Spatial Variability of Seedling Disease Pressure in Cotton

University of Arkansas, Fayetteville University of Arkansas, Fayetteville

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Graduate Theses and Dissertations

5-2017

Spatial Variability of Seedling Disease Pressure in Cotton Fields Spatial Variability of Seedling Disease Pressure in Cotton Fields

Kyle Douglas Wilson University of Arkansas, Fayetteville

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Spatial Variability of Seedling Disease Pressure in Cotton Fields

A thesis submitted in partial fulfillment

of the requirements for the degree of

Master of Science in Plant Pathology

by

Kyle Wilson

Arkansas State University

Bachelor of Science in Biology, 2013

May 2017

University of Arkansas

This thesis is approved for recommendation to the Graduate Council.

Dr. Craig Rothrock

Thesis Director

Dr. Terry Spurlock

Co-Director

Dr. Tina Teague

Committee Member

Abstract

Seedling diseases are important factors in cotton stand establishment, and seedling

disease pathogens are widespread in fields in Arkansas. Little is known about the variability of

seedling disease pressure within fields. With expanded adoption of site-specific management and

other precision agriculture approaches, cotton producers are increasingly interested in predicting

seedling disease pressure, particularly in spatially variable fields. The cotton seedling disease

pathogens include the soilborne pathogens Thielaviopsis basicola, Rhizoctonia solani, Pythium

spp., and Fusarium spp. These pathogens can survive in soil for long periods and, and when the

environment is conducive, these pathogens can act individually or in combination to cause a

range of symptoms on seed, roots and hypocotyls, which can affect germination, emergence, and

early-season growth and development of plants. Seedling diseases reduce stand density and

seedling vigor, which in turn results in variable plant growth and maturity. Results from

experiments conducted at the Judd Hill Cooperative Research Foundation in Poinsett Co.

Arkansas showed field-scale increases of cotton seedling disease pressure where minimal soil

temperature was lower (20.0 °C) and lower seedling disease pressure where minimal soil

temperature was higher (21.5 °C) for both years of this study. This study indicates the

importance of the role of the environment in disease development and supports the site-specific

management zone approaches being adopted by cotton producers.

Acknowledgments

There are many people I would like to thank for all the support and help they have given

me. First and foremost, I offer thanks to my family and friends for always being there supporting

me in countless ways. To my colleagues that became close friends, I couldn’t have done it

without you all. I never would have made it here without the encouragement from committee

member, Dr. Tina Teague, who taught me the value of hard work and research. Of course, I offer

a huge thanks to advisor and mentor, Dr. Craig Rothrock, who I met in Eastern Arkansas and

saw enough potential in me to bring me to the University of Arkansas to study Plant Pathology. I

also owe a big thanks to my other advisor, Dr. Terry Spurlock, for the instruction and wisdom

that has led me to accomplishments I never thought possible for myself. Thanks to Dr. Andy

Mauromoustakos and the other Agricultural Statistics personnel for being kind and offering

much help and encouragement. Thanks to David Wildy and Bruce Bond for allowing me to do

research on their farms. Thanks to the Judd Hill Foundation for providing resources for this and

other cotton research. Thanks to Scott Winters for helping me learn many valuable skills. Thanks

to Chris Cochran, Jorge Lopez, and Chris Winters who deserve much credit for often going the

extra mile helping me with field and lab work. Last, but not least, a very big thanks to all of my

fellow students and staff in the Plant Pathology Department. I wish the best of luck to all the

students and faculty in their endeavors.

Dedication

I would like to dedicate my thesis to my family who have been there my whole life and

supported me through everything. I would also like to dedicate this thesis to Dr. Tina Teague at

Arkansas State University, if she hadn’t hired me as an undergraduate worker, I would have

never found my calling in agricultural research. Thank you all so much, for everything.

Table of contents

Chapter 1 – Literature review ..................................................................................................... 1

Cotton production – field preparation and planting overview. ............................................ 2

Plant populations and yield. ..................................................................................................... 3

The environment and seedling diseases of cotton. .................................................................. 4

Cotton seedling disease pathogens. .......................................................................................... 5

Cotton seedling disease management. ..................................................................................... 8

Importance of study. ................................................................................................................. 9

References ................................................................................................................................ 11

Chapter 2. Spatial examination of seedling disease pressure ................................................. 16

Abstract .................................................................................................................................... 16

Introduction ............................................................................................................................. 18

Materials and Methods ........................................................................................................... 20

Results ...................................................................................................................................... 25

Discussion ................................................................................................................................. 28

References ................................................................................................................................ 33

Tables........................................................................................................................................ 36

Figures ...................................................................................................................................... 50

Appendix .................................................................................................................................. 52

Chapter 3 - Spatial examination of cotton stands in growers’ fields ..................................... 64

Abstract .................................................................................................................................... 64

Introduction ............................................................................................................................. 65

Materials and Methods ........................................................................................................... 67

Results ...................................................................................................................................... 72

Discussion ................................................................................................................................. 74

References ................................................................................................................................ 78

Tables........................................................................................................................................ 80

1

Chapter 1 – Literature review

Cotton is grown for its fiber and seed which are important commodities across many

countries (Oerke, 2006). Agricultural production of cotton has a long history in many regions of

the world, but the origin of its first domestication is not known. There are four primary species

which were cultivated for various plant growth characteristics and are produced in the modern

world for lint and seed; Gossypium arboreum and G. herbaceum, from Africa and Asia, and G.

hirsutum and G. barbadense from the Americas (Wendel and Cronn, 2003). G. hirsutum is

commonly known as upland cotton which accounts for up to 90% of current production because

of its high yields and wide environmental adaptation (Lee, 1984). G. barbadense is known as

Pima cotton, Egyptian cotton, or extra-long staple (ELS) producing longer, stronger, and finer

fibers that are used to manufacture silkier yarns woven for luxury textiles, but agricultural

production of this species is restricted to more specific environments reducing the number of

regions able to successfully grow this crop (Avci et al., 2013).

Upland cotton is grown in tropical, subtropical, and temperate climates around the globe.

It can be found as far north as 47 °N in China, and as far south as 32 °S in Australia (Lee, 1984).

The major cotton producing countries are the U.S., Uzbekistan, China, and India. Other leading

cotton-growing countries are Australia, Brazil, Pakistan and Turkey. The U.S. is currently the

third largest producer of upland and ELS cotton in the world with 3.4 million hectares (8.5

million acres) planted and 3.2 million hectares (8 million acres) harvested with 12.9 million 260

kg (480 pound) bales produced between 2015 and 2016 (National Agricultural Statistics

Service). Cotton is grown in several states across the Southern United States, the Cottonbelt,

with concentrations in the Texas High Plains, irrigated valleys in Arizona and California, the

Mid-South, and Southeast. Arkansas is a major cotton producing state. In 2015, 84,000 hectares

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(210,000 acres) of cotton were planted in the state resulting in 471,000 bales of lint

produced. The average yield for Arkansas was 1,234 kilograms of lint per hectare. Arkansas

ranks 5th currently in lint yield production per hectare, and 7th for hectares planted (National

Agricultural Statistics Service, 2015).

Cotton production – field preparation and planting overview.

Cotton is a perennial plant grown as an annual plant from seed planted each year.

Environmental conditions at planting are important to establishing vigorous cotton seedlings at

desired plant populations, setting the crop up for early fruiting, strong fruit retention, and

maximizing the primary fruiting cycle. The environment affects cotton physiology as well as

biological pests present in the field. Historically there have been many different practices for

growing cotton in the different regions of the U.S., but ordinarily, land preparation begins post-

harvest in the fall by shredding the stalks of the old crop. Some fields may be tilled to reduce

soil compaction and to establish raised seed beds for the next year, or left non-tilled if not needed

or if under conservation practices. Many producers leave the shredded crop debris on the surface

to reduce soil erosion. Winter cover crops are sometimes used to prevent soil erosion, and/or

manage pests. Planting preparation for fields under conventional tillage usually begin in the

early spring by tilling and/or hipping the soil to create raised seed beds. Just prior to planting,

the top few cm of the beds are usually dragged to form a flat-top ridge. In most of the

Cottonbelt, 96 cm (38 inch) row spacing is used, although in some regions under certain growing

conditions, primarily in west Texas, “stripper cotton” is planted on much narrower row spacing

or broadcast.

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Plant populations and yield.

Establishing and maintaining a stand of healthy plants with uniform spacing and plant

density is critical for uniform crop development, managing the crop, good fiber qualities, and

yield (Christiansen and Rowland, 1981). Research on optimal cotton plant populations for

maximum yield and quality have produced variable results, however, much of the available

literature suggests comparable yield may be obtained within a wide range of plant populations.

Ray et al. (1959), and Franklin et al. (2000), in Texas, found plant densities between 37,050 –

185,250 plants per hectare, and 64,531 – 129,111 plants per hectare, respectively, did not affect

yield. Hawkins and Peacock (1970), in Georgia, found yield reduction with populations outside

the range of 96,000 – 144,000 plants per hectare. Bridge et al. (1973), in Mississippi, found

highest yields with plant densities between 70,000 and 121,000 plants per hectare. Smith et al.

(1979), in Arkansas, found highest yields were obtained with 101,573 plants per hectare from a

range of 33,969 – 169,841 plants per hectare. In North Carolina, Jones and Wells (1998)

reported populations ranging from 20,372 – 122,235 plants per hectare did not influence yield.

Siebert et al. (2005) found no yield differences for population ranges of 37,750 – 152,833 plants

per hectare in Louisiana, but they did find hill-drop spaces greater than 40 cm reduced yield.

Wrather et al. (2008), in the Mississippi Delta, found over the years 2002 – 2004, plant

populations between 67,952 – 135, 904 plants per hectare produced higher yields than plant

populations of 33,976 plants per hectare when planted in mid-April, but when planted at later

dates, there were no significant yield differences between plant populations of 33,976 and

135,904 plants per hectare. Comparable yield production through the wide ranges of plant

populations in these studies may be partly explained by the cotton plant’s capacity for adapting

various growth characteristics for a given environment including plant density. Brown and Ware

4

(1958) found cotton plants in denser plant populations tend to grow taller and have more

vegetative growth that can cause a delay in fruiting. Bednarz et al. (2000) found cotton, in

thinner plant populations, produced more monopodial branches, and in denser plant populations

more boll shedding occurred.

The environment and seedling diseases of cotton.

If cotton is planted too early, the stand will commonly suffer from stresses brought on

from diseases, cold temperatures and unfavorable rainfall, but if planted too late, the plants

commonly become more vegetative, are difficult to manage, and have lower yield potential

(Silvertooth and Norton. 2000). Depending on climate, cotton in the US is planted in some

southern regions as early as March and as late as the end of June. Mid-south regions have a

shorter planting window typically ranging from late April to early May. Early planting is

common for maximizing the length of the growing season, limiting late-season insect pressure,

and allowing for favorable weather at harvest. Colyer et al. (1991) in Louisiana, found that poor

stands and increased seedling disease pressure are often associated with early planting dates;

with early April plantings resulting in low plant populations, late April and early May plantings

resulting in intermediate plant populations, and mid-May plantings resulting in high plant

populations. Calculation of the accumulated heat units for particular growth stages are often

used to explain duration of stages in cotton crop development (Oosterhuis, 1990). The

calculation for heat units is Degree Day 60 (DD-60), based on the premise that cotton growth is

proportional to daily temperatures above a threshold of 60 °F (15.6 °C) and the common formula

is ((HT + LT) / 2) – 60 in which HT is the highest temperature of the day, LT is the lowest

temperature of the day, and 60 refers to DD-60 (cottonheatunits.com). Kerby et al. (1987) found

reduced emergence for cotton planted with less than 16 heat units within the first 5 days after

5

planting showing the importance of temperature on emergence. Both soil temperature and soil

moisture have been shown to be important during the first few weeks after planting for cotton

stand establishment because of effects on plant vigor and susceptibility to disease (Johnson et al.

1969).

Cotton seedling diseases affect germination, emergence, survival, and early-season

development of seedlings. Cotton production around the globe is impacted by seedling diseases

(DeVay, 2001, Hillocks, 1992; Melero-Vara and Jimenaz-Diaz, 1990). In 1952, The Cotton

Disease Loss Estimate Committee was formed by the Cotton Disease Council to compile and

publish an annual estimate of losses caused by individual diseases in each state. The U.S. Cotton

disease loss estimates for the U.S. from 1952 to 2009 for seedling diseases averaged 2.8% with

loss estimates accounting for 23% of the total estimated losses in lint production over these years

(Disease database, http://www.cotton.org/tech/pest/seedling/index.cfm).

Cotton seedling disease pathogens.

The pathogens associated with the cotton seedling disease complex are Thielaviopsis

basicola (Berk. & Broome) Ferraris (syn. Chalara elegans Nag Raj & Kendrick), Rhizoctonia

solani Kuhn, teleomorph Thanatephorus cucumeris (A. B. Frank) Donk, Pythium spp., and

Fusarium spp. (DeVay, 2001; Rothrock and Buchanan, 2017). These soilborne pathogens can

act individually or in combination to cause a range of symptoms on seed, roots and hypocotyls

when the environment is conducive. Lack of emergence from rotted seed, or stand failure from

damping-off causes moderate to severe consequences for the crop.

Geographically, pathogens in the cotton seedling disease complex are found in almost all

fields used to grow cotton in all cotton growing regions (Bird, 1973; Johnson et al., 1978). Many

of the pathogens associated with cotton affect many plant species (Minton and Garber, 1983).

http://www.cotton.org/tech/pest/seedling/index.cfm

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Other organisms that may be associated with the seedling disease complex are pathogenic

nematodes, which in combination with pathogenic fungi often cause more severe damage to the

seedling than the fungi alone (Mai and Abawi, 1987; Powell, 1971). The most important

nematodes on cotton are Sting (Belonolaimus longicaudatus), Lance (Hoplolaimus spp.), Root-

knot (Meloidogyne spp.), and Reniform (Rotylenchulus spp.) (DeVay, 2001).

Most soilborne pathogens often exist in the soil as a dormant propagule requiring a

trigger from a plant to come out of dormancy or germinate before interacting with the plant

(Huisman, 1988). When a structure of a plant such as a seed, root, or hypocotyl influences a

pathogenic propagule or combination of pathogens under the appropriate conditions, the

pathogen(s) will attempt to infect and colonize the plant resulting in one or more symptoms.

