IMPACTS OF NITROGEN FERTILITY, SOILBORNE DISEASE,

AND SOCIOECONOMIC FACTORS ON DIVERSE SMALL

GRAIN PRODUCTION SYSTEMS OF THE

PACIFIC NORTHWEST

By

LUCAS J. PATZEK

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

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY Department of Crop and Soil Sciences

AUGUST 2012

© Copyright by LUCAS J. PATZEK, 2012 All Rights Reserved © Copyright by LUCAS J. PATZEK, 2012

All Rights Reserved ii

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of LUCAS J.

PATZEK find it satisfactory and recommend that it be accepted.

Stephen S. Jones, Ph.D., Chair

Lindsey J. du Toit, Ph.D.

Jessica R. Goldberger, Ph.D.

Carol A. Miles, Ph.D.

Timothy C. Paulitz, Ph.D. iii

ACKNOWLEDGMENTS

The assistance, support, and guidance of the following persons are acknowledged:

My parents, who chanced a life in a foreign land removed from the comforts of family and their mother culture, and laid the spiritual and intellectual foundation without which I would not be who I am today. Their eternal love is my inspiration, challenging me to believe in the fundamental good of humanity, and avoid the many paths of ignorance. My sisters, who have journeyed with me through life as my best friends, and whose accomplishments warrant the greatest pride. My love, Sarah Szewczyk, who was patient and loving through thick and thin.

My mentor and major advisor, Dr. Stephen Jones, who welcomed me into his re- search group, and instilled in me a profound respect for the history, ethics, and fundamental principles of biology. He trusted and challenged me in a traversal of disciplinary bound- aries, international boundaries, and the perfidious, self-erected boundaries of doubt and fear, and for that I am forever grateful.

My committee members, who with great generosity and patience, took the time to teach me concepts and methods, advise me in my research, provide me with insights from the “real world” of applied science, and support me in my (occasionally wild) endeavors.

The many WSU-CAHNRS and USDA-ARS faculty, researchers, and staff who opened the iv

doors to their offices and labs, offered vital scholarly and material support, and provided me a congenial atmosphere in which to relate my sloppiest brainstorms and more refined thoughts. Carlo Leifert, Mohammed Almuairfi, Chris Seal, and Kirsten Brandt for warmly welcoming me as a visiting scientist to the School of Agriculture, Food & Rural Develop- ment, Newcastle University, UK.

The Jones Lab, past and present, including Meg Gollnick, Steve Lyon, Kerry Balow,

Kevin Murphy, Lori Hoagland, Jennifer Moran, Matt Arterburn, Glafera Janet Matangui- han, James Keach, Jeffrey Endelman, Karen Hills, Steve Hinton, Caitlin Price, and Brook

Brouwer.

The fantastic students of WSU Pullman, Mount Vernon, and Puyallup.

Thank you. v

IMPACTS OF NITROGEN FERTILITY, SOILBORNE DISEASE,

AND SOCIOECONOMIC FACTORS ON DIVERSE SMALL

GRAIN PRODUCTION SYSTEMS OF THE

PACIFIC NORTHWEST

Abstract

by Lucas J. Patzek, Ph.D. Washington State University August 2012

Chair: Stephen S. Jones

Small grains are grown in rotation with many temperate crops in the Columbia Basin and Puget Sound regions, functioning to improve the profitability of the cash crops. Dur- ing 2009-10, 45 isolates of Rhizoctonia spp. were collected from Columbia Basin fields in which onions were cultivated in sequence with cereal cover crops. Isolates were character- ized as R. solani AGs 2-1, 3, 4, 5, 8, and 9; Waitea circinata; and binucleate Rhizoctonia spp. AGs A, E, and I. At 8–15◦C, stunting of onion was caused by isolates of AG 2-1, 3,

4, 8, E, and W. circinata. The most virulent isolates belonged to AG 8, confirming a possi- vi

ble cereal-onion disease bridge, although one isolate each of AG 3 and E were also highly virulent. AG 2-1 and 3 isolates were of moderate virulence. During 2009-10 and 2010-

11, the influence of nitrogen (N) rate (0, 85, and 170 kg N/ha) and source (poultry feather meal and sulfur-coated urea) on the agronomic performance, flour quality, and phenolic acid (PA) content of four hard red winter wheat cultivars was assessed in Skagit County.

Yields of up to 10.1 Mg/ha were realized, as were grain protein contents of 12 to 14%.

High N rates reduced test weights up to 4%, but increased protein content by 1% on aver- age. Micro-SDS volumes averaged 10.6-12.7 cm3/g, values correlated in other studies with desirable loaf characteristics. Seven PAs were identified by HPLC in grain of the 2009-

10 cultivars, as were four ferulic acid dehydrodimers (DiFAs). N fertilization was only a significant source of variation for ferulic acid and DiFA concentrations. A mail survey was conducted of past, current, and prospective grain growers in the Puget Sound to assess their marketing strategies, information uses, and challenges. Although current growers rely on the commodity market, many were interested in direct-to-consumer markets. Product segmentation is limited by infrastructure, without which regional markets are difficult to access. Animal feed was the crop use of greatest importance. Compared with current grow- ers, prospective growers were more apt to value university- and Extension-based sources of production information, and to be guided by sustainable agriculture principles. vii

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS...... iii

ABSTRACT...... v

LIST OF TABLES...... ix

LIST OF FIGURES...... xiv

Chapter

1. Introduction...... 1

2. Stunting of onion caused by Rhizoctonia spp. isolated from the Columbia Basin7

2.1 Abstract...... 7 2.2 Introduction...... 8 2.3 Materials and methods...... 22 2.4 Results...... 34 2.5 Discussion...... 58 2.6 Acknowledgments...... 67

3. Effect of nitrogen fertility on the agronomic performance, flour quality and

phenolic acid content of hard red winter wheat in western Washington...... 68

3.1 Abstract...... 68 3.2 Introduction...... 70 3.3 Materials and methods...... 80 3.4 Results...... 92 3.5 Discussion...... 117 viii

3.6 Acknowledgments...... 134

4. Marketing strategies and information needs of small grain growers in the

Puget Sound region of Washington State...... 135

4.1 Abstract...... 135 4.2 Introduction...... 136 4.3 Methods...... 143 4.4 Results...... 146 4.5 Discussion...... 167 4.6 Acknowledgements...... 172

5. Summary of findings and conclusions...... 173

BIBLIOGRAPHY...... 176

APPENDIX...... 222

A Stunting of onion caused by Rhizoctonia spp. isolated from the Columbia

Basin...... 222 B Effect of nitrogen fertility on the agronomic performance, flour quality

and phenolic acid content of hard red winter wheat in western Washington 246 C Marketing strategies and information needs of small grain growers in the

Puget Sound region of Washington State...... 275 ix

LIST OF TABLES

Table Page

2.1 Rhizoctonia isolates obtained from three commercial onion fields in Mor- row County, OR in 2009 and 2010...... 36

2.2 ED50 estimate and maximum reduction ranges (low to high (mean)) modeled for the combined plant weight, height, and total root length re- sponses of 6-week-old onion seedlings inoculated with Rhizoctonia iso- lates and grown at 15◦C. Data for repeats of the experiment are shown in separate columns...... 41

2.3 ED50 estimate and maximum reduction ranges (low to high (mean)) modeled for the combined plant weight, height, and total root length responses of 8-week-old onion seedlings inoculated with Rhizoctonia isolates and grown at 13/8◦C day/night temperatures. Data for repeats of the experiment shown in separate columns...... 45 2.4 Radial colony growth rate (mm/day) of Rhizoctonia isolates on agar medium at different temperatures (results of two experiments combined).a 57 3.1 Crop management, climatic conditions, and soil characteristics at two sites per crop year at the WSU Mount Vernon NWREC research farm during the 2009-10 and 2010-11 crop years...... 84 3.2 Marginal means for the effect of nitrogen (N) source and rate on plant height, stem lodging, yield, test weight, falling number, grain protein, as well as constant weight and constant protein micro-SDS values for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA in 2009-10 and 2010-11...... 95 3.3 The effect of nitrogen (N) source and ratea on the grain protein content of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009- 10, and north and south sites during 2010-11...... 101 x

3.4 The effect of nitrogen (N) source and ratea on the micro-SDS volumes, based on constant wholemeal flour weight, of hard red winter wheat cul- tivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Ver- non, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 103 3.5 The effect of nitrogen (N) source and ratea on the micro-SDS volumes, based on constant wholemeal flour protein (10%), of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 105

3.6 Diagnostic absorption wavelengths of peaks (λmax) and of troughs (λmin), as well as retention times (tR) for phenolic acid monomers and dimers measured by HPLC...... 112 3.7 Composition of individual phenolic acid contents in hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites at Mount Vernon, WA during 2009-10...... 114 4.1 Demographic characteristics of all respondents (Global, N = 54), of those currently growing small grains (Group A, N = 25), of those no longer growing grains (Group B, N = 8), and of those interested in grow- ing grains (Group C, N = 22). Respondents were in the Puget Sound region of western Washington State...... 148

4.2 Factorsa limiting production for respondents growing small grains (Group A, N = 25), and factors influencing the decision to stop grain production for those no longer growing small grains (Group B, N = 8) in the Puget Sound region of western Washington State...... 154 4.3 Market outlets used by respondents growing small grains (Group A, N = 25), and those of interesta to respondents wanting to grow small grains (Group C, N = 21) in the Puget Sound region of western Washington State. 159 4.4 The use of market outlets by respondents no longer growing small grains in the Puget Sound region of western Washington State (Group B, N = 8). 160 xi

4.5 Crop usesa of importance to respondents currently growing small grains (Group A, N = 24), and those of interest to respondents interested in growing small grains (Group C, N = 21) in the Puget Sound region of western Washington State...... 161 4.6 The use or importance of information sourcesa to growers currently grow- ing small grains (Group A), growers who no longer grow small grains (Group B), and growers who have an interest in growing small grains (Group C). Respondents were in the Puget Sound region of western Washington State...... 165 A1 Effect of inoculum density of Rhizoctonia spp. on the dry plant weight (PW), height (PH), rooting depth (RD), and total root length (TRL) of 6-week-old onion seedlings grown at 15◦C...... 223 A2 Best-fit values for all model parameter estimates used in the analysis of the effect of inoculum density and Rhizoctonia spp. on dry plant weight (PW), height (PH), rooting depth (RD), and total root length (TRL) of 6-week-old onion seedlings grown at 15◦C...... 226 A3 Effect of inoculum density and Rhizoctonia isolate on the dry plant weight (PW), height (PH), total root length (TRL), rooting depth (RD), and root number (RN) of 8-week-old onion seedlings grown at a 13/8◦C day/night temperature regime...... 230

A4 Best-fit values for all model parameter estimates used in the analysis of the effect of inoculum density and Rhizoctonia isolate on the dry plant weight (PW), height (PH), total root length (TRL), rooting depth (RD), and root number (RN) of 8-week-old onion seedlings grown at a 13/8◦C day/night temperature regime...... 234 A5 Effect of inoculum density and Rhizoctonia spp. on the emergence of onion seedlings grown at a 13/8◦C day/night temperature regime...... 245

B1 The effect of nitrogen (N) source and ratea on the plant height of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 248 xii

B2 The effect of nitrogen (N) source and ratea on the stem lodging of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 250 B3 The effect of nitrogen (N) source and ratea on the grain yield of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 252 B4 The effect of nitrogen (N) source and ratea on the test weight of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 254 B5 The effect of nitrogen (N) source and ratea on the falling numbers of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11...... 256

B6 The effect of nitrogen (N) source and ratea on the plant height, yield, and test weight of hard red winter wheat cultivars Norwest 553 and WA8120 grown at two sites (north and south) in Mount Vernon, WA during the 2010-11 growing season...... 258

B7 The effect of nitrogen (N) source and ratea on the falling number and grain protein content of hard red winter wheat cultivars Norwest 553 and WA8120 grown at two sites (north and south) in Mount Vernon, WA during the 2010-11 growing season...... 259 B8 The effect of nitrogen (N) source and ratea on the micro-SDS volumes, based both on constant flour weight and on a 10% constant protein rate, of hard red winter wheat cultivars Norwest 553 and WA8120 grown at two sites (north and south) in Mount Vernon, WA during the 2010-11 growing season...... 260 B9 Marginal means for phenolic acid concentrations of hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at east and west sites in Mount Vernon, WA during 2009-10 (µ/g dry matter)...... 261 xiii

B10 The effect of nitrogen (N) source and ratea on the concentration (µg/g dm) of trans-ferulic acid for hard red winter wheat cultivars Bauermeis- ter, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season...... 263 B11 The effect of nitrogen (N) source and ratea on the content (µg/g dm) of p- coumaric acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season...... 265 B12 The effect of nitrogen (N) source and ratea on the total phenolic acid content (µg/g dm) for hard red winter wheat cultivars Bauermeister, Mc- Call, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season...... 267 B13 The effect of nitrogen (N) source and ratea on the content (µg/g dm) of sinapic acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season...... 269

B14 The effect of nitrogen (N) source and ratea on the content (µg/g dm) of 2- hydroxycinnamic acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season...... 271

B15 The effect of nitrogen (N) source and ratea on the content (µg/g dm) of 2,4-dihydroxybenzoic acid for hard red winter wheat cultivars Bauer- meister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season...... 273 xiv

LIST OF FIGURES

Figure Page

2.1 (A) Infrared aerial photograph of a 46 ha commercial onion field irri- gated by center pivot in Morrow County, Oregon taken June 14, 2011. Healthy foliage appears red, while patches of severely stunted plants appear as small, discolored spots distributed throughout the field. The larger areas of discoloration are due to sand-drift. (Courtesy Bill Dean, River Point Farms), (B) Patch of stunted onions (foreground) in a com- mercial field in Morrow County. Strips of wheat stubble between onion beds are all that remain of a cover crop used to protect onion seedlings from sand-blasting...... 11

2.2 (A) Onion seedlings inoculated with R. solani AG 8 isolate Rh070943 at 16 colony forming units per gram of soil (CFU/g soil) showing typical symptoms, including short and sparse roots with brown discoloration, and senescent leaf tips. Melanized mycelium of Rhizoctonia growing on the surface of an onion root visible microscopically at 24X (B) and 100X (C) magnification. (Courtesy Lindsey du Toit, Washington State University)...... 16 2.3 Rhizoctonia solani AG 8 isolate Rh070943 significantly reduced the growth of 8-week-old onion seedlings at inoculum densities of 4 and 16 CFU/g soil compared with noninoculated control plants (0 CFU/g soil). The plants were grown in a plant growth chamber under a 12 h photoperiod and 13/8◦C day/night temperatures...... 48

2.4 Effect of inoculum density and Rhizoctonia spp. on the emergence of onion seedlings expressed as 100 - % of noninoculated control. The four-parameter sigmoidal regression analysis was performed on the com- bined responses to three isolates of each Rhizoctonia solani AG (AG 2- 1: Rh060811, Rh070913, and Rh070937; AG 3: Rh060801, Rh070933, and Rh070942; AG 4: Rh010901, Rh070909, and Rh070929; AG 8: Rh070922, Rh070927, and Rh070943), but only one isolate of binucle- ate Rhizoctonia AG E (Rh070923), over two repeats of the experiment... 52 xv

2.5 Radial colony growth rate (mm/day) of Rhizoctonia solani AG 3 (iso- lates Rh060801, Rh070933, and Rh070942), R. solani AG 4 (Rh010901, Rh070909, and Rh070929), R. solani AG 8 (Rh070922, Rh070927, and Rh070943), and a binucleate Rhizoctonia AG E (Rh070923) on an agar medium at 5 to 35◦C. Each data point is the mean of eight replications... 56 3.1 High-performance liquid chromatography (HPLC) elution profile of phe- nolic acid monomers and dimers in alkali-hydrolysed extracts of hard red winter wheat wholemeal flour (cv. WA8022) with detection at 320 nm. Key to identity of peaks: 1, syringic + vanillic acids; 2, 2,4-dihydroxybenzoic acid; 3, p-coumaric acid; 4, sinapic acid; 5, trans-ferulic acid; 6, cis- ferulic acid; 7, 2-hydroxycinnamic acid; (*) compounds having UV-vis spectra corresponding to phenolic acid monomers and dimers...... 111

A1 Study of the influence of inoculum density on disease severity caused by Rhizoctonia spp. on onion grown at 15◦C. Results shown for binucleate Rhizoctonia AG A (isolate Rh010913), AG I (Rh070914); Waitea circi- nata var. circinata (Rh070924); and R. solani AG 5 (Rh070930). Mean reductions in dry weight, height, and rooting depth for 6-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars rep- resent standard errors (n = 9). Results of repeat experiments are shown in separate columns...... 229

A2 Effect of inoculum density and Rhizoctonia solani AG 2-1 isolate (Rh060811, Rh070913, and Rh070937) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoc- ulated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns...... 239 A3 Effect of inoculum density and Rhizoctonia solani AG 3 isolate (Rh060801, Rh070933, and Rh070942) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoc- ulated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns...... 240 A4 Effect of inoculum density and Rhizoctonia solani AG 4 isolate (Rh010901, Rh070909, and Rh070929) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoc- ulated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns...... 241 xvi

A5 Effect of inoculum density and Rhizoctonia solani AG 8 isolate (Rh070922, Rh070927, and Rh070943) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoc- ulated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns...... 242 A6 Effect of inoculum density and binucleate Rhizoctonia AG E isolate Rh070923 on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns...... 243 A7 Scatter plot comparing the maximum modeled reductions in onion seedling height, weight, and total root length with infection by Rhizoctonia solani AG 2-1 (isolates Rh060811, Rh070913, and Rh070937), AG 3 (Rh060801, Rh070933, and Rh070942), and AG 8 (Rh070922, Rh070927, and Rh070943), as well as binucleate Rhizoctonia AG E (Rh070923). Response variables expressed as 100 - % of noninoculated control...... 244 B1 Total monthly precipitation (bar graphs) and mean monthly air tempera- ture (line graph) as recorded by the WSU AgWeatherNet station located 1 km from research plots over the duration of the study from October 2009 until September 2011...... 246 B2 The experiment was replicated at two sites per crop year over two crop years at the WSU Mount Vernon NWREC, separated 0.6 km, and uti- lized a split-plot design with five N fertility treatments as the main plot factor arranged in a CRD with four replications per main plot. Hard red winter wheat cultivar was the subplot factor. N fertility treatments in- cluded an non-fertilized control, 85 kg N/ha poultry feather meal (PFM), 170 kg N/ha PFM, 85 kg N/ha sulfur-coated urea (SCU), and 170 kg N/ha SCU. Within a pass, subplots were separated by mowed alleys, and main plots by a plot of soft white winter wheat. Adjacent passes were separated by planting of soft white winter wheat...... 247 xvii

Dedication

For those who cherish life in all its forms, and sojourn in the infinite mystery and beauty of the universe. Especially my parents, Tadeusz and Joanna, my sisters, Sophie and Julie,

and my love, Sarah Szewczyk.

“It cannot be denied that science, in its inevitable applications, has given unprecedented extremes of scale to the technologies of land use, manufacturing, and war, and to their bad effects. One response to the manifest implication of science in certain kinds of destruction

is to say we need more science, or more and better science. I am inclined to honor this

proposition, if I am allowed to add that we also need more than science.”

Wendell Berry – The Way of Ignorance 1

CHAPTER 1. INTRODUCTION

The Pacific Northwest, narrowed herein to Washington and Oregon States, is an im- portant North American small grains growing region. The 2010 harvest of the most preva- lent grain crop in the region, wheat (Triticum aestivum L.), amounted to 5.7 million metric tons (Mt), or 10% of the total U.S. production from 1.3 million hectares (ha). Washington was the fourth top wheat producing state in the union. Seventy-eight percent of the Wash- ington crop had a winter habit, while 67% belonged to the common white class, uniquely suited for cookies, pastries, flatbreads, and other products [USDA-NASS, 2011a]. Forty- two percent of all U.S. white wheat was produced in Washington, and 92% of the U.S. total was produced in Washington, Oregon, and Idaho. The hard red class, used for bread

flours, accounted for 22% of the Washington wheat production area, or a production figure of about 880 thousand Mt, 2% of the U.S. total. Following wheat, 239 thousand Mt of barley (Hordeum vulgare L.) was harvested from 49,000 ha in the Pacific Northwest, and

71 thousand Mt of oats (Avena sativa L.) was harvested from 11,000 ha [USDA-NASS,

2012].

Based on the value of production, wheat is the third most important agricultural com- modity in Washington State, after apples and milk [WSDA, 2010]. It is estimated that the wheat industry has a 1.18 billion USD impact on the Washington economy, and 86% of 2

that economic impact is realized east of the Cascade Mountains [Fortenbery, 2011]. Be- tween 85 and 90 percent of all Washington wheat is exported, particularly to Asian and

Middle Eastern markets [Washington Grain Commission, 2012], and the export value of wheat grain and wheat products was 435.6 million USD in 2010 [USDA-NASS, 2011].

Wheat is the primary cash crop for many dryland central and eastern Washington farms, those relying entirely on rainfall for moisture, and figures centrally in the character of many communities in the region. Nearly 90% of the wheat area in the state is non-irrigated, and

52% of the 2010 Washington wheat crop was grown under non-irrigated conditions in just three counties: Whitman, Lincoln, and Adams [USDA-NASS, 2011]. Farms having the lowest annual precipitation levels often employ a summer-fallow rotation system, in which a portion of a farmer’s ground is not cropped to allow for storage of winter precipitation in the soil to be used by a succeeding grain crop. Where soil moisture is less limiting, two- and three-year crop rotations are commonly employed in which broadleaf crops, such as peas, lentils, canola, and camelina, are rotated with spring and winter wheat crops, as well as barley crops.

A majority of the research focused on grains and their farmers, from all disciplinary perspectives, has been restricted to those regions, communities, and farming systems which are most reliant on grain crops for their livelihood. As a result, there is a considerable body of literature on dryland grain systems of central and eastern Washington. However, there are numerous farms across the state on which wheat and other small grains function pri- 3

marily to improve the quality, yields, and ultimately profitability of other cash crops. This doctoral research explores two unique Pacific Northwest production regions outside of the dominant dryland grain production regions. The first is the irrigated Columbia Basin pro- duction region where graminaceous winter cover crops and windbreaks are part of complex cropping sequences including many temperate vegetables, vegetable seed crops, legumes, and herbs [Mansour, 1999]. The second is the high rainfall, maritime western Washington production region where small grains are commonly cultivated in rotation with high-value fruit, vegetable, and bulb crops, primarily to reduce nutrient loss and erosion, provide or- ganic matter to the soil, as well as to break disease and pest cycles [Miles et al., 2009].

The study presented in Chapter 2 emerged from the observation that stunting of onion caused by Rhizoctonia spp. in irrigated onion crops of the Columbia Basin appeared only to occur after the incorporation of small grain cover or wind-break crops. In these production systems, onion seed is commonly planted in the spring into beds formed after incorporation of winter wheat or barley cover crops in strips, and strips of cover crop are left standing between the onion beds early in the spring growing season, when the seedlings are vulner- able to sand-blasting. The primary objectives of the study were to identify the Rhizoctonia spp. inhabiting stunted patches in commercial onion fields in north-central Oregon and south-central Washington, and to investigate the pathogenicity of isolates of each identified

Rhizoctonia spp. on onion seedlings over a range of inoculum densities and a range of temperatures characteristic of the region between February and April, when spring-planted 4

onions appear to be most susceptible to infection by the pathogen. We discovered that the complex cropping sequences of the region allow for a great diversity of Rhizoctonia spp., but the study confirmed that the most virulent isolates on onion belonged to R. solani AG

8, commonly associated with wheat and barley. A similar grains-onion disease bridge has emerged as a challenge to Australian onion producers [Wicks et al., 2010].

Chapters 3 and 4 explore the production of graminaceous crops, particularly wheat, in western Washington. This region, not commonly associated with small grains farming, differs markedly in soil types, precipitation levels, and summer temperatures from the ma- jor wheat growing regions of central and eastern Washington. The upsurge in consumer interest in locally-produced grains, particularly for artisanal hearth breads [Brown, 2012,

Hills et al., 2012], has provided a new and growing revenue source for many vegetable, fruit and bulb farmers in western Washington. Therefore, farmers are interested in grains not only for their role as rotational crops improving the profitability of their non-grain cash crops, but also as an increasingly important source of farm income. However, the alluvial farmlands of western Washington, which are exposed to high levels of precipitation and commonly have a seasonally perched water table [Klungland and McArthur, 1989], are a challenging area in which to grow hard red winter wheat, or bread wheat, of consistently high breadmaking quality. This class of wheat has a unique high nitrogen (N) require- ment, because baking and nutritional qualities relate in part to protein content [Bell and

Simmonds, 1963, Finney and Barmore, 1948, Park et al., 2006], which in turn relates to 5

soil N availability. Because N readily dissolves and moves with water, too much applied N fertilizer can pose an unnecessary financial burden to farmers and result in harmful environ- mental impacts [Delgado, 2002, Galloway et al., 2004, McIsaac et al., 2002, Vitousek et al.,

1997], but poor N fertility will result in low yields and undesirable flour quality. Therefore,

Chapter 3 investigates the effect of different rates of poultry feather meal and sulfur-coated urea on the agronomic performance, flour quality, and phenolic acid content of hard red winter wheat cultivars grown in Skagit County, which has the largest area planted to wheat of any western Washington county [USDA-NASS, 2012].

Eastern Washington grain farms are overwhelmingly export-driven. Therefore, there exists an opportunity for farmers west of the Cascades to produce wheat and other grains for regional markets, including the Seattle and Portland metropolitan areas, which have a high concentration of direct-to-consumer market outlets [Martinez et al., 2010]. However, there exist many unknowns concerning the characteristics, marketing strategies, informa- tion source usages, needs, opinions, and challenges of prospective, current, and past small grains growers in the region. For instance, access to prime farmland might be difficult due to the close proximity of many western Washington farms to populated areas, which are ex- erting development pressures [Canty and Wiley, 2004]. Developing markets outside of the commodity chain may be a challenge due to a lack of critical handling and processing in- frastructure. Beyond anecdotal evidence, it is unclear how limiting various weather-related factors are to grains production in western Washington, including the high seasonal rain- 6

fall which can cause pre-harvest sprouting and lodging, the short growing season which demands fast crop maturation and timely harvest, and the cool maritime climate which is ideal for stripe rust (Puccinia striiformis) epidemics. Chapter 4 addresses these funda- mental questions through the results of a mail survey sent to growers in thirteen western

Washington counties. The findings can help influence the future of university research and extension, and may help resolve local grain market barriers. 7

CHAPTER 2. STUNTING OF ONION CAUSED BY RHIZOCTONIA

SPP. ISOLATED FROM THE COLUMBIA BASIN

2.1 Abstract

Forty-five isolates of Rhizoctonia spp. were collected from three commercial dry

bulb onion fields in Morrow County, Oregon during 2009 and 2010. The majority of the

isolates (29 out of 45) were identified as Rhizoctonia solani, but isolates of Waitea circinata

(Rhizoctonia oryzae) (5 out of 45) and binucleate Rhizoctonia spp. (8 out of 45) were also

identified. Characterization of species and subspecific grouping, including anastomosis

groups (AGs), was performed by sequence analysis of the rDNA ITS region. Of the R.

solani isolates, the most prevalent AG was AG 4 (8 isolates), followed by AG 8 (7), AG

3 (6), AG 2-1 (3), AG 5 (3), and AG 9 (2). The influence of inoculum density on disease

severity caused by Rhizoctonia spp. on onion seedlings at 13/8 and 15/15◦C day/night

temperatures was determined. Stunting of onion was caused by isolates of R. solani AGs

2-1, 3, 4, 8, W. circinata var. circinata, and binucleate Rhizoctonia AG E. Isolates of R. solani AG 5, as well as binucleate Rhizoctonia AG A and AG I were nonpathogenic on onion. The most virulent isolates belonged to R. solani AG 8, although one AG 3 and one 8

AG E isolate were also highly virulent on onion. Isolates of R. solani AG 2-1 and AG 3 caused moderate levels of disease, and the least virulent isolates belonged to W. circinata var. circinata and R. solani AG 4, one isolate of which was nonpathogenic on onion. AG

4 and AG 8 isolates did not significantly reduce onion emergence, but isolates of AG 2-1,

AG 3, and AG E did. This is the first report on the virulence of W. circinata var. circinata on onion.

2.2 Introduction

In 2010, approximately 3.32 million Mt of onion (Allium cepa) bulbs were harvested from 60,600 ha in the U.S., with an estimated farmgate value of 1.46 billion USD [USDA-

NASS, 2011b]. The total value of this horticultural crop to the U.S. economy is consid- erably larger if indirect revenue garnered from seed, fertilizer and sales of other farming inputs, as well as from processing, distribution and retail are considered. The Columbia

Basin vegetable production region of central Washington and north central Oregon States produced 27% of the national onion crop on nearly 13,300 ha in 2010, yielding an estimated farmgate value of 544 million USD [USDA-NASS, 2011b]. Both storage and non-storage types of dry bulb onion are grown in the Columbia Basin, but late summer and fall storage types account for 92% of the region’s total production [National Onion Association, 2011].

Although official statistics on the cultivar makeup of the dry bulb onion crop do not exist, 9

it is generally believed that yellow cultivars account for 87% of the total national crop, red cultivars 8%, and white cultivars 5% [Mininger, 2011].

Dry bulb onions in the Columbia Basin are commonly grown in 3- to 4-year rota- tions with small grains, peas, potatoes, sweet corn, carrots, and other crops [Pelter and

Sorensen, 2003]. The onions are primarily planted from late February to mid-April as seed or transplants, when average daily air temperatures range from 8 to 15◦C. However, a small percentage is direct-seeded in the fall, when air temperature averages are substan- tially higher, 26 to 32◦C, and over-wintered to produce bulbs for early harvest. Onions are usually planted in multiple rows on beds formed at or just before planting. Each bed contains 2 to 12 rows of plants spaced 8 to 10 cm apart, and a typical arrangement is two double-rows spaced 30 cm apart on 86- or 112-cm wide beds [Pelter and Sorensen, 2003,

Sullivan et al., 2001].

The Columbia Basin has an arid climate due to its position in the rain-shadow of the

Cascade Mountains; thus, irrigation, particularly by central pivot [Umatilla County Criti- cal Groundwater Task Force, 2008], is a defining characteristic of the region’s vegetable agriculture. The average annual precipitation is 15 to 51 cm over much of the region, and about 57% of total annual precipitation falls between October and February. Morrow

County, Oregon, a major onion producing county in the Columbia Basin, receives 22 cm of precipitation annually, with 61% falling outside of the spring and summer growing season

[Hosler, 1983, Johnsgard, 1963, USDA-NRCS, 2011]. Prior to the construction of modern 10

irrigation systems, the fertile but arid lands of the region were used primarily for livestock

grazing and dryland, small grains cultivation [Dietrich, 1996, Hosler, 1983, Johnson and

Makinson, 1988].

Winters in the region are cold but generally not severe, and summers are hot during

the day and cool at night, making for a particularly good growing environment for many

common vegetable crops [Agricultural Extension Service, 1951]. Along the Columbia

River, the average winter temperature is 3◦C, the average summer temperature is 23◦C, and there are anywhere between 125 and 220 frost free days a year. The average relative humidity at dawn is 60% and decreases in mid-afternoon to about 50%. Frequent strong winds sweep across the Columbia Basin, periodically exceeding 48 km/h, and the average wind speed in Morrow County is highest in April at 18 km/h [Hosler, 1983, USDA-NRCS,

2011]. Because light sandy soils with low organic matter predominate in this region, the strong winds lead to rapid soil loss and the formation of dunes in fields. In addition, the blowing soil damages crops by sandblasting plants, uncovering roots and uprooting plants.

Measures that minimize the unfavorable effects of blowing soil include planting row crops and cover crops, minimizing tillage, as well as irrigating, cultivating and planting in a timely fashion [Hosler, 1983, Johnson and Makinson, 1988]. 11

Figure 2.1: (A) Infrared aerial photograph of a 46 ha commercial onion field irrigated by center pivot in Morrow County, Oregon taken June 14, 2011. Healthy foliage appears red, while patches of severely stunted plants appear as small, discolored spots distributed throughout the field. The larger areas of discoloration are due to sand-drift. (Courtesy Bill Dean, River Point Farms), (B) Patch of stunted onions (foreground) in a commercial field in Morrow County. Strips of wheat stubble between onion beds are all that remain of a cover crop used to protect onion seedlings from sand-blasting. 12

Over the past decade, stunting of onion caused by Rhizoctonia spp. has become an

increasing problem in irrigated onion crops planted in the Columbia Basin after the incor-

poration of small grain cover crops [du Toit, 2009]. The genus Rhizoctonia, belonging to

the class Basidiomycota, is a complex of filamentous fungal species that are plant parasitic,

saprotrophic, soilborne, entirely hyphal, and mostly asexual [Harvais and Hadley, 1967,

Sneh et al., 1991, Warcup, 1981]. Economically important Rhizoctonia diseases have been

identified on many crop, tree, pasture and horticultural species worldwide [Agrios, 1997,

Savary and Mew, 1996], including seed decay, damping-off, seedling blight, root rot, crown

rot, various stem infections, storage rots and foliar blights [Baker, 1970, Schwartz and Mo-

han, 2008]. The taxonomy of the genus continues to evolve nearly two centuries after its

establishment [De Candolle, 1815, Ogoshi, 1996], especially with the advent of molecu-

lar techniques; at present, between 30 and 50 epithets are believed to exist [Andersen and

Stalpers, 1994, Roberts, 1999]. Perhaps most fundamentally, the Rhizoctonia complex is

separated into two forms based on the nuclear number per hyphal cell [Parmeter and Whit-

ney, 1970]. The multinucleate species have three or more nuclei per cell and a teleomorph

genus of either Thanatephorus Donk or Waitea Warcup and Talbot. R. solani belongs to the first genus while R. oryzae and R. zeae belong to the latter. The binucleate Rhizoctonia

spp. have predominantly two nuclei per cell and a teleomorph genus of Ceratobasidium

Rogers containing about 15 species [Garcia et al., 2006, Hjortstam and Larsson, 1998,

Ogoshi, 1987]. Further sub-specific grouping of the Rhizoctonia spp. has been attempted 13

using a number of methods [Anderson, 1982, Kuninaga et al., 1997, Parmeter and Whit-

ney, 1970, Sweetingham et al., 1986], but the anastomosis group (AG) system, based on

hyphal anastomosis reactions, has been the most widely adopted for R. solani and binu-

cleate Rhizoctonia spp. [Carling, 1996, Sneh et al., 1991]. Each AG may be treated as an

evolutionary unit, perhaps even as a biological species [Anderson, 1982]. The subgroups

can have different host specificities and vary in pathogenicity depending on the soil envi-

ronment. At present, there are 14 AGs (AG-1 through AG-13 and AG-BI) described for

R. solani [Carling et al., 1999, 2002], and 7 AGs (CAG-1 to CAG-7) described in the U.S.

and 19 (AG-A to AG-S) in Japan for binucleate Rhizoctonia [Burpee et al., 1980b, Garcia

et al., 2006, Hjortstam and Larsson, 1998, Ogoshi, 1987].

Rhizoctonia-induced stunting of onion is not believed to impact plant survival in the

field, but can reduce onion size at harvest, and because larger bulb onions have greater market value than smaller size classes, the grower may nonetheless be dealt an economic blow [Sullivan et al., 2001]. The disease appears primarily to be a problem in onions cultivated in rotation with small grain cover crops or nurse crops, predominately wheat and barley, but the disease has also been observed in onion cultivated a season after the incorporation of green manure crops, such as mustard [Huppert, 2012]. Most commonly, onion seed is planted in the spring into beds formed after incorporation of the cover crops in strips, and strips of cover crop are left standing between the onion beds early in the spring growing season, when the seedlings are vulnerable to sand-blasting. Once the onions have 14

matured adequately, or before the cover crop plants achieve adequate size to compete with the onion seedlings, the strips of cover crops are killed selectively with herbicide or tillage.

Beginning in May to mid-June, infection is identifiable in the field as circular or irregular patches of stunted, but not killed, onions. Patches range from <1 m to >10 m in diameter.

The patches may extend in the direction of the row planting, and the greatest degree of stunting is typically observed in onion rows immediately adjacent to the small grain cover crop. The most severely impacted fields can have nearly 15% of total crop area stunted

(Figure 2.1), and anecdotal evidence from growers suggests that onion cultivars do not differ greatly in susceptibility to the pathogen [Dean, 2010].

R. solani AG 8, responsible for the Rhizoctonia root rot of small grain crops in Wash- ington and Oregon States [Paulitz et al., 2001, 2002, Pumphrey et al., 1987, Mazzola et al.,

1996, Smiley and Uddin, 1993, Weller et al., 1986], was hypothesized as the causative agent of onion stunting for several reasons. The occurrence of damage in distinct patches is a commonality between onion stunting and Rhizoctonia root rot, the latter also known as bare patch. Fields lacking copious quantities of small grain cover crop or green manure crop detritus in the rooting zone of developing onion seedlings do not appear to be affected by the disease, suggesting a disease "bridge" between cover crop residues and onions. Only onion seedlings between one and six weeks after emergence are impacted by the disease, which is consistent with R. solani generally maintaining pathogenicity only on juvenile plant tissues [Anderson, 1982, Parmeter and Whitney, 1970, Weinhold and Sinclair, 1996]. 15

Suggestive of small grain root rot symptoms, the root systems of onion seedlings develop

a spear-tipping effect, remain short and sparse, and may display light brown discoloration

without any distinct lesions [Schwartz and Mohan, 2008]. Yellowing and senescence of

the older leaves is also common. Additionally, wide (up to 17 µm diameter), melanized

hyphae typical of R. solani are commonly found affixed to affected roots and basal plates upon inspection with a microscope (Figure 2.2). 16

Figure 2.2: (A) Onion seedlings inoculated with R. solani AG 8 isolate Rh070943 at 16 colony forming units per gram of soil (CFU/g soil) showing typical symptoms, including short and sparse roots with brown discoloration, and senescent leaf tips. Melanized mycelium of Rhizoctonia grow- ing on the surface of an onion root visible microscopically at 24X (B) and 100X (C) magnification. (Courtesy Lindsey du Toit, Washington State University) 17

Abiotic soil conditions in the Columbia Basin, in particular soil type and temperature

in the early months of spring, favor rapid growth and increased pathogenicity of R. solani

AG 8 in comparison to other soilborne fungi [Brewster, 2008]. The pathogen has been

found to cause eight times the number of stunted patches in dryland wheat fields with sandy

soils compared with crops on heavier soils [De Beer, 1965]. Gill et al.[2000] confirmed

that Rhizoctonia root rot of wheat seedlings was more severe in sand than in loamy sand or sandy clay loam when tested in a greenhouse, and that the pathogen spreads radially twice as rapidly in sand compared with loamy sand, and four times as rapidly compared with sandy clay loam soil. It should be expected, then, that R. solani AG 8, and probably other

Rhizoctonia spp., can grow rapidly in the Sagehill series soils in which irrigated onions are commonly grown in the Columbia Basin. This soil is a very fine sandy loam, consisting of 52 to 69% sand and 0.47 to 1.4% organic matter in the rooting zone [Hosler, 1983,

USDA-NRCS]. Studies of Rhizoctonia root rot have found that R. solani AG 8 tends to be

favored over other Rhizoctonia spp. by low day/night soil temperatures, which are common during the spring onion planting season in the Columbia Basin. For instance, Ogoshi et al.

[1990] found R. solani AG 8 to be highly pathogenic on both wheat and barley at 10◦C but only mildly so at 20◦C, and Smiley and Uddin[1993] concluded that R. solani AG 8 was most damaging to wheat and barley at low (6 to 19◦C) and ambient (11 to 23◦C) soil temperatures than at high (16 to 27◦C) temperatures.

Optimum temperatures for the growth and infectivity of other Rhizoctonia spp. in- 18

digenous to the region may differ from that of R. solani AG 8. For instance, studies have shown that the optimum temperature for the development of R. solani AG 3 isolates ranges from 20 to 25◦C while AG 4 isolates reach optimum growth at a temperature ranging from

25 to 28◦C[Anguiz and Martin, 1989, Sherwood, 1969]. A study by Kaminski and Verma

[1985] found that optimum growth of Canadian R. solani AG 2-1 isolates was observed at 24◦C while that of AG 4 isolates was observed at 26◦C, and AG 2-1 isolates grew at

2◦C but not at 36◦C while AG 4 isolates grew at 36◦C but not at 2◦C. Field experiments in Canada demonstrated that damping-off in rapeseed more commonly resulted from AG

2-1 isolates before soil temperatures had warmed to 15◦C, but at 20◦C, damping-off due to

AG 4 increased in importance [Teo et al., 1988]. Inter-isolate variability in growth rates is commonly observed for Rhizoctonia spp., but growth rate may not necessarily be correlated with disease severity for a given species-host interaction, as discovered in pathogenicity studies of R. solani AG 3 isolates on potato [Carling and Leiner, 1990, Lehtonen et al.,

2008]. Also, temperature optima for isolates of the same species originating from different geographic regions probably vary as a result of local adaptation, as demonstrated in a com- parison of W. circinata isolates from Washington State versus Japan [Leiner and Carling,

1994], and the observations made for R. solani AG 4 isolates originating from different

U.S. Northern Plains locations [Caesar et al., 1993].

Previous studies have demonstrated that infections on onion are successfully initiated by isolates of R. solani AGs 1, 2-1, 3, 4, 5, and 8 [Farrokhi-Nejad et al., 2007, Ichielevich- 19

Auster et al., 1985, Juan-Abgona et al., 1996, Sumner et al., 1997, Wicks et al., 2010, Ya- mamoto and Uehara, 1972]; binucleate Rhizoctonia spp. AGs A, Ba, Bi, E, F, G, K, O, and

R[Burpee et al., 1980a, Erper et al., 2006, Farrokhi-Nejad et al., 2007, Ichielevich-Auster et al., 1985, Juan-Abgona et al., 1996]; as well as Waitea circinata var. zeae (Rhizocto- nia zeae)[Erper et al., 2006, Ichielevich-Auster et al., 1985]. In Israel, Ichielevich-Auster et al.[1985] showed that one of five R. solani AG 1 isolates, six of seven AG 4 isolates, and two binucleate Rhizoctonia AG K isolates were highly virulent on onion, while one R. zeae isolate and one binucleate Rhizoctonia AG F isolate were only moderately virulent, and isolates of R. solani AGs 3, 5, and 6 were not pathogenic on onion. Juan-Abgona et al.[1996] baited Rhizoctonia isolates from 13 locations in Gifu Prefecture in Japan and observed a wide range of variation in disease severity for onion. One of three AG 4 iso- lates was found to be highly virulent on onion, but the other two isolates exhibited much lower virulence. Isolates of binucleate Rhizoctonia spp. AG A, AG Ba, AG G, and AG O were observed to be only moderately virulent on onion. A series of pathogenicity tests per- formed in environmental chambers comparing Rhizoctonia spp. isolated from U.S. sweet onions found R. solani AG 4 to be virulent at diurnal temperature ranges of both 10 to 21 and 21 to 32◦C, but AG A, AG E, AG F, AG R, AG 1, AG 2-1 and AG 2-2 IIIB isolates were avirulent [Sumner et al., 1997]. In Turkey, Erper et al.[2006] also observed isolates of R. solani AG 4 to cause the greatest disease severity in onion, while W. circinata var. zeae was moderately virulent and the binucleate Rhizoctonia spp. were of low virulence. In 20

contrast, Wicks et al.[2010] did not isolate R. solani AG 4 frequently from stunted onions in the “Mallee” region of South Australia, while isolates of AG 2-1, AG 3, and AG 8 were common in onion field soil. The authors found that isolates of R. solani AG 2-1 and AG 8 consistently caused severe stunting in onion seedlings grown in inoculated soils, but only quantification of the DNA of AG 8 was significantly correlated with stunted patches in the

field, leading them to conclude that onion stunting in South Australia is primarily associ- ated with isolates of AG 8, though interactions with other pathogens may also be involved.

In neighboring New Zealand, a pathogenicity test of Rhizoctonia spp. cultured from potato tubers concluded that four isolates of R. solani AG 8 were of low virulence on onion. The authors concluded that R. solani AG 3 isolates were the most virulent, followed closely by

AG 4 and AG 5 isolates. R. solani AG 2-2 IIIB isolates and binucleate Rhizoctonia AG Ba,

AG Bi, AG D, AG E, and AG K isolates were of moderate virulence [Farrokhi-Nejad et al.,

2007].

