Phenotypic and Biochemical Characterization of the United States Department of Agriculture Core Peanut (Arachis Hypogaea L.) Germplasm Collection

PHENOTYPIC AND BIOCHEMICAL CHARACTERIZATION OF THE UNITED STATES DEPARTMENT OF AGRICULTURE CORE PEANUT (ARACHIS HYPOGAEA L.) GERMPLASM COLLECTION

STANLEY W. DEZERN

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2018

To Emily

ACKNOWLEDGMENTS

This research was made possible through the generous support of The Peanut Foundation, the Georgia Peanut Commission, and the Florida Peanut Producers Association. I would like to thank the University of Florida Plant Science Research and Education Unit for their assistance planting, managing, and harvesting our experiment.

My committee has been incredibly helpful in shaping this research, and I would like to thank them for their commitment to this project through many ups and downs. I would especially like to thank Dr. Edzard van Santen for his generously given time and effort assisting with the statistical analysis and editing process.

I would like to recognize the Forage Evaluation lab team led by Richard Fethiere for very kindly assisting me on the protein analysis. I would also like to recognize the immensely important contributions of the following former and current members of our lab team: Leah

Aidif, Adina Grossman, Louisa Aarrass, Bob Querns, Anthony Cappello, and Jacob Allen.

Thank you to Mike Durham and Dr. Jay Ferrell for your constant guidance and encouragement. I am especially grateful for the friendship and support of Candice Prince, who has helped me every step of the way during the past two years. I would like to thank Dr.

MacDonald for providing me this opportunity and patiently supporting me throughout this project.

I would like to thank my parents for twenty-four years of love and encouragement. And finally, I would not have been able to complete this project were it not for the patient understanding and loving support of my wife Emily.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 14

LIST OF ABBREVIATIONS ...... 15

ABSTRACT ...... 16

CHAPTER

1 INTRODUCTION ...... 18

Overview ...... 18 Global and Domestic Peanut Production ...... 19 Origin of Arachis ...... 20 Section Arachis ...... 22 Arachis hypogaea: Morphology and Taxonomy ...... 23 Peanut Germplasm ...... 25 Peanut Germplasm Maintenance ...... 25 USDA Germplasm Collection ...... 25 USDA Core Germplasm Collection ...... 26 USDA Core Collection Selection Methodology ...... 27 Uses of the Core Collection ...... 27 Mini Core Collection ...... 29 Comparison of USDA Collection to Other Major Collections ...... 31 Breeding and Genetics ...... 32 Overview of Peanut Breeding ...... 32 Disease resistance ...... 32 Abiotic stressors ...... 33 Peanut Genome ...... 34 Genetics and Breeding Strategies ...... 35

2 MORPHOLOGICAL CHARACTERIZATION ...... 39

Background ...... 39 Research Objectives ...... 42 Materials and Methods ...... 42 Experimental Background ...... 42 Planting ...... 43 In-Season Measurements ...... 44 Procedure: Phenotypic ...... 44 Harvest ...... 44

Processing ...... 44 Yield and Grade ...... 45 Pod Volume ...... 46 Standard Descriptors ...... 46 Main Stem Flowering Pattern ...... 46 Statistical Analysis ...... 47 Results...... 48 Yield and Grade ...... 48 Pod Traits ...... 49 Plant Architecture ...... 49 Leaf Traits ...... 50 Canonical Discriminate Analysis (CDA) ...... 51 First CDA ...... 51 Second CDA ...... 52 Third CDA ...... 53 Revised Agronomic Traits ...... 54 Revised Pod Traits ...... 55 Revised Plant Traits ...... 57 Revised Leaf Traits ...... 58 Discussion ...... 59

3 BIOCHEMICAL CHARACTERIZATION ...... 81

Background ...... 81 Research Objectives ...... 84 Experimental Background ...... 84 Planting ...... 85 Harvest ...... 86 Processing ...... 86 Sample Preparation ...... 87 Raw Protein Content ...... 87 Total Oil Content ...... 88 Fatty Acid Composition ...... 88 Statistical Analysis ...... 89 Results...... 90 Biochemical Analysis ...... 90 Canonical Discriminate Analysis (CDA) ...... 93 First CDA ...... 93 Second CDA ...... 94 Third CDA ...... 95 Revised Biochemical Traits ...... 95 Discussion ...... 96

4 CONCLUSIONS ...... 116

APPENDIX

A SUPPLEMENTAL TABLES ...... 120

B DESCRIPTOR GUIDE ...... 127

REFERENCES ...... 153

BIOGRAPHICAL SKETCH ...... 159

LIST OF TABLES

Table page

2-1 Accession type within experimental design per year ...... 63

2-2 Mean, minimum, maximum, and standard error of peanut agronomic traits as a function of market type grouped by collection...... 64

2-3 Mean, minimum, maximum, and standard error of pod characteristics as a function of market type grouped by collection...... 65

2-4 Mean, minimum, maximum, and standard error of plant architecture as a function of market type grouped by collection...... 66

2-5 Mean, minimum, maximum, and standard error of leaflet characteristics as a function of market type grouped by collection, ...... 67

2-6 Between Canonical Structure for core collection accessions...... 68

2-7 Canonical discriminate analysis of market type split by main stem flowering pattern. ....69

2-8 Probabilities of distance based on canonical correlations for all market types plus unclassified and mixed accession split by flowering data...... 70

2-9 Yield (kg·ha-1) of peanuts as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 71

2-10 Yield (kg·ha-1) of peanuts as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 71

2-11 Percentage sound mature kernels per adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 71

2-12 Sound mature kernel percent by market type for the adjusted mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 71

2-13 Meats per 200g of peanuts as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 71

2-14 Meats per 200g of peanuts as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 72

2-15 Meat to hull ratio as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 72

2-16 Meat to hull ratio as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 72

2-17 Percent Fancy pods as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 72

2-18 Percent Fancy pods as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 72

2-19 Pod volume as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 73

2-20 Pod volume as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 73

2-21 Plant height at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 73

2-22 Plant height at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 73

2-23 Plant width at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 73

2-24 Plant width at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 74

2-25 Plant height to width ratio at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 74

2-26 Plant height to width at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 74

2-27 Leaflet length at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 74

2-28 Leaflet length at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 74

2-29 Leaflet width at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 75

2-30 Leaflet width at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 75

2-31 Leaflet internode distance at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 75

2-32 Leaflet internode distance at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 75

2-33 Leaflet length to width ratio at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 75

2-34 Leaflet length to width ratio at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 76

3-1 Accession type within experimental design per year ...... 99

3-2 Total protein and oil content as a function of market type grouped by core collection, mini-core collection, and commercial standards...... 100

3-3 Gadoleic acid and lignoceric acid content as a function of market type grouped by core collection, mini-core collection, and commercial standards ...... 101

3-4 Peanut arachidic acid, behenic acid, and stearic acid content as a function of market type grouped by core collection, mini-core collection, and commercial standards...... 102

3-5 Peanut palmitic acid, linoleic acid, and oleic acid content as a function of market type grouped by core collection, mini-core collection, and commercial standards...... 103

3-6 Peanut oleic to linoleic acid ratio and unsaturated fat content as a function of market type grouped by core collection, mini-core collection, and commercial standards...... 104

