1*C2 *1)

- (15)
I+COShT(2*C1 *C2 *1)

where:
2" is seed velocity in the drop tube

t is transit time in the drop tube.

20

Upon hitting the ground, seed bounce may disrupt the uniformity of spacing.
Panning et al. (1997) reported that planters which are used in the US today have the
ability to reduce horizontal velocity of the seed. Drop tubes with a rearward curve lead
the seed in the opposite direction of the planter. Directing the seed in the opposite
direction will reduce the horizontal velocity of the seed. With reduced horizontal velocity
there is less potential for the seed to roll in the furrow. If the exit velocity is at an angle 0.,
from the vertical, then the x-component of velocity at the exit, relative to the planter is:
it, =z'*tan9e (16)
where:

it, is x-component of velocity at the exit of the drop tube

0.3 is angle of the seed from vertical during the exit velocity.
If the x-component of seed velocity relative to the planter is equal to the forward velocity
of the planter, then the seed will drop with a zero horizontal velocity, hence the chance
for seed bounce is eliminated.

In the case of the pneumatic planters, the seed attains a speed due to the air
velocity in the hose. Consequently, the horizontal velocity of a seed at the exit of the
planter is:

5:, =Va*sinGe (17)
where:

Val is velocity of the air in the hose

0c is the angle of the seed at the exit of the drop tube.

Giles and Cattanach (1999), reported that the use of an insert into any style of

seed tube reduced the number of plants at the target spacing of five inches. An insert is a

21

tube which contains a sensor for monitoring the flow of seed. The insert is placed in the
drop tube and for this reason the diameter of the tube from where the seed is dropped, is
reduced. Based on planter test stand tests with a speed of 6.5 km/h (4 mph) and a medium
size seed, they found that the most uniform plant spacing was achieved with a straight
tube without an insert (of the 240 seeds on the belt , the 73 seeds/ 100 it were spaced at
exactly 5 inches apart from each other). Small beet tubes and curved tubes with an insert
caused an erratic spacing ( the straight tube with an insert gave 50 uniform seed
spacing/1001i from the total of 240 seeds, and the small beet tube gave 64 uniform seed
spacing/100fi from the total of 240 seeds found on the belt).
2.5.3 Seed-to-Soil Contact

Seed requires firm contact with moist soil to germinate. Early in the season soil
moisture content increases with depth because drying occurs through moisture transfer to
the surface (Srivastava et al., 1994). Even though deep planting provides good moisture,
the choice for an optimum seed depth is a compromise because one should consider not
only the soil moisture, but two other factors: 1) soil is warmer near the surface of the soil,
and warm soil promotes seed germination, and 2) planting deep can stop emergence
because the seed may not have the strength to reach the surface (sugar beet seed emerges
in approximately 10 days). There is an optimum depth of planting which varies with type
of crop and other factors (Morrison and Gerik, 1985).
2.6 COMPARISON OF LABORATORY AND FIELD TESTS

Precision planters should meter one seed at a time and place them in the furrow at
the desired spacing (Srivastava, et a1. 1994). Sometimes planter malfunctions occur due

to component wear error and adjustments. It is recommended that growers should test

22

their planters prior to planting. This is made possible with the use of test stands that are
available for most planters. “A well maintained drill could give full establishment of
uniform crop. On the other hand though, poorly maintained drills can result in a loss of
2500 English pounds on a typical 850 tonne beet contract” (Ecclestone, 1995).

The test stand is useful to identify malfunctions of the planter from faulty or
unadjusted components. The test stand allows field simulation with the only exaggerated
issue of the seed bounce. The inability of the test stand to simulate a seed bounce during
seed drop is a weakness that forces the method to be used only as an indicator of faulty
planter components (Srivastava, et al., 1994; Panning et al., 1997 ). Even though test
stand results are helpful in identifying faulty parts, they cannot be used to represent field
tests. Seed spacing performance results from laboratory testing were generally higher
than results from field-testing (Panning et al., 1997). In addition, the analysis of variance
indicated that the interaction of test method, planter type and travel speed was significant.
More specifically the analysis of variance showed that planter seed spacing performance
from laboratory testing was generally higher than results from field testing. Having this
interrelation between the three factors, test method, planter type, and travel speed,
Panning et a1. (1997) concluded that the laboratory test method cannot be used to predict
field test method results. Laboratory testing methods could be used as identifiers
(screening tests) of faulty components in the planters, and to determine which planters
have poor seed spacing uniformity. Planters that give good seed spacing uniformity in
laboratory testing must be tested in the field to determine which ones give the best seed
spacing results when the effects of seed bounce and roll are included in the field (Panning

etal,1997)

23

Generally, planter vibrations in the field disrupt the seed drop by causing the seed
to bounce inside the tube. It is more likely that the planter bounce will not be uniform,
and each seed will vary causing variations in seed spacing (Panning et al., 1997). The
inability of test stand to simulate seed bounce is attributed to the fact that the planter unit
that is mounted on the test stand is propelled by an integrated electric motor. As a result,
there are no vibrations on the planter unit. When the seed falls from the stationary drop
tube it hits a moving grease belt where the seed sticks on the belt. When the seed sticks
on the belt it does not roll, as it would normally do in the furrow.

The test stand operator can perform several adjustments on the planter units, and
identify faulty parts based on how well the planter spaces the seeds on the greased belt
(Harrigan, 1999b). The investment in drill testing pays handsome dividends towards
achieving a profitable sugar beet crop (Ecclestone, 1994; Ecclestone, 1995). Giles, and
Cattanach (1998), supported the above idea, by identifying several recommendations
which were derived from testing on a test stand John Deere plate type planters. The most
important recommendations were: 1) seed cutoff replacement or adjustment, avoids profit
losses 2) seed plate cells should be round and not oblong and of the proper size according
to the seed size that is used 3) proper vacuum setting for specific sizes of seed are desired
4) seed plate should have little if any contact with the meter housing, while the seed
ejection wheel should run smooth in the cell holes of the seed plate 5) the rubber seal in
the meter door should not be cracked or grooved on the seed plate surface 6) the large
straight seed tube without the insert is recommended.

A disadvantage of the test stand is that it is time consuming to obtain numerical

representation of seed spacing uniformity (Seaborn and Hofinan, 1999). Moreover, the

24

seed might still slide or bounce on high belt speeds (Kocher et al., 1998). In addition, the
belt is limited to about ten feet of travel (Seaborn and Hofman, 1999). This limitation of
the belt length restricts the consecutive seed spacing data that can be obtained (Seaborn
and Hofrnan, 1999).

2.7 SEED TREATMENT

There are four seed treatment options: 1) untreated seed, 2) seed treatment for
soil-borne pests, 3) seed treatment for insects, and 4) seed treatment for early emergence.
(http://www.britishsug2_rr.co.ul_g). The untreated seed does not contain any insecticide. It is
intended for areas where no pest attacks are expected, or where granular insecticide is
being used to control docking disorder.

The seed treatment for soil-borne pests is polymer coated. This seed treatment
controls soil-borne pests which eat below-ground parts of seedlings e. g. springtails,
millipedes, symphylids, pygmy beetles and wireworrns.

The seed treatment for insecticides is polymer coat too. This synthetic pesticide
will control most insect and insect-like pests of seedling roots and leaves, and will persist
long enough to control an early migration of aphids. Lastly, the seed treatment for early
emergence is a treatment where some of the germination process is completed before the
seed is pelleted. This treatment is designed for early sowing opportunities
(h@://www.bfitishsugar.co.uk).

Seed placement and performance can be improved by altering the shape of the
seed or by adding chemicals on the seed to get a better germination. There are two types
of seed coatings in the market: pelleting (pelleted seed) and the coating (fasonated seed)

(http://wwwbritishsuggco.uk). Seed pelleting and coating technologies have been

25

developed to improve plantability of flat seeds and also to permit the addition of
bioactive chemicals, nutrients and beneficial microbes (Halmer, 1988). The regular
pelleted sugar beet seed ranges from 3.8 to 4.6mm. The medium coated (fasonated) sugar
beet seed ranges from 3.2 to 3.6mm in diameter and 1.8 to 2.6mm in thickness (Lan et al.,
1999)

At present, all sugar beet seed receives fungicides such as Tachigaren which
insure against the damaging effects of diseases carried on seed and in the soil.
Tachigaren, which is applied during pelleting in a discrete layer at a distance from the
seed, controls soil-home fungal pathogens, particularly Aphanomyces.

In addition, two insecticide seed treatments are available: the tefluthrin and
imidacloprid. Both these treatments are applied by a thin—film coating process. This
application technique uses specialist high precision equipment to spray, and
simultaneously dry, chemical formulations mixed with binder and color pigment onto
batches of pre-pelleted seed (Prince et al., 1997).

2.7.1 Seed Pelleting

Seed pelleting improves plantability and performance. The non spherical shapes
of many vegetable seed including sugar beet seed prevent efficient precision planting.
Seed processors apply coatings on the seed to get a heavier and rounder seed.

Pelleted seed is made up of fillers (clays, limestone, calcium carbonate, talc,
vermiculite) and cementing additives (gum arabic, gelatin, methylcellulose, polyvinyl
alcohol, polyoxylethylene glycol-based waxes) inoculants, and fungicides (Taylor and
Harman, 1990). Pellets are believed to reduce anaerobic damage when calcium oxide and

peroxides are added to the pelleting material. These compounds release oxygen to the

26

seed when there is a shortage of oxygen due to flooding or soil compaction (Ollerenshaw,
1985; Langan et al., 1986). Despite the processing problems and extra costs of pelleting,
pelleted seeds are recognized as an important aspect of precision planters.
2.7.2 Seed Coating

Seed coating improves seed performance. The coating is a material that is applied
to the seed but does not affect its shape. The purpose of seed coating is to avoid the stress
of the planting environment by adding substances such as fungicides, insecticides,
safeners, and micronutrients. Rushing (1988) reported that the ideal traits of a seed
coating polymer are: 1) a water-based polymer, 2) a low viscosity range, 3) a high
concentration of solids, 4) an adjustable hydrophilic-hydrophobic balance, and 5) form a
hard film upon drying. These traits should lead to excellent plantability, contain no dust
from additives, and provide for excellent germination. One of the major benefits of a seed
coating is that it is placed directly on the seed and in the immediate vicinity of the
germinating seedling. Less chemicals are required compared to broadcast or furrow
applications with far less cost, avoiding environmental damage from excess pesticide use.

Pelleted and fasonated seed come in a variety of sizes and breeding techniques.
There is a distinct visual difference between the two though. Pelleted sugar beet seed has
a uniform spherical shape, size and mass, while the fasonated seed has an irregular shape,
and less mass (Pahl, 1996).

Uniformly shaped pelleted sugar beet seed generally outperformed non-pelleted
seed in Nebraska, by having a higher seeding percentage, with fewer doubles (two seeds
at the same point) or skips (no seed at a desired point) (Smith et. al., 1991b). The physical

characteristics (uniform shape, size and mass) of pelleted seed generally provided better

27

seed and plant spacing than medium sized non-pelleted seeds over the wide range of test
parameters (Pahl, 1996). Pelleted seed improved the ability of the planter to perform
better by having less skips and multiples.
2.8 SEEDBED CULTIVATION

Uniformity of planting depth and firming soil around the seed are important
aspects for the performance of precision planters (Srivastava et al., 1994). Consistent
planting depth and the firming of soil, can positively influence seed germination and
emergence. “There is no trade-off between a seedbed that is good for the drill and one
that is good for the seed” (Gummerson, 1986). Minimal spring cultivation has been
shown to be beneficial. With no spring cultivation there is no compaction under the rows,
spring work is reduced, and moisture is conserved (Harrigan, 1999a; Gummerson, 1989).

A desirable seedbed is the one with a firm soil below, and fine aggregates above
(Harrigan, 1999a). An extension of Harri gan’s statement was made by Proctor (1994)
who said that the soil particle should be fine enough to be in touch with the seed, keeping
it moist. Smith et al. (19913) suggested that the tillage should be shallow, 3-6cm deep.
Shallow tillage conserves soil moisture and causes minimal disturbance. Deep tillage
brings up big clods and moisture, hence drier soil below the surface (Smith et. al., 1991a).
Gummerson (1989) concluded that a soil which was not leveled could affect the ability of
the planter to place seed at a uniform depth. “The drill usually takes its depth control
fi'om wheels in front of the coulter so that on an undulating surface the coulter often rides
out of the soil, leaving seeds uncovered” (Gummerson, 1989). The greatest range of seed
depths was found in the seedbed without spring cultivation, due to the unevenness of the

soil. Drilling into land leveled in autumn or winter with little or no cultivation in spring

28

proved generally successful. In 1983 and 1984 a light spiked roll was used to break the
crust on the soil surface. This decreased final emergence in 1983 by 5% but increased it
by 4% in 1984. In 1984 and 1985 autumn leveling was achieved using a furrow press
attached to the plough. This resulted in about 5% less emergence than the best cultivated
treatments.