Several other economically important soilborne pathogens commonly afflict onions in the Columbia Basin [Pelter and Sorensen, 2003]. However, it is unlikely that these other pathogens are primary culprits in the onion stunting disease because either the symptoms typical of these organisms are not found on the stunted onions, or the environmental con- ditions under which the stunting occurs do not favor the growth or pathogenicity of these organisms. Potentially confusing a disease diagnosis in the field, Fusarium oxysporum f. sp. cepae can cause stunting and damping-off in onion seedlings, although the pathogen 21

grows best at temperatures ranging from 28 to 32◦C[Srivastava and Qadri, 1984], much warmer than the temperatures common to the seedling phase of spring-planted onion crops in the Columbia Basin [Pelter and Sorensen, 2003]. Stunting and damping-off can also be caused by Pythium spp., although isolates of this genus tend to be destructive in wetter soils than those favored by Rhizoctonia spp. [Schwartz and Mohan, 2008]. Nematode pests of onion, particularly stubby-root nematodes (Paratrichodorus spp. and Trichodorus spp.), root-lesion nematodes (Pratylenchus spp., particularly P. penetrans and P. neglectus), and northern root-knot nematodes (Meloidogyne hapla), occur throughout the Columbia Basin

[Hafez et al., 1992, Ingham et al., 1999] and can cause a range of symptoms including stunting of plants, often in patches, potentially confusing diagnosis.

The objectives of the present work were: (a) to identify the Rhizoctonia spp. inhab- iting stunted patches in onion fields in the Columbia Basin; (b) to investigate the effect of inoculum density on the disease severity caused by one isolate of each of the Rhizoctonia spp. associated with stunted patches in onion crops; (c) to investigate the effect of inocu- lum density and isolate on disease severity caused by pathogenic Rhizoctonia spp. and

AGs on onion seedlings; and (d) to study the growth characteristics of these pathogenic

Rhizoctonia spp. on an agar medium at different temperatures. 22

2.3 Materials and methods

2.3.1 Isolation of Rhizoctonia spp. from onion fields in the Columbia Basin

Isolates of Rhizoctonia spp. were baited from naturally infested agricultural soils in Morrow County, OR using toothpicks [Paulitz and Schroeder, 2005], and were isolated directly from the roots of crop plants growing in the same soils. Soils for toothpick baiting were collected from three commercial bulb onion fields, both inside and outside of stunted patches, over two growing seasons in Morrow County, OR using a steel soil sampling tube

(Humboldt Manufacturing Co., Schiller Park, IL). Each sample was a composite of five soil cores, 15 cm deep and 2.2 cm in diameter, extracted along a transect either completely within a stunted patch, or completely external of a stunted patch. Soil samples were stored in polyethylene bags at 4◦C until use. At the time of baiting, each sample was placed in a 5 cm x 5 cm x 6.5 cm plastic pot, watered until completely saturated and incubated in a temperature-controlled growth room at a constant 16◦C. After 48 h, five flat, white birch wooden toothpicks (6 cm long x 1 mm deep x 1 to 2 mm wide; Diamond Brands,

Minneapolis, MN) were inserted into the soil at an even spacing to a depth of 5 cm. After another 48 h, the five toothpicks were removed, gently tapped clean of soil and placed on a plate of 2% water agar. Onion plants, as well as potato and pea plants growing as volunteers in onion fields, were removed from within and around stunted onion patches 23

over two growing seasons in Morrow County. Roots from each plant were cut into 2 cm long segments, washed gently three times with sterile distilled water, blotted dry with a paper towel, and placed on plates of 2% water agar. The subsequent steps were identical for both the toothpick-baiting and the root-isolation methods.

2.3.2 Identification of Rhizoctonia isolates

The morphology of mycelia growing on the water agar plates was examined with a dissecting microscope (Olympus SZ, 10 to 40 X) every 12 h. A hyphal tip was excised from each mycelium identified putatively as Rhizoctonia, and placed on a plate of potato dextrose agar (PDA) medium (Difco, Sparks, NV). If a PDA culture was found to be free of contaminating organisms, a working culture was maintained on PDA, and a PDA slant was made and archived in a laboratory collection at 4◦C. In preparation for DNA isolation, an isolate was cultured for up to 7 d in 15 mL of potato dextrose broth (PDB) (Difco,

Sparks, NV) in a 100 mm diameter x 15 mm deep petri dish at 25◦C. The mycelial mat of each isolate culture was washed three times in sterile distilled water and blotted dry on a

Whatman No. 1 filter disk. Cleaned mycelium was stored at -20◦C for, at most, two days if DNA extraction could not be performed immediately.

Total DNA was obtained from mycelia using the FastDNA Kit (QBIOgene, Carls- bad, CA) and FastPrep FP120 homogenizer (QBIOgene) according to the manufacturer’s 24

protocol. The DNA template for sequencing included the ITS1 + 5.8S + ITS2 rDNA re- gions amplified with the universal eukaryotic primers UN-UP18S42 (5’-CGTAACAAGG-

TTTCCGTAGGTGAAC-3’) and UN-LO28S576B (5’-GTTTCTTTTCCTCCGCTTATTA-

ATATG-3’) [Bakkeren et al., 2000, Schroeder et al., 2006]. PCR reactions consisted of

2 µL DNA template, 6 µL 5X buffer, 1.5 mM MgCl2, 0.2 mM of each deoxynucleotide,

10 pmol of each primer, and 1.25 U Taq polymerase (Promega Corp., Madison, WI) in a total volume of 30 µL. The PCR assay was performed using a PTC-200 thermocycler (MJ

Research, Reno, NV) using the following program: 94◦C for 3 min (1 cycle); 92◦C for

45 s, 60◦C for 45 s, 72◦C for 60 s (31 cycles); 72◦C for 10 min (1 cycle); held at 4◦C.

Agarose gel electrophoresis was used to verify PCR amplification before continuing with the sequencing procedure.

Unconsumed nucleotides and salts were removed from the PCR product mixture us- ing ExoSAP-IT (USB Corporation, Cleveland, OH) according to the manufacturer’s pro- tocol. The resultant ExoSAP-IT product was used for sequencing reactions, which were set up using a 10 µl total volume consisting of 4 µl ABI PRISM BigDye Terminator Cycle

Sequencing Ready Reaction Kit (Applied Biosystems, Foster City, CA), 4 pmol forward primer (listed above), and 5 µl ExoSAP-IT product. DNA amplification was performed with the following program: 2 min at 94◦C (1 cycle); and 2 min at 94◦C, 1 min at 50◦C, and 1.5 min at 60◦C (24 cycles). Sequencing reactions were cleaned of dye terminators, dNTPs and other low molecular weight materials using Performa DTR Gel Filtration Car- 25

tridges (EdgeBio, Gaithersburg, MD) according to the manufacturer’s protocol. This final product was dried in a DNA speed-vac concentrator and run on a 3730 DNA Analyzer

(Applied Biosystems, Foster City, CA) at the Laboratory of Biotechnology and Bioanaly- sis at Washington State University in Pullman, WA. One complete sequence was obtained for each region of the ITS1, 5.8S, and ITS2 sequence, and stretches of junk sequence at the ends were deleted using CLC Sequence Viewer Vers. 6 (CLC Bio, Aarhus, Denmark).

Sequence analysis involved a query against the NCBI sequence database using the online

BLAST tool. Only sequence homologies of 98 to 100% were considered in assigning a species, subgroup, and AG to each isolate.

2.3.3 Inoculum preparation and enumeration

A modification of the protocol developed by Paulitz and Schroeder[2005] was used in the preparation of Rhizoctonia inoculum. Briefly, 800 mL whole oats were mixed with

800 mL distilled water in a 1 gal (3.78 L) high-density polyethylene (HDPE) jug, and sealed with an autoclaveable foam plug and aluminum foil. The jug and contents were autoclaved twice for 90 min, once on each of two consecutive days. The sterilized oats were then inoculated with 30 1 cm diameter agar plugs taken from a 1 week-old PDA culture of the appropriate isolate. Jugs were incubated in the dark at 25◦C for 6 weeks, and the contents shaken once per week to facilitate complete colonization of oat kernels. The inoculum was 26

air-dried on Kraft paper, ground in a coffee grinder, and sieved to obtain particle sizes of

250 to 1000 µm. Inoculum was stored in plastic zip-lock bags at 4◦C in the dark until enumeration and use (no longer than six months).

Inoculum density was quantified by dilution plating the oat inoculum on a 2% water agar medium. In brief, 1:10 and 1:100 dilutions were made in triplicate from an initial suspension of 100 mg colonized oat inoculum in 5 mL sterile distilled water. A 200 µl aliquot of each suspension was spread on plates of 2% water agar of each dilution. Colony forming units (CFU) were counted using a dissecting microscope over a 48 h period, and inoculum densities were determined algebraically. Enumerated inoculum was stored in plastic zip-lock bags at 4◦C in the dark for use in pathogenicity studies within 1 week of enumeration.

2.3.4 Steam pasteurization of field soils

Field soil, instead of a potting medium, was utilized in the pathogenicity studies to emulate chemo-physical and biotic attributes of the natural soil environment of Columbia

Basin onion fields as closely as possible. Determining the effects of Rhizoctonia isolates on the health of onion plants required that other naturally-occurring pathogens be eradicated as they may have confounded experimental results. Prior studies have determined that most soilborne plant pathogenic fungi do not survive pasteurization at 60◦C, while a substantial 27

fraction of the saprophytic soil flora is unharmed following this treatment [Bollen, 1969,

Cook and Baker, 1983, Mitchell and Shaw, 1975, van Loenen et al., 2003]. Complete

sterility is not desirable, as the pathogen under study will colonize sterile soils much more

rapidly than soils having some measure of biological activity, and will therefore lead to con-

clusions that are seldom, if ever, realized in nature [Baker, 1962]. All pathogenicity studies

were performed using Sagehill fine sandy loam soils originating from uncultivated portions

of River Point Farms in Morrow County, OR (latitude 45.7887◦N, longitude 119.8433◦W).

Prior to inoculation of the soils with Rhizoctonia spp., soils were steam-pasteurized for 1 h at 60◦C, air-dried on Kraft paper, and sieved to a particle size of 2 mm.

2.3.5 Influence of inoculum density on disease severity caused by Rhizoc- tonia spp. on onion seedlings

An exploratory disease assay was conducted using only one isolate of each Rhizocto- nia taxon isolated from the Columbia Basin with the purpose of determining the influence of inoculum density on disease severity caused by each of the following 10 Rhizoctonia iso-

lates on onion: R. solani AG 2-1 (isolate Rh060811), AG 3 (Rh060801), AG 4 (Rh010901),

AG 5 (Rh070930), AG 8 (Rh070927); binucleate Rhizoctonia AG A (Rh010913), AG E

(Rh070923), AG I (Rh070914); and Waitea circinata var. circinata (Rh070924). Steam-

pasteurized field soil was infested with each Rhizoctonia isolate at seven inoculum con-

centrations: 0, 2.5, 4, 20, 100, 250 and 500 CFU/g soil. This inoculum concentration 28

series is a modification of that used by Paulitz and Schroeder [Paulitz and Schroeder, 2005,

Schroeder, 2004] in Rhizoctonia studies of wheat and barley. About 70 g infested soil was placed into each conetainer (16 cm long; 2.5 cm inside diameter; 66 ml volume) (Stuewe and Sons, Inc., Corvallis, OR) and watered with 30 ml water. One onion seed (cv. Talon,

Bejo Zaden B.V., Warmenhuizen, Netherlands) was placed on the soil surface of each tube and covered with 16 g noninoculated, pasteurized soil. Conetainers were arranged in a completely randomized design (CRD) in plastic trays. The conetainers were then covered with clear polyethylene plastic until emergence to maintain a high level of relative humid- ity. Plants were grown for a total of 6 weeks at 15/15◦C day/night temperatures with a 12 h photoperiod in a temperature-controlled greenhouse at the USDA-ARS Root Disease and

Biological Control Lab, Pullman, WA. Plants were watered 2 days after emergence and then at 2- to 3-day intervals to maintain a conetainer weight within 5% of the initial weight.

The study was carried out twice, and nine replicate plants were used per treatment.

Plants were harvested by destructively cutting open the conetainers and gently wash- ing the soil from the roots under running tap water. Plants were maintained on moistened paper towels until fresh plant observations could be completed on the same day. The fol- lowing dependent variables were measured immediately: plant shoot height (cm), rooting depth (cm), and total root length (cm). Plants were dried in paper bags at 60◦C for 72 hours, and the dry plant biomass (mg) was recorded. 29

2.3.6 Influence of inoculum density and isolate on disease severity caused by Rhizoctonia spp. on onion seedlings

Steam-pasteurized field soil was infested with Rhizoctonia isolates at each of eight concentrations: 0, 4, 8, 16, 32, 64, 128 and 256 CFU/g soil. The experimental conditions were identical to those described above except that plants were grown for a total of 8 weeks under a 12 h photoperiod with a 13◦C day temperature and 8◦C night temperature in a Percival PGC-20ALB plant growth chamber (Percival Scientific, Inc., Perry, IA) at the WSU Mount Vernon Northwest Research and Extension Center (NWREC), Mount

Vernon, WA. Plants were watered 2 days after emergence and then at 4- to 5-day intervals to maintain a conetainer weight within 5% of the initial weight. Plants were fertilized three times over the course of the trial with 20-20-20 NPK + micronutrients (JR Peters, Inc.,

Allentown, PA) dissolved in water.

Limited room in the plant growth chamber required that the experiment be divided into two studies. The first study focused on the influence of inoculum density and isolate on disease severity caused by isolates of R. solani AG 4 and AG 8 on onion seedlings. Three isolates of R. solani AG 4 (Rh010901, Rh070909, and Rh070929), and three isolates of R. solani AG 8 (Rh070927, Rh070922, and Rh070943) were used in the study. The second study focused on the influence of inoculum density and isolate on disease severity caused by isolates of R. solani AG 2-1, AG 3, and binucleate Rhizoctonia AG E on onion seedlings.

Three isolates of R. solani AG 2-1 (Rh060811, Rh070913, and Rh070937), three isolates 30

of R. solani AG 3 (Rh060801, Rh070933, and Rh070942), and one isolate of binucleate

Rhizoctonia AG E (Rh070923) were used. Each study was conducted twice, and 10 repli- cate plants were used per treatment. The required number of replications was determined from observed variances for several dependent variables in the study, as explained in the above section.

Plants were harvested from the conetainers as described in the previous section. The following dependent variables were measured immediately on the freshly harvested plants: shoot height (cm), rooting depth (cm), total root length (cm), total number of roots, and number of branched roots. Plants were dried in paper coin envelopes at 60◦C for 72 hours,

and dry plant biomass (mg) was recorded as the final dependent variable.

2.3.7 Growth of Rhizoctonia isolates on agar at different temperatures

To understand the potential for Rhizoctonia disease development in an onion field,

an investigation of the influence of temperature on colony growth rate of those Rhizoc-

tonia spp. determined previously to be pathogenic on onion was completed. This study

included 11 isolates of the following Rhizoctonia spp.: R. solani AG 3 (isolates Rh060801,

Rh070933, and Rh070942), R. solani AG 4 (Rh010901, Rh070909, and Rh070929), R.

solani AG 8 (Rh070922, Rh070927, and Rh070943), and binucleate Rhizoctonia AG E

(Rh070923). The isolates were transferred onto 1/5-strength PDA in 60 mm x 15 mm 31

sterile, polystyrene petri dishes using a 4-mm diameter mycelial disk of each isolate taken from the margin of a 5-day-old PDA culture. Each dish was sealed with Parafilm (Structure

Probe, Inc., West Chester, PA, USA) and incubated in the dark at 5, 10, 15, 20, 25, 30, or

35◦C, with four replicate plates/isolate/temperature. Colony diameter (mm) was measured

12, 24, 36, 48, 60, 72, 84, and 96 hours after inoculation along two perpendicular lines intersecting at the center of the colony. The experiment entailed a completely randomized design (CRD) and was carried out twice.

2.3.8 Data analyses

Studies of the effects of inoculum density and isolate of Rhizoctonia on disease sever- ity each entailed a CRD experimental design with either 9 or 10 replications. The depen- dent variable responses were normalized to a percentage of the control treatment (0 CFU/g soil), and the data were analyzed for interactions and mean separations as a mixed effects model using the PROC MIXED procedure with the default REML (Restricted Maximum

Likelihood) method of estimation in SAS 9.2 (SAS Institute, Cary, NC).

Plant-host interactions across a range of inoculum densities are highly amenable to quantitative analysis using the sigmoidal regression model [Baker, 1971], but in cases where no maximum asymptote is easily discernible, a linear function is better-suited, thus disease progress curves were described with either a linear or a nonlinear four-parameter 32

sigmoidal model. The linear function was of the form: Y = M ∗ X + B, where Y is the

control-treatment-normalized response at a log-transformed inoculum density of X = log(CFU +

1)/gsoil, M is the slope coefficient, and B is the y-intercept. The four-parameter sig- moidal function was of the form: Y = Min + (Max − Min)/(1 + 10(logED50−X)∗nH ), where

Y is the control-treatment-normalized response at a log-transformed inoculum density,

X, logED50 represents the log-transformed inoculum density at the point of inflection of the sigmoid curve, Min represents the lowest observed response, Max the highest ob- served response, and nH specifies a Hillslope. The effective inoculum dose (ED) which yielded 50% of the maximum modeled response (ED50) was either provided as a coeffi- cient in the sigmoidal model, logED50, or was derived algebraically from the linear model,

(((2.41∗M+B)∗0.5)−B)/M ED50 = 10 − 1. The ED90 was derived algebraically from the sig-

logED ∗9(1/nH ) moidal model, ED90 = 10 50 − 1, as well as from the linear model, ED90 =

10(((2.41∗M+B)∗0.9)−B)/M − 1. The effect of inoculum density and Rhizoctonia spp. on

the emergence of onion seedlings was assessed by four-parameter sigmoidal regression

analysis on control-treatment-normalized emergence response means plotted against log-

transformed inoculum densities. Means were determined by combining percent emergence

responses from two experiment replications and three isolates for each R. solani AG (AG

2-1: Rh060811, Rh070913, and Rh070937; AG 3: Rh060801, Rh070933, and Rh070942;

AG 4: Rh010901, Rh070909, and Rh070929; and AG 8: Rh070922, Rh070927, and

Rh070943), but only one isolate of binucleate Rhizoctonia AG E (Rh070923) because 33

there was only one isolate of the latter in the isolate collection. It is well established

2 2 2 that the coefficient of determination (R ) and the bias-corrected R (R ad j) are inappro- priate for demonstrating the performance or validity of nonlinear models, including the four-parameter sigmoidal model [Spiess and Neumeyer, 2010], and were, therefore, omit- ted for all analyses. Instead, the P-value derived from the ANOVA F-test was used as a measure of the validity of model fit. If a significant percent reduction in a response vari- able was observed at a particular inoculum density level, but neither a nonlinear sigmoidal nor a linear model could be fit to the data as a whole, it was concluded that no significant

(P ≤ 0.05 in the ANOVA F-test) reduction in that response variable occurred. A significant regression was required to model ED50 and ED90 estimates, as well as maximum reduc- tions in response variables as a percent of the noninoculated control treatment. Linear and nonlinear regression analyses were performed using SigmaPlot 11.0 (Systat Software Inc.,

San Jose, CA).

The study of growth of Rhizoctonia isolates on agar at different temperatures was treated as a one-way ANOVA. The data was analyzed as a mixed effects model using the

PROC MIXED procedure with the REML method of estimation in SAS 9.2. The main effect of experiment and the treatment-by-experiment interactions were not significant, thus results from the two experiments were combined for analysis to improve the statistical power of the test. 34

2.4 Results

2.4.1 Characterization of Columbia Basin Rhizoctonia isolates by rDNA ITS sequence analysis

Forty-five isolates of Rhizoctonia spp. were recovered from three commercial onion

fields in Morrow County, OR in which patches of severely stunted plants were observed during spring of 2009 and 2010 growing seasons. The isolates were each assigned to a species, subgroup, and AG based on high (98 to 100%) rDNA ITS sequence homologies with GenBank accessions: three isolates (7%) were R. solani AG 2-1, six isolates (13%)

were AG 3, eight isolates (18%) were AG 4, three isolates (7%) were AG 5, seven iso-

lates (16%) were AG 8, two isolates (4%) were AG 9; three isolates (7%) were binucleate

Rhizoctonia AG A, one isolate (2%) was AG E, one isolate (2%) was AG I; four isolates

(9%) were Waitea circinata var. circinata, and one isolate (2%) was W. circinata var. zeae.

For the remaining six isolates, three were identified as binucleate Rhizoctonia spp., but a further subgroup designation could not be assigned, and three isolates were identified as

Rhizoctonia spp., but no further designation could be assigned (Table 2.1).

Forty-four percent of the total isolate collection was recovered directly from the roots of onion plants growing within stunted patches. This subsection of the collection repre- sented a relatively wide genotypic range: out of 20 isolates, one isolate was R. solani AG

2-1, one was AG 3, four were AG 4, five were AG 8; three were binucleate Rhizoctonia AG 35

A, two were unknown binucleate Rhizoctonia spp.; two were W. circinata var. circinata, one was W. circinata var. zeae; and one was an unknown Rhizoctonia spp. Thirty-one percent of the total isolate collection was recovered directly from the roots of pea plants growing as volunteers within stunted onion patches: out of 14 isolates, two isolates were

R. solani AG 2-1, two were AG 3, four were AG 4, one was AG 8, two were AG 9; one was binucleate Rhizoctonia AG I, one was an unknown binucleate Rhizoctonia spp.; and one was an unknown Rhizoctonia spp. Thirteen percent of the total isolate collection was recovered directly from the roots of potato plants growing as volunteers in onion fields.

Half of these were recovered from potato plants growing outside patches of stunted onions, all of which were R. solani AG 3, and half were recovered from potato plants growing within patches of stunted onions, all AG 5. The remaining five isolates were baited from soils. Soils extracted from patches of stunted onions yielded one isolate each of R. solani

AG 8, W. circinata var. circinata, and an unknown species of Rhizoctonia. One isolate each of binucleate Rhizoctonia AG E and W. circinata var. circinata were baited from soils

extracted from unaffected portions of the onion fields. 36

Table 2.1: Rhizoctonia isolates obtained from three commercial onion fields in Morrow County, OR in 2009 and 2010.

Speciesa AG/subgroup Isolate Sourceb Locationc Accessiond Rhizoctonia AG 2-1 Rh060811 Pea Stunted FM867592, EU591804 solani AG 2-1 Rh070913 Pea Stunted DQ355130, AB054853 AG 2-1 Rh070937 Onion Stunted DQ355130, FJ435129 AG 3 Rh060801 Pea Stunted AB000024 AG 3 Rh070912 Pea Stunted AB019020, AY387569 AG 3 Rh070933 Potato Asymptomatic AB000041, AY387526 AG 3 Rh070934 Potato Asymptomatic AB019020, AY387528 AG 3 Rh070935 Potato Asymptomatic AY387528.1, AB019010 AG 3 Rh070942 Onion Stunted AB000024, GQ885147 AG 4 Rh010901 Onion Stunted FJ746956, EU591803 AG 4 Rh070908 Pea Stunted EU591803, EU591801 AG 4 Rh070909 Pea Stunted EU591803, EU591801 AG 4 Rh070910 Pea Stunted EU591809, HQ629872 AG 4 Rh070915 Pea Stunted EU591755, EU591759 AG 4 Rh070929 Onion Stunted EU591803, EU591801 AG 4 Rh070939 Onion Stunted AF354074, AF153776 AG 4 Rh070940 Onion Stunted FJ435140, FM867594 AG 5 Rh070930 Potato Stunted DQ355140, AF354113 AG 5 Rh070931 Potato Stunted DQ355140, EU591752 AG 5 Rh070932 Potato Stunted DQ355140, AF354112 AG 8 Rh060828 Onion Stunted AF354067, AF354068 AG 8 Rh070918 Onion Stunted AF354067, AF153797 AG 8 Rh070919 Onion Stunted DQ356413, AF354067 AG 8 Rh070922 Soil Stunted AF354068, AF354067 AG 8 Rh070927 Onion Stunted DQ356413, AF153798 AG 8 Rh070941 Onion Stunted DQ356413, AF354067 AG 8 Rh070943 Pea Stunted DQ356413, AF354067 AG 9 Rh070921 Pea Stunted AY154315, AB000037 AG 9 Rh070938 Pea Stunted AY154315, AB000046

Binucleate AG A Rh010907 Onion Stunted AJ242900, AY927356 Rhizoctonia AG A Rh010913 Onion Stunted AJ242900, AY927356

(continued on next page) 37

Table 2.1:(continued from preceding page) Speciesa AG/subgroup Isolate Sourceb Locationc Accessiond spp. AG A Rh090801 Onion Stunted AJ242900, EU591764 AG E Rh070923 Soil Asymptomatic DQ279013, AB290018 AG I Rh070914 Pea Stunted DQ356409, DQ356407 – Rh010905 Onion Stunted DQ356407, AJ242882 – Rh070911 Pea Stunted – – Rh090108 Onion Stunted DQ356407, EU645602

Waitea var. circinata Rh010909 Onion Stunted EU693449, DQ356414 circinata var. circinata Rh070924 Soil Stunted EU693448, FJ755858 var. circinata Rh070925 Soil Asymptomatic EU693449, DQ356414 var. circinata Rh070936 Onion Stunted EU693449, GQ521107 var. zeae Rh060815 Onion Stunted EU591763, DQ356414

Unknown – Rh010915 Onion Stunted DQ421232, AF407006 Rhizoctonia – Rh070920 Pea Stunted – spp. – Rh070926 Soil Stunted – aIsolated species included Rhizoctonia solani, binucleate Rhizoctonia spp., Waitea circinata, and unknown Rhizoctonia spp. bIsolates baited from naturally infested onion soils, or cultured from the roots of onion, pea, and potato plants growing in onion fields. cSoil and roots collected for fungal isolations originated either from patches of stunted onions, or from asymptomatic areas of onion fields. dGenBank accessions with 98 to 100% rDNA ITS sequence homologies with that of field isolates were used to assign species and AG/subgroup. 38

2.4.2 Influence of inoculum density on disease severity caused by Rhizoc- tonia spp. on onion seedlings

For the initial study of the effect of inoculum density on disease severity caused by one isolate of each representative Rhizoctonia spp. on onion seedlings, the four-parameter sigmoidal model accurately described 92% of the relationships between control-treatment- normalized response variables and log-transformed inoculum densities (Tables A1 and

A2 in AppendixA). The exception was for R. solani AG 2-1 in the first experiment, which required the fit of a linear model.

Table A1 in AppendixA shows that in the initial study of one isolate of each repre- sentative Rhizoctonia species, isolates of R. solani AGs 2-1, 3, 4 and 8, as well as binucleate

Rhizoctonia AG E caused reductions in plant biomass ranging from 29.9 to 57.1%, and re- ductions in shoot height ranging from 24.5 to 53.4% of that of the noninoculated control plants. The isolate of R. solani AG 5 (Rh070930) originated from volunteer potato roots, and reduced onion seedling height by 28.0%, total root length by 36.7% and rooting depth by 62.5% in Experiment 2, but the disease response was observed only at high inoculum densities (100 to 500 CFU/g soil), and the results were not observed in Experiment 1.

The isolate of W. circinata var. circinata (Rh070924) baited from onion soil consistently reduced onion seedling weight and rooting depth, although the reductions were relatively small (20.7 to 24.5% of the noninoculated control plants), and the disease response was ob- served only at the two greatest inoculum densities, 250 and 500 CFU/g soil (Figure A1 in 39

AppendixA). Below inoculum densities of 250 CFU/g soil, the W. circinata isolate signif-

icantly increased plant weight, height, and rooting depth compared with the noninoculated

control plants. Isolates of binucleate Rhizoctonia AGs A (Rh010913) and I (Rh070914)

were nonpathogenic, and significantly increased plant weight, height, and rooting depth

compared with the noninoculated control plants at low and high inoculum densities (Fig-

ure A1). None of the Rhizoctonia spp. induced significant root branching over that of the

noninoculated control plants.

Mean reductions in total root length were 1.9 times greater than mean reductions in

plant height for the pathogenic isolates of R. solani AGs 3, 4 and 8, as well as binucle-

ate Rhizoctonia AG E. However, the pathogenic isolate of R. solani AG 2-1 caused nearly

equivalent mean reductions in these above- and below-ground variables (Table A1 in Ap-

pendixA). Modeled reductions in plant weight and height ranged from 24.5 to 57.1% for

the pathogenic isolates and, excluding the R. solani AG 2-1 isolate, modeled reductions in total root length and rooting depth ranged from 51.3 to 88.5%. The R. solani AG 2-1 isolate reduced plant weight by 36.0 to 41.9%, height by 24.5 to 40.7%, and total root length by

38.6 to 40.6%. The AG 3 isolate reduced plant weight by 35.7 to 57.1%, height by 28.3 to

48.6%, and total root length by 65.6 to 86.2%. The AG 4 isolate reduced plant weight by

29.9 to 53.3%, height by 28.9 to 43.9%, and total root length by 64.4 to 79.2%. The AG

8 isolate reduced plant weight by 34.4 to 44.4%, height by 27.9 to 38.0%, and total root length by 65.8 to 70.5%. The binucleate Rhizoctonia AG E isolate reduced plant weight by 40

30.8 to 42.8%, height by 35.5 to 53.4%, and total root length by 51.3 to 81.1%. 41 C. Data for repeats ◦ Max. . 50 ED isolates and grown at 15 Rhizoctonia 100 - % of noninoculated control c Max. AG 2-1 (isolate Rh060811), AG 3 (Rh060801), AG 4 (Rh010901), and AG 8 Experiment 1 Experiment 2 b AG E (Rh070923). 50 R. solani Rhizoctonia Isolate ED a estimate and maximum reduction ranges (low to high (mean)) modeled for the combined plant weight, height, and 50 species included isolates of ED Species AG 2-1AG 3 Rh060811AG 4 24.0–40.4AG (32.8) Rh060801 8AG Rh010901 77.4–252.1 E (159.1) 24.5–38.6 (33.1) 135.2–153.6 Rh070927 (145.6) 28.3–65.6 (43.2) 28.9–64.4 Rh070923 (41.1) 3.3–13.8 (7.1) 6.3–93.7 144.8–254.0 28.6–139.6 (35.8) (192.4) 2.6–3.9 (96.7) (3.2) 40.6–41.9 30.8–51.3 43.9–79.2 (41.1) (39.2) (58.8) 48.6–86.2 (64.0) 47.1–103.0 (81.6) 27.9–65.8 (42.7) 42.8–81.1 (59.1) 9.3–10.4 (9.8) 38.0–70.5 (51.0) estimates expressed as colony forming units/g soil. 50 Rhizoctonia ED Mean maximum modeled reductions in response variables expressed as a (Rh070927), as well asb a binucleate c Table 2.2: total root length responses of 6-week-old onion seedlings inoculated with of the experiment are shown in separate columns. 42

Of the five isolates which were consistently pathogenic in both experiments, only

R. solani AG 8 achieved maximal disease response at relatively low inoculum densities, as ED50 estimates ranged from 2.4 to 10.4 CFU/g soil, and ED90 estimates from 3.3 to

17.6 CFU/g soil for all response variables. Therefore, a low inoculum concentration could induce a 34.3 to 44.4% reduction in seedling biomass. In one repeat of the experiment,

ED50 estimates for plant weight and height ranged from 4.0 to 13.8 CFU/g soil for isolates of R. solani AG 2-1 and 3. Only ED90 estimates for plant weight and total root length were derivable for the AG 2-1 isolate in Experiment 2, and the estimates were 8.3 and 5.5

CFU/g soil, respectively. The results differed in Experiment 1, in which the ED50 estimates for all response variables ranged from 24.0 to 40.6 CFU/g soil for the AG 2-1 isolate and

77.4 to 252.1 CFU/g soil for the AG 3 isolate. In the same experiment, ED90 estimates for all response variables ranged from 274.0 to 303.5 CFU/g soil for the AG 2-1 isolate. The

R. solani AG 4 and binucleate Rhizoctonia AG E isolates caused reduced growth at greater inoculum densities, as signified by ED50 estimates for all dependent variables ranging from

28.6 to 254.0 CFU/g soil. All ED90 estimates for the AG 4 isolate fell outside of the range of inoculum densities tested, while half of the ED90 estimates were derivable for the AG E isolate, and ranged from 157.1 to 426.7 CFU/g soil.

Averaging maximum modeled reductions and ED50 estimates over plant height, weight, and total root length responses revealed specific patterns in isolate virulence (Table 2.2).

Of the five pathogenic isolates, R. solani AG 3 isolate Rh060801 caused the greatest reduc- 43

tions in onion growth in both repeats of the experiment (43.2 and 64.0% in Experiments 1 and 2, respectively), but had the second and third largest mean ED50 estimates (159.1 and

35.8 CFU/g soil). The reverse was true for AG 2-1 isolate Rh060811, which caused the smallest reductions in onion growth in both repeats of the experiment (33.1 and 41.1%), but had the second smallest (32.8 CFU/g soil) and smallest (7.1 CFU/g soil) mean ED50 estimates. AG 8 isolate Rh070927 caused moderate levels of stunting, and had the smallest

(3.2 CFU/g soil) and second smallest (9.8 CFU/g soil) mean ED50 estimates. Binucleate

Rhizoctonia AG E isolate Rh070923 also caused moderate levels of stunting, but had the greatest (192.4 CFU/g soil) and second greatest (81.6 CFU/g soil) mean ED50 estimates.

Similar results were observed for R. solani AG 4 isolate Rh010901. Considering virulence as a gradient, with the most virulent isolate being that which causes the greatest reduction in plant growth and has the lowest ED50 estimate, the R. solani AG 8 isolate was highly virulent on onion, while the AG 2-1 and AG 3 isolates were moderately virulent, and the

R. solani AG 4 and binucleate Rhizoctonia AG E isolates were of low virulence at 15◦C.

The purpose of the second study was to test multiple isolates (if available) of each

Rhizoctonia spp. and AG found to be pathogenic on onion in the above study, using slightly cooler (13/8◦C) day/night temperatures. The four-parameter sigmoidal model accurately described 68% of the relationships between control-treatment-normalized response vari- ables and log-transformed inoculum densities (Table A4). Eighteen percent of the relation- ships were described with a linear model, and 14% percent of the relationships could not 44

be fit with either model. The linear model was primarily used to describe the root-related

responses of two R. solani AG 2-1 isolates (Rh060811 and Rh070937), as well as that of

the binucleate Rhizoctonia AG E isolate. Table A3 and Figures A2 to A6 (AppendixA)

show that all three isolates of R. solani AG 2-1, all three isolates of AG 3, two isolates of

AG 4, all three isolates of AG 8, and the binucleate Rhizoctonia AG E isolate significantly reduced growth of onion seedlings, while one isolate of AG 4 was nonpathogenic on onion. 45 C day/night ◦ Max. . 50 isolates and grown at 13/8 ED Rhizoctonia 100 - % of noninoculated control c Max. Experiment 1 Experiment 2 b 50 AG 2-1 (isolates Rh060811, Rh070913, and Rh070937), AG 3 (Rh060801, Rh070933, and R. solani AG E (Rh070923). Isolate ED Rh070913Rh070937 4.9–28.7 (15.0) 10.6–19.2 (14.9)Rh070933 49.3–83.2 (60.7)Rh070942 47.2–59.2 (53.2) 5.2–24.5 (12.8) 13.4–41.2 (24.8) 13.3–44.4 (32.6) 13.9–30.8 (22.3)Rh070909 36.0–74.0 50.9–99.8 (50.3) (71.3)Rh070929 27.7–40.3 48.1–74.2 (34.0) (59.2) 7.0–19.0 (14.7) 3.2–28.7 11.9–67.6 (14.9) (35.7)Rh070927 –Rh070943 53.8–75.1 33.7–75.1 (64.0) (51.4) 10.0–25.6 (18.6) 26.9–34.4 (31.1) 2.5–5.1 (3.9) 46.6–90.7 7.9–24.7 (63.1) (14.8) 15.4–34.8 (28.0) 4.6–6.1 54.8–93.4 (5.4) (70.0) 54.9–86.9 1.9–4.0 (70.9) – (3.1) 42.6–89.8 (63.1) – – estimate and maximum reduction ranges (low to high (mean)) modeled for the combined plant weight, height, a 50 species included ED Rhizoctonia Species AG 2-1 Rh060811 5.1–11.0 (8.0) 47.6–48.2 (47.9) 8.0–43.7 (21.8) 53.6–65.8 (60.8) AG 3 Rh060801AG 4 3.3–20.4 (10.9) Rh010901 44.1–84.1 100.8–153.7AG (61.0) (127.3) 8 25.7–30.7 (28.2) 6.0–55.5 (28.4) Rh070922 15.4–103.0 (59.2)AG 38.5–57.3 E (46.3) 17.0–27.5 (22.3) 0.8–20.3 (8.3) Rh070923 18.6–49.7 (35.4) 2.9–14.4 (7.0) 2.9–33.0 (14.2) 44.1–100.0 (71.6) 36.9–69.7 (55.8) 4.1–13.8 (8.7) 62.3–100.0 (89.2) estimates expressed as colony forming units/g soil. 50 ED Rhizoctonia Mean maximum modeled reductions in response variables expressed as c b a Rh070942), AG 4 (Rh010901, Rh070909, andbinucleate Rh070929), and AG 8 (Rh070922, Rh070927, and Rh070943), as well as a and total root length responses of 8-week-old onion seedlings inoculated with Table 2.3: temperatures. Data for repeats of the experiment shown in separate columns. 46

Based on significant model fits, the isolates of R. solani AG 2-1 reduced plant weight by 36.0 to 62.9%, height by 27.7 to 53.6%, and total root length by 40.3 to 83.2%. All regressions were significant (P ≤ 0.05), the exceptions being weakly significant fits for total root length for isolate Rh060811 in the first experiment (P = 0.0551) and for plant weight for isolate Rh070937 in both experiments (P = 0.0941 and 0.0587), as well as a non-significant fit for root number for isolate Rh070913 in the first experiment. The R. solani AG 3 isolates reduced plant weight by 38.5 to 63.3%, height by 33.7 to 53.8%, and total root length by 57.3 to 99.9%. All regressions were significant, the exceptions being weakly significant fits for root number for isolate Rh060801 in the second experiment

(P = 0.0799) and for isolate Rh070942 in the first experiment (P = 0.0630). R. solani AG

4 isolates reduced plant weight by 25.7 to 34.4%, height by 15.4 to 30.7%, and total root length by 22.0 to 34.8%. The greatest within-AG variability was observed among the R. solani AG 4 isolates, with only one isolate, Rh070929 originating from onion roots, causing consistently significant disease responses across all dependent variables. R. solani AG 4 isolate Rh010901, baited from onion roots, caused significant reductions in onion seedling weight and height in both repeats of the experiment. Interestingly, the total root length, rooting depth, and number of roots were not reduced significantly, other than one weakly significant reduction (P = 0.1025) in total root length in the first experiment. R. solani AG

4 isolate Rh070909 baited from pea roots caused a significant reduction in onion seedling dry weight in one experiment, but not in the repeat experiment. No significant reductions 47

occurred for the other response variables, thus the isolate was considered nonpathogenic on

onion. In fact, this isolate significantly stimulated aboveground seedling growth, but root

growth was not stimulated (Figure A4 in AppendixA). Plant weight increased significantly

at higher inoculum densities in the first experiment, but not in the repeat experiment. R.

solani AG 8 isolates reduced plant weight by 37.9 to 61.7%, height by 18.6 to 54.8%, and

total root length by 49.7 to 93.4%. AG 8 isolate Rh070922 did not significantly reduce root

number, and neither did AG 8 isolate Rh070943 in the second experiment. The plant height

response for AG 8 isolate Rh070927 in the second experiment was weakly significant (P =

0.0847). The binucleate Rhizoctonia AG E isolate caused 85.0 to 97.6% reductions in total root length at inoculum densities ranging from 64 to 256 CFU/g soil, and reduced seedling weight by 56.9 to 90.5%, and height by 44.1 to 62.3%. 48

Figure 2.3: Rhizoctonia solani AG 8 isolate Rh070943 significantly reduced the growth of 8-week- old onion seedlings at inoculum densities of 4 and 16 CFU/g soil compared with noninoculated control plants (0 CFU/g soil). The plants were grown in a plant growth chamber under a 12 h photoperiod and 13/8◦C day/night temperatures. 49

Comparing maximum modeled reductions and effective doses for three key response variables (plant weight, height, and total root length) elucidated some patterns in virulence among the pathogenic isolates (Table 2.3 and Figure A7 in AppendixA). Two isolates, R. solani AG 8 isolate Rh070943 (Figure 2.3) and the binucleate Rhizoctonia AG E isolate, caused severe onion stunting at low inoculum densities in both repeats of the experiment, and were, therefore, the most virulent isolates on onion at the 13/8◦C day/night tempera- ture regime. The AG 8 isolate caused a 54.8 to 93.4% (mean 70.0%) reduction in growth in the first experiment, and a 42.6 to 89.8% (mean 63.1%) reduction in the second exper- iment. The ED50 estimate for this isolate ranged from 2.5 to 5.1 CFU/g soil (mean 3.9

CFU/g soil) in the first experiment, and 1.9 to 4.0 CFU/g soil (mean 3.1 CFU/g soil) in the second experiment. Additionally, of all the pathogenic isolates studied, only AG 8 isolate

Rh070943 had consistently low ED90 estimates for all three response variables, ranging from 12.4 to 22.1 CFU/g soil (mean 15.7 CFU/g soil) in the first experiment, and 6.4 to

8.9 CFU/g soil (mean 7.2 CFU/g soil) in the second experiment. The AG E isolate caused a 44.1 to 100.0% (mean 71.6%) reduction in growth in the first experiment, and a 62.3 to 100.0% (mean 89.2%) reduction in the second experiment. The ED50 estimate for this isolate ranged from 2.9 to 14.4 CFU/g soil (mean 7.0 CFU/g soil) in the first experiment, and 4.1 to 13.8 CFU/g soil (mean 8.7 CFU/g soil) in the second experiment. Also highly virulent on onion, both AG 8 isolate Rh070927 and AG 3 isolate Rh070933 had low ED50 estimates and greatly reduced plant growth, averaging 63.1 and 70.9% for the AG 8 iso- 50

late in Experiments 1 and 2, respectively, and 71.3 and 64.0% for the AG 3 isolate. The

ED50 estimate for AG 8 isolate Rh070927 ranged from 10.0 to 25.6 CFU/g soil (mean 18.6

CFU/g soil) in the first experiment, and 4.6 to 6.1 CFU/g soil (mean 5.4 CFU/g soil) in the second experiment. The ED50 estimate for AG 3 isolate Rh070933 ranged from 5.2 to

24.5 CFU/g soil (mean 12.8 CFU/g soil) in the first experiment, and 7.0 to 19.0 CFU/g soil

(mean 14.7 CFU/g soil) in the second experiment.