3-7 Between Canonical Structure for core collection accessions...... 105

3-8 Total oil content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 106

3-9 Gadoleic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 106

3-10 Lignoceric acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 106

3-11 Arachidic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 106

3-12 Behenic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 106

3-13 Stearic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 107

3-14 Palmitic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 107

3-15 Linoleic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 107

3-16 Oleic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 107

3-17 Oleic to linoleic acid ratio as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 107

3-18 Unsaturated fat content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 108

3-19 Protein content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL...... 108

3-20 Total oil content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 108

3-21 Gadoleic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 108

3-22 Lignoceric acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 108

3-23 Arachidic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 109

3-24 Behenic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 109

3-25 Stearic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 109

3-26 Palmitic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 109

3-27 Linoleic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 109

3-28 Oleic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 110

3-29 Oleic to linoleic acid ratio as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 110

3-30 Unsaturated fat content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 110

3-31 Protein content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL...... 110

A-1 Yield (kg·ha-1) of peanuts as a function of market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 120

A-2 Sound mature kernel percent by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 120

A-3 Meat content per 200g of peanuts by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 120

A-4 Yield (kg·ha-1) of peanuts by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 120

A-5 Meat content per 200g of peanuts by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 121

A-6 Sound mature kernel percentage by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 121

A-7 Meat to hull ratio by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 121

A-8 Fancy pod percentage by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 121

A-9 Pod volume by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 122

A-10 Meat to hull ratio by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 122

A-11 Percent Fancy pods by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 122

A-12 Pod volume by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 122

A-13 Plant height at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 123

A-14 Plant width at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 123

A-15 Plant height-width ratio at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 123

A-16 Plant height at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 123

A-17 Plant width at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 124

A-18 Plant height-width ratio at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 124

A-19 Leaflet length at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 124

A-20 Leaflet width at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 124

A-21 Leaflet length to width ratio at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL...... 125

A-22 Leaflet internode distance at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL ...... 125

A-23 Leaflet length at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 125

A-24 Leaflet width at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 125

A-25 Leaflet length to width ratio at 75 days after planting by market type for the mini- core collection of the USDA peanut germplasm collection grown in Citra, FL...... 126

A-26 Leaflet internode distance at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL...... 126

LIST OF FIGURES

Figure page

1-1 Major clades of the Papilionoid legumes ...... 37

1-2 Average annual precipitation in South America...... 37

1-3 Sectional relationships in genus Arachis ...... 38

1-4 Taxonomy of Arachis hypogaea ...... 38

2-1 Plot spacing in field at Citra, FL in 2013 ...... 77

2-2 Leaflet measurements were collected by selecting fully mature leaves from two randomly selected plants...... 77

2-3 Plant height and width was determined by taking the average height and width of three plants per row ...... 78

2-4 Apparatus for measuring pod volume...... 78

2-5 A Spanish type (left) with a pod volume of 20mL, and a Virginia type (right) with a pod volume of 90mL...... 79

2-6 A Valencia type (left) and a Virginia type (right), both with a pod volume of 80mL...... 79

2-7 Plotted group centroids of canonical variables...... 80

3-1 Average fatty acid profile of Runner accessions in the core of the USDA peanut germplasm collection grown in Citra, FL...... 111

3-2 Average fatty acid profile of Spanish accessions in the core of the USDA peanut germplasm collection grown in Citra, FL...... 112

3-3 Average fatty acid profile of Valencia accessions in the core of the USDA peanut germplasm collection grown in Citra, FL...... 112

3-4 Average fatty acid profile of Virginia accessions in the core of the USDA peanut germplasm collection grown in Citra, FL...... 113

3-5 Average fatty acid profile of unclassified accessions in the core of the USDA peanut germplasm collection grown in Citra, FL...... 113

3-6 Average fatty acid profile of mixed accessions in the core of the USDA peanut germplasm collection grown in Citra, FL...... 114

3-7 Canonical group centroids for the first, second, and third canonical analysis...... 115

LIST OF ABBREVIATIONS

EMBRAPA Brazilian Agricultural Research Corporation

GRIN Germplasm Resource Information Network

GWAS Genome-Wide Association Study

GxE Genetic by Environment

ICRISAT International Crops Research Institute for the Semi-Arid Tropics

OCRI-CAAS Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences

PCR Polymerase Chain Reaction

QTL Quantitative Trait Locus

SNP Single Nucleotide Polymorphism

TSWV Tomato Spotted Wilt Virus

USDA United States Department of Agriculture

USDA-AMS USDA-Agricultural Marketing Service

USDA-FAS USDA-Foreign Agricultural Service

USDA-NASSCP USDA National Agricultural Statistics Service Crop Production

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

PHENOTYPIC AND BIOCHEMICAL CHARACTERIZATION OF THE UNITED STATES DEPARTMENT OF AGRICULTURE CORE PEANUT (ARACHIS HYPOGAEA L.) GERMPLASM COLLECTION

Stanley W. Dezern

May 2018

Chair: Gregory MacDonald Major: Agronomy

Peanut (Arachis hypogaea L.) is an economically important leguminous oilseed crop.

Low levels of genetic diversity have been observed in the peanut genome due to the genetic similarity of the two diploid progenitors, the relatively recent evolution of A. hypogaea (2.3-2.9 million years ago), the highly self-pollinating nature of peanut reproduction, and the impact of intense selection by humans using peanut for thousands of years. To maintain the genetic variability availability in cultivated peanut, the USDA peanut germplasm collection was created, maintaining around 10,000 unique accessions collected from throughout the world. To allow breeders more access to the large collection, a core collection was developed in 1993 containing

831 unique accessions chosen by clustering and randomly sampling the accessions in the full collection. A mini-core collection was later developed using similar methods, consisting of 112 of the core accessions. The core and mini-core collections have been effective in breeding, but the accessions within have not been fully characterized across morphological and biochemical traits in a single experiment. This study aimed to collect and analyze morphological and biochemical data from the core and mini-core collections in a single augmented design study.

Phenotypic descriptors were collected representing agronomic performance, standard industry

descriptors, plant architecture, leaf structure, pod and seed traits, and flowering pattern.

Additionally, biochemical data were collected on protein content, oil content, and oil fatty acid composition. High levels of variation were found across most traits in the core collection, generally with less diversity observed in the mini-core collection. A not unsubstantial number of accessions in the core and mini-core collection did not display the main stem flowering pattern that would be expected based on their subspecies designation. A series of canonical discriminate analysis of phenotypic characteristics indicated that these accessions are not grouped correctly by market type, differing significantly across canonical variables compared to the correctly flowering accessions of the market type. The core collection showed a large amount of variation across biochemical traits, notably in oleic acid and linoleic acid content, as well as in minor fatty acids. Biochemical data further supported the revision of market type designation. These findings can help to identify inconsistencies in the core and mini-core collections and provide robust data for QTL analysis.