Pythium is a genus of Oomycota that contains many plant pathogenic species that have

long been known to cause disease on a range of host plants. Not all species are known to be

pathogenic, but some are capable of causing serious economic loss to a crop (Hendrix and

Campbell, 1973). Pythium spp. can severely reduce stands in cotton crops by causing symptoms

like seed rot and pre-emergence damping-off, as well as post-emergence damping-off on newly

emerged seedlings (Hendrix and Campbell, 1973; DeVay, et al., 1982; Howell, 2002). Soil

temperature at planting is an important environmental factor. Temperatures ranging from 16-20

°C in cotton growing areas are more conducive to disease; moreover, wet soil conditions also

favor disease (DeVay, 2001). Parasitism by Pythium spp. is generally limited to juvenile or

succulent tissues of seedlings or root tips of older plants.

Rhizoctonia solani Kuhn is an important pathogenic species to a range of host plants.

Within the species, there are anastomosis groups (AG) and intraspecific groups (ISGs) (Ogoshi,

1987). Rhizoctonia isolates that have the ability to fuse hyphae (anastomose) with each other are

7

considered to be genetically related. Anastomosis groups are used to characterize and identify

Rhizoctonia because there are many different biotypes and they do not produce easily

identifiable or distinguishable structures but have different pathogenic capabilities. Parmeter et

al. (1969) studied 138 isolates, and most fell into 1 of 4 anastomosis groups. Since this study,

there has been several more AGs characterized, (Carling et. al. 1994; Carling et. al. 2002). The

primary seedling disease group of cotton is R. solani is AG-4 (Rothrock and Buchanan, 2017).

R. solani is known to be a major pathogen to subterranean portions of cotton plants causing seed

rot, preemergence death, and postemergence damping-off (Rothrock, 1996).

Thielaviopsis basicola is a significant pathogen that affects seedling development and

yield of cotton in most cotton growing areas. T. basicola is a hemi-biotrophic plant pathogen

that survives in the soil as chlamydospores (Hood and Shew, 1997). T. basicola causes black root

rot on cotton which primarily affects early-season growth delaying crop maturity. Affected

plants may look stunted, chlorotic, and have blackened roots. Temperature plays an important

role in the ability of T. basicola to survive and colonize roots. Rothrock, (1992) found survival

to be significantly greater in soil with a temperature of 16 °C than 24 or 28 °C. Mauk and Hine

(1988) found that disease severity was greater at 18 to 20 °C than 24 to 26 °C.

Several species of Fusarium are isolated from diseased cotton plants (Colyer, 1988).

Pathogenicity was determined for isolates in the species F. solani, F. oxysporum, F. equiseti, F.

moniliforme, and F. graminearum. F. solani, and F. oxysporum were shown to be most virulent.

F. oxysporum Schlechtend. f. sp. vasinfectum (Atk.) Snyd. & Hans has been shown to cause wilt

and seedling death on cotton (DeVay, 2001).

8

Cotton seedling disease management.

Limiting the stand loss and damage on cotton from seedling diseases relies on planting

high quality seed, bedding row, and planting when the soil environment and weather forecast

favors rapid cotton germination and growth. Chemical control is also an important management

strategy. Combination fungicide seed treatments are used throughout the Cotton belt to protect

the crop from multiple seedling disease pathogens. All cottonseed sold in the U.S., is treated

with multiple fungicides. In-furrow liquid and granular fungicides have been used with some

success in the past but are not commonly used currently.

Each year a National Cottonseed Treatment Program is conducted by the Cotton Disease

Council in which seed treatments are tested at multiple locations in diverse environments across

the Cotton belt. In an 11-year study (Rothrock et al., 2012), the importance of seedling diseases

and fungicide seed treatments on cotton was examined by stand improvement from seed

treatment combinations used by the cottonseed industry and experimental compounds compared

to no fungicide treatment (black seed). They also examined the importance of specific pathogens

by comparing selective fungicide treatments to black seed in which metalaxyl was used to assess

the role of Pythium and PCNB for R. solani. This study also examined the role of environment

on stand establishment of cotton by collecting soil temperature, soil moisture, and rainfall data

for each of the trials. This study found fungicide seed treatments significantly improved stands

in most of the trials, showing the importance of seedling diseases in stand establishment.

Moreover, they found both selective treatments, metalaxyl and PCNB, improved stands showing

the importance and widespread distribution of Pythium and R. solani respectively. This study

found that the combination seed treatments improved stand over black seed in all environmental

conditions, but notably, environment had major impact on level of fungicide responses. When

9

minimal soil temperature decreased from 25 °C to 12 °C, fungicide response increased

dramatically. Pythium disease pressure increased as minimal soil temperature decreased and

rainfall increased. R. solani disease pressure was not largely affected by varying planting

environment, suggesting that other factors such as inoculum level may be important for disease.

This study made important ecological discoveries on the cotton seedling disease complex which

can be used to further improve crop production.

Importance of study.

Increasing costs of cotton seed due to technology fees and products applied to the seed

has resulted in a more difficult task for producers to balance planting expenses and obtaining

desired plant populations. Several decades ago, producers often over-seeded and then thinned

plants to their target population after emergence, but this practice is not economically practical

on modern farms because of the high planting and labor costs, therefore, seed is planted at rates

that will potentially result in a stand that is closest to the desired plant population, but emergence

is not guaranteed because of the environment and seedling diseases. Many cotton producers are

trying to reduce input costs by reducing seeding rates for entire fields or sites within fields using

variable rate planting techniques. The environment and seedling diseases become more

important when aiming for the most efficient seeding rate for establishing a given plant

population. Planting at high rates when emergence conditions are good can result in excessive

plant densities, likewise, low planting rates when emergence conditions are not favorable can

result in deficient plant densities or crop failure. As seeding rates are reduced, accurate

assessment of emergence and stand potential of the planting environment becomes more critical

for reducing the risk of planting error.

10

Field-scale studies of soil factors and seedling disease could provide more information

needed to better assess conditions for planting. Spatial studies could be important for examining

the relationships among soilborne plant pathogens, their environments, and disease allowing

improved understanding of pathogen ecology and disease management (Campbell and Noe,

1985). Soil variability is the outcome of many processes that act and interact across the space of

the field and over time (Parkin, 1993). Throughout the growing season, cotton generally uses the

top 1.2 meters of a soil profile which is composed of physical soil factors such as texture, hard

pans, gravel layers or water tables. These factors can gradually or abruptly change throughout

fields horizontally and vertically and can influence water, oxygen availability, and temperature.

Classical statistical methods assume observations are independent of each other. Tobler’s

law states observations made close to one another are more similar than observations further

apart. Because of gradients of varying soil factors often present in agricultural fields, statistical

methods accounting for spatial variability may improve analyses (Delmelle, 2014). This study

uses statistical methods that utilize spatial analyses to elucidate field-scale variability of soil

factors that influence variability of seedling diseases and stands, the importance of soil

population densities of T. basicola and R. solani on seedling disease, and the importance of

seedling diseases in reduced stands for cotton in research and commercial cotton fields in

Arkansas.

11

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Chambers, A. Y., Newman, M. A., Kirkpatrick, T. L., Barham, J. D., Phipps, P. M.,

Shokes, F. M., Littlefield, L. J., Padgett, G. B., Hutmacher, R. B., Davis, R. M.,

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16

Chapter 2. Spatial examination of seedling disease pressure

Abstract

Cotton is an important crop in the United States and many other countries. Establishing

and maintaining a stand of healthy plants with uniform spacing and plant density is critical for a

good crop, therefore it is important to manage seedling diseases which affects germination,

emergence, survival, and early-season development of seedlings. Cool soils saturated with

moisture are conducive to reduced seedling vigor and more severe disease. The objective of this

study was to characterize field-scale spatial variation in seedling disease incidence and severity,

cotton stands and abiotic soil factors and elucidate their spatial relationships. Spatial field trials

were established at the Judd Hill Cooperative Research Foundation in which the overall level of

seedling disease was assessed by stand improvements among fungicide seed treatments. A

complete broad-spectrum seed treatment (ipconazol + muclobutanil + metalaxyl + penflufen +

prothioconazol) improved stand over non-treated seed by 12.2% in 2014 and 8.8% in 2015. In a

field with 50 replicates, spatial variability was determined for soil populations of select

pathogens, disease severity, and relative fungicide response as stand among the complete broad-

spectrum treated seed, or selective fungicide seed treatments, metalaxyl or PCNB, and non-

treated seed. Soil populations of T. basicola, root disease severity, and T. basicola incidence

were each found to be spatially aggregated suggesting spatial field properties were influencing

the observed patterns. Relative fungicide response, T. basicola soil populations and incidence,

and root disease were found to be negatively spatially correlated with minimal soil temperature

and soil texture (% clay). These results suggest seedling disease severity increased across the

field where soil temperature and soil texture (% clay) decreased, and severity decreased where

soil temperature and clay increased. Based on the findings of this study, seedling disease varied

17

spatially across the field based on predictable soil environment factors. These findings give

important insights into the role of soil environment in disease development which may be

valuable for improved management strategies. These findings also suggest variable rate planting

could be adjusted for seedling disease pressure based on easily measurable soil factors to more

consistently obtain optimal stands.

18

Introduction


countries (Oerke, 2006). Cotton is grown in several states across the Southern United States, the

Cottonbelt, with concentrations in the Texas High Plains, irrigated valleys in Arizona and

California, the Mid-South, and Southeast. Establishing and maintaining a stand of healthy plants

with uniform spacing and plant density is critical for uniform crop development, managing the

crop, good fiber qualities, and yield (Christiansen and Rowland, 1981). Research on optimal

cotton plant populations for maximum yield and quality have produced variable results, however,

much of the available literature suggests comparable yield may be obtained over a wide range of

plant populations. Environmental conditions at planting are important to getting cotton seedlings

off to a vigorous start with desired plant populations.

Colyer et al. (1991) in Louisiana, found that poor stands and increased seedling disease

pressure are often associated with early planting dates; with early April plantings resulting in low

plant populations, late April and early May plantings resulting in intermediate plant populations,

and mid-May plantings resulting in high plant populations. Cotton production around the globe

is impacted by seedling diseases (DeVay, 2001, Hillocks, 1992; Melero-Vara and Jimenaz-Diaz,

1990). Cotton seedling diseases affect germination, emergence, survival, and early-season

development of seedlings. The U.S. Cotton disease loss estimates for the U.S. from 1952 to

2009 for seedling diseases averaged 2.8% with loss estimates accounting for 23% of the total

estimated losses in lint production over these years (Disease database,

http://www.cotton.org/tech/pest/seedling/index.cfm).

The pathogens associated with the cotton seedling disease complex include Thielaviopsis

basicola (Berk. & Broome) Ferraris (syn. Chalara elegans Nag Raj & Kendrick), Rhizoctonia


19

solani Kuhn, teleomorph Thanatephorus cucumeris (A. B. Frank) Donk, Pythium spp., and

Fusarium spp. (DeVay, 2001; Rothrock and Buchanan, 2017). These soilborne pathogens can

act individually or in combination to cause a range of symptoms. Limiting the stand loss and

damage on cotton from seedling diseases relies on planting high quality seed, land preparation,

and planting when the soil environment and weather forecast favors rapid cotton germination and

growth. Combination fungicide seed treatments are used throughout the Cotton belt to protect

the crop from multiple seedling disease pathogens. Rothrock et al., (2012) documented the

importance of the environment on seedling disease, in field trials across the Cottonbelt, in which

stand responses among seed treated with fungicides compared to seed not treated with fungicides

increased in trials with cooler soils and increasing rainfall the first three days after planting.

The high price of seed due to technology fees and products applied to the seed has led

many producers to look towards reducing planting costs by planting less seed. Seeding rates have

dramatically decreased across the Cotton Belt and producers are looking towards using variable

rate planting to improve stand uniformity, but this increases the importance of each seed to

germinate, emerge, and become established, and therefore increases the importance of seedling

diseases and planting environment. Assessing the spatial variability of seedling disease pressure

and soil environment factors across a field could provide useful information for producers and

researchers. The objectives of this study were to characterize spatial variation in seedling

disease incidence and severity and cotton stands within cotton fields and elucidate abiotic and

biotic soil factors that explain spatial differences. Spatial analysis could identify important

relationships between select seedling pathogens and disease and soil environment or physical

factors in order to predict seedling diseases on cotton.

20

Materials and Methods

In 2014 and 2015 a research field with a history of cotton monoculture at Judd Hill

Cooperative Research Foundation in Poinsett County in Northeast Arkansas was selected. Soils

are classified overall as a Dundee silt loam. The field was under conventional tillage, and fall

field preparation included re-building seed beds in the spring prior to planting. Trials were

planted with a 4-row research cone-planter. Plots were furrow irrigated and maintained using

standard practices according to the University Of Arkansas Division Of Agriculture Cooperative

Extension Service.

Five, 4-row strips were planted to represent the area of the field. Each strip was divided

into 10, 15.25 meter long replicates. A 4-row replication had a randomly selected row planted

with Delta Pine 1044B2RF (Gossypium hirsutum) seed which was treated with one of each of the

four fungicide seed treatments (1) no fungicide treatment, (2) metalaxyl, (3) PCNB or (4)

ipconazol + myclobutanil + metalaxyl + penflufen + prothiooconazole + penflufen + metalaxyl

(Table 1). All seed were treated with imidacloprid (528.4 g a.i./100 kg seed), CaCO3 (463.5

g/100 kg seed), polymer (Secure 65 ml/100 kg seed, Syngenta Inc.), and dye (Color Coat Red 65

ml/100 kg seed, Syngenta Inc.). Seed were treated using a Hege 11 liquid seed treater (Hege

Maschinen GmbH, Waldenburg, Germany). Each 15.25 meter row was planted with 150 seed.

The field was planted on 6 May in 2014, and the center of each replicate was georeferenced

using a Trimble® Yuma 2 Rugged Tablet GPS unit (Trimble Navigation, Ltd., Sunnyvale,

California), and these plots were used as sample sites in order to represent the entire field.

Twenty-one days after planting, stand counts were performed for the length of each

replicate which consisted of 4, 15.25 meter long rows. After stands were counted, 10 seedlings

from the non-treated row in each replicate were dug, and placed in a re-sealable bag on ice for

21

transportation to the laboratory in Fayetteville, AR. Height was measured above the cotyledon

and nodes counted for 5 arbitrarily selected seedlings from each sample. Height was measured

from the cotyledon node to the apex of the apical meristem. Weight was recorded for all

seedlings in each sample. Skip indices (Chamber, 1986) were determined for each 15.25 meter

long row in each plot 42 days after planting. A skip is defined as a distance greater than 30.5 to

45.7 cm between seedlings. A skip index was calculated by assigning a value of 1 for every 30.5

cm skip and adding 1 for every additional 15 cm in a skip. Five representative plants from each

row in each plot were selected and the height was measured from the soil line to the apex of the

apical meristem 42 days after planting. These height measurements were averaged together for

each row. Yield for each row of each replicate was harvested with a two – row spindle picker

fitted with a weigh cell capable of being tarred for each row.