Again, considering plant weight, height, and total root length together, AG 8 isolate

Rh070922 caused symptoms at relatively low inoculum densities, as ED50 estimates ranged from 0.8 to 20.3 CFU/g soil (mean 8.3 CFU/g soil) in the first experiment, and 2.9 to 33.0

CFU/g soil (mean 14.2 CFU/g soil) in the second experiment. The isolate caused mod- erate reductions in plant growth: 35.4 and 55.8% mean reductions in Experiments 1 and

2, respectively. All AG 2-1 and AG 3 isolates, caused moderate levels of stunting and had relatively small ED50 estimates, the exception being AG 3 isolate Rh070942, which had the second greatest ED50 estimates of the pathogenic isolates, averaging 32.6 CFU/g soil in the

first experiment, and 35.7 CFU/g soil in the second experiment. The largest ED50 values were estimated for AG 4 isolate Rh010901, ranging from 100.8 to 153.7 CFU/g soil (mean

127.3 CFU/g soil) in the first experiment, and 15.4 to 103.0 CFU/g soil (mean 59.2 CFU/g soil) in the second experiment. The two pathogenic AG 4 isolates caused the smallest reductions in plant growth: 28.2 and 22.3% mean reductions for isolate Rh010901 in Ex- periments 1 and 2, respectively, and 31.1 and 28.0% mean reductions for isolate Rh070929. 51

Using the definition of virulence previously described, R. solani AG 8 isolate Rh070943

and the binucleate Rhizoctonia AG E isolate were more virulent on onion than all other pathogenic isolates at the 13/8◦C day/night temperature regime. AG 8 isolate Rh070927 and AG 3 isolate Rh070933 were highly virulent on onion. AG 4 isolates Rh010901 and

Rh070929, as well as AG 3 isolate Rh070942 were of low virulence, while all other isolates were of moderate virulence. 52

Figure 2.4: Effect of inoculum density and Rhizoctonia spp. on the emergence of onion seedlings expressed as 100 - % of noninoculated control. The four-parameter sigmoidal regression analysis was performed on the combined responses to three isolates of each Rhizoctonia solani AG (AG 2-1: Rh060811, Rh070913, and Rh070937; AG 3: Rh060801, Rh070933, and Rh070942; AG 4: Rh010901, Rh070909, and Rh070929; AG 8: Rh070922, Rh070927, and Rh070943), but only one isolate of binucleate Rhizoctonia AG E (Rh070923), over two repeats of the experiment. 53

There were no degrees of freedom available for the statistical analysis of percent emergence for each treatment combination (isolate-by-inoculum density), thus data were combined for all isolates belonging to a particular Rhizoctonia spp. and for both repeats of the experiment (Figure 2.4). Fitting the combined data with a four-parameter sigmoidal model it was found that isolates of all Rhizoctonia spp. studied except the R. solani AG

4 and 8 isolates significantly reduced emergence compared with that of the noninoculated control plants, and the maximum modeled reductions in emergence ranged from 37 to

100% (Table A5 in AppendixA). R. solani AG 2-1 isolates caused a significant (P =

0.0218) reduction in emergence, leading to a maximum modeled reduction of 37.1% of that of the noninoculated control plants, as well as the smallest derived ED50 estimate of

13.8 CFU/g soil. R. solani AG 3 isolates caused a significant maximum modeled reduction of 41.7% (P = 0.0075), and an intermediate ED50 estimate of 29.9 CFU/g soil. The isolate of binucleate Rhizoctonia spp. AG E caused the most severe pre-emergence damping-off of the isolates studied, leading to a maximum modeled reduction of 100% and an ED50 of 34.1 CFU/g soil (P = 0.0006). In both repeats of the experiment, the AG E isolate prevented seedling emergence at the greatest inoculum density (256 CFU/g soil). In the first experiment, there was no emergence at the second highest inoculum density (128 CFU/g soil), and only one plant emerged at this inoculum level in the second experiment. 54

2.4.3 Growth of Rhizoctonia isolates on agar at different temperatures

Results of the initial disease assay demonstrated that single field isolates of R. solani

AGs 3, 4, and 8, as well as binucleate Rhizoctonia AG E were pathogenic on onion seedlings, thus this growth rate study was undertaken to determine the temperatures under which op- timum growth of isolates of these Rhizoctonia spp. occurs on an agar medium. Because no significant interaction was observed between repeats of the experiment and other main fac- tors, the data from the two experiments were combined for statistical analysis. The colony growth rate response of all Rhizoctonia isolates studied was best described as having a neg- atively skewed normal distribution with elongated tails between 5 and 25◦C (Figure 2.5).

Every isolate showed a maximum rate of radial growth from 25 to 30◦C. Only R. solani

AG 4 isolate Rh070929 grew significantly more rapidly at 30 than at 25◦C, while isolates

Rh070942 (AG 3), Rh070909 (AG 4), and Rh070918 (AG 8) each grew as rapidly at 30 as at 25◦C (data not shown). All other isolates grew most rapidly at 25◦C. A rapid reduction in growth rate occurred from 30 to 35◦C, from near-optimal growth rates to 1 to 2 orders of magnitude below the optimum (Table 2.4). All isolates except the three R. solani AG

4 isolates grew significantly more rapidly at 5 than at 35◦C, although the growth rate dif- ferences between the temperature extremes were small. At 35◦C, four isolates had growth rates which did not differ significantly from 0 mm/day: R. solani AG 3 isolate Rh070933,

R. solani AG 8 isolates Rh070927 and Rh070943, and binucleate Rhizoctonia AG E iso- 55

late Rh070923. All three R. solani AG 8 isolates had negligible growth rates at this high

temperature.

Averaged across isolates of the same AG at 25◦C, R. solani AG 4 had the largest

growth rate maximum, 34.5 mm/day, followed by the binucleate Rhizoctonia AG E isolate at 30.9 mm/day, R. solani AG 3 at 30.6 mm/day, and R. solani AG 8 at 18.8 mm/day. At this temperature, significant within-AG variation was observed for both AG 3 isolates (ranging from 26.7 to 33.0 mm/day) and AG 4 isolates (ranging from 32.0 to 37.1 mm/day). How- ever, growth rates for all three AG 8 isolates did not differ significantly. Again averaging across isolates, AG 4 isolates had significantly slower growth rates at 5◦C than all other

Rhizoctonia spp., but beginning at 10◦C, the three isolates of AG 8 grew significantly more slowly than all other Rhizoctonia spp., and this held true across the temperature gradient tested. The growth rates exhibited by three AG 8 isolates were nearly half that of the AG

3, AG 4, and AG E isolates from 15 to 30◦C. 56

Figure 2.5: Radial colony growth rate (mm/day) of Rhizoctonia solani AG 3 (isolates Rh060801, Rh070933, and Rh070942), R. solani AG 4 (Rh010901, Rh070909, and Rh070929), R. solani AG 8 (Rh070922, Rh070927, and Rh070943), and a binucleate Rhizoctonia AG E (Rh070923) on an agar medium at 5 to 35◦C. Each data point is the mean of eight replications. 57

Table 2.4: Radial colony growth rate (mm/day) of Rhizoctonia isolates on agar medium at different temperatures (results of two experiments combined).a

Temperature (◦C) Speciesb Isolate 5 10 15 20 25 30 35 AG 3 Rh060801 4.5 bc 10.2 ab 19.9 bc 23.3 d 33.0 bc 31.3 c 2.3 b Rh070933 3.5 d 8.8 cd 17.9 de 22.5 d 26.7 e 22.4 e 0.1 cd Rh070942 5.0 ab 11.1 a 20.8 ab 25.3 c 32.2 cd 31.8 bc 2.6 ab AG 4 Rh010901 2.3 e 8.7 cde 19.3 cd 26.3 bc 37.1 a 33.4 ab 2.8 a Rh070909 2.2 e 9.2 bc 21.4 a 28.1 a 34.6 b 34.1 a 2.9 a Rh070929 1.8 e 7.5 efg 17.5 e 22.1 d 32.0 cd 34.1 a 2.5 ab AG 8 Rh070922 4.6 b 7.8 def 11.8 f 14.2 e 19.1 f 18.4 f 0.4 c Rh070927 3.8 d 6.5 fg 11.1 fg 15.1 e 19.5 f 17.1 f 0.2 cd Rh070943 3.9 cd 6.4 g 10.4 g 14.1 e 17.9 f 15.2 g 0.3 cd AG E Rh070923 5.4 ab 11.4 a 20.0 abc 27.2 ab 30.9 d 25.9 d 0.0 d aMeans within a column followed by the same letter are not significantly different (P ≥ 0.05) based on least square means comparisons. bAG = anastomosis group. Rhizoctonia spp. studied included three isolates each of R. solani AG 3, AG 4, and AG 8, as well as one binucleate Rhizoctonia AG E isolate. 58

2.5 Discussion

This is the first published report of the pathogenicity of Rhizoctonia spp. on onions

for isolates obtained from the Pacific Northwest, semi-arid, irrigated region of onion bulb

production in the United States. Rhizoctonia-induced stunting of onion has only recently

been identified as a problem by Columbia Basin growers, thus a limited number of af-

fected fields was available for collecting Rhizoctonia isolates in 2009 and 2010. Even so, a great diversity of multinucleate and binucleate Rhizoctonia spp. was found to inhabit the sampled onion soils, perhaps associated with the complex cropping sequences of the region which include many temperate vegetables, graminaceous crops, legumes, and herbs.

Diversity of host crops has been correlated with diversity of the species complex in other diverse vegetable systems in temperate climates [Juan-Abgona et al., 1996, Budge et al.,

2009, Ohkura et al., 2009]. In all, eleven unique Rhizoctonia genotypes were identified to species and subspecific grouping, including R. solani AGs 2-1, 3, 4, 5, 8, and 9; binucleate

Rhizoctonia AG A, E, and I; as well as Waitea circinata var. circinata and var. zeae. The most dominant species were AG 4 (18%), AG 8 (16%), and AG 3 (13% of isolates), which is not surprising considering that AG 4 has a wide host range [Anderson, 1982], while AG

8 is commonly associated with wheat and barley [Ogoshi, 1987], and AG 3 with potato

[Carling et al., 1986], all important rotational crops in the region. This is the first report of R. solani AG 9 isolated from naturally infected pea plants in Oregon, although the AG 59

had previously been collected from soils in wheat and barley fields in the Pacific Northwest

[Carling et al., 1987, Ogoshi et al., 1990]. Isolates of AG 9 are believed not to be pathogens

of pea [Carling et al., 1987, Yang et al., 1996], and the pathogenicity of AG 9 isolates on

onion were not tested. This is also the first report of binucleate Rhizoctonia AG I isolated

from pea in Oregon, a fungal species recently implicated as a member of the Rhizoctonia

complex causing root rot of canola in Washington, and for which pathogenicity screenings

have shown stunting of pea [Schroeder and Paulitz, 2012]. Although AG-5-potato associa-

tions have been observed in Maine [Bandy et al., 1988], North Dakota [Carling and Leiner,

1990], and other temperate regions [Farrokhi-Nejad et al., 2007, Woodhall et al., 2007,

Lehtonen et al., 2008], this is the first report of the association in Oregon.

Isolates of R. solani AGs 2-1, 3, 4, 8, W. circinata var. circinata, and binucleate

Rhizoctonia AG E were found consistently to be pathogenic on onion seedlings, although

virulence differed among isolates of a species and among species. AG 8 isolates caused se-

vere stunting of onion at the low inoculum densities expected of agricultural soils [Paulitz

and Schroeder, 2005, Sumner et al., 1995], but did not reduce seedling emergence over a

range of temperatures characteristic of north-central Oregon and south-central Washington

between February and April, when spring-planted onions appear to be most susceptible to

infection by Rhizoctonia species. Because wheat and barley are commonly used as win- ter cover crops in this region, as early-season wind-break crops in fields in which onion stunting develops, this finding affirms the possibility of a disease bridge between the gram- 60

inaceous and onion crops. AG 8 was also implicated as the main cause of onion stunting in Australia, where wheat and barley are similarly utilized to protect onion seedlings from wind-blown sand [Wicks et al., 2010]. Conversely, Farrokhi-Nejad et al.[2007] found that isolates of AG 8 recovered from potato tubers in New Zealand caused the lowest disease severity on onion of the Rhizoctonia spp. studied.

The R. solani AG 8 isolate collected from onion (Rh070927) caused up to a 1.7 times greater reduction in plant growth (average of plant weight, height, and total root length responses) at 13/8◦C day/night temperatures than at 15/15◦C, although symptoms occurred at lower inoculum densities at the warmer temperatures. At the lower temperature regime,

AG 8 isolate Rh070943, collected from pea, was markedly more virulent than the other two

AG 8 isolates studied, causing severe reductions in plant weight at low inoculum densities.

Seemingly rare in agricultural soils of the Pacific Northwest, one isolate of binucleate Rhi- zoctonia AG E, a species previously observed to be either weakly virulent [Burpee et al.,

1980a, Farrokhi-Nejad et al., 2007] or avirulent [Sumner et al., 1997], was highly virulent at 13/8◦C, but caused limited disease at 15/15◦C. The AG E isolate also significantly re- duced onion emergence, leading to complete pre-emergence damping-off at high inoculum densities. The finding that AG 2-1 and AG 3 isolates were mostly of moderate virulence on onion is consistent with other studies [Farrokhi-Nejad et al., 2007, Wicks et al., 2010].

In this study, however, one AG 3 isolate (Rh070933) was highly virulent at the cooler tem- peratures, while at the warmer temperatures, another AG 3 isolate (Rh060801) caused the 61

most severe onion stunting of the isolates tested. This is the first published finding that

AG 2-1 and AG 3 isolates caused substantial reductions in onion emergence: 37.1 ± 8.8

and 41.7 ± 10.4%, respectively. Two AG 4 isolates were of low virulence, and one isolate was nonpathogenic on onion at the cooler temperature regime. Some researchers have also observed considerably variable disease responses to isolates of this AG [Ichielevich-Auster et al., 1985, Juan-Abgona et al., 1996, Wicks et al., 2010], although not all [Erper et al.,

2006, Farrokhi-Nejad et al., 2007].

One isolate each of binucleate Rhizoctonia AG A and AG I were nonpathogenic on onion and, instead, significantly stimulated plant growth across a range of inoculum densities. AG I isolates have not been studied on onion, and it is likely that significant within-AG variation exists among isolates of AG A, as Juan-Abgona et al.[1996] found three significantly different levels of virulence, ranging from low to moderate. On the other hand, Burpee et al.[1980a], Ichielevich-Auster et al.[1985], and Sumner et al.[1997] demonstrated that 14 isolates of AG A among these studies were all nonpathogenic to onion. An isolate of W. circinata var. circinata evaluated in this study was weakly virulent on onion, causing limited reductions in plant growth (20.7 to 21.9% in biomass) at high inoculum densities. Although isolates of W. circinata var. zeae can be moderately virulent

on onion [Ichielevich-Auster et al., 1985, Erper et al., 2006], this is the first report on the

virulence of W. circinata var. circinata on onion. An isolate of R. solani AG 5 did not

cause significant reductions in plant biomass, and inconsistently impacted other response 62

variables. AG 5 isolates have been shown to cause moderate levels of disease on onion

[Farrokhi-Nejad et al., 2007], and to be nonpathogenic [Ichielevich-Auster et al., 1985].

Eighty-two percent of Rhizoctonia isolates used in this study were obtained from plant roots rather than soil, so it is difficult to assess if those Rhizoctonia strains isolated directly from crop roots were more virulent than the isolates baited from bulk soil, as some researchers have observed [Yitbarek et al., 1987]. Both the binucleate AG E isolate

(Rh070923) baited from soil in a healthy portion of the field, and the R. solani AG 8 isolate

(Rh070922) baited from soil in a stunted patch caused significant stunting of onion. Isolate

Rh070922 caused smaller reductions in onion growth than did AG 8 isolates from onion and pea roots. Many isolates baited from the roots of pea and potato plants that were volunteers in onion fields proved to be pathogenic on onion, demonstrating that rotational vegetable crops can potentially act as a disease bridge for Rhizoctonia to infect onion plants.

Interestingly, the AG 8 isolate from pea (Rh070943) and the AG 3 isolate from potato

(Rh070933) were more virulent on onion than isolates of those AGs baited from onion.

Similarly, an AG 2-1 isolate from pea (Rh060811) caused a 1.8 times greater reduction in onion growth than did the AG 2-1 isolate from onion (Rh070937) in the second experiment, and it induced symptoms at lower inoculum densities than did isolate Rh070937 in both repeats of the experiment. However, this was not true for AG 4, as the two isolates from onion (Rh010901 and Rh070909) caused significant disease, while the AG 4 isolate from pea (Rh070909) was nonpathogenic on onion. 63

Consistent with the literature [Carling and Leiner, 1990, Lehtonen et al., 2008], rela-

tive growth rate of Rhizoctonia isolates was not found to be significantly positively cor-

related with virulence within an AG. At 5 to 15◦C, the temperature range used in the

pathogenicity experiments, R. solani AG 3 isolate Rh070933 grew significantly slower

than the other two AG 3 isolates but caused the most severe onion stunting. Similarly, of

the R. solani AG 8 isolates studied, Rh070922 grew significantly faster than Rh070943 at 5

to 15◦C, but isolate Rh070943 nonetheless caused substantially greater reductions in onion

growth. Isolate Rh070909 grew fastest of the three R. solani AG 4 isolates studied at 10

to 15◦C, but was not pathogenic on onion, while the slowest-growing isolate, Rh070929, consistently caused disease at 13/8◦C day/night temperatures. Some of the isolates used in

the pathogenicity studies at both 13/8◦C and 15/15◦C day/night temperatures were more

virulent at the cooler temperature regime, which is interesting considering that all isolates

grew significantly faster at 15◦C than at 8 to 13◦C. For instance, AG 8 isolate Rh070927

reduced onion growth up to 1.7 times more (based on an average of plant weight, height,

and total root length responses), and the AG E isolate (Rh070923) up to 2.3 times more,

at the cooler than at the higher temperature regimes. Some isolates, like Rh070923 and

AG 3 isolate Rh060801, produced symptoms at lower inoculum densities at 13/8 than at

15/15◦C, although the reverse was true for AG 8 isolate Rh070927.

Natural variability in onion seed germination and seedling vigor is a notorious prob-

lem [Gamiely et al., 1990], making onion a potentially difficult crop to study with indi- 64

vidual replicate plants grown in small-volumed conetainers. Although substantial variation in above- and below-ground seedling growth was observed for the noninoculated control plants, resulting in excessive variation in the regression analyses, seed germination rates for the commercial cultivar used were excellent (≥95%), and statistically significant disease curves were modeled using basic growth variables, specifically plant dry weight, height, total root length, rooting depth, and number of roots. The conetainer experimental system was chosen primarily for the small footprint, which allowed for the study of large numbers of treatment combinations in the highly-controlled environments possible in plant growth chambers. Therefore, this study demonstrated the usefulness of the small-footprint, low- cost method of studying soilborne pathogens on onion seedlings planted from a seed lot with a high level of germination.

Potentially misidentified by growers, researchers, and consultants as the cause of onion stunting, stubby-root nematodes, root-lesion nematodes, and northern root-knot ne- matodes are commonly encountered in the Columbia Basin and can cause symptoms sim- ilar to those caused by Rhizoctonia spp., including reduced plant height, shortened roots, and reduced bulb size [Hafez et al., 1992, Ingham et al., 1999]. Paratrichodorus allius can cause roughly circular patches of stunted onions in the Columbia Basin [Ingham et al.,

1999], and wheat is an excellent host for the nematode [Ingham et al., 2000], suggesting that P.allius alone could be the cause of stunting in onion fields grown in rotation with small grain cover crops, or could interact with Rhizoctonia spp. to produce stunted patches. How- 65

ever, this nematode species develops and reproduces optimally at 21 to 24◦C[Ayala et al.,

1970] and, similarly, Meloidogyne hapla is most pathogenic at 25 to 30◦C[Griffin and

Jensen, 1997], temperatures well in excess of those characteristic of the seedling phase of spring-planted onion crops, when infection by Rhizoctonia most commonly occurs. Only P. penetrans is known to be highly pathogenic on onion at cooler temperatures, and can cause severe damage to onion at 7 to 13◦C[Ferris, 1970]. Several years of in-season nematode testing by a Columbia Basin grower lead to the conclusion that populations of nematode pests did not differ markedly between stunted patches and healthy portions of the fields

[Dean, 2010], which suggested that nematodes alone are not always responsible for stunt- ing in onion crops. This was expected as growers in the region commonly use preplant fumigants and in-season, non-fumigant nematicides, especially oxamyl (Vydate). Findings from Australia support this conclusion because, although Wicks et al.[2010] observed the most severe stunting of onion in pot experiments with both Pratylenchus spp. and R. solani

AG 8 isolates, the researchers found that plant parasitic nematode numbers generally were low in fields and rarely were associated with stunted onion plants.

Most Columbia Basin onion growers, and potato growers who rotate with onions, fumigate their fields once during each rotation cycle, either immediately prior to planting a crop or a year in advance of crop cultivation [Pelter and Sorensen, 2003]. Metam-sodium is the primary commercial fumigant in use for the control of soilborne onion pathogens [Hup- pert, 2012], while other fumigants applied include chloropicrin, dichloropropene (Telone 66

II), and dichloropropene + chloropicrin (Telone C-17 or Telone C-35) [DeFrancesco, 2004].

These fumigants can reduce field populations of arbuscular mycorrhizae (AM), as well as the intensity of crop infection by AM [Menge, 1982, Trappe et al., 1984, Davis et al., 1996,

Schreiner et al., 2001], the ubiquitous soil fungi of the phylum Glomeromycota known to colonize roots of the vast majority of land plants, including most Allium species [Brewster,

2008]. AM root colonization has been observed to occur in the earliest stages of onion root development [Afek et al., 1990], which is the infection window for Rhizoctonia. Be- cause research on potatoes has demonstrated that plant-AM associations can significantly reduce R. solani disease severity [Yao et al., 2002] through accumulations of antifungal phytoalexins in the roots [Yao et al., 2003], there is the potential for AM root colonization to reduce the severity of stunting of onion caused by Rhizoctonia species. Commercial AM inoculants, although rarely used in the Columbia Basin [Huppert, 2012], could be of ben- efit even to those growers who regularly fumigate their fields, as nearly twice the level of onion root colonization occurred in fumigated than in nonfumigated field soil when Glomus intraradices inoculum was banded below the seeds at planting [Afek et al., 1990].

In summary, stunting of onion from 8 to 15◦C in fine sandy loam field soils was caused by different Rhizoctonia species and AGs, with the isolates virulent on onion be- longing to R. solani AGs 2-1, 3, 4, 8, W. circinata var. circinata, and binucleate Rhizoctonia

AG E. However, only R. solani AG 8 isolates consistently caused severe symptoms at low inoculum densities, suggesting the existence of a disease bridge between wheat and barley 67

cover crops (or nurse crops) and dry bulb onion crops, as AG 8 is especially pathogenic

to cereals. One AG 3 isolate was highly virulent, and two were moderately virulent on

onion, suggesting that rotational potato crops or potato volunteers in onion fields might

also act as a disease bridge, as AG 3 is an important potato pathogen. AG 2-1 isolates were

moderately virulent on onion, and AG 4 isolates caused low levels of disease, as did the

W. circinata var. circinata isolate. AG 2-1, AG 3, and AG E isolates significantly reduced onion emergence, but isolates of AG 4 and AG 8 did not.

2.6 Acknowledgments

I am forever indebted to Timothy Paulitz and Lindsey du Toit for mentoring me through this entire research project. Also, I would like to thank the USDA-ARS Root Dis- ease and Biological Control Research Unit in Pullman, WA for the use of their laboratory equipment and greenhouse facilities. Thank you to Sarah Szewczyk, Bridget Tinsley, Ch- asity Watts, Barbara Holmes, Adriana Flores, Erica Turnbull, and Kylie McDermott for helping in the capacity of research technicians, and to Marc Evans for help with data analy- sis. Lastly, thank you to Bill Dean and River Point Farms for their support and cooperation in this project. 68

CHAPTER 3. EFFECT OF NITROGEN FERTILITY ON THE

AGRONOMIC PERFORMANCE, FLOUR QUALITY AND

PHENOLIC ACID CONTENT OF HARD RED WINTER WHEAT

IN WESTERN WASHINGTON

3.1 Abstract

During 2009-10 and 2010-11, the influence of nitrogen (N) rate (0, 85, and 170 kg

N/ha) and source (poultry feather meal and sulfur-coated urea) on the agronomic perfor- mance, flour quality, and phenolic acid content of four hard red winter wheat (Triticum aestivum L.) cultivars was assessed in Skagit County, Washington. Although N fertility was not a significant source of variation for yield, yield was 0.5 to 2.0 Mt/ha greater in the second year compared with the first year; and the two cultivars developed after 2005,

Bauermeister and WA8022, produced two- to three-fold greater yields (4.6 and 6.3 Mt/ha, respectively) than the two cultivars, Relief and McCall, that were developed prior to 1965

(2.2 and 2.3 Mt/ha, respectively). Test weights tended to be low in both years of the study, and only WA8022 met the U.S. No. 2 grade requirements in three site-years. The greatest 69

rates of PFM and SCU significantly reduced test weights by 3 to 4%, but site-year was a 12-fold larger source of variation for the response variable. The greatest rates of PFM and SCU increased grain protein content by an average of 1%. The two “modern” culti- vars averaged 9.7 and 10.2% protein, while the two “historic” cultivars averaged 11.5 and

12.4% protein. N fertilization had a small, but significant (P = 0.0413) influence on cultivar protein quality, evaluated by constant protein sodium dodecyl sulfate-microsedimentation

(micro-SDS) tests. Mean cultivar constant protein micro-SDS volume ranged from 10.6 to 12.7 cm3/g, values which were significantly positively correlated in other studies with desirable loaf characteristics. High protein quality, a complex trait not influenced strongly by N fertility, can obviate the effects of seasonal variability in grain protein content, which is a concern in western Washington, so long as the mean protein content achieved is beyond a certain threshold. The 170 kg N/ha rate produced grain protein contents similar to those achieved in central and eastern Washington, but lower rates did not significantly increase grain protein contents over unfertilized controls. Pre-harvest sprouting of wheat grain was not a major problem, and N fertilization treatments had insignificant effects on HFN. Seven phenolic acids were identified by HPLC in grain of the 2009-10 cultivars, as were four com- pounds with characteristics of ferulic acid dehydrodimers (DiFAs). The total phenolic acid content averaged 607.0 µg/g dry matter (dm), and ferulic acid was the dominant pheno- lic acid. Other important phenolic acids were sinapic, p-coumaric, and syringic + vanillic acids. N fertilization significantly influenced the concentrations of trans-ferulic acid and 70

the DiFAs, while site-by-N fertility was a significant source of variation for p-coumaric acid concentration. However, genotype and site were much larger sources of variation for phenolic acid concentrations.

3.2 Introduction

Most soils lack a mineral-bound, weatherable pool of N, a nutrient that is essential for plant growth and reproduction, and is a critical constituent of proteins, various coen- zymes, chlorophyll, nucleic acids, and other plant compounds [Wiese, 1993]. Traditionally, humankind relied on the nutrient stores of soil organic matter to sustain agriculture, and ac- tively supplemented N availability through the cultivation of N-fixing crops and by the input of organic amendments [Robertson and Vitousek, 2009]. Crop production environments, as well as the global N cycle, were dramatically altered with the commercialization of the

Haber-Bosch process following World War II. In 2005, industrial N fixation produced 121

Tg of N, mostly for synthetic fertilizer (NH4, NO3), which is more than triple the 40 Tg of

N estimated to have come from cultivation-induced biological N fixation [Galloway et al.,

2008, Robertson and Vitousek, 2009]. An increased input of N does not translate entirely into increased biomass production, as research consistently demonstrates that over half of the applied N is not recovered in crops at harvest [Smil, 1999, Cassman et al., 2002]. Much of the surplus N is lost from cropland by various processes to surface and ground waters 71

as well as the atmosphere, often becoming a significant pollutant and conservation concern

[Vitousek et al., 1997, Delgado, 2002, McIsaac et al., 2002, Galloway et al., 2004, Crutzen et al., 2008]. The threat of N leaching and eutrophication is especially great in the alluvial farmlands of western Washington State, which are exposed to high levels of precipitation and commonly have a seasonally perched water table that is at or above the surface [Klung- land and McArthur, 1989]. Skagit Valley, the geographic focus of this study, contains the

Skagit River watershed, which accounts for over 30% of all freshwater flowing into the

Puget Sound. Indeed, the Skagit County Monitoring Program reported in 2011 that most of the deleterious trends in water quality were linked to increases in nutrient values, and that ammonia levels in the drainage infrastructure occasionally approached chronically toxic levels [Carruthers et al., 2011].

Winter wheat, which has a shorter winter dormancy period under the mild climate of western Washington compared with central and eastern Washington, has the potential for superior soil N recovery compared with spring wheat, but careful nutrient planning is required to realize and maximize this efficiency. Nearly four times the wheat hectares in western Washington are planted to winter wheat versus spring wheat, and in 2010, winter wheat production reached 14,343 Mt of grain with an average yield of 5.1 Mt/ha [USDA-

NASS]. Although roots of spring and winter wheat cultivars penetrate the soil at the same rate [Thorup-Kristensen et al., 2009], maximum rooting depth is much deeper for winter wheat, 140 to 200 cm [Gregory et al., 1978], compared with spring wheat at 80 to 120 cm 72

[Herrera et al., 2007, Siddique et al., 1990], by virtue of the longer growing period. In their study of N uptake in spring versus winter wheat, Thorup-Kristensen et al.[2009] found that winter wheat removed a substantial amount of N from the 1.0 to 2.5 m soil layer. Averaged over three years, 130 kg N/ha remained in the deeper soil profile after spring wheat harvest, whereas only 49 kg N/ha remained after winter wheat harvest. However, high inputs of N have been shown to reduce depletion of N in the deeper soil layers by winter wheat despite substantial root growth in these layers [Anderson et al., 1998, Kuhlmann et al., 1989]. Thus, over-fertilization might reduce the N efficiency of winter wheat.

In western Washington, wheat has been in cultivation since the establishment of farms connected with Forts Vancouver, Nisqually, and Langley during the fur trade era of the mid-1800s [Hussey, 1957]. Today, winter wheat is cultivated in rotation with high- value fruit, vegetable, and bulb crops, primarily to reduce nutrient loss, provide organic matter to the soil, as well as to break disease and pest cycles [Miles et al., 2009]. An up- surge in interest in locally-produced wheat [Hills et al., 2012] has improved the profit po- tential of rotational grain crops and kindled grower interest in better management practices to improve crop quality. The baking and nutritional qualities of the hard red class of bread wheat are determined in part by grain protein content [Bell and Simmonds, 1963, Finney and Barmore, 1948, Park et al., 2006], which is determined by a combination of genetic and environmental factors, particularly N availability [Daniel and Triboï, 2000, Johansson et al., 2001, 2004, Triboï et al., 2003, Wieser and Seilmeier, 1998]. Increased total protein 73

content is positively correlated with increased levels of all protein subclasses [Park et al.,

2006], but not all protein subclasses are positively correlated with baking quality. There- fore, although protein quantity is important for the expansion of bread loaves, improved protein quality (dough elasticity or ‘strength’) can compensate for this effect to yield de- sirable loaf characteristics from lower protein flours [Færgestad et al., 2000, Uhlen et al.,

2004]. This is particularly important in cool, high-rainfall environments, which are not ideal for achieving protein levels of 14 to 15% that is often targeted by commercial flour mills [Altenbach et al., 2003, Gooding et al., 2003, Zhu and Khan, 2001]. Most gener- ally, it is the storage proteins (‘gluten’), both monomeric gliadins and polymeric glutenins, which determine the unique breadmaking properties of wheat by conferring water absorp- tion capacity, cohesivity, viscosity, and elasticity on dough, while the albumin and globulin fractions, predominantly structural, metabolic and protective proteins, are not believed to influence baking properties [Veraverbeke and Delcour, 2002, Wrigley et al., 2006]. Even within the highly complex mixture of proteins that form wheat gluten, not all constituent proteins and protein subunits improve baking quality, and it is understood that high molecu- lar weight (HMW) subunits of glutenin are particularly important in conferring high dough elasticity [Schofield, 1994, Shewry et al., 1992] and, thereby, have large measurable effects on loaf volume [Aamodt et al., 2005, Lukow et al., 1989, Payne et al., 1987].

The scientific literature on this topic is primarily focused on the influence of flour protein quantity and protein quality on the loaf characteristics of pan bread, the style of 74

bread with which American consumers are most familiar. Unlike pan bread, hearth bread is baked without the support of a pan, directly on the hearth (sole) of the oven, and expands both horizontally and vertically. Therefore, the form ratio (height/width) is an important quality parameter for hearth bread, in addition to loaf volume, which is considered a key criterion of pan bread quality [Dowell et al., 2008]. Assessing loaf form ratio and volume with a baking test is time-consuming, and is difficult if only small quantities of grain are available, thus indirect chemical or physical parameters of flour are often used as predictors of processing and end-product qualities. The sodium dodecyl sulfate (SDS) sedimentation test has been shown to be fairly robust and reproducible over a diverse range of protein quality and quantity [Dick and Quick, 1983, Mansur et al., 1990, Morris et al., 2007], and is highly correlated with the loaf volume (83% to 94%) and form ratio (76% to 85%) of hearth bread [Tronsmo et al., 2003, Uhlen et al., 2004]. Grain nitrogen or protein content, although widely used as a predictor of baking performance, has been found to be both positively correlated [Tronsmo et al., 2003] and not correlated [Aamodt et al., 2005, Uhlen et al., 2004] with certain desirable hearth bread characteristics.

High α-amylase enzymatic activity, which is indirectly measured by quantifying the rheological properties of starch hydrolyzed by the enzymes during the Hagberg falling number test [Hagberg, 1961], adversely impacts baking quality by reducing flour mix- ing strength, causing sticky dough, and reducing loaf volume and shelf life [Dobraszczyk,

2001]. For these reasons, wheat grain scoring below 250 s is discounted or rejected by 75

most millers, while grain scoring 300 s and above is considered of good quality. Tolerance of wheat grain to pre-harvest sprouting is under genetic control, but continual moist condi- tions after the grain is mature can lead to germination in even the most tolerant genotypes

[Trethowan, 2001], an important consideration for wheat growers in western Washington.

Additionally, N fertilization can lower HFN by increasing lodging, which forces the heads closer to the moist soil and decreases drying rate through reduced air circulation [Brun,

1982]; or, where lodging is not a problem, N fertilization can increase rain-induced pre- harvest sprouting in wheat cultivars with low or moderate levels of sprouting tolerance

[Morris and Paulsen, 1984]. Conversely, in regions with high levels of seasonal precip- itation, N fertilization has been shown to increase HFN [Ayoub et al., 1994, Smith and

Gooding, 1996, Tanács et al., 2005]. A variety of mechanisms has been suggested to ex- plain the increase in HFN caused by N application, including a delay in crop maturity

[Gooding et al., 1986, Pushman and Bingham, 1976], an increase in post-harvest grain dor- mancy [Kindred et al., 2005], an increase in the grain drying rate [Kettlewell, 1999], and a change in grain size and morphology [Evers et al., 1995].

Both the loaf volume of pan and hearth breads [Aamodt et al., 2005, Lai et al., 1989,

Moder et al., 1984, Rogers and Hoseney, 1982], as well as the form ratio of hearth bread

[Aamodt et al., 2005], are reduced by additions of wholemeal flour and bran through the interruption of the continuous gluten matrix by nonendosperm material. For a growing por- tion of American consumers interested in whole wheat bread [Oldways Preservation Trust, 76

2012], purchasing decisions are weighing not only loaf size and shape, but also taste and texture, and the potential benefits of whole grain foods to human health (reviewed by Jon- nalagadda et al.[2011] and Borneo and León[2012]). The potential benefits of the high dietary fiber content of whole wheat foods is well-studied, but the benefits of the enhanced phenolic content of these foods are not as well known. Universal among plants, pheno- lics are secondary metabolites possessing one or more aromatic rings with one or more hydroxyl groups, and diverse biological functions, from structural to protective [Lattanzio et al., 2012, Nicholson and Hammerschmidt, 1992]. Generally categorized as phenolic acids, flavonoids, stilbenes, coumarins, and tannins, these compounds affect the appear- ance, taste, odor, oxidative stability, as well as the nutritional and pharmacological value of plant-based foods and nutraceuticals [Shahidi and Naczk, 2003]. Considerable attention is being focused on phenolic acids in crop plants, in part due to their potential health ben- efits as antioxidants, anti-inflammatories, antimutagens, and anticarcinogens [Graf, 1992,

Thompson, 1994, Liyana-Pathirana and Shahidi, 2006, Abraham et al., 2012], and in part due to their potential role in crop disease and pest resistance [Cabrera et al., 1995, McKee- hen et al., 1999, Abdel-Aal et al., 2001].

Phenolic acids are grouped into derivatives of benzoic and cinnamic acids, the latter being the predominant group in wheat grain. Hydroxycinnamic acid derivatives occur- ring in wheat kernels include caffeic, o-coumaric, p-coumaric, ferulic, and sinapic acids.

Present in smaller amounts, the hydroxybenzoic acid derivatives include p-hydroxybenzoic, 77

syringic, and vanillic acids. Phenolic acids exist in either soluble free, soluble conjugated, or insoluble bound forms, although the bound forms present in the aleurone layer and peri- carp of wheat kernels account for a vast majority of the total phenolic acids [Hatcher and

Kruger, 1997, Adom et al., 2005, Liyana-Pathirana and Shahidi, 2006, Li et al., 2008,

Verma et al., 2008]. Ferulic acid, the dominant phenolic acid in wheat grain [Sosul- ski et al., 1982, Klepacka and Fornal, 2006, Zuchowski et al., 2011], is ester-bound to structural polysaccharides, such as arabinoxylans, in cell wall matrices. Dehydrodimers of ferulic acid effect polysaccharide-polysaccharide cross-linking, while both the monomers and dimers effect polysaccharide-lignin cross-linking, thereby controlling wall organiza- tion and structural integrity [Hatfield et al., 1999]. Ferulic acid is so scarce in the starchy endosperm of mature grains that presence of the compound can reflect the purity of flours

[Pussayanawin et al., 1988]. The large phenolic acid content differences between milling fractions probably have important human health effects, as whole-grain and bran-rich breads have been shown to contain significantly higher amounts of bioaccessible phenolic acids than white bread [Hemery et al., 2010, Mattila et al., 2005], and the regular consumption of whole-grain wheat cereal has been linked to a prolonged presence of antioxidant phenolic acids in the bloodstream [Costabile et al., 2008].

In addition to the effect of wheat genotype, growing conditions have been shown to influence the antioxidant properties of wheat, as well as the amounts of individual and total phenolic acids present in the grain. Studies conducted at five different Colorado lo- 78

cations found that under irrigated production conditions significantly greater levels of total phenolics were produced than under rainfed conditions in bran extracts of both a hard red

[Yu et al., 2003] and a hard white [Yu and Zhou, 2004] winter wheat cultivar. The same research group observed that the total phenolic content of three hard wheat cultivars grown at the same five locations was significantly negatively correlated (r = −0.417, P < 0.01) with the number of hours exceeding 32◦C during the 6-week grain-filling period [Moore et al., 2006]. A study of 26 soft and durum wheat cultivars over two years of production in

Italy also correlated high temperatures prior to harvest with a significant drop in the grain phenolic content, while differences among cultivars within a species were less significant

[Heimler et al., 2010]. Conversely, Menga et al.[2010] found that two cultivars each of soft and durum wheat, grown at three different rainfed locations in Italy and fertilized iden- tically, had significantly greater total phenolic content at the warmest and driest location in southern Italy. The researchers observed that location contributed to the greatest pro- portion of total variance for the total phenolic content of soft (42.5%) and durum (72.2%) wheat. Similarly, location had a greater influence than cultivar on the total phenolic con- tent of wholemeal flour made from three wheat cultivars grown at different organic farms in Quebec, Canada [Gélinas and McKinnon, 2006]. Furthermore, in their analysis of 20 hard wheat cultivars grown at two Colorado locations, Moore et al.[2006] found that the environment contributed the greatest proportion of total variance (ranging from 57 to 79%) for the concentrations of five major phenolic acids, as well as for total phenolic content. 79

Also, Mpofu et al.[2006] found that environmental effects were considerably larger than genotype effects for total phenolic content, as well as for the concentrations of vanillic, syringic, and ferulic acids in a study of six hard red and white spring wheat cultivars grown at four western Canada locations. Interestingly, the authors could not attribute the observed environmental variation to either growing temperature nor to rainfall from anthesis to matu- rity. In a comparison of organic and conventional production systems in Poland, Zuchowski et al.[2011] observed that out of four cultivars each of winter and spring wheat, total phe- nolic acid concentrations were significantly increased by organic management for only one cultivar each of winter and spring wheat. However, one winter wheat cultivar had sig- nificantly greater total phenolic acid concentrations under the conventional system, while phenolic acid concentrations of two cultivars of winter wheat and three of spring wheat did not differ significantly between production systems. Organic management significantly increased the concentrations of p-coumaric acid for two cultivars each of winter and spring wheat. Conversely, levels of p-hydroxybenzoic and vanillic acids were significantly greater under conventional management for six of the eight cultivars tested.

Little information exists regarding the effect of N fertilization on bread type hard red winter wheat in western Washington, a region which has markedly different soils, greater precipitation and cooler summer temperatures than the major wheat growing regions of central and eastern Washington. Additionally, little is known about the effect of N fertility on the phenolic acid make-up of wheat grain. Therefore, this study was conducted to 80

measure the effect of, and interactions between, two N input types commonly applied by farmers in the region (granulated poultry feather meal and sulfur-coated urea) and three N input levels (0, 85, and 170 kg N/ha) on the: (1) agronomic performance, (2) flour quality, and (3) grain phenolic acid content of hard red winter wheat cultivars grown in the high- rainfall environment of northwestern Washington with moderate, year-round temperatures.

3.3 Materials and methods

3.3.1 Experimental treatment

The experiment was replicated at two sites in each of two seasons from 2009 to

2011 at the Washington State University (WSU) Mount Vernon Northwest Research and

Extension Center (NWREC) research farm located in the Skagit Valley of northwestern

Washington State. Within a growing season, the distance between the two sites was ap- proximately 0.7 km. Soil type at all sites was classified as Skagit silt loam, consisting of silt loam, silty clay loam, and some very fine sandy loam, with a moist bulk density ranging from 1.15 to 1.30 g/cm3. Wheat cultivars planted included Bauermeister, McCall,

Relief, and WA8022 in both years. All cultivars were selected because of their high per- formance in cultivar trials in high precipitation zones of eastern Washington: Bauermeister and WA8022 performed well under conventional management, while McCall and Relief performed well under organic management. Additionally, Bison and Itana were grown 81

in the first year, but near complete yield-loss incurred as a result of the combination of stripe rust and lodging resulted in these cultivars not being included in the data analyses or grown in the second year. Cultivars Norwest 553 and WA8120, having shown promise in western Washington variety trials, were grown only in the second year as replacements for Bison and Itana and are, therefore, only discussed briefly. The cropping history at each site-year varied, and included conventionally-managed peas, organically-managed mixed vegetables, and fallow (Table 3.1). Cultivation included chisel-plowing to a depth of 41 cm one month prior to planting, followed by one pass of a cultimulcher tillage implement on the morning of the planting date. In-season weed control in both years was achieved by hand-weeding and mowing, but only in the second year were broadleaf herbicides applied, including dimethylamine 2,4-dichlorophenoxyacetate (rate: 1.8 L/ha, Base Camp Amine

4, Wilbur-Ellis Company, San Francisco, CA) and thifensulfuron + tribenuron (rate: 36.5 mL/ha, Harmony Extra XP, DuPont, Wilmington, DE). Stripe rust in the second year was controlled with azoxystrobin (rate: 585 mL/ha, Quadris, Syngenta Crop Protection, LLC,

Greensboro, NC).

A split-plot design was utilized with N fertility treatments randomly assigned to whole plots, and six hard red winter wheat cultivars randomly assigned to split plots. Whole plots were arranged in a completely randomized design (CRD) with four replications per site. N fertility treatments included a non-fertilized control treatment, 85 kg N/ha PFM

(PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha SCU (SCU low), and 170 kg N/ha 82

SCU (SCU high) (Figure B2 in AppendixB). Sulfur was added to PFM treatments to ac- count for the addition of sulfur in the SCU treatments, but no other nutrients were applied.

Wheat was planted at a seeding rate of 123 kg/ha to a depth of 2.5 cm in six-row passes with a row spacing of 17.8 cm, using a Hege 1000 cone planter (Hege Equipment, Inc., Col- wich, KS, USA). In the first year, split plots were each 3.1 m long, and adjacent plots were separated by 91 cm of bare ground that was managed by mowing. In the second year, split plots were each 3.7 m long, and adjacent plots were separated by passes of non-fertilized soft white winter wheat (cv. Xerpha). First and second year experiments were planted on 7

October 2009 and 4 October 2010, fertilized by topdressing on 12 April 2010 and 7 April

2011, and harvested on 19 August 2010 and 18 August 2011, respectively.

3.3.2 Soil sampling and weather data

Soils were sampled in non-fertilized plots at planting, mid-season, and post-harvest for analysis of total organic matter (OM), pH, cation exchange capacity (CEC), nitrogen

(N), weak Bray phosphorus (P), potassium (K), and sulfur (S). Each sample comprised a composite of 15 soil cores (0 to 30 cm depth) collected per site with a 25-mm internal diameter soil probe. Soil samples were air-dried within 1 day then sent for analysis (A&L

Western Agricultural Laboratories, Portland, OR). Soil N concentrations were calculated on the basis of an average soil bulk density of 1.225 g/cm3. In-season soil N availabil- 83

ity was calculated on the basis of the following two assumptions appropriate for western

Washington [Cogger, 2010]: that the N mineralization rate was 11.3 kg/N released/1% soil

OM, and that soil nitrate-N measured at planting and mid-season were leached away from the root-zone. Weather data during the trial period were obtained from the WSU Mount

Vernon NWREC AgWeatherNet station, located 1 km from the research plots (Table 3.1, and Figure B1 in AppendixB). 84

Table 3.1: Crop management, climatic conditions, and soil characteristics at two sites per crop year at the WSU Mount Vernon NWREC research farm during the 2009-10 and 2010-11 crop years.