CHAPTER 1 INTRODUCTION

Overview

Peanut (Arachis hypogaea, L.), also known as groundnut, is an economically important leguminous crop. It is in the family Fabaceae and is notable for its geocarpic fruit, which is used for a variety of products including peanut butter and peanut oil. Originating in South America, species in the genus Arachis are found in many diverse environments, and over 80 species have been named. Additional species may exist in some regions of South America where collection efforts have not been thorough, and are further limited due to political restrictions (Ferguson et al., 2005). Wild Arachis species are highly diverse and contain considerable genetic variability, but intersectional infertility, the inability to produce viable offspring between members of different sections, and ploidy differences hinders efforts to utilize wild species in breeding programs (Stalker et al., 2013). Cultivated peanut, however, has a relatively narrow genetic base, making breeding progress even more difficult. Despite this, improved cultivars have been developed to solve many challenging issues facing peanut growers, all while continually increasing yield (Holbrook et. al., 2014). The development of the USDA peanut core germplasm collection (Holbrook et al., 1993), and later the mini-core collection (Holbrook and Dong, 2005), have allowed for many recent advances in peanut breeding by allowing breeders to more easily access the diversity maintained in the whole USDA collection in a more manageable number of accessions. While this resource has already been utilized to advance peanut breeding, only the mini-core portion of the collection has been adequately characterized (Chen et al., 2014). This study aims to phenotype and biochemically characterize the entire USDA core collection across a number of standard traits to assess the full diversity represented in the USDA germplasm, to identify superior lines for traits of interest to breeders, and to assist in trait-marker identification

by providing geneticists with complete phenotypic data across all USDA core germplasm accessions.

Global and Domestic Peanut Production

Peanut is a legume crop of global economic importance. Globally, as of December 2017, peanut is the fourth most important oilseed crop in terms of production. In the past five years, it has been among the top five oilseed crops with total production between 40-45 million metric tons. Global peanut production was comparable to cottonseed and sunflower seed in most recent years, and behind rapeseed (69-73 million metric tons annually) and soybean (280-350 million metric tons annually) (USDA-FAS, 2017).

The United States is a major producer of peanuts, which are used domestically in a variety of products, including peanut oil, peanut butter, and peanut candies and confectionaries.

In 2017, domestic peanut production was estimated to be 3.53 million metric tons, planted on 1.8 million acres. Georgia has been by far the most productive state, with 1.7 million metric tons produced in 2017, followed by Texas (436,000 metric tons), Alabama (366,000 metric tons), and

Florida (291,000 metric tons) (USDA-NASSCP, 2017).

In 2002, Roveredo and Fletcher analyzed 30 years of global peanut production data and compiled a report outlining the trends in the peanut market. Overall, peanut production has increased by 136% since the 1970s, but most of the growth has been the result of huge production increases in the Asian markets. East Asia has seen a production increase from 2419 metric tons in the 1970s to over 14,000 metric tons by 2013, a 579% increase in production.

Other regions that experienced large amounts of growth in this period were West Africa (179%) and Southeast Asia (132%). North American peanut production has increased 28% since the

1970s, from 1735 metric tons to 2221 metric tons produced.

Origin of Arachis

Arachis is a legume genus. It belongs to the sub-family Papilionoideae, along with nearly all other economically important legume crops (Fig. 1-1). However, Arachis is from the more basal Dalbergioids, apart from other important legumes found in the Phaseoloids, which contain beans, cowpeas, and soy, and Galegoids, which contain clover, lentil, chickpea, and alfalfa

(David J. Bertioli et al., 2011). Arachis species currently exist in large areas of South America, with the exact origin of genus Arachis thought to be the Mato Grosso do Sul region of Brazil

(Gregory, Krapovickas, & Gregory, 1980), as a geocarpic form of Stylosanthes (Simpson,

Krapovickas, & Valls, 2001) (Figure 1 2). The Mato Grosso area is a highland, and transportation via flowing water is thought to have occurred, spreading Arachis species to lowland areas. This location is noted as being one of five central points of diversity in the

Arachis genus (Krapovickas, 1968), the others being the Paraná and Paraguay river basin, Goias,

Brazil, the eastern slopes of the Bolivian Andes, and Peru. The evolutionary dispersion of

Arachis began in wet highlands and later evolved to survive in drier lowlands, but because of the geocarpic nature of Arachis, dispersal was generally slow (approximately 1m/year), with flowing water being the primary vehicle for longer dispersal distances (Simpson et al., 2001). Arachis species are found in many diverse habitats, with various soil, temperature and moisture conditions. The ability of Arachis to evolve and speciate to perform well in these diverse environments is attributed to the geocarpic nature of the peanut fruit. While the underground fruit slowed the dispersal of Arachis, it is likely one of the key factors that allowed peanut to quickly acclimate to new environments, protecting the fruit from a variety of otherwise unfavorable environmental factors. The underground fruiting body is also thought to contribute to the high rate of speciation in Arachis compared to other flowering plants. The closest relatives of Arachis have estimated speciation rates of 0.15 new species per million years, but the

speciation rate of Arachis is estimated to be much higher for several reasons (Magallón &

Sanderson, 2009). When seeds are distributed at an unusually long-range due to infrequent biotic or abiotic means, the isolated populations are unlikely to interact with any other populations.

This genetic bottleneck likely created many isolated, highly self-pollinated populations that had very little interaction or genetic exchanges with other Arachis populations (Bertioli et al., 2016), but greatly increased the rate of speciation to around 0.95 speciation events per million years

(Moretzsohn et al., 2013).

Taxonomic groupings for peanut were developed by Krapovickas and Gregory (1994).

Using similarities found in root systems, rhizomes, and fruiting bodies, Arachis species were sorted into clusters. Clustered groups contained species that were similarly adapted to a common environment. These findings led to the creation of 9 distinct sections within the Arachis genus:

Erectoides, Procumbentes, Trierectoides, Extranervosae, Triseminatae, Heteranthae,

Caulorrhizae, Rhizomatosae, and Arachis, which contains cultivated peanut (Arachis hypogaea

L.). The relatedness of sections of Arachis is demonstrated in Figure 1-3 (Krapovickas and

Gregory, 1994). While there are different levels of infertility between species found within sections, there is near total infertility between species of different sections. Gregory and Gregory

(1979) attempted 40 intersectional crosses across seven Arachis sections and created only six successful hybrids (Gregory & Gregory, 1979). The dispersal and evolution of Arachis can also be examined on a genetic basis, where there exists a large amount of genetic variation. While there are still some unresolved groupings, molecular work using microsatellites has shown that the Arachis sectional groups developed by Krapovickas and Gregory, based primarily on morphological similarities and cross-compatibility, are largely accurate (Hoshino et al., 2006).

Section Arachis

Section Arachis contains two tetraploid (2n=4x=40) species (A. hypogaea and A. monticola), 26 diploid species (2n=2n=20), and three aneuploid species (2n=2x=18) and can be further divided by cytological groups. Husted (1936) described two distinct sets of chromosomes in A. hypogaea: the “A” pair, which were distinguished for their smaller size, and the “B” pair, which had a “conspicuous secondary constriction”, in addition to the primary constriction, which was subterminal rather than median (Husted, 1936). These distinguishing pairs led to the common terminology of the “A” genome and the “B” genome. This is the terminology used to describe the allotetraploid A. hypogaea, which has an “AABB” genome (2n=4x=40).

Additionally, a “D” genome is found in A. glandulifera (Stalker, 1991). Crosses between species of different cytological groups, like intersectional crosses, show near infertility, while crosses within cytological groups may have moderate to full fertility. The most current classification for section Arachis species contains one clade composed of the “B” and “D” genome species (“B” genome also covers those species referred to as having “F” and “K” genomes), and a second clade containing the “A” genome species.

The origin of A. hypogaea is thought to be a hybridization event between an “A” genome contributor and a “B” genome contributor. The most likely candidates are A. duranensis Krapov. and W.C. Gregory, which contains an “AA” diploid genome, and A. ipaensis Krapov. and W.C.