The above ground portion were cut from sampled seedlings leaving the remaining

hypocotyl and roots. The roots/hypocotyls for a sample were washed by first placing each

sample in a jar with a modified lid that allowed water to flow in and out while containing the

plant matter inside. This initial washing lasted 20 minutes. Next, the lids were removed from

the jars, and the roots/hypocotyls were surface disinfested with a 0.5% sodium hypochlorite

solution for 30 seconds. The roots/hypocotyls were removed and blotted dry in paper towels.

Disease indices were taken for the roots and hypocotyls of each seedling sampled

(Rothrock et al., 1995). The hypocotyl disease severity index was based on a scale of 1 to 5, in

which 1=no symptoms, 2=few pinpoint lesions or diffuse discolored areas, 3=distinct necrotic

lesions, 4=girdling lesions, and 5=seedling death. The hypocotyl severity index was analyzed as

the percentage of seedlings with a rating of 3 or greater. The root disease severity index was

based on a scale of 1 to 5, in which 1=no symptoms, 2=1-10% of the root system discolored,

22

3=11-25% of the root system discolored, 4=26-50% of the root system discolored, 5= greater

than 50% of the root system discolored and analyses were done using the mid-percentile value

for each category.

The seedling root/hypocotyls were individually placed in Petri dishes containing water

agar, 0.8% (Gelidium agar, Mooragar Inc., Rocklin, CA) amended with 10 and 250 mg of the

antibiotics rifampicin and ampicillin, respectively, and the miticide fenpropathrin (0.14 mg

a.i./liter, Danitol 2.4 EC, Valent Chemical Co.). After 48 hours, emerging colonies were

transferred by hyphal tip removal using a flame sterilized scalpel to Petri dishes containing an

amended potato dextrose agar medium, PDArad (18g Difco potato dextrose agar, 10 and 250 mg

of the antibiotics rifampicin and ampicillin, respectively), and the miticide fenpropathrin (0.14

mg a.i (Danitol 2.4 EC, Valent Chemical Co.)/liter). The isolated filamentous colonies were

sorted based on morphological characteristics, and they were identified to genus under a

microscope and recorded. After 5 days, the seedling roots/hypocotyls were transferred from the

WA Petri dishes to Petri dishes containing a modified TB-CEN carrot juice media, selective for

Thielaviopsis basicola (Rothrock et al., 2012). The plated roots/hypocotyls were examined

under a dissecting microscope and rated based on the percentage of the root colonized by T.

basicola. The T. basicola colonization rating was based on a scale of 0 to 10 in which 0 = no

colonization, 1 = 1 – 10%, 2 = 11 – 20%, 3 = 21 – 30%, 4 = 31 – 40%, 5 = 41 – 50%, 6 = 51 –

60%, 7 = 61 – 70%, 8 = 71 – 80%, 9 = 81- 90%, and 10 = 91 – 100%. The mid-percentile values

were used for analysis. The number seedlings per sample in which T. basicola was found was

also recorded to quantify frequency of isolation.

A quantitative assay using wooden toothpicks as baits inserted into soil samples was used

for determining the soil populations of Rhizoctonia spp. for each replicate in the Judd Hill field

23

locations (Spurlock et al., 2015). For this study 4 intact soil cores were taken per replicate, 1 per

row in a diagonal pattern for a replicate. The cores were retrieved using a bulb planter (Bulb

Hound bulb planter; (Hound Dog Products, Inc., Edna, MN). Each core was placed in a 473 ml

Styrofoam cup (Dow Chemical Worldwide), and transported back to the laboratory in

Fayetteville, AR. The soil cores were bottom watered to bring the soil close to saturation and

allowed to drain for 12 h, and 5 autoclaved toothpicks were inserted 5 cm deep into the soil

spaced at least 2 cm apart in each cup. After 2 days of incubation, the toothpicks were removed

from the soil cores and were placed on Petri dishes containing TS1 medium (Spurlock et al.,

2015). Colonies growing into the medium away from the toothpicks with Rhizoctonia- like

morphological characteristics were marked, measured from the top of the toothpick, and hyphal

tips transferred aseptically to Petri dishes containing PDArad for identification. Isolates were

grouped based on morphological characteristics, counted and data recorded by field, replicate,

cup, toothpick, and depth on the toothpick (1-5 cm). The quantity of Rhizoctonia propagules per

100 cm3 of soil was calculated (Spurlock et al., 2015).

To provide quantitative population data for Theilaviopsis basicola for each replicate, a

pour-plate method with the modified TB-CEN medium was used (Rothrock et al. 2012). Soil

from the intact cores mentioned previously was used for this procedure. Soil from each of the 4

cores collected from each replicate was placed in a plastic bag and mixed together, oven dry

weight was measured, and 25 g oven dry weight equivalent of soil was placed in a flask along

with 238 ml of 0.2% dilute water agar and placed on a wrist action shaker for 20 min. For each

sample, 1 ml of the 1:10 dilution was pipetted into 10 sterile Petri dishes. The Petri dishes were

filled with molten medium and rotated gently to mix the diluted soil and medium. The plates

were incubated at room temperature for 21 days. The resulting T. basicola colonies were

24

counted and recorded for each plate. Colony forming units per gram of soil was recorded for

each replicate.

Minimal soil temperature and soil water content 10 cm deep was recorded for each

replicate 1 and 5 days after planting before sunrise each morning. Minimal soil temperature was

measured with a Digital Thermometer (Durac®), and soil water content was measured with an

ML3 ThetaProbe Soil Moisture Sensor (Delta-T Devices Ltd). The strength of soil crusting is a

combination of factors that can be determined by observing the pressure required to insert an

object into the soil. The strength of soil crusting for each replication was measured with a Soil

Test Pocket Penetrometer, a device with a spring loaded 0.5 cm diameter rod that measures the

pressure in kg/cm2. For this experiment, a 3.0 cm diameter disk was pressed into the soil with

the instrument to better measure the crusting of the upper layer of soil. The pressure required to

press the disk 3.0 cm into the soil was recorded 5 days after planting on top of the raised seed

bed for all replicates in 2014. Due to rain saturated soils experienced in 2015, soil crusting

strength was not measured. Soil texture for each plot was measured by the hydrometer method

(Bouyoucos, 1962).

Spatial auto correlation and regression models were performed in GeoDa (Anselin et al.,

2006). Spatial autocorrelation for variables was determined by calculating the Moran’s I values.

Values of I range for one or 2 variables from -1 to +1. Negative values indicate negative spatial

autocorrelation or a uniform spatial distribution. I values close to 0 indicates a random spatial

pattern. Positive I value indicates a positive spatial autocorrelation or an aggregated spatial

distribution. Univariate Moran’s I was calculated for each variable, and bi-variate Moran’s I was

calculated for pairs of variables that were individually spatially auto correlated. Simple OLS

regression models were used to examine the relationships between variables. Diagnostics for

25

spatial dependence (Moran’s I for residuals and Lagrange multiplier for error and lag) were used

in each analysis in which spatial lag or spatial error were applied to the models when diagnostics

indicated spatial dependencies among variables. Analysis of variance in JMP®, 12.1 (SAS

Institute Inc., Cary, NC) was used for calculating the overall fungicide seed treatment response.

Results

In both years, the trial at Judd Hill had air temperatures reaching a high of approximately

23 °C and a low of approximately 13 °C one day after planting (DAP), and average minimal soil

temperatures across this field were 20.7 °C in 2014 and 21.3 °C in 2015 (Table 2). Between the

first and 5th day after planting there was at least 4 cm more rainfall in 2015 than in 2014 resulting

in overall lower minimal soil temperatures and wetter soils; average minimal soil temperature 5

DAP was 22.15 °C in 2014 and 17.4 °C in 2015.

Overall fungicide seed treatment effects on stands were examined by comparing the least

squared means with a student’s t-test (α=0.05) in a completely randomized block design. Stand

counts among the complete broad-spectrum seed treatment (ipconazol + muclobutanil +

metalaxyl + penflufen + prothioconazol) had a mean stand of 70.4% in 2014 and mean stand of

72.5% in 2015 which were significantly higher than the stand counts for the non-treated seed

which had a mean stand of 58.2% in 2014 and mean stand of 63.84% in 2015 (Table 3). This

stand response indicated the level of seedling disease pressure in this field, and the importance of

seedling diseases in stand establishment. The selective fungicide seed treatment, metalaxyl

(Allegiance FL), significantly improved stand counts in 2014 and numerically increased stands

in 2015 showing the importance of Pythium in stand establishment (Table 3). PCNB (RTU-

PCNB) improved stands numerically but not significantly both years suggesting Rhizoctonia was

less important in these 2 years in this field (Table 3).

26

To evaluate the spatial associations soil factors had with stand and seedling disease, the

spatial variability of soil factors were characterized. Within this field, minimal soil temperature

was aggregated and consistent across years as was soil water (Tables 4 and 5). Soil texture was

also aggregated. The spatial variation of soil water positively correlated with percent clay across

the field (Table 6). Regression models also showed minimal soil temperature was positively

correlated with soil water 5 DAP, and soil water was also positively correlated with clay (data

not shown). These correlations show abiotic soil factors were spatially variable based on each

other. Soil populations of T. basicola were found to be spatially aggregated both years but were

not consistent across years, and R. solani soil populations were found to be spatially random both

years (Tables 4 and 5). Bivariate Moran’s I showed T. basicola populations had a negative

spatial correlation with minimal soil temperature 1 DAP and clay content both years (Table 6).

Regression models also showed T. basicola soil populations were negatively correlated with soil

temperature and clay content (Data not shown).

Spatial variation of isolation frequency of seedling pathogens and seedling disease ratings

were examined. Isolation frequencies were mostly spatially random, except for T. basicola

incidence on seedlings on selective medium which was spatially aggregated and consistent

across years (Tables 4 and 5). Bivariate Moran’s I indicated positive spatial correlation with T.

basicola incidence and soil populations (Table 7).

Stands and skip indices were associated with soil factors in 2014, but not in 2015.

Univariate Moran’s I indicated in 2014 stands and skip indices were aggregated (Table 4).

Bivariate Moran’s I comparing the 2014 stands and skips with soil factors indicated that higher

plant stands for the non-treated seed with fewer skips were aggregated with higher minimal soil

temperature and clay content, and lower plant stands with more skips were aggregated with

27

lower minimal soil temperature and lower clay content (Table 6). Regression models also

showed for 2014 that plant stands were positively correlated with minimal soil temperature (Data

not shown). Bivariate Moran’s I between weight and number of nodes of seedlings measured 21

DAP with soil factors indicated that weight was aggregated with minimal soil temperature, soil

water 5 DAP, and percent clay both years (Table 6). Bivariate Moran’s I indicated yield among

the complete broad-spectrum treated rows were higher in this field where minimal soil

temperature, soil water 5 DAP, and percent clay was lower both years, and yield among the non-

treated rows were also dispersed with minimal soil temperature, soil water 5 DAP, and percent

clay in 2014 but randomly dispersed with soil factors in 2015 (Table 6).

The role of soil factors on seedling disease severity were examined as change in stand

between the complete broad-spectrum fungicide seed treatment (ipconazol + muclobutanil +

metalaxyl + penflufen + prothioconazol) and seed that did not receive fungicide treatment for

replications. The mean stand improvement across this field was 12.1% per replication in 2014

and 8.6% per replication in 2015. Univariate Moran’s I indicated that the spatial distribution of

relative fungicide response was dispersed randomly across this field in 2014 but was evenly

dispersed in 2015 indicating a high-low, and low-high spatial autocorrelation (Table 4).

Bivariate Moran’s I spatial comparison of fungicide response and soil factors indicated

high-low, and low-high correlations with minimal soil temperature both years of this study, and

soil water, and percent clay in 2014 (Table 6 and figure 1). This indicates that replicates that had

high levels of response had neighboring replicates with lower minimal soil temperatures. Spatial

examination of soil factors and seedling disease pressure showed root disease indices were

spatially consistent among years, and root and hypocotyl disease indices had negative spatial

correlations within minimal soil temperature, soil water, and percent clay (Tables 5 and 6).

28

Regression models also showed negative correlations between root disease indices and minimal

soil temperature, and percent clay (Data not shown).

Discussion

The importance of using fungicide seed treatments for stand establishment and therefore

the role of the cotton seedling disease complex in reducing stands examined by comparing stand

counts among the 4 seed treatments. The complete broad-spectrum (ipconazole + myclobutanil

+ metalaxyl + penflufen + prothioconazole) fungicide seed treatment improved stands over the

non-treated seed in both years. The complete treatment also improved stands over the selective

treatments, metalaxyl and PCNB showing the benefit of combination seed treatments and the

role of multiple pathogens causing stand loss. Metalaxyl improved stands over the non-treated

seed demonstrating the importance of Pythium in stand establishment. Rhizoctonia may not

have been strong in reducing stands in these 2 years in this field as indicated by the PCNB

response. However, PCNB is a protectant fungicide and is less effective than some of the newer

chemistries and thus may underestimate the importance of R. solani in this study. The benefits

of fungicide seed treatments on cotton stand establishment have been previously documented.

Wang and Davis, (1997) found seed treatment with carboxin + PCNB for the control of

damping-off from Rhizoctonia improved stand over non-treated seed in all their greenhouse trials

and half of their field trials for all 12 cultivars they tested. Davis et al., (1997) found, across the

San Joaquin Valley, the combination seed treatment of myclobutanil + metalaxyl or

myclobutanil improved stands relative to non-treated seed in 22 of 25 field trials. Metalaxyl had

a positive response in trials in 1995, but not in the 1993 or 1994 trials. Wheeler et al., (1997)

found seed treatments triadimenol improved 21-day emergence over non-treated or Captan seed

treatments in the High Plains of Texas. Rothrock et al., (2012) found fungicide seed treatments

29

increased stands compared to non-treated seed, in 119 out of 211 field trials across the Cotton

Belt, and metalaxyl or PCNB improved stands relative to non-treated seed in 40 or 44,

respectively, of these 119 trials with a fungicide response.

General planting conditions were conducive to disease both years. Planting environment

has been shown to be an important factor in stand establishment, and planting too early is not

recommended because it often results in poor stands and increased disease. Colyer et al. (1991),

in Louisiana, found cotton plant populations were low when planted in early April but improved

with later planting dates. In Tennessee, Johnson et al. (1969) found good stands at minimal soil

temperatures of 19 C or higher, but poor stands at 10 C or lower. Rothrock et al. (2012) showed

increased seedling disease pressure and increased fungicide response as soil temperature

decreased from 25 °C to 12 °C and rainfall increased the first 3 days after planting. Davis et al.