Year 1 (2009–10) Year 2 (2010–11) East site West site South site North site Crop managementa Planting 7 Oct 7 Oct 4 Oct 4 Oct Harvest 19 Aug 19 Aug 18 Aug 18 Aug Fertilization 12 Apr 12 Apr 7 Apr 7 Apr Previous crop Fallow Peas Vegetables Fallow Weed control HW, M HW, M HW, M, H HW, M, H Stripe rust control – – F F

Climatic conditionsb Precipitation (mm) 698 698 768 768 Heat units 1751 1751 1535 1535

Soil characteristics (0–30 cm)c OM (%) 2.9 2.7 3.9 5.3 pH 6.4 6.8 5.7 5.9 Est. N release (kg/ha) 81.2 75.6 109.2 148.4 P (kg/ha) 152.9 301.8 141.1 156.8 K (kg/ha) 823.2 933.0 517.4 956.5 aWeeds controlled by hand-weeding (HW), mowing of alleys (M), and application (on 23 March 2011) of dimethylamine 2,4-dichlorophenoxyacetate and thifensulfuron + tribenuron herbicides (H). Stripe rust controlled by application (on 23 March 2011) of azoxystrobin (F). bWeather data recorded by WSU AgWeatherNet station located 1 km from research plots. Precipitation and base 5◦C heat units accumulated from the planting date until crop maturity. cIn-season soil N release estimated by a mineralization rate of 11.3 kg/N released per 1% soil organic matter (OM), and the assumption was made that nitrate-N measured at planting or mid-season leached away from the root-zone. Phosphorus (P) measured by the weak Bray method. 85

3.3.3 Agronomic performance

Stripe rust (Puccinia striiformis) severity was rated as percent severity on a plot basis in May and June of each year, and reaction type was scored using a 0 to 100% scale (0%

= immune, 100% = highly susceptible). Observations for plant height and stem lodging, plants fallen over, were made after crop maturity in July and August each year, respectively.

Heading date and maturity date were monitored each year. Wheat heads in each split plot were harvested into an individual paper bag with a Hege 140 plot combine (Wintersteiger

AG, Ried im Innkreis, Austria), and grain samples were air-dried at room temperature

(23◦C) to achieve a standard moisture content of 13.5%. The mass of each uncleaned grain sample was measured on a digital balance (Mettler-Toledo, Greifensee, Switzerland). Test weight, or bulk density of the grain, was measured according to AACC Method 55-10

[AACC, 2000] with some modifications. Uncleaned grain was emptied from a spouted sample pan at a fixed height into a one-quart (946 mL) cup until overflowing, then the excess grain was scraped off and the full cup was weighed. In preparation for flour quality and phenolic acid analyses, wheat samples were cleaned of chaff, straw, weeds, and other detritus with an air blast seed cleaner (ALMACO, Nevada, IA). 86

3.3.4 Grain milling

For the falling number, micro-SDS sedimentation, and LECO protein tests, whole grain wheat samples were ground in a Udy Cyclone sample mill (Udy Corp., Fort Collins,

CO) to pass through a 0.5-mm mesh screen at the United States Department of Agriculture

(USDA) Western Wheat Quality Laboratory in Pullman, WA. The mill was cleaned be- tween samples with a brush and vacuum. Milled samples were stored at room temperature in Udy-compliant, screw-cap glass jars at room temperature until analysis within five days.

For the phenolic acid analysis, whole grain wheat samples were milled using a Retsch ZM

200 mill (Retsch GmbH, Brinkmann, Germany) to pass through a 0.25-mm mesh screen at the Newcastle University Human Nutrition Research Centre in Newcastle, UK. The mill was cleaned between samples with a vacuum and 70% ethanol. Milled samples were imme- diately cooled to -20◦C and stored at this temperature in the dark in sterile plastic screw-cap vials until analysis to protect bioactive components from degradation.

3.3.5 Determination of sprouting damage

The α-amylase enzymatic activity and associated pre-harvest sprouting damage of each wholemeal flour sample was indirectly determined as a falling number, expressed in seconds, in a manner that simulates some of the changes flour undergoes during baking 87

using a Shakematic 1095 and a Falling Number 1600 apparatus (Perten Instruments AB,

Huddinge, Sweden) according to AACC Method 56-81B [AACC, 2000]. In brief, 25 mL of distilled water was added to a 7 g flour sample in a standardized viscometer tube, and an aqueous flour gel was developed through vigorous shaking of the tube for approximately 5 sec in the Shakematic 1095. The falling number is the time it took for a viscometric stirrer to fall a fixed distance through the aqueous flour gel under its own weight after mechanical stirring for 60 sec in a boiling water bath of the Falling Number 1600 apparatus. Falling numbers are reported on a 14% moisture basis, and are corrected for an elevation of 762 m

(2500 ft) to attain sea-level standardized values [USDA-FGIS, 2009].

3.3.6 Determination of grain protein content

The nitrogen content of the wholemeal flours was determined on a 0.25 g sample using a Leco model FP-528 Protein/Nitrogen Determinator (Leco Corporation, St. Joseph,

MI), and protein content was calculated using a protein factor of 5.7 as indicated in AACC

Method 46-30 [AACC, 2000].

3.3.7 Determination of grain protein quality

A micro-SDS sedimentation test was conducted as an indirect measure of flour qual- ity following a modification of Carter et al.[1999]. In brief, two stock solutions were 88

prepared in advance: (1) 85% lactic acid (LA) in water (1:8, v/v), and (2) 20%, w/v, SDS solution. The two stock solutions were mixed on the day of the test to create a working solution of 1:48, v/v, LA/SDS. Tests were performed on 0.5 g wholemeal flour samples in screw-cap, borosilicate glass tubes (150 mm long, 14 mm inside diameter) placed into racks

(single row of 20 tubes/rack) engraved with measurement scales. Samples were initially mixed with 4 ml distilled water for 20 sec on a high-speed vortex mixer. After a resting interval of 7 min, samples were again mixed for 10 sec. Once rested for 3.5 min, 12 ml

LA/SDS solution was added to each sample, and samples were agitated on a Zeleny type rocker shaker for 40 sec (20 inversions), rested for 2 min, and then agitated again for 40 sec. Micro-SDS sedimentation volume, which reflects gluten strength governed by protein quality and protein quantity, was recorded as the height (mm) of the solid-liquid interface line after 10 min sedimentation in an upright position. Each micro-SDS sedimentation test was performed once per treatment replicate, with control sample flours of cultivars

Stephens (soft white winter wheat, 7.8% flour protein) and UI Silver (hard white winter wheat, 10.4% flour protein) included in each rack of 20 samples. To evaluate the protein quality differences among samples, protein-corrected micro-SDS sedimentation volumes were used, which were calculated by dividing the observed volumes by grain protein con- tent and multiplying by 10 to obtain volumes on the basis of 10% protein [Baik et al.,

1994]. 89

3.3.8 Chemicals for HPLC analysis

All chemicals were purchased from Sigma-Aldrich (Dorset, U.K.). Phenolic acid standards (2,4-dihydroxybenzoic, 4-hydroxybenzoic, 2-hydroxycinnamic, caffeic, ferulic, p-coumaric, sinapic, syringic, and vanillic acids) were prepared as stock solutions at 1.5 mg/mL in 80:20 methanol:water, and were stored at -20◦C in the dark until high-performance liquid chromatography (HPLC) analysis was performed, within one month. Acetic acid, acetonitrile, ethyl acetate, hydrochloric acid (HCl), methanol (MeOH), and sodium hy- droxide (NaOH) were of analytical grade. Deionized water (18.2 Mω-cm) was prepared using a Barnstead Nanopure Ultrapure water purification system (Thermo Fisher Scientific,

Waltham, MA).

3.3.9 Extraction of phenolic acids from wholemeal wheat flour

Extraction of total phenolic acids from wholemeal wheat flour was achieved accord- ing to established methods [Adom and Liu, 2002, Adom et al., 2003, Li et al., 2008] with some modifications. In brief, flour samples (each 250 mg) were each combined with 1 mL

80% MeOH in glass vials. Samples were agitated by vortexing for 2 min until the flour was suspended, then sonicated for 10 min. Samples were subjugated to a 4 h alkaline hy- drolysis reaction with the addition of 400 µL 2 M NaOH per sample. After centrifugation 90

for 20 m at 4000 rpm, the supernatant was transferred to a clean glass vial and acidified to

pH 2 with 12 M HCl, then extracted twice with ethyl acetate (800 µL). The organic phase

was retained and evaporated to dryness under nitrogen at 40◦C (Techne Dri-Block DB-3D,

Bibby Scientific Limited, Staffordshire, UK), resuspended in 2% (v/v) aqueous acetic acid

(600 µL), then passed through a 0.2 µm PVDF syringe filter (Chromacol, Thermo Fisher

Scientific, Inc.). The filtrate was stored in the dark at 4◦C in amber glass sample vials and

subsequently analyzed by HPLC within one day.

3.3.10 Identification and quantification of phenolic acids

Wholemeal wheat flour extracts were analyzed by HPLC on a Shimadzu Prominence

HPLC system equipped with an LC-20AD pump, SIL-20AC autosampler, and SPD-M20A

photodiode array detector (Shimadzu Corp., Kyoto, Japan). Data collection and integration

were performed using Shimadzu LCsolution software. Phenolic acids were separated on a

reverse-phase Thermo Scientific Hypersil C18 column (250 × 4.6 mm, 5 µm). The column

was heated at 28◦C while the sample tray temperature was set to 4◦C. Eluent A was 2%

(v/v) aqueous acetic acid while eluent B was 100% acetonitrile, and the solvent gradient

was programmed as follows: at 0 m, 15% B; at 10 m, 20% B; at 16 m, 23% B; at 24 to 28 m,

27% B; at 30 to 35 m, 100% B; and at 37 to 42 m, 15% B. The flow rate of the mobile phase

was 1.5 mL/m, and the injection volume was 20 µL. Scanning was performed from 225 to 91

600 nm, and phenolic acids were identified by comparing retention times and UV-VIS

spectra with those of pure standards. Concentrations, expressed in µ/g of dry matter, were calculated at either 250, 280, or 320 nm using calibration curves of phenolic acid standards, that underwent the same extraction procedure. The following phenolic acid peaks were identified and quantified: 4-hydroxybenzoic, vanillic + syringic, 2,4-dihydroxybenzoic, p- coumaric, sinapic, ferulic (trans- and cis- forms), and 2-hydroxycinnamic. Cis-ferulic acid was putatively identified according to its spectra and relative retention time [Andreasen et al., 2000, Waldron et al., 1996], and was quantified against standards of trans-ferulic acid. In addition to the standard-verified phenolic acids, four major peaks having UV- vis spectra and retention times corresponding to ferulic acid dehydrodimers (DiFAs) were quantified against standards of trans-ferulic acid [Andreasen et al., 2000, García-Conesa et al., 1997, Ralph et al., 1994, Waldron et al., 1996], and the total phenolic acid content of a flour sample was determined by summing the concentrations of all individually identified phenolic acids with the concentrations of the four unknown phenolic acids.

3.3.11 Data analysis

Data were analyzed by analysis of variance (ANOVA) with a mixed effects model us- ing the PROC MIXED procedure with the default Restricted Maximum Likelihood (REML) estimation method, as well as the Kenward and Roger[1997] method for determining de- 92

nominator degrees of freedom in SAS 9.2 (SAS Institute, Cary, NC, USA). Fixed effects

included cultivar, site nested within year, nitrogen treatment, and the interaction terms.

The random effect was replicate nested within the interaction term of the main effects, site

and nitrogen treatment. Post-hoc comparisons of the least-squares means was performed

using Fisher’s protected least square differences (LSD) test with significance evaluated at

P < 0.05, and letter groupings were assigned using the PDMIX800 SAS macro [Saxton,

1998]. In the repeated measures statement, the covariance structure for each response vari- able was defined as either having compound symmetry (type=cs) or as being unstructured

(type=un) depending on the results of the null model likelihood ratio test. The normality assumption was tested with the PROC UNIVARIATE procedure.

3.4 Results

3.4.1 Agronomic performance of hard red winter wheat cultivars

Cultivars Bauermeister, McCall, Relief, and WA8022 were grown in both years of the study and showed large variation in agronomic traits under the field conditions of this study, including yield (0.3 to 10.1 Mt/ha), hectoliter weight (52.8 to 79.2 kg/hL), plant height (94.0 to 170.2 cm), stem lodging (0% to 100%), and stripe rust severity (5% to

90%) (Tables B1, B2, B3, and B4 in AppendixB). Yields and plant heights were 138 and 107% greater, respectively, in the 2010-11 growing season compared with the 2009-10 93

season. Although cultivar (C) accounted for the largest proportion of variation in yield and plant height according to the ANOVA F-test (85 and 89%, respectively), these response variables were also significantly affected by site-year (SY) and C×SY (10 to 12% of the variation). This suggests that even with 216 fewer accumulated heat units (base 5◦C) in the second year, crop productivity might have improved over that of the first year due, in part, to 70 mm more rainfall, as well as early-spring applications of broadleaf herbicide in the second year versus mowing and hand-weeding in the first year (Table 3.2). Stripe rust severities were similar among cultivars in both crop years (data not shown), so it is unlikely that an application of fungicide in the second year was the cause of enhanced yields in that year compared with the first year. Relief was the tallest cultivar across all site-years (mean ± standard error: 142.6 ± 1.5 cm), followed by WA8022 (129.7 ± 1.0 cm), McCall (126.7 ± 1.3 cm), and Bauermeister (117.4 ± 0.9 cm). Relief and McCall, the two cultivars that were developed prior to 1965, yielded the least across all site-years

(2.2 ± 0.1 Mt/ha and 2.3 ± 0.1 Mt/ha, respectively), due in part to greater susceptibility to stripe rust (75.3 ± 2.0% and 69.7 ± 2.2%, respectively) than the other cultivars. WA8022

(released in 2007) yielded three times greater than the two “historic” cultivars (6.3 ± 0.2

Mt/ha), and Bauermeister (released in 2005) yielded two times greater (4.6 ± 0.2 Mt/ha).

WA8022 and Bauermeister had low to moderate susceptibility to stripe rust (14.1 ± 1.1% and 30.5 ± 1.7%, respectively).

Plant height was significantly affected by N fertility, although only in the 2010-11 94

crop year, but yield was not affected in either crop year. At the north site, the greatest rates of PFM and SCU treatments both significantly increased the height of McCall by

13.4% over that of the non-fertilized control plants, while all PFM and SCU treatments significantly increased the height of Relief by 14.7 to 19.9%. At the south site, the greatest rate of SCU significantly increased the height of Bauermeister by 16.4% over that of the non-fertilized control plants, and the high PFM treatment level significantly increased the height of Relief by 12.9%. N application significantly improved yields only for the two modern cultivars, Bauermeister and WA8022, and only at the south site in 2010, suggesting that either maximum yield potentials were met at most site-years, or that other nutrients or environmental factors were limiting. At the south site, all PFM and SCU treatment levels significantly increased Bauermeister and WA8022 yields by 30.4 to 63.0% and 24.1 to

56.9%, respectively. 95 ) ) 3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.4 0.2 0.3 0.2 0.2 0.2 ± ± ± ± ± ± ± ± ± ± ± ± ± 0.4 11.6 0.4 11.2 0.4 12.0 0.4 11.1 0.4 11.1 0.3 10.6 0.3 10.6 0.5 11.7 0.3 12.7 0.4 10.2 0.2 12.8 0.3 12.9 0.2 9.7 ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( Micro-SDS values (cm 0.2 13.1 0.2 12.3 0.2 14.1 0.2 11.7 0.2 11.6 0.1 13.2 0.1 13.6 0.1 13.0 0.2 9.9 0.2 10.9 0.1 15.0 0.2 14.4 0.1 10.4 ± ± ± ± ± ± ± ± ± ± ± ± ± 9.5 10.4 4.6 9.7 7.2 11.5 5.8 10.2 6.7 10.2 8.3 11.7 8.4 11.2 10.0 11.2 12.0 10.9 11.2 11.7 10.7 10.5 11.8 12.4 11.8 10.6 ± ± ± ± ± ± ± ± ± ± ± ± ± 0.7 377.8 0.6 368.9 0.8 356.7 1.1 371.7 0.5 383.4 0.2 416.2 0.9 384.0 0.7 290.7 0.5 397.9 0.7 377.2 0.9 334.3 0.3 413.7 0.3 361.8 ± ± ± ± ± ± ± ± ± ± ± ± ± 0.3 70.4 0.3 71.0 0.3 69.7 0.3 70.2 0.2 72.5 0.2 75.5 0.1 70.3 0.1 66.7 0.2 70.5 0.3 69.1 0.3 66.5 0.2 74.5 0.2 73.0 ± ± ± ± ± ± ± ± ± ± ± ± ± 4.4 4.0 4.9 3.9 4.5 3.8 4.4 3.9 3.6 3.5 3.2 6.3 3.7 2.2 3.4 2.3 3.8 4.6 3.3 5.0 4.6 4.0 2.7 3.5 3.6 3.0 ± ± ± ± ± ± ± ± ± ± ± ± ± 1.6 46.4 1.9 44.4 1.8 50.4 1.7 35.8 1.7 22.3 1.0 23.9 1.5 68.5 1.3 28.3 0.9 38.4 1.9 17.8 1.8 40.0 0.7 63.6 1.2 37.1 ± ± ± ± ± ± ± ± ± ± ± ± ± (cm) (%) (Mt/ha) (kg/hL) no. (s) (%) weight protein Height Lodging Yield Test weight Falling Protein Constant Constant b a Marginal means for the effect of nitrogen (N) source and rate on plant height, stem lodging, yield, test weight, falling num- Relief 142.6 McCall 126.7 WA8022 129.7 2009 East 128.7 SCU Low 128.4 PFM Low 129.5 SCU High 130.2 2009 West 121.0 PFM High 132.6 2010 North 135.9 2010 South 130.6 Bauermeister 117.4 Table 3.2: ber, grain protein, as well as constantMcCall, weight Relief, and and constant WA8022 protein grown micro-SDS at values Mount for Vernon, hard WA red in winter 2009-10 wheat and cultivars 2010-11. Bauermeister, Non-fertilized 124.7 N Fertility (NF) Cultivar (C) Site-year (SY) 96 ) 3 Micro-SDS values (cm ) (cm) (%) (Mt/ha) (kg/hL) no. (s) (%) weight protein -values based on the ANOVA F-test. Height Lodging Yield Test weight Falling Protein Constant Constant P C 11.47 3.51 8.97 20.78 17.47 3.14 14.11 17.44 C 0.63 1.09 0.84 1.11 1.18 0.9 0.92 0.54 C 1.44 1.39C <0.0001 0.74 0.0005 1.32 <0.0001 <0.0001 1.41 <0.0001 0.0016 1.42 <0.0001 1.33 <0.0001 0.9 C 0.8183 0.375 0.6051 0.3593 0.2997 0.5487 0.5329 0.8893 C 0.0653 0.0839 0.8598 0.1414 0.0745 0.0723 0.1152 0.6361 NF 1.36 2.94 1.52 2.34 2.23 1.71 3.16 2.02 NF 0.2125 0.0029 0.1425 0.0187 0.0213 0.0872 0.0016 0.0382 × × × × × × × × continued from preceding page SY NF NF SY NF NF Cultivar 220.18 67.35 391.07 137.52 95.49 193.25 71.66 68.91 Cultivar <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 :( SY SY × × Site-year 19.41 37.63 34.58 96.23 34.98 20.91 75.09 55.28 Site-year <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 c N fertility 3.35 10.83 1.6 8.15 2.17 12.94 11.12 2.66 N fertility 0.0153 <0.0001 0.1872 <0.0001 0.0838 <0.0001 <0.0001 0.0413 c SY SY F > value -values and associated Table 3.2 F P Wheat was grown at the east and west sites during theN 2009-10 fertility growing treatments season, included and an in non-fertilized the control, north 85 and kg south N/ha sites poultry during feather the meal 2010-11 (PFM low), 170 kg N/ha PFM (PFM F a growing season. All sitesb were at the WSU Mount Vernon NWREChigh), in 85 Mount kg Vernon, N/ha WA. c sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). 97

Of the variation in test weight, a measure of grain bulk density and general grain quality, 51% was explained by genotype, and WA8022 had the largest test weight (75.5

± 0.2 kg/hL), followed by Bauermeister (70.5 ± 0.5 kg/hL), Relief (70.3 ± 0.9 kg/hL), and McCall (66.7 ± 0.7 kg/hL) (Table 3.2). The environment also had a large influence on test weight, as site-year and C×SY together accounted for 44% of the variation in test weight, and test weight was 109% greater in 2009-10 than in 2010-11. N fertilization treatments significantly reduced test weight in three site-years, although N fertility (NF) and NF×SY accounted for a small proportion (4%) of total variation in test weight. At the west site in 2009, the greatest rate of SCU significantly reduced the test weight of McCall and WA8022 by 5.7 and 3.4%, respectively, compared with non-fertilized control plants, and the smallest rate of SCU reduced the test weight of McCall by 5.0%. At both sites in

2010, the greatest rate of PFM significantly decreased the test weight of Bauermeister and

Relief by 12.4 and 26.4%, respectively, compared with non-fertilized control plants. At the south site, the greatest rate of PFM significantly reduced the test weights of Bauermeister,

McCall, and Relief by 7.7, 9.2, and 11.7%, respectively. The test weight of McCall was also significantly reduced by the smallest rate of PFM by 8.4% and the greatest rate of SCU by 10.3%.

Norwest 553 and WA8120 were grown only in the second year in place of two cul- tivars, Bison and Itana, which incurred near complete yield-loss in the first year, so results for these two cultivars are discussed only briefly. The two cultivars produced high yields 98

at both sites: 8.5 ± 0.3 Mt/ha and 7.7 ± 0.3 Mt/ha, respectively. All N fertilization treat-

ments significantly increased the yield of Norwest 553 compared with non-fertilized con-

trol plants (Table B6 in AppendixB). Only at the south site were yields in plots with the

smallest rate of SCU significantly less than in plots with the greatest rate of PFM. Except for

the greatest rate of PFM, which nearly doubled the yield compared with the non-fertilized

control plants, N fertilization did not significantly increase yield of WA8120 at the north

site. In contrast, all N treatments except the smallest rate of SCU significantly increased

yield in the south site. The test weight of Norwest 553 was significantly increased in plots

fertilized with the greatest rate of PFM compared with the non-fertilized control plants, and

in plots with the smallest rate of SCU, at both sites. In contrast, the test weight of WA8120

was significantly decreased in plots with the greatest rates of PFM and SCU compared with

the non-fertilized control plants, and the smallest rates of PFM and SCU, at both sites.

3.4.2 Wholemeal wheat flour quality

Cultivars Bauermeister, McCall, Relief, and WA8022 showed large variation in whole-

meal wheat flour quality traits under the field conditions of this study, including grain

protein content (7.0 to 14.4%), constant weight micro-SDS sedimentation volume (5.3 to

19.5 cm3/g), constant protein micro-SDS volume (4.6 to 16.7 cm3/g), and Hagberg falling number (HFN) (119.0 to 632.5 s) (Tables 3.3, 3.4, and 3.5, as well as Table B5 in 99

AppendixB). Of the variation in grain protein, 82% was explained by the main effect of

cultivar (C), and Relief had the greatest average grain protein (12.4 ± 0.1%), followed by

McCall (11.5 ± 0.1%), Bauermeister (10.2 ± 0.1%), and WA8022 (9.7 ± 0.1%) (Table

3.2). Site-year (SY) and C×SY explained 10% of the variation in grain protein, and grain

protein was about 1% greater in 2009-10 (11.2 ± 0.2% and 11.7 ± 0.1% in the east and

west sites, respectively) than in 2010-11 (10.6 ± 0.2% and 10.2 ± 0.2% in the north and south sites, respectively). N fertility treatments also had a significant effect on grain pro- tein. The greatest rates of PFM and SCU resulted in grain protein contents ranging from

9.6 to 14.4% (mean: 12.5%) for the two “historic” cultivars, and from 7.1 to 13.8% (mean:

10.5%) for the two “modern” cultivars. The greatest rate of PFM significantly increased grain protein compared with non-fertilized control plants at all site-years, except for the south site in 2010: Bauermeister by 15.8, 16.0, and 33.0% at the east, west, and north sites, respectively; Relief by 8.5, 9.1, 31.7% also at the east, west, and north sites; McCall by 9.5 and 22.6% at the west and north sites; and WA8022 by 10.8% at the west site. The greatest rate of SCU also significantly increased grain protein content compared with non-fertilized control plants, but mostly in 2009: Bauermeister by 16.8 and 18.0% at the east and west sites, respectively; WA8022 by 14.8 and 11.8% at the same sites; McCall by 8.6% at the west site; and Relief by 14.7% at the 2009 west site, as well as by 25.0% at the 2010 north site. Grain protein was statistically similar in plots which were non-fertilized, and in those receiving the smallest rates of PFM and SCU with one exception: the smallest rate of SCU 100

significantly increased grain protein of Bauermeister at the west site, and this response did not differ from the responses of Bauermeister to the greatest rates of SCU and PFM. 101 ) 0.7 a 0.4 abcd 0.4 ab 0.3 bcde 0.6 abc 0.2 defg 0.6 abcde 0.4 cdef 0.5 defg 0.3 efgh 0.5 gh 0.6 efgh 0.3 defg 0.1 h 0.5 h ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 0.8 ab 12.5 0.8 defg 11.4 0.3 a 12.2 0.7 bcde 10.9 0.6 defgh 12.1 0.6 abcd 10.7 0.9 cdef 11.2 0.3 abc 10.8 0.6 fghi 10.6 0.7 ghi1.3 efghi 9.2 0.2 9.9 defg 10.6 0.7 bcde 9.9 0.8 defgh 8.6 0.6 hi 8.9 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Grain protein (%) 0.3 a 13.0 0.3 cd 11.0 0.2 ab 13.7 0.4 cd 11.6 0.3 cde 10.4 0.1 bc 11.9 0.2 cde 11.3 0.1 abc 13.0 0.2 cde 9.6 0.1 fg0.1 def 9.6 0.2 9.8 ef 10.6 0.4 ef 11.7 0.1 hi 10.3 0.2 i 8.8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the grain protein content of hard red winter wheat cultivars Bauermeister, 10.0 a b 2009-10 2010-11 0.3 ab 13.3 0.6 bc 12.4 0.1 a 13.2 0.4 cd 12.4 0.4 bc 12.1 0.2 def 12.6 0.4 fg 12.2 0.3 cde 12.7 0.2 efg 12.2 0.4 ij0.2 fg 11.2 0.2 11.8 efg 11.6 0.1 gh 11.6 0.2 hi 10.4 0.3 ijk East West North South ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 12.7 SCU Low 11.4 SCU Low 10.0 PFM Low 12.5 PFM Low 11.6 PFM Low 10.3 SCU High 13.5 SCU High 11.8 SCU High 11.1 PFM High 14.0 PFM High 12.4 PFM High 11.0 Non-fertilized 12.9 Non-fertilized 11.6 Non-fertilized 9.5 WA8022 Relief McCall Bauermeister The effect of nitrogen (N) source and rate Table 3.3: McCall, Relief, and WA8022 grown2010-11. at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 102 0.6 h 0.9 fgh 0.4 h 0.1 h 0.4 h ± ± ± ± ± 0.5 ghi1.0 efghi 9.5 8.9 0.6 defgh 8.9 0.3 i 8.7 0.2 hi 8.7 ± ± ± ± ± Grain protein (%) 0.3 gh0.3 fg 9.5 9.8 0.2 fg 10.7 0.2 hi 8.3 0.1 hi 8.7 ) ± ± ± ± ± 2009-10 2010-11 0.2 jk0.2 ij 10.7 11.4 0.4 ijk 11.3 0.2 k 10.3 0.1 k 10.2 East West North South ± ± ± ± ± standard error. Within each column, means with the same letter are not significantly different ± continued from preceding page :( SCU Low 9.4 PFM Low 9.0 SCU High 10.1 PFM High 9.7 Non-fertilized 8.8 Table 3.3 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry expressed feather as meal mean (PFM low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 103 ) 0.5 cefgi 1.4 bcdefgh 0.3 a 0.6 bcdef 0.9 abc 0.5 efghj 1.2 fgh 1.2 bcdefghj 0.7 ghj 1.0 defghij 0.4 abd 0.6 bcdefg 0.2 a 0.8 abcde 0.6 a ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ) 3 continued on next page ( 1.4 cdef0.5 bcde1.5 9.7 a 9.7 2.4 bcd 12.5 1.4 11.7 def1.8 f1.3 9.2 ab1.0 9.0 cdef 9.5 1.2 9.1 f0.9 def1.3 ab 11.6 10.2 1.4 13.1 abc 13.0 1.3 cdef 10.1 1.3 cdef 8.0 0.7 ef 11.1 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.5 hijk0.5 ghij0.6 10.3 cdefg 12.2 0.4 16.4 abcde 12.1 0.4 abcd1.4 abc0.4 9.6 a0.2 7.7 a 15.8 0.5 efghi0.5 11.3 defgh0.4 8.1 efghi 9.5 0.4 15.4 bcdef 13.4 0.4 cdefgh 11.2 0.2 ab 10.5 0.9 ghij 8.8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the micro-SDS volumes, based on constant wholemeal flour weight, of hard a 13.7 Micro-SDS volume, constant weight (cm b 2009-10 2010-11 0.6 def 13.8 0.3 bc0.4 16.3 bcd0.5 ab 16.7 16.8 0.4 ab0.4 17.8 cde1.0 cdef 14.8 15.0 0.4 bc 15.8 0.3 cde 15.5 0.4 a 17.9 0.6 bc 14.6 0.6 fgh 0.5 efg 15.3 0.5 ab 17.4 0.4 cde 13.9 East West North South ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 13.2 SCU Low 16.8 SCU Low 14.8 PFM Low 13.9 PFM Low 17.0 PFM Low 14.2 SCU High 15.8 SCU High 17.3 SCU High 15.8 PFM High 15.0 PFM High 17.9 PFM High 15.8 The effect of nitrogen (N) source and rate Non-fertilized 15.5 Non-fertilized 14.9 Non-fertilized 12.5 McCall Relief WA8022 Bauermeister Table 3.4: red winter wheat cultivars Bauermeister,2009-10, and McCall, north Relief, and and south sites WA8022 grown during 2010-11. at Mount Vernon, WA at east and west sites during 104 0.1 hj 0.6 j 0.8 fghj 1.0 fghj 1.0 fghj ± ± ± ± ± ) 3 1.1 f1.8 bcd1.1 7.4 9.0 def 8.1 1.4 f1.0 7.7 def 8.9 ± ± ± ± ± 0.4 kl1.3 jkl0.3 7.5 fghij 13.0 9.4 0.5 l1.0 ijkl 7.8 9.2 ) ± ± ± ± ± Micro-SDS volume, constant weight (cm 2009-10 2010-11 1.4 ghi 12.0 0.4 fg 14.1 0.5 hi 12.6 0.5 i 11.7 1.0 i 13.0 East West North South ± ± ± ± ± standard error. Within each column, means with the same letter are not significantly different ± continued from preceding page :( SCU Low 10.4 PFM Low 11.8 SCU High 12.9 PFM High 11.0 Non-fertilized 10.1 Table 3.4 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry expressed feather as meal mean (PFM low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 105 ) 0.7 bcdeg 0.6 cdefgh 0.3 bcdef 0.6 efghi 0.5 cdefgh 0.9 fghj 0.6 j 1.4 ghj 1.0 hj 0.4 ghj 0.8 ab 0.1 abce 0.2 a 1.1 abcd 0.5 abcde ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ) 3 continued on next page ( 0.6 bcdefgh 10.4 0.7 h 9.8 0.8 bcdefg 10.7 0.3 h 9.3 1.5 h 9.6 0.8 defghi 8.5 0.5 eghi 7.2 1.1 abcd 8.9 1.4 h 8.5 1.2 fghi 8.7 0.7 abc 11.9 0.6 abdf 10.9 0.5 a 12.6 0.7 abcd 11.1 1.3 abcde 10.9 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.1 de 10.2 0.5 e 8.0 0.4 e 11.2 0.5 cde 8.1 0.2 cde 8.0 0.3 a 9.6 0.1 a 9.2 0.3 a 12.2 1.1 a 7.9 0.4 a 9.0 0.3 a 12.1 0.4 ab 11.6 0.4 abc 13.9 0.4 abcd 11.9 0.2 ab 11.6 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the micro-SDS volumes, based on constant wholemeal flour protein (10%), a 13.7 Micro-SDS volume, constant protein (cm b 2009-10 2010-11 0.4 efgh 11.9 0.6 fgh 11.2 0.4 h 11 0.5 gh 12.1 0.4 gh 12.2 0.4 a 14.6 0.2 a 13.8 0.3 ab 14.1 0.2 a 13.8 0.3 abcd 14.5 0.3 abc 13.9 0.1 bcde 13.6 0.2 abcd 13.4 0.3 abcd 13.3 0.3 bcde East West North South ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 11.7 SCU Low 14.7 SCU Low 13.2 PFM Low 11.3 PFM Low 14.6 PFM Low 13.5 SCU High 11.8 SCU High 14.6 SCU High 14.2 PFM High 11.2 PFM High 14.4 PFM High 13.6 The effect of nitrogen (N) source and rate Non-fertilized 11.5 Non-fertilized 13.4 Non-fertilized 13.2 WA8022 Relief McCall Bauermeister Table 3.5: of hard red winter wheatduring 2009-10, cultivars and Bauermeister, north McCall, and Relief, south and sites during WA8022 grown 2010-11. at Mount Vernon, WA at east and west sites 106 0.8 fhj 0.3 dfghi 0.3 bcdefgh 0.5 hj 0.1 fghj ± ± ± ± ± ) 3 0.5 defghi 8.9 0.6 ceghi 9.3 1.1 abcd 10.0 1.2 efgh 8.4 1.4 gh 8.8 ± ± ± ± ± 0.2 bcde 9.5 0.6 cde 9.6 1.1 e 12.0 0.3 e 9.0 0.4 e 8.8 ± ± ± ± ± ) Micro-SDS volume, constant protein (cm 2009-10 2010-11 0.3 defg 12.3 0.8 h 12.1 1.4 cdef 11.2 0.2 gh 11.6 0.5 fgh 11.5 East West North South ± ± ± ± ± standard error. Within each column, means with the same letter are not significantly different ± continued from preceding page :( SCU Low 11.0 PFM Low 11.2 SCU High 12.7 PFM High 13.0 Non-fertilized 11.4 Table 3.5 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry expressed feather as meal mean (PFM low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 107

Micro-SDS sedimentation volumes based on constant wholemeal flour weight reflect

gluten strength governed by both protein quantity and protein quality [Baik et al., 1994,

Mikhaylenko et al., 2000]. Therefore, paralleling the grain protein response, the constant

weight micro-SDS volumes were greater in 2009-10 (14.4 ± 0.3 cm3/g and 15.0 ± 0.2 cm3/g at the east and west sites, respectively) than in 2010-11 (10.9 ± 0.4 cm3/g and 9.9

± 0.2 cm3/g at the north and south sites) (Table 3.2). Similarly, N fertilizer treatments accounted for roughly the same proportion of variation (6%) among both grain protein content and constant weight micro-SDS volumes. On average, the greatest rate of PFM increased micro-SDS volumes by 21.6% (2.5 cm3/g) compared with non-fertilized con- trol plots, while the greatest rate of SCU increased volumes by 12.9% (1.5 cm3/g), and

the smallest rates of PFM and SCU did not influence this response variable significantly.

Compared with the non-fertilized control treatments, the greatest rate of PFM significantly

increased micro-SDS volumes of Bauermeister by 20.0, 13.1, 59.2, and 28.9% at the east,

west, north, and south sites, respectively; McCall by 15.5 and 64.6% at the east and north

sites; Relief by 95.1% at the north site; and WA8022 by 66.7% at the north site (Table 3.4).

The greatest rate of SCU significantly increased micro-SDS volumes of the two “modern”

cultivars at both 2009-10 sites, and increased volumes of one of the two “historic” cultivars

at one of the 2010-11 sites: Bauermeister by 26.4 and 19.0% at the east and west sites,

respectively; WA8022 by 27.7 and 20.5% at the same two sites; and Relief by 65.4% at the

north site. 108

Determining micro-SDS volumes based on a constant 10% protein level revealed

that many of the significant responses of constant weight volumes to N fertilizer treatments

were positively correlated to the grain protein content responses (Table 3.2). The ANOVA

F-test similarly showed that N fertility treatments had a smaller influence (2 versus 6%)

on the variation observed for constant protein, versus the constant weight, micro-SDS vol-

umes. Only the greatest rate of PFM at the north site resulted in significantly increased

micro-SDS volumes: McCall by 35.6%, Relief by 40%, and WA8022 by 36.4% (Table

3.4). On average, Bauermeister had the greatest constant protein micro-SDS volume (12.7

± 0.2 cm3/g), followed by McCall (11.7 ± 0.4 cm3/g), then Relief and WA8022 (both

10.6 ± 0.2 cm3/g). Interestingly, site-year explained roughly 40% of the variation in both the constant weight and constant protein micro-SDS volumes and, paralleling the constant weight responses, the constant protein micro-SDS volumes were greater in 2009-10 (12.9

± 0.2 and 12.8 ± 0.2 cm3/g at the east and west sites, respectively) than in 2010-11 (10.2

± 0.3 and 9.7 ± 0.2 cm3/g at the north and south sites).

Hagberg falling number (HFN) is a measure of pre-harvest sprouting, and is thus an important indicator of baking quality for wheat grown in high-rainfall environments. Of the variation in HFN, 62% was explained by cultivar (C), and on average, WA8022 had the greatest falling number (416.2 ± 4.6 s), followed by Bauermeister (397.9 ± 5.8 s),

Relief (384.0 ± 11.8 s), and McCall (290.7 ± 7.2 s). Site-year (SY) explained 23% of the variation in HFN, and C×SY explained 11% (Table 3.2). Although N fertility was 109

not a significant source of variation according to the ANOVA F-test, significant responses

were observed for two cultivars: compared with non-fertilized control plants, the greatest

rates of PFM and SCU decreased Bauermeister HFNs at the west site by 125 and 119 s,

respectively; and the smallest rate of PFM, as well as the greatest rates of PFM and SCU,

decreased Relief HFNs at the north site by 111, 181, and 202 s (Table B5 in AppendixB).

Grown only in the second year, Norwest 553 and WA8120 had mean grain protein

contents of 9.7 and 8.6%, respectively, which was low compared with Bauermeister, Mc-

Call, and Relief. N fertilizer significantly increased the protein content of WA8120, but

not of Norwest 553. The greatest rate of SCU increased WA8120 protein content by 33

and 20% at the north and south sites, respectively, while the greatest rate of PFM increased

protein by 31% at the north site only (Table B7 in AppendixB). Constant weight micro-

SDS volumes were nearly identical for the two cultivars, averaging 12.8 and 12.2 cm3/g for

Norwest 553 and WA8120, respectively, and were significantly increased by the greatest

rate of each N fertilizer. The greatest rate of PFM increased volumes for both cultivars

at both sites by 27 to 76% over that of non-fertilized control plants, resulting in 16.2 and

16.9 cm3/g for Norwest 553 and WA8120, respectively, at the north site, and 13.8 and

14.0 cm3/g at the south site (Table B8 in AppendixB). The greatest rate of SCU signifi- cantly increased volumes of WA8120 only, and the responses did not differ from those at the greatest rate of PFM. Added PFM at the greatest rate significantly increased constant protein micro-SDS volumes for both cultivars at both sites by 13.6 to 35.2%, and WA8120 110

produced significantly greater constant protein volumes than Norwest 553 at both sites.

Hagberg falling numbers averaged 397.3 and 363.8 s for Norwest 553 and WA8120, re-

spectively, and N fertilizer treatments did not influence this response variable significantly

(Table B7 in AppendixB).

3.4.3 Amount and composition of total phenolic acids in wholemeal wheat flour

Seven phenolic acid monomers were identified in wholemeal flours produced of the four hard red winter wheat cultivars: trans-ferulic, cis-ferulic, sinapic, p-coumaric, 2-

hydroxycinnamic (o-coumaric), syringic + vanillic, and 2,4-dihydroxybenzoic acid (Figure

3.1 and Table 3.6). All were identified with pure standards, other than cis-ferulic acid,

which was putatively identified according to published UV-vis spectra and relative reten-

tion times [Andreasen et al., 2000, Waldron et al., 1996]. Vanillic and syringic acid peaks

could not be separated and were, therefore, quantified together at 280 nm using a mean

response factor determined from the calibration curves for both phenolic acid standards.

Caffeic and 4-hydroxybenzoic acids were not detected. 111 , 1 * 20.0 , cis-ferulic acid; 6 17.5 , trans-ferulic acid; 5 15.0 , sinapic acid; 4 *** 12.5 Time (min) Time 7 -coumaric acid; p , 3 10.0 6 5 4 7.5 3 ) compounds having UV-vis spectra corresponding to phenolic acid monomers and dimers. * , 2,4-dihydroxybenzoic acid; 2 2 5.0 1 High-performance liquid chromatography (HPLC) elution profile of phenolic acid monomers and dimers in alkali-

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

mAU (x1,000) , 2-hydroxycinnamic acid; ( Figure 3.1: 7 hydrolysed extracts of hard red winter wheat wholemeal flour (cv. WA8022) with detection at 320 nm. Key to identity of peaks: syringic + vanillic acids; 112

Table 3.6: Diagnostic absorption wavelengths of peaks (λmax) and of troughs (λmin), as well as retention times (tR) for phenolic acid monomers and dimers measured by HPLC.

Phenolic acid tR (min) λmax (nm) λmin (nm) 4-hydroxybenzoic acid 4.33 254 235 vanillic acid 4.74 260, 292 237, 280 syringic acid 4.86 274 240 2,4-dihydroxybenzoic acid 5.19 255, 294 236, 276 p-coumaric acid 6.92 309 247 sinapic acid 7.55 323 263 trans-ferulic acid 7.79 322 261 cis-ferulic acida 9.07 309 259 2-hydroxycinnamic acid 11.60 276, 324 245, 304 aPutative assignment based on the literature [Andreasen et al., 2000, Waldron et al., 1996]. 113

Identified monomeric phenolic acids ranged in concentration from 174.5 to 594.0

µ/g of wholemeal flour dry matter (dm), while the total phenolic acid content (identified

monomers plus unidentified phenolic acids) ranged in concentration from 299.2 to 802.1

µg/g dm (Table 3.7). Trans-ferulic acid was the dominant phenolic acid identified in all

samples, with concentrations ranging from 141.5 to 463.1 µg/g dm (mean: 323.2 µg/g dm). This compound represented 74 to 87% of the total concentration of the individually identified phenolic acid monomers, or 42 to 69% of the total phenolic acid content. For the remaining compounds, the concentration of sinapic acid ranged from 16.8 to 82.7 µg/g dm (0.2 to 7.3% of the total phenolic acid content), cis-ferulic acid from 4.9 to 24.7 µg/g dm (0.1 to 1.9%), p-coumaric acid from 4.2 to 14.3 µg/g dm (0.01 to 1.60%), syringic + vanillic acids from 3.5 to 10.2 µg/g dm (0.02 to 1.10%), 2,4-dihydroxybenzoic acid from

1.5 to 5.0 µg/g dm (0.01 to 0.50%), and 2-hydroxycinnamic acid from 0.5 to 3.9 µg/g dm

(0.01 to 0.20%). 114

Table 3.7: Composition of individual phenolic acid contents in hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites at Mount Vernon, WA during 2009- 10.