Gregory, which has a “BB” genome, based on molecular comparisons of the two diploid species to the tetraploid A. hypogaea (Seijo et al., 2004). These two progenitor species exist in the southern region of Bolivia, making the region a likely candidate for the origin of A. hypogaea

(Stalker and Simpson, 1995) (Simpson et al., 2001). However, archeological data from the

Northwest region of Peru indicates that this region may be the actual origin of A. hypogaea

(Simpson et al., 2001). Simpson postulates that these two seemingly contradictory pieces of

evidence may in fact point to a two-event origin of A. hypogaea, but this has not been fully supported. In either case, A. monticola is thought to be the immediate wild predecessor to the domesticated A. hypogaea due to the genomic similarities between the two species (Grabiele,

Chalup, Robledo, & Seijo, 2012). The single hybridization event between A. duranesis and A. ipanesis resulting in A. monticola and later A. hypogaea can account for the low levels of genetic diversity available in cultivated peanut.

Additionally, due to the ploidy differences between A. hypogaea and other Arachis species, which are predominately diploid, exploitation of beneficial traits from wild species in cultivated peanut is difficult. Furthermore, inter-clade and intersectional barriers make hybridization, and ultimately accumulation of beneficial traits in wild types to be used in the breeding of cultivated peanut, burdensome. This is especially disappointing considering many beneficial traits that have been discovered in wild Arachis species. Many traits, such as aflatoxin contamination resistance, tomato spotted wilt virus resistance, late leaf spot (Mycosphaerella berkeleyi) and early leaf spot (Mycosphaerella arachidis) resistance, general insect resistance, and root knot nematode (Meloidogyne arenaria) resistance have all been found in wild Arachis species (Stalker et. al., 2016).

Arachis hypogaea: Morphology and Taxonomy

Arachis hypogaea can be divided into two subspecies containing a total of six botanical varieties. Subspecies fastigiata contains the botanical varieties fastigiata, aequatoriana, vulgaris, and peruviana. Subspecies hypogaea contains varieties hypogaea and hirsuta (Fig. 1-4). The key distinguishing feature that separates the two subspecies is the presence or absence of flowering on the main stem, with hypogaea lacking flowers on the main stem, although some studies have shown that there is less distinction between botanical varieties and even subspecies than

previously thought (He & Prakash, 2001). Furthermore, extensive breeding has led to many lines that have mixed lineage, making it difficult to cleanly sort them into a single botanical variety

(Stalker and Simpson, 1995). These botanical varieties roughly correspond to the USDA market types: A. hypogaea fastigiata var. fastigiata is the Valencia market type, A. hypogaea fastigiata var. vulgaris is the Spanish type, A. hypogaea hypogaea var. hypogaea is the Virginia market type. The Runner market type does not have a corresponding botanical variety but is grouped with the hypogaea subspecies. Flowering pattern is one of the key distinguishing traits between subspecies fastigiata and subspecies hypogaea. Subspecies fastigiata flowers on the main stem and contains varieties vulgaris and fastigiata, which approximately correspond to the Spanish and Valencia market types, respectively. Subspecies hypogaea does not flower on the main stem, and contains the hypogaea varieties, which are designated as Virginia and Runner market types.

Runner varieties are a hybrid between var. vulgaris and var. hypogaea, but does not flower on the main stem, and therefore is placed with Virginia types in var. hypogaea.

The Germplasm Resource Information Network (GRIN) is a web tool developed by the

USDA Agricultural Research Service and U.S. National Plant Germplasm System to organize and distribute genetic information for agriculturally relevant organisms to breeders, researchers, and growers. The GRIN peanut market type system is one of the most commonly used methods to classify the accessions in the USDA germplasm collection and is used commercially to apply market standards to different kinds of peanuts. Four market types are recognized, and each is represented in the core collection: Virginia, Runner, Valencia, and Spanish. Also included in the collection are unclassified and mixed lines, which are yet to be sorted into a market type. GRIN market types are used commercially to separate different cultivars based on genetic and phenotypic classes. Virginia types, which total about 15% of US peanut production, are the

largest type, and are commonly sold as roasted in-shell peanuts. These types have the largest kernels and are largely grown in Virginia and the Carolinas. Spanish type peanuts account for about 4% of total production and are identified as being smaller and having darker skins on the kernels. They also have higher oil content and are used for a variety of products like candy and peanut butter. Valencia peanuts only account for 1% of the market and can be identified as having three or more kernels per pod. Valencia types are also notably sweeter tasting than other varieties. Runner types account for most of the U.S. market –about 80%. This is due to a combination of factors, including their high yield, consistent kernel size, good taste, and high oil content, and are primarily used for peanut butter, which is the largest consumption of peanuts.

Peanut Germplasm

Peanut Germplasm Maintenance

Germplasm collections are global or regional collections consisting of the entirety of the genetic material available for a crop species and are a common fixture for most agriculturally relevant plant species. There are four major germplasm collections for peanut germplasm: the

USDA collection in the United States, the ICRISAT collection in India, the EMBRAPA collection in Brazil, and the OCRI-CAAS collection in China. There are many major problems facing peanut germplasm maintenance. In this section the USDA collection, which is the focus of this study, will be discussed and compared to the other major peanut germplasm collections in the following sections

USDA Germplasm Collection

The USDA collection consists of 9321 accessions of cultivated peanut and 655 accessions of wild peanut as of 2016 (Barkley et al., 2016). This large collection is maintained at the USDA Plant Genetic Resources Conservation Unit in Griffin, Georgia. The cultivated peanut germplasm is stored only as seed. If stored at 4°C and 25% humidity; samples can maintain high

levels of germination for up to 15 years. This storage method is most often used for samples that will be shipped to breeders and researchers. Long-term storage can preserve seed for up to 25 years when samples are stored at -18°C. Each accession is divided with part stored at 4°C and part at -18°C.

Seed viability while in storage is a concern for germplasm maintenance, and seed must be regenerated to recover losses during storage. Storage performance is understudied, and the factors that determine the length of time peanut seed can be stored before reaching 50% viability are not fully understood (Walters et al., 2005). Although not statistically verified, the current status quo in the germplasm and breeding community is that 15 years is the maximum time peanuts can be stored and maintain reasonable viability, after which seeds lose viability at an unspecified but dramatic rate. Walters et al. also suggests that storage at -18°C can increase the maximum storage time to 25 years as opposed to 15. This difference of 10 years of viability has important implications on the management of the germplasm collection. At 15°C storage, 1000 lines per year would need to be regenerated to avoid rapid loss of viability, whereas at -18°C storage, only 400 lines per year would need to be regenerated (Barkley et al., 2016). As more resources are required to maintain germplasm collections as the numbers of accessions increases, storage temperature plays a key role in the management of these genetic materials. Regeneration of accessions is carried out primarily under field conditions. Typically, 100 seed per accession are planted, with an average return of around 1050 seeds per accession with 80% viability

(Barkley et al., 2016).

USDA Core Germplasm Collection

Core collections are commonplace in most major agronomic crops. The concept of a core germplasm collection was developed by Frankel and Brown in 1984, suggesting that it would be more efficient to have a smaller representative collection to allow easier access to a cumbersome

complete germplasm bank, which often contain thousands of accessions (Brown, 1989). The term was coined by Frankel as a subset of accessions that acted to sample “representative variability” of a larger germplasm collection. Brown showed that it is possible to subset accessions to achieve this in 1989 and proved that a core collection allowed more efficient utilization of a complete germplasm collection, making it more accessible to breeders. The

USDA core germplasm collection for peanut was developed by Holbrook et al. in 1993 and has proved a useful tool in peanut breeding (Holbrook, 1999).