2.9 PLANTING DATE

Selecting the most desirable planting date can enhance the growing season by
taking advantage of the available soil moisture for germination and emergence and
reduce the risk of frost damage (Yonts et al., 1999). So, is it better to plant early or late in
the season? According to Yonts et al. (1999) when soil moisture and temperature are not
favorable for germination, uneven stands due to low emergence rates result. On the other
hand, when soil moisture and temperature are favorable, germination and emergence
occur in a short period of time. But, even if the planting conditions are ideal and plants
emerge early, there is no guarantee that the seedlings will survive due to the freezing
conditions that may occur.

The primary reason for planting early is to increase the length of the growing
season and increase total production. But there is always the chance of an additional costs
from replanting due to stand loss and hence a reduction of the growing season. Planting
later in the spring provides faster germination and decreases the potential for frost injury
(Yonts et al., 1999). Similarly, Mayo and Dexter (1997), stated that an early planting date

is critical to increase the yield, but it is always under the risk of frost damage.

29

CHAPTER 3

3. METHODS AND MATERIALS

The purpose of this study was to evaluate the field performance of four planters
for sugar beet. The study was replicated twice in 2000 and once in 2001 at the Saginaw
bean and beet research farm. A randomized complete block in combination with a
factorial treatment design was applied to 6 blocks. Each block consisted of 14 treatments.
The 14 treatments were based on 4 planters (Kverneland-Accord plate type planter, John
Deere 7300 vacuum planter, Monosem vacuum planter, Stanhay-Ralley vacuum planter),
2 types of seed (variety E17 4m PAT pellet, fasonated #3) and 2 planter speeds, 4.8 km/h
and 7.3 km/h (3.0 mph, 4.5 mph). Tables 1 and 2, provide an explanation on what
components each treatment consists. Data for rate of emergence, plant spacing, pre-
harvest stand, beet size, yield, percent sugar, percent clear juice purity (CJP), and
recoverable white sugar per ton (RWST) were collected. An analysis of variance with
mean separation by the least significant difference (LSD) procedure, the multiple linear
regression, and principal component analysis (PCA) were used. Finally, a stepwise

regression method was used to identify important independent variables.

30

Table 1. Treatments for trials one and two in 2000.

 

 

 

 

 

Treatment Planter Seed Speed, km/h (mph)
1 Accord Pellet 7.3 (4.5)
2 Accord Pellet 4.8 (3.0)
3 John Deere Pellet 7.3 (4.5)
4 John Deere Pellet 4.8 (3.0)
5 John Deere Fasonated 7.3 (4.5)
6 John Deere Fasonated 4.8 (3.0)
7 Monosem Pellet 7.3 (4.5)
8 Monosem Pellet 4.8 (3.0)
9 Monosem F asonated 7.3 (4.5)
10 Monosem Fasonated 4.8 ( 3.0)
11 Stanhay Pellet 7.3 (4.5)
12 Stanhay Pellet 4.8 (3.0)
13 Stanhay Fasonated 7.3 (4.5)
14 Stanhay Fasonated 4.8 (3.0)
Table 2. Treatments for trial three in 2001.

Treatment Planter Sid Speed. l_(m/h (mph)
1 Accord Pellet 7.3 (4.5)
2 Accord Pellet 4.8 (3.0)
3 John Deere Pellet 7.3 (4.5)
4 John Deere Pellet 4.8 (3.0)
5 John Deere Fasonated 7.3 (4.5)
6 John Deere Fasonated 4.8 (3.0)
7 MonosemI Pellet 7.3 (4.5)
8 Monosem] Pellet 4.8 (3.0)
9 MonosemI F asonated 7.3 (4.5)
10 Monoseml Fasonated 4.8 (3.0)
11 Monosem2 Pellet 7.3 (4.5)
12 Monosemz Pellet 4.8 (3.0)
13 Monosem2 F asonated 7.3 (4.5)
14 Monosem2 Fasonated 4.8 (3.0)

 

Monosem]: The press-wheels of the Monosem planter were set 1.25 in. apart
Monosemzz The press-wheels of the Monosem planter were set 2.25 in. apart

The fields were located at the Saginaw Bean and Beet farm. The soil was a
Zilwaukee Silty Clay. For the first two replicated trials of the year 2000, the field length
extended from north to south. Each plot was 100 feet long and 10 feet wide. The third

replicated trial was performed the year 2001. The length of the field extended from east

31

to west and the plots were 70 feet long and 10 feet wide. The randomization for the 14
treatments was the same for all three studies. The Stanhay planter was unavailable during
the last study and it was replaced by Monosem with its press-wheels at a wide setting
(2.25 in.). A schematic drawing is provided in figure 1. All treatments were fall
moldboard plowed and fit with a field cultivator. Seedbed tillage for trial 1 and 3 was a
single shallow pass with a spike-tooth/rolling harrow finishing tool within an hour of
planting. The seedbed tillage for trial 2 was with a field cultivator followed by a Danish
tine cultivator with a rolling basket to level and firm the seedbed. The finishing
implement was at a right angle to the direction of planting and at a very shallow depth to
level the soil compaction and break the soil crust yet conserve soil moisture. The tractor
was a John Deere 2355 two-wheel-drive equipped with dual tires at 10 psi (rear) and at
12 psi on the front.

Figure 1, provides a visual representation of how the design of the study was

plotted.

32

Figure 1. The design and dimensions of the field plot.

 

A

 

i

 

A

 

H4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

D
4

1 3456789101112131489 713111014125 462
Block] Block2

121014369121148513413 78593121411210
Block3 Block4

3 4114119131262105783 1213251146 71014
Block5 Block6

 

 

Year 2000 (A = 380 feet, 8 = 100 feet, C = 40 feet, D = 280 feet)
Year 2001 (A = 290 feet, B = 70 feet, C = 40 feet, D = 280 feet)

33

 

 

 

 

 

 

3.1 PLANTER ADJUSTMENTS

The planters were adjusted to achieve a target plant spacing of about 12.7 to 14
cm (5 to 5.5 in) for the first two trials and 13 cm (5.125in) for the third trial, and planting
depth of 2.6 cm (1 in.) for all the trials. Initial planter adjustments were based on
manufacturer recommendations. Final adjustments were made in the field based on expert
opinion of planter performance prior to planting (Appendix B). Special attention was
given to depth seed placement, press wheel adjustment for proper seed-to-soil contact and
minimal seed skips or multiple seed drops.
3.2 DATA COLLECTION
3.2.1 Moisture

The first month after sowing was the most critical period for the seed germination
and emergence. Given suitable moisture and temperature, sugar-beet seed started to
germinate 7 to 10 days after sowing. Thirty days after planting, seed which had not
emerged were most probably dead due to problems with: 1) soil moisture, 2) soil
temperature, 3) aeration, 4) seed processing, 5) crusted soil, 6) planter skips, and 7) frost.

Soil moisture was measured in three locations in each block. The three locations
were evenly distributed throughout each block. Figure 2 indicates which treatments were

selected for soil moisture testing.

34

Figure 2. The selected treatments (shaded) for soil moisture testing.

 

 

12 34 5 6 7 8 910111213148917131110141253462

i {if i‘fil rf

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Block 1 Block 2

 

12710143 6 912114 8 51341317859.312146111210

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Block 3 Block 4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Block 5 Block 6

 

 

Cores of soil were extracted from 0-1 inch depth, and 1—2 inch depth. From each
of the selected treatments, six soil samples from each block were collected to form a
representative sample for each block. The 0-1 inch and 1-2 inch cores were placed in
separate air tight bags to prevent soil moisture loss. Prior to drying the soil samples, their
weight (grams) was measured. Samples were placed in a forced-air dryer for 5 days at
120° Fahrenheit to dry. After drying, the weight (grams) of the samples was again
measured. The percent soil moisture was calculated as:

(Wet weight—Dry weight) (18)
Dry weight

 

Percent soil moisture =

Graphical representation of the soil moisture of the trials is shown in APPENDIX E.

35

3.2.2 Rate of Emergence

Stand counts were made at 10, 20 and 30-days after planting. The stand count for
the first two studies in 2000 measured the middle 50 feet of the third row from the west
for each treatment. The last study of 2001 measured the 70 feet of the second row from
south. The second or third row were selected to avoid variations in performance among
planter units on the planter.
3.2.3 Plant Spacing

Plant spacing uniformity was measured following the 30-day stand count. Plant
spacing measurements were made in the same area where stand counts were made. Plant
spacing measurements were made by laying a measuring tape along the row and
recording the spacing, to within ‘/2 centimeter ( 0.2 in) for each successive plant. A new
plant—spacing measuring technique was used for trial 3. A beet buggy was equipped with
a counter which sensed axle revolution, and translated revolution to distance (cm). An
equal number of spacing counts were collected from every row for comparison purposes.
The row with the minimum number of plants was the common divisor for all the
treatments. From the three studies a minimum of 120 plant spacing were collected. When
all the spacing between plants for all the treatments was gathered, the 3 cm mode range
was used to measure the ability of the planters to space plants at the target spacing (Smith
et. al., 1991b). The 3cm mode range provides the percentage frequency of spacing that
occur within the desired range. The desired range includes the target spacing plus or
minus 1.5cm (0.6 in.). In other words, from the 120 plant spacing collected for each
replication of each treatment, the 3cm mode range measures the percent frequency of

plant spacing that were included in the desired range. Each treatment has its own value

36

that characterize its ability to space plants uniformly. The higher the 3cm mode range
value, the better the plant spacing uniformity. The 3cm mode range was calculated as:

(Frequency of occurrence based on the targeted spacing)

 

3cm mode = * 100 (19)

(total number of observations)

3.3 HARVEST PROCEDURE
3.3.1 Hand Harvest

A sub-sample of 6 treatments was selected to measure beet size uniformity.
Treatments were selected based on the 30-day spacing uniformity measurements. The
best treatments were those with the greatest frequency of desirable spacing. The
treatments selected for hand harvesting provided a range of spacing uniformity from
relatively poor spacing uniformity to the best uniformity. Hand-harvest was used to avoid
broken beets or skips since small beets could not always be collected by the machine
harvester. Figure 3 shows treatments selected for hand-digging.

A representative sample of 40 beets was collected from the middle 50 feet of the
3rd row from the west for each of the best 6 treatments of each block. This representative
sample was sorted by size, (0-0.99lbs., 1-1.99lbs., 2-2.99lbs., 3-3.99lbs., 4-4.99lbs., 5
lbs>). The juice extracted from the sorted beets was sampled for percent sugar, percent

CJP, RWST and recoverable white sugar per acre (RWSA).

37

Figure 3. The selected treatments (shaded) for hand harvest.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Block 1 Block 2

 

12710143691211485134131785931214611210

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Block 3 Block 4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Block 5 Block 6

 

 

 

3.3.2 Machine Harvest
The middle two rows of each treatment were machine harvested for yield per acre.
In plots that were hand harvested for beet size, 85% of the weight of the beets that were
hand-dug were also added to the yield. Only the 85% of the weight was added in order to
account for likely machine loss. Several beets from each treatment were randomly
selected for sugar sampling. During harvest, beets were sampled for percent sugar,

percent CJ P, RWST and recoverable white sugar per acre (RWSA).

38

3.4 STATISTICAL ANALYSIS

A three factor randomized complete block experimental design was used. Each of
the blocked treatments consisted of three factors, planter type, seed treatment, forward
speed. Use of the RCBD eliminates experimental error and provides control over
environmental variations. The criteria for using the RCBD were: 1) the study was
experimental, 2) the number of experimental units (EU) in each block was equal to the
number of treatments, and 3) the treatments were randomly assigned within each block.
The random allocation of treatments to the EU ( 1 EU = 1 plot = 1 treatment) simulates
the effect of independence and allows the observations to be considered independent and
normally distributed. Independent and normally distributed observations were critical for
the estimation and the test of hypothesis since they provided valid estimates of
experimental error.

Since the study was based on the RC BD with a factorial treatment design, the
Statistical Analysis Software, SAS, was used to identify significant factors based on the
F-values and P-values derived from the Analysis of Variance, ANOVA. The ANOVA
table for a RCBD with a 3—factor factorial design is shown in table 3.

The tests of significance were set at an alpha level, a, of 0.05. After identifying
the factors that were significant, the Least Significance Difference, LSD, was used to
make multiple comparisons between these factors, and find any differences between
planters, seeds, speeds, or interactions of planter*speed and seed*speed.

The multiple linear regression and principal component analysis were tools used
to identify a relationship between beet size categories (lbs.) and plant spacing uniformity

(CP3).

39

Finally, an automated variable selection method was used to find out which

variables are affecting the depended variable the most The method used is known as the

stepwise regression. Stepwise regression begins with no independent variables. It enters

the most significant variable first, and continues step by step identifying the most

significant variables left, and adds them to the model. While new variables are added to

the model, the method checks to see if any variation can be dropped (P>.15 can drop a

variable).

Table 3. ANOVA table.