Concentration (µ/g dm) Phenolic acid Meana Range syringic + vanillic acids 6.56 ± 0.10 3.49–10.22 2,4-dihydroxybenzoic acid 3.06 ± 0.06 1.49–5.03 p-coumaric acid 9.50 ± 0.16 4.23–14.30 sinapic acid 45.24 ± 1.35 16.76–82.73 trans-ferulic acid 323.19 ± 5.23 141.53–463.06 cis-ferulic acidb 11.75 ± 0.36 4.93–24.67 2-hydroxycinnamic acid 1.32 ± 0.06 0.52–3.87 unknown phenolic acidsc 206.39 ± 4.45 89.68–350.88 total phenolic acidsd 607.01 ± 7.51 299.21–802.12 aValues each expressed as mean ± standard error (N = 160). bPutatively identified according to published UV-vis spectra and relative retention times [Andreasen et al., 2000, Waldron et al., 1996]. cUnidentified phenolic acids having UV-vis spectra and retention times expected of this class of compounds [Andreasen et al., 2000, García-Conesa et al., 1997, Ralph et al., 1994, Waldron et al., 1996]. dTotal phenolic acid monomers and dimers, determined by summing concentrations of individually identified phenolic acids with the concentrations of unidentified phenolic acids. 115

The cultivar effect significantly influenced the concentration of each phenolic acid detected, although the proportion of variation explained by cultivars varied widely among the phenolic acids. The greatest total phenolic acid content was found in McCall (691.7

± 12.6 µg/g dm), followed by Bauermeister (600.9 ± 8.1 µg/g dm), Relief (581.5 ± 9.7

µg/g dm), and WA8022 (554.0 ± 15.8 µg/g dm) (Table B9 in AppendixB). McCall had the greatest concentration of all compounds analyzed, except for syringic + vanillic and

2,4-dihydroxybenzoic acids, as Relief had the greatest concentration of the former, and

WA8022 of the latter. Of the four cultivars tested, McCall had the lowest concentration of 2,4-dihydroxybenzoic acid. A large proportion of total variation was explained by the main effect of cultivar for the concentrations of 2,4-dihydroxybenzoic acid (81.1%), sinapic acid (78.0%), and syringic + vanillic acids (56.2%). Cultivar and site explained similar proportions of variation for the concentrations of cis-ferulic acid (47.6 and 47.9%, respectively), and p-coumaric acid (51.6 and 32.7%). Site had the greatest influence on trans-ferulic and 2-hydroxycinnamic acid, explaining 77.3 and 84.5% of the total variation in the concentrations of each phenolic acid, respectively. Site did not significantly influ- ence the concentrations of syringic + vanillic or 2,4-dihydroxybenzoic acids. Compared with the east site, cultivars grown at the west site had greater total phenolic acid concen- trations, due primarily to higher concentrations of trans-ferulic acid, although also due, in part, to greater concentrations of cis-ferulic, p-coumaric, and sinapic acids. The reverse was true for 2-hydroxycinnamic acid. Similar concentrations of syringic + vanillic and 116

2,4-dihydroxybenzoic acids were found in the cultivars at both sites.

N fertilizer treatments had a significant influence on the concentration of trans-ferulic

acid, the dominant constituent of the total phenolic content, while the interaction of S×NF

treatment significantly influenced the concentrations of trans-ferulic and p-coumaric acids

(Table B9 in AppendixB). The greatest rates of PFM and SCU significantly increased

concentrations of trans-ferulic acid for McCall at the west site, but not the east site, by 83

µg/g dm over that of the non-fertilized control plants. At the east site, the smallest rate of

SCU significantly increased the concentration of this phenolic acid in Bauermeister (Table

B10 in AppendixB). Concentrations of p-coumaric acid tended to decrease with N fertil- izer treatments versus non-fertilized plots at the east site, but increase with N application at the west site, suggesting that some other environmental factors were interacting with N treatments to influence the concentrations of this phenolic acid. The greatest rate of PFM significantly reduced the concentration of p-coumaric acid in McCall at the east site by

1.9 µg/g dm (16.7%) compared with the non-fertilized control plants, but significantly in-

creased the concentration at the west site by 2.5 µg/g dm (23.1%) (Table B11 in Appendix

B). The greatest rate of PFM significantly reduced the total phenolic content of McCall

and WA8022 at the east site by 17.3 and 22.6%, respectively, while the greatest rate of

SCU significantly increased the total phenolic content of McCall by 15.3% at the west site

(Table B12 in AppendixB). Concentrations of sinapic acid were significantly increased in

McCall at the west site at both rates of SCU (19.5 and 30.5%), as well as the greatest rate 117

of PFM (33.0%) (Table B13 in AppendixB). Concentrations of 2-hydroxycinnamic acid were significantly reduced at the east site for McCall at the smallest rate of PFM (33.1%), as well as for WA8022 at both rates of SCU (54.3 and 46.9%) (Table B14 in AppendixB).

At the east site, concentrations of 2,4-dihydroxybenzoic acid were significantly reduced for

Relief and WA8022 (26.6 and 21.0%) at the smallest rate of SCU and the smallest rate of

PFM, respectively (Table B15 in AppendixB).

3.5 Discussion

The hard red class of winter wheat accounts for 39% of U.S. wheat production, is used for bread flours, and is cultivated primarily in the Great Plains states of Kansas,

Montana, Colorado, Oklahoma, South Dakota, Nebraska, and Texas. However, the Pa- cific Northwest region produced 920 thousand Mt in 2010, or 3% of the total U.S. hard red winter wheat production, and Washington had greater production volumes than either Ore- gon or Idaho [USDA-NASS, 2011a]. Growing consumer interest in localized small grains production, particularly in the Seattle metropolitan area, which represents just under half of Washington State’s population, has revealed a series of challenges associated with the production of bread wheat that has consistently high baking quality. The relatively high rainfall and mild maritime temperature of the region promote high yields, but hinder the re- alization of high grain protein contents often demanded by large commercial mills, worsen 118

the problem of pre-harvest grain sprouting and lodging, exacerbate stripe rust epidemics, and induce water-logging damage. The challenge is two-fold: (1) breeding for improved bread-making quality, sprouting tolerance, stripe rust resistance, and straw strength under the unique conditions of the region; and (2) determining best management practices for the region, particularly N fertility. This study has provided some insights into the effects of poultry feather meal and sulfur-coated urea N fertilizers on the agronomic performance,

flour quality, and phenolic acid content of hard red winter wheat produced in the Skagit

Valley in northwestern Washington State.

Wheat genotype accounted for the greatest proportion of variation in yield. The cul- tivars studied represented a range of pedigrees, although most were bred in Washington.

The two “modern” cultivars, Bauermeister (released in 2005) and WA8022 (2007), pro- duced two- to three-fold greater yields than the two “historic” cultivars, Relief (1931) and

McCall (1965), predominantly due to genetic gains in yield potential and improved biotic resistances in the modern versus historic cultivars and, most importantly, resistance traits against stripe rust. The historic cultivars also competed poorly against weeds as a result of poorer canopy closure, lower leaf area index, and moderate tillering compared to the modern cultivars. Bauermeister yields were similar to the average winter wheat yields of western Washington, while WA8022 yielded 26% greater than the average. WA8022 was not only the greatest yielding cultivar, but also was the least susceptible to stripe rust of the cultivars studied. The non-race specific, high-temperature, adult-plant resistance carried 119

by Bauermeister was probably less effective in western Washington than in the warmer wheat production areas of eastern Washington. Yields were 14 to 67% greater in 2010 than

2009, but this was not likely due to applications of fungicide in 2010, as cultivar stripe rust severity was similar in both years of the study. N fertilizer treatment was not a significant source of variation in yield, suggesting either that N was not limiting crop productivity at any of the study sites, or that the timing of fertilizer application was not ideal [Mahler et al., 1994]. Soil tests confirmed adequate levels of phosphorus and potassium at the sites, and sulfur was applied with both the organic and mineral N fertilizers, so it is unlikely that these macronutrients were limiting.

Grain density tended to be low in both years of the study, and only WA8022 met the

U.S. No. 2 grade requirements for the hard red class of winter wheat on average in three site-years. Relief met the U.S. No. 2 and 3 grade requirements in 2009 only. Bauermeister and McCall were graded U.S. No. 4 and 5 in all site-years [USDA-FGIS, 2006]. The low average test weights of these three cultivars was due, in part, to the moderate to high stripe rust susceptibilities as, in addition to reducing yields, infection by the stripe rust pathogen before or during flowering commonly results in kernel shriveling and reduced test weight

[Bockus et al., 2010]. Environment contributed significantly to grain density variability, and greater rainfall in 2010-11, particularly after all cultivars had reached physiological maturity in the month of July, resulted in test weight reductions ranging from 5 to 12% compared with the first year of the study. Rainfall between maturity and harvest, partic- 120

ularly the number of rainfall events, can markedly reduce grain test weight [Farrer et al.,

2005]. Added N fertilizer reduced test weight by 3 to 4%, on average, with the largest

reductions occurring with the highest rate of organic and mineral N fertilizers, a finding

consistent with that of other studies [Johnson et al., 1972, McGuire et al., 1988, Syltie and

Dahnke, 1983].

Grain protein content and gluten strength, as indicated by SDS sedimentation vol-

ume, are important flour quality parameters which are highly positively correlated with

hearth bread loaf characteristics including loaf volume, form ratio, and crumb structure

[Tronsmo et al., 2003, Uhlen et al., 2004]. Genotype, site-year, and N fertility all con-

tributed significantly to grain protein variability, although genotype explained by far the

greatest proportion of variation (82%). Although low (<10%) grain protein contents were observed for 28% of samples, 12 to 14% grain protein levels were attained in both years, and the first year average of 11.5% was consistent with the grain protein averages of hard red winter wheat produced in central and eastern Washington [Knodel et al., 2008, Plains

Grains, Inc., 2011]. The considerably lower grain protein average (10.0%) in the second year suggests that year-to-year climate variability can be a challenge for achieving consis- tently high flour quality over time, particularly for cultivars developed after the widespread use of pesticides and fertilizers. The two historic cultivars, McCall and Relief, maintained an average protein advantage of 1 to 4% greater than that of the two modern cultivars,

Bauermeister and WA8022, with all N fertilizer treatments. Because grain yield and grain 121

protein concentration commonly have a strong negative genetic relationship [Löffler and

Busch, 1981, Simmonds, 1995, Slafer et al., 1990], it makes sense that these two traits were not simultaneously improved in the “modern” genotypes studied.

Improving protein quality, determined by the proportion and composition of the gliadin and glutenin fractions and the size distribution of protein aggregates in wheat flour, might reduce seasonal variability in breadmaking quality even as protein contents change in response to the weather and soil N availability [Osman et al., 2012]. Indeed, for hearth breads baked with flours ranging from 11 to 13% protein, protein quality was found to be critical for doughs to retain proper shape during proving and baking, an effect which could not be compensated for by increasing protein content [Færgestad et al., 2000]. Similarly, for hearth breads baked with flours ranging from 8 to 11% protein, variability in protein content had only a minor influence on baking properties, while the high molecular weight glutenin subunit composition of the flour (protein quality) was highly positively correlated with baking quality [Tronsmo et al., 2003]. In this study, grain protein was increased 1% on average by both organic and mineral N fertilizers compared with non-fertilized plants, particularly at the greater rate of application, suggesting that PFM and SCU had similar mineral-N release patterns in the cool, wet soils of this region. The work of Engelsjord et al.[1997] confirmed that the rate of N release from surface-applied SCU pellets is slow at cool temperatures common to western Washington, as <10% of the N was released as ammonium and nitrate within six weeks at 12◦C on a moist peat medium, while 15 to 20% 122

was released over this same period when soils were maintained at 21◦C.

N fertility had a significant influence on both constant weight and constant protein micro-SDS sedimentation volumes, although the relative importance of this source of vari- ation was markedly reduced for the constant protein volumes. Micro-SDS volumes based on constant flour weight are influenced both by grain protein content and quality [Baik et al., 1994, Mikhaylenko et al., 2000], whereas micro-SDS volumes based on constant protein correct for the influence of protein content to reveal differences in protein quality.

Organic and mineral N fertilizers, almost exclusively at the greater application rate tested, increased both protein content and constant weight micro-SDS volume across three site- years and all four cultivars. However, only the high PFM rate, and only at the north site in the second year, significantly increased constant protein micro-SDS volume. Other studies also conclude that protein quality is mostly under genetic control [Schofield, 1994, Shewry et al., 1992], although N nutrition can influence the relative proportions of proteins in the grain [Godfrey et al., 2010, Wieser and Seilmeier, 1998]. In this study, constant protein micro-SDS volumes ranged from an average of 10.6 for cultivars Relief and WA8022, to

12.7 cm3/g for Bauermeister. Similar volumes were obtained for wholemeal of the cultivar

Finley, a hard red winter wheat cultivar with excellent baking quality known to contain the

5+10 HMW glutenin subunits, when grown in Pullman, WA and Lind, WA by the WSU

Variety Testing Program: 11.0 and 11.4 cm3/g. Grown at the same two locations, Estica, a hard red winter wheat cultivar known to contain the 2+12 HMW glutenin subunits, had 123

substantially lower constant protein micro-SDS volumes than those observed in this study:

7.7 and 8.2 cm3/g [Morris et al., 2007].

The correlation between high breadmaking quality and the 5+10 HMW glutenin sub- units has long been known [Payne, 1987]. Flours containing 5+10 HMW glutenin subunits produce hearth breads with substantially larger loaf volume and greater form ratio com- pared with flours containing 2+12 HMW glutenin subunits, even for loaves baked with up to 60% wholemeal flour [Aamodt et al., 2005]. Cultivar WA8022 has Finley in its pedigree, so likely contains the desirable 5+10 HMW glutenin subunits. Because the constant pro- tein micro-SDS volumes observed in this study parallel those which have been linked with high breadmaking quality in other studies, it is possible that the other cultivars evaluated in this study contain these desirable protein subunits. A direct comparison between flour and hearth bread loaf characteristics corresponding to hard red winter wheat cultivars grown in western Washington State has not been attempted systematically. However, local com- mercial bakers have tested flours produced from hard spring and winter cultivars grown by Stephen Jones’ WSU Mount Vernon NWREC research group, including Bauermeis- ter, which was found to have excellent dough strength, and to produce voluminous loaves having good flavor [DePasquale, 2011, Mangold, 2011]. Although the bakers observed rel- atively high enzymatic activity in flours produced from western Washington grown wheat,

83% of flour samples in this study had HFNs of 300 s and higher, suggesting generally low levels of α-amylase enzymatic activity. McCall accounted for 66% of the samples 124

with HFNs below 300 s, while the “modern” cultivars collectively accounted for only 15%,

suggesting that breeding efforts have improved pre-harvest sprouting tolerance.

Seven benzoic and cinnamic acid derivatives were identified in all wholemeal flour

samples in this study, with phenolic acids of the latter group, particularly trans-ferulic,

sinapic, and p-coumaric acids, occurring in the greatest concentrations. Additionally, four

major HPLC peaks with UV-vis absorption spectra and retention times consistent with

ferulic acid dehydrodimers were present in all samples. Without access to pure standards,

precise identification of these compounds was not possible. However, three peaks had char-

acteristics similar to those known for 5,5’-DiFA, 8-O-4’-DiFA, and 8,5’-DiFA benzofuran

form [Waldron et al., 1996]. Ferulic acid dehydrodimers have been observed in concen-

trations approaching one-third that of trans-ferulic acid, or 280 to 298 µg/g wholemeal

flour [García-Conesa et al., 1997, Lempereur et al., 1998, Mattila et al., 2005], consistent

with the mean concentration of the unknown phenolic acids (207.1 µg/g dm) observed in

this study. The total phenolic acid content, determined by summing the concentrations

of the seven identified phenolic acid monomers with the concentrations of the unknown

monomers and dimers, averaged 607.0 µg/g dm, ranging from 299.2 to 802.1 µg/g dm.

These findings are consistent with those of Li et al.[2008], who observed total phenolic

acid content to range from 326 to 1171 µg/g dm, and averaged 664 µg/g dm, in their de- tailed analysis of 130 winter wheat genotypes. Caffeic acid was not detected in this study.

However, studies have demonstrated that NaOH hydrolysis, the method employed in this 125

study, leads to severe loss of the compound [Krygier et al., 1982, Verma et al., 2009]. Sim- ilarly, 4-hydroxybenzoic acid, which was not detected in this study, has either been found not to be recoverable by alkaline hydrolysis [Verma et al., 2009], or to be recoverable in only trace amounts [Arranz and Calixto, 2010]. However, utilizing similar extraction pro- cedures to this study, Li et al.[2008] reported concentrations of 4-hydroxybenzoic acid ranging from 2.5 to 21.2 µg/g dm.

In comparison with white flour, whole wheat and bran-enriched flours have substan- tially higher phenolic acid contents [Mattila et al., 2005], compounds with a range of poten- tial human health benefits [Dykes and Rooney, 2007]. In particular, concentrations of fer- ulic acid, found predominately in the outer bran layer of wheat kernels [Liyana-Pathirana and Shahidi, 2006], are highly positively correlated with free radical scavenging activity

[Mpofu et al., 2006, Verma et al., 2009, Zhou et al., 2004a]. Ferulic acid (combined trans- and cis- isomers) was the dominant phenolic acid monomer detected in this study, with concentrations in wholemeal flour samples ranging from 146.5 to 487.7 µg/g dm (mean:

334.9 µg/g dm), which accounted for 83.6% of total identified phenolic acid monomers on average. These findings are consistent with those of Li et al.[2008], who observed total ferulic acid concentration to range from 181 to 742 µg/g dm (mean: 395 µg/g dm). Mpofu et al.[2006] also reported ferulic acid concentrations in this range for six hard wheat culti- vars grown in Canada, as did Stracke et al.[2009] for wheat grown in a long-term field trial in Switzerland, and Zuchowski et al.[2011] for winter wheat cultivars grown in Poland. 126

However, these values are an order of magnitude greater than those reported by Zhou et al.

[2004a]. A number of studies report ferulic acid concentrations as a proportion of bran mass [Moore et al., 2006, Verma et al., 2008, Zhou et al., 2004b], but because the rela- tionship between the phenolic content of bran versus flour is cultivar dependent [Klepacka and Fornal, 2006], reliable comparisons cannot be made with the findings of this study.

Occurring naturally in its stable trans form, ferulic acid can be converted to its cis iso- mer by the action of light [Ribereau-Gayon, 1972], and it has been suggested that relative amounts of cis-ferulic acid increase in the latter part of grain development, when trans- parency of the bracts allows greater exposure of wheat kernels to UV light [McCallum,

1989]. In this study, the concentration of cis-ferulic acid averaged 11.7 µg/g dm, or 3.6% of that of trans-ferulic acid. Mccallum and Walker[1991] observed quantities of cis-ferulic acid ranging from 0.5 to 4.0% of the concentration of trans-ferulic acid, while Verma et al.

[2009] reported a range of 2.6 to 3.4% for alkaline-hydrolyzed bran of six wheat cultivars.

Conversely, Hartley and Keene[1984] observed the concentration of cis-ferulic acid to be

71% of the concentration of trans-ferulic acid in alkaline-hydrolyzed wheat bran cell walls.

Other important phenolic acid monomers detected in wholemeal wheat flour in this study were sinapic acid (mean: 45.2 µg/g dm), p-coumaric acid (mean: 9.5 µg/g dm), and syringic + vanillic acids (mean: 6.6 µg/g dm). The range of observed sinapic acid concentrations is consistent with published findings for wholemeal wheat flours [Li et al.,

2008, Mattila et al., 2005, Stracke et al., 2009, Zuchowski et al., 2011], and many studies 127

have similarly reported this compound as being the second most dominant phenolic acid monomer [Mattila et al., 2005, Stracke et al., 2009, Zuchowski et al., 2011]. However,

Verma et al.[2009] observed higher contents of p-coumaric acid in the bran of five of the six wheat cultivars tested. Concentrations of p-coumaric acid determined in this study were consistent with some studies [Li et al., 2008, Stracke et al., 2009, Zuchowski et al.,

2011], although other studies reported substantially lower concentrations [Menga et al.,

2010, Zhou et al., 2004a], and others reported concentrations two to three times greater than those observed in this study [Hernández et al., 2011, Mattila et al., 2005, Mpofu et al.,

2006]. Syringic and vanillic acids had near-identical retention times, so could not be sepa- rated, but instead, showed as a single peak representing 1.0 to 3.1% of identified phenolic acid monomers, up to a maximum concentration of 10.2 µg/g dm. Several studies report similar concentrations for these two phenolic acids combined [Menga et al., 2010, Stracke et al., 2009]; however, other studies report combined values two to three times greater

[Hernández et al., 2011, Li et al., 2008, Mattila et al., 2005, Zhou et al., 2004a]. There is no consensus on the relative importance of syringic acid versus vanillic acid, as the con- centration of syringic acid has been reported as being three [Zhou et al., 2004a] to seven times [Hernández et al., 2011] greater than that of vanillic acid, as well as near-identical

[Li et al., 2008, Mattila et al., 2005, Mpofu et al., 2006, Stracke et al., 2009, Verma et al.,

2009].

Of least importance among the phenolic acids detected in this study were 2,4-dihydroxybenzoic 128

acid and trans-2-hydroxycinnamic acid, also known as o-coumaric acid. Although rarely reported in the literature, Li et al.[2008] observed concentrations for 2,4-dihydroxybenzoic acid second only to the dominant ferulic acid, or up to two orders of magnitude greater than the mean concentration (3.1 µg/g dm) determined in this study. Trans-2-hydroxycinnamic acid was detected in low concentrations, from 0.5 to 3.9 µg/g dm. Li et al.[2008] and

Mpofu et al.[2006] both utilized an extraction procedure involving alkaline hydrolysis followed by aqueous-organic extraction, like that employed in this study, and found drasti- cally different concentrations of this phenolic acid in wholemeal wheat flours: the former reported a range from 3.7 to 10.9 µg/g, and the latter study 145.5 to 229.2 µg/g. The com- pound was also observed in high concentrations across a range of Chinese wheat cultivars, and accounted for 21% of total identified phenolic acid monomers in a cultivar with white seed color [Li et al., 2005]

Concentrations of phenolic acids and total phenolic contents are commonly reported to differ significantly among wheat cultivars, as exemplified by a 3.5-fold range of total phenolic acid concentrations observed among the wholemeal flours of 130 winter wheat cultivars [Li et al., 2008], as well as a 2.0-fold range of concentrations observed among the bran fractions of 51 spring wheat cultivars [Verma et al., 2008]. Therefore, it is no surprise that genotype had a highly significant effect on all phenolic acids assayed in this study, explaining roughly 50% of the variation in total phenolic acid content, and making possi- ble up to a 20% mean content difference among cultivars. Wheat genotype had a smaller 129

relative influence on total phenolic content in a number of studies [Moore et al., 2006,

Mpofu et al., 2006], probably because substantial distances among experimental sites en- hanced the influence of environment, while the two experimental sites chosen for this study were less than 1 km apart and, therefore, had similar soil characteristics, climate, as well as weed and disease pressures. Genotype explained most of the variation for sinapic, 2,4- dihydrobenzoic, and syringic + vanillic acids. Zuchowski et al.[2011] also found genotype to account for a majority of the variation observed in sinapic acid concentrations for spring and winter wheat, although the main effect of production system and the interaction with genotype exerted a substantial influence on vanillic acid. Similarly, about 75% of the vari- ation in both vanillic and syringic acids was explained by the environment in a study of six hard wheat cultivars grown in four western Canada locations [Mpofu et al., 2006], as well as in a study of 20 hard wheat cultivars grown in two eastern Colorado locations [Moore et al., 2006]. In this study, site was a highly significant (P < 0.0001) source of variation for all phenolic acids except syringic + vanillic acids and 2,4-dihydroxybenzoic acid. Relative to genotype, site had a particularly strong influence on the concentration of trans-ferulic acid in this study, substantially more so than reported by others [Moore et al., 2006, Mpofu et al., 2006, Zuchowski et al., 2011].

N fertilizer treatments had a small but significant influence on the concentration of trans-ferulic acid and, because this was the dominant phenolic acid detected, the signifi- cance of the main effect was extended to the total phenolic acid content. Predominantly at 130

the west site in 2009-10, additions of both organic and mineral forms of N fertilizer signif-

icantly increased trans-ferulic acid levels for both Bauermeister and McCall. P-coumaric

acid was significantly influenced by the interaction of site-by-N fertility, with significantly

reduced concentrations in McCall at the east site, but significantly increased concentra-

tions at the west site in plots fertilized with the higher concentration of organic fertilizer

(PFM). Concentrations of sinapic acid were also increased in McCall at the west site with

inputs of both organic and mineral forms of N fertilizer. The effect of N fertility was

nearly significant for both p-coumaric and sinapic acid (P = 0.0681 and P = 0.0712, re- spectively), suggesting that hydroxycinnamic acid concentration can be influenced by N fertilizer. Interestingly, N fertilizer treatments had a significant influence on the combined concentration of the four unknown phenolic acids, further supporting the hypothesis that the compounds were dimers of ferulic acid. Mean cultivar concentrations of nearly all phe- nolic acids were reduced in the east site compared with the west site in 2009-10, so an environmental stress at the east site other than N availability was apparently affecting the biosynthesis of cinnamic acid derivatives. Large trees at one end of the east site resulted in extended periods of shading on plots at that end, thus plant N might have been directed toward morphological and physiological changes favoring more efficient light capture in response to the shading [Li et al., 2010], at the expense of phenolic acid production, even with adequate levels of available N. Cinnamic acids are produced through the deamination of phenylalanine, an essential aromatic amino acid [Gleason and Chollet, 2012]. Inputs of 131

N fertilizer might increase levels of phenylalanine [Dubetz et al., 1979, Eppendorfer, 1978] and phenylalanine-derived metabolites, while a N-stressed environment might mobilize a greater proportion of N sources for biomass production and grain fill [Blacklow and Incoll,

1981], resulting in reduced concentrations of ferulic, sinapic and p-coumaric acids. As suggested by the finding that yield was not significantly influenced by N fertility, N might not have been limiting at either site, so the degree of influence of N on hydroxycinnamic acid concentration could be more pronounced in more N-stressed environments.

Lempereur et al.[1998] found that N fertilization did not significantly influence fer- ulic acid concentrations in durum wheat kernels. Zuchowski et al.[2011] observed that concentrations of both ferulic acid and p-coumaric acid were significantly greater in the grain of spring and winter wheat cultivars managed organically, assumed to be the more

N-limited environment, compared with conventional management. However, three-year studies of wheat in Switzerland [Stracke et al., 2009] and oats in Sweden [Dimberg et al.,

2005] concluded that no statistically significant differences in phenolic acid concentrations existed between the organically produced cereals compared to the conventionally grown cereals. Variations in temperature and soil moisture, both among production areas within the same growing season [Heimler et al., 2010, Menga et al., 2010, Moore et al., 2006] and year-to-year variations at the same location [Stracke et al., 2009], probably have a greater influence on the phenolic acid content of wheat than N fertilization.

In summary, while N fertilization did not influence grain yields significantly in this 132

study, both organic and mineral N fertilizers applied at a rate of 170 kg N/ha significantly increased the grain protein content of hard red winter wheat cultivars in western Wash- ington. Protein content differed among cultivars, as the higher yielding modern cultivars had substantially lower average protein contents than historic cultivars. Although the cool, wet environment of the region is not ideal for maximizing grain protein content, relatively high protein contents (12 to 14%) were realized. Although year-to-year protein variability was substantial, which is a concern for producing a consistently high quality bread flour, the protein quality of all cultivars, evaluated by constant protein micro-SDS sedimenta- tion tests, was consistently at levels positively correlated with high breadmaking quality in other studies. High protein quality, a complex trait not influenced strongly by N fertility can, therefore, obviate the effects of seasonal variability in grain protein content so long as the mean protein content achieved is beyond a certain threshold. According to research in northern Europe, high quality hearth breads can be baked with flours ranging from 10 to

13% protein. Pre-harvest sprouting was not a major problem in this study, particularly for the modern cultivars which have undergone genetic gains in sprouting tolerance compared to historic cultivars; however, consistently meeting U.S. No. 1 and No. 2 grade require- ments for hectoliter weight may be a challenge for western Washington wheat growers.

Phenolic acid composition and total phenolic acid concentration of grain were not influ- enced differentially by organic versus mineral forms of N, and only the concentration of trans-ferulic acid, the dominant compound identified in all flour samples, was significantly 133

influenced by type of N fertilizer. Additionally, the interaction between site and N fertility significantly influenced the concentrations of trans-ferulic and p-coumaric acids as well as the total phenolic acid content. The near-significant influence of N fertilizer treatments on the concentrations of sinapic and p-coumaric acids suggests the cinnamic acid derivatives are more likely to be influenced by N fertility than benzoic acid derivatives. High inputs of N fertilizer significantly decreased total phenolic acid content at the east site in 2009-10 and significantly decreased total phenolic acid concentration at the west site in 2009-10.

Thus, other environmental factors may interact with soil N availability to influence pheno- lic acid production. In this study, it is possible that tree-shading at the east site directed plant N to metabolic processes other than phenolic acid production.

Some hard red winter wheat cultivars improved under eastern Washington conditions can achieve grain protein contents of 10 to 12%, as well as 5 to 9 Mt/ha yields, under western Washington conditions. If these cultivars also carry the genetic traits express- ing desirable protein quality, which are only minimally influenced by N fertilization, then breadmaking quality of western Washington flours is competitive with the quality of east- ern Washington flours. However, flour yields, positively correlated with grain test weights, will be lower on average in western Washington compared with drier and warmer parts of the state. It is unclear from this study if single applications of fungicide are effective against stripe rust, but greater test weights and yields were achieved in more genetically resistant cultivars. The environmental conditions of the region dictate continuous breeding 134

for stripe rust resistance. Desirable breadmaking quality traits from historic, or heritage, cultivars will most likely have to be bred into modern genetic backgrounds to be useful in western Washington, as the cultivars lack effective stripe rust resistance traits, have lower yield potential, are too prone to lodging, and are too prone to pre-harvest sprouting.

3.6 Acknowledgments

I am grateful to Stephen Jones and Carlo Leifert for mentoring me throughout this study, to Alfredo Aires for supporting me in the analysis of phenolic acids, as well as to

Marc Evans and Kimberly Garland Campbell for their help with statistical analysis. I would also like to thank Steve Lyon for assistance with crop planting, management, and harvest, as well as Bozena Paszczynska and Doug Engle for training me in wheat quality analysis methods and allowing me to use the USDA-ARS Western Wheat Quality Laboratory at

Pullman, WA. Finally, thank you to Mohammed Almuairfi and Kirsten Brandt for training me in HPLC methods for the analysis of phenolic acids at the School of Agriculture, Food

& Rural Development, Newcastle University, UK. This study was financially supported by the European Commission in the Communities 7th Framework Programme, Project

NUE-Crops (KBBE-2007-1-2-15), as well as by the Harry E. Goldsworthy Wheat Research

Fund, and Organically Grown Company. 135

CHAPTER 4. MARKETING STRATEGIES AND INFORMATION

NEEDS OF SMALL GRAIN GROWERS IN THE PUGET SOUND

REGION OF WASHINGTON STATE

4.1 Abstract

Efforts to relocalize the grain economy are gaining momentum in the Puget Sound region of western Washington State, a region having a long history of grain production but not commonly associated with the crops today. A mail survey of past, current, and prospec- tive small grains growers in the region was conducted in order to assess their use of and interest in different market outlets, end-uses, and sources of grain production information, as well as to identify factors limiting grain production. Aside from weather, three inter- related economic factors were most limiting to the current production of grains: availability and/or cost of infrastructure, limited market outlets, and prices received. These limitations meant that 50 percent of wheat growers “very often” to “always” sold through a grain ele- vator, while 50 percent of non-wheat grain growers sold to animal operations with the same frequency. However, many growers are interested in alternative, direct-to-consumer mar- kets. Feed grain was the crop use of primary interest to both current and prospective grain 136

growers. Compared with current grain growers, prospective grain growers operated smaller farms, were younger, and had a higher level of formal education. Additionally, this cohort predominantly valued university- and Extension-based sources of production information, most notably conferences/workshops/seminars, and tended to have a greater interest in prin- ciples of sustainable agriculture, including animal-integration, cover-cropping, and organic management practices than current grain growers. All respondents highly valued informa- tion from other growers, but current grain growers were substantially more receptive to information from input suppliers than were prospective grain growers.

4.2 Introduction

Small grain crops have been in cultivation in western Washington State since the es- tablishment of farms connected with outposts of the Hudson’s Bay Company during the fur trade era of the mid-1800s [Hussey, 1957]. The first wheat crop on the Puget Sound was planted in the fall of 1834 at the British Ft. Nisqually, and tracts of farmland were promptly developed throughout the region to promote self-sufficiency. Although challenged with occasional spring frosts and disease outbreaks, the company’s farms annually produced thousands of bushels of wheat, oats, barley, and rye, generally cultivated in rotation with peas and potatoes. Production was sufficiently expanded by the 1840s to allow for tons of wheat to be traded to the Russian America Company for export to the Russian Far East 137

[Scheuerman, 2012]. By 1845, the first Oregon Trail immigrants were settling in the De- schutes Valley near present-day Tumwater and Olympia, where Washington Territory’s

first American settlement was located. A grist mill built by the Simmons and Bush fam- ilies was the region’s first American-owned milling venture [Zionitz, 1982]. The region became known for high quality grain crops and high yields. William Owen Bush, son of the pioneering African American George Washington Bush, was awarded the gold medal for “World’s Best Wheat” at the 1876 Centennial International Exhibition in Philadelphia for wheat grown at his south Puget Sound farm [Lockley, 1916]. The fertile soils and mild maritime climate of Whidbey Island enabled one farmer to set the dry-land wheat record of

117.5 bushels per acre in 1894 [Baker, 1931].

Wheat continues to be the primary grain crop in the region, followed distantly by bar- ley, and in 2010, 27.7 thousand metric tons of the two crops were harvested from 12,800 acres in western Washington. This same region also produced 367,000 tons of non-alfalfa hay on 166,000 acres, an unknown portion of which was cereal straw hay [USDA-NASS,

2012]. The impact of the wheat industry on western Washington’s economy has been es- timated at 160.2 million USD, or about 14 percent of the total economic impact of this commodity on the state [Fortenbery, 2011], which is substantial considering that 99.5 per- cent of Washington wheat is harvested east of the Cascade Mountain Range [USDA-NASS,

2011a]. Today, between 85 and 90 percent of all Washington wheat is exported from Pa- cific Northwest ports to international markets, particularly to Asia and the Middle East 138

[Washington Grain Commission, 2012]. The Washington wheat export market mirrors the national market, which acquired its architecture in the 1950s, when import demand shifted away from Europe to Asia and emerging third-world markets. In the 2011 fiscal year, 57 percent of total U.S. wheat was exported, or 34.5 million metric tons of grain valued at 11.5 billion USD [U.S. Department of Commerce - Bureau of the Census, 2012], and the U.S. share of world wheat exports averaged 24 percent from 2001 to 2009 [USDA-FAS, 2012].

Wheat and wheat products have been major export commodities of the present U.S., subject to production controls and price supports, since the British colonial era [Johnson et al., 1915]. A period of protectionism and industrialization following independence el- evated agriculture as a source of demand for U.S. domestic use, yet continuous growth of wheat acreage following the Civil War drove the exploit of foreign markets to absorb wheat grain and flour surpluses [Rothstein, 1960]. As early as the nineteenth century, agriculture was divided into “increasingly specialized sectors linked in input chains that crossed national boundaries to create food products marketed transnationally” [Friedmann and McMichael, 1989]. By the end of World War II, the U.S. had achieved an unprece- dented level of global dominance in agro-food production and trade [Friedmann, 1978], including its new claim as the world’s largest wheat exporter [Liefert et al., 2010]. A heav- ily subsidized U.S.-Soviet wheat deal in 1972, the largest private grain sale in U.S. history up to that point, launched a new era of agro-food “globalization,” during which U.S. grain exports and food aid combined with a process of transnational accumulation by U.S. cor- 139

porations, undercut the independent capacities of nations to regulate domestic agricultural production and trade [Friedmann and McMichael, 1989, Kliukin, 1981].

Beginning with the New Deal of the 1930s, commodity price support programs re- warded large family farms, and the size and productivity of farms expanded with the advent of new machines, chemicals, cultivars, and production practices. Greater capital investment by farms necessitated greater production specialization, and “subordinated farms to emerg- ing agro-food corporations, both as buyers of machines, chemicals, and animal feeds, and as sellers of raw materials to food manufacturing industries or livestock operations” [Fried- mann, 1995]. Today, large-scale family farms, although only 9 percent of all U.S. farms, account for a 66 percent share of the value of production, hold 29.4 percent of all farm assets, and receive 45 percent of commodity-related payments [Hoppe and Banker, 2010].

Representative of a certain prominent economic viewpoint, Blank[1999] posits that the decline of smaller family farms combined with the concentration of production in fewer large farms over the last half century is a necessary market correction for agriculture’s poor efficiency. Others counter that agriculture is more than a generator of products, and this development will “dramatically change the very landscape of rural America, jeopardize the future productive capacity of the land, and severely limit our food choices” [Kirschenmann et al., 2005].

Agriculture has become increasingly restructured in the direction of concentration and consolidation through the combination of three processes: horizontal integration, ver- 140

tical integration, and globalization. Since the late 1980s, the proportion of the total market share that is held by the four largest firms has been consistently increasing for every ma- jor sector of the agro-food system, which concerns many economists, who concede that a market loses its competitive character if the four firm concentration ratio is in excess of 40 percent [Heffernan et al., 1999]. Wheat processing in the U.S. is representative of this trend, as the top four firms had 34 percent of total milling capacity in 1974 and over

65 percent in 1991, and over that time-frame the number of mills declined by 25 percent and the average mill capacity nearly doubled [Brester and Goodwin, 1993, Wilson, 1995].

By 2007, just three firms had 55 percent of the flour milling capacity, and Archer Daniels

Midland (ADM) and Cargill, two of the largest U.S. wheat millers, also owned a combined annual animal feed capacity of 11.2 million tons [Hendrickson and Heffernan, 2007]. The complete picture of vertical integration is difficult to ascertain, but as an example: Cargill owns a 67 percent stake in one of the world’s largest fertilizer companies, as well as more than 500 million bushels of grain storage capacity, while ADM has more than 700 mil- lion bushels of storage capacity, as well as truck, rail, barge, port, and ocean-going vessel handling and freight capacity [Gale Group, 2012, Hendrickson and Heffernan, 2007].

The globalized, industrialized agro-food system excels in several areas: mass pro- duction of food and fiber, access to capital, and an intense focus on the bottom line which simplifies decision-making [Hendrickson and Heffernan, 2002]. These strengths are ide- ologically and morally legitimated by neoclassical economics and a capitalist culture, yet 141

various counter-movements seek food system alternatives which are rooted in particular places and emphasize economic viability, ecologically sound practices, health, as well as enhanced social equity and democracy [Feenstra, 1997]. Expanding on the first characteris- tic mentioned, localization is a counterpoint to globalization which culminates in a system where “‘food is grown, harvested, processed, marketed, sold, [and] consumed as close to home as possible”’ [Kloppenburg et al., 2000], and sometimes this can be viewed from a historical perspective as relocalization, or “a return to the greater regional food self-reliance of the past” [Hinrichs, 2003]. The “localness” of a local food system is not uniformly de-

fined, but is dependent on an array of social, cultural, and bio-physical particularities [Fea- gan, 2007, Kneafsey, 2010]. Therefore, “local food” is sometimes geographically-charged, being measured in “food miles” between production and consumption, or delineated by po- litical boundaries [Pirog et al., 2001]. At other times, the distance is an emotional, spiritual, or otherwise nonrational one [DeLind, 2006]. A prominent effect of the (re)localization movement has been the creation of new marketing arrangements, particularly direct-to- consumer sales through farmers markets, roadside stands, and community-supported agri- culture. Although increasing in economic importance, direct-to-consumer sales are not the dominant marketing channels for local food [Gale, 1997, Martinez et al., 2010].

Fresh produce, as well as meat and dairy products feature prominently in the (re)localization movement, while small grains have been mostly ignored as they remain firmly rooted in the dominant commodity chain. However, over the last five years, interest in regionally grown 142

wheat and other small grains has been gaining momentum in areas not popularly identi-

fied with these crops, including New England [Bruce, 2007], North Carolina [McGreger,

2009], and western Washington [Brown, 2012]. In a recent survey of western Washington commercial bakers, sixty-one percent of respondents were interested in purchasing region- ally produced wheat or flour, although key considerations were that the flour quality be high and the supply reliable [Hills et al., 2012]. Bakers and other regional grain processors are probably sensitive to the high level of consumer support for locally produced foods

[Loureiro and Hine, 2002, Pirog, 2004], particularly when “local food” is associated with helping local farmers and the local economy [Ostrom, 2006].

In the Puget Sound region, small grains are commonly cultivated in rotation with high-value fruit, vegetable, and bulb crops, primarily to reduce nutrient loss and erosion, provide organic matter to the soil, as well as to break disease and pest cycles [Miles et al.,

2009]. Therefore, small grain crops in this region are often “secondary” to other “primary” crops, functioning to improve the quality, yields, and ultimately profitability of the primary crops. East of the Cascade Mountains, grains are central to the profitability and character of many dryland farms, and wheat is one of the largest economic drivers. As the eastern Wash- ington wheat farms are overwhelmingly export-driven, there is an opportunity for western

Washington farms to produce wheat and other grains for the regional market, including the Seattle metropolitan area, which represents just under half of Washington’s population, as well as the Portland metropolitan area. However, the close proximity of many western 143

Washington farms to populated areas has resulted in rapid conversion of prime farmland soils to other uses, primarily urban development, as dramatically exemplified by changes in Thurston County, seat of the state capital: between 1950 and 2008, 90,023 acres of farmland were lost and 193,116 people were gained [Fisher and Mitchell, 2009]. In fact,

49 percent of the Puget Sound’s farmland is outside of agricultural zones [Canty et al.,

2012], making this one of most highly threatened agricultural areas in the country [Canty and Wiley, 2004].

Grain growers might be faced with a unique set of challenges in this region, including poor availability of specialized equipment (combines, threshers, etc.) and infrastructure

(storage, dryers, mills, etc.), high annual precipitation, intense disease and weed pressure, poor access to or inadequate knowledge of information resources, as well as high land costs and pressures from urbanization. Since researchers have typically overlooked small grains production in western Washington, this study was undertaken to answer some fundamental questions regarding the characteristics, marketing strategies, information sources, needs, opinions, and challenges of small grain growers in the Puget Sound region.

4.3 Methods

Using a modified Tailored Design Method [Dillman et al., 2008], data were obtained from a mail survey about the characteristics, marketing strategies, information sources, 144

needs, opinions, and challenges of small grain growers in western Washington State. Mod- ifications to the method included the use of return envelopes with printed postage instead of real first-class stamps, and the exclusion of personalized correspondence, which was not possible because mailings were administered by the Washington State Office of the

USDA’s National Agricultural Statistics Service (NASS) in order to preserve respondent anonymity. Cover letters were printed on the official letterhead of the Washington State

University NWREC and were hand-signed. The questionnaire was designed as a two-sided booklet twelve pages in length, and included Likert-type scales, check all that apply re- sponses, and fill in the blank responses (see AppendixC).

The geographic focus was the following 13 counties surrounding the Puget Sound in

Washington, west of the Cascade Mountain Range: Clallam, Grays Harbor, Island, Jeffer- son, King, Kitsap, Mason, Pierce, San Juan, Skagit, Snohomish, Thurston, and Whatcom.

Farms located in the above-mentioned counties were chosen from a 2010 list of farm oper- ations maintained by the USDA-NASS, supplemented with lists maintained by Washington

State University Extension county offices. For this survey, small grain crops were defined as wheat, barley, oats, triticale, rye, emmer, and spelt. The target population (N = 500) included all known growers of small grain crops (N = 257), as well as a random sampling of the 3,866 pasture and/or hay farming operations larger than ten acres in size (N = 243).

This latter group of producers, predominantly beef ranchers, dairy operators, and horse ranchers, was targeted because their crop-related needs and challenges are typically over- 145

looked by researchers. Also, it was hypothesized that some of these producers once grew, but no longer grow, small grains. There is a risk that producers of grass hay or feed grain might consider such activities of secondary importance to their livestock operations, and therefore, be disinclined to complete a survey on this subject. However, an analysis of non- response bias was not conducted as part of the overall study. Such an analysis would have yielded information about why certain individuals chose not to participate in the survey.

A cover letter and questionnaire were sent on November 19, 2010. A reminder post- card was sent on December 3, 2010, followed by the mailing of a replacement survey on January 4, 2011 only to those non-respondents who requested one by email or phone upon receipt of the postcard. Because the mailings were anonymously administered by a third-party organization, there existed no mechanism for tracking non-respondents, mak- ing effective repeated contacts a challenge. Low response rates are known to occur without repeated contacts [Fox et al., 1988, Yammarino et al., 1991], and a large percentage of farmers do not respond to mail surveys [Pennings et al., 2002].