USDA Core Collection Selection Methodology

After confirming existence of consistent morphological data in the GRIN database for accessions from the same country of origin, clusters were formed based on the following morphological traits that represent genetically distinct groups: plant type, pod type, seed size, testa color, number of seeds per pod, and average seed weight. Then, 10% of accessions were randomly selected from each cluster. Accessions from countries with few accessions were pooled and 10% were chosen, and similarly 10% of accessions with incomplete data were chosen by country of origin. The result was a collection of 831 lines, roughly 10% of the entire USDA collection at the time, representing nearly all the diversity contained in the USDA collection

(Holbrook et al., 1993).

Uses of the Core Collection

Trait mining, or the screening of the entire core collection for specific traits, is one utility offered by the core collection. A two-stage approach is typically used. First, all core accessions are screened for a specific trait. Then, clusters of the larger collection containing the core accessions displaying the trait are assessed. Other clusters are ignored, greatly reducing the amount of germplasm to be analyzed, instead focusing on clusters that are more likely to display the trait based on the trait-positive core accessions. This core collection was screened for tomato

spotted wilt virus resistance. Twenty-eight (28) accessions were found that displayed higher levels of resistance to the disease than moderately resistant cultivars (Anderson et. al., 1996).

Trait mining has been successfully utilized in discovering potential germplasm for resistance to many different diseases, including major diseases such as late leaf spot (Holbrook and Anderson,

1995) and early leaf spot (Isleib et al., 2001). Other traits which either the entire core collection or a section of the core collection has been screened for include root-knot nematode resistance

(Holbrook et al., 2000), aflatoxin contamination resistance (Holbrook et al., 2009), and fatty acid composition (Hammond et al., 1997).

Another potential use for the core collection is to enhance and improve the larger collection. Core collections have been shown to be able to find gaps or areas of shallow diversity in larger collection, which can help curators to target geographic locations from which to bring in more accessions (Steiner et al., 2001). Further, core collections demonstrate the genetic availability in a crop. In peanut, this task is particularly important for two reasons: one, breeders tend to rely on relatively few germplasm sources in breeding programs (Knauft & Gorbet, 1989); and two, cultivated peanut has a narrow genetic base and limited access to wild type germplasm integration due to ploidy differences and hybrid viability loss. The former is likely the more easily solved; breeders do not tend to use germplasm which is under-analyzed, an issue this experiment hopes to help remedy. Wild type integration is a continuing issue, due to high cost and low agronomic performance of wild lines. With better documentation of core accessions, breeders may be more inclined to assume the risk of including foreign germplasm into their working collections. Because most lines in the core collection have relatively low levels of agronomic performance, only through well-documented benefits of the germplasm will the risk be deemed worth the cost of lowering overall agronomic performance in the working collection,

especially considering the not unsubstantial progress made through more traditional means.

Through better documentation of available germplasm however, the genetic ceiling of gains from crosses can be raised by using new germplasm sources with minimal drag (Kannenberg & Falk,

1995).

Mini Core Collection

Although the core collection initially allowed breeders to screen available germplasm for a number of different phenotypic traits, the high per sample cost of many biochemical measurements prevented the entire core collection from being analyzed for these traits.

Furthermore, the cost and land requirements continued to grow and studying all accessions in the core collection was burdensome and expensive. These issues were resolved by the creation of the mini core collection (Holbrook & Dong, 2005). A mini core, or “core of the core”, is a subset of a core collection that represents a large portion of the diversity in the larger collection, not unlike the relationship between a core collection and a full collection. The selection of the mini core was similar to the selection of the core collection. Using data from seven above- and seven below-ground traits (growth habit, plant size, main stem prominence, flowering pattern, leaf color, stem color, maturity, pod shape, pod constriction, pod reticulation, seed per pod, 100-pod weight, market type, and testa color), the core collection was clustered into groups. Then, 10% of each group was randomly selected to create a smaller collection containing 112 accessions: the mini core. The mini-core collection offers a comparable level of diversity to the core collection in a much smaller number of lines. This allowed for trait mining and documentation of traits that require more time, space, replication, and cost per accession, notably biochemical and genetic tests.

An additional benefit offered by the smaller number of accessions in the mini core collection is the focus that can now be given to each individual accession. These observations

can point to improvements that need to be made in the larger collections and procedures can be tested to make these changes. For instance, one issue in the USDA collection is a considerable number of accessions showing heterogeneity. Heterogeneity in the core collection is expected because lines were not purified before being included in the collection. A purified mini-core was developed (Chen et al., 2014), but a purified core collection has not been developed; This is an issue for multiple reasons.

Firstly, heterogeneous accessions can lead to confusion in descriptions, which can cause mislabeling and inconsistencies in databases. Market type designation is based on phenotypic trait expression, and heterogeneous accessions reduce the ability to maintain distinct traits in a single accession. This can lead to a situation in which one group is favored within a heterogeneous line based on the previous description of the “correct” phenotype of the accession.

If seed from only some plants in the accession are regenerated, the novel genetics of the other seeds are lost. Alternatively, splitting heterogeneous accessions into two distinct accessions in the collection could greatly increase the size of the collection, which is already resource intensive to maintain.

Secondly, inconsistent data can be interpreted as highly variable environmental responses by the accessions instead of differences in genetics within the accession. If one group within a heterogeneous accession is highly resistant to Tomato Spotted Wilt Virus, and the other group is only mildly resistant, the overall rating of the “accession” may be lower, potentially causing researchers to overlook valuable genetic characteristics.

Thirdly, for meaningful genomic analysis, accessions need to be not only phenotypically uniform but genetically uniform. Phenotypic heterogeneity is a good sign of genetic

heterogeneity, which can lead to confusing and contradictory results in genetic analysis such as

QTL mapping and SNP identification.

Lastly, these inconsistencies reduce the robustness of the information available to breeders and can cause mistrust in breeding resources. Confused labelling, poor phenotyping results, and conflicting information from genetic analysis can cause unneeded frustration in the peanut research community and could hinder further developments in peanut breeding.

In peanut, four major core collections have been developed: the USDA core collection, the ICRISAT core collection (Upadhyaya, Ortiz, Bramel, & Singh, 2003), the Chinese core collection (Jiang et al., 2008), and the Asian core collection (Upadhyaya et al., 2001).

Comparison of USDA Collection to Other Major Collections

Although the USDA peanut germplasm collection is very large, it is not the largest. At around 15,000 accessions, the ICRISAT germplasm collection in India is the largest collection globally. Like the USDA collection, each accession in the ICRISAT collection is stored at both a short-term storage and a long-term storage, although the ICRISAT accessions are stored at a temperature of -20°C as opposed to -18°C. Additionally, regeneration methods are different from the USDA methods. ICRISAT regenerates lines in field using 4-row plots of 4m in length containing 160 seeds. However, accessions not acclimated to environmental conditions in the region are grown in greenhouse.