 

 

Source D.F SS MS F -value

Total rabc-l SS total

Blocks r-l SS blocks

A' a-l ss A MSA=SSA/(a-l) FA==MSA/MSE

B2 b-l SS B MSB=SSB/(b-1) FB=MSB/MSE

C3 c-l SS C MSC=SSC/(c-1) FC=MSC/MSE
A*C (a-1)*(c-l) SS AC MSAC=SSAC/(a-l)*(c-l) FAC=MSAC/MSE
B*C (b-1)*(c-l) SS BC MSBC=SSBC/(b-l)*(c-l) FBC=MSBC/MSE
Error (abc-l)*(r-l) SS E MS E

 

1 = Planter, 2 = Seed, 3 = Speed
D.F.z Degrees of freedom

SS: Sum of squares

MS: Mean square

40

CHAPTER 4

4. RESULTS AND DISCUSSION

The study was replicated three times. The first two replications were during the
year 2000 and the third replication was in 2001. Trials one and three were planted in late
April in a firm, level seedbed following spring seedbed tillage with a single pass
combination spike-tooth/rolling harrow perpendicular to the direction of planting. Trial
two was planted May 25th in a coarse seedbed created following demolition of an earlier
planted sugar beet crop damaged by heavy rains. Tillage was a single pass of a field
cultivator (4 inch depth) followed by two passes with a Danish tine/rolling harrow to firm
and level the surface. The three trials were analyzed both individually and across years
and locations. Individual analysis reveals conditions that might have affected the
treatments, group analysis provides an overall performance of the treatments.

Tables 4-11 provide the least significant difference (LSD), the coefficient of
variation (CV) and means for each variable (yield, percent sugar, percent CJP, percent
RWST, percent RWSA, 3cm mode range, rate of emergence, pre-harvest stand). Figures
4-9 provide a visual representation of how the treatments performed in terms of rate of
emergence and plant spacing uniformity. Images in this thesis are presented in color.

4.1 RATE OF EMERGENCE AND FINAL STAND

The rate of emergence was calculated as:

Actual plant population (20)

 

rate of emergence (%) = .
Theoretical plant population

41

The actual plant population was the population that was measured in the field. The
theoretical plant population was the seeding rate based on the seed spacing expected for
the planter setting. The percent germination reported on the seed bag label was 95-97%.
Plants may not emerge because of : a) poor seed-to-soil contact, b) environmental stress,
c) diseases and d) seed damage during treatment.

Rainfall, soil temperature and soil moisture information is listed in APPENDIX E.
4.1.1 John Deere Planter

In trial 1, the John Deere planter provided rapid emergence and a 10-day stand
equal to 79 to 93% of the desired seeding rate (table 4, fig.4). Heavy rains following the
lO-day count caused soil crusting and diminished stands in the following weeks. The 20-
day and 30-day stands were from 76 to 90% and 76 to 88% respectively. The final stand
ranged from 76 to 88% of the desired seeding rate (table 4). The John Deere planter
provided a significantly greater 10-day stand than the Accord or Stanhay planters.

There was no significant differences due to seed treatment (table 9). However, the
fasonated seed tended to emerge more quickly. The 10-day stand was averaged nine
percentage units greater and the pre-harvest stand was seven percentage units greater than
the pelleted seed. Pelleted seed has been shown to provide a significant improvement in
emergence in cool soil (Poindexter, 1999). Based on this work, that advantage appears to
diminish in warm soil.

In trial 2, The John Deere planter provided a 10-day stand equal to 46 to 56% of
the desired seeding rate (table 5, fig. 5). The fasonated seed provided a significant

improvement in rate of emergence compared to the pelleted seed (table 9). The lO-day

42

stand was seven percentage units higher and the pre-harvest stand was fourteen
percentage units higher than the pelleted seed.

Trial 2 was planted late in the season, May 25th, as a replanted stand following
seedbed tillage with a field cultivator (4-inch depth) and two passes with a Danish tine
rolling harrow to level and firm the seedbed. The poor emergence might be attributed to
the fact that the press-wheels of the planter were unable to close the seed furrow in all
locations. Poor seed-to-soil contact likely explains the relatively poor stand.

In trial 3, the John Deere provided a lO-day stand equal to 39 to 45% of the
desired seeding rate (table 6, fig.6). The soil was dry and early emergence was relatively
slow. Timely rains lead to a large increase in emergence for the 20-day and 30-day stand
(table 6, fig. 6). There was little difference in 10-day stand among seed treatment, but the
fasonated seed provided a 20-day stand eleven percentage units higher and a 30-day stand
8 percentage units higher than the pelleted seed.

Combined over years and locations, the John Deere planter provided a good rate
of emergence comparable to the other planters tested. Other than the difficulty in closing

the seed furrow in trial two, the planter appeared to provide good seed-to-soil contact.

43

 

 

 

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4.1.2 Monosem Planter

In trial 1 the Monosem planter provided a 10-day stand equal to 77 to 84% of the
desired seeding rate (table 4, fig. 4). The 20 and 30 day stands provided similar
emergence rates since few plants emerged following the heavy rains twelve days after
planting. The final stand ranged from 71 to 79% of the desired rate (table 4). There were
no significant differences in rate of emergence among seed treatments, but the fasonated
seed averaged one or two percentage units higher (table 9).

In trial 2, the Monosem planter provided lO-day stand equal to 55 to 64% of the
desired seeding rate (table 5, fig. 5). The 20 and 30 day rates of emergence were among
the best of all treatments when using fasonated seed. The fasonated seed provided a
significantly higher pre-harvest stand, and average of fourteen percentage higher than the
pelleted seed (table 9).

In trial 3, the Monosem planter provided a lO-day stand equal to 35 to 44% of the
desired seeding rate (table 6, fig. 6). The soil was cool and dry which likely delayed
emergence. Rainfall on May 7‘“, 0.55 inch and May 11‘“, 0.29 inches (Appendix E)
increased emergence at the 20 and 30-day stands to 73 to 77% and 79 to 83%
respectively. The pelleted seed provided a significantly higher lO-day stand, but there
was no difference in the 20 and 30-day stands.

The Monosem planter was also tested with the V style press-wheels set at the
wide setting, 2.25 inch, similar to what would be used in planting com. This was
designed to simulate the press-wheel settings commonly found on other general purpose
planters. This press-wheel configuration provided a slower emergence rate than the

narrow setting (table 6 and 9, fig. 6). This was likely due to improved seed-to-soil contact

53

in the dry soil. The 20— and 30-day stands showed a dramatic increase with increasing soil
moisture, ranging from 69 to 75% and 74 to 78% respectively (Appendix E).

Pelleted seed provided a significantly higher lO-day stand than fasonated seed in
each press-wheel configuration (nine percentage unit advantage with the press-wheel in,
thirteen percentage units with the wide setting) (table 9). However, there were no
differences in 20-day or 30-day stands. Increasing soil temperature and moisture allowed
the fasonated seed to emerge successfully prior to the final stand counts.

Combined over years and locations, the Monosem planter provided stands among
the highest in emergence during the emergence period (table 7, 9). The fasonated seed
provided a significantly higher 20-day and 30-day stand than the pelleted seed.

4.1.3 Stanhay Planter

In trial 1, the Stanhay planter provided a 10-day stand equal to 59 to 75% of the
desired seeding rate (table 4, fig.4). The 20-day and 30-day stands ranged from 57 to 73%
of the desired seeding rate. The final stand ranged from 55 to 67% of the desired seeding
rate (table 4). The pelleted seed provided a higher lO-day emergence rate (ten percentage
units) and final stand (seven percentage units) than the fasonated seed (table 9).

In trial 2, the Stanhay planter provided a lO-day stand equal to 50 to 71% of the
desired seeding rate (tableS, fig.5). The pre-harvest stand ranged from 60 to 64% with
fasonated seed and 75 to 76% with pelleted seed (table 5). The pelleted seed provided a
eighteen percentage unit increase in the 10-day rate of emergence and an increase of eight
percentage units in pre-harvest stand (table 9).

In trials 1 and 2, across locations, the Stanhay planter provided for a stand equal

to 55 to 73% of the desired seeding rate (table7). The final stand ranged from 58 to 72%

54

of the desired rate (table 7). In general, the Stanhay planter may have been too light to
provide adequate seed depth and coverage in the seedbeds tested. Planters such as this are
generally designed for a more intensively tilled seedbed. The runner opener did not allow
the planter units to maintain a constant depth and the press-wheel was designed for tilled
soil. The planter would likely perform better in more intensively tilled fields. The
Stanhay planter was not available for trial three in 2001.

4.1.4 Accord Planter

The Accord planter was designed for seeding sugar beet with pelleted seed.
Similar to the Stanhay, the Accord was a relatively light planter designed for tilled soil.
In trial 1, the Accord planter provided a 10-day stand equal to 62 to 70% of the desired
seeding rate (table 4, fig. 4). The 20-day and 30-day stands ranged from 59 to 65% and
59 to 64% of the desired seeding rate, respectively. Some features of the Accord planter
which may hinder planting in a firm seedbed are: a) light weight, b) the runner style
opener and c) press-wheels. These design features may cause the planter units to ride
over the seedbed, place the seed at a shallow depth and provide little loose soil for
covering and firming around the seed. The final stand ranged from 57 to 61% of the
desired rate (table 4).

In trial 2, the Accord planter provided a 10-day stand equal to 60 to 63% (table 5,
fig. 5). There was an increase to the seeding rate in the 20 and 30-day stands, ranging
from 70 to 72%. The seedbed was very coarse and little loose soil was available for
covering the firming around the seed. However, when ranked with all treatments, the

Accord was among the best in 10-day, 20-day, 30-day and pre-harvest stand.

55

In trial 3, the Accord planter provided a 10-day stand equal to 26 to 30% of the
desired seeding rate (tab1e6, fig. 6). Rate of emergence and pre-harvest stand were among
the lowest of the planters tested. The average 30-day stand was about 60%, 10 to 20
percentage units lower than the other planters. The soil was dry after planting and the
seedbed was firm. The Accord planter was designed for a tilled seedbed.

In trials 1, 2 and 3, across years and locations, the Accord planter provided a 10-
day stand equal to 52% of the desired seeding rate (table 8). There was a slight increase
in the seeding rate during the 20 and 30 day stands, but still lower than the seeding rate
provided by other planters. This indicates poor soil-to-seed contact in the firm seedbeds
tested.

4.2 PLANT SPACING UNIFORMITY
4.2.] John Deere Planter

The 3 cm mode range (CP3) was used together with plant spacing frequency
histograms to analyze plant spacing uniformity. The 3 cm mode range is a measure of the
percentage of plants established within 1.5 cm of the target spacing for each planter. A
higher CP3 value indicates better planter performance. Frequency histograms provide a
visual representation of the planter performance by grouping plant spacing by frequency
of occurrence in 3 cm increments. An ideal histogram would show a single, high spike at
the 12 to 15 cm increment indicating all plants spaced at the targeted spacing. An
undesirable spacing could be shown as several spikes of intermediate height, or high
spikes at an interval other than that which includes the target spacing.

In trial 1, the John Deere planter provided a 3cm mode range ranging from 15.5 to

25.7% (table 4, fig. 7). When planter speed was held constant at 4.8 km/h, (3.0 mph)

56

there was little difference due to seed treatment. The CP3 was 25.7% for the pelleted seed
and 25.5% for the fasonated seed (table 10). Pelleted seed (17.9%) performed slightly
better than fasonated seed (15.5%) at the faster travel speed of 7.3 km/h (table 4) but the
difference was not statistically significant.

Spacing uniformity was significantly better at the slower travel speed. Pelleted
seed sown at 4.8 km/h (3.0 mph) provided a CP3 of 25.7% while a speed of 7.3 km/h (4.5
mph) provided a CP3 of 17.9% (table 4). Similar results were found with fasonated seed.
At 4.8 mm (3.0 mph) the CP3 was 25.5%, falling to 15.5% at 7.3 km/h (4.5 mph).

Figure 7 indicates that the John Deere planter provided frequent narrow plant
spacing. Narrow seed spacing can be explained by: a) a long drop distance from the
metering mechanism to the furrow (53 cm/21 in), b) fast forward speed and c) poorly
functioning seed delivery units. Based on the high frequency of plants at a frequency
narrower than the target spacing, the John Deere planter may have been delivering seed at
a higher rate than intended.

In trial 2, the John Deere planter provided a 3cm mode range ranging fiom 10.0 to
14.4% (table 5, fig. 8). Figure 8, indicates that the planter provided many narrow spacing
and a lot of skips. In the coarse seedbed characteristic of trial two, the planter press-
wheels did not effectively close the furrow. As the soil dried in the days after planting the
seed furrow opened in numerous locations, exposing the seed to dessication and other
hazards.

In trial 3, the John Deere planter provided a 3cm mode range ranging from 18.3 to
23.8% (table 6, fig. 9). At a forward travel speed of 4.8 km/h (3.0 mph), pelleted seed,

23.8% provided a significantly better seed spacing uniformity than fasonated seed, 8.3%.

57

Table 10. Paired comparisons for the 3cm mode range value (CP3) within and across
trials.