The survey closed on March 1, 2011 with a 13 percent adjusted response rate. Of the

500 farmers surveyed, 55 were eligible respondents, 69 were ineligible respondents, 364 were non-respondents, and there were 12 whose survey was sent back marked return-to- sender. Respondents who answered that they were currently growing small grain crops are herein referred to as Group A (N = 25), respondents who answered that they no longer grew small grain crops are referred to as Group B (N = 8), and respondents who answered that 146

they were interested in growing small grain crops are referred to as Group C (N = 21). Be- cause the Group B respondents are probably not representative of the entire Puget Sound farming population having once grown small grains, this group will only be minimally considered in the discussion and conclusions. Although the adjusted response rate was rel- atively low, Group A operations totaled 15,774 tillable acres in seven counties. Assuming half of their tillable farmland was in small grains, Group A respondents represented 62 percent of the wheat and barley acreage in the 17 counties of western Washington, a larger region than the 13 counties of interest to this study [USDA-NASS, 2012]. Therefore, it is possible that between one-third to one-half of all current grains acreage in the geographic focus area was captured in this study.

4.4 Results

4.4.1 Respondent demographics

Many characteristics of the study’s principal operators parallel those of the average

Washington farmer (Table 4.1). According to the 2007 Census of Agriculture [USDA-

NASS, 2007], the mean principal operator age in the state was 57 years, near-equal to the survey respondent average age of 56 years. Reflective of the larger worrying trend of an ag- ing farmer population, fewer than 10 percent of surveyed principal operators were younger than 34 years, while 60 percent were 55 years or older. Little diversity was observed in 147

the race and gender make-up of the respondents, 91 percent of whom were white and 86 percent of whom were male, similar to the state average of 97 percent white and 79 per- cent male. Nationally, white farmers are more likely than those of another race to produce grains; however, in this study there were some demographic differences between those cur- rently growing grains and those having an interest in growing grains. Three-quarters of the non-white and female respondents belonged to Group C. Also, this Group tended to be younger, as just 43 percent were 55 years old or older, and households belonging to this

Group averaged three persons, compared with just two for Group A. Additionally, Group

C respondents belonged to families having resided half as many generations in Washington on average, and were twice as likely to have attained a bachelor’s degree or higher. 148 22). = N 3 0 0 ± ± ± 3 54 00 3 2 ± ± ± 3 58 0 4 0 2 ± ± ± 2 56 0 4 0 2 54), of those currently growing small grains (Group A, ± ± ± = N SE) 3 8), and of those interested in growing grains (Group C, ± = (%) N n SE) 3 ± (%) n SE) 56 (%) 47 (86) 25 (100) 6 (75) 16 (73) ± n American Indian or Alaska NativeAsian or Asian AmericanBlack or African AmericanSpanish, Hispanic or LatinoWhite (not Spanish, Hispanic orOther Latino) 2 (3)Some high school or lessHigh 49 school (91) 0 diploma (0) or equivalentSome 25 college (100) but no degree 0Vocational (0) or 1 Extension (2) certificate 0 6Two-year 1 (0) (74) college (2) degreeFour-year 0 college (0) degree 0 (0) 19Some (86) graduate 0 school 2 (0) (9) 6Graduate (11) degree 0 (0) 0 (0) 4 1 0 (16) (13) (0) 2 (4) 19 (35) 0 (0) 1 2 (5) (25) 0 0 (0) (0) 10 2 (40) (8) 1 3 (2) (6) 0 4 (0) 10 (50) 0 (18) (0) 0 (0) 4 0 2 (16) (0) (8) 5 6 (24) (11) 0 (0) 0 (0) 0 1 (0) (13) 0 0 (0) (0) 8 (15) 6 0 (29) (0) 3 0 1 (12) (0) (5) 2 (25) 6 (29) 3 (13) Generations family has lived in WA (mean CharacteristicAge (mean Male sex, Race/ethnicity, Highest level of education completed, Global Group A Group B Group C People in household (mean Demographic characteristics of all respondents (Global, 25), of those no longer growing grains (Group B, = Table 4.1: N Respondents were in the Puget Sound region of western Washington State. 149

4.4.2 Farm characteristics

One-third of survey respondents were located in Skagit County, which is ranked first in total value of crops among the 13 surveyed western Washington counties [USDA-NASS,

2007], and another one-third of respondents were located in Grays Harbor, Island, and Sno- homish Counties. Only one respondent was located in Pierce County, while two were in

Kitsap County, three each were in King and Whatcom Counties, four each were in San

Juan and Thurston Counties, and two did not provide a county. None was located in Clal- lam, Jefferson, or Mason Counties. Seventy-eight percent of respondents located in Skagit

County and 47 percent of those located in Snohomish County belonged to Group A, while all respondents located in heavily populated King and Pierce Counties, which contain the

Seattle-Tacoma metropolitan area, belonged to Group C.

Group A operations totaled 15,774 tillable acres in seven counties, Group B opera- tions totaled 1,198 tillable acres in seven counties, and Group C operations totaled 3,003 tillable acres in 11 counties. Respondents operated 370 tillable acres on average (median:

80 acres), and although operations ranged in size from 0.13 to 3,800 acres, 65 percent of operations were from 5 to 499 acres in size. Only two respondents did not own any of the land on which they farmed (4 percent), but thirty-one respondents rented some amount of farmland (57 percent). The mean area of owned and operated land was 232.4 acres (me- dian: 97.5 acres), and the mean area of rented and operated land was 400.0 acres (median: 150

113.0 acres). Group A respondents operated four times the mean tillable acreage compared with Group B and C respondents, and the largest Group A operation was six times larger than the largest Group B operation, and three times larger than the largest Group C opera- tion. Of the respondents, 2 operated their current farm for one to three years (4 percent), 8 for four to nine years (15 percent), and 43 for ten or more years (81 percent). On average,

Group A respondents had operated their current farm for 29 years, Group B respondents for 27 years, and Group C respondents for 21 years. Nearly half of Group C respondents were first generation farmers, while 88 percent of both Group A and B respondents were at least third generation farmers.

Animal-oriented operations, either those producing hay, silage, and feed, or those managing livestock or poultry, accounted for 61 percent of all surveyed operations. Twenty- one percent of operations managed vegetable crops, 11 percent managed vegetable seed crops, and 7 percent managed berry crops. Ninety percent of vegetable seed crop opera- tions belonged to Group A respondents, while vegetable crop operations were roughly split between Group A and C respondents, and 57 percent of berry operations belonged to Group

C respondents. Eighty-one percent of livestock operations were managed by Group A and

C respondents, and Group C respondents managed 86 percent of poultry operations. Group

A respondents accounted for 48 percent of operations producing hay, silage, or feed, while

Group B and C respondents each accounted for 26 percent of such operations.

Group A respondents have been growing small grains for 30 years on average; 27 151

percent have grown for one to fours years, while 73 percent have grown for twenty or more years. Wheat was the predominant grain grown by this Group (52 percent of operations), followed by barley (25 percent), oats (11 percent), triticale (8 percent), and emmer (4 per- cent). Less than 1 percent of the Group A wheat acres, 3 percent of the barley acres, 37 percent of the oat acres, 64 percent of the triticale acres, and all the emmer acres were either certified organic or were transitioning to organic. Twenty-seven percent of Group A respondents did not know the name of the small grain crop varieties they grew. Seventy percent of those growing wheat used the soft white winter wheat varieties Cashup (37 per- cent of wheat growers), Stephens (20 percent), and Goetze (13 percent), although seven other wheat varieties were also grown. Barley growers used Camelot, Baroness, Bob, and

Steptoe. Oat growers used Cayuse and Victory, and triticale growers used Juan. Group

B respondents grew small grain crops for 9.5 years on average, and 63 percent stopped growing small grains between the year 2000 and 2010. Of the small grain acreage once managed by Group B respondents, 45 percent was in wheat, 31 percent was in barley, 13 percent was in rye, and 11 percent was in oats. Only one Group B grower could identify a variety name. 152

4.4.3 Limitations to the production of small grain crops

An important goal of this study was to determine which factors were limiting to the production of small grain crops in the Puget Sound region, and which crop traits were of greatest importance to growers. Group A respondents rated each factor on a 1 (not limiting) to 5 (very limiting) response scale. No factor averaged 4 or higher on this scale, and the

five most limiting factors for Group A were weather (3.83), prices received (3.50), limited market outlets (3.25), availability and/or cost of infrastructure (storage, drying, milling, etc.) (3.17), and weed pressure (3.17) (Table 4.2). The primary weeds were identified as grasses (ryegrass and wild oats) (42 percent of respondents), bedstraw (10 percent), thistles

(8 percent), lambsquarter (6 percent), and vetch (6 percent), and 59 percent of respondents applied an herbicide. The five least limiting factors were fertility (2.33), availability and/or cost of labor (2.25), insect pressure (2.21), financial assistance (2.04), and grain/straw qual- ity for animals (1.67). When asked which agronomic and end-use traits were important in choosing varieties to grow, Group A respondents identified the following traits rated on a similar 1 (not important) to 5 (very important) response scale: high grain yield (4.43), pre-harvest sprout tolerance (4.26), lodging resistance (4.13), disease resistance/tolerance

(4.09), and earliness (4.04). The latter four traits relate to weather, as high seasonal rain- fall in western Washington can cause pre-harvest sprouting and lodging, the short growing season requires fast crop maturation and timely harvest, and the cool maritime climate of 153

the region is ideal for stripe rust (Puccinia striiformis) epidemics. Indeed, stripe rust was identified as a top disease by 60 percent of Group A wheat growers, and 18 percent of them applied a fungicide.

When asked to rate the importance of twenty-two factors in influencing their deci- sion to stop growing small grains on a scale from 1 (not important) to 5 (very important),

Group B respondents rated only two factors above 3 on average: the availability and/or cost of infrastructure (3.25) and of land (3.13) (Table 4.2). None of the Group B respondents claimed retirement to be an important factor in their decision-making, and most found in- sect pressure, financial assistance, and the availability and/or cost of technical assistance or educational resources not to be important. Compared with Group A respondents, high grain yield was not nearly as important of a crop trait to Group B respondents (3.29), while grain protein content was rated as the most important trait (4.43), and grain quality for animal consumption was second in importance (3.86). Both Group A and B respondents ranked high performance under organic conditions as the least important trait, which corresponds to the relatively low number of acres that were certified organic by Group A respondents

(31.3 percent of operations and 2.2 percent of all acres), and Group B respondents (0 acres). 154 ) 0.52 0.50 0.61 0.58 0.54 0.61 0.25 0.59 0.60 0.47 0.61 0.67 0.43 0.25 0.25 0.42 0.35 0.25 0.35 0.43 0.00 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.260.26 2.25 0.30 2.38 0.29 2.00 0.28 3.25 0.29 2.88 0.29 2.25 0.28 1.50 0.28 2.50 0.31 2.13 0.28 2.00 0.31 2.25 0.31 3.13 0.26 2.00 0.25 1.38 0.23 1.38 0.25 2.25 0.26 1.50 0.24 1.38 0.20 1.38 2.00 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 25), and factors influencing the decision = N 8) in the Puget Sound region of western Washington = N limiting production for respondents growing small grains (Group A, a Factors Prices receivedLimited market outletsAvailability and/or cost of infrastructure (storage,Weed pressure drying, milling, etc.)DrainageDisease pressure 3.17 Availability and/or cost of farming equipmentLack (combine, of grain access drill, to etc.) marketAvailability outlets and/or cost of seedAvailability and/or 2.88 cost of transportationAvailability and/or cost of landGrain quality, for humansAvailability and/or cost of technical assistanceAvailability and/or cost of educational resourcesFertilityAvailability and/or cost of laborInsect pressureFinancial assistanceGrain/straw quality, for animalsRetired 3.25 3.50 2.75 2.79 2.46 2.33 3.17 2.79 3.00 2.71 3.08 2.63 2.25 1.67 2.04 2.21 2.33 Doesn’t apply 1.00 FactorWeather Group A 3.83 Group B State. Table 4.2: to stop grain production for those no longer growing small grains (Group B, 155 standard error. ± ) continued from preceding page :( Table 4.2 Factor Group A Group B Responses were on a five-point Likert scale, and displayed values are expressed as mean a 156

4.4.4 Market outlets and crop uses

Sixty-seven percent of wheat growers “very often” or “always” sold through a grain elevator, and of this group, 67 percent “sometimes” or more frequently sold to other market outlets as well. One barley grower “sometimes” sold through an elevator, while growers of oats and triticale “never” utilized this market outlet. Thirty percent of Group A respon- dents, and 50 percent of those growing grains other than wheat, “very often” or “always” sold directly to animal operations. Direct sales to processors and retailers were conducted primarily by wheat growers, 22 percent of whom “very often” or “always” engaged in these market relationships. Interestingly, barley, oat, and triticale growers “never” sold directly to restaurants, institutions, or customers at farmers markets and roadside stands, while three wheat growers “sometimes” or “very often” utilized these outlets. Four grains grow- ers had formed direct relationships with customers outside of the established markets, and while most of these growers did not elaborate on the structure of such relationships, one mentioned the importance of online advertisements through Craigslist, as well as “word- of-mouth” referrals through Facebook and Myspace.

On an identical five-point Likert scale, 63 percent of Group B respondents “some- times” or more frequently had sold directly to animal operations, while 39 percent of the cohort had sold directly to processors and 38 percent had sold through a grain elevator with the same frequency (Table 4.4). However, only one respondent “always” sold to animal 157

operations, one “always” to processors, and one “very often” through grain elevators, so it is unlikely that these findings accurately reflect the relative importance of market outlets to all Puget Sound growers no longer producing small grains. Group B respondents “never” made direct sales to restaurants, institutions, or customers at roadside stands, while one respondent “sometimes” sold directly to retailers and to customers at farmers markets.

Most Group C respondents rated sales through grain elevators as “not important” to their future small grain operations on a five-point interval scale, while direct sales to ani- mal operations (mean: 3.30), to retailers (3.10), and at farmers markets (3.05) were rated as being of greatest importance (Table 4.3). In an open-ended question, Group C respondents were asked “Why are you interested in small grains?” Most respondents provided multi- ple answers, thus responses were coded and grouped according to themes. Production of on-farm poultry or livestock feed was the most frequently mentioned reason (47 percent), followed by a means of farm diversification (42 percent), a desire to enhance the local food system (32 percent), economic sustainability of the farm (26 percent), better land steward- ship (cover-cropping and closing nutrient cycles) (26 percent), and production of grains for human food uses (21 percent). Some examples of interests mentioned by respondents were:

• “We use and could use a lot of organic grain for poultry feed and cover cropping.”

• “Diversify farm crops and reduce off-farm inputs.”

• “To reduce dependence on off-farm suppliers, utilize on-farm resources more effi- ciently, expand product diversity, and supply community needs as possible.” 158

• “My cropland needs to be used for something, and I know more about the end use of small grains than I do about other crop options.” • “Profitability, some crop to grow after we retire from dairy farming.”

Asked to rate the importance of 12 uses for grains in their current operation on a scale from 1 (not important) to 5 (very important), Group A respondents stated the top three uses to be feed grain (3.54), noodles (2.75), and breads (2.67) (Table 4.5). Feed grain was of lower importance to wheat growers (3.40) than to growers of barley, oats, and triticale

(4.00), and the same was true for feed hay (1.67 vs. 2.67), understandable considering that most Washington wheat is grown for human consumption [Washington Grain Commission,

2012]. Conversely, noodles (3.33) and breads (3.22) were important to wheat growers, while these uses along with cakes, pastries, breakfast cereals, other food products, as well as malting, fermentation, and distillation uses were not important (1.00) to growers of other small grains. Bird seed was of low importance to wheat growers (1.33), and was not important to all other growers (1.00). Nineteen respondents were not interested in targeting different small grain end-use markets in the future (56 percent), but of the fifteen who were, eight had an interest in breads and pastries, four in malting and brewing, and one each in noodles, livestock feed, and ethanol production. Asked to rate their interest in the same crop uses on a scale from 1 (not interested) to 5 (very interested), Group C respondents stated the top three uses of interest to be feed grain (4.43), cover crop (3.90), and breads

(3.52). Although not rated as low compared with Group A responses, bird seed (2.05) was again ranked lowest among the crop uses. 159 ) SE 0.3 0.4 0.4 0.4 0.3 0.3 0.0 0.4 0.4 ± ± ± ± ± ± ± ± ± ± Group C mean ( to respondents wanting a ) % 25), and those of interest ( n = N Group A, 21) in the Puget Sound region of western Washington State. = N Market outlets used by respondents growing small grains (Group A, Market outletSell through a grain elevator 8 (33) 2 (8) Never Rarely 2 Sometimes (8) Very often Always 7 (29) 5 (21) 1.8 Sell directly to animal operationsSell directly to processorsSell directly to 7 retailers (29)Sell at roadside stands andSell markets 2 directly (8) to institutions 20Sell (83) directly to 15 customers (63)Sell directly 0 to 8 (0) restaurants (33) 3 (13)Sell at 15 farmers (63) markets 20 (83) 3 (13) 1 2 (4) 4 (8) (17) 21 1 (88) (4) 22 (92) 1 2 (4) (8) 3 (13) 1 2 (4) 1 (8) 1 22 (4) (4) (92) 3.3 1 (4) 1 4 (4) (17) 1 (4) 3 1 (13) (4) 2 (8) 1 1 (4) (4) 2.9 0 2.3 (0) 0 (0) 0 (0) 3.1 0 (0) 0 (0) 0 (0) 2.2 1.0 0 (0) 2.9 3.1 Responses were on a five-point Likert scale. a Table 4.3: to grow small grains (Group C, 160 ) % ( n Group B, 8). = N Market outletSell directly to animal operationsSell directly to processorsSell through a 3 grain (38) elevatorSell directly to retailersSell 0 directly (0) to restaurantsSell directly to institutions 5 (63)Sell 4 at (50) farmers 4 Never markets (50)Sell at 0 roadside 1 (0) Rarely stands (13) andSell markets directly 8 Sometimes to 7 (100) customers (88) 7 Very (88) 8 often 0 (100) 2 (0) 1 (25) 0 (13) (0) 0 Always (0) 0 0 (0) (0) 8 (100) 1 (13) 0 1 1 1 (0) (13) 8 (13) (13) (100) 0 (0) 1 (13) 0 (0) 0 (0) 1 0 (13) (0) 0 0 0 (0) (0) (0) 0 (0) 0 0 (0) (0) 0 0 (0) (0) 0 0 (0) (0) 0 (0) 0 (0) 0 (0) 0 (0) The use of market outlets by respondents no longer growing small grains in the Puget Sound region of western Washington Table 4.4: State (Group B, 161

Table 4.5: Crop usesa of importance to respondents currently growing small grains (Group A, N = 24), and those of interest to respondents interested in growing small grains (Group C, N = 21) in the Puget Sound region of western Washington State.

Crop use Group A Group C Feed grain 3.54±0.33 4.43±0.22 Noodles 2.75±0.33 2.52±0.37 Breads 2.67±0.36 3.52±0.37 Cover crop 2.46±0.29 3.90±0.34 Cakes and pastries 2.42±0.32 2.81±0.35 Seed production 2.42±0.29 3.19±0.35 Other food products 2.04±0.28 2.71±0.36 Malting, fermentation, and distillation 2.04±0.29 2.90±0.34 Breakfast cereals 1.96±0.25 2.33±0.33 Forage 1.96±0.27 3.14±0.36 Feed hay 1.92±0.26 3.33±0.36 Bird seed 1.25±0.12 2.05±0.33 aResponses were on a five-point Likert scale, and displayed values are expressed as mean ± standard error. 162

4.4.5 Sources of information in decision-making

Little is known concerning the sources of information western Washington growers are utilizing for the management of their small grain crops. Table 4.6 presents data on the importance attributed to different sources of information on a scale from 1 (not im- portant) to 5 (very important). Group A respondents rated the following four sources of information most highly: other growers (3.73), university scientists (3.35), input suppliers

(3.15), and extension educators (3.00). Although magazines, newspapers, and books were considered important to decision-making (2.69), TV and radio programs were not impor- tant (1.27). Written material on the internet (2.35) was rated as being more important than video/audio material and social-networking on the internet (1.63). Wheat growers mostly rated information sources similarly to barley, oat, and triticale growers, however, there were a few differences: wheat growers rated input suppliers more highly (3.30) than did other growers (2.67), but they rated written material on the internet (2.20) and the USDA Nat- ural Resources Conservation Service (USDA-NRCS) (1.85) lower than did other growers

(2.83 and 2.33). Organic certifiers were not an important source of information to Group A respondents.

Respondents no longer growing small grain crops also rated other growers (3.38) as their most important source of information on an identical scale. No other information source averaged 3 or higher for Group B respondents; however, the three other highest 163

ranked sources were extension educators (2.63), buyers (2.63), and university publications

(2.50). Again, video/audio material and social-networking on the internet were of low importance (1.25), and written material on the internet was only slightly more important

(1.75), probably because internet use was less prominent when many of these respondents were growing small grains. TV and radio programs were of no importance (1.00), and magazines, newspapers, and books were of low importance (1.50).

Group C respondents would be more likely to utilize conferences, workshops, and seminars (4.43) as a source of information to learn about small grain production than any other source. This cohort rated five additional information sources 4.0 or higher on a scale from 1 (not likely) to 5 (very likely): other growers (4.19), extension educators (4.14), farm tours and field days (4.14), university scientists (4.05), and university publications (4.00).

Internet sources were ranked in the following order: written material (3.81), video/audio material (2.71), and social-networking (1.90). Magazines, newspapers, and books was a likely source of information (3.19), while TV and radio programs was ranked lowest of all sources (1.67). Input suppliers (2.10) and family members (1.71) were rated lower by

Group C than Group A respondents (3.15 and 2.54, respectively), but were rated similarly to Group B (2.38 and 2.00, respectively). Organic certifiers were more highly valued as a source of information by Group C respondents (2.86) than by the other two Groups.

Formal education was ranked similarly by Group A (2.65), Group B (2.25), and Group C

(2.81) respondents. Commodity or grower organizations ranked relatively low for all three 164

cohorts. 165 ) d 0.27 0.27 0.27 0.27 0.31 0.33 0.34 0.29 0.24 0.21 0.24 0.27 0.27 0.31 0.28 0.24 0.29 0.26 0.34 0.18 0.32 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Group C c 0.35 4.19 0.42 4.05 0.47 2.10 0.50 4.14 0.50 2.52 0.25 3.19 0.49 2.81 0.48 3.62 0.35 1.71 0.35 4.43 0.47 4.00 0.45 4.14 0.39 3.81 0.35 2.48 0.45 2.81 0.42 3.71 0.40 3.29 0.15 1.90 0.15 2.71 0.00 1.67 0.25 2.86 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Group B continued on next page ( b 0.25 3.38 0.28 2.25 0.29 2.38 0.28 2.63 0.32 2.63 0.24 1.50 0.28 2.25 0.25 1.88 0.28 2.00 0.27 1.63 0.23 2.50 0.27 2.13 0.28 1.75 0.23 1.63 0.22 2.13 0.21 2.25 0.19 2.00 0.20 1.25 0.20 1.25 0.12 1.00 0.10 1.38 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± to growers currently growing small grains (Group A), growers who no a Information sourceOther growersUniversity scientistInput supplierExtension educatorBuyerMagazines, newspapers, booksFormal educationWashington State Department of AgricultureFamily membersConferences/workshops/seminarsUniversity Group publications A 2.58 Farm tours/field daysInternet - written material 3.35 Commodity 2.69 or 3.73 grower organizationsCommercial newsletter, advisory, product pamphlet 3.00 USDA publications 3.15 2.12 2.50 USDA Natural Resources Conservation DistrictInternet - social networkingInternet - 2.65 video, audio material 1.96 TV, radio programs 2.27 2.73 Organic certifiers 2.46 2.54 2.35 2.38 1.63 1.63 2.12 1.27 1.27 The use or importance of information sources Table 4.6: longer grow small grains (GroupPuget B), Sound and region growers of who western have Washington an State. interest in growing small grains (Group C). Respondents were in the 166 d Group C c Group B b ) standard error. continued from preceding page 26) were asked to rate how important the information sources are to their decision-making in managing 21) were asked to rate how likely they would be to utilize the information sources to learn about small 8) were asked to rate how important the information sources were to their decision-making in managing ± :( = = = N N N Table 4.6 Information source Group A Group C respondents ( Values expressed as mean Group A respondents ( Group B respondents ( their small grain productiond system on a scale from 1grain (not production important) on to a 5 scale (very from important). 1 (not likely) to 5 (very likely). their current small grainc production system on a scale from 1 (not important) to 5 (very important). a b 167

4.5 Discussion

In contrast to the dominant Washington grain market, which is centered around the export of white wheat for pastries, cookies, cereals, flatbreads, and noodles, animal feed was the end-use of greatest importance to both Group A (including wheat growers) and

Group C producers, and both cohorts highly ranked direct sales to animal operations, either as current or as prospective market outlets. While about one-fifth of Group A respondents and one-tenth of Group C respondents raised dairy heifers, one-third of Group C respon- dents raised poultry, and were interested in growing small grains as a source of on-farm poultry feed. The interests of the Group C cohort reflect the evolving economics of live- stock production in the Puget Sound region, which has had a steep decline in the number of dairy operations, while the number of poultry farms, particularly egg operations, increased

73 percent between 2002 and 2007. The 13 counties of interest currently contain 45 percent of Washington’s poultry farms, and a large number of them are operations having less than

50 animals [USDA-NASS, 2007]. There is a developing trend of Washington dairy farmers

“trying to gain better control over their operating costs, by growing feed for their cows themselves” [Mance, 2012], however, it is likely that land costs in the region, identified by

Group B respondents as the second most important factor influencing their decision to stop growing small grains, outweigh the savings dairy producers could achieve with on-farm feed production in the Puget Sound. 168

Selling often to grain elevators and considering high grain yield as a substantially more important trait than either grain or straw quality, one might conclude that Group A respondents were devotees of the commodity market. However, limited market outlets was identified as a major impediment by this cohort, and 41 percent expressed interest in using direct-marketing channels in the future, thus the commitment to a local small grains system and the reliance on a global export market need not be mutually exclusive, a conclusion of a recent study of Austrian cereal and bread producers, processors, and marketers [Milestad et al., 2010]. Consistent with the literature that farmers adopting direct-marketing strate- gies are often younger and have more formal education [Galt et al., 2012], and tend to operate smaller farms [Detre et al., 2011, Monson et al., 2008], Group C respondents were not interested in sales to grain elevators, but instead expressed a desire to sell directly to animal operations, to retailers, and to customers at farmers markets. In general, western

Washington is known to have some of the highest concentrations of farmers marketing food locally in the nation [Low and Vogel, 2011].

Except for grower-to-animal operation sales and low-volume whole grain and flour sales at farmers markets, the Puget Sound region is lacking in direct-market opportunities for small grain growers. The community supported agriculture model as a conduit for local grain to consumers is becoming a viable option in some areas on the Pacific coast of North

America, such as the Urban Grains CSA serving Vancouver, British Columbia, as well as the online CSA operated by Bluebird Grain Farms of the Upper Methow Valley in central 169

Washington. However, developing markets outside of the commodity chain is proving dif-

ficult for growers in areas not commonly associated with modern-day grain production, as critical handling and processing infrastructure has been moved, dismantled, or repurposed for non-agricultural uses [Hills et al., 2011]. For instance, no grain dryers currently op- erate in Thurston and Grays Harbor Counties, but at least four were operating in the area just two decades ago [Thompson, 2012]. This challenge is not unique to grain production, as numerous comprehensive studies of the Washington State food system cite a lack of infrastructure as a universal and serious hurdle preventing producers from being able to respond to strong consumer interest in buying locally grown foods [EO 10-02 Inter Agency

Working Group, 2012, Johnson et al., 2010, Washington State Department of Agriculture,

2008].

Availability and/or cost of infrastructure was ranked as the fourth most limiting factor by current small grains producers, and was the top factor influencing the decision of Group

B operators to stop growing small grains. Prices received and limited market outlets were also identified as primary limitations to the current production of grains. All three factors are connected, as appropriate infrastructure allows for greater quality differentiation and product segmentation (e.g., organic vs. conventional, hard red wheat vs. soft white wheat, wheat vs. barley), which in turn leads to diversification of markets and a command of price premiums outside of the commodity system [Elbehri, 2007]. Accessible and appropriately scaled grain storage is a fundamental requirement for product segmentation. Grain storage 170

capacity in the 13 counties of interest amounts to 1.25 million bushels on 67 farms [USDA-

NASS, 2007]. Although this represents 1.8 percent of Washington’s total grain storage capacity, the volume should be placed into context, as it suffices to store enough wheat to bake 94 million one-pound loaves of bread annually assuming a 70 percent flour extraction rate during milling.

In addition to the aforementioned demographic differences, Group C respondents were more likely than those in Group A to operate farms integrating animals with crop production, were more interested in non-harvest, ground cover and forage crop functions, and were more “likely” to use organic certifiers as a source of production information, operational characteristics which can be understood as key elements of sustainable agri- culture: diversity of production, and harmony with nature [Beus and Dunlap, 1990]. The cohort’s commitment to the principles of sustainable agriculture is combined with a high re- gard for academic sources of production information, most notably conferences/workshop- s/seminars, which is unusual given that agricultural research and extension are often fo- cused on the maximization of food production, ignoring various environmental, social, and economic factors, and inhibiting orientations towards multidisciplinary problem-solving

[Francis et al., 2003, Kroma, 2006]. Other growers were the second most highly esteemed source of information, perhaps supporting the assertion that those active in grower net- works are more likely to be interested in sustainable agricultural practices, as posited in a study of eastern Washington wheat growers [Jussaume Jr. and Glenna, 2009]. While Group 171

A producers also had a high estimation of university scientists, Extension educators, and other growers, unlike the Group C cohort, they ranked input suppliers among the top three sources of production information utilized. This is consistent with the finding that larger- scale farmers increasingly stress technical consultants as meaningful or important sources of information, and these consultants are generally tied to the sale of farm inputs [Foltz et al., 1996, Patrick and Ullerich, 1996, Wolf, 1998].

The evidence we have presented suggests that although growers currently producing grains, particularly wheat, continue to rely on the global commodity market, many are in- terested in direct-to-consumer markets. Product segmentation is limited by the availability and/or cost of infrastructure, without which regional markets outside of the commodity chain are difficult to access, and prices remain mostly out of the growers’ control. Produc- tion of feed grains and sales to animal operations are of great importance to current and prospective grains growers, a characteristic which deviates from the dominant grains econ- omy of the state. Growers interested in grains production are more apt to be guided by sus- tainable agriculture principles, including animal-integration, cover-cropping, and organic management practices. Other growers were a highly valued source of information among all respondents, but while the decision-making of current grain growers was commonly guided by input suppliers, prospective grain growers predominantly valued university- and

Extension-based sources of production information. 172

4.6 Acknowledgements

I am grateful to Jessica Goldberger for mentoring me throughout this project, and to the following individuals for their expert advice and support: Stephen Jones, Carol Miles,

Andrew Corbin, Karen Hills, Annabel Kirschner, Steve Lyon, Justin Smith, and David

Knopf. This study was financially supported by the Harry E. Goldsworthy Wheat Research

Fund, as well as by an Alexander A. Smick Scholarship in Rural Community Service and

Development. 173

CHAPTER 5. SUMMARY OF FINDINGS AND CONCLUSIONS

Findings of the Chapter 2 study demonstrated that stunting of onion seedlings was

caused by different Rhizoctonia species and AGs in fine sandy loam field soils at temper-

atures ranging from 8 to 15◦C, with the isolates virulent on onion belonging to R. solani

AGs 2-1, 3, 4, 8, W. circinata var. circinata, and binucleate Rhizoctonia AG E. However,

only R. solani AG 8 isolates consistently caused severe symptoms at low inoculum densi-

ties, suggesting the existence of a disease bridge between wheat and barley cover crops or

wind-break crops, and dry bulb onion crops. One AG 3 isolate was highly virulent, and two

were moderately virulent on onion, suggesting that rotational potato crops or potato volun-

teers in onion fields might also act as a disease bridge. Supporting the field observations

that stunting of onion can occur a season after the incorporation of brassicaceous green

manure crops, AG 2-1 isolates were moderately virulent on onion. AG 4 isolates caused

low levels of disease, as did the W. circinata var. circinata isolate. AG 2-1, AG 3, and AG

E isolates significantly reduced onion emergence, but isolates of AG 4 and AG 8 did not.

Chapter 3 results led to the conclusion that some hard red winter wheat cultivars improved under eastern Washington conditions can achieve grain protein contents of 10 to 12%, as well as 5 to 9 Mt/ha yields, under western Washington conditions. According to research in northern Europe, as long as genetic traits for protein quality are carried by 174

the cultivars, traits which are minimally influenced by N fertilization, high quality hearth breads can be baked with flours ranging from 10 to 13% protein. Although grain yields were not significantly influenced by application of either poultry feather meal nor sulfur- coated urea in this study, the 170 kg N/ha rate of both fertilizers significantly boosted grain protein contents by 1% on average. Although year-to-year protein variability was substan- tial, which is a concern for producing a consistently high quality bread flour, the protein quality of all cultivars, evaluated by constant protein micro-SDS sedimentation tests, was consistently at levels positively correlated with high breadmaking quality in other stud- ies. Although flour yields, positively correlated with grain test weights, will be lower on average in western Washington compared with drier and warmers parts of the state, the breadmaking quality of western Washington flours is competitive with the quality of east- ern Washington flours. It is unclear from this study if single applications of fungicide are effective against stripe rust, but greater test weights and yields were achieved in more ge- netically resistant cultivars. The environmental conditions of the region dictate continuous breeding for stripe rust resistance. Desirable breadmaking quality traits from historic, or heritage, cultivars will most likely have to be bred into modern genetic backgrounds to be useful under western Washington conditions, as these cultivars lack effective stripe rust resistance traits, have lower yield potential, are too prone to lodging, and are too prone to pre-harvest sprouting. Phenolic acid composition and total phenolic acid concentration of grain were not influenced differentially by organic versus mineral forms of N, and only the 175

concentration of trans-ferulic acid, the dominant compound identified in all flour samples, was significantly influenced by type of N fertilizer.

The evidence presented in Chapter 4 suggests that although growers currently pro- ducing grains, particularly wheat, continue to rely on the global commodity market, many are interested in direct-to-consumer markets. Product segmentation is limited by the avail- ability and/or cost of infrastructure, without which regional markets outside of the com- modity chain are difficult to access, and prices remain mostly out of the growers’ control.

Production of feed grains and sales to animal operations are of great importance to current and prospective grains growers, a characteristic which differs from the dominant central and eastern Washington grains economy. Compared with current grain farmers in west- ern Washington, those interested in producing small grains are more apt to value academic sources of production information, and to be guided by sustainable agriculture principles, including animal-integration, cover-cropping, and organic management practices. 176

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A Stunting of onion caused by Rhizoctonia spp. isolated from the Columbia Basin 223 ) -value P Max. 90 continued on next page ED ( 50 ED d -value P c Max. b 90 C. ◦ Experiment 1 Experiment 2 ED b spp. on the dry plant weight (PW), height (PH), rooting depth (RD), and total 50 PH 24.0 274.0 24.5PH 0.0109 77.4 13.8 – –PH 28.3 40.7 135.2 0.0100 0.0020 – 7.6PH 28.9 – 0.0018 – 48.6 28.6 0.0036 – – 43.9 – 0.0002 0.1106 71.2 – 28.0 0.0473 RD 40.6 303.5 41.2RD 0.0103 143.7 5.1 – –RD 64.2 29.7 130.9 0.0022 0.0421 116.2 – –RD 75.5 88.5 0.0011 – 0.0023 136.6 – – 78.0 – 0.0006 0.1660 259.3 483.6 62.5 0.0007 Rhizoctonia TRL 40.4 303.2 38.6TRL 0.0197 252.1 3.3 325.4 65.6 5.5 0.0017TRL 40.6 148.1 0.0122 93.7 – – 64.4 86.2TRL 0.0024 0.0023 – 139.6 – – 79.2 – 0.0011 0.1728 247.1 397.6 36.7 0.0904 Isolate ED a AG 3 Rh060801 PW 147.9 –AG 4 Rh010901 PW 35.7 153.6 0.0001 –AG 6.3 5 Rh070930 PW 29.9 – 0.0093 – 57.1 121.7AG 0.0016 8 Rh070927 – – PW 53.3 3.1 – 0.0187 5.0 1.0000 34.3 – 0.0046 – 9.7 10.9 – 44.4 0.9995 0.0106 AG 2-1 Rh060811 PW 34.1 293.4 36.0 0.0010 4.0 8.3 41.9 0.0039 Species Effect of inoculum density of Table A1: root length (TRL) of 6-week-old onion seedlings grown at 15 224 ) -value P Max. 90 continued on next page ED ( 50 ED d -value P c Max. b 90 Experiment 1 Experiment 2 ED b ) 50 PH 3.9 4.2 27.9 0.0004 10.4 10.4 38.0 0.0056 PH – –PH 178.2 – – 0.6601PH 35.5 – 0.0069 – 94.7 – 157.1 –PH 53.4 – 107.1 0.0005 – 0.9856 167.8 18.5 0.1135 0.0828 – – – – – – 0.9304 1.0000 RD 2.4 3.3 73.4 0.0015 6.0 17.6 77.3 0.0002 RD – –RD 242.2 – 426.7 60.7 0.9007 <0.0001RD 103.6 – 161.7 – 82.0 – 0.0029 –RD – 246.3 – 0.6628 406.2 23.8 0.6358 0.0274 – 251.9 370.3 24.5 – 0.0598 – 0.8485 TRL 2.6 4.8 65.8 0.0018 9.3 10.6 70.5 0.0180 TRL – –TRL 254.0 – – 0.6045 51.3TRL – <0.0001 – 103.0 166.9 – 81.1 –TRL 0.0109 – – – 0.9702 0.7883 – – – – 0.4563 – – 0.6623 – – 0.7059 Isolate ED Rh070924 PW 179.8 204.4 21.9 0.0383 174.8 – 20.7 0.0644 a continued from preceding page AG I Rh070914 PW – – – 0.9077 – – – 0.7470 AG E Rh070923 PW 144.8 – 30.8 0.0100 47.1 – 42.8 0.0183 AG A Rh010913 PW – – – 0.8938 – – – 0.6823 :( Species Table A1 Waitea circinata 225 -value P circinata var. Max. 90 ED W. circinata 50 ED . d -value P c Max. b 90 Experiment 1 Experiment 2 ED 100 - % of noninoculated control b ) 50 AG A (Rh010913), AG E (Rh070923), AG I (Rh070914); and AG 2-1 (isolate Rh060811), AG 3 (Rh060801), AG 4 (Rh010901), AG 5 (Rh070930), AG 8 Isolate ED R. solani Rhizoctonia estimates for significant responses which fell beyond the range of tested inoculum densities are not shown. a 90 continued from preceding page are the inoculum densities, in colony forming units/g soil, at which 50 and 90% of the maximum reduction in :( 90 Species spp. include and ED Table A1 50 -value derived from the ANOVA F-test. See Table A2 in the Appendix for details on the models used for fitting the data. P ED Rhizoctonia Maximum modeled reduction in response expressed as (Rh070924). Experiment repeats areb shown in separate columns. response occurs. ED c a (Rh070927); binucleate d 226 ) ne ne ne 1.7 1.3 0.3 0.4 3.2 0.6 0.3 4.2 0.3 0.5 4.4 11.6 M ± ± / ± ± ± ± ± ± ± ± ± ± ± ± ± H n Rhizoctonia 50 ne 26.6 ne 20.7 ne 1.0 0.5 3.5 0.1 3.6 0.0 3.8 0.2 1.0 0.3 0.9 0.1 3.6 0.2 1.2 0.4 0.6 0.0 8.9 0.3 0.6 0.4 0.7 0.0 6.8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( ne 2.4 ne 0.8 ne 2.4 6.0 1.9 9.4 2.1 5.8 2.1 4.5 1.5 7.7 0.9 7.2 0.9 2.5 0.6 1.8 0.7 12.4 2.0 13.1 2.1 11.3 2.1 11.9 1.2 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 4.7 36.7 4.1 28.0 4.3 79.2 3.0 78.0 3.0 43.9 ne 29.7 B Max logED 1.2 62.5 8.0 86.2 2.8 53.3 6.2 88.5 5.0 40.6 3.6 41.9 11.8 48.6 11.7 57.1 17.0 40.7 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Min b ne 3.8 5.3 0.0 4.8 0.0 2.3 0.0 4.1 -2.1 2.7 -1.6 2.5 -1.5 8.3 6.5 0.8 7.4 0.4 -5.9 0.9 -6.7 2.4 -7.6 M ± ± ± ± / ± ± ± ± ± ± ± ± H n b 50 ne 48.9 0.1 5.1 0.1 4.1 0.1 3.6 0.1 6.2 0.2 1.6 0.2 1.0 0.1 3.3 ± ± ± ± ± ± ± ± logED – – 17.8 – – 19.1 – – 9.4 – – 15.6 4.5 2.1 6.7 2.2 9.9 2.4 9.7 2.2 3.8 1.9 3.4 2.2 b 10.3 2.2 10.2 2.1 ± ± ± ± ± ± ± ± ± ± ± ± Experiment 1 Experiment 2 Max b 8.0 41.2 4.5 64.4 4.6 75.5 1.9 28.9 2.9 29.9 4.0 65.6 4.7 64.2 0.8 35.7 8.8 38.6 4.0 24.5 3.8 36.0 2.5 28.3 B / ± ± ± ± ± ± ± ± ± ± ± ± Min Best-fit values for all model parameter estimates used in the analysis of the effect of inoculum density and a PH – – – – -4.1 PH -1.0 PH 1.0 PH -0.9 RD – – – – 2.2 RD -1.7 RD -6.5 RD -10.3 PW – – – – – – – – PW -3.2 PW -1.6 PW -6.1 TRL – – – – -7.9 TRL -2.9 TRL -6.0 TRL -9.5 C. ◦ Spp. AG 2-1 AG 5 AG 4 AG 3 spp. on dry plant weight15 (PW), height (PH), rooting depth (RD), and total root length (TRL) of 6-week-old onion seedlings grown at Table A2: 227 ) ne ne ne ne ne ne 4.2 ± 17.1 12.6 M ± ± ± ± ± / ± ± ± H n 50 ne 22.4 ne 43.9 nene 21.0 24.2 nene 54.1 7461.6 0.4 7.2 0.3 5.4 0.4 3.2 ± ± ± ± ± ± ± ± ± continued on next page ( ne 1.0 ne 1.0 ne 1.1 3.5 0.8 5.3 2.0 7.7 2.2 9.5 1.7 7.0 2.0 12.3 2.0 ± ± ± ± ± ± ± ± ± 6.2 81.1 3.8 20.7 8.0 42.8 ne 44.4 ne 38.0 3.3 53.4 3.7 82.0 ne 70.5 B Max logED 5.3 77.3 / ± ± ± ± ± ± ± ± ± Min b ne – – – – ne -11.1 ne -9.8 ne -2.1 47.7 0.9 9.3 -16.3 0.9 -6.8 3.9 4.2 1.2 -14.6 4.5 -4.5 M ± ± ± ± / ± ± ± ± ± ± H n b 50 ne 24.2 ne 90.7 ne 22.5 ne 74.8 0.0 7.5 0.2 1.3 0.0 7.0 0.4 1.3 0.0 13.0 0.0 9.0 ± ± ± ± ± ± ± ± ± ± ) logED ne 2.3 6.2 2.0 1.5 2.4 8.34.2 2.3 2.4 3.6 0.6 3.8 0.5 2.51.2 0.6 0.7 b 17.4 2.2 ± ± ± ± ± ± ± ± ± ± Experiment 1 Experiment 2 Max b 3.1 18.5 0.6 51.3 3.51.7 35.5 60.7 5.1 65.8 2.4 30.8 3.51.4 34.3 27.9 ne 21.9 5.3 73.4 B / ± ± ± ± ± ± ± ± ± ± continued from preceding page Min :( a PH -4.6 PH – – – – – – – – PH -5.1 PH – – – – – – – – PH -0.9 RD – – – – – – – – RD -5.3 RD – – – – – – – – RD 0.0 PW –PW 2.1 – – – – – – – PW –PW -3.0 – – – – – – – PW -0.2 TRL – – – – – – – – TRL -1.0 TRL – – – – – – – – TRL -0.2 AG A AG E Table A2 Spp. AG 8 AG I W.c. 228 is Y ne M ± / H n circinata , where ) H n 10) not shown. var. . ∗ ) 0 X 50 − ≥ 0.2 32.7 50 P ± logED ( 10 + Waitea circinata 1 5.6 2.4 ( / ) ± is the control-normalized response at a Min Y − Max is the point of inflection of the sigmoid curve, specifies the Hillslope. , where is the modeled response at the highest inoculum 2.5 24.5 B Max logED B H 50 + ( / n ± + Max X Min Min ∗ logED = , M Y X = Y b ne -4.9 M ± / H n column: is the y-intercept. 50 B b 50 ne 25.4 logED ± standard errors (ne = nonestimable). Insignificant estimates ( ) ± logED is the highest observed response, and is the slope, and M Max b AG A (Rh010913), AG E (Rh070923), AG I (Rh070914); and , 411.8 2.4 AG 2-1 (isolate Rh060811), AG 3 (Rh060801), AG 4 (Rh010901), AG 5 (Rh070930), AG 8 X Experiment 1 Experiment 2 ± Max R. solani Rhizoctonia b 2.3 23.8 B / ± continued from preceding page Min spp. include :( a RD -5.8 is the lowest observed response, TRL – – – – – – – – Table A2 Spp. Best-fit values for model parameter estimates Rhizoctonia (Rh070924). Experiment repeats areb shown in separate columns. a (Rh070927); binucleate Linear model fits are those withlog-transformed no inoculum estimate density in the density. All other data was fit with the four-parameter sigmoidal model: the control-normalized response at a log-transformed inoculum density Min 229