Another collection of note is the OCRI collection in Wuhan, China. Although the collection is smaller than the USDA collection, only around 8500 accessions compared to around

10000, the strategies of maintaining the germplasm is quite different. OCRI stores their seed at -

5C to -10C, considerably warmer than the -18C and -20C of the USDA and ICRISAT, respectively. However, while ICRISAT stores their long-term storage seed at up to 8% relatively

humidity, ORCI reduces the humidity to a super-dry level of 3.5%. In 2011, Yu showed this method to be sufficient to maintain germination rates of 75% after 12 years of storage.

Breeding and Genetics

Overview of Peanut Breeding

Peanut breeding, despite the numerous challenges that will be outlined in this section, has consistently progressed towards higher yield, while also overcoming or tolerating many biotic and abiotic stressors (Holbrook et. al., 2014). Through a combination of traditional breeding and new genetic breeding technologies, peanut breeders are poised to continue this trend.

Disease resistance

There are 55 diseases of note that affect peanut production globally (Kokalis-Burelle,

1997). In the United States, the pathogens that regularly cause the most significant damage to peanut crops are late leaf spot (Mycosphaerella berkeleyi), early leaf spot (Mycosphaerella arachidis), white mold (Sclerotinia sclerotiorum), limb rot (Rhizoctonia solani), root-knot nematodes (Meloidogyne arenaria), and tomato spotted wilt virus. Due to annual differences in environmental conditions that may favor or hinder disease instance, yearly losses to different diseases is varied.

Cultivated peanut is limited in genetic diversity available to breeders, and as a result, progress towards breeding goals can be slow or impossible. One area of interest has been in wild peanut accessions, which have been shown to have high resistance or sometimes complete immunity to many of the diseases (Stalker et al., 2013). While this may seem an obvious direction to pursue, the necessity of wild type hybridization for disease resistance is not absolute.

There are several reasons for this. First, disease resistance is not always the most important trait to growers when deciding which cultivar to plant. Georgia-06G is the most widely grown cultivar because of yield potential, despite little resistance to disease compared to other similar

cultivars. This, coupled with the fact that pathogenic issues such as root knot nematode can be adequately controlled,mean that a high yielding variety paired with a good pesticide management program can mitigate the most destructive aspects of disease issues. For instance, in a 2011 survey of growers in Miller County, GA, 95% of the peanut acreage was planted with the slightly higher yielding Georgia-06G, which is susceptible to root knot nematode, as opposed to

Tifguard, which is nearly immune to root knot nematode (Hollis, 2011). Thus, the focus of breeding programs is not always to achieve complete or even moderate resistance if the cost in yield and agronomic performance is sacrificed. Another reason wild types have not been used extensively despite the obvious benefits is that often, manageable levels of disease resistance can and have been achieved through conventional breeding with the available cultivated peanut germplasm resources. While tolerance or moderate resistance in a cultivar is less appealing than full immunity, when used in a well-managed cropping system, often moderate resistance can still return good yields and profits for growers. Because the integration of wild types into a breeding collection is difficult and comes with baggage, it is easy to see why the breeding community at large has focused on exploiting the resistance within the cultivated peanut species, such as

TSWV resistance (Anderson et al. 1996).

Abiotic stressors

The main abiotic stressor of interest to peanut breeders is drought stress. Drought stress can be a major issue in peanut production, especially in areas where irrigation is not available, such as West Africa. Many studies have addressed the effect of moderate drought stress on yield, with mixed findings, although some trends seem evident: drought effects on yield early in the growing season showed little effect on yield to increasing yield (Puangbut et al., 2009); midseason drought has been associated with moderate declines in yield (Lal et al., 2009); drought stress towards the end of the growing season has been linked to reduced yields. Because

drought is unexpected and ranges in severity, breeding efforts to increase overall drought tolerance are a priority among breeders.

Drought tolerance is a particularly complex trait to breed towards because the molecular machinery involved in drought tolerance includes many post transcriptional interactions which cannot be accounted for in traditional trait inheritance studies (Jain, Basha, & Holbrook, 2001).

However, studies on the USDA minicore collection (Kottapalli et al., 2009) and the ICRISAT minicore collection (Hamidou et al., 2012) indicated that there is genetic variability for drought tolerance in peanut germplasm. However, even with genetic material available, drought tolerance breeding is problematic. High variability in environmental conditions makes replication difficult, and GxE interactions necessitate the testing of germplasm in diverse locations. Additionally, although wild diploid Arachis species show promise in drought tolerance, little work has been done to move these wild genes into cultivated peanut.

Peanut Genome

Peanut has a relatively large genome at 2.7-3.2Gb, which is similar to the size of the human genome. The large size, combined with the allotetraploid nature of the genome, has proved to be difficult to understand, hindering some progress. Specifically, the sequenced genome fragments were too complex to assemble into one cohesive genome (Bertioli et al.,

2013). To work around this issue, the genomes of A. hypogaea progenitors A. duranensis and A. ipaensis were sequenced, assembled, and combined to form a working genome for A. hypogaea.

The full genome was made available on PeanutBase (https://peanutbase.org/), a tool developed at

Iowa State University to provide references to breeders and geneticists working on QTL analysis, trait mapping, GWAS studies, and so forth (Cannon et al., 2014).

The complexity of the peanut genome originates from the progenitors of A. hypogaea.

Peanut is described as having an “AABB” genome, with a full chromosome set from two

parents. The difficulty in studying the peanut genome is due to the similarity between the A and

B genome; regions of the genome have highly conserved sections nearly identical between the A and B genome, with only small regions in between showing differences (Bertioli et al., 2013).

Additionally, it is estimated that repetitive content makes up around 64% of the peanut genome

(Dhillon, Rake, & Miksche, 1980). Because artificial intersectional crosses between Arachis species have been shown to be largely infertile except in rare cases (Gregory & Gregory, 1979), and artificial allotetraploid crosses of A. duranensis and A. ipaensis have been shown to produce healthy, fertile offspring (Foncéka et al., 2009), it is likely that the two donor progenitors were closely related. Additionally, intron nucleotide substitutions indicate that the evolutionary divergence of the A and B genomes took place only 2.3-2.9 million years ago, relatively recently in evolutionary terms (Moretzsohn et al., 2013). This close relationship, combined with the already highly repetitive genomes and low polymorphism, makes genetic mapping and marker development difficult. Additionally, SNP mapping has proven challenging because of the tetraploid nature of the genome, which produces many homeologous polymorphisms, which do not distinguish well and cannot be used effectively for mapping.

Genetics and Breeding Strategies

One of the most common strategies currently used in peanut breeding that utilizes modern genetic techniques is marker assisted breeding, or MAS. While the technology is still relatively young, across many crops, MAS has produced notable impacts (Collard & Mackill,

2008). In peanut breeding, a modified version of MAS, called marker assisted back-crossing, or

MABC, is used to take a specific advantageous genetic region from an otherwise lacking accession of germplasm and backcross it with an elite line with minimal genetic baggage. This method has been shown to greatly reduce the time required to recover the original recurrent parent genome when adding backcrossing for a single trait (Frisch, Bohn, & Melchinger, 1999).

MAS techniques are best used when targeting a trait controlled by one or few genes, as opposed to a quantitative trait, because the effects of QTLs are spread across large regions and cannot all be effectively targeted using MAS or MABC (Dekkers and Hospital, 2002). Limitations of this breeding method have been described in the previous sections. High similarity and low RFLP variability in the genome means that high quality markers are less frequent than in other crops

(Kochert et al., 1991).