 

 

 

 

 

 

 

58

Planter Speed Seed CP3
Trial 1 Trial 2 Trial 3 Trial 1 2 Trial 1 2 3
Accord 4.8 Pellets 38.8“ 42.7“ 37.2“ 40.8“ 39.6“
-5999?! ........ 7 ;3_------.1??-1.1?-t§ ......... 39:6.“ ...... ‘1 9.“.-------3-‘}.-3_“ ...... 4.2;3“--_-------f19.-.1.“ ............
J.D. 4 8 Pellets 25.7“ 12.2“ 23.8“ 19“ 20.6“
-.J_-_I?.- ............ 4 -3 ........ 1? 3_sqnatsQ----2.5_.5-? ...... 1.4.74.“ ...... 1 -33.“. ..... .29“ ............. 1.9.4.“. ............
JD. 7 3 Pellets 17.9“ 10“ 23.6“ 14“ 17.2“
3.1.).- ............ 7 -3 ........ 1? 3.8-9241351.---33-3“ ______ 1-.1--9.“------2.0.-.1.“.-----.131? ........... 1- 3.3.“. ............
JD. 4 8 Pellets 25.7“ 12.2“ 23.8“ 19“ 20.6“
-133.- ............ 7 -3 ........ 1? 91.153}? ......... 1.729.“ ...... 1.9-“--------.2_3.-§.“------.1.‘1“-“ ............. 1- 7:3.“- ............
J.D. 4 8 Fasonated 25.5“ 14.4“ 18.3“ 20“ 19.4“
-.J-.1?.- ............ 7 .3------_.1??§9!12t¢s1.--__l.5--5.‘-’ ...... 1.1.-.9.“-----_2.9.-.1.“.-----.1-3-.7“ ........... 1. 3.3.". ............
Monosem 4.8 Pellets 35.7“ 27.3“ 33.4“ 31.5“ 32.2“
-M9n9se-II} ..... 4 :3 ........ 1? a.s.911.at¢st----3§.§‘_’ ...... 2. 8.“.__----_.3.9.-_7.“ ...... .2-8.3?---------_.2_9-.1-“ ............
Monosem 7.3 Pellets 24.4“ 25.4“ 32.9“ 24.9“ 27.6“
-M9n9-san ..... 7 :3--_-_---1?@§911§t@si.----3.1.-.5.“ ...... 2- §.-ft“.-----.2-4.3-“_ ..... 3.3. 193-------__-?:4." ...............
Monosem 4.8 Pellets 35.7“ 27.3“ 33.4“ 31.5“ 32.2“
34999.89?) ..... 7. :3 -------- 1? 3.1-1?}? ......... 3.4-f1“ ...... 3 .5.-.‘1“-----_3_3.--9.“ ...... 3.4-.9“.------_--.2-7.-.6.“ ............
Monosem 4.8 F asonated 28.6“ 28“ 30.7“ 28.3“ 29.1“
M99933?) ..... 7 13 ........ 1? a.sqnat¢si---_2-1_.5.‘.’ ...... 2. .6--&“--_--.2.4.-3.“. ..... 3.3-.9“.------_--.2_4.“ ...............
Stanhay 4.8 Pellets 30“ 16.6“ - 23.3“ -
31911-139): ....... 4 :3”----_E?.S_9nat¢si---_2§“ ........ 2. Lilia--.- ........... 3-3175‘ ........... - ..................
Stanhay 7.3 Pellets 24“ 19.2“ - 21.6“ -
-Stanhax ....... 7. .3-"nu-13389231351.-_--3.2.-_4“ ...... 1. 3.-§“_--__- ___________ 3.9.6“----------: ..................
Stanhay 4.8 Pellets 30“ 16.6“ - 23.3“ -
-Stanhax ....... 7 :3 ........ 1? 53.1.1.6}? ......... 3.41 ........ 1.9.--2.“-----: ........... 3.1-:9“W ..................
Stanhay 4. 8 Fasonated 26“ 2 1 .4“ - 23 .7“ -
-3123111132 ...... 7 .3 ________ 1? gamed-2-3.2.4“ ...... 1.3.3.1"--.- ........... .2.Q-.6f‘----------- ..................
Monosem“ 4.8 Pellets - - 31.5“ - -
-M9B9§9@1---.‘1:8 ........ 12‘ 3.891131%": ........... - 291 ...... ..................
Monosem“ 7.3 Pellets - - 28.8“ - -
-M999ssm‘---2;3 ________ 1? wagging ........... -. -__---_---.2.‘}“ ........ ..................
Monosem“ 4.8 Pellets - - 31.5“ - -
-M99953!!!“--.7_;3--_----.1?.e_1_1§_t_s ......... -. ___________ -. 288 ------ __________________
Monosem“ 4. 8 Fasonated - - 29. l“ - -
Monoseml 7.3 Fasonated - - 24b — -
LSD 6.42 6.36 4.62 4.57 3.17
CV (%) 20.90 23.8 14.27 22.76 18.12
Monosem“: The press-wheels are set 2.25 in. apart
LSD (P<0.05)

 

 

 

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Combined over years and locations, the John Deere planter provided a 3cm mode
range ranging from 15.8 to 20.6% (table 10). When speed was held constant, pelleted
seed tended to provide a more uniform spacing, but the improvement was not significant.
When seed treatment was held constant, the slower forward travel speed (4.8 km/h)
provided a more uniform plant spacing than the faster travel speed (7.3 km/h).

4.2.2 Monosem Planter

In trial 1, the Monosem planter provided a 3cm mode range ranging from 21.5 to
35.7% (table 4, fig.7). This trial experienced heavy rains twelve days after planting which
likely interfered with emergence. Rapid emergence provided an advantage in this trial
since there were no improvements in emergence measured after the 10—day emergence
count. At 4.8 km/h (3.0 mph), pelleted seed, 35 .7%, provided a better stand than
fasonated seed, 28.6%, (table 10). At 7.3 km/h (4.5 mph) there were no differences in
spacing uniformity. When seed treatment was held constant and travel speed varied,
spacing uniformity was better at the slower travel speed with both pelleted (35.7% at 4.8
km/h, 24.4% at 7.3 km/h) and fasonated seed (28.6% at 4.8 mm, 21.5% at 7.3 km/h).
The spherical shape of the pelleted seed reduces friction in tube and its increased mass
reduces seed bounce within the drop tube. The slower speed reduces seed bounce in the
fiHTO“L

In trial 2, the Monosem planter provided a 3cm mode range ranging from 25.4 to
28.0% (table 5, fig. 8). In this coarse seedbed, the planter generally provided a more
uniform spacing than either the Deere or Stanhay planters. However, there were no
significant differences in plant spacing uniformity due to either seed selection or travel

speed.

62

In trial 3 with the press-wheels adjusted to the narrow spacing (3.2 cm, 1.25 in.),
the Monosem planter provided a 3cm mode range ranging from 24.3 to 33.4% (table 6,
fig. 9). When travel speed was held constant and seed treatment varied there were no
differences in seed spacing uniformity at 4.8 km/h (3.0 mph) (pelleted at 33.4%,
fasonated at 30.7%), but at 7.3 km/h (4.5 mph), pelleted seed (32.9%) provided a more
uniform spacing than fasonated seed (24.3%) (table 10). When pelleted seed was used
there was no difference in seed spacing uniformity between 4.8 km/h (33.4%) and 7.3
km/h (32.9%). However, when fasonated seed was used, the slower travel speed provided
a more uniform stand (30.7%) than the faster travel speed (24.3%). Pelleted seed was
handled better because of its increased weight and its spherical shape.

In trial 3, the Monosem planter was also evaluated with the press-wheels set at the
wide spacing (5.7 cm, 2.25 in.), typical of the spacing used for corn and other row crops.
This spacing would likely provide less effective firming of the soil around the small sugar
beet seed than the narrow setting. At this press-wheel spacing the planter provided a CP3
value ranging from 24.0 to 31.5% (table 6 and 10, fig. 9). Although the use of pelleted
seed and a slower travel speed tended to provide a small improvement in seed spacing

uniformity, the improvement was not significant.

63

Table 11. Paired comparisons for the 3cm mode range value (CP3) and rate of emergence
on the Monosem planter with the press-wheels adjusted for sugar beet (1.25 in, narrow)

and the press-wheels adjusted for corn (2.25 in, wide) in Trial 3.

 

 

 

 

 

 

Press-wheels Planter Speed $991 Q3 Rate of emergence. %
lO-dav 20-dav 30-day
Narrow Monosem 4.8 Pellets 33.4“ 44“ 73“ 80“

-1819? ................. M93393??? ..... 4;.8. ........ P911933 ......... 3.1-.51--.-24? ........ 79? _______ 7.5? ______
Narrow Monosem 4.8 F asonated 30.7“ 35“ 77“ 79“

.333 i919 ................. 15619992992 ..... 4.8. ........ 1.393998%----3.9.;1“._---.2_4.“ ........ 7.53----18“ ......
Narrow Monosem 7.3 Pellets 32.9“ 44“ 77“ 79“

-W 19.9 _________________ M99999!!! ..... 7.3. ........ 11631-919 ......... 2.8-.31---.49.“ ........ 73.“-___-_-_7_4“ ......
Narrow Monosem 7.3 Fasonated 24.3“ 36“ 75“ 83“
Wide Monosem 7.3 F asonated 24“ 25b 69“ 74b
L.S.D (P<0.05) 4.62 7 7 7
c.v (%) 14.27 16.92 8.99 7.96

 

In trials 1, 2, and 3, across years and locations, the Monosem planter with the
press-wheels set at the narrow spacing provided a 3cm mode range ranging from 24 to
32.2% (table 10). There was no difference in seed treatment at 4.8 km/h (3.0 mph), but at
7 .3 km/h (4.5 mph) the pelleted seed improved seed spacing uniformity (27.6% for
pellets, 24% for fasonated seed). The slower travel speed provided a more uniform stand
(about a five percentage unit increase in CP3) when both pelleted seed and fasonated seed
were used.

4.2.3 Stanhay Planter

The Stanhay planter is a vegetable planter with a runner type furrow opener, a
short seed drop, vertical press behind the opener and a semi-pneumatic press-wheel
behind the furrow closers. The Stanhay was used in trials one and two. In trial 1, the
Stanhay planter provided a 3cm mode range ranging from 22.4 to 30.0% (table 4, fig. 7).

While the planter tended to provide a slightly more uniform stand with pelleted seed or at

64

the slower travel speed, the differences between treatments were not significant in trial
one (table 10).

In trial 2, the Stanhay planter provided a 3cm mode range ranging from 16.6 to
21.4% (table 5, fig.8). Seed spacing uniformity was not as good as in trial 1 due to the
coarse seedbed and difficulty in obtaining good seed-to-soil contact. The differences
between treatments were small and not significant (table 10).

In trials 1 and 2, across locations, the Stanhay planter provided a 3cm mode range
ranging from 20.6 to 23.7% (table 7). Once again, while the pelleted seed and slower
travel speed tended to provide a more uniform stand, the differences between treatments
were small and not significant (table 10).

4.2.4 Accord Planter

The Accord planter was designed specifically for pelleted sugar beet seed. It has a
ground driven seed delivery mechanism with a short seed drop. Seed spacing uniformity
was among the best in each of the three trials (table 10). There were no significant
differences in seed spacing uniformity due to travel speed in any of the three trials. The
uniform spacing could be due to: a) the short seed drop distance b) the seed packer wheel
located right behind the furrow opener which helps eliminate seed bounce, and c) the
pelleted seed is heavier and improves the planter handling ability. The Accord planter
provided the best seed spacing uniformity across locations and years.

4.3 BEET SIZE UNIFORMITY

Sugar content is partially a function of beet size. Large beets tend to have lower

sugar content than small beets. Uniform plant spacing may help provide a more uniform

beet size. Uniform plant spacing allows each beet equal access to moisture, nutrients and

65

sunlight. Based on an optimal sugar content and ease of harvest a desirable beet weight is

one to two pounds.

Six treatments in each of trial 1 and trial 2 were selected for beet size sampling

(Table 12). Treatments were selected based on the treatment CP3 value to represent a

range of plant spacing uniformity from among the most to the least uniform. Prior to

machine harvest, fifiy consecutive beets from each of six replications of the six

treatments were hand harvested. Individual beet weights were recorded. In order to

characterize beet size uniformity, a comparison of descriptive statistics, an analysis of

variance and mean separation of beet size categories, three dimensional contour charts,

correlation coefficients, multiple linear regression and principle component analysis were

used.

Table 12. Treatments selected for beet size sampling.

 

 

Trial 1 Trial 2 Treatment CP3 Planter Seed Speed, km/h
X 1 49.0 Accord Pelleted 7 .3
X 2 3 8.8 Accord Pelleted 4.8
X 3 10.0 Deere Pelleted 7.3
X 5 l 1.9 Deere Fasonated 7.3
X 5 l 5 .5 Deere Fasonated 7.3
X 8 35 .7 Monosem Pelleted 4.8
X 8 27.3 Monosem Pelleted 4.8
X 9 21.5 Monosem Fasonated 7.3
X 9 26.4 Monosem Fasonated 7.3
X 12 30.0 Stanhay Pelleted 4.8
X 1 3 22 .4 Stanhay Fasonated 7.3
x 13 18.8 Monosem“ Fasonated 7.3

 

4.3.1 Descriptive Statistics

Standard descriptive statistics for the beet weights of trials 1 and 2 are listed in

Table 13. Beets in trial 1 tended to be larger than in trial 2. An average beet weight in

66

trial 1 was 2.37 lb and the mode was 1.5 lb. In trial 2, an average beet weight was 2.08 lb

while the mode was 1.2 lb.