(1997) found fungicide seed treatments improved stands compared to seed without fungicide

over environments with mean soil temperatures that ranged from 19.7 to 22.2 C for the first 5

days after planting suggesting that even at more favorable soil temperatures seedling diseases

can be important in stand establishment.

Spatial variability of minimal soil temperature, soil water content, and soil texture within

this field were examined to see how these factors influence spatial variability of seedling disease.

Minimal soil temperature was aggregated in this field and was consistent across years as was soil

water and clay. Minimal soil temperature was positively correlated with soil water and clay

across this field. Soil water and temperatures may have been influenced by textural changes.

Minimal soil temperature (temperature measurement taken at the end of the night period of the

diurnal cycle) relies on the soil’s ability to retain heat. In agricultural fields under conventional

tillage, soil texture and water holding capacity are important factors in how much solar energy

30

will be retained during the day, and how much will be stored at night (Farouki, 1981). Diurnal

oscillations of temperature in a moist soil are less than those in a dry soil. Moist soils warm and

cool more slowly, and dry soils warm and cool more quickly (Mount and Paetzold, 2002).

Isolation frequencies of Fusarium, R. solani, and Pythium were randomly dispersed and

provided little information on disease ratings as did R. solani soil population. Seedlings infected

by R. solani or Pythium often suffer from acute symptoms that cause pre or post-emergence

damping off that may kill the host potentially limiting the value of isolation data on surviving

seedlings. Root disease indices, T. basicola isolation, and soil populations of T. basicola were

each aggregated within this field, and all but T. basicola soil populations were found to be

consistent across years. The use of a selective medium (TB-CEN) for isolating T. basicola may

have been more precise, and the chronic nature of black root rot may have allowed for a better

representation than other pathogens and diseases. Soil environmental conditions are important

for development of black root rot and pathogen survival in which cooler and wetter soils are

favored. Rothrock et al. (1992) found higher chlamydospore survival of T. basicola in soils at 16

°C and a decrease in population at 24 °C. The importance of soil environment on survival could

potentially lead to populations being related to spatial variability of soil factors within fields.

Stand counts were positively correlated with minimal soil temperature 1 and 5 days after

planting and to a lesser degree soil water content 5 days after planting and percent clay in 2014,

but correlations were not significant in the second year of this study. In 2015, the weather was

much more overcast with increased rainfall. Soil temperature and water content may have been

favorable to reduced emergence and increased seedling disease even for the warmer areas of the

field (approx. 18 °C) leading to the spatially random stands observed in 2015. Seedling growth

was found to increase both years where minimal soil temperature, soil water, and percent clay

31

were higher indicating more vigorous plants in soil environments similar to those that had

improved stands in 2014. However, Yield was higher in areas of the field with lower minimal

soil temperatures, soil water, and percent clay. Improved yields where stands were lower in

2014 and where seedlings had less mass and less nodes both years may be attributed to the cotton

plant’s growth characteristics that have been shown to vary with plant density. Brown and Ware

(1958) found cotton plants in denser plant populations tend to grow taller and have more

vegetative growth that can cause a delay in fruiting. Bednarz et al. (2000) found cotton, in

thinner plant populations, produced more monopodial branches, and in denser plant populations

more boll shedding occurred.

Although stand count data were spatially random in 2015, relative fungicide response was

spatially uniform indicating stand reduction from seedling disease followed a spatial pattern that

may have been influenced by field characteristics. In both years of this study, relative fungicide

response was negatively correlated with minimal soil temperature, soil water, and percent clay

suggesting these soil factors influenced the variability of seedling disease pressure. Areas of the

field with lower minimal soil temperature, soil water, and percent clay were also spatially

associated with increased seedling disease pressured measured as root and hypocotyl disease

indices. These finding suggest seedling disease varied in a field based on predictable

environment factors.

Seedling diseases are important and a concern for cotton producers especially as seeding

rates are decreased; therefore, anticipating disease pressure is crucial for establishing good

stands. The objectives of this study were to characterize seedling disease incidence and severity

and cotton stands within cotton fields and elucidate abiotic soil factors that explain differences.

Using spatial analysis to identify relationships in order to predict seedling disease on cotton.

32

This study showed the importance of combination fungicide seed treatments with broad-

spectrum ranges of control for the cotton seedling disease complex across spatially variable soil

environments. Seedling disease pressure varied in a field based on predictable environment

factors. Higher disease pressure was present where soil temperatures were lower at night within

across this field. This information is valuable for improving management within and across fields

by adjusting planting rate, and decisions on planting date or fungicide treatments in cotton.

33

References

Anselin, L., Syabri, I., and Youngihn, K. 2006. GeoDa: An Introduction to Spatial Data

Analysis. Geographical Analysis 38:5-22.

Bednarz, C. W., Bridges, D. C., and Brown, S. M. 2000. Analysis of Cotton Yield Stability

Across Population Densities. Agron. J. 92:128-135. doi:10.2134/agronj2000.921128x

Bouyoucos, G.J. 1962. Hydrometer method improved for making particle size analysis of soils.

Agron. J. 54:464-465.

Brown, H.B. and Ware, J. O. 1958. Cotton. McGraw-Hill Book Co. Inc., New York.

Chambers, A. Y. 1986. Comparative effects of selected skip levels in stands and replanting on

cotton yields. Pages 19 in: Proc. Beltwide Cotton Prod. Conf., Natl. Cotton Counc.,

Memphis, TN.




Colyer, P. D., Micinski, S., and Nguyen, K. T. 1991. Effect of planting date on the efficiency of


742.

Davis, R. M., Nunez, J. J., and Subbarao, K. V. 1997. Benefits of cotton seed treatments for the

control of seedling diseases in relation to inoculum densities of Pythium species and

Rhizoctonia solani. Plant Dis. 81:766-768.



Farouki, O. T. 1981. Thermal properties of soils. U.S. Army Cold Regions Research and

Engineering Laboratory. Hanover, New Hampshire 03755. CRREL Monograph 81-1.

34







Mount, H. R., and R. F. Paetzold. 2002. The temperature regime for selected soils in the United

States. United States Departments of Agriculture, Natural Resources Conservation

Service, National Soil Survey Center, Lincoln, Nebraska, Soil Survey Investigation

Report No. 48.


Rothrock, C. S. 1992. Influence of soil temperature, water, and texture on Thielaviopsis basicola

and black root rot of cotton. Phytopathology 82:1202 – 1206.




Rothrock, C. S., Kirkpatrick, T. L., Frans, R. E., and Scott, H. D. 1995. The influence of winter

legume cover crops on soilborne pathogens and cotton seedling diseases. Plant Dis.

79:167-171.









35

Spurlock, T. N., Rothrock, C. S., Monfort, W. S. 2015. Evaluation of Methods to Quantify

Populations of Rhizoctonia in Soil. Plant Dis. 99:836-841.

Wang, H., and Davis, R. M. 1997. Susceptibility of selected cotton cultivars to seedling disease

pathogens and benefits of chemical seed treatments. Plant Dis. 81:1085-1088.

Wheeler, T. A., Gannaway, J. R., Kaufman, H. W., Dever, J. K., Mertley, J. C., and Keeling, J.

W. 1997. Influence of tillage, seed quality, and fungicide seed treatments on cotton

emergence and yield. J. Prod. Agric., 10, 394-400.

36

Tables

Table 1. Fungicide seed treatments used in field experiments planted at Judd Hill in 2014 and 2015.

Treatment Product name Common name Rate (g a.i./100 kg seed) Chemical name

1. none none none none

2. Allegiance FL metalaxyl 32.32 N-(2,6-dimethylphenyl)-N-(methoxyacetyl) alanine methyl

ester

3. RTU-PCNB PCNB 843.375 Pentachloronitrobenzene

4. Vortex + Spera

+ Allegiance +

EverGol Prime

+ Evergol

Energy

ipconazol +

myclobutanil +

metalaxyl +

penflufen +

prothiooconazole

+ penflufen +

metalaxyl

2.035 + 29.75 + 32.32 +

5.675 + 10.63 + 5.254 +

8.496

2-[(4-chlorophenyl)methyl]-5-(1-methylethyl)-1-(1H-1,2,4-

triazol-1-ylmethyl) cyclopentanol + alpha-butyl-alpha-(4-

chlorophenyl)-1H-1,2,4-triazole 1-propanenitrile + N-(2,6-

dimethylphenyl)-N-(methoxyacetyl) alanine methyl ester +

N-[2-(1,3-dimethylbutyl)phenyl]-5-fluoro-1,3-dimethyl-

1Hpyrazole-4-carboxamide + 2-[2-(1-chlorocyclopropyl)-3-

(2-chlorophenyl)-2-hydroxypropyl]-1H-1,2,4-triazole-3-

thione

All seed were treated with imidachloprid (1-[( 6-Chloro-3-pyridinyl )methyl]-N-nitro-2-imidazolidi nimine, Gaucho 600® 528.4 g

a.i./100 kg seed)

37

Table 2. Range, mean, and median values for all variables measured across 50 sites for a field at

Judd Hillo

2014 2015

Variable Range Mean Median Range Mean Median

Minimal soil temperature

1 DAP (°C)

20.22 –

21.46

20.73 20.68 20.68 –

21.68

21.3 21.33

Minimal soil temperature

5 DAP (°C)

21.7 –

22.6

22.15 22.1 16.5 –

18.55

17.44 17.55

Soil water content 1 DAP

(%)

9.4 –

16.18

13.47 13.72 9.25 –

16.88

13.15 13.35

Soil water content 5 DAP

(%)

12.05 –

20.1

15.52 15.16 23.66 –

36.5

33.27 34.08

Soil texture (% silt + %

clay)

33.77 –

78.4

55.7 57.16

Soil texture (% sand) 21.59 –

66.2

44.3 42.84

Soil texture (% clay) 1.20 –

17.45

8.45 9.77

T. basicola soil population

(propagules/g of soil)

0 – 166.66 26.8848 13.535 0 – 21.9 6.318 5.2

R. solani soil population

(propagules/100g of soil)

0 – 25.92 2.85 0 0 –

187.92

70.5 58.32

Pythium isolationp 0 - 5 1.34 1 0 – 2 0.6 0.5

38

Table 2. (Cont.) Range, mean, and median values for all variables measured across 50 sites for a

field at Judd Hillo

2014 2015

Variable Range Mean Median Range Mean Median

R. solani isolationq 0 - 4 0.32 0 0 - 10 4.7 4

T. basicola incidencer 0 – 100 30.6 30 0 - 100 81.1 100

Stand complete broad-

spectrums

63 - 134 105.6 106 76 - 135 108.7 109

Stand metalaxylt 40 - 127 92.6 94.5 72 - 135 102 102

Stand PCNBu 44 - 130 90.3 89.5 52 - 126 95.7 98

Stand non-treatedv 38 - 126 87.4 83.5 39 - 126 96.4 96

Relative fungicide responsew 77.8 –

242.5

127.4 116.6 75.6 –

276

116.8 107.9

Skip index complete broad-

spectrumx

4 - 40 18.4 19 3 - 25 11.2 11.5

Skip index non-treated 6 - 37 22.5 24 3 - 30 14.6 14.5

Seedling weight (g)y 2.17 – 7.66 4.66 4.5 7.6 –

17.5

11.3 11.2

Nodes per seedling 0.8 – 2.2 1.83 1.8 1 -2 1.76 1.8

Root disease indexz 12.2 – 85.5 50.3 53 6.3 - 59 27.7 25.9

Yield complete broad-

spectrum (kg)

2.64 – 14.4 9.56 9.8 4.7 –

13.2

10.44 10.75

Yield non-treated (kg) 3.11 –

13.51

8.56 8.65 6.1 –

12.3

9.93 9.96

o Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas on

6 May 2014 and 7 May 2015

p Percentage of seedlings with Pythium spp.

39

q Percentage of seedlings with R. solani

r Percentage of seedlings on TB-CEN selective media with Thielaviopsis basicola

s Plant stand for each replicate of the row treated with ipconazol + muclobutanil + metalaxyl +

penflufen + prothioconazol +penflufen + metalaxyl (2.035 + 29.75 + 32.32 + 5.675 + 10.63 +

5.254 + 8.496 a.i. g/100 kg seed) out of 150 seed planted

t Plant stand of the metalaxyl (32.32 a.i. g/100 kg seed) treated row for each replicate 21 DAP

u Plant stand of the PCNB (843.375 a.i. g/100 kg seed) treated row for each replicate 21 DAP

v Plant stand of the non-treated row for each replicate 21 DAP

w Stand response of the complete broad-spectrum treated compared to the non-treated

x A skip index was calculated by assigning a value of 1 for every 30.5 to 45.5 cm skip and adding

1 for every additional 15 cm in a skip 42 DAP

y Seedlings were recovered from field 21 DAP

z Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-

percentile values were averaged for each replication

40

Table 3. ANOVA for fungicide seed treatment response across 50 replications w for a field at

Judd Hill x in 2014 and 2015 to determine overall level of seedling disease pressure throughout

the field.

Seed treatment y Rate (g a.i./100 kg seed) Plant stand z

2014

Plant stand z

2015

1. ipconazol + myclobutanil +

metalaxyl + penflufen +

prothiooconazole + penflufen +

metalaxyl

2.035 + 29.75 + 32.32 +

5.675 + 10.63 + 5.254 +

8.496

105.6 A 108.7 A

2. Metalaxyl 32.32 92.6 B 102.0 AB

3. PCNB 843.375 90.3 BC 96.4 B

4. None 87.4 C 95.76 B

w Each replicate was 15.25 meters long and 4 rows wide and each row was planted with 150 seed

treated with one of the fungicide seed treatments.

x Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas on

6 May 2014 and 7 May 2015.

y All seed were treated with imidachloprid (1-[( 6-Chloro-3-pyridinyl )methyl]-N-nitro-2-

imidazolidi nimine, Gaucho 600® 528.4 g a.i./100 kg seed).

z Stands were counted 21 days after planting. Means within a column and main effect followed

by the same letter are not significantly different, LSD (P=0.05).