Figure A1: Study of the influence of inoculum density on disease severity caused by Rhizoctonia spp. on onion grown at 15◦C. Results shown for binucleate Rhizoctonia AG A (isolate Rh010913), AG I (Rh070914); Waitea circinata var. circinata (Rh070924); and R. solani AG 5 (Rh070930). Mean reductions in dry weight, height, and rooting depth for 6-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 9). Results of repeat experiments are shown in separate columns. 230 ) -value P Max. 90 continued on next page ED ( C day/night temperature regime. 50 ◦ ED d -value P c Max. b 90 Experiment 1 Experiment 2 isolate on the dry plant weight (PW), height (PH), total root length (TRL), ED b 50 Rhizoctonia PH 9.0 – 44.1 0.0048 55.5 – 43.3 0.0225 PH 11.3 –PH 49.3 19.2 0.0020 153.5 19.8 47.2 194.9 40.8 0.0085 <0.0001 30.8 168.2 27.7 0.0166 PH 11.0 – 48.2 0.0071 43.7 – 53.6 0.0050 RDRN 40.3 4.5 – 76.8 79.4 38.9 0.0031 0.0361 76.7 7.1 – 8.0 65.9 26.1 0.0033 0.0799 RDRN 40.6 – –RD 66.6 –RN 128.9 0.0006 182.0 16.8 – 57.3 66.6 149.6 50.2 0.1709 0.0081 – 144.3 0.0035 6.8 57.7 – 126.9 13.1 0.0096 30.6 142.8 51.1 32.2 0.0171 0.0512 0.0300 RDRN 21.1 13.8 156.4 144.3 49.5 25.8 0.0066 0.0407 69.6 12.0 – 140.6 42.3 67.8 0.0042 0.0030 TRL 20.4 – 84.1 0.0128 23.8 160.0 57.3 0.0070 TRL 28.7 –TRL 83.2 10.6 0.0075 137.4 41.2 59.2 – 0.0168 74.0 13.9 144.4 0.0023 40.3 0.0524 TRL 2.3 – 64.9 0.0551 13.6 143.8 65.8 0.0027 Isolate ED Rh070937 PW 2.1 9.8 36.1 0.0941 7.0 – 40.2 0.0587 Rh070913 PW 4.9 – 49.5 0.0034 13.4 – 36.0 0.0059 a AG 3 Rh060801 PW 3.3 68.6 54.7 0.0038 6.0 6.2 38.5 0.0175 AG 2-1 Rh060811 PW 5.1 – 47.6 0.0015 8.0 – 62.9 0.0141 Effect of inoculum density and Species Table A3: rooting depth (RD), and root number (RN) of 8-week-old onion seedlings grown at a 13/8 231 ) -value P Max. 90 continued on next page ED ( 50 ED d -value P c Max. b 90 ) Experiment 1 Experiment 2 ED b 50 PH – –PH 12.8 – – 0.5935 26.9 – 0.0221 – 24.7 161.1 15.4 – 0.0093 0.1724 PH 153.7 – 30.7 0.0335 103.0 – 17.0 0.0047 PH 8.6 –PH 50.9 40.0 0.0023 – 18.2 48.1 – 0.0041 53.8 27.7 0.0010 – 33.7 0.0009 RDRN – – – – – – 0.1701 0.5026 – – – – – – 0.5439 0.9839 RDRN – – – – – – 0.1691 1.0000 – – – – – – 0.7504 1.0000 RDRN 31.7 3.6 – –RD 81.4RN 42.0 33.4 0.0001 7.5 0.0175 33.0 – 129.0 5.2 – 30.7 66.9 206.8 88.9 0.0630 0.0011 34.7 <0.0001 13.6 40.0 0.0060 143.9 – 25.8 0.0021 60.6 0.0005 TRL – – – 0.4435 – – – 0.9581 TRL 3.1 4.3 22.0 0.1025 – – – 0.8736 TRL 24.5 –TRL 99.8 44.4 0.0039 19.0 – – 74.2 75.1 0.0132 0.0044 67.6 – 75.1 0.0016 Isolate ED continued from preceding page Rh070929 PW 28.7 – 34.4 0.0178 11.9 – 33.8 0.0358 Rh070909 PW – – – 0.8184 34.7 – 29.1 0.0035 Rh070942 PW 13.3 143.2 55.5 0.0110 11.9 – 45.5 0.0141 Rh070933 PW 5.2 – 63.0 0.0060 7.0 – 63.3 0.0013 :( a AG 4 Rh010901 PW 100.8 – 25.7 0.0445 15.4 147.3 27.5 0.0135 Table A3 Species 232 ) -value P Max. 90 continued on next page ED ( 50 ED d -value P c Max. b 90 ) Experiment 1 Experiment 2 ED b 50 PH 2.9 37.3 44.1 0.0149 4.1 – 62.3 0.0097 PH 25.6 –PH 46.6 4.1 0.0009 22.1 49.3 54.8 – 0.0024 34.5 4.0 0.0847 6.4 42.6 0.0212 PH 0.8 0.9 18.6 0.0319 33.0 93.0 36.9 0.0454 RDRN 4.4 31.4 – –RD 82.7RN 50.7 2.2 0.0012 4.0 0.0044 13.8 2.9 16.7 47.8 91.2 51.2 – <0.0001 – 0.0304 1.9 81.1 42.0 0.0135 7.1 0.0065 – 90.0 – 0.0004 – 0.1621 RDRN 2.1 33.4 12.1 42.6 50.3 16.9RD 0.0310 0.0354RN 3.8 – 28.2 – 4.2 – – 58.2 – 29.3 0.0015 – 0.0384 – 2.5 0.3797 38.8 0.6069 70.7 – 0.0321 – – 0.4675 TRL 14.4 145.4 113.9 0.0003 13.8 144.3 114.8 <0.0001 TRL 10.0 –TRL 90.7 2.5 0.0007 12.4 4.6 93.4 – <0.0001 86.9 1.9 0.0494 6.4 89.8 0.0004 TRL 3.2 12.8 32.1TRL 0.0378 3.7 7.9 4.3 9.3 34.8 49.7 0.0073 0.0082 2.9 64.5 69.7 0.0119 Isolate ED continued from preceding page Rh070943 PW 5.1 12.6 61.7 0.0153 3.4 8.9 56.8 0.0119 Rh070927 PW 20.3 – 52.0 0.0008 6.1 – 54.9 0.0451 :( a AG 8 Rh070922 PW 20.3 – 37.9 0.0080 6.7 – 60.6 0.0545 AG E Rh070923 PW 3.6 88.9 56.9 0.0120 8.1 – 90.5 0.0042 Table A3 Species 233 AG E -value P Rhizoctonia Max. 90 ED 50 . ED d -value P c Max. b 90 ) 100 - % of noninoculated control Experiment 1 Experiment 2 ED b 50 RDRN 17.3 11.0 150.5 138.3 109.8 44.1 0.0014 0.0209 20.4 155.4 14.5 103.6 145.5 0.0008 87.1 0.0003 AG 2-1 (isolates Rh060811, Rh070913, Rh070937), AG 3 (Rh060801, Rh070933, Rh070942), R. solani Isolate ED estimates for significant responses which fell beyond the range of tested inoculum densities are not shown. continued from preceding page 90 :( a are the inoculum densities, in colony forming units/g soil, at which 50 and 90% of the maximum reduction in 90 spp. include Table A3 Species and ED 50 -value derived from the ANOVA F-test. See Table A4 in the Appendix for details on the models used for fitting the data. P ED Rhizoctonia Maximum modeled reduction in response expressed as (Rh070923). Experiment repeats areb shown in separate columns. response occurs. ED c a AG 4 (Rh010901, Rh070909, Rh070929), and AG 8 (Rh070922, Rh070927, Rh070943), as well as binucleate d 234 ) 4.2 4.3 6.4 3.1 5.4 3.6 0.9 0.8 0.9 0.8 0.5 1.1 1.3 0.9 M 384.2 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± H ± n Rhizoctonia 50 2.3 8.9 0.2 1.3 0.00.3 4.0 0.2 0.8 1.6 0.1 2.6 0.2 1.7 0.2 1.7 0.4 0.8 ± ± ± ± ± ± ± ± ± continued on next page ( ne – 12.8 nene – – 15.3 16.3 ne – 10.1 nene – – 26.4 16.3 7.3 0.9 1.3 1.3 3.3 1.2 9.4 1.7 70.9 2.2 25.0 1.6 15.8 1.8 15.8 1.8 17.6 1.0 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 6.8 27.7 9.7 74.0 1.4 40.8 4.8 36.0 B Max logED 6.7 32.2 3.8 51.1 5.7 57.7 8.5 40.2 4.7 30.6 4.6 67.8 5.6 42.3 8.3 65.8 4.8 53.6 31.1 62.9 10.1 40.3 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Min b ne 7.4 4.6 1.4 6.7 1.0 5.5 -9.1 5.7 2.3 4.0 3.0 ne -1.0 ne -5.0 0.6 4.6 0.5 -1.3 1.6 2.2 0.6 -5.0 0.7 4.4 10.6 0.0 M ± ± ± ± ± ± ± ± / ± ± ± ± ± ± H n b 50 ne 32.3 ne 0.7 ne 1.3 0.8 2.9 0.1 1.9 0.2 1.0 3.6 0.5 0.1 1.7 0.4 0.7 ± ± ± ± ± ± ± ± ± logED ne – 21.6 ne – 22.0 ne – 21.4 ne 1.5 ne –ne 0.8 23.2 ne – 10.4 8.0 2.1 4.4 0.5 6.0 1.6 6.4 1.1 2.5 0.8 b 62.5 0.5 15.3 1.1 ± ± ± ± ± ± ± ± ± ± ± ± ± ± Experiment 1 Experiment 2 C day/night temperature regime. Max ◦ b 7.1 50.2 8.5 47.2 8.4 49.3 8.8 49.5 5.7 47.6 ne 83.2 ne 49.5 6.1 25.8 3.6 66.6 3.5 66.6 B 23.3 64.9 19.9 48.2 10.3 59.2 23.5 36.1 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± Min Best-fit values for all model parameter estimates used in the analysis of the effect of inoculum density and a PH -4.2 PH -3.7 PH -5.0 RN -1.9 RD 5.7 RDRN 4.8 – – – – 6.3 RD -6.5 RN 0.7 PW 0.0 PW 0.0 PW -2.0 TRL 6.1 TRL 0.0 TRL -3.0 isolate on the dry plant weightonion (PW), seedlings height grown (PH), at total a root 13/8 length (TRL), rooting depth (RD), and root number (RN) of 8-week-old Table A4: Species AG 2-1 Rh060811 AG 3 Rh070937 Rh070913 235 ) ne ne 3.1 2.0 7.0 0.7 1.0 0.2 1.1 0.7 1.3 0.5 0.4 1.0 1.9 0.4 1.0 M ± ± / ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± H n 50 ne 47.6 ne 181.4 0.0 4.9 0.1 2.5 0.10.0 2.0 0.1 2.0 2.1 0.30.1 0.9 0.2 3.4 1.2 0.1 1.1 0.2 1.0 0.1 2.0 0.2 1.5 ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( ne – 11.5 ne – 10.4 ne – 28.2 ne 0.8 1.4 2.0 4.3 1.6 8.52.2 1.3 2.4 1.5 0.8 2.4 1.5 6.9 1.3 6.2 0.9 4.1 0.9 12.1 1.1 20.1 1.8 13.8 1.9 11.2 1.8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ne 38.5 5.0 27.5 1.65.1 88.9 34.7 2.3 33.7 6.1 53.8 8.2 26.1 10.8 57.3 B Max logED 0.7 17.0 3.1 25.8 6.3 45.5 5.0 75.1 2.8 60.6 9.4 75.1 3.9 65.9 4.7 43.3 12.6 63.3 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Min b ne – – – – 4.6 0.8 6.1 0.0 ne 0.5 ne -4.1 ne -7.5 ne -10.7 1.9 -0.2 1.4 4.4 1.01.3 -0.2 -0.8 1.0 -4.1 1.0 1.2 1.2 -4.0 0.6 5.4 0.5 4.4 1.3 -2.9 M 314.6 1.7 ± ± ± ± ± ± ± / ± ± ± ± ± ± ± ± ± ± ± H n b 50 ne 12.4 ne 0.9 ne 0.9 ne 1.1 ne 0.7 3.3 8.0 0.3 2.0 0.2 2.1 0.00.3 3.8 1.4 0.1 2.0 0.1 2.8 0.1 2.4 0.2 1.2 0.2 1.0 0.1 2.1 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ) logED ne – 10.4 ne – 22.1 ne 1.4 ne 1.0 ne 0.8 ne 1.3 2.2 0.6 3.03.6 1.5 0.7 6.4 1.6 5.2 1.6 2.9 0.7 5.7 1.0 3.1 0.6 b 13.7 2.0 12.9 1.7 18.3 1.6 100.3 2.2 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Experiment 1 Experiment 2 ± Max b 7.2 74.2 3.0 25.7 4.5 38.9 9.2 54.7 ne 99.8 ne 50.9 ne 63.0 ne 84.1 9.3 22.0 2.2 30.7 7.0 30.7 9.3 55.5 2.5 81.4 3.9 48.1 3.4 66.9 8.3 79.4 8.5 44.1 B 13.1 33.4 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Min continued from preceding page :( a PH 3.6 PH 3.6 PH 0.0 PH 0.0 RN 5.7 RDRN 2.5 -3.6 RD 4.9 RN -0.6 RD 0.8 PW -0.6 PW 2.2 PW 0.0 PW -2.6 TRL 0.0 TRL 10.5 TRL 0.0 TRL 0.0 AG 4 Rh010901 Rh070942 Table A4 Species Rh060801 Rh070933 236 ) ne ne ne ne 0.8 1.9 1.1 1.3 1.0 93.6 M ± / ± ± ± ± ± ± ± ± ± H n 50 ne 1.0 ne 2.0 ne 33.8 ne 2.0 0.3 0.9 0.4 1.0 0.40.2 1.2 8.7 0.1 3.1 ± ± ± ± ± ± ± ± ± continued on next page ( ne 0.9 ne 0.5 ne – 7.7 ne 0.6 1.6 0.9 8.6 1.1 5.0 1.5 2.3 1.6 12.9 1.5 12.6 0.9 ± ± ± ± ± ± ± ± ± ± 7.8 29.3 3.0 15.4 1.4 29.1 ne 54.9 ne 70.7 ne 69.7 B Max logED 3.1 34.8 4.6 36.9 6.8 33.8 25.7 60.6 / ± ± ± ± ± ± ± ± ± ± Min b ne -0.5 nene 3.9 0.0 1.1 0.0 5.3 –53.4 – 0.0 – – 5.4 0.0 0.5 -5.8 0.9 -3.2 1.3 0.0 M ± ± ± / ± ± ± ± ± ± ± H n b 50 ne 33.8 nene 23.4 31.5 0.1 3.3 0.5 2.7 0.0 41.3 0.2 3.7 0.3 1.0 0.3 1.0 0.2 1.8 ± ± ± ± ± ± ± ± ± ± ) logED 3.2 1.3 4.15.2 0.5 1.5 1.9 0.7 2.7 0.6 9.2 1.3 2.8 0.7 7.0 1.1 2.1 0.2 7.2 1.5 b ± ± ± ± ± ± ± ± ± ± Experiment 1 Experiment 2 Max b 3.7 16.9 3.4 52.0 5.3 32.1 8.3 26.9 5.7 34.4 4.6 58.2 5.5 37.9 6.2 49.7 5.1 18.6 B 21.3 50.3 / ± ± ± ± ± ± ± ± ± ± Min continued from preceding page :( a PH -1.8 PH 0.0 PH – – – – – – – – RN – – – – – – – – RD 0.0 RN -10.7 RD 0.0 RDRN – – – – – – – – – – – – – – – – RDRN – – – – – – – – – – – – – – – – PW -4.4 PW 0.0 PW -2.5 PW – – – – -0.4 TRL -0.2 TRL 0.0 TRL – – – – – – – – Rh070927 AG 8 Rh070922 Rh070929 Rh070909 Table A4 Species 237 3.9 6.7 2.0 ne ne ne ne 5.7 4.4 0.6 1.4 113.7 10.0 M / ± ± ± ± ± ± ± ± ± ± ± ± ± H 10) . n 0 AG E ≥ P 50 ne 1.4 ne 1.1 ne 1.4 ne 1.0 0.40.3 3.5 3.2 0.0 10.0 0.1 5.1 0.2 1.4 0.7 1.0 ± ± ± ± ± ± ± ± ± ± Rhizoctonia is the control-normalized ne – 48.0 ne – 46.3 Y ne – 35.7 ne 0.7 ne 1.0 ne 0.6 ne 0.7 2.42.4 0.5 0.5 3.3 0.7 3.6 0.6 7.8 1.7 24.7 1.7 is the highest observed ± ± ± ± ± ± ± ± ± ± ± ± ± is the modeled response at the Max , where B + Max X ∗ 9.1 103.6 3.5 42.0 ne 90.5 ne 81.1 ne 86.9 B Max logED 5.4 87.1 2.7 114.8 7.3 42.6 8.0 56.8 8.0 34.5 21.1 89.8 M 62.3 62.3 20.7 90.0 / ± ± ± ± ± ± ± ± ± ± = ± ± ± Y Min is the y-intercept. column: B b 4.5 1.1 6.1 -12.1 3.9 3.1 ne 0.0 1.1 0.0 0.44.1 0.0 0.7 – 0.0 – – – 0.5 -2.2 1.6 0.0 4.3 0.0 1.3 -1.9 1.20.4 0.8 0.0 50 M ± ± ± ± / ± ± ± ± ± ± ± ± ± ± H n is the lowest observed response, logED Min is the control-normalized response at a log-transformed inoculum density b 50 Y ne 1.5 0.1 2.2 0.00.1 2.6 3.8 0.1 2.0 0.0 3.1 0.1 3.3 0.1 5.9 0.1 2.4 0.10.1 3.4 1.2 is the slope, and ± ± ± ± ± ± ± ± ± ± ± M , standard errors (ne = nonestimable). Insignificant parameter estimates ( ) logED X , where ) ± H n ∗ ) X ne – 47.8 ne – 46.6 ne – 16.6 ne 0.7 2.1 0.6 0.64.5 0.5 0.7 2.7 0.7 0.6 0.5 2.4 0.7 4.8 0.8 6.6 1.5 2.86.7 1.4 1.0 − b 50 ± ± ± ± ± ± ± ± ± ± ± ± ± ± Experiment 1 Experiment 2 AG 2-1 (isolates Rh060811, Rh070913, Rh070937), AG 3 (Rh060801, Rh070933, Rh070942), Max logED ( 10 + 1 R. solani ( b / 7.2 109.8 3.39.7 91.2 51.2 2.0 93.4 5.3 54.8 5.3 50.7 ne 82.7 ) 5.3 44.1 4.7 113.9 4.4 44.1 4.3 56.9 2.7 46.6 B 10.0 90.7 10.5 61.7 / ± ± ± ± ± ± ± ± ± ± ± ± ± ± Min Min − continued from preceding page spp. include is the point of inflection of the sigmoid curve, Max :( a 50 PH 0.0 PH -0.2 PH 0.7 + ( RN 4.1 RD -5.5 RDRN -4.4 -0.1 RN -6.7 RD 0.0 PW 0.0 PW 0.0 TRL 1.6 TRL -2.0 TRL -3.5 Min logED = , AG E Rh070923 Rh070943 Table A4 Species Best-fit values for model parameter estimates Rhizoctonia not shown. Linear model fitsresponse are at those a with log-transformed no inoculum estimate density in the highest inoculum density. All other data was fit with the four-parameter sigmoidal model: X (Rh070923). Experiment repeats areb shown in separate columns. a AG 4 (Rh010901, Rh070909, Rh070929), and AG 8 (Rh070922, Rh070927, Rh070943), as well as binucleate Y 238 specifies the Hillslope. H n response, and 239

Figure A2: Effect of inoculum density and Rhizoctonia solani AG 2-1 isolate (Rh060811, Rh070913, and Rh070937) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns. 240

Figure A3: Effect of inoculum density and Rhizoctonia solani AG 3 isolate (Rh060801, Rh070933, and Rh070942) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns. 241

Figure A4: Effect of inoculum density and Rhizoctonia solani AG 4 isolate (Rh010901, Rh070909, and Rh070929) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns. 242

Figure A5: Effect of inoculum density and Rhizoctonia solani AG 8 isolate (Rh070922, Rh070927, and Rh070943) on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns. 243

Figure A6: Effect of inoculum density and binucleate Rhizoctonia AG E isolate Rh070923 on the dry weight, height, and total root length of 8-week-old onion seedlings expressed as 100 - % of noninoculated control. Error bars represent standard errors (n = 10). Results of repeat experiments are shown in separate columns. 244 AG E (Rh070923). Response variables expressed Rhizoctonia . AG 2-1 (isolates Rh060811, Rh070913, and Rh070937), AG 3 (Rh060801, Rh070933, and Rh070942), Scatter plot comparing the maximum modeled reductions in onion seedling height, weight, and total root length with in- Rhizoctonia solani 100 - % of noninoculated control as and AG 8 (Rh070922, Rh070927, and Rh070943), as well as binucleate Figure A7: fection by 245

Table A5: Effect of inoculum density and Rhizoctonia spp. on the emergence of onion seedlings grown at a 13/8◦C day/night temperature regime.

a b c d Species ED50 Maximum P-value R. solani AG 2-1 13.81 37.08 ± 8.79 0.0218 R. solani AG 3 29.88 41.67 ± 10.43 0.0075 R. solani AG 4 64.00 16.67 ± 21.68 0.1049 R. solani AG 8 – – 0.5673 Binucleate Rhi. AG E 34.10 100.00 ± 11.29 0.0006 aFour-parameter sigmoidal model was fit to the combined responses of three isolates of each Rhizoctonia solani AG (AG 2-1: Rh060811, Rh070913, and Rh070937; AG 3: Rh060801, Rh070933, and Rh070942; AG 4: Rh010901, Rh070909, and Rh070929; AG 8: Rh070922, Rh070927, and Rh070943), but only one isolate of binucleate Rhizoctonia AG E (Rh070923), over two repeats of the experiment. b ED50 is the inoculum density, in colony forming units/g soil, at which 50% of the maximum reduction in emergence occurs. cMaximum reduction in emergence expressed as 100 - % of noninoculated control ± standard error (SE). dP-value derived from the ANOVA F-test. 246

B Effect of nitrogen fertility on the agronomic performance, flour quality and phenolic acid content of hard red winter wheat in west- ern Washington

Figure B1: Total monthly precipitation (bar graphs) and mean monthly air temperature (line graph) as recorded by the WSU AgWeatherNet station located 1 km from research plots over the duration of the study from October 2009 until September 2011. 247

Figure B2: The experiment was replicated at two sites per crop year over two crop years at the WSU Mount Vernon NWREC, separated 0.6 km, and utilized a split-plot design with five N fertility treatments as the main plot factor arranged in a CRD with four replications per main plot. Hard red winter wheat cultivar was the subplot factor. N fertility treatments included an non-fertilized control, 85 kg N/ha poultry feather meal (PFM), 170 kg N/ha PFM, 85 kg N/ha sulfur-coated urea (SCU), and 170 kg N/ha SCU. Within a pass, subplots were separated by mowed alleys, and main plots by a plot of soft white winter wheat. Adjacent passes were separated by planting of soft white winter wheat. 248 ) 4.5 ab 7.1 bcd 6.1 ab 1.3 a 2.9 efgi 3.7 efg 5.2 ghi 4.3 efg 1.5 efg 8.3 defh 5.6 i 3.3 efgi 3.4 fgi 6.3 gi 11.6 bc ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 3.0 abc 152.4 4.9 abc3.3 ab4.5 153.0 a 161.3 147.3 2.6 defg 142.9 4.8 bcd 124.5 2.9 efgh8.7 defg2.2 114.9 cd 123.8 124.5 3.6 hi 125.7 5.3 ghi3.9 fghi4.7 ghi 121.9 123.6 117.5 7.3 i 108.0 10.4 defg 128.9 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Plant height (cm) 2.4 abcd 153.7 1.9 ab1.3 abcd2.3 abc 153.0 159.4 160.0 4.1 a 133.4 2.0 ef 144.1 2.3 fghi1.1 efg1.0 127.0 efgh1.1 134.0 efgh 144.1 133.4 1.2 ghi 116.8 2.2 i1.2 hi1.1 ghi 121.3 125.7 120.7 1.3 hi 113.7 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the plant height of hard red winter wheat cultivars Bauermeister, McCall, a 114.0 b 1.6 abcd 127.8 3.6 abc 128.2 2.9 ab1.7 efg 127.7 127.6 1.8 a 129.0 1.8 efg 121.5 1.5 fg 120.5 3.1 fg3.4 116.9 efg5.5 gh 118.3 118.5 2.4 i 115.8 3.9 hi 112.5 1.0 hi2.9 i 114.7 115.6 2.4 i ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 129.8 SCU Low 124.7 SCU Low 115.1 PFM Low 140.3 PFM Low 127.6 PFM Low 118.1 SCU High 138.4 SCU High 129.1 SCU High 115.6 PFM High 143.4 PFM High 129.3 PFM High 117.8 The effect of nitrogen (N) source and rate Non-fertilized 145.1 Non-fertilized 126.7 Non-fertilized 114.9 WA8022 McCall Cultivar &N TreatmentBauermeister East 2009-10 West North 2010-11 South Relief Table B1: Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11. 249 2.2 cdef 8.8 efg 4.8 defg 3.6 cde 4.5 efg ± ± ± ± ± 2.2 defg 132.7 8.5 defgh2.7 cde6.3 129.5 cdef 132.7 124.5 6.0 defgh 121.9 ± ± ± ± ± Plant height (cm) 1.3 ef 135.3 0.7 cde1.7 ef1.2 129.5 de 142.2 139.1 1.5 bcde 131.4 ± ± ± ± ± ) 2.5 efg 121.6 2.8 bcde 123.0 2.2 cdefg3.1 121.6 cdef 123.2 4.7 defg 123.3 standard error. Within each column, means with the same letter are not significantly different ± ± ± ± ± ± continued from preceding page :( SCU Low 132.3 PFM Low 135.1 SCU High 126.8 PFM High 132.4 Non-fertilized 131.9 Table B1 Cultivar &N Treatment East 2009-10 West North 2010-11 South 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 250 ) 4.8 bc 8.8 a 6.3 de 5.0 e 0.0 e 2.5 e 2.5 e 2.5 e 0.0 e 17.5 ab 23.3 cd 18.6 de 15.0 de 14.1 de 12.0 de ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 1.4 a 72.5 2.3 a 83.5 3.5 ef 22.5 0.0 f9.0 ef 2.5 2.5 19.7 cdef 33.3 21.6 cdef 52.5 26.0 bcde 23.3 15.5 bcdef 5.0 22.1 cdef 15.0 15.5 abcd 20.0 13.8 ab26.0 bcdef 16.7 0.0 20.0 abc 2.5 14.6 def 0.0 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Lodging (%) 6.0 abc 97.5 3.8 abd3.8 a 96.0 32.5 1.3 ab 31.3 3.1 ab 10.0 2.5 abcde 28.8 4.8 j 0.0 3.8 cefgh6.1 bcdef 81.0 45.0 8.3 defg 70.0 7.2 efh 16.3 13.8 fhi 47.5 16.9 efhi 46.3 14.1 hij12.1 cefgh 62.5 13.8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 58.8 on the stem lodging of hard red winter wheat cultivars Bauermeister, McCall, a b 7.1 ab 87.5 3.8 a 86.3 2.5 h9.6 22.5 efgh 58.8 2.9 bcd 77.5 9.4 efgh 40.0 6.6 gh 63.8 9.1 efgh 62.5 4.3 gh 10.1 ab 91.3 10.1 ab 88.8 17.4 bc 88.8 10.0 cdef 53.8 12.6 cdefg12.3 cde 63.8 65.0 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 78.8 SCU Low 25.0 SCU Low 42.5 PFM Low 78.8 PFM Low 23.8 PFM Low 20.0 SCU High 80.0 SCU High 40.0 SCU High 55.0 PFM High 91.3 PFM High 11.3 PFM High 31.3 The effect of nitrogen (N) source and rate Non-fertilized 58.8 Non-fertilized 2.5 Non-fertilized 11.3 WA8022 Relief McCall Cultivar &N TreatmentBauermeister East 2009-10 West North 2010-11 South Table B2: Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11. 251 0.0 e 0.0 e 5.0 e 0.0 e 0.0 e ± ± ± ± ± 4.7 ef5.0 ef 0.0 0.0 21.3 cdef 0.0 22.2 bcdef 0.0 21.2 bcdef 5.0 ± ± ± ± ± Lodging (%) 3.8 abcdef 28.8 8.7 ij8.7 efhi7.5 fhi 6.3 5.0 43.8 11.7 fhi 37.5 ± ± ± ± ± ) 9.0 fgh 55.0 4.8 gh 47.5 11.9 efgh 71.3 15.9 defgh 51.7 11.4 efgh 35.0 standard error. Within each column, means with the same letter are not significantly different ± ± ± ± ± ± continued from preceding page :( SCU Low 30.0 PFM Low 13.8 SCU High 25.0 PFM High 7.5 Non-fertilized 16.3 Table B2 Cultivar &N Treatment East 2009-10 West North 2010-11 South 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 252 ) 0.5 ij 0.3 j 0.3 hij 0.2 ij 0.3 j 0.7 hij 0.5 gh 0.1 hi 0.3 hij 0.7 def 0.2 bcde 0.5 ij 0.2 ij 0.3 ef 0.4 cdef ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 0.6 fg0.3 efg0.2 g0.3 2.1 g 3.1 0.3 cefgh 2.3 2.8 3.4 0.2 efg 3.5 0.2 defgh 3.3 0.2 fg 3.8 0.2 bcdefg0.2 2.7 defgh 3.0 0.8 bcde 4.6 0.2 bcdef0.7 bcde 6.1 7.5 0.3 bc0.6 bd 6.0 6.9 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Yield (Mt/ha) 0.4 g0.2 g0.4 2.1 g0.4 2.4 g0.1 1.7 g 1.8 2.6 0.1 g 2.4 0.1 g 2.7 0.5 cef 3.9 0.6 bcde0.3 3.6 e 3.9 0.3 g 2.3 0.5 e0.4 def 4.2 0.1 4.1 g0.4 g 2.7 2.5 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the grain yield of hard red winter wheat cultivars Bauermeister, McCall, 4.4 a b 0.4 c0.2 c0.2 2.0 c0.2 2.1 c 2.6 2.2 0.1 c 2.0 0.2 ab 0.2 c 2.1 0.4 ab 4.6 0.2 c 2.0 1.1 b 3.9 0.1 c 2.0 0.1 ab 4.1 0.1 c 2.0 0.1 ab0.1 4.3 c 1.9 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 1.8 SCU Low 1.5 SCU Low 4.4 PFM Low 2.1 PFM Low 4.3 PFM Low 1.8 SCU High 1.6 SCU High 1.1 SCU High 3.8 PFM High 1.5 PFM High 3.6 PFM High 1.4 Non-fertilized 1.4 Non-fertilized 4.3 Non-fertilized 1.4 WA8022 Relief Cultivar &N TreatmentBauermeister East 2009-10 West North 2010-11 South McCall The effect of nitrogen (N) source and rate Table B3: Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11. 253 0.9 fg 0.8 abc 0.5 a 1.0 bcd 0.6 ab ± ± ± ± ± 0.9 a 5.8 0.8 a1.3 a 7.9 9.1 0.6 a0.3 a 7.2 8.2 ± ± ± ± ± Yield (Mt/ha) ) 0.4 abd 6.6 0.6 abc0.5 a 7.4 6.9 0.3 abc0.3 abcd 7.3 7.9 ± ± ± ± ± 0.2 ab 5.3 0.4 a 5.4 1.5 ab 5.9 0.6 ab 5.6 0.8 ab 5.2 ± ± ± ± ± standard error. Within each column, means with the same letter are not significantly different ± continued from preceding page :( SCU Low 4.7 PFM Low 5.1 SCU High 4.6 PFM High 4.4 Non-fertilized 4.5 Table B3 Cultivar &N Treatment East 2009-10 West North 2010-11 South 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 254 ) 0.6 gh 4.8 efg 2.0 ef 0.7 cde 0.6 ef 0.6 de 0.5 def 1.7 ef 1.6 gh 1.8 h 2.8 fgh 2.6 h 1.5 cde 1.7 ef 0.5 bcd ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 2.5 cde 70.3 2.0 abc 73.0 0.5 bcde1.0 66.6 cde 67.1 2.4 de1.0 bcde1.9 67.4 69.9 cde1.2 68.9 cde2.6 cde3.1 67.7 ef0.8 62.0 cde2.2 61.5 ef 64.9 0.8 60.7 abcd2.0 70.3 cde 68.5 11.8 f 62.1 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Test weight (kg/hL) 0.8 abcd1.2 abcde 67.0 65.1 1.6 efg0.9 bcde0.7 fg 61.9 67.1 0.8 bcd1.8 65.4 def0.5 63.9 def0.8 g 62.3 0.9 fg 59.9 0.9 62.4 a0.8 59.0 abc1.1 ab 69.6 62.3 51.2 0.9 cde 64.7 1.1 cdef 70.7 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the test weight of hard red winter wheat cultivars Bauermeister, McCall, 74.3 a b 0.5 ab1.2 bcde 76.1 75.0 0.3 fgh 72.8 0.3 efgh0.5 fghi 73.4 1.5 72.2 j1.2 ghij0.5 ij0.9 72.8 75.5 hij0.9 fghij 73.1 1.3 71.7 71.2 bcdef1.6 bcdefg0.5 77.8 77.4 cdef 77.4 0.8 efghi 75.0 0.5 defg ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 75.9 SCU Low 72.7 SCU Low 69.3 PFM Low 69.3 PFM Low 74.1 PFM Low 73.3 SCU High 74.5 SCU High 72.2 SCU High 70.8 PFM High 71.9 PFM High 68.9 PFM High 72.6 Non-fertilized 73.1 Non-fertilized 67.9 Non-fertilized 73.6 WA8022 McCall Relief Cultivar &N TreatmentBauermeister East 2009-10 West North 2010-11 South The effect of nitrogen (N) source and rate Table B4: Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11. 255 0.6 ab 1.4 abc 0.6 a 0.3 ab 0.6 ab ± ± ± ± ± 0.4 ab 76.1 0.6 a 75.2 0.6 a 75.7 1.2 ab0.8 ab 74.6 77.5 ± ± ± ± ± Test weight (kg/hL) 0.5 bcde0.5 efg 74.8 74.8 0.7 bcde 75.1 0.4 abcd 75.9 0.7 abcd 76.4 ± ± ± ± ± ) 0.7 bcdef0.8 abcd 74.9 73.2 0.6 abc 75.0 0.3 abcd 75.6 0.3 a 75.8 ± ± ± ± ± standard error. Within each column, means with the same letter are not significantly different ± continued from preceding page :( SCU Low 74.6 PFM Low 75.6 SCU High 75.9 PFM High 76.4 Non-fertilized 76.0 Table B4 Cultivar &N Treatment East 2009-10 West North 2010-11 South 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 256 ) 9.0 bc 6.1 ab 18.6 abc 13.9 ab 31.9 ab 25.3 ab 17.7 d 19.0 d 31.5 cd 24.4 ab 11.2 ab 27.3 cd 37.2 cd 20.8 ab 11.7 ab ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 25.8 e 409.0 18.2 c 363.7 13.6 de 385.8 46.6 d 403.3 22.4 bc 363.0 16.6 de 284.0 20.1 d 299.0 25.4 d 309.0 15.7 ab34.5 417.5 d37.4 d 307.5 314.0 21.0 a 411.8 18.3 abc 419.7 18.3 ab 413.8 11.0 ab 405.0 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Falling number (s) 9.0 bcdef 242.0 44.0 abc 161.0 62.5 abc 322.8 57.9 abc 182.3 29.6 a 251.8 33.5 ab 362.8 18.7 ef 216.3 33.7 def 234.8 14.2 f 229.5 37.1 def 419.3 24.8 ef 246.8 19.8 abcde 439.8 15.6 ef 385.3 24.3 abcd 426.8 12.1 abc 413.8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± on the falling numbers of hard red winter wheat cultivars Bauermeister, McCall, 466.4 a b 16.9 a 440.4 26.6 ab 448.5 26.2 abc 458.9 38.3 def 497.3 22.2 abcd 474.0 26.8 fgh 338.0 22.4 gh 348.5 37.9 h10.0 h 398.0 330.3 26.4 bcd 347.3 20.7 efgh 337.3 27.9 cde 419.3 24.2 de 341.5 40.2 defg 428.6 21.9 bcd ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± SCU Low 438.1 SCU Low 275.1 SCU Low 366.3 PFM Low 352.6 PFM Low 294.4 PFM Low 337.4 SCU High 464.8 SCU High 282.6 SCU High 373.3 PFM High 432.8 PFM High 262.0 PFM High 356.8 The effect of nitrogen (N) source and rate Non-fertilized 407.0 Non-fertilized 264.3 Non-fertilized 374.3 WA8022 Relief McCall Cultivar &N TreatmentBauermeister East 2009-10 West North 2010-11 South Table B5: Relief, and WA8022 grown at Mount Vernon, WA at east and west sites during 2009-10, and north and south sites during 2010-11. 257 5.3 a 14.5 ab 16.9 ab 11.0 ab 28.2 ab ± ± ± ± ± 17.3 a 399.5 40.6 ab 415.5 13.3 ab19.3 ab21.6 409.0 ab 381.5 425.3 ± ± ± ± ± Falling number (s) 2.7 abc3.9 abc4.3 418.8 cdef 409.5 427.8 11.7 abc 441.0 14.1 abc 411.0 ± ± ± ± ± ) 14.2 abcd 457.5 26.6 bcd 449.2 14.9 bcd13.7 457.9 abcd 390.3 20.8 abcd 454.5 standard error. Within each column, means with the same letter are not significantly different ± ± ± ± ± ± continued from preceding page :( SCU Low 388.1 PFM Low 397.3 SCU High 409.0 PFM High 396.3 Non-fertilized 376.0 Table B5 Cultivar &N Treatment East 2009-10 West North 2010-11 South 05) according to a comparison of least squares means. . 0 < N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM P a high), 85 kg N/hab sulfur-coated urea (SCU low), and 170 kg( N/ha SCU (SCU high). 258 0.1 c 0.7 d 1.0 d 0.2 c 0.5 c 0.9 b 0.5 ab 0.8 b 0.4 ab 0.4 a ± ± ± ± ± ± ± ± ± ± 0.3 c1.5 d 74.2 72.0 0.9 d 71.2 0.6 c 74.0 0.5 c 73.9 0.2 b0.2 ab 76.9 77.2 0.8 b0.5 ab0.4 76.7 77.4 a 78.7 ± ± ± ± ± ± ± ± ± ± 0.4 ab 70.9 1.4 bcd 74.8 0.5 abc 70.8 0.8 abc 74.3 0.3 de 74.1 0.3 abc 78.7 1.0 cd 79.3 0.7 e0.6 abc0.6 77.4 78.0 a 78.1 ± ± ± ± ± ± ± ± ± ± 05) according to a comparison of least . 0 < P 1.0 bcd 9.4 0.7 d 7.8 0.9 ab 9.3 0.5 cd 8.6 0.8 d 6.2 0.7 abc 9.3 1.2 d0.8 abc 5.2 8.5 0.7 ab 7.4 1.0 a 9.9 ± ± ± ± ± ± ± ± ± ± on the plant height, yield, and test weight of hard red winter wheat cultivars a 5.0 a 7.8 4.7 a 5.9 5.3 a 10.1 5.8 a 6.9 5.7 a 5.4 4.4 b 6.1 2.1 b 9.3 1.2 b4.8 8.7 b 10.0 1.0 b 10.6 ± ± ± ± ± ± ± ± ± ± 84.5 c e standard error. ± b 4.2 cde 90.8 4.1 de 92.1 4.3 cde 94.0 1.0 ab 119.4 5.3 cde 113.5 2.2 a 120.0 4.0 bc 113.7 7.9 cd 108.0 3.6 de 91.4 4.5 ± ± ± ± ± ± ± ± ± ± The effect of nitrogen (N) source and rate SCU Low 102.9 SCU Low 98.4 PFM Low 111.8 PFM Low 100.3 SCU High 119.4 SCU High 95.9 PFM High 101.0 PFM High 130.2 Table B6: Norwest 553 and WA8120 grown at two sites (north and south) in Mount Vernon, WA during the 2010-11 growing season. Cultivar &N TreatmentNorwest 553 Non-fertilized North 90.2 Height (cm) South North Yield (Mt/ha) South Test weight (kg/hL) North South WA8120 Non-fertilized 102.2 N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM Within each column, means with the same letter are not significantly different ( a high), 85 kg N/hab sulfur-coated urea (SCU low), andc 170 kg N/ha SCU (SCU high). squares means. 259 0.1 abcd 0.2 abc 0.3 a 0.3 ab 0.3 abcd 0.1 e 0.6 de 0.6 bcde 0.9 cde 0.4 abc ± ± ± ± ± ± ± ± ± ± 0.5 ab0.7 a0.7 9.4 a1.1 10.3 a 10.1 9.5 0.3 bc0.4 c0.4 8.0 a0.5 c 8.3 1.1 a 9.1 8.8 9.6 0.4 abc 9.3 05) according to a comparison of least . ± ± ± ± ± ± ± ± ± ± 0 < P 4.6 a 10.2 4.6 bcd9.9 7.7 abc 7.3 22.7 a31.4 ab11.4 9.3 10.3 a16.8 9.9 cd13.4 abcd 7.5 35.8 9.8 d 10.0 24.7 abc 8.7 ± ± ± ± ± ± ± ± ± ± on the falling number and grain protein content of hard red winter wheat 383.0 a c ab b 6.7 bc9.2 abc 331.0 335.8 6.4 a 313.5 33.1 ab25.2 ab27.9 396.3 ab28.0 391.8 abc 406.3 411.5 23.0 ab15.8 c 368.8 379.7 13.8 ± ± ± ± ± ± ± ± ± ± standard error. ± SCU Low 403.3 SCU Low 338.5 PFM Low 400.5 PFM Low 379.0 SCU High 370.8 SCU High 430.5 PFM High 404.5 PFM High 400.0 Cultivar &N TreatmentNorwest 553 Non-fertilized 405.3 North Falling number (s)WA8120 SouthNon-fertilized 365.0 Grain protein (%) North South The effect of nitrogen (N) source and rate N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM Within each column, means with the same letter are not significantly different ( a high), 85 kg N/hab sulfur-coated urea (SCU low), andc 170 kg N/ha SCU (SCU high). squares means. cultivars Norwest 553 and WA8120 grown at two sites (north and south) in Mount Vernon, WA during the 2010-11 growing season. Table B7: 260 1.1 ab 0.7 bc 0.5 a 0.5 bc 0.8 bc 0.5 c 0.6 c 0.7 b 0.4 c 0.3 c ± ± ± ± ± ± ± ± ± ± ) 3 05) according to a comparison of least 0.8 bc 14.4 0.5 c 13.2 0.7 a 15.3 0.5 bc 13.2 1.4 c 12.9 0.2 bc 12.6 0.6 c 12.2 1.1 b 13.4 0.7 c 12.4 1.0 c 11.8 . 0 ± ± ± ± ± ± ± ± ± ± < P 1.4 a 14.8 1.5 ab 12.8 1.3 a 17.3 0.9 b 13.7 0.4 b 12.8 0.5 ab 14.1 0.8 ab 13.4 0.8 a 16.0 0.3 b0.2 ab 12.8 13.4 ± ± ± ± ± ± ± ± ± ± Micro-SDS volumes (cm 10.9 c on the micro-SDS volumes, based both on constant flour weight and on a 10% a cd b 1.1 d 10.4 1.6 abc 12.0 0.9 abc 12.3 1.1 a 13.8 0.4 bcd 11.7 2.1 ab 13.9 0.7 d 11.7 1.3 a 14.0 0.7 cd 11.0 1.0 ± ± ± ± ± ± ± ± ± ± standard error. ± SCU Low 13.6 SCU Low 9.3 PFM Low 10.6 PFM Low 12.4 SCU High 14.9 SCU High 13.9 PFM High 16.9 PFM High 16.2 WA8120 Non-fertilized 9.6 Cultivar &N TreatmentNorwest 553 Non-fertilized North 11.2 Constant weight South Constant protein North South The effect of nitrogen (N) source and rate N fertility treatments included an non-fertilized control, 85 kg N/haValues poultry each feather expressed meal as (PFM mean low), 170 kg N/ha PFM (PFM Within each column, means with the same letter are not significantly different ( a high), 85 kg N/hab sulfur-coated urea (SCU low), andc 170 kg N/ha SCU (SCU high). squares means. Table B8: constant protein rate, of hardVernon, WA during red the winter 2010-11 wheat growing cultivars season. Norwest 553 and WA8120 grown at two sites (north and south) in Mount 261 ) 9.7 8.1 9.2 16.1 18.4 19.0 17.2 13.6 15.8 12.6 11.4 ± ± ± ± ± ± ± ± ± ± ± 0.1 618.3 0.2 621.2 0.1 590.4 0.1 593.7 0.1 611.6 0.1 554.0 0.1 581.5 0.1 691.7 0.1 600.9 0.1 628.9 0.1 585.2 ± ± ± ± ± ± ± ± ± ± ± continued on next page ( 0.2 3.0 0.1 3.1 0.1 3.0 0.2 3.0 0.2 3.2 0.1 3.6 0.1 3.0 0.1 2.5 0.1 3.2 0.0 3.1 0.1 3.0 ± ± ± ± ± ± ± ± ± ± ± 0.2 1.3 0.3 1.3 0.2 1.3 0.2 1.3 0.2 1.4 0.2 1.4 0.2 1.1 0.2 1.6 0.2 1.2 0.1 1.0 0.2 1.7 ± ± ± ± ± ± ± ± ± ± ± /g dry matter). µ 0.3 6.4 0.2 6.6 0.3 6.4 0.4 7.0 0.4 6.6 0.3 6.4 0.3 6.4 0.3 5.9 0.2 7.2 0.2 6.7 0.2 6.5 ± ± ± ± ± ± ± ± ± ± ± 3.2 9.8 3.2 9.7 3.4 9.6 2.8 8.9 2.6 9.5 1.4 8.7 1.6 8.5 1.9 11.3 1.9 10.0 0.6 9.6 1.9 9.0 ± ± ± ± ± ± ± ± ± ± ± 0.9 47.4 0.7 46.5 0.8 46.2 0.8 43.4 0.8 42.7 0.4 37.7 0.4 51.1 0.8 62.4 0.5 48.4 0.4 42.1 0.4 29.7 ± ± ± ± ± ± ± ± ± ± 2 3 4 5 6 7 Total ± 8.1 10.8 9.1 12.6 7.2 11.8 6.6 13.4 9.3 8.6 5.8 10.1 11.9 11.7 13.0 11.7 14.5 11.9 12.0 10.3 10.6 16.2 ± ± ± ± ± ± a ± ± ± ± ± 1 b NF 3.98 1.11 1.98 3.45 1.86 1.59 0.46 3.81 Site 82.11 58.23 26.05 24.89 0.17 65.02 2.54 18.79 East 291.4 West 355.0 × Marginal means for phenolic acid concentrations of hard red winter wheat cultivars Bauermeister, McCall, Relief, and Relief 295.8 S McCall 349.8 Cultivar 11.79 57.78 136.77 39.26 7.36 4.33 24.87 34.97 WA8022 311.6 c SCU Low 332.3 PFM Low 299.8 N Fertility 4.13 1.79 2.25 2.28 1.68 0.45 0.88 1.58 SCU High 342.1 PFM High 324.4 Bauermeister 335.5 Non-fertilized 317.5 Table B9: WA8022 grown at east and west sites in Mount Vernon, WA during 2009-10 ( F value N Fertility (NF) Cultivar (C) Site (S) 262 , syringic + vanillic 5 -coumaric acid; p , 4 , sinapic acid; , total phenolic acid monomers and dimers, determined 3 Total , cis-ferulic acid; 2 standard error. ± ) 2 3 4 5 6 7 Total , trans-ferulic acid; , 2,4-dihydroxybenzoic acid; and 1 7 a 1 -values based on the ANOVA F-test. P with the concentrations of unidentified phenolic acids with UV-vis spectra and retention times expected of this C 2.32 1.34 6.56 4.77 1.20 3.11 0.63 4.24 CC 0.76 1.18 0.81 0.44 0.85 0.88 0.51 0.97 0.28 0.54 1.37 1.08 0.27 1.02 1.09 0.92 C 0.0815 0.2665 0.0005 0.0042 0.3153 0.0311 0.5961 0.0079 CC 0.6840 0.3091 0.6375 0.9437 0.6032 0.5671 0.9052 0.4861 0.9909 0.8839 0.1981 0.3908 0.9921 0.4417 0.3816 0.5291 7 NF 0.0054 0.3559 0.1056 0.0120 0.1254 0.1848 0.7676 0.0070 × × × × × × Site <0.0001 <0.0001 <0.0001 <0.0001 0.6816 <0.0001 0.1148 <0.0001 S S × to continued from preceding page S NF NF NF NF 1 Cultivar <0.0001 <0.0001 <0.0001 <0.0001 0.0002 0.0071 <0.0001 <0.0001 :( × × S S N Fertility 0.0043 0.1383 0.0712 0.06810 0.1626 0.7750 0.4802 0.1887 c , 2-hydroxycinnamic acids; F 6 > -values and associated Table B9 P N fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), Quantified phenolic acids include: F b 85 kg N/ha sulfur-coatedc urea (SCU low), and 170 kg N/ha SCU (SCU high). class of compounds. Values each expressed as mean acids; by summing a 263

Table B10: The effect of nitrogen (N) source and ratea on the concentration (µg/g dm) of trans- ferulic acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season.