Table 13. Descriptive statistics for trial 1 and 2

 

Statistic

1“t quartile, lbs
3'“ quartile, lbs

Mean

Standard Error

Median
Mode

Standard Deviation
Sample Variance
Kurtosis

Skewness

Range

Minimum
Maximum

Sum
Count

Trial 1
1.4
3.1
2.37
0.035
2.2

1.5
1.359
1.84
1.393
0.988
8.85
0.05
8.9
3545.7
1495

Trial 2
1.2
2.8
2.08
0.031
1.85
1.2
1.261
1.591
1.287
1.006
7.45
0.05
7.5
3337.8
1602

 

4.3.2 Analysis of Variance and Mean Separation

An analysis of variance with mean separation was performed to identify important

differences in beet size categories among treatments. Beets were sorted by weight in one-

pound increments (Tables 14 and 15).

Table 14. Beet weight frequencies as a percent of the beets collected for Trial 1.

 

 

 

 

Treat # Planter _(1 Speed fl Beet size (lbs)
km 19.2.9. 1 1.99 2299 3-399 4499 5_>
2 Accord Pellet 4.8 38.8 8.4c 32.4”“ 30.6“ 20.1a 4.6b 3.3"a
5 1.0 Fasonated 7.3 15.5 23.1' 38.7' 23.3' 9.8c 3.6“ 1.3b
8 Monosem Pellet 4.8 35.7 1 1.9“ 35.9““ 300' 14.7”“ 4.6“ 2.6“
9 Monosem Fasonated 7.3 21.5 17.5‘” 37.9“ 28.6‘ 10.0“ 4.2” 1.4”
12 Stanhay Pellet 4.8 30.0 11.2he 28.9“c 23.7' 16.2““ 11.5' 8.2'
13 Stanhay Fasonated 7.3 22.4 8.0c 28.0c 26.1' 16.7““ 12.7“ 8.3“
LSD (.05) 7.3 9.3 7.8 6.7 6.7 5.5
CV (%) 46.6 23.4 24.6 38.8 83.0 111.7

 

There was no difference between treatments in the 2-2.99 lb category, and no apparent

differences due the CP3 measure of plant spacing uniformity. A similar relationship

between beet size and plant spacing uniformity exists for ma] 2.

Table 15. Beet weight frequencies as a percent of the beets collected for Trial 2.

 

 

 

 

 

Treat # Planter Sccd Speed CP3 Beet size (lbs)
K_1_1_ 00.99 1-1.99 2299 33.99 4.4.99 53
l Accord Pellet 7.3 49.0 18.1”ac 42.1” 26.7' 7.0c 4.1”” 1.7”
3 1.1) Pellet 7.3 10.0 11.0c 26.0c 27.9‘ 179‘ 9.9a 7.1”
5 1.1) Fasonated 7.3 11.9 17.2”‘ 34.4”“ 25.7” 13.9”” 5.0” 3.6”
8 Monosem Pellet 4.8 27.3 184”" 37.2”” 25.7' 12.2”ac 2.7”” 3.5”
9 Monosem Fasonated 7.3 26.4 26.9“ 42.2” 19.8' 9.5”” 0.9c 0.5”
13 Stanhay Fasonated 7.3 18.8 21.0”” 28.0”c 26.1“ 16.7“ 5.3” 2.7”
LSD (.05) 9.3 10.6 9.9 5.8 3.9 3.4
CV (%) 42.2 25.9 33.1 38.5 71.7 90.2

 

4.3.3 Three-dimensional representation of plant spacing uniformity (CP3) and beet
size frequency (%).

Three-dimensional continuous surface charts displaying CP3 values on the X-
axis, beet weight frequencies on the Y-axis and beet size categories on the Z-axis were
used to create a visual representation of the relationship between variables and identify
potential trends in beet size with plant spacing uniformity (Fig. 10-13). The relationship
between CP3 values and beet size uniformity varied among trials. In trial 1 (Fig. 10-11), a
greater concentration of the smallest beets appeared to be associated with a poor plant
spacing uniformity (low CP3 value). In trial 2 (Fig. 12-13) the greatest concentration of
small beets was associated with the best plant spacing uniformity. A low CP3 value
indicates few plants within three centimeters of the target spacing. This measure does not
differentiate between spacing that was too narrow or too wide. Since a narrow spacing
tends to produce a small beet, it appears that the low CP3 values were due to narrow plant

spacing in trial 1 and wide plant spacing in trial 2.

68

 

Figure 10. SD chart of beet size frequencies vs beet size categories
vs CP3 values for Trial 1

60.0 \

 

 

so. I

 

 

40.0-

Beet size
mammal “°'°‘

 

 

20.0
10.0 n 3
- z 3 “ n
0'09 '9 A” 2 .. NCP3
.0 N L '
. 8 ‘0 (D
Beetsrze ‘° ‘0

(lbs
.0.0-10.0 .10.0-20.0 13200-300 0300-40-0 .40.0-50.0 .50.0-60.0"

 

 

 

Figure 11. Top view of beet size categories vs CP3 values for Trial 1

 

 

I 0.0-10.0 .10.0—20.0 13200-300 D 30.0-40.0 .40.0—50.0 I 50.0-60.0]

 

69

 

 

 

 

 

Figure 12. 3D chart of Beet size frequencies vs beet size categories
vs CP3 values for Trial 2
60.0]

      
     

 

 

50.0

 
     

'1“

 

 

40.0
Beetslze \Wd‘
N_
Beet ('7 (:2 P3
size § g 8:; 5 C
(lbs) <5 m 3

 

. 0.0-10.0 .10.0—20.0 El 20030.0 0 300-400 . 40.0600 . 50.0-60.0

 

 

Figure 13. Top view of beet size categories vs CP3 values for Trial 2

 

 

 

. 0.0-10.0 .10.0-20.0 CI 20.0-30.0 CI 30.0-40.0 . 40.0-50.0 . SOD-60.01

 

7O

 

4.3.4 Correlation Coefficients

Correlation coefficients were calculated with the CP3 values as the dependent

variable and the beet size categories as the independent variables (Table 16, 17). The

correlation coefficients support the relationships revealed in the three-dimensional

surface graphs. In trial 1, there was a negative correlation between CP3 and the smaller

beet sizes. Fewer small beets were associated with higher CP3 values. In trial 2, there was

a negative correlation between CP3 and the larger beet categories. As the CP3 value

increased there were fewer large beet and a greater percentage of small beet. The

correlation coefficients also indicate that a number to the dependent variables were highly

correlated.

Table 16. Correlations between CP3 and beet size categories for Trial 1.

 

 

CP3 0-0.99 1-1.99 22.99 33.99 4-4.99 5>
CP3 1
0.099 02362 1
12:Y91.99.----Q-.1_.6.5._5 ................................................................................
1-1.99 -0.3630 0.1887 1
P:Y§19§----9~9.2.9..6. ..... 9.2.79.2- .................................................................
2299 0.3435 0.0485 -0.1647 1
12:Y.a.1.99.---.9-94.Q-2- ..... 9.27.8.5 ...... 9.33.6.9. ...................................................
33.99 0.2427 05049 03351 01474 1
P:Y§199----9-_1..5_3.7. ..... 9:99.17. ..... 11.-94.5.7. ..... 9.3-9.9.7. .....................................
4-4.99 0.1226 -0.5058 -05741 02461 0.2293 1
P:Y9199_-_--9f1.79-2. _____ 9:99.19. ..... 9.9993 ...... 1),-14.7.3 ..... 9.1.7.34. .......................
5> 0.0589 0.4131 -0.2785 -0.5189 0.0914 0.5917 1
p-value 0.7327 0.0123 0.0999 0.0012 0.5958 0.0001

 

71

Table 17. Correlations between CP3 and beet size categories for Trial 2.

 

 

CP3 O-O.99 1-1.99 2-2.99 3-3.99 4-4.99 5>
CP3 1
0.099 0.0954 1
P:Y9199----_9-.5._7.9_.9_ ..............................................................................
1-1.99 0.4269 0.1435 1
12:791-119----99994 ..... 9.49.3.6. ................................................................
22.99 -00023 -04277 -0.5585 1
P:Y919.e.----9-.9.3.9.9 ..... 9.99.9.3 ______ 9.99.94- ..................................................
3—3.99 -O.5568 -O.4154 -O.5120 0.0570 1
RtY§199-_--9.99.Qf1 ..... 9.91-1.3 ...... 9.99.1.4. ..... 9.14.1.2. ....................................
4-4.99 -0.3182 -O.4761 -O.5301 0.2104 0.4335 1
P:Y§199.----9-9.5§-6. ..... 9.99.33 ...... 9.99.9.9. ..... 1),-2.1.7.9.---9-9933 ........................
5> 03443 04979 04666 0.3386 0.3774 0.5231 1
p-value 0.0397 0.0020 0.0041 0.0433 0.0232 0.0011

 

4.3.5 Multiple Linear Regression

A multiple linear regression (MLR) was performed to evaluate the strength of the
relationship between the dependent variable, CP3, and the beet weight categories. A goal
of multiple linear regression is to assign a relative importance to each independent
variable. In this case, an objective was to determine how important beet size distribution
was in explaining beet spacing uniformity when measured as CP3. The F -test associated
with the analysis of variance table is a test of the hypothesis that [3A = [33 = BC = [in = BE =
BF = 0. It is a test of whether there is a linear relationship between the dependent variable
and the entire set of independent variables.

The preceding correlation matrix indicates a problem of intercorrelated
independent variables. In such a situation the overall regression may be significant while
none of the individual coefficients are significant. The signs of the regression coefficients
may be counterintuitive. The estimates of the B and the sum of squares attributed to each
variable are dependent on the other variables in the equation and a unique, unbiased least-

squares solution does not exist. However, multicollinearity does not affect the usefulness

72

of the fitted model for making inferences on the response function as long as the
inferences are made within the range of observations. Regression results for trial 1 are
provided in Table 18. The fitted equation was significant at the relaxed level of (p <.10).
The R2 value was 0.295 indicating a great deal of scatter in the data.

Table 18. ANOVA and multiple linear regression coefficients for trial 1.

 

 

 

 

 

D.F S.S M.S F Significance F

Regression 6 1 102.527 183.7544 2.028053 0.093903

Residual 29 2627.583 90.60632
Total 35 3730.11
Variable D.F Parameter estimate S.E t-value Pr > It]
Intercept 1 12.47 27.11 0.46 0.648
0-0.99 lbs 1 -0.08 0.34 -0.25 0.803
1-1.99 lbs 1 -0.24 0.32 -0.78 0.443
2-2.99 lbs 1 0.71 0.37 1.89 0.068
3-3.99 lbs 1 0.37 0.39 0.94 0.355
4-4.99 lbs 1 -0.19 0.45 -0.44 0.666
5 lbs> l 0.54 0.52 1.04 0.306
R2 0.295

 

The fitted equation for trial 2 was significant (p < .017) and more effectively
described the data (table 19). The R2 was 0.393, again indicating a high degree of scatter
about the fitted equation. The fact that some independent variables are intercorrelated
eliminates the common interpretation of regression coefficients as measuring the change
in the expected value of the dependent variable when an independent variable is
increased by one unit when all other independent variables are held constant. However,
multicollinearity does not interfere with the ability to obtain a good fit. Although the
regression results indicated that there was a relationship between CP3 and beet size, the
regression equations were only useful in describing rather than predicting the relationship

among variables.

73

Table 19. ANOVA and multiple linear regression coefficients for trial 2.

 

 

 

 

 

D.F S.S M.S F Significance F

Regression 6 2722.349 453.7249 3.134563 0.017141
Residual 29 4197.721 144.749
Total 35 6920.07
Variable D.F Parameter Estimate Standard Error t-value Pr >jtj

Intercept 1 36.05 41.02 0.88 0.38
0-0.99 lbs 1 -0.35 0.48 -0.73 0.46
l-l.99 lbs 1 0.22 0.48 0.45 0.65
2-2.99 lbs 1 0.18 0.50 0.37 0.71
3-3.99 lbs 1 —1.16 0.58 -l.98 0.05
4—4.99 lbs 1 -0. 14 0.78 -0.19 0.85
5 lbs > 1 -0.89 0.81 -l.09 0.28
R“ 0.393

 

4.3.6. Principle Component Analysis

Since the problems associated with multicollinearity of the independent variables
made it impossible to interpret the coefficients of the multiple linear regression, a
procedure known as principle component analysis (PCA) was used to further examine the
relationship of CP3 and the beet size categories. PCA transforms a set of correlated
variables to a set of uncorrelated variables called factors. Factors are not single
independent variables, rather labels for groups of variables. The factors are inferred from
the observed variables and can be estimated as linear combinations of them.

There are four steps to factor analysis: 1) compute the correlation matrix for all
variables, 2) extract the number of factors necessary to represent the data, 3) transform
the factors to make them more understandable, and 4) compute the scores for each factor.
For the purpose of this study, a principal components factor analysis with a varimax
rotation was performed with Kaiser normalization.

Referring to trial 1, the correlation matrix indicates that there was a highly
significant correlation among several variables (Table 20). In order to create a set of

uncorrelated factors, principal component factor analysis of the correlation matrix was

74

performed (Table 21). Factor 1 accounted for 38% of the variance among factors while
factor 2 accounted for 24%. These two factors explained 62% of the total variance.
Generally, only factors with a variance greater than 1 are used since factors with a
variance less than 1 are no better than a single variable.

Trial 20. Correlation (Pearson) coefficients for trial 1.