41

Table 4. The spatial autocorrelation (Univariate Moran’s I) of soil factor, and plant and disease

response variables measured across the 50 sites established in 2014 and 2015 at the Judd Hill

fieldu

2014 2015

Variable Moran’s I v P Moran’s I o P

Minimal soil temperature 1 DAP 0.68 0.001 0.57 0.001

Minimal soil temperature 5 DAP 0.45 0.001 0.79 0.001

Soil water content 1 DAP 0.49 0.001 0.44 0.001

Soil water content 5 DAP 0.66 0.001 0.43 0.002

Soil texture (% sand) 0.76 0.001

Soil texture (% clay) 0.75 0.001

Soil texture (% silt + clay) 0.77 0.001

T. basicola soil population 0.21 0.020 0.23 0.012

R. solani soil population -0.05 0.425 0.02 0.344

Fusarium isolation % -0.05 0.388 0.01 0.352

Pythium isolation % 0.03 0.297 0.02 0.364

R. solani isolation % 0.11 0.110 -0.03 0.471

T. basicola isolation % 0.16 0.051 0.53 0.001

Stand complete broad-spectrumt 0.32 0.002 -0.05 0.401

Stand non-treatedu 0.50 0.001 -0.06 0.384

Relative fungicide responsev 0.08 0.195 -0.18 0.036

Skip index complete broad-spectrumw 0.23 0.010 0.16 0.055

Skip index non-treated

0.38 0.001 0.07 0.207

42

Table 4 (Cont.) The spatial autocorrelation (Univariate Moran’s I) of soil factor, and plant and

disease response variables measured across the 50 sites established in 2014 and 2015 at the Judd

Hill fieldu

2014 2015

Variable Moran’s I v P Moran’s I o P

Nodes per seedling 0.13 0.073 -0.14 0.138

Seedling heightx -0.01 0.443 0.01 0.383

Root disease indexy 0.12 0.099 0.44 0.001

Hypocotyl diseasez 0.02 0.272 0.47 0.001

Yield complete broad-spectrum 0.48 0.001 -0.02 0.466

Yield non-treated 0.38 0.002 -0.05 0.421

Yield total 0.75 0.001 0.04 0.271

u Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas on

6 May 2014 and 7 May 2015

v The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1

to -1 where values approaching 1 are considered to be aggregated and values approaching

-1 are considered to be dispersed. Values approaching 0 are randomly distributed.

w A skip index was calculated by assigning a value of 1 for every 30.5 to 45.5 cm skip and

adding 1 for every additional 15 cm in a skip 42 DAP

x Seedlings were recovered from field 21 DAP

y Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-


z Hypocotyls as the percentage of seedlings with lesions was calculated for each replicate

43

Table 5. The spatial correlation between aggregated variables measured in 2014 with the same

aggregated variables measured in 2015 (Bivariate Moran’s Iv) to observe how spatial

distributions changed or remained consistent from one growing season to the next across the 50

sites established in the same locations each year at the Judd Hill field.

Moran’s Iw P value

Minimal soil temperature 1 DAP 0.48 0.001

Minimal soil temperature 5 DAP 0.30 0.001

Soil water content 1 DAP 0.39 0.001

Soil water content 5 DAP 0.47 0.001

T. basicola soil population 0.05 0.236

T. basicola incidence 0.28 0.001

Stand complete broad-spectrum -0.03 0.346

Stand non-treated -0.02 0.423

Skip index complete broad-spectrum x -0.14 0.042

Skip index non-treated -0.01 0.478

Seedling weight y 0.10 0.088

Root disease index z 0.17 0.015

v Bi-variate Moran’s I compares a spatially referenced variable with the neighboring variables.

w The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1



x A skip index was calculated by assigning a value of 1 for every 30.5 cm skip and adding 1 for

every additional 15 cm in a skip 42 DAP


z Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-


44

Table 6. The spatial correlations (Bivariate Moran’s Iv) between minimal soil temperature, soil water (measured one and five days

after planting), and soil texture showing how these soil factors spatially influence each other and T. basicola soil populations, disease

ratings on roots and hypocotyls, plant growth, plant stands, relative fungicide response, and yield.

Variables Minimal soil

temp 1 DAP

Minimal soil temp 5

DAP

Soil water 1

DAP

Soil water 5

DAP

Soil texture (%

clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Minimal soil

temperature 1 DAP

I w

Pw

0.68

0.001

0.57

0.001

0.38

0.001

0.63

0.001

-0.02

0.387

-0.07

0.172

0.31

0.001

0.20

0.009

0.50

0.001

0.35

0.001

Minimal soil

temperature 5 DAP

I

P

0.35

0.001

0.60

0.001

0.44

0.001

0.79

0.001

-0.20

0.010

0.12

0.053

-0.16

0.025

0.40

0.001

0.04

0.286

0.46

0.001

Soil water content 1

DAP

I

P

0.01

0.441

-0.14

0.037

-0.22

0.010

0.08

0.169

0.50

0.001

0.48

0.001

0.50

0.001

0.33

0.002

0.46

0.001

0.28

0.002


DAP

I

P

0.28

0.001

0.19

0.010

-0.19

0.012

0.43

0.001

0.50

0.001

0.38

0.001

0.66

0.001

0.43

0.002

0.61

0.001

0.46

0.001

T. basicola isolation % I

P

-0.33

0.001

-0.36

0.001

-0.15

0.018

-0.37

0.001

-0.22

0.002

0.04

0.329

-0.20

0.006

-0.14

0.026

-0.36

0.001

-0.40

0.001

Hypocotyl disease x I

P

-0.20

0.009

-0.44

0.001

-0.12

0.057

-0.49

0.001

-0.16

0.015

-0.02

0.369

-0.15

0.030

-0.31

0.001

-0.17

0.015

-0.57

0.001

45

Table 6. (Cont.) The spatial correlations (Bivariate Moran’s Iv) between minimal soil temperature, soil water (measured one and five

days after planting), and soil texture showing how these soil factors spatially influence each other and T. basicola soil populations,

disease ratings on roots and hypocotyls, plant growth, plant stands, relative fungicide response, and yield.


temp 1 DAP

Minimal soil temp 5

DAP

Soil water 1

DAP

Soil water 5

DAP

Soil texture (%

clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Seedling weight y I

P

0.30

0.001

0.35

0.001

0.17

0.012

0.38

0.001

0.09

0.117

0.06

0.235

0.31

0.002

0.18

0.007

0.22

0.003

0.38

0.001

Nodes per seedling I

P

0.20

0.004

0.05

0.234

0.04

0.246

0.01

0.447

0.16

0.036

-0.14

0.041

0.26

0.002

-0.10

0.125

0.25

0.002

-0.01

0.457

Stand complete broad-

spectrum

I

P

0.48

0.001

-0.17

0.015

0.25

0.001

-0.20

0.006

0.05

0.308

-0.04

0.297

0.32

0.001

-0.12

0.050

0.40

0.001

-0.23

0.005

Stand non-treated I

P

0.48

0.001

-0.01

0.481

0.30

0.001

-0.04

0.290

0.22

0.006

-0.12

0.055

0.35

0.001

-0.09

0.095

0.46

0.001

-0.20

0.008

Relative fungicide

response y

I

P

-0.23

0.004

-0.12

0.070

-0.16

0.009

-0.15

0.027

-0.20

0.017

-0.03

0.333

-0.18

0.014

-0.08

0.119

-0.23

0.004

-0.06

0.187

Skip index complete

broad-spectrum z

I

P

-0.41

0.001

0.14

0.026

-0.14

0.041

0.14

0.031

-0.09

0.142

0.01

0.471

-0.33

0.001

0.10

0.097

-0.43

0.001

0.28

0.001

46

Table 6. (Cont.) The spatial correlations (Bivariate Moran’s I r) between minimal soil temperature, soil water (measured one and five

days after planting), and soil texture showing how these soil factors spatially influence each other and T. basicola soil populations,

disease ratings on roots and hypocotyls, plant growth, plant stands, relative fungicide response, and yield.


temp 1 DAP

Minimal soil temp 5

DAP

Soil water 1

DAP

Soil water 5

DAP

Soil texture (%

clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Skip index non-treated I

P

-0.56

0.001

-0.07

0.175

-0.32

0.001

-0.02

0.418

-0.11

0.073

0.17

0.018

-0.32

0.001

0.08

0.136

-0.45

0.001

0.16

0.018

Yield complete broad-

spectrum

I

P

-0.51

0.001

-0.25

0.002

-0.20

0.010

-0.24 0.001 -0.11

0.071

-0.03

0.318

-0.37

0.001

-0.14

0.046

-0.35

0.001

-0.13

0.058

Yield non-treated I

P

-0.42

0.001

-0.03

0.313

-0.16

0.014

-0.05

0.246

-0.07

0.171

-0.01

0.487

-0.30

0.001

-0.01

0.410

-0.23

0.001

0.07

0.222

Yield total I

P

-0.60

0.001

-0.11

0.088

-0.21

0.007

-0.12

0.059

-0.18

0.018

-0.12

0.055

-0.47

0.001

-0.16

0.020

-0.44

0.001

-0.08

0.142


w The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1 to -1 where values approaching 1

are considered to be aggregated and values approaching -1 are considered to be dispersed. Values approaching 0 are randomly

distributed.

x Hypocotyls as the percentage of seedlings with lesions was calculated for each replicate


47

z A skip index was calculated by assigning a value of 1 for every 30.5 to 45.5 cm skip and adding 1 for every additional 15 cm in a

skip 42 DAP

48

Table 7. Spatial correlations (Bivariate Moran’s I s) of soil population levels of the pathogens T.

basicola and R. solani with plant and disease measurements.

Variable T. basicola soil

population

R. solani soil population

2014 2015 2014 2015

T. basicola incidence I t

Pt

0.31

0.001

0.33

0.001

-0.08

0.151

-0.06

0.240

Hypocotyl disease u I

P

0.08

0.172

0.23

0.002

-0.03

0.300

-0.11

0.085

Root disease index v I

P

0.10

0.072

0.33

0.001

-0.08

0.141

-0.06

0.179

Stand complete broad-spectrum w I

P

-0.21

0.004

-0.01

0.426

0.01

0.452

0.02

0.399

Stand non-treated x I

P

-0.23

0.003

-0.10

0.114

0.13

0.048

-0.10

0.113

Relative fungicide response y I

P

0.13

0.054

0.06

0.190

-0.14

0.033

0.04

0.260

Skip index complete broad-spectrum z I

P

0.19

0.008

-0.04

0.313

0.05

0.263

-0.03

0.355


P

0.27

0.001

0.11

0.090

-0.05

0.269

0.16

0.029

Yield complete broad-spectrum I

P

0.21

0.004

0.09

0.122

0.04

0.294

-0.12

0.049

Yield non-treated I

P

0.12

0.067

0.01

0.468

0.03

0.380

-0.11

0.068

s Bi-variate Moran’s I compares a spatially referenced variable with the neighboring variables.

49

t The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1



u Hypocotyls was the percentage of seedlings with lesions

v Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-


w Plant stand for each replicate of the row treated with ipconazol + muclobutanil + metalaxyl +

penflufen + prothioconazol +penflufen + metalaxyl (2.035 + 29.75 + 32.32 + 5.675 + 10.63 +

5.254 + 8.496 a.i. g/100 kg seed)

x Plant stand of the non-treated row for each replicate 21 DAP

y Stand response of the complete broad-spectrum treated compared to the non-treated

(treated/non-treated)

z A skip is defined as a distance greater than 30.5 cm between seedlings. A skip index was

calculated by assigning a value of 1 for every 30.5 cm skip and adding 1 for every additional 15

cm in a skip 42 DAP

50

Figures

Figure 1. Trend surface maps graphically representing the spatial variability of minimal soil

temperature and relative fungicide response across the 50 sites established in Judd Hill field in

2014 and 2015. (A) Minimal soil temperature (°C) measured during the first week after planting

(B) Relative fungicide response

(C) Minimal soil temperature (℃) (D) Relative fungicide response

(A) Minimal soil temperature (℃)

51

in 2014. (B) Relative fungicide stand response (treated/non-treated) calculated 21 days after

planting in 2014. (C) Minimal soil temperature measured during the first week of planting in

2015. (D) Relative fungicide stand response (treated/non-treated) calculated 21 days after

planting in 2015.

52

Appendix

Table 1. Regression modelsu showing the relationships of selected soil pathogen populations and pathogen isolation from plants with

seedling disease ratings, plant stands, relative fungicide response, and yield across the 50 sites established at the same locations in

2014 and 2015 in a research field at Judd Hillv.


population

R. solani soil

population

Fusarium isolationp Pythium isolation q R. solani isolation r

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

T. basicola

isolation %

Tw

P w

0.248

0.8052

3.397

0.00138

-0.544

0.58891

-0.726

0.47112

2.610

0.01205

-0.325

0.74660

0.823

0.41430

-0.157

0.87585

0.611

0.54414

0.492

0.62505

Hypocotyl

disease x

T

P

0.722

0.4739

3.677

0.00024

-2.222

0.03101

-1.931

0.05940

2.885

0.00584

-0.239

0.81190

0.231

0.81849

0.853

0.39799

0.790

0.43315

-1.305

0.19799

Root disease y T

P

0.515

0.6091

3.440

0.00122

-1.883

0.06579

-1.274

0.20882

2.916

0.00537

-1.089

0.28173

1.121

0.26795

2.366

0.02205

1.522

0.13462

-0.252

0.80200

Stand complete

broad-spectrum

T

P

-1.211

0.2319

-0.964

0.34003

0.454

0.65202

-1.557

0.12612

-1.710

0.09367

-0.985

0.32958

-2.559

0.01370

-0.563

0.57578

0.760

0.45077

0.908

0.36828

Stand non-treated T

P

-2.512

0.0120

-1.719

0.09199

1.837

0.06625

-1.641

0.10743

-1.641

0.10731

-0.028

0.97766

-1.455

0.15210

0.978

0.33285

-0.698

0.48930

-1.398

0.16859

53

Table 1. (Cont.) Regression modelsu showing the relationships of selected soil pathogen populations and pathogen isolation from

plants with seedling disease ratings, plant stands, relative fungicide response, and yield across the 50 sites established at the same

locations in 2014 and 2015 in a research field at Judd Hill v.


population

R. solani soil

population

Fusarium isolation Pythium isolation R. solani isolation

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Relative

fungicide

response

T

P

2.086

0.0423

0.280

0.78069

-1.581

0.12054

0.038

0.96966

0.703

0.48516

-0.833

0.40900

0.032

0.97428

-1.222

0.22754

1.381

0.17375

0.956

0.34378

Skip index

complete broad-

spectrum z

T

P

1.170*

0.2422

0.767

0.44694

0.416

0.67900

1.082

0.28482

0.702

0.48617

0.562

0.57571

2.234

0.03016

-0.212

0.83304

0.227

0.82159

-0.396

0.69384

Skip index non-

treated

T

P

2.622*

0.0087

1.172

0.24682

-0.582

0.56315

0.616

0.54087

1.633

0.10899

0.287

0.77516

0.669

0.50698

-0.370

0.71286

0.851

0.39877

1.004

0.32033

Yield complete

broad-spectrum

T

P

3.388

0.0014

-0.782

0.43791

-0.812

0.42094

-0.914

0.36539

2.020

0.04893

0.731

0.46845

-3.04**

0.00240

-1.230

0.22490

0.718

0.47601

-1.473

0.14733

Yield non-treated T

P

0.361

0.7199

-0.115

0.90919

-0.743

0.46131

-0.865

0.39119

1.038*

0.29937

-1.780

0.08135

-0.207

0.83727

0.523

0.60312

0.063

0.94962

1.149

0.25629

54

* Spatial lag model was used

** Spatial error model was used

u Simple ordinary least squares regression was used unless diagnostics indicated spatial lag or error models were more appropriate

v Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas on 6 May 2014 and 7 May 2015

w T is the regression statistic and P is the probability

x Percentage of seedling hypocotyls with lesions for each replicate

y Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-percentile values were averaged for

each replication


skip 42 DAP

55

Table 2. Regression models u showing the relationships between minimal soil temperature, soil water content, and soil texture. And

showing relationships of these soil factors with T. basicola soil populations, seedling disease ratings, plant stands, relative fungicide

response, and yield across the 50 sites established at the same locations in 2014 and 2015 in a research field at Judd Hill v.