Perhaps the most successful and well-known application of genomic technology in peanut breeding is the creation of Hi-Oleic peanuts. The Hi-Oleic trait causes peanuts to drastically reduce the conversion of oleic acid to linoleic acid, which greatly increases the oleic acid content of the oil. This process is controlled by the recessive homeologous genes ahFAD2A and ahFAD2B (Chu et al., 2009a). Homeologous genes are pairs of highly similar sequences resulting from a ploidy change, which is not uncommon in plants. In the case of the FAD2 gene, both homeologs must be recessive, and thus not active, to prevent the conversion of linoleic to oleic acid (Wang et al., 2011). Real-time PCR techniques have been developed to quickly identify the desired alleles (Barkley et al., 2010).

Figure 1-1. Major clades of the Papilionoid legumes (Bertioli et al., 2011).

Figure 1-2. Average annual precipitation in South America. The Mato Grosso do Sul region is centered on the crosshairs. Note the diverse precipitation patterns surrounding this region.

Figure 1-3. Sectional relationships in genus Arachis (Krapovickas and Gregory, 1994).

Figure 1-4. Taxonomy of Arachis hypogaea (Bertioli et al., 2011).

CHAPTER 2 MORPHOLOGICAL CHARACTERIZATION

Background

Peanut (Arachis hypogaea L.) is an economically important leguminous oilseed crop.

Found in family Fabaceae, peanut is notable for the geocarpic nature of its fruit. Peanut is native to South America, originating in either the northwest region of Peru or the southern region of

Bolivia (Simpson et al., 2001), evolving 2.3-2.9 million years ago from a genome-doubling hybridization event between progenitors A. duranensis Krapov. and W.C. Gregory and A. ipaensis Krapov. and W.C. Gregory, resulting in a tetraploid AABB genome (Seijo et al., 2004).

Low levels of genetic diversity have been observed in the peanut genome due to the close relationship of progenitors, the relatively recent evolution of A. hypogaea, the highly self- pollinating nature of peanut reproduction, and the impact of intense selection by humans using peanut as a crop (Moretzsohn et al., 2013). Despite the difficulties in breeding peanuts that arise from low genetic variation, peanut breeding has been successful in increasing yield and, more recently providing protection from certain pathogens and abiotic stressors (Holbrook et al.,

2014).

The genetic variability available to breeders is contained in the USDA peanut germplasm collection, which is curated at the Agricultural Research Service Plant Genetic Resources

Conservation Unit repository in Griffin, Georgia. This collection is one of the largest in the world, containing nearly 10,000 unique accessions from around the globe. Accessions are stored exclusively as seed. Two methods of storage are utilized. In short-term storage, samples are stored at 4°C and 25% humidity and can stay viable for 15 years. These samples are shipped to breeders and researchers. Long-term storage samples are maintained for seed preservation and

restoration; they can remain viable for up to 25 years when stored at -18°C. Regenerated seed is divided evenly between short- and long-term storage (Barkley et al., 2016).

The USDA peanut germplasm collection is a vital resource but is too large to be used effectively. One strategy to managing access to large germplasm banks is through a core collection. Core collections have been developed for most major crops, beginning with the concept developed by Frankel and Brown (1989), of a representative sample of approximately

10% of a larger collection that would act as a guide to the full set of germplasm. The peanut core germplasm collection was developed in 1993 by Holbrook, using a system of clustering by origin and morphological traits. 10% of each cluster was randomly selected, producing a smaller collection of 831 unique accessions, known as the USDA core peanut germplasm collection. The core collection is most effective when used as a guide for trait mining the full collection. In this way, a smaller number of accessions can be searched for a trait of interest and eliminate large amounts of the full collection that are unlikely to contain the desired trait, allowing breeders and researchers to focus on a smaller subsection of the full collection (Holbrook et al., 2000).

To further assist researchers a core of the core or “mini-core” collection was developed by Holbrook and Dong in 2005. This collection was chosen using the core selection methodology, but drew 10% of clusters from the core accessions, resulting in a collection of 112 accessions. The benefits of the mini-core collection are the ability to examine each accession more closely, and to more easily experiment on the entire mini-core collection, where field space and management resources would be highly limited to support the entire core collection.

Cultivated peanut is composed of two subspecies: A. hypogaea hypogaea and A. hypogaea fastigiata. These two subspecies are differentiated by their main stem flowering pattern; subspecies hypogaea does not produce flowers on the main stem, whereas subspecies

fastigiata produces flowers on the main stem. There are a total of five botanical varieties of A. hypogaea, but only three are commercially relevant: variety hypogaea, variety fastigiata, and variety vulgaris. Varieties fastigiata and vulgaris belong to subspecies fastigiata while variety hypogaea belongs to subspecies hypogaea. These three botanical varieties roughly encompass three of the USDA market types: Virginia types, Valencia types, and Spanish types, respectively.

A fourth market type, Runner type, is a hybrid between Virginia and Spanish types, but is botanically classified with Virginia types. Botanical variety designation and market type designation are not strictly interchangeable because botanical varieties are grouped by genetic and taxonomic relatedness, while market types are grouped based on commercial purpose and, consequently, different agronomic standards. Market types are a useful categorization strategy, but there is evidence that market type designations are blurred. For instance, Runner and Virginia types have been shown to contain a notable amount of genetic introgression from subspecies fastigiata, whereas the Spanish type is the purest genetically (Isleib et al., 2001).

There are three major breeding goals in peanut: increasing yield, increasing market value, and biotic and abiotic stress resistance. Yield has steadily increased at a rate of 29.9 kg/ha/year

(Holbrook et al., 2014) and many cultivars have been developed to some level of resistance to major peanut diseases. A relatively recent breakthrough in peanut breeding was the discovery and successful breeding of the Hi-Oleic trait. Hi-Oleic peanuts have a higher oleic to linoleic acid ratio due to a mutation in the FAD2B gene (Burton et al., 2004). Hi-Oleic cultivars display several beneficial qualities, including improved shelf life (Mugendi et al., 1998) and possible health benefits (Derbyshire, 2014). The market demand for Hi-Oleic peanuts has resulted in the release of Hi-Oleic cultivars for all market types (Holbrook et al., 2016).

Although the core and mini-core collections have been crucial in the breeding of new cultivars and understanding of the peanut genome, neither collection has been fully characterized in a single experiment. The advantage of phenotyping the entire core and mini-core collection within a single experiment is that more direct comparisons can be made between accessions because confounding factors such as location, general climate, and management practices are more consistent. The data generated from this project will help better describe and organize the germplasm available to breeders and researchers in the core and mini-core collection and will provide a complete, cohesive data set for genomic analysis.

Research Objectives

1. Provide robust phenotypic data for use in breeding and genetic research by:

a) Phenotyping the core collection (including the mini-core) of the USDA germplasm collection across traits representing diverse characteristics including: plant architecture, agronomic performance, pod traits, seed traits, leaf traits, and flowering pattern.

b) Assessing the level of phenotypic diversity among traits represented by the core and mini-core subsections of the USDA germplasm collection.

2. Analyze the core and mini-core collections for any inconsistencies, redundancies, or documentation errors that could jeopardize the utility and function of the USDA germplasm collection.