 

 

CP3 <0.99 1-1.99 22.99 33.99 44.99
<0.99 -0.236

R:Y91!£§..-.0.-.1.9-5 ..........................................................
1-1.99 -0.363 0.189

12:731-199---9-939.---9..2-7-9 ...............................................
22.99 0.344 0.049 -0.165

12:1'9199---9-949----9:719 ..... 9.3-3.7 ....................................
33.99 0.243 -0505 -0335 -0147
19.-39.199---9-_1.34.----9.-99.2 ..... 9.9-4.6 ..... 9.3.9.1 .........................
4499 0.123 -0.506 -0574 -0.246 0.229
12:139-199--9479.----9.-99.2 ..... 9.9.99 ..... 9 .149 ..... 9 4.7.8 ..............
5> 0.059 -0413 -0279 -0519 0.091 0.592
p-value 0.733 0.012 0.100 0.001 0.596 0.000

 

Table 21. Factor pattern matrix and communalities for trial 1.

 

 

 

V variable Factor 1 Factor 2 Factor 3 Communality
CP3 -0.354 -0.699 0.045 0.616

<.99 0.728 0.067 0.416 0.708

l-1.99 0.648 0.406 -0.420 0.761

2-2.99 0.276 -0.841 0.153 0.807

3-3.99 -0.575 -0.214 -0.665 0.819

4-4.99 -0.834 0.134 0.316 0.814

5 > -0.709 0.478 0.248 0.792
Variance 2.6820 1.6575 0.9782 5.3176

% Var. 0.383 0.237 0.140 0.760

 

The factor pattern matrix contains the unrotated factor loadings. These are the

coefficients that relate the variables to the three factors. They are the standardized

regression coefficients in the multiple regression equation with the original variable as

the dependent variable and the factors as the independent variables. They indicate how

much weight is assigned to each factor. For instance, the CP3 index can be expressed as:

75

CP3 = -.354F1 -.699F2 +.045F3

In order to identify meaningful factors that summarize sets of closely related
variables, a varimax rotation was performed (table 22). The variables that had large
loadings for the same factor were grouped together. Small factor loadings with an
absolute value less than 0.5 were removed from the table. The rotated factor matrix
indicates that factor 1 was highly negatively correlated with beets of 1-1.99 lb and
positively correlated with beets of 4 lb and larger. Factor 2 was positively correlated with
the CP3 value and beets of 2-2.99 lb. Factor 3 was highly negatively correlated with the
smallest beets and positively correlated with beets weighing 3-3.99 1b. Factor 1 could be
labeled Big Beets, factor 2, Desirable Beets and factor 3, Above Average. These are not
independent variables, rather labels for groups of variables.

Table 22. Rotated factor loadings and communalities (V arimax rotation).

 

Variable Factor] Factor2 Factor3 Communality

 

CP3 - 0.726 - 0.616
< .99 - - -0.768 0.708
1-1 .99 -O.626 -0.598 - 0.761
2-2.99 - 0.801 - 0.807
3-3.99 - - 0.899 0.819
4-4.99 0.872 - - 0.814
5 > 0.832 - 0.792

 

Variance 2.1157 1.6226 1.5794 5.3176
% Var. 0.302 0.232 0.226 0.760

 

Referring to the unrotated factor loadings, the CP3 value can now be expressed

as:
CP3 = -.354(Large Beets) -.699(Desirable Beets) +.045(Above Average)

A similar analysis was performed for trial 2. The correlation coefficients are

provided in table 23. As in trial 1, several variables are highly intercorrelated.

76

Table 23. Correlation (Pearson) coefficients for trial 2.

 

 

CP3 <99 1-1.99 22.99 33.99 44.99
<99 0.095

RtY9111-9---Q-.5.39. .........................................................
1-1.99 0.427 0.144

1209.19.62---9-99-9-2--9.4.94 ...............................................
22.99 -0002 -0.428 -0559

12:119-199---9.--9_3.9.----9.99.9 ..... 9:999 ....................................
33.99 -0557 -0415 -0512 0.057
1219-19-9---9-999.-__-9-9.1.?- ..... 9.9.91 ..... 9.7.4.1 .........................
4499 -0.318 -0.476 -0530 0.210 0.434
12:119-196.---9-932----9_-993 ..... 9.9.9.1 ..... 9.2.1.3 _____ 9 .99-3 ..............
5> -0344 -0.498 -0.467 0.339 0.377 0.523
p-value 0.040 0.002 0.004 0.043 0.023 0.001

 

Principal component factor analysis of the correlation matrix provided three factors
accounting for about 7 8% of the variance among variables (Table 24).

Table 24. Factor pattern matrix and communalities for trial 2.

 

 

 

Variable Factor 1 Factor 2 Factor 3 Communality
CP3 0.574 -0.647 0.140 0.767

< .99 0.631 0.406 -0.573 0.890

1-1.99 0.774 -0.017 0.567 0.920

2-2.99 -0.514 -0.684 -0.418 0.907

3-3.99 -0.719 0.440 0.149 0.732

44.99 -0.758 0.006 0.194 0.612

5 > -0.760 -0.128 0.156 0.618
Variance 3.2571 1.2625 0.9276 5.4472

% Var. 0.465 0.180 0.133 0.778

 

The CP3 index can now be expressed as: CP3 = .574Fl -.647F2 +.140F3

The rotated factor matrix (Table 25) indicates that each of the three factors

accounted for a similar proportion of the variance. Factor 1 was highly correlated

negatively correlated with CP3 and beets of 3-3.99 lb, factor 2 was highly positively

correlated with the smallest beets and negatively correlated with the largest beets, and

factor 3 was positively correlated with beets of l-1.99 1b and negatively correlated with

77

beets of 2-2.99 lb. Factor 1 can be labeled Above Average, factor 2 Small, and factor 3
Desirable.

Table 25. Rotated factor loadings and communalities (V arimax rotation).

 

Variable Factorl Factor2 Factor3 Communality

 

CP3 -0.875 - - 0.767
< .99 - 0.937 - 0.890
l-l.99 -0.574 - 0.763 0.920
2-2.99 - - -0.897 0.907
3-3.99 0.759 - - 0.732
44.99 - -0.608 - 0.612
5 > - -0.63l - 0.618

 

Variance 2.0052 1.8915 1.5506 5.4472
% Var. 0.286 0.270 0.222 0.778

 

Based on the rotated factor loading, the CP3 can be expressed as:

CP3 = .5 74(Above Average)F 1 -.647(Small)F2 +.140(Desirable)

The use of principle component analysis to characterize the relationship between
CP3 and beet size uniformity provided very different results in each of the trials
evaluated. A low CP3 score can be obtained by either having excessive narrow plant
spacing or excessive wide plant spacing. The effect of poor plant spacing is likely to be
quite different in either case. Multiple linear regression and principle component analysis
provide a means to describe and understand the impact of CP3 on a particular sampling
of sugar beets, but such tools have no useful predictive capabilities. While plant spacing
does affect beet size uniformity, a measure of plant spacing uniformity other than CP3
will be needed.
4.4 STEPWISE REGRESSION

Even though the variables, 3cm mode range and rate of emergence, provided
significant differences between the treatments, the yield was not affected much. A
stepwise regression was performed, to see what is it that affects yield the most. Table 26,

shows the independent variables that affect the dependent variables the most.

78

Table 26. Stepwise regression for the dependent variables of RWSA, CP3, Stanle,

Stand20 and Stand30 in trials 1, 2 and 3.

 

 

 

 

 

Trial Depended variable Independent variable Partial R“ Model R“ F -value P-value
1 RWSA 1. Yield 0.9157 0.9157 890.76 0.0001
2. RWST 0.0836 0.9993 10175.3 0.0001
2 RWSA 1. yield 0.9826 0.9826 4636.58 0.0001
2RWST00168 ....... 9 9.9.9.4 ....... 24.7.2...34----9.-9991.---
1 CP3 1 Speed 0 1678 0.1678 16 53 0 0001
2 Seed 0.1217 0.2895 13.88 0.0004
3. Planter 0.0204 0.3099 2.36 0.1280
2 CP3 1. Planter 0.1331 0.1331 12.59 0.0006
2. Seed 0.0331 0.2938 3.96 0.0501
3 CP3 1. Seed 0.0784 0.3689 10.06 0.0021
2. Speed 0.0329 0.4018 4.41 0.0390
3P1ant00175 ....... 9 4.1.9.3 ....... 2.3.8. ........ 9.1.20.9.--
2 Stanle 1 Seed 0.0488 0.7368 15 01 0 0002
.3 ......... 9 9991.9 ................. 1 Sd02386 ....... 9 3.9.99 ....... 3.8.1.1 ....... 9.9991,---
-.1 ......... 8. tand2018eed00008 ....... 9 29.1.2 ....... 3,-2.8. ........ 99.7.3.8.---
2 Stand30 1 Seed 0.0015 0.9636 3 28 0 0738
3 Stand30 1 Seed 0.0264 0.7617 2 52 0 1163

 

4.4.1 Recoverable White Sugar per Acre (RWSA)

The RWSA for trial 1 and 2, was mainly influenced by the yield, tons/acre, with
the partial R“ ranging from 0.9157 to 0.9826. This outcome was expected because RWSA
is related to yield. It is the amount of sugar which was recovered from the crop. The fact
that the RWSA was not affected by the plant spacing uniformity implies that the planter’s
seed and speeds which are directly related to an improved plant spacing uniformity did
not help a lot for the improvement of the amount of recoverable white sugar per acre.
4.4.2 3cm Mode Range (CP3)

The CP3 for trial 1, 2 and 3 was influenced by planter seed and speed with a
partial R“ ranging from 0.0175 to 0.1678. There was not a specific pattern however, on
which is first or last. Different trials ranked the three variables in different order. This
outcome was again expected and proved that the plant spacing uniformity does get

influenced by planter seed and speed. In addition, there was no sign that the plant spacing

79

uniformity is influencing yield. This might suggest that planter seed speed does not help
for an improved yield.
4.4.3 Rate of Emergence

The 10, 20 and 30-day stands, dependent variables were influenced by the type of
seed used. The partial R2 was ranging from 0.0008 to 0.2386. The importance of seed
was one of the main issues for an improved rate of emergence. This outcome provides a
prove that indeed the seed is an important factor for better rate of emergence. However,
the rate of emergence did not improve yield which is one of the goals of the study. This

might suggest that seed does not help for an improved yield.

80

CHAPTER 5

5. CONCLUSION

Evaluating the field performance of sugar beet planters is a useful tool in

measuring the suitability of planting systems.

1.

Plant-to-stand planter performance was greatly influenced by available
soil moisture. When soil moisture was adequate, there was little difference
among planters measured as rate of emergence or final plant population. In
dry soils the general purpose planters tended to provide a more rapid
emergence, likely due to their ability to maintain a constant seeding depth
and provide good seed to soil contact.

The fasonated seed tended to provide a more rapid rate of emergence in

warm soil, the pelleted seed in cool soil.

'The Accord planter provided the best plant spacing uniformity at both 4.8

km/h and 7.3 km/h.

When the forward travel speed of the planter was held constant there was
little difference in plant spacing uniformity between the pelleted and
fasonated seed treatments.

The slower forward travel speed (4.8 km/h) tended to provide for a more
uniform plant spacing than the faster travel speed (7.3 km/h).

The three centimeter mode range alone was not a good predictor of beet
size uniformity.

There were no consistent differences in beet yield, sugar content, clear

81

juice purity or other standard measures of beet yield and quality due to

seed treatment or planter selection.

82

CHAPTER 6

6. RECOMMENDATIONS

One problem that was faced in the analysis was the small number of plant spacing
and beets collected. The relatively small amount of data made it difficult to differentiate
components. It will be helpful to collect a population of at least 200. That will reduce the
standard error and eliminate any misleading issues.

The comparison of plant spacing uniformity with beet size generated problems
during analysis. Multicollinearity did not allow a normal explanation of the analysis. A
more tedious but more accurate approach would be to compare beets with plant spacing
that come fiom the exact same location. In other words, identify specific locations and
from there, count the plant spacing at that location, and also the weight of the beet at that
location.

A comparison of the performance of the planter in the field with the performance
of the planter in the lab could identify parts of the planter that are not working properly

and help in improving the general performance of the planter in the field.

83

APPENDIX A

Table A. Dated activities of trials 1 and 2, year 2000

 

 

Activities Date
Moldboard plowed Fall 1999
Fertilizer 46-0-0 application 3/3/00
Field plot traced 4/14/00
Shallow cultivation 4/17/00
Planter adj ustrnents 4/18/00
Planting 4/1 8/ 00
soil moisture sampling (wet sample weight) 4/28/00
soil moisture sampling (dry sample weight) 5/4/00
soil moisture sampling (wet sample weight) 5/9/00
soil moisture sampling (dry sample weight) 5/16/00
soil moisture sampling (wet sample weight) 5/15/00
soil moisture sampling (dry sample weight) 5/18/00
soil moisture sampling (wet sample weight) 5/21/00
soil moisture sampling (dry sample weight) 6/1/00
Stand count, 10-day 5/8/00
Stand count, 20-day 5/21/00
Stand count, 30-day 5/31/00
Plant spacing count 5/30/00
Herbicide application (Norton and Pyramin DF) 6/31/00
Fungicide application (Super Tin and Emminent) 6/31/00
Row cultivation 6/5/00
Topping the beets 10/2/00
Final stand measurement 10/2/00
Hand-digging 10/3/00
Machine harvest 10/4/00
Laboratory sample results 11/1/00
Analysis of variables 12/1/00

 

84

Table B. Dated activities of trial 3, year 2001.