Variables Minimal soil temp 1

DAP

Minimal soil temp 5

DAP


DAP


DAP

Soil texture

(%clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Minimal soil

temp 1 DAP

T t

P t

1 1 4.327*

0.00002

4.135*

0.00004

-0.380

0.70552

-1.311

0.19608

1.040*

0.29823

1.729

0.09029

2.143*

0.03213

2.828

0.00681

Minimal soil

temp 5 DAP

T

P

3.501**

0.00046

3.940*

0.00008

1 1 -3.248

0.00213

1.065

0.29208

-2.340

0.02352

2.299*

0.02152

0.821

0.41574

4.379

0.00006

Soil water

content 1 DAP

T

P

-0.380

0.70552

-1.311

0.19608

-3.248

0.00213

1.065

0.29208

1 1 6.357

0.00001

4.967

0.00001

2.095*

0.03615

2.020

0.04898

Soil water

content 5 DAP

T

P

2.123

0.03892

1.729

0.09029

-2.339

0.02352

4.052

0.00018

4.335*

0.00001

3.838*

0.00012

1 1 3.613*

0.00030

5.270

0.00001

T. basicola soil

population

T

P

-2.406

0.02004

-3.198

0.00245

-0.916

0.36406

-1.902

0.06315

-2.129

0.03845

2.601

0.01233

-2.491

0.01625

-0.145

0.88559

-3.881

0.00032

-1.720

0.09195

T. basicola

isolation %

T

P

-2.611

0.01202

-2.734

0.00875

-1.787

0.08028

-3.183

0.00256

-0.676

0.50233

1.029

0.30869

-1.799

0.07831

-0.622

0.53706

-3.889

0.00031

-3.469

0.00111

56

Table 2. (Cont.) Regression models u showing the relationships between minimal soil temperature, soil water content, and soil texture.

And showing relationships of these soil factors with T. basicola soil populations, seedling disease ratings, plant stands, relative

fungicide response, and yield across the 50 sites established at the same locations in 2014 and 2015 in a research field at Judd Hill v.


DAP

Minimal soil temp 5

DAP


DAP


DAP

Soil texture

(%clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Hypocotyl

disease w

T

P

-0.478

0.63454

-4.001*

0.00006

-1.061

0.29412

-5.232

0.00001

-0.317

0.75236

0.108

0.91484

-1.088

0.28186

-2.263*

0.02363

-0.684

0.49730

-5.525

0.00001

Root disease

index x

T

P

-1.653

0.10488

-3.909

0.00029

-1.865

0.06829

-4.268

0.00009

-0.457

0.64952

-0.797

0.42923

-1.569

0.12313

-2.142

0.03731

-1.801

0.07791

-5.319

0.00001

Seedling

weighty

T

P

1.208

0.22720

4.601

0.00003

0.273

0.78597

3.745

0.00048

0.779

0.43969

-0.369

0.71403

1.837

0.07231

0.826

0.41312

1.433

0.15847

3.412

0.00132

Nodes per plant T

P

1.732

0.08954

1.477

0.14628

1.051

0.29857

0.433

0.66730

0.333

0.74079

-0.649

0.51944

1.443

0.15541

-1.107

0.27353

1.417

0.16308

-0.598

0.55236

Stand complete

broad-spectrum

T

P

4.889

0.00001

-1.583

0.12004

2.530

0.01475

-1.523

0.13424

0.355

0.72436

-0.436

0.66456

1.523

0.13441

-0.581

0.56383

3.085

0.00337

-2.603

0.01227

57





DAP

Minimal soil temp 5

DAP


DAP


DAP

Soil texture

(%clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Stand non-

treated

T

P

2.872*

0.00408

1.022

0.31144

2.080*

0.03754

-0.190

0.85007

1.645

0.10640

-0.535

0.59493

3.511

0.00098

0.565

0.57482

4.715

0.00002

-0.296

0.76871

Relative

fungicide

response

T

P

-1.762

0.08443

-1.946

0.05747

-1.407

0.16598

-1.324

0.19172

-1.696

0.09630

-0.892

0.37680

-2.681

0.01002

-2.294

0.02618

-2.669

0.01035

-1.932*

0.05334

Skip index

complete broad-

spectrum z

T

P

-5.372

0.00001

1.115

0.27052

-2.066

0.04425

-1.640**

0.10097

-0.020

0.93630

1.420

0.16201

-1.292

0.20242

1.294

0.20176

-2.425

0.01912

1.997

0.05154

Skip index non-

treated

T

P

-6.002

0.00001

-1.860

0.06907

-3.837

0.00036

0.081

0.93576

-0.194

0.84663

2.292

0.02632

-2.368

0.02197

-0.468

0.64156

-4.553

0.00004

1.003

0.32049

58





DAP

Minimal soil temp 5

DAP


DAP


DAP

Soil texture

(%clay)

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015

Yield complete

broad-spectrum

T

P

-3.240*

0.00119

-1.178

0.24446

-1.991

0.05218

-1.715

0.09271

-1.656

0.10415

-1.022

0.31188

-3.285

0.00191

-2.075

0.04338

-3.009

0.00334

-1.964

0.05538

Yield non-

treated

T

P

-2.191

0.03332

-0.274

0.78558

-0.502

0.61810

-0.429

0.66980

-0.512

0.61179

0.489

0.62692

-2.056

0.04527

0.043

0.96597

-1.942

0.05808

-0.028

0.97778

Yield total T

P

-4.603

0.00003

-0.957

0.34336

-1.584

0.11985

-1.244

0.21945

-1.908

0.06233

-1.173

0.24628

-2.484

0.01299

-1.898

0.06378

-3.989

0.00023

-1.575

0.12190



t T is the regression statistic and P is the probability

u Simple ordinary least squares regression was used unless diagnostics indicated spatial lag or error models were more appropriate

v Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas on 6 May 2014 and 7 May 2015

w The percentage of seedling hypocotyls with lesions for each replicate

x Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-percentile values were averaged for

each replication


59

z A skip is defined as a distance greater than 30.5 cm between seedlings. A skip index was calculated by assigning a value of 1 for

every 30.5 cm skip and adding 1 for every additional 15 cm in a skip 42 DAP

60

Table 3. Spatial correlations (Bivariate Moran’s I v) looking at the spatial relationships of

hypocotyl and root disease ratings with plant growth measurements, plant stands, relative

fungicide response, and yield across the 50 sites established at the same locations in 2014 and

2015 in a research field at Judd Hill u.

Variables Hypocotyl disease w Root disease index x

2014 2015 2014 2015

Hypocotyl disease w I v

P v

0.02

0.288

0.47

0.001

0.16

0.042

0.44

0.001

Root disease index x I

P

0.12

0.068

0.49

0.001

0.44

0.001

0.12

0.076

Seedling weight y I

P

-0.21

0.006

-0.42

0.001

-0.20

0.006

-0.40

0.001

Stand complete broad-spectrum I

P

-0.08

0.155

0.05

0.252

-0.10

0.097

0.14

0.034

Stand non-treated I

P

-0.18

0.017

0.05

0.293

-0.30

0.001

-0.01

0.439

Fungicide relative response I

P

0.13

0.052

0.01

0.465

0.22

0.002

-0.09

0.116

Skip index complete broad-spectrum z I

P

0.04

0.322

-0.17

0.023

0.08

0.149

-0.19

0.010


P

0.14

0.042

-0.01

0.432

0.29

0.002

-0.04

0.319

Yield complete broad-spectrum I

P

0.14

0.030

0.21

0.008

0.26

0.002

0.17

0.013

Yield non-treated I

P

0.15

0.032

-0.03

0.373

0.17

0.013

-0.08

0.147

Yield total I

P

0.16

0.015

0.09

0.128

0.28

0.001

0.03

0.364


61

u Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas on

6 May 2014 and 7 May 2015

v The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1



w Hypocotyls were rated based on disease symptoms on a 1 to 5 scale and the percentage of

seedlings with lesions (ratings greater than 3) was calculated for each replicate

x Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-



z A skip index was calculated by assigning a value of 1 for every 30.5 to 45.5 cm skip and adding


62

Table 4. Regression models u looking at the relationships of hypocotyl and root disease ratings

with plant growth measurements, plant stands, relative fungicide response, and yield across the

50 sites established at the same locations in 2014 and 2015 in a research field at Judd Hill w.

Variables Hypocotyl diseasex Root disease indexy

2014 2015 2014 2015

Hypocotyl disease x T v

P v

1 1 6.108

0.00001

4.129*

0.00004

Root disease index y T

P

6.108

0.00001

3.940*

0.00008

1 1

Seedling weight T

P

-1.572*

0.11600

-4.441

0.00005

-2.59*

0.0096

-6.054

0.00001

Stand complete broad-spectrum T

P

-0.497*

0.61897

0.593

0.55602

-1.877

0.06667

0.857

0.39569

Stand non-treated T

P

-2.533*

0.01130

0.184

0.85516

-3.956

0.00025

0.773

0.44354

Fungicide relative response T

P

2.080

0.04288

0.527

0.60048

2.082

0.04269

0.356

0.72521

Skip index complete broad-spectrum z T

P

0.243

0.80915

-2.270

0.02771

1.479

0.14564

-1.542

0.12962

Skip index non-treated T

P

1.983*

0.04734

0.267

0.79098

3.248

0.00212

-0.837

0.40656

Yield complete broad-spectrum T

P

2.076*

0.03787

0.976

0.33374

2.410

0.01983

0.040

0.96793

Yield non-treated T

P

0.564

0.57570

0.573

0.56943

0.548

0.58651

1.375

0.17537

Yield total T

P

2.660*

0.00781

1.258

0.21453

2.040*

0.04140

0.925

0.35937



63

u Simple ordinary least squares regression was used unless diagnostics indicated spatial lag or

error models were more appropriate

v T is the regression statistic and P is the probability

w Tests were planted at the Judd Hill Cooperative Research Foundation, Poinsett Co. Arkansas

on 6 May 2014 and 7 May 2015

x The percentage of seedling hypocotyls with lesions was calculated for each replicate

y Roots of seedlings were rated based on discoloration from disease on a 1 to 10 scale and mid-


z A skip index was calculated by assigning a value of 1 for every 30.5 to 45.5 cm skip and adding


64

Chapter 3 - Spatial examination of cotton stands in growers’ fields

Abstract

Cotton is an important crop in the United States and many other countries.

Establishing and maintaining a stand of healthy plants with uniform spacing and plant density is

critical for maximum yields. Therefore, it is important to manage seedling diseases which affect

germination, emergence, survival, and early-season development of seedlings. Cool and wet

soils are conducive to reduced seedling vigor and more severe disease. The objective of this

study was to characterize field-scale spatial variation of cotton stands and elucidate the spatial

relationships of soil factors and pathogen soil populations in causing variation in growers’ fields.

Spatial sampling was performed in two growers’ fields in Arkansas over the years 2014 and

2015. In the Bond field, 100 sample points were established in a grid pattern that encompassed

5.8 ha. In the Wildy field, 100 sample points were established across the 31 ha field based on

soil texture. Variability of stands in fields were slightly positively correlated with soil

temperature and water measured within the first week after planting in the Bond field in 2014.

Controlled environment studies were performed to assess the role of seedling disease in stand

variability observed in the two fields under a uniform environment by using sites with differing

histories of stand establishment. Stand differences for soil from these sites when under uniform

environmental conditions were similar. These results indicate variable seedling disease in the

field is the result of field-scale environmental variability.

65

Introduction


countries (Oerke, 2006). Cotton is grown in several states across the Southern United States, the

Cottonbelt, with concentrations in the Texas High Plains, irrigated valleys in Arizona and

California, the Mid-South, and Southeast. Establishing and maintaining a stand of healthy plants

with uniform spacing and plant density is critical for uniform crop development, managing the

crop, good fiber qualities, and yield (Christiansen and Rowland, 1981). Research on optimal

cotton plant populations for maximum yield and quality have produced variable results, however,

much of the available literature suggests comparable yield may be obtained within a reasonably

wide range of plant populations. Environmental conditions at planting are important to getting

cotton seedlings off to a vigorous start with desired plant populations.

Colyer et al. (1991) in Louisiana, found that poor stands and increased seedling disease

pressure are often associated with early planting dates; with early April plantings resulting in low

plant populations, late April and early May plantings resulting in intermediate plant populations,

and mid-May plantings resulting in high plant populations. Cotton production around the globe

is impacted by seedling diseases (DeVay, 2001, Hillocks, 1992; Melero-Vara and Jimenaz-Diaz,

1990). Cotton seedling diseases affect germination, emergence, survival, and early-season

development of seedlings. The U.S. Cotton disease loss estimates for the U.S. from 1952 to

2009 for seedling diseases averaged 2.8% with loss estimates accounting for 23% of the total

estimated losses in lint production over these years (Disease database,

http://www.cotton.org/tech/pest/seedling/index.cfm).

The pathogens associated with the cotton disease complex are Thielaviopsis basicola

(Berk. & Broome) Ferraris (syn. Chalara elegans Nag Raj & Kendrick), Rhizoctonia solani


66

Kuhn, teleomorph Thanatephorus cucumeris (A. B. Frank) Donk, Pythium spp., and Fusarium

spp. (DeVay, 2001; Rothrock and Buchanan, 2015). These soilborne pathogens can act

individually or in combination to cause a range of symptoms. Limiting the stand loss and

damage on cotton from seedling diseases relies on planting high quality seed, land preparation,

and planting when the soil environment and weather forecast favors rapid cotton germination and

growth. Combination fungicide seed treatments are used throughout the Cotton belt to protect

the crop from multiple seedling disease pathogens. Rothrock et al., (2012) documented the

importance of the environment in seedling diseases, in field trials across the Cottonbelt, in which

stand responses among seed treated with fungicides compared to seed not treated with fungicide

increased in trials with cooler soils and had increased rainfall after planting.