Site Cultivar N Treatment Trans-Ferulic Acid East Bauermeister Non-fertilized 285.6 ± 4.6 klmnob PFM Low 285.3 ± 1.3 klmno PFM High 291.3 ± 7.2 jklmno SCU Low 353.2 ± 21.6 cdefghij SCU High 311.8 ± 26.2 fghijklmn McCall Non-fertilized 331.1 ± 4.6 defghijklm PFM Low 297.2 ± 4.5 ijklmno PFM High 275.9 ± 34.1 mno SCU Low 346.5 ± 34.6 cdefghijk SCU High 335.3 ± 11.6 cdefghijklm Relief Non-fertilized 291.3 ± 15.2 jklmno PFM Low 274.8 ± 5.7 mno PFM High 257.6 ± 18.0 no SCU Low 278.2 ± 6.8 mno SCU High 280.9 ± 36.7 lmno WA 8022 Non-fertilized 280.7 ± 10.3 lmno PFM Low 260.6 ± 13.2 no PFM High 238.9 ± 18.0 o SCU Low 235.7 ± 47.2 o SCU High 315.5 ± 32.8 fghijklmn West Bauermeister Non-fertilized 362.0 ± 30.9 bcdefgh PFM Low 319.7 ± 35.8 efghijklmn PFM High 395.9 ± 8.5 abc SCU Low 367.8 ± 16.5 abcdefg SCU High 382.4 ± 28.1 abcd McCall Non-fertilized 341.9 ± 25.5 cdefghijkl PFM Low 349.5 ± 28.5 cdefghij PFM High 425.3 ± 16.3 a SCU Low 372.3 ± 37.1 abcdef SCU High 423.5 ± 19.8 ab Relief Non-fertilized 292.9 ± 22.1 ijklmno PFM Low 308.8 ± 22.8 ghijklmn

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Table B10:(continued from preceding page) Site Cultivar N Treatment Trans-Ferulic Acid PFM High 348.8 ± 29.3 cdefghij SCU Low 317.2 ± 18.7 efghijklmn SCU High 307.7 ± 16.8 ghijklmn WA 8022 Non-fertilized 354.3 ± 34.4 cdefghi PFM Low 302.5 ± 22.6 hijklmn PFM High 361.3 ± 11.7 bcdefgh SCU Low 387.2 ± 12.2 abcd SCU High 379.5 ± 16.0 abcde aN fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). bValues each expressed as mean ± standard error. Within each column, means with the same letter are not significantly different (P < 0.05) according to a comparison of least squares means. 265

Table B11: The effect of nitrogen (N) source and ratea on the content (µg/g dm) of p-coumaric acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season.

Site Cultivar N Treatment p-Coumaric Acid East Bauermeister Non-fertilized 8.6 ± 0.3 hijklmnob PFM Low 8.7 ± 0.2 hijklmno PFM High 8.9 ± 0.4 ghijklmn SCU Low 10.3 ± 0.5 bcdefghi SCU High 9.1 ± 0.8 fghijklmn McCall Non-fertilized 11.7 ± 0.3 abc PFM Low 10.6 ± 0.2 bcdefg PFM High 9.7 ± 1.0 efghijkl SCU Low 11.6 ± 1.4 abcd SCU High 11.1 ± 0.4 bcde Relief Non-fertilized 9.1 ± 0.6 fghijklmn PFM Low 7.6 ± 0.4 mnop PFM High 8.0 ± 0.9 lmnop SCU Low 8.7 ± 0.6 hijklmno SCU High 8.5 ± 1.1 ijklmno WA 8022 Non-fertilized 8.0 ± 0.3 lmnop PFM Low 7.3 ± 0.2 nop PFM High 6.6 ± 0.6 p SCU Low 6.9 ± 1.4 op SCU High 8.7 ± 0.8 hijklmno West Bauermeister Non-fertilized 10.0 ± 0.8 cdefghijk PFM Low 8.8 ± 0.8 ghijklmn PFM High 10.9 ± 0.1 bcdef SCU Low 10.2 ± 0.4 cdefghij SCU High 10.3 ± 0.9 bcdefghi McCall Non-fertilized 10.7 ± 1.0 bcdef PFM Low 11.2 ± 0.8 bcde PFM High 13.2 ± 0.5 a SCU Low 11.2 ± 1.0 bcde SCU High 12.0 ± 0.3 ab Relief Non-fertilized 8.3 ± 0.6 jklmnop PFM Low 8.3 ± 0.4 klmnop

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Table B11:(continued from preceding page) Site Cultivar N Treatment p-Coumaric Acid PFM High 9.2 ± 0.8 fghijklm SCU Low 8.6 ± 0.4 hijklmno SCU High 8.2 ± 0.4 lmnop WA 8022 Non-fertilized 9.8 ± 0.7 defghijkl PFM Low 8.6 ± 0.6 hijklmno PFM High 10.1 ± 0.5 cdefghijk SCU Low 10.3 ± 0.3 bcdefghi SCU High 10.4 ± 0.2 bcdefgh aN fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). bValues each expressed as mean ± standard error. Within each column, means with the same letter are not significantly different (P < 0.05) according to a comparison of least squares means. 267

Table B12: The effect of nitrogen (N) source and ratea on the total phenolic acid content (µg/g dm) for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season.

Site Cultivar N Treatment Total Phenolic Acid Concentration East Bauermeister Non-fertilized 582.2 ± 21.8 ghijkb PFM Low 582.3 ± 16.7 ghijk PFM High 571.8 ± 17.8 hijkl SCU Low 600.8 ± 16.0 ghijk SCU High 567.3 ± 19.7 hijklm McCall Non-fertilized 696.7 ± 16.8 abcdef PFM Low 641.9 ± 6.4 defghij PFM High 576.2 ± 66.4 ghijkl SCU Low 702.1 ± 23.2 abcde SCU High 713.1 ± 6.0 abcd Relief Non-fertilized 629.2 ± 56.5 defghijk PFM Low 559.0 ± 25.9 ijklm PFM High 561.5 ± 44.1 ijklm SCU Low 605.7 ± 25.6 ghijk SCU High 574.0 ± 41.6 hijkl WA 8022 Non-fertilized 567.0 ± 33.7 hijklm PFM Low 489.4 ± 57.0 lmn PFM High 438.6 ± 43.2 n SCU Low 477.8 ± 90.1 mn SCU High 566.6 ± 14.2 hijklm West Bauermeister Non-fertilized 607.4 ± 22.4 fghijk PFM Low 570.8 ± 37.6 hijkl PFM High 647.4 ± 11.4 cdefghi SCU Low 665.1 ± 7.2 abcdefg SCU High 613.6 ± 27.6 efghijk McCall Non-fertilized 654.1 ± 30.6 bcdefgh PFM Low 741.1 ± 19.8 ab PFM High 734.6 ± 6.5 abc SCU Low 702.6 ± 50.8 abcde SCU High 754.1 ± 25.8 a Relief Non-fertilized 549.8 ± 36.6 klm PFM Low 599.9 ± 18.6 ghijk

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Table B12:(continued from preceding page) Site Cultivar N Treatment Total Phenolic Acid Concentration PFM High 593.1 ± 15.3 ghijk SCU Low 588.4 ± 11.1 ghijk SCU High 554.6 ± 16.5 jklm WA 8022 Non-fertilized 606.0 ± 32.8 ghijk PFM Low 565.0 ± 42.9 hijklm PFM High 600.1 ± 11.7 ghijk SCU Low 626.6 ± 7.7 defghijk SCU High 602.8 ± 14.4 ghijk aN fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). bValues each expressed as mean ± standard error. Within each column, means with the same letter are not significantly different (P < 0.05) according to a comparison of least squares means. 269

Table B13: The effect of nitrogen (N) source and ratea on the content (µg/g dm) of sinapic acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season.

Site Cultivar N Treatment Sinapic Acid East Bauermeister Non-fertilized 27.6 ± 1.7 opb PFM Low 28.0 ± 0.7 op PFM High 26.6 ± 0.9 p SCU Low 30.3 ± 0.7 op SCU High 26.7 ± 3.0 p McCall Non-fertilized 57.4 ± 1.8 bcde PFM Low 53.4 ± 2.6 defgh PFM High 49.7 ± 6.5 efghi SCU Low 61.8 ± 3.9 bcd SCU High 61.9 ± 1.2 bcd Relief Non-fertilized 48.2 ± 11.2 efghij PFM Low 49.5 ± 4.0 efghi PFM High 55.8 ± 6.6 cdefg SCU Low 56.6 ± 8.9 cdef SCU High 49.0 ± 6.3 efghij WA 8022 Non-fertilized 34.6 ± 1.1 lmnop PFM Low 31.9 ± 2.4 nop PFM High 29.4 ± 2.4 op SCU Low 28.6 ± 5.9 op SCU High 35.0 ± 2.1 klmnop West Bauermeister Non-fertilized 30.3 ± 1.1 op PFM Low 28.5 ± 2.1 op PFM High 33.5 ± 0.7 mnop SCU Low 31.8 ± 1.1 nop SCU High 34.0 ± 1.0 lmnop McCall Non-fertilized 56.7 ± 4.4 cde PFM Low 66.0 ± 4.3 abc PFM High 75.4 ± 4.4 a SCU Low 67.8 ± 5.2 ab SCU High 74.0 ± 4.4 a Relief Non-fertilized 44.9 ± 0.9 ghijkl PFM Low 51.6 ± 2.6 defghi

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Table B13:(continued from preceding page) Site Cultivar N Treatment Sinapic Acid PFM High 54.9 ± 2.0 defgh SCU Low 49.6 ± 2.0 efghi SCU High 50.6 ± 2.4 efghi WA 8022 Non-fertilized 42.1 ± 2.7 ijklmn PFM Low 38.0 ± 1.8 jklmno PFM High 44.3 ± 2.6 hijklm SCU Low 45.6 ± 2.3 fghijk SCU High 48.0 ± 1.8 efghij aN fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). bValues each expressed as mean ± standard error. Within each column, means with the same letter are not significantly different (P < 0.05) according to a comparison of least squares means. 271

Table B14: The effect of nitrogen (N) source and ratea on the content (µg/g dm) of 2- hydroxycinnamic acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season.

Site Cultivar N Treatment 2-Hydroxycinnamic Acid East Bauermeister Non-fertilized 1.5 ± 0.2 cdefghib PFM Low 1.2 ± 0.2 cdefghi PFM High 1.2 ± 0.1 cdefghi SCU Low 1.9 ± 0.6 bcd SCU High 1.3 ± 0.1 cdefghi McCall Non-fertilized 2.5 ± 0.1 ab PFM Low 1.7 ± 0.3 cdefg PFM High 1.7 ± 0.2 bcdef SCU Low 1.9 ± 0.1 bcd SCU High 2.9 ± 0.8 a Relief Non-fertilized 1.6 ± 0.2 cdefgh PFM Low 1.8 ± 0.7 bcde PFM High 1.3 ± 0.1 cdefghi SCU Low 1.4 ± 0.1 cdefghi SCU High 1.2 ± 0.0 defghi WA 8022 Non-fertilized 2.5 ± 0.6 ab PFM Low 2.0 ± 1.0 bc PFM High 1.8 ± 0.3 bcde SCU Low 1.1 ± 0.2 defghi SCU High 1.3 ± 0.1 cdefghi West Bauermeister Non-fertilized 1.0 ± 0.1 fghi PFM Low 0.9 ± 0.1 ghi PFM High 1.2 ± 0.1 defghi SCU Low 1.1 ± 0.0 efghi SCU High 1.0 ± 0.1 efghi McCall Non-fertilized 0.8 ± 0.0 hi PFM Low 1.0 ± 0.1 efghi PFM High 1.0 ± 0.1 fghi SCU Low 1.0 ± 0.2 fghi SCU High 1.2 ± 0.1 defghi Relief Non-fertilized 0.8 ± 0.1 i

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Table B14:(continued from preceding page) Site Cultivar N Treatment 2-Hydroxycinnamic Acid PFM Low 0.9 ± 0.1 ghi PFM High 0.8 ± 0.0 hi SCU Low 0.9 ± 0.0 hi SCU High 0.8 ± 0.1 hi WA 8022 Non-fertilized 0.9 ± 0.2 hi PFM Low 1.0 ± 0.1 efghi PFM High 1.0 ± 0.0 efghi SCU Low 1.0 ± 0.1 efghi SCU High 1.1 ± 0.1 efghi aN fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). bValues each expressed as mean ± standard error. Within each column, means with the same letter are not significantly different (P < 0.05) according to a comparison of least squares means. 273

Table B15: The effect of nitrogen (N) source and ratea on the content (µg/g dm) of 2,4- dihydroxybenzoic acid for hard red winter wheat cultivars Bauermeister, McCall, Relief, and WA8022 grown at two sites (east and west) in Mount Vernon, WA during the 2009-10 growing season.

Site Cultivar N Treatment 2,4-Dihydroxybenzoic Acid East Bauermeister Non-fertilized 3.2 ± 0.2 abcdefghib PFM Low 3.4 ± 0.1 abcde PFM High 3.2 ± 0.2 bcdefghi SCU Low 3.3 ± 0.1 abcdef SCU High 2.9 ± 0.3 cdefghijkl McCall Non-fertilized 2.5 ± 0.4 ghijkl PFM Low 2.4 ± 0.3 ijkl PFM High 2.3 ± 0.3 jkl SCU Low 2.4 ± 0.4 ijkl SCU High 2.3 ± 0.1 l Relief Non-fertilized 3.3 ± 0.3 abcdef PFM Low 2.7 ± 0.5 defghijkl PFM High 3.2 ± 0.2 abcdefgh SCU Low 2.5 ± 0.5 hijkl SCU High 2.6 ± 0.5 efghijkl WA 8022 Non-fertilized 4.0 ± 0.3 ab PFM Low 3.1 ± 0.4 cdefghij PFM High 3.2 ± 0.2 abcdefghi SCU Low 3.6 ± 0.8 abc SCU High 3.6 ± 0.2 abc West Bauermeister Non-fertilized 3.4 ± 0.2 abcde PFM Low 3.2 ± 0.1 abcdefghi PFM High 3.1 ± 0.1 cdefghijkl SCU Low 3.1 ± 0.1 cdefghijk SCU High 3.0 ± 0.2 cdefghijkl McCall Non-fertilized 2.8 ± 0.1 defghijkl PFM Low 2.3 ± 0.1 kl PFM High 2.5 ± 0.1 fghijkl SCU Low 2.6 ± 0.2 efghijkl SCU High 2.7 ± 0.1 defghijkl Relief Non-fertilized 3.1 ± 0.1 cdefghijkl

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Table B15:(continued from preceding page) Site Cultivar N Treatment 2,4-Dihydroxybenzoic Acid PFM Low 3.1 ± 0.1 cdefghij PFM High 3.0 ± 0.1 cdefghijkl SCU Low 3.3 ± 0.3 abcdefg SCU High 3.3 ± 0.1 abcdefgh WA 8022 Non-fertilized 3.6 ± 0.4 abc PFM Low 4.0 ± 0.2 a PFM High 3.6 ± 0.3 abc SCU Low 3.7 ± 0.4 abc SCU High 3.5 ± 0.5 abcd aN fertility treatments include: non-fertilized control, 85 kg N/ha poultry feather meal (PFM low), 170 kg N/ha PFM (PFM high), 85 kg N/ha sulfur-coated urea (SCU low), and 170 kg N/ha SCU (SCU high). bValues each expressed as mean ± standard error. Within each column, means with the same letter are not significantly different (P < 0.05) according to a comparison of least squares means. 275

C Marketing strategies and information needs of small grain growers in the Puget Sound region of Washington State 276

______

Dear Grower,

Production of small grain crops—wheat, barley, oat, triticale, and rye—has been a mainstay of northwestern Washington agriculture for generations. University researchers have typically overlooked small grains west of the Cascades, but due to increasing grower interest, a small grains plant breeding research program has recently been established at WSU Mount Vernon under Dr. Stephen Jones. Research is currently underway on wheat, barley, and triticale, and this season marks the first in many years that WSU has performed wheat and barley variety yield trials in northwestern Washington. To better serve the local agricultural community, this survey has been developed to answer some fundamental questions regarding the characteristics, marketing strategies, information sources, needs, opinions, and challenges of small grain growers in northwestern Washington. Such information can help influence the future of university research and extension.

Therefore, I am writing to ask for your help in answering important questions about small grain production in northwestern Washington. Your participation in this study is voluntary. However, for the results of this study to truly reflect the views and experiences of all growers producing or interested in producing small grains in northwestern Washington, it is important that each questionnaire be completed and returned in the prepaid envelope provided.

The enclosed questionnaire should be filled out by the lead operator (or one of the lead operators) of your farm. We define lead operator as an individual who makes most of the day-to-day management decisions on the farm. We know you are pressed for time, so we have tried to make the survey as easy as possible to fill out. Based on our experience, the survey should take roughly 30 minutes to complete. Please return your completed paper questionnaire in the enclosed postage paid envelope.

You may be assured of complete confidentiality. Your name will never be placed on the questionnaire itself, nor will your name or address be part of our survey data files. These steps are taken to ensure that at no time will information about individual farm operations ever be released to any person, organization, or government agency. To further ensure your privacy, all published results will be based on combined data from all survey respondents.

If you have any questions about the project or enclosed survey, please do not hesitate to call me at (360) 848- 6120, or e-mail me at [email protected].

Sincerely,

Lucas J. Patzek

Project Coordinator NW-WA Small Grains Project

16650 State Route 536, Mount Vernon, WA 98273-4768 360-848-6120 Fax: 360-848-6159 TDD: 1-800-833-6388 http://mtvernon.wsu.edu

Serving People and Industries through: Instruction, Research and Extension

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Small Grains in Northwest Washington Grower Survey

Lucas Patzek Department of Crop and Soil Sciences Washington State University Northwestern Washington Research & Extension Center

Production of small grain crops—wheat, barley, oat, triticale and rye—has been a mainstay of northwestern Washington agriculture for generations. University researchers have typically overlooked small grains west of the Cascades, but due to increasing grower interest, a small grains plant breeding research program has recently been established at WSU Mount Vernon under Dr. Stephen Jones. Research is currently underway on wheat, barley and triticale, and this season marks the first in many years that WSU has performed wheat and barley variety yield trials in western Washington. To better serve the local agricultural community, this survey has been developed to answer some fundamental questions regarding the present state of small grain production and the needs of small grain growers in northwestern Washington. If you can spare approximately 30 minutes, we would like to ask you some questions about your experience with, and interest in, small grain crops. Your participation in this survey is completely voluntary. By participating in this survey you agree that researchers can use the information you provide for academic and research purposes. All responses and personal information will be held confidential. If you have questions about the study, please contact Lucas Patzek at [email protected] or 360-848- 6120. If you have any questions about your rights as a study participant, you can call the WSU Institutional Review Board at 509-335-9661. Survey results will be available in the winter of 2011 on the WSU Mount Vernon Plant Breeding website (http://plantbreeding.wsu.edu).

Thank you for your participation!

Contact Lucas Patzek ([email protected], 360-848-6120) if you are interested in the following:

 Participating in surveys, interviews or workshops related to small grain production within the next 1-3 years.  Hosting on-farm research trials within the next 1-3 years.  Working directly with WSU scientists in participatory small grain breeding programs within the next 1-3 years.

Do not fill out this survey if you have already completed another copy.

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A Interest in Small Grain Crops

For this survey, “small grain crops” are defined as wheat, barley, oat, triticale and rye.

A1 Does your current farming operation include small grain crops?

 Yes Please skip to Section B “Currently Growing Small Grain Crops” below.  No Please continue to the next question (A2).

A2 Has your farming operation ever included small grain crops?

 Yes Please skip to Section C “No Longer Growing Small Grain Crops” on page 7.  No Please continue to the next question (A3).

A3 Are you interested in growing small grain crops in the future?

 Yes Please skip to Section D “Interested in Small Grain Crops” on page 10.  No Please stop here and return the survey in the envelope provided.

B Currently Growing Small Grain Crops

B1 For how many years have you been growing small grain crops? ______

B2 What rotations involving small grains have you tried?

a) Rotation 1: ______b) Rotation 2: ______

B3 Which small grain crops (state if winter or spring types) are included in your current farming operation and how many acres (conventional, organic, and in transition to organic) of each do you grow in a season? Small grain crop Conventional Organic Transition Acres Acres Acres Example Hard red winter wheat 8 2 1 a) b) c) d) e) f)

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B4 What are your top three preferred small grain varieties?

Small grain crop Variety Example Hard red winter wheat Bauermeister a) b) c)

B5B5 In choosing small grain varieties to grow, how important are the following agronomic and end-use traits? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important. Not Very Important Important   a) Seasonal growth habit (spring-seeded 1 2 3 4 5 vs. fall-seeded) b) Cold/frost tolerance 1 2 3 4 5 c) High grain yield 1 2 3 4 5 d) High straw yield 1 2 3 4 5 e) Weed competitiveness 1 2 3 4 5 f) Herbicide tolerance 1 2 3 4 5 g) Disease resistance/tolerance 1 2 3 4 5 h) Insect resistance/tolerance 1 2 3 4 5 i) Short stature 1 2 3 4 5 j) Lodging resistance 1 2 3 4 5 k) Waterlogging tolerance 1 2 3 4 5 l) Nutrient efficiency 1 2 3 4 5 m) High performance under organic 1 2 3 4 5 conditions n) Earliness 1 2 3 4 5 o) Shatter resistance 1 2 3 4 5 p) Easy threshability 1 2 3 4 5 q) Pre-harvest sprout tolerance 1 2 3 4 5 r) Long-term seed germination 1 2 3 4 5 s) Grain protein content 1 2 3 4 5 t) Grain quality, for human consumption 1 2 3 4 5 u) Grain quality, for animal consumption 1 2 3 4 5 v) Straw quality 1 2 3 4 5 w) Other (Please list: ______) 1 2 3 4 5

B6 Which two agronomic and/or end-use traits need the greatest improvement in small grain varieties recommended for western Washington?

a) Small grain crop: ______Trait 1: ______b) Small grain crop: ______Trait 2: ______Mark here  if you do not know.

B7 What are the top two diseases/insects in your small grain production system?

a) Small grain crop: ______Problem 1: ______b) Small grain crop: ______Problem 2: ______Mark here  if you do not know. 3 | Page

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B8 What are the top two weeds in your small grain production system?

a) Small grain crop: ______Problem 1: ______b) Small grain crop: ______Problem 2: ______Mark here  if you do not know.

B9 What is your standard herbicide and fungicide (organic and/or conventional) program?

a) Small grain crop: ______Product 1: ______No. of applications: ____ b) Small grain crop: ______Product 2: ______No. of applications: ____ c) Small grain crop: ______Product 3: ______No. of applications: ____ d) Small grain crop: ______Product 4: ______No. of applications: ____ e) Small grain crop: ______Product 5: ______No. of applications: ____ Mark here  if you do not apply herbicides or fungicides.

B10 In managing your small grain production system, how important are the following sources of information to your decision-making? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important. Not Very Important Important   a) Formal education 1 2 3 4 5 b) Other growers 1 2 3 4 5 c) Family members 1 2 3 4 5 d) University scientist 1 2 3 4 5 e) Extension educator 1 2 3 4 5 f) Washington State Department of 1 2 3 4 5 Agriculture (WSDA) g) Natural Resources Conservation Service 1 2 3 4 5 (NRCS) h) Organic certifiers 1 2 3 4 5 i) Commodity or grower organizations 1 2 3 4 5 j) Input supplier 1 2 3 4 5 k) Buyer 1 2 3 4 5 l) Magazines, newspapers, books 1 2 3 4 5 m) TV, radio programs 1 2 3 4 5 n) Internet – written material 1 2 3 4 5 o) Internet – video, audio material 1 2 3 4 5 p) Internet – social networking 1 2 3 4 5 q) Commercial newsletter, advisory, 1 2 3 4 5 product pamphlet r) University publications 1 2 3 4 5 s) USDA publications 1 2 3 4 5 t) Farm tours/field days 1 2 3 4 5 u) Conferences/workshops/seminars 1 2 3 4 5 v) Other (Please list: ______) 1 2 3 4 5

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B11 What is the importance of the following end-uses to your current small grain marketing? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important. Not Very Important Important   a) Breads 1 2 3 4 5 b) Cakes and pastries 1 2 3 4 5 c) Noodles 1 2 3 4 5 d) Breakfast cereals 1 2 3 4 5 e) Other food products 1 2 3 4 5 f) Malting, fermentation and distillation 1 2 3 4 5 g) Seed production 1 2 3 4 5 h) Feed hay 1 2 3 4 5 i) Feed grain 1 2 3 4 5 j) Forage 1 2 3 4 5 k) Bird seed 1 2 3 4 5 l) Cover crop 1 2 3 4 5 m) Other (Please list: ______) 1 2 3 4 5

B12 Are you interested in targeting different small grain end-use markets in the future?

 Yes Which end-use markets would you be interested in targeting? a) Small grain crop: ______End-use 1: ______b) Small grain crop: ______End-use 2: ______

 No Please continue to the next question (B13).

B13 How often do you use the following small grain marketing methods? On each line, please circle a number ranging from 1 = Never to 5 = Always. Very Never Rarely Sometimes Often Always      a) Sell through a grain elevator 1 2 3 4 5 b) Sell directly to animal operations 1 2 3 4 5 c) Sell directly to processors 1 2 3 4 5 d) Sell directly to retailers 1 2 3 4 5 e) Sell directly to restaurants 1 2 3 4 5 f) Sell directly to institutions 1 2 3 4 5 g) Sell at farmers markets 1 2 3 4 5 h) Sell at roadside stands and markets 1 2 3 4 5 i) Other (Please list: ______) 1 2 3 4 5  I’m having difficulty finding a buyer

B14 Are you interested in using different small grain marketing methods in the future?

 Yes Which marketing method(s) would you be interested in using? c) Small grain crop: ______Method 1: ______d) Small grain crop: ______Method 2: ______

 No Please continue to the next question (B15).

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B15 How limiting are the following factors to your production of small grains? On each line, please circle a number ranging from 1 = Not Limiting to 5 = Very Limiting.

Not Very Limiting Limiting   a) Availability and/or cost of technical 1 2 3 4 5 assistance b) Availability and/or cost of educational 1 2 3 4 5 resources c) Limited market outlets 1 2 3 4 5 d) Lack of access to market outlets 1 2 3 4 5 e) Financial assistance 1 2 3 4 5 f) Prices received 1 2 3 4 5 g) Availability and/or cost of land 1 2 3 4 5 h) Availability and/or cost of seed 1 2 3 4 5 i) Availability and/or cost of farming 1 2 3 4 5 equipment (combine, grain drill, etc.) j) Availability and/or cost of infrastructure 1 2 3 4 5 (storage, drying, milling, etc.) k) Availability and/or cost of 1 2 3 4 5 transportation l) Availability and/or cost of labor 1 2 3 4 5 m) Weather 1 2 3 4 5 n) Drainage 1 2 3 4 5 o) Weed pressure 1 2 3 4 5 p) Disease pressure 1 2 3 4 5 q) Insect pressure 1 2 3 4 5 r) Fertility 1 2 3 4 5 s) Grain quality, for humans 1 2 3 4 5 t) Grain/straw quality, for animals 1 2 3 4 5 u) Other (Please list: ______) 1 2 3 4 5

B16 What is a reasonable selling price if direct marketing one pound, one bushel or one ton of your small grain product?

Small grain crop $ per ton $ per lb $ per bushel a) b) c) d)

Proceed to Section E “Current Farm” on page 11.

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C No Longer Growing Small Grain Crops

C1 For how many years did you grow small grain crops? ______

C2 In what year did you stop growing small grain crops? ______

C3 What rotations involving small grains have you tried?

a) Rotation 1: ______b) Rotation 2: ______

C4 How important were the following factors in influencing your decision to stop growing small grains? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important.

Not Very Important Important   a) Availability and/or cost of technical 1 2 3 4 5 assistance b) Availability and/or cost of educational 1 2 3 4 5 resources c) Limited market outlets 1 2 3 4 5 d) Lack of access to market outlets 1 2 3 4 5 e) Financial assistance 1 2 3 4 5 f) Prices received 1 2 3 4 5 g) Availability and/or cost of land 1 2 3 4 5 h) Availability and/or cost of seed 1 2 3 4 5 i) Availability and/or cost of farming 1 2 3 4 5 equipment (combine, grain drill, etc.) j) Availability and/or cost of infrastructure 1 2 3 4 5 (storage, drying, milling, etc.) k) Availability and/or cost of 1 2 3 4 5 transportation l) Availability and/or cost of labor 1 2 3 4 5 m) Weather 1 2 3 4 5 n) Drainage 1 2 3 4 5 o) Weed pressure 1 2 3 4 5 p) Disease pressure 1 2 3 4 5 q) Insect pressure 1 2 3 4 5 r) Fertility 1 2 3 4 5 s) Grain quality, for humans 1 2 3 4 5 t) Grain/straw quality, for animals 1 2 3 4 5 u) Retired 1 2 3 4 5 v) Other (Please list: ______) 1 2 3 4 5

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C5 Which small grain crops (state if winter or spring types) were included in your farming operation and how many acres (conventional and organic) of each did you grow in a season?

Small grain crop Conventional Organic Acres Acres Example Hard red winter wheat 8 2 a) b) c) d) e) f)

C6 What were your top three preferred small grain varieties?

Small grain crop Variety Example Hard red winter wheat Bauermeister a) b) c)

C7 In choosing small grain varieties to grow, how important were the following agronomic and end-use characteristics? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important. Not Very Important Important   a) Seasonal growth habit (spring-seeded 1 2 3 4 5 vs. fall-seeded) b) Cold/frost tolerance 1 2 3 4 5 c) High grain yield 1 2 3 4 5 d) High straw yield 1 2 3 4 5 e) Weed competitiveness 1 2 3 4 5 f) Herbicide tolerance 1 2 3 4 5 g) Disease resistance/tolerance 1 2 3 4 5 h) Insect resistance/tolerance 1 2 3 4 5 i) Short stature 1 2 3 4 5 j) Lodging resistance 1 2 3 4 5 k) Waterlogging tolerance 1 2 3 4 5 l) Nutrient efficiency 1 2 3 4 5 m) High performance under organic 1 2 3 4 5 conditions n) Earliness 1 2 3 4 5 o) Shatter resistance 1 2 3 4 5 p) Easy threshability 1 2 3 4 5 q) Pre-harvest sprout tolerance 1 2 3 4 5 r) Long-term seed germination 1 2 3 4 5 s) Grain protein content 1 2 3 4 5 t) Grain quality, for human consumption 1 2 3 4 5 u) Grain quality, for animal consumption 1 2 3 4 5 v) Straw quality 1 2 3 4 5 w) Other (Please list: ______) 1 2 3 4 5 8 | Page

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C8 What were the top two diseases/insects that existed in your small grain production system?

a) Small grain crop: ______Problem 1: ______b) Small grain crop: ______Problem 2: ______Mark here  if you do not know.

C9 What were the top two weeds that existed in your small grain production system?

a) Small grain crop: ______Problem 1: ______b) Small grain crop: ______Problem 2: ______Mark here  if you do not know.

C10 What was your standard herbicide and fungicide (organic and/or conventional) program?

a) Small grain crop: ______Product 1: ______No. of applications: ____ b) Small grain crop: ______Product 2: ______No. of applications: ____ c) Small grain crop: ______Product 3: ______No. of applications: ____ d) Small grain crop: ______Product 4: ______No. of applications: ____ e) Small grain crop: ______Product 5: ______No. of applications: ____ Mark here  if you did not apply herbicides or fungicides.

C11 In managing your small grain production system, how important were the following sources of information to your decision-making? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important. Not Very Important Important   a) Formal education 1 2 3 4 5 b) Other growers 1 2 3 4 5 c) Family members 1 2 3 4 5 d) University scientist 1 2 3 4 5 e) Extension educator 1 2 3 4 5 f) Washington State Department of 1 2 3 4 5 Agriculture (WSDA) g) Natural Resources Conservation Service 1 2 3 4 5 (NRCS) h) Organic certifiers 1 2 3 4 5 i) Commodity or grower organizations 1 2 3 4 5 j) Input supplier 1 2 3 4 5 k) Buyer 1 2 3 4 5 l) Magazines, newspapers, books 1 2 3 4 5 m) TV, radio programs 1 2 3 4 5 n) Internet – written material 1 2 3 4 5 o) Internet – video, audio material 1 2 3 4 5 p) Internet – social networking 1 2 3 4 5 q) Commercial newsletter, advisory, 1 2 3 4 5 product pamphlet r) University publications 1 2 3 4 5 s) USDA publications 1 2 3 4 5 t) Farm tours/field days 1 2 3 4 5 u) Conferences/workshops/seminars 1 2 3 4 5 v) Other (Please list: ______) 1 2 3 4 5

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C12 How often did you use the following small grain marketing methods? On each line, please circle a number ranging from 1 = Never to 5 = Always. Very Never Rarely Sometimes Often Always      a) Sell through a grain elevator 1 2 3 4 5 b) Sell directly to animal operations 1 2 3 4 5 c) Sell directly to processors 1 2 3 4 5 d) Sell directly to retailers 1 2 3 4 5 e) Sell directly to restaurants 1 2 3 4 5 f) Sell directly to institutions 1 2 3 4 5 g) Sell at farmers markets 1 2 3 4 5 h) Sell at roadside stands and markets 1 2 3 4 5 i) Other (Please list: ______) 1 2 3 4 5

Proceed to Section E “Current Farm” on page 11.

D Interested in Small Grain Crops

D1 How likely would you be to utilize the following sources of information to learn about small grain production? On each line, please circle a number ranging from 1 = Not Likely to 5 = Very Likely. Not Very Likely Likely   a) Formal education 1 2 3 4 5 b) Other growers 1 2 3 4 5 c) Family members 1 2 3 4 5 d) University scientist 1 2 3 4 5 e) Extension educator 1 2 3 4 5 f) Washington State Department of 1 2 3 4 5 Agriculture (WSDA) g) Natural Resources Conservation Service 1 2 3 4 5 (NRCS) h) Organic certifiers 1 2 3 4 5 i) Commodity or grower organizations 1 2 3 4 5 j) Input supplier 1 2 3 4 5 k) Buyer 1 2 3 4 5 l) Magazines, newspapers, books 1 2 3 4 5 m) TV, radio programs 1 2 3 4 5 n) Internet – written material 1 2 3 4 5 o) Internet – video, audio material 1 2 3 4 5 p) Internet – social networking 1 2 3 4 5 q) Commercial newsletter, advisory, 1 2 3 4 5 product pamphlet r) University publications 1 2 3 4 5 s) USDA publications 1 2 3 4 5 t) Farm tours/field days 1 2 3 4 5 u) Conferences/workshops/seminars 1 2 3 4 5 v) Other (Please list: ______) 1 2 3 4 5 10 | Page

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D2 Which small grain end-use markets interest you? On each line, please circle a number ranging from 1 = Not Interested to 5 = Very Interested. Not Very Interested Interested   a) Breads 1 2 3 4 5 b) Cakes and pastries 1 2 3 4 5 c) Noodles 1 2 3 4 5 d) Breakfast cereals 1 2 3 4 5 e) Other food products 1 2 3 4 5 f) Malting, fermentation and distillation 1 2 3 4 5 g) Seed production 1 2 3 4 5 h) Feed hay 1 2 3 4 5 i) Feed grain 1 2 3 4 5 j) Forage 1 2 3 4 5 k) Bird seed 1 2 3 4 5 l) Cover crop 1 2 3 4 5 m) Other (Please list: ______) 1 2 3 4 5

C12D3 How important would the following small grain marketing methods be to your operation? On each line, please circle a number ranging from 1 = Not Important to 5 = Very Important. Not Very Important Important   a) Sell through a grain elevator 1 2 3 4 5 b) Sell directly to animal operations 1 2 3 4 5 c) Sell directly to processors 1 2 3 4 5 d) Sell directly to retailers 1 2 3 4 5 e) Sell directly to restaurants 1 2 3 4 5 f) Sell directly to institutions 1 2 3 4 5 g) Sell at farmers markets 1 2 3 4 5 h) Sell at roadside stands and markets 1 2 3 4 5 i) Other (Please list: ______) 1 2 3 4 5

D4 Why are you interested in small grains? ______

Proceed to Section E “Current Farm” below.

E Current Farm

E1 In what county is your farm located? ______

E2 Of the total acres of farm/ranch land you currently operate, how many are in the categories listed below? Acres owned and operated: ______Acres rented (from others) and operated: ______Tillable acres: ______11 | Page

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E3 What are your primary crops and/or animals? ______

E4 For how many years have you operated your current farm? ______

E5 How much longer do you plan on operating your current farm?  1-3 years  4-9 years  10 years or more

E6 What is your interest in working directly with WSU scientists in participatory small grain breeding programs within the next 1-3 years? (Participatory Breeding uses both breeder and farmer expertise to develop varieties particularly suited to a specific set of environmental challenges).  Not interested  Somewhat interested  Very interested

Proceed to Section F “Respondent” below.

F Respondent

F1 What is your gender:  Male  Female

F2 What is your current age? ______

F3 What is your race/ethnicity? Please check all that apply.  American Indian or Alaska Native  Asian or Asian American  Black or African American  Spanish, Hispanic or Latino  White (not Spanish, Hispanic or Latino)  Other (describe: ______)

F4 What is the highest level of formal education that you have completed?  Some high school or less  Two-year college degree  High school diploma or equivalent  Four-year college degree  Some college but no degree  Some graduate school  Vocational or Extension certificate  Graduate degree

F5 How many people are in your household? ______

For the following questions, “family” is defined as your children, parents, grandparents and so on.

F6 For how many generations has your family lived in Washington State? ______

F7 For how many generations has your family farmed in Washington State? ______

F8 For how many generations has your family farmed anywhere? ______

F9 How many of your relatives farmed in the following years? 1950: ______1970: ______1990: ______2010: ______

Thank you for completing the survey! Please return the survey in the envelope provided. 12 | Page