 

 

Activities Date
Moldboard plowed Fall 2000
Fertilizer 46-0-0 application 3/20/01
Field plot traced 4/26/01
Shallow cultivation 5/01/01
Planter adjustments 5/01/01
Planting 5/01/01
Soil moisture sampling (Weather station start date ) 5/09/01
Soil moisture sampling (Weather station end date) 5/31/01
Stand count, lO-day 5/07/01
Stand count, 20-day 5/20/01
Stand count, 30-day 5/31/01
Plant spacing count 6/07/01

 

85

 

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86

APPENDIX C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Trial 1.
1. Yield .
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 555.10 30.84 7.76 <.0001
Error 65 258.16 3.97
Corrected
total 83 813.26
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 6.22 1.24 0.31 0.90
treat 13 548.88 42.22 10.63 <.0001
2. Sugqr.
Significance
df Sum of Squares Mean Sjuare F-value p-value
Model 18 2.49 0.14 0.92 0.56
Error 65 9.77 0.15
Corrected
total 83 12.26
Sum of Significance
Variables df Squares Mean Square F-value Q-value
block 5 1.16 0.23 1.55 0.19
treat 13 1.33 0.10 0.68 0.77
3. CJP.
Significance
df Sum of Squares Mean Square F-value p;value
Model 18 5.03 0.28 1.05 0.42
Error 65 17.32 0.27
Corrected
total 83 22.35
Sum of Significance
Variables df Squares Mean Square F -value j-value
block 5 1.66 0.33 1.25 0.30
treat 13 3.37 0.26 0.97 0.49

 

87

4. RWST.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F -value p-value
Model 18 594.26 33.01 0.70 0.80
Error 65 3078.03 47.35
Corrected
total 83 3672.29
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 334.09 66.82 1.41 0.23
treat 13 260.16 20.01 0.42 0.96
5. RWSA.
Significance
df Sum of Squares Mean Square F -value p-value
Model 18 32305 877. 1 9 179477096 5.30 <.0001
Error 65 2199170538 338333.93
Corrected
total 83 5429758257
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 364691.29 7293 8.26 0.22 0.95
treat 13 3194118590 245701430 7.26 <.0001
6. CP3.
Significance
df Sum of Squares Mean Sgtare F-value p-value
Model 18 4088.57 227.14 7.17 <.0001
Error 65 2060.23 31 .70
Corrected
total 83 6148.80
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 146.61 29.32 0.93 0.47
treat 13 3941.97 303.23 9.57 <.0001
7. Stand 10.
Significance
df Sum of Euares Mean Square F-value p-value
Model 18 0.91 0.05 5 .43 <.0001
Error 65 0.61 0.01
Corrected
total 83 1.52

 

88

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 0.15 0.03 3.16 0.01
treat 13 0.77 0.06 6.31 <.0001
8. Stand 20.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.94 0.05 6.61 <.0001
Error 65 0.51 0.01
Corrected
total 83 1.46
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 0.20 0.04 5.12 0.00
treat 13 0.74 0.06 7.18 <.0001
9. Stand 30.
Significance
df Sum of Sculares Mean Square F-value J-value
Model 18 0.88 0.05 5.75 <.0001
Error 65 0.55 0.01
Corrected
total 83 1.43
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 0.20 0.04 4.80 0.00
treat 13 0.67 0.05 6.11 <.0001
10. Final Stand.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 1.04 0.06 6.30 <.0001
Error 65 0.60 0.01
Corrected
total 83 1.63
Variables Sum of Significance
df Squares Mean Square F -value p-value
block 5 0.20 0.04 4.29 0.00
treat 13 0.84 0.06 7.07 <.0001

 

89

Trial 2.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1. Yield .
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 961.76 53.43 3.13 0.00
Error 65 1108.60 17.06
Corrected
total 83 2070.36
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 « 45.17 9.03 0.53 0.75
treat 13 916.59 70.51 4.13 <.0001
2. Sugar.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 1.26 0.07 1.01 0.46
Error 65 4.50 0.07
Corrected
total 83 5.75
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 0.54 0.11 1.56 0.18
treat 13 0.72 0.06 0.80 0.66
3. CJP.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 18.00 1.00 0.96 0.52
Error 65 67.82 1.04
Corrected
total 83 85.83
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 3.92 0.78 0.75 0.59
treat 13 14.08 1.08 1.04 0.43

 

90

4. RWST.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F -value p—value
Model 18 848.43 47.13 0.90 0.58
Error 65 3398.52 52.28
Corrected
total 83 4246.95
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 319.81 63.96 1.22 0.31
treat 13 528.62 40.66 0.78 0.68
5. RWSA.
Mean Significance
df Sum of Squares Square F -value p—value
Model 18 6442153570 357897420 3.07 0.00
Error 65 7574835830 116535940
Corrected
total 83 14016989400
Significance
Variables df Sum of Squares Mean Square F -value p-value
block 5 347124054 6942481 1 0.60 0.70
treat 13 6095029520 468848425 4.02 <.0001
6. CP3.
Significance
df Sum of Syres Mean Square F-value J-value
Model 18 6366.78 353.71 10.92 <.0001
Error 65 2104.98 32.38
Corrected
total 83 8471.76
Sum of Significance
Variables df Squares Mean Swre F-value p-value
block 5 181.50 36.30 1.12 0.36
treat 13 6185.28 475.79 14.69 <.0001

 

91

7. Stand 10.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.51 0.03 3.71 <.0001
Error 65 0.50 0.01
Corrected
total 83 1.01
Sum of Significance
Variables df Squares Mean Square F -value p-value
block 5 0.14 0.03 3.60 0.01
treat 13 0.38 0.03 3.76 0.00
8. Stand 20.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.90 0.05 7.71 <.0001
Error 65 0.42 0.01
Corrected
total 83 1.33
Sum of Significance
Variables df Squares Mean Square F -value p-value
block 5 0.25 0.05 7.61 <.0001
treat 13 0.65 0.05 7.75 <.0001
9. Stand 30.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.87 0.05 8.22 <.0001
Error 65 0.38 0.01
Corrected
total 83 1.25
Sum of Significance
Variables df Squares Mean Square F -va1ue p-value
block 5 0.22 0.04 7.35 <.0001
treat 13 0.65 0.05 8.55 <.0001

 

92

10. Final Stand.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.72 0.04 5.32 <.0001
Error 65 0.49 0.01
Corrected
total 83 1.22
Variables Sum of Significance
df Squares Mean Square F—value p-value
block 5 0.14 0.03 3.60 0.01
treat 13 0.59 0.05 5.99 <.0001
Trial 3.
1. Stand 10.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.47 0.03 6.94 <.0001
Error 65 0.25 0.00
Corrected
total 83 0.72
Sum of Significance
Variables df Squares Mean Sgare F-value p-value
block 5 0.05 0.01 2.88 0.02
treat 13 0.42 0.03 8.50 <.0001
2. Stand 20.
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.69 0.04 8.88 <.0001
Error 65 0.28 0.00
Corrected
total 83 0.98
Sum of Significance
Variables df Squares Mean Square F -value p-value
block 0.05 0.01 2.12 0.07
treat 13 0.65 0.05 l 1.48 <.0001

 

93

3. Stand 30.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F-value p-value
Model 18 0.45 0.02 6.61 <.0001
Error 65 0.24 0.00
Corrected
total 83 0.69
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 0.04 0.01 1.93 0.10
treat 13 0.41 0.03 8.42 <.0001
4. CP3 .
Significance
df Sum of Squares Mean Square F-value p-value
Model 18 4896.35 272.02 11.24 <.0001
Error 65 1573.28 24.20
Corrected
total 83 6469.64
Sum of Significance
Variables df Squares Mean Square F-value p-value
block 5 267.68 53.54 2.21 0.06
treat 13 4628.67 356.05 14.71 <.0001
Trial 1, 2.
1. Yield.
Significance
df Sum of Squares Mean Square F-value y-value
Model 32 6733.54 210.42 20.40 <.0001
Error 135 1392.83 10.32
Corrected
total 167 8126.37
Significance
Variables df Sum of Squares Mean Square lF-value R-value
block 5 25.31 5.06 0.49 0.78
treat 13 1043.94 80.30 7.78 <.0001
trial 1 5242.75 5242.75 508.15 <.0001
treat*tn'al 13 421.54 32.43 3.14 0.00

 

94

2. Sugar.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F-value p-value
Model 32 8.64 0.27 2.33 0.00
Error 135 15.66 0.12
Corrected
total 167 24.30
Significance
Variables df Sum of Scniares Mean Square F-value p-value
block 5 0.30 0.06 0.52 0.76
treat 13 0.85 0.07 0.56 0.88
trial 1 6.29 6.29 54.21 <.0001
treat*trial 13 1.20 0.09 0.79 0.67
3. CJP.
Significance
df Sum of Squares Mean Square F-value p-value
Model 32 84.71 2.65 4.11 <.0001
Error 135 87.01 0.64
Corrected
total 167 171.73
Significance
Variables df Sum of Squares Mean Square F-value p-value
block 5 3.71 0.74 1.15 0.34
treat 13 12.69 0.98 1.51 0.12
trial 1 63.55 63.55 98.60 <.0001
treat*trial 13 4.76 0.37 0.57 0.88
4. RWST.
Significance
df Sum of Squares Mean Square F-value y-value
Model 32 1021.20 31.91 0.62 0.94
Error 135 6917.93 51.24
Corrected
total 167 7939.13
Significance
Variables df Sum of Squares Mean Square F-value mvalue
block 5 212.53 42.51 0.83 0.53
treat 13 316.46 24.34 0.48 0.94
trial 1 19.89 19.89 0.39 0.53
treat*trial 13 472.32 36.33 0.71 0.75

 

95

5. RWSA.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mean Significance
df Sum of Squares Square F -value p-value
13810331.7
Model 32 44193061390 0 18.79 <.0001
Error 135 99201661.70 734827.10
Corrected
total 167 54113227570
Significance
Variables df Sum of Squares Mean Square F-value p-value
block 5 237433380 474866.80 0.65 0.66
treat 13 65 88283 1 .90 506791010 6.90 <.0001
trial 1 34666479910 34666479910 471.76 <.0001
treat*trial 13 2700864920 207758840 2.83 0.00
6. CP3.
Significance
df Sum of Squares Mean Square F-value p-value
Model 32 10260.70 320.65 9.86 <.0001
Error 135 4390.38 32.52
Corrected
total 167 14651.07
Significance
Variables df Sum of Squares Mean Square F—value p-value
block 5 102.93 20.59 0.63 0.67
treat 13 2139.07 164.54 5.06 <.0001
trial 1 30.52 30.52 0.94 0.33
treat*trial 13 7988.18 614.48 18.89 <.0001
7. Stand 10.
Significance
df Sum of Squares Mean Square F-value p-value
Model 32 2.86 0.09 10.17 <.0001
Error 135 1.18 0.01
Corrected
total 167 4.04
Significance
Variables df Sum of Squares Mean Square F-value p-value
block 5 0.21 0.04 4.78 0.00
treat 13 0.49 0.04 4.29 <.0001
trial 1 1.50 1.50 171.34 <.0001
treat*tria1 13 0.65 0.05 5.73 <.0001

 

96

8. Stand 20.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F-value p-value
Model 32 2.01 0.06 8.45 <.0001
Error 135 1.00 0.01
Corrected
total 167 3.01
Significance
Variables df Sum of Squares Mean Square F-value p-value
block 5 0.39 0.08 10.38 <.0001
treat 13 0.50 0.04 5.17 <.0001
trial 1 0.23 0.23 30.61 <.0001
treat*trial 13 0.90 0.07 9.27 <.0001
9. Stand 30.
Significance
df Sum of Squares Mean Square F-value J-value
Model 32 1.94 0.06 8.47 <.0001
Error 135 0.96 0.01
Corrected
total 167 2.90
Significance
Variables df Sum of Squares Mean Square F-value p-value
block 5 0.39 0.08 10.87 <.0001
treat 13 0.43 0.03 4.63 <.0001
trial 1 0.22 0.22 30.81 <.0001
treat*trial 13 0.90 0.07 9.66 <.0001
10. Final Stand .
Significance
df Sum of Squares Mean Square F-value Lvalue
Model 32 1.83 0.06 6.74 <.0001
Error 135 1.15 0.01
Corrected
total 167 2.98
Significance
Variables df Sum of Squares Mean Square F-value p-value
block 5 0.27 0.05 6.44 <.0001
treat 13 0.54 0.04 4.90 <.0001
trial 1 0.13 0.13 14.90 0.00
treat*trial 13 0.89 0.07 8.06 <.0001

 

97

Trials 1, 2 and 3.