To reduce planting costs, seeding rates have dramatically decreased across the Cotton

Belt and producers are looking towards using variable rate planting to improve stand uniformity,

but this increases the importance of each seed to germinate, emerge, and become established, and

therefore increases the importance of seedling diseases and planting environment. Assessing the

spatial variability of seedling disease pressure and soil environment factors across a field could

provide useful information for producers and researchers. The objectives of this study were to

characterize spatial variation of plant populations in growers’ fields and examine the roles of the

environment and seedling disease pressure on stand variability.

67

Materials and Methods

Two commercial cotton fields were examined for spatial variability of stand

establishment and soil factors. In 2014 and 2015 a 31.6 hectare field farmed by Wildy Farms

Inc. was used. This field had a history of cotton monoculture and has variable soil textures. Soil

textural zones were designated by soil electrical conductivity maps and standard soil textural

analysis then georeferenced and drawn in ArcGIS (T. G. Teague, personal communication). The

field was prepared under conventional tillage with a 0.96 m row spacing. In both years this field

was planted with a John Deere® 1720 Max Emerge 12 row vacuum planter equipped with a

variable seeding rate controller. This field was planted in 4, 12 row strips, replicated 7.5 times.

Each strip had 3 sample points. One point in a sandy loam zone, one point in a heavy clay zone,

and one point in a course sand zone. Each of the 4 strips per replication was planted with a low,

intermediate, high, or variable rate seeding rate. The seeding rates were 1.5, 3, 4.5 seed/ft

(50,986 seed/ha, 101,873 seed/ha, and 152,960 seed/ha). The variable rate strip adjusted seeding

rate according to soil textural zones. Course sand zones were planted with the low rate, sandy

loam soil zones were planted with the intermediate rate, and heavy clay soil zones were planted

with the high rate. Planting dates were 4 May 2014 and 5 May 2015.

The second commercial field examined in 2014 and 2015 for this study was farmed by

Bruce Bond Farms and is located in Ashley County in Southeast Arkansas. This is a 71 ha (176

acre) field with a cotton monoculture cropping history and variable soil textures. Planting

occurred 4 May in 2014 and 5 May 2015 and was seeded at 3 seed/ft (101,973 seed/ha) on 0.96

m rows.

To spatially examine the stand variability in the Wildy field, 100 points were selected to

perform stand counts, skip indices, and plant height measurements. The sites were selected by

68

seeding rate and soil textural zone. Three sites were chosen per 12 row strip, with 30 sites

located in course sand soil textural zones, 30 located in heavy clay textural zones, and 30 located

in loamy sand textural zones. An additional 10 sites were selected based on proximity to other

sites in order to represent the space in this field. To spatially examine disease variability of the

Bond field , a 14.25 acre (5.8 ha) area was established for both years in this field which consisted

of 100 georeferenced sample sites in a 10 by 10 grid pattern. The 100 sites of both fields were

georeferenced with a Trimble® Yuma 2 Rugged Tablet GPS unit (Trimble Navigation, Ltd.,

Sunnyvale, California).

Stand counts were performed 21 days after planting by counting surviving plants in two

adjacent 7.6 m long sections of the middle rows of the 12 row strips in order to avoid

inconsistencies sometimes observed on the outside rows of a planter swath at each of the 100

georeferenced sites. Skip indices (Chamber, 1986) were determined for the same two adjacent

7.6 m long sections of rows for each site 42 days after planting. A skip is defined as a distance

greater than 30.5 cm between plants. A skip index was calculated by assigning a value of 1 for

every 30.5 cm skip and adding 1 for every 15 cm greater than 30.5 cm. The total number of

skips per site was the sum of the values assigned to each skip for that site. Five plants from each

site were arbitrarily selected and measured from the soil line to the apex of the apical meristem

42 days after planting for height measurements and were averaged together for each site.

For the Bond field location in 2014 and 2015, 10 sites across the field were selected in a

zig-zag pattern in which 10 seedlings were collected from each site. Shoots were cut from the

plants leaving the hypocotyls and roots. The roots/hypocotyls were washed by first placing each

sample in a jar with a modified lid that allows tap water to flow in and out while containing the

plant matter inside for 20 minutes.

69

Disease indices were taken for the roots and hypocotyls for seedlings recovered

(Rothrock et al., 1995). The hypocotyl disease severity index was based on a scale of 1to5, in

which 1=no symptoms, 2=few pinpoint lesions or diffuse discolored areas, 3=distinct necrotic

lesions, 4=girdling lesions, and 5=seedling death. The percentage of samples with a hypocotyl

rating of 3 or greater was calculated. The root disease severity index was based on a scale of

1to5, in which 1=no symptoms, 2=1-10% of the root system discolored, 3=11-25% of the root

system discolored, 4=26-50% of the root system discolored, 5= greater than 50% of the root

system discolored. For roots, analyses were done using the mid-percentile value for each

category.

At the Bond field location in 2014, a spatial examination of nematode galling and plant

growth 42 days after planting was performed by sampling 10 plants from each of the 100 sites

and transporting on ice to the laboratory and refrigerated until processing. The taproots and

secondary roots were gently hand washed in water to remove soil and debris to eliminate

obstruction for visual assessment of galls formed by the cotton plant by feeding of the root knot

nematode, Meloidogyne incognita. The galls were counted for each of the 10 plants from each

sample and recorded. Height measurements were recorded for the length between the cotyledon

nodes and the apical meristem, and the number nodes above the cotyledon were recorded for 5

plants arbitrarily selected from each sample.

Controlled environmental studies were performed in 2015 from soil collected from

specific locations across the growers’ fields. From the Bond field, 10 sites with the highest and

10 sites with lowest stand counts were selected. From the Wildy field, 3 sites with the highest

and 3 with the lowest stand counts for each of the 3 soil textural zones were selected; making 18

sites overall. The soil samples were collected from the Bond field on 18 June 2015 and from the

70

Wildy field on 10 September 2015. Samples of soil were recoverd from the top 15 cm of beds

with a shovel and filling plastic containers for transport to the laboratory in Fayetteville, AR.

Soils were stored in cool rooms at 4 °C to limit biological changes until use.

The Bond field controlled environmental study was set up as a randomized complete

block design with two fungicide treatments; a complete broad-spectrum fungicide treated seed

and non-fungicide treated seed. The relative fungicide response was calculated by the change of

stand between the complete broad-spectrum treated seed and the non-treated seed for each site.

The Wildy field controlled environment study was similar except with 3 soil textures as

additional factors. Each experiment was performed twice with 4 replications per site in each

field.

For each experimental run, soil from each site was potted in 8 pots (12.7 x 17.1 cm with a

depth of 5.7 cm) for a total of 160 pots for the Bond field, and 144 pots for the Wildy field. Two

pots having different seed treatments from each site were placed in 1 of 4 growth chambers

(Conviron® Adaptis CMP6010), replications. To condition the soil to resemble the field planting

conditions prior to planting, the pots were arranged 6 per tray and bottom irrigated with two

liters of water per tray to saturate the soil and excess water was allowed to drain. Each chamber

was set to 21.8 °C with a 12-hour photoperiod, and the pots remained in the chambers for 4 days.

Four pots from each soil sample site were planted with 24 cotton seeds total (Gossypium

hirsutum) with the complete broad-spectrum fungicide seed treatment which included the

fungicides: ipconazol (2-[(4-chlorophenyl)methyl]-5-(1-methylethyl)-1-(1H-1,2,4-triazol-1-

ylmethyl) cyclopentanol, Vortex® 2.035 g a.i./100 kg seed), myclobutanil (alpha-butyl-alpha-(4-

chlorophenyl)-1H-1,2,4-triazole 1-propanenitrile, Spera® 29.75 g a.i./100 kg seed), metalaxyl (N-

(2,6-dimethylphenyl)-N-(methoxyacetyl) alanine methyl ester, Allegiance® 32.32 g a.i./100 kg

71

seed), penflufen (N-[2-(1,3-dimethylbutyl)phenyl]-5-fluoro-1,3-dimethyl-1Hpyrazole-4-

carboxamide, EverGol Prime® 5.675 g a.i./100 kg seed), and prothiooconazole (10.63 g a.i./100

kg seed), penflufen (5.254 g a.i./100 kg seed), and metalaxyl (8.496 g a.i. g/100 kg seed) (2-[2-

(1-chlorocyclopropyl)-3-(2-chlorophenyl)-2-hydroxypropyl]-1H-1,2,4-triazole-3-thione, N-[2-

(1,3-dimethylbutyl)phenyl]-5-fluoro-1,3-dimethyl-1Hpyrazole-4-carboxamide, and N-(2,6-

dimethylphenyl)-N-(methoxyacetyl) alanine methyl ester, EverGol Energy® 24.38 g a.i./100 kg

seed). The other 4 pots from each soil sample site were planted with 24 non-fungicide treated

seed. All seed were treated with imidachloprid (1-[( 6-Chloro-3-pyridinyl )methyl]-N-nitro-2-

imidazolidi nimine, Gaucho 600® 528.4 g a.i./100 kg seed), CaCO3 (463.5 g/100 kg seed),

polymer (Secure 65 ml/100 kg seed, Syngenta Inc.), and dye (Color Coat Red 65 ml/100 kg seed,

Syngenta Inc.). Seed were treated using a Hege 11 liquid seed treater (Hege Maschinen GmbH,

Waldenburg, Germany). Seed was planted in each pot by making impressions 2 cm deep with a

number 2 pencil, 3 cm apart, and placing an individual seed in each hole. The pots were bottom

irrigated and randomized within each growth chamber once per week for 21 days after planting.

Twenty-one days after planting, the pots were removed from the growth chambers and

stand was counted from seedlings with developed cotyledons or more advanced vegetative

growth for each pot. The relative fungicide response was determined by the quotient of the stand

count of the fully-treated seed divided by stand count of the non-treated seed. For each

replication, 10 seedlings from each of the pots planted with the non-treated seed were collected

and placed in plastic bags and refrigerated until further processed. Seedlings were processed by

the same procedures as seedlings from field samples.

Spatial auto correlation and regression models were performed in GeoDa (Anselin, 2006)

for the field experiments. Spatial autocorrelation for variables was determined by calculating the

72

Moran’s I values. Values of I range for one or 2 variables from -1 to +1. Negative values

indicate negative spatial autocorrelation or a uniform spatial pattern. I values close to 0 indicate

a random spatial pattern. A positive I value indicates a positive spatial autocorrelation or an

aggregated spatial pattern. Univariate Moran’s I was calculated for each variable, and bi-variate

Moran’s I was calculated for pairs of variables that were individually spatially auto correlated.

Simple OLS regression models were used to examine the relationships between variables.

Diagnostics for spatial dependence (Moran’s I for residuals and Lagrange multiplier for error and

lag) were used in each analysis in which spatial lag or spatial error were applied to the models

when diagnostics indicated spatial dependencies among variables. JMP®, 12.1 (SAS Institute

Inc., Cary, NC) was used to perform analysis of variance to compare fungicide response, disease

indices, and isolation frequencies of pathogens from seedlings between soils in the controlled

environment experiments.

Results

From the Bond field, general seedling disease data from 2014 was lost. From the

seedlings recovered in 2015, root discoloration ratings ranged from 0 to 75%, with a mean of

6.8%, and 2.0% of hypocotyls had lesions. In 2014, root discoloration ratings ranged from 18 to

88% with a mean of 50.6%, and 17.0% of hypocotyls had lesions. In 2015, the seedlings

recovered from the 10 sample sites had root discoloration ratings ranging from 5 to 75% with a

mean of 31.6%, and 9.0% of hypocotyls had lesions.

Weather and field conditions at the Bond field in Ashley Co. Arkansas during the first

week of planting in 2014 had maximum air temperatures reaching approx. 24 °C and lows of

around 21 °C with 1 cm of precipitation which led to minimal soil temperatures ranging from

20.4 to 22.5 °C and soil water content ranging from 7.5 to 13.6% across sampling area which

73

consisted of 100 GPS marked sample sites. Minimal soil temperature and soil water were both

found to be aggregated (Table 1). In 2015, air temperatures were similar with highs of 24 °C and

lows of 21 °C, but there was more rainfall in this year (4.5 cm) which led to minimal soil

temperatures ranging from 19.5 to 21.9 °C. Minimal soil temperature was also aggregated across

the same 100 sample sites (Table 1). At the Wildy field in Mississippi Co. in 2014, air

temperatures were a high of 26 °C and low of 21 °C during the first week of planting, and

minimal soil temperatures averaged 17.5 °C. In 2015, air temperatures reached highs of 28 °C

and lows of 24 °C, and minimal soil temperatures averaging a high of 16.6 °C.

In the Bond field, stands and skip indices were each aggregated in 2014 and stands were

spatially negatively correlated with skip indices which indicates there were smaller and fewer

skips where stand counts were higher (Tables 1 and 2). Stand counts in 2015 showed a trend

toward being uniformly spatially autocorrelated (P=0.1280) and skip indices were random (Table

1). In the Wildy field, stands were random in 2014, but stands, skip indices, and plant height

were each aggregated in 2015 (Table 3). Plant height measurements taken 42 days after planting

were spatially aggregated in both fields and years.

The role of soil factors in the Bond field were examined with stands, skips, and plant

height. In 2014, stands were found to be negatively spatially correlated with minimal soil

temperature and positively spatially correlated with soil water, as well as, skips were negatively

spatially correlated with soil water (Tables 2).

Controlled environment studies were performed to assess the role of seedling disease in

stand variability observed in the two Growers’ fields under a uniform environment. Fungicide

response, stand counts, pathogen isolation frequency, and disease ratings on seedlings were

compared between naturally infested soils collected some sites within the Bond field location

74

that had either the highest stand rates (86% - 90%) (Soil H) or the lowest stand rates (73% -

76%) (Soil L) 21 days after planting in 2015. Fungicide seed treatment significantly improved

stand. Overall, stand counts among the complete broad-spectrum treated seed had a 69.4%

(16.65 of 24 seed per pot), and the seed that did not receive fungicide had a 44.8% stand rate

(10.77 of 25 seed per pot). Stand improvement of the complete broad-spectrum fungicide seed

treatment compared to the non-treated seed planted soil H was not significantly different than the

stand improvement of the complete broad-spectrum fungicide seed treatment to the non-treated

seed planted in soil L (Table 6). There was no significant difference of root disease indices

between soil H or soil L for the first experiment run, but root disease indices were higher in soil

L (Ls mean = 51.3%) than soil H (Ls mean = 46.8%) for the second experiment run (Table 6).