 

 

 

 

 

 

 

 

 

 

 

1. CP3.
Significance
df Sum of Squares Mean Square F-value p-value
Model 44 27463.54 624.17 24.46 <.0001
Error 135 3445.55 25.52
Corrected
total 179 30909.09
Sum of Significance

Variables df Squares Mean Square F-value p-value
year 1 14391.91 14391.91 563.89 <.0001
trial*year 1 144.32 144.32 5.65 0.02
block*trial*year 15 244.61 16.31 0.64 0.84
treat 9 3210.36 356.71 13.98 <.0001
treat*year 9 3297.21 366.36 14.35 <.0001
treat*trial*year 9 6849.53 761.06 29.82 <.0001
2. Stand 10.

Significance
df Sum of Squares Mean Square F -va1ue p-value
Model 44 6.33 0.14 21.19 <.0001
Error 135 0.92 0.01
Corrected
total 179 7.25
Sum of Significance

Variables df Squares Mean Square F-value p-value
year 1 3.60 3.60 530.53 <.0001
trial*year 1 1.68 1.68 247.84 <.0001
block*trial*year 15 0.25 0.02 2.47 0.00
treat 9 0.24 0.03 3.96 0.00
treat*year 9 0.17 0.02 2.73 0.01
treat*trial*year 9 0.40 0.04 6.48 <.0001

 

98

3. Stand 20.

 

 

 

 

 

 

 

 

 

 

 

Significance
df Sum of Squares Mean Square F-value p-value
Model 44 2.39 0.05 8.68 <.0001
Error 135 0.84 0.01
Corrected
total 179 3.23
Sum of Significance

Variables df Squares Mean Square F-value p-value
year 1 0.03 0.03 5.23 0.02
trial*year 1 0.44 0.44 70.17 <.0001
block*trial*year 15 0.33 0.02 3.53 <.0001
treat 9 0.71 0.08 12.62 <.0001
treat‘year 9 0.30 0.03 5.26 <.0001
treat*tria1*year 9 0.66 0.07 1 1.68 <.0001
4. Stand 30.

Significance
df Sum of Squares Mean Square F-value p-value
Model 44 2.30 0.05 8.45 <.0001
Error 135 0.83 0.01
Corrected
total 179 3.13
Sum of Significance

Variables df Squares Mean Square F -value p-value
year 1 0.22 0.22 35.71 <.0001
trial*year 1 0.40 0.40 64.44 <.0001
block*tria1*year 15 0.32 0.02 3 .44 <.0001
treat 9 0.52 0.06 9.36 <.0001
treat*year 9 0.18 0.02 3.24 0.00
treat*trial*year 9 0.69 0.08 12.47 <.0001

 

99

APPENDIX D

Figure A. Rainfall data from April 15th to May 31“, 2000.

 

 

Rainfall, April and May 2000
2
1.8
.E 1.6
2 1.4
5 1.2
.. 1
g 0.8
u 0.6
E 0.4
0.2
0

0 Q Q Q
9 9 9° 9 9 9° 9°
9" a"? a“? ‘3‘" "5 «30° 49
Dates

 

 

 

Figure B. Rainfall data from April 15'11 to May 31“, 2001.

 

Rainfall, April and May 2001

lnchesofraln
PPPPPPPPP
o-INOOAOICDNCDCD-t

 

 

 

 

100

Figure C. Soil temperature data from April 15th to May 31“, 2000.

 

Temperature, F

 

cue-58888838

Soil temperature, April and May 2000

 

8 a e 8 a a

5’ a a a :2 a

? v v m B :0
Dates

5/27/00

 

Figure D. Soil temperature data from April 15‘“ to May 31“, 2001.

 

Temperature, F

 

Soil temperature, April and May 2001

 

 

101

 

 

 

Figure B. Soil moisture data for the first two trials (04/28-05/04).

 

Soil moisture 2000 (04128-0504)

 

Soil moletu

 

.
‘x99999s9

 

 

 

Figure F. Soil moisture data for the first two trials (05/09-05/16).

 

Soil moisture 2000 (05l09-05I16)

 

15.0
10.0

    

Soil moisture, %

‘5

xu§§ §.\,§\5°.s§0a't°a
“\""\\\‘**g‘99¢‘*'*0+++

 

Treatments (left column - 1 In. depth, right column - 2 in. depth)

 

102

 

Figure G. Soil moisture data for the first two trials (05/21-06/01).

 

Soil moisture 2000 (05I21 -06I01)

30.0
25.0
20.0
15.0
10.0

5.0

0.0

‘5 \

Soil moisture, °/.

 

QQQ \.£%
\ \{y ‘x ‘5‘\* §§N§$\$g¢\ _‘ i "\§.§.’+‘+§

Treatments (left column - 1 in. depth, right column - 2 In. depth)

 

 

Figure H. Soil moisture data for the first two trials (05/31-06/06).

 

Soll moisture 2000 (05I31-06I06)

30.0

a! 25.0
2 20.0
15.0
10.0
5.0

0.0

moistu

So

 

I°QQQKQKI99619
\\\ °°9‘\°‘~°9‘99‘9\°°4‘4‘¢4‘

Treatments (left column - 1 In. depth, rightcolumn I 2 In. depth)

 

 

 

103

 

Bibliography

British Sugar company. 2001. Sugar Beet-A Growers Guide-Seed and Varieties.
http://www.britishsugar.co.uk/bsweb/growers/seedvar.htm.

Brooks, D., B. Church. 1987. Drill performance assessments: A changed approached.
British Sugar Beet Review 55(4) :50-51.

Brown, S. 1999. Review of sugar beet cultivations. British Sugar Beet Review 6 7(2):30-
33.

Ecclestone, P.. 1994. Drill testing. British Sugar Beet Review 62(3) :24-25.
Ecclestone, P.. 1995. Drill testing. British Sugar Beet Review 63(3):]6-1 7.

F ornstrom, K. J ., SD. Miller. 1989. Comparison of sugar beet planters and planting depth
with two sugar beet varieties. Journal of sugar beet research 26(3):]0-16.

Deere and Co.. Fundamental of Machine Operation. 1992. Planting third edition

Giles, J. F ., A.W.Cattanach, L.J. Smith, N.R. Cattanach. 1990. Effect of planter ground
speed and seed spacing on sugar beet production. Journal of sugar beet research
2 7(3) .- 39.49.

Giles, J. F., NR Cattanach. 1998. Sugar beet stand establishment with proper planter
maintenance. Sugar beet research and extension reports. January 1999, pp. 202-
203. North Dakota State University.

Giles, J. F., NR. Cattanach. 1999. The effect of John Deere Max Emerge 2 planter
attachments on sugar beet spacing, yield, and quality. Sugar beet research and
extension reports. 1 79-18 7. North Dakota State University.

Goering, C.E., L.E. Bode and MR Gebhardt. 1972. Mathematical modeling of spray
droplet deceleration and evaporation. Transactions of the ASAE 15(2):220-225.

Gummerson, B. 1986. The effect of seedbed cultivation on seed placement and seed bed
environment. British Sugar Beet Review 64(4) :25-32.

Gummerson, R. J.. 1989. Seedbed cultivation and sugar beet seedling emergence. J.agric.
sci. Carnb. [12:159-169.

Halmer, P. 1988. Technical and commercial aspects of seed pelleting and film coating,
pp. 19 Thorton Heath, UK: British Crop Protection Council.

104

Harrigan, T.M. 1999a. Sugar beet tillage and planting. December 1999 meetings.
Agricultural Engineering Department. Michigan State University, East Lansing,
Michigan.

Harrigan, T. M. 1999b. Personal Communication. Agricultural Engineering Department.
Michigan State University, East Lansing, Michigan.

Harrigan, TM. 2000. Sugar beet tillage and planting. January 2000 meetings.
Agricultural Engineering Department. Michigan State University, East Lansing,
Michigan.

Hofman, V. 1988. Maximum yields need accurate planting. The Sunflower 14(1):]0-11.

Hollewell, W. 1982. Drill performance assessments. British Sugar Beet Review 50(3): 13-
15.

International Organization of Standardization. 1984. Sowing equipment-Test methods-
Part 1: Single seed drills (precision drills), 7256/1.

Jaggard, K. 1990. Are there too many beets in our fields? British Sugar Beet Review
58(1): 6-9.

Jasa, P.J., E.C. Dickey. 1982. Tillage factors affecting corn seed spacing. Transactions of
the ASAE 25(6):]516—1519.

Kachman, S.D., J.A. Srnith. 1995. Alternative measures of accuracy in plant spacing for
planters using single seed metering. Transactions of the ASAE 38(2):379-387.

Kepner, RA 1978. Principles of Farm Machinery. Westport, CT: AVI Publishing Inc.

Kocher, M. F., Y. Lan, C. Chen, J .A Smith. 1998. Optoelectronic sensor system for rapid
evaluation of planter seed spacing uniformity. Transactions of the ASAE
41(1):237-245.

Lari, Y., M.F. Kocher, and J .A. Smith. 1999. Optoelectronic sensor system for laboratory
measurement of planter seed spacing with small seeds. Journal Of Agricultural
Engineering Research 72(2) .' 1 19-12 7.

Langan, T. D., J. W. Pendleton, and E. S. Oplinger. 1986. Peroxide coated seed
emergence in water-saturated soil. Agronomy Journal 78:769-772.

McClleland DP. 1997. Sowing Modemity—America ’s First Agricultural Revolution.

Mayo, CM. and AG. Dexter. 1997. Influence of Betanex plus frost on sugarbeet. Sugar
Beet Research and Extension Reports. January 1998, pp.111-112.

105

Michigan Agricultural Statistics Service (MASS). 1999. Michigan agricultural statistics —
1999. Michigan Agricultural Statistics Service, Michigan Dept. of A griculture,
Lansing, M.

Morrison, J .E., Jr. and TI. Gerik. 1985. planter depth control: 1. Prediction and projected
effect on crop emergence. Transactions of the ASAE 28(5):]415-1418.

Ollerenshaw, J. H. 1985. Influence of waterlogging on the emergence and grth of
Lolium perenne L. shoots from seed coated with calcium peroxide. Plant and Soil
85:131-141.

Pahl, S. J .. 1996. The reintroduction of pelleted sugar beet seed in the southern Minnesota
sugar cooperative. Sugar beet Research and Extension Reports. 182-188.
University of Minnesota.

Panning, J. W., M.F. Kocher, J .A. Smith. 1997. Laboratory and field-testing of seed
spacing uniformity for sugar beet planters. ASAE Paper No. 97-3011. ASAE: St.
Joseph, M

Pitt, R.E., G.S. Farmer, and LP. Walker. 1982. Approximating equations for rotary
distributor spreader patterns. Transactions of the ASAE 25(6) :1544-1552.

Poindexter, S. 1999. Pelleted/ seed size trial. 1999 Research Report. Saginaw Valley Bean
and Beet Research Farm and Related Bean-Beet Research:25.

Prince, J ., J. Rose, B. Brown. 1997. Quality assured seed treatments. British Sugar Beet
Review 65(4).'24-25.

Proctor, J .. 1994. Good husbandry achieves high sugar yields. British Sugar Beet Rreview
62(4) :4-5.

Rushing, K. W. 1988. Additives to enhance seed quality and field performance.
Proceedings Mississippi State University, Mississippi State, MS State Seed Short
Course Vol. 30.

Shafii, S., R.G. Holmes. 1990. Air-jet seed metering, a theoretical and experimental
study. Transactions of the ASAE 33(5):]432-1438.

Shearer, S.A., RG. Holmes. 1991. Precision seed metering with a submerged turbulent
air-jet. Transactions of the ASAE 34(3): 781-786.

Smith, A E., and R Miller. 1987. seed pellets for improved seed distribution of small
seed forage crops. Journal of Seed Technology 11:42-51.

Smith, J. A., C.W. Peck, J .W.F. Prince. 1991a. Sugar beet planting technology in the
UK. ASAE paper No. 911560. ASAE: St. Joseph, 1141.

106

Smith, J. A., K.L. Palm, CD. Yonts, RG. Wilson, 1991b. Seed spacing accuracy of sugar
beet planters. ASAE Paper No. 911551.ASAE: St. Joseph, M

Smith, J. A. 2001. Personal communication. Biological Systems Engineering
Department. University of Nebraska, Lincoln, Nebraska.

Srivastava, A. K., C.E. Goering, R.P. Ronrbach. Revised printing August 1994.
Engineering Principles of Agricultural Machines. Chapter 6, crop planting.
ASAE.

Seaborn, J ., V. Hofrnan. 1999. An optical sensor for seed spacing measurements. Sugar
beet research and extension reports. 166-1 70. North Dakota.

Sugarbeet Update. Sugarbeet advancement in Michigan. Summer, 2000. p.4-7.

Tabachnick, B. G., & Fidell, L. S. 2001. Using multivariate statistics. Boston: Allyn and
Bacon.

Taylor, AG, and G. E. Harman. 1990. concepts and technologies of selected seed
treatment. Annual Review of Phytopathologz 28: 321-339.

Thomson, M. 1986. Drill performance assessments. British Sugar Beet Review 54(4) :1 1-
14.

Yonts, C. D., J .A. Smith, R.G. Wilson. 1999. Effect of seed type, planter type and depth
of planting on sugar beet emergence. Journal of sugar beet research 36(4) :1-9.

Yonts, C.D., R. G. Wilson and J .A. Smith. 1999. influence of planting date on stand, yield

and quality of sugarbeet. Journal of Sugar Beet Research 36(3): [-14 (Journal
series No. 12218).

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