For soils from the Wildy field, overall, stand counts among the complete broad-spectrum

treated seed treatment had a 54% (12.96 of 24 seed per pot), and the seed that did not receive

fungicide had a 37% stand rate (8.9 of 24 seed per pot). Relative fungicide response did not

differ in both experiment runs between clay soil H, clay soil L, loamy sand soil H, loamy sand

soil L, course sand H, and course soil L (Table 7). Root disease indices were higher among

seedlings recovered from loamy sand soils than clay or course sand soils (Table 7).

Discussion

Seedling pathogens and disease were identified on seedlings at the Wildy field location

both years and at the Bond location in 2015. Pathogens were isolated and considerable disease

was present based on root and hypocotyl symptoms on seedlings recovered 21 days after planting

from the Wildy field in 2014 and 2015 and from the Bond field in 2015. The four main groups

of pathogens associated with the cotton seedling disease complex are Pythium spp., Fusarium

spp., R. solani, and T. basicola, and with the exception T. basicola, these pathogens are

75

considered ubiquitous in cotton fields (Bird, 1973). T. basicola was found in over 70% of fields

surveyed in Arkansas (Rothrock, 1997). Wheeler et al. (2000), in Texas, identified T. basicola in

55% of surveyed fields in 1995 and in 73% of the irrigated fields surveyed in 1996. Rothrock et

al. (2012) found root disease was positively correlated with T. basicola isolation frequency, and

hypocotyl disease was positively correlated with isolation frequency of R. solani and T. basicola.

Stands varied within both field locations. Spatial variability of plant populations at the

Wildy field location, and plant populations and soil factors at the Bond field location were

measured. Stand counts and skip indices were found to be aggregated in the Bond field in 2014,

and in 2015, stand counts were spatially uniform and skips were spatially random. At the Bond

field in 2015, seed were planted under a hill-drop practice, with 3 seed per drop at approx. 30 cm

apart along the row, which may explain some of the spatial differences found between years.

Stand counts and skip indices at the Wildy field were found to be spatially aggregated in 2015.

Stand counts were weakly associated with minimal soil temperature and soil water content taken

within the first few days after planting at the Bond field in 2014 but not in 2015. This suggests

field-scale variability of soil factors may influence stands. Soil temperature and soil water have

been shown to affect stands (Colyer et al, 1991; Johnson et al, 1969; and Rothrock et al, 2012).

Planting environment has been shown to be an important factor in stand establishment, and

planting too early is not recommended because it often results in poor stands and increased

disease. Colyer et al. (1991), in Louisiana, found cotton plant populations were low when

planted in early April but improved with later planting dates. Reduced stand establishment is

often associated with low soil temperature and increased rainfall which increase abiotic stresses,

and increases susceptibility to seedling diseases. In Tennessee, Johnson et al. (1969) found good

stands at minimal soil temperatures of 19 °C or higher, but poor stands at 10 °C or lower.

76

Rothrock et al. (2012) showed increased seedling disease pressure and increased fungicide

response as soil temperature decreased and soil water content increased. Davis et al. (1997)

found fungicide seed treatments improved stands compared to seed without fungicide over

environments with mean soil temperatures that ranged from 19.7 to 22.2 °C for the first 5 days

after planting suggesting that even at favorable soil temperatures seedling diseases can be

important in stand establishment. Soil factors that affect soil temperature and water content

often vary within fields which may influence seedling disease.

The role of seedling disease pathogens explaining stand variability observed in the field

studies were assessed under uniform soil temperature and soil water conditions in controlled

environment experiments from naturally infested soils recovered from select sites within each

field location having different stand establishment. Stands of seed treated with the complete

broad-spectrum seed treatment, and the seed that did not receive fungicide both varied in the

experiments, and the treated seed had significantly higher stands than the non-treated, but this

relative response did not significantly vary between soils recovered from sites, within each field,

that had a history of high and low stands. There were considerable amounts of disease on non-

treated seedlings planted in each of the soils. From the Bond field, root disease severity and T.

basicola incidence did not differ consistently across experimental runs. In the Wildy field,

disease symptoms under a uniform environment did not consistently respond to soil texture or

high or low stand

The results show the role of seedling disease in stand reduction and the importance of

fungicide seed treatments for managing seedling disease. Seedling disease pressure was present

in all of the soils tested and fungicide seed treatments significantly reduced stand loss. The

variability of stands within each field was not explained by seedling disease pathogen pressure

77

alone but stand differences when under the uniform environmental conditions of this study were

consistent. This indicates variable amounts seedling disease pathogen population did not

determine stand variability observed in the field suggesting the importance of field-scale

environmental variability in seedling disease development.

78

References

Anselin, L., Syabri, I., and Youngihn, K. 2006. GeoDa: An Introduction to Spatial Data

Analysis. Geographical Analysis 38:5-22.

Bird, L. S. 1973. The dynamics of cotton seedling diseases. Pages 75-79 in: The relation of soil

microorganisms to soilborne plant pathogens. G. C. Papavizas, ed. South. Coop. Ser.

Bull. 183. Va. Polytech. Inst. State Univ., Blacksburg.

Colyer, P. D., Micinski, S., and Nguyen, K. T. 1991. Effect of planting date on the efficiency of


742.

Chambers, A. Y. 1986. Comparative effects of selected skip levels in stands and replanting on

cotton yields. Pages 19 in: Proc. Beltwide Cotton Prod. Conf., Natl. Cotton Counc.,

Memphis, TN.




Davis, R. M., Nunez, J. J., and Subbarao, K. V. 1997. Benefits of cotton seed treatments for the

control of seedling diseases in relation to inoculum densities of Pythium species and

Rhizoctonia solani. Plant Dis. 81:766-768.







79




Rothrock, C. S. 1997. Prevalence and distribution of Thielaviopsis basicola. Pages 75-76 in:

Proc. Beltwide Cotton Prod. Conf. National Cotton Council of America, Memphis, TN.

Rothrock, C. S., Kirkpatrick, T. L., Frans, R. E., and Scott, H. D. 1995. The influence of winter

legume cover crops on soilborne pathogens and cotton seedling diseases. Plant Dis.

79:167-171.












Specht, L. P., and Griffin, G. J. 1985. A selective medium for enumerating low populations of

Thielaviopsis basicola. Can. J. Plant Pathol. 7:438-441.

Wheeler, T. A., Hake, K. D., and Dever, J. K. 2000. Survey of Meloidogyne incognita and

Thielaviopsis basicola: Their impact on cotton fruiting and producers’ management

choices in infested fields. J. Nematol. 32(4S):576-58

80

Tables

Table 1. Spatial autocorrelation (univariate Moran’s I u) of soil and plant variables measured

across 100 sites within the Bond field in Ashley Co., Arkansas used in 2014 and 2015.

2014 2015

Variable Moran’s I u P value Moran’s I P value

Minimal soil temperature v 0.22 0.0.250 0.36 0.003

Soil water content w 0.15 0.0930 0.21 0.031

Plant height x 0.04 0.3340 0.27 0.010

Nodes 0.13 0.1050

Galls 0.35 0.0010

Skips y 0.25 0.0060 0.04 0.334

Stands z 0.21 0.0300 -0.14 0.128

u The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1



v Minimal soil temperature was measured before 7:00 AM within the first week of planting

w Soil water content was measured within the first week of planting

x Plant height was measured 42 days after planting

y A skip index was calculated by assigning a value of 1 for every 30.5 to 45.5 cm skip and adding

1 for every additional 15 cm in a skip 42 days after planting for 7.6 meter sections of two

adjacent rows at each sample site

z Stand counts were performed 21 days after planting for 7.6 meter sections of two adjacent rows

at each sample site

81

Table 2. Spatial correlations (Bivariate Moran’s I v) of minimal soil temperature and soil water content with plant variables for 2014

and 2015 and across years across the 100 sample site established in the Bond field in Ashley Co. Arkansas used for this study.

2014 2015

Minimal soil

temperaturew

Soil water

contentx

Standsy Skipsz Minimal soil

temperature

Soil water

content

Stands Skips Plant

Height Variable

Minimal soil

temperature 2014

I v

P v

0.22

0.017

0.01

0.439

-0.17

0.027

0.08

0.159

-0.02

0.373

0.15

0.044

-0.06

0.258

0.01

0.434

-0.16

0.048

Soil water content

2014

I

P

0.01

0.476

0.15

0.098

0.12

0.076

-0.18

0.018

-0.20

0.019

-0.04

0.300

-0.14

0.050

-0.07

0.217

-0.07

0.214

Stands 2014 I

P

-0.13

0.073

0.14

0.053

0.21

0.029

-0.21

0.010

-0.30

0.001

-0.26

0.002

0.06

0.229

-0.06

0.228

0.06

0.231

Skips 2014 I

P

0.06

0.233

-0.20

0.009

-0.18

0.038

0.25

0.010

0.21

0.008

0.37

0.001

0.01

0.450

0.09

0.142

-0.10

0.136

Plant height 2014 I

P

-0.08

0.173

0.05

0.258

0.08

0.172

-0.16

0.029

-0.17

0.018

-0.29

0.001

-0.12

0.087

-0.10

0.121

0.02

0.364

82

Table 2. (Cont.) Spatial correlations (Bivariate Moran’s I v) of minimal soil temperature and soil water content with plant variables for

2014 and 2015 and across years across the 100 sample site established in the Bond field in Ashley Co. Arkansas used for this study.

2014 2015

Minimal soil

temperature

Soil water

content

Stands Skips Minimal soil

temperature

Soil water

content

Stands Skips Plant

Height Variable

Minimal soil

temperature 2015

I

P

0.01

0.481

-0.26

0.001

-0.37

0.001

0.27

0.001

0.36

0.002

0.14

0.051

0.03

0.337

-0.01

0.460

-0.02

0.424

Stands 2015 I

P

-0.07

0.218

-0.12

0.071

0.03

0.366

0.05

0.285

-0.07

0.193

-0.01

0.493

-0.14

0.128

-0.07

0.176

-0.22

0.006

Skips 2015 I

P

0.09

0.151

-0.05

0.243

-0.11

0.097

0.11

0.083

-0.03

0.382

0.03

0.319

-0.02

0.407

0.04

0.309

-0.03

0.379

Plant height 2015 I

P

-0.20

0.013

-0.01

0.438

0.14

0.039

-0.18

0.023

-0.04

0.317

-0.20

0.011

-0.14

0.059

-0.03

0.369

0.27

0.011

v Bi-variate Moran’s I statistic gives a value ranging between -1 and 1. As value approaches 1, distributions between two variables are

more aggregated together. As value approaches -1, distributions between two variables are more uniformly dispersed from each other.

The Moran’s I values indicate significance at the P=0.05 and below

w Minimal soil temperature was measured before 7:00 AM within the first week of planting

x Soil water content was measured within the first week of planting

y Stand counts were performed 21 days after planting for 7.6 meter sections of two adjacent rows at each sample site


skip 42 days after planting for 7.6 meter sections of two adjacent rows at each sample site

83

Table 3. Spatial autocorrelations (univariate Moran’s I w) of plant variables collected across the

100 sample sites established in 2014 and 2015 across the Wildy field in Mississippi Co.

Arkansas used for this experiment

Variable Moran’s I w P value

Stands 2014 x -0.08 0.112

Stands 2015 0.16 0.006

Skip index 2015 y 0.31 0.001

Plant height 2015 z 0.43 0.001

w The Moran’s I values indicate significance at the P=0.05 and below. Moran’s I ranges from 1



x Stand counts were performed 21 days after planting for 7.6 meter sections of two adjacent rows

at each sample site




z Plant height was measured 42 days after planting

84

Table 4. Spatial correlations (Bivariate Moran’s I w) between plant variables for 2014 and 2015

and across years collected from 100 sample site established in the Wildy field in Mississippi Co.

Arkansas.

Stands 2014 x Stands 2015 Skips 2015 y Plant height 2015 z

Variable

Stands 2014 I w

P

-0.08

0.083

0.03

0.214

-0.05

0.141

0.01

0.435

Stands 2015 I

P

0.05

0.114

0.16

0.007

-0.27

0.001

0.32

0.001

Skips 2015 I

P

-0.07

0.046

-0.25

0.001

0.31

0.002

-0.38

0.001

Plant height 2015 I

P

0.04

0.137

0.31

0.001

-0.40

0.001

0.43

0.001

w Bi-variate Moran’s I statistic gives a value ranging between -1 and 1. As value approaches 1,

distributions between two variables are more aggregated together. As value approaches -1,

distributions between two variables are more uniformly dispersed from each other. The Moran’s

I values indicate significance at the P=0.05 and below.

x Stand counts were performed 21 days after planting for 7.6 meter sections of two adjacent rows

at each sample site




z Plant height was measured 42 days after planting

85

Table 5. Wildy regression

Stand counts

2014

Stand counts

2015

Skip index 2015 Plant height

2015

Stand counts

2014

1 (+)0.00067 (+)0.83753 (+)0.76362

Stand counts

2015

(+)0.00020* 1 (-)0.00005 (+)0.00027

Skip index 2015 (+)0.83753 (-)0.00005 1 (-)0.00001

Plant height 2015 (+)0.76362 (+)0.00027 (-)0.00001 1

86

Table 6. Bond. Relative fungicide response and root disease and root disease indices compared

between soils

LS Mean (Fungicide

response)

Letter LS Mean (Root disease

index)

Letter

Experiment

1

Soil L 1.805205 A 49.66471 A

Soil H 2.083943 A 48.87421 A

Experiment

2

Soil L 2.402876 A 51.3091 A

Soil H 1.892784 A 46.80098 B

Values not connected by a different letter indicate no significant difference. Student’s t-test

(α=0.05)

87

Table 7. Wildy. Relative fungicide response, and root disease severity compared between soils.

LS Mean (Fungicide

response)

Letter LS Mean (Root disease

index)

Letter

Course sand L 2.22302

A 47.33442

ABC

Loamy sand

H

2.214862

A 55.61339

A

Course sand H 1.844624

A 43.37179

CD

Loamy sand L 1.790521

A 52.52249

AB

Clay soil H 1.699449

A 36.28623

D

Clay soil L 1.677364

A 47.0941

BC

Values not connected by a different letter indicate no significant difference. Student’s t-test

(α=0.05)

Documents

Spatial Variability of Seedling Disease Pressure in Cotton