3. Replenish seed for the continued maintenance of the germplasm repository in Griffin, GA.

Materials and Methods

Experimental Background

The University of Florida Plant Science Research and Education Unit was chosen for the study, located in Citra, Florida. Fields consisted of Candler fine sand (Coarse-loamy, micaceous, mesic Typic Dystrudepts), and peanuts were grown according to University of Florida recommended practices for irrigation, fertility, and pest management (Wright et al., 2000). The experiment was arranged in an augmented randomized block design with three blocks. In each

block, fourteen check varieties of commonly grown commercial cultivars from each USDA

GRIN market type (Valencia, Virginia, Spanish, and Runner), called “commercial standards”, were planted. Eight of the check varieties were replicated three times in each block, and six of the check varieties were replicated once in each block due to lack of seed availability (Table 2-

1). Replicated three times per block were Georgia-06G (Runner), FL-107 (Hi-Oleic Runner),

Bailey (Virginia), Florida Fancy (Hi-Oleic Virginia), H&W-136 (Valencia), NM Valencia A

(Valencia), OLin (Hi-Oleic Spanish), and Tamnut OL-06 (Hi-Oleic Spanish). Replicated once per block were NM 309-2 (Hi-Oleic Valencia), Florida-07 (Hi-Oleic Runner), Tifguard

(Runner), Tamrun OL-11 (Hi-Oleic Runner), Red River (Hi-Oleic Runner), and Jupiter

(Virginia). Overall there were six Runner check varieties, three Virginia check varieties, two

Spanish check varieties, and three Valencia check varieties. Eight of the fourteen check varieties contained the Hi-Oleic trait. Mini-core accessions (n = 107) were replicated once in each block.

Additionally, the experiment was augmented with 687 core lines randomly distributed between the blocks. The three blocks were arranged against the slope of the fields, running north-south. In

2013, peanuts were planted May 12-15, and in 2015 peanuts were planted April 27-30.

Planting

Seeds were obtained from the USDA Germplasm Repository in Griffin, Georgia. Seeds were planted using the standard planting procedures used by the USDA Germplasm Repository.

Each accession was planted in a two-row plot 3 m in length with 75 cm row spacing (Figure 2 1).

There was a 3 m space between each plot in the planting direction, and a 1.5 m gap between rows to reduce the risk of cross contamination. Seeds were planted at a density of 50 seeds per row (5 cm seed spacing) and a depth of 3.5 cm.

In-Season Measurements

At 75 days after planting, fully expanded mature leaves were pulled from two randomly selected plants from each plot. The length and width of the four leaflets from each leaf were measured to the nearest mm and the distance between the leaflet pairs was recorded (Figure 2-2).

Leaflet width was measured at the widest point of the leaflet. Canopy architecture was determined by measuring the height and width of plants in each row, taking the average of three measurements per row (Figure 2-3).

Procedure: Phenotypic

Harvest

Each plot was harvested based on the USDA GRIN market type designation of the accession to ensure uniform maturity at harvest time: Valencia types were harvested 90-95 days after planting, Spanish types were harvested 110-115 days after planting, and Runner and

Virginia types were harvested 135-140 days after planting. After digging, one healthy plant from each plot was selected and photographed to provide visual estimation of overall plant architecture including height, pod density, and pod placement. After 2-3 days of drying, plants were thrashed using commercial plot harvesters and pods bagged in mesh sacks. Sacks were temporarily stored in a semi-truck trailer and fumigated with phostoxin (aluminum phosphide) to control insects. Samples were then transported to the University of Florida Weed Science

Building on main campus for processing.

Processing

Samples were cleaned, removing any stems and leaves left from the thrashing process.

The pods were then placed in brown paper bags and labelled. Approximately 10 mL of spinosad

((2R,3aS,5aR,5bS,9S,13S,14R,16aS,16bR)-2-(6-deoxy-2,3,4-tri-O-methyl-αL- mannopyranosyloxy)-13-(4-dimethylamino-2,3,4,6-tetradeoxy-β-D-erythropyranosyloxy)-9-

ethyl-2,3,3a,5a,5b,6,7,9,10,11,12,13,14,15,16a,16b-hexadecahydro-14-methyl-1H-as- indaceno[3,2-d]oxacyclododecine-7,15-dione and 50-5%

(2S,3aR,5aS,5bS,9S,13S,14R,16aS,16bS)-2-(6-deoxy-2,3,4-tri-O-methyl-α-L- mannopyranosyloxy)-13-(4-dimethylamino-2,3,4,6-tetradeoxy-β-D-erythropyranosyloxy)-9- ethyl-2,3,3a,5a,5b,6,7,9,10,11,12,13,14,15,16a,16b-hexadecahydro-4,14-dimethyl-1H-as- indaceno[3,2-d]oxacyclododecine-7,15-dione) insecticide (Tracer1) was applied at a rate of at 1.0 mg-ai/kg individually to each bag for further insect control. Peanuts were stored in shell under ambient conditions.

Yield and Grade

Pods from each plot were weighed (kg) and converted to total yield (kg/ha). A subsample of 20 pods free from noticeable defect were selected, weighed, and stored separately at 4°C for later analysis. The grading process followed a modified version of the grading procedure outlined in the American Peanut Council guide. All USDA GRIN Market Types were graded on the Virginia in-shell measurements for “Fancy” size pods and on Runner or Virginia standard for kernel size. A subsample of clean, undamaged pods was selected from each bag (approximately

250g to ensure uniform sample size), and weights recorded. Pods were size sorted on one lane of a 2-lane roller sizer set to a width of 32/64” to allow filtering of Virginia Fancy size pods.

Virginia Fancy pods were weighed and added back in with the rest of the pods. Peanuts were placed on the second lane of the sizer, which was set to sort the peanuts into sheller compartments based on pod size for efficient shelling with minimal kernel splitting. Shelling was performed using a commercially available three-chamber rub-type sheller. Due to differences in seed size in relation to pod size based on market type, different sheller screen arrangements were

1 Dow AgroScience, 2016

used to accommodate each market type. Runner and Virginia screen standards were used for kernel sizing on a shaker for all market types to allow for direct comparisons between groups.

The screen sizes were as follows: (Runner) extra-large kernels (21/64” x ¾” slot),

(Runner/Virginia) medium kernels (18/64” x ¾” slot), (Virginia) No. 1 kernels (15/64” x ¾” slot) (USDA ARS, 2017). Each category was weighed. Splits were removed and weighed independently. Remaining kernels were weighed as “others”. The resulting mature seeds were placed in brown paper bags and stored at 4°C.

Pod Volume

Pod volume was calculated using a specially designed apparatus (Figure 2-4). A plastic jar with 1-2 mm diameter holes in the bottom and top was placed at the bottom of the graduated cylinder and water was added to a volume of 500 mL. The plastic jar was brought to the top of the cylinder and 20 undamaged mature pods were selected and placed into the plastic jar. The plastic jar was then inserted into the water to the bottom of the cylinder, agitated slightly to allow for trapped bubbles to escape, and the new volume recorded. The original volume of 500 mL was subtracted from the new volume to determine peanut volume (cm3).

Standard Descriptors

The procedure for standard descriptor data collection can be found in the Appendix B.

Traits measured included 100-seed weight, pod constriction, pod reticulation, seed coat color pattern, and seed coat color. This data will not be presented here within but will be used to amend the current descriptions found in the USDA GRIN database and future subsequent descriptor databases.

Main Stem Flowering Pattern

Flowering on the main stem was determined at 75 days after planting in the 2013 trial. At maturity, two randomly selected plants per plot were examined for the presence or absence of

flowers on the main stem. If the plants showed different flowering patterns, for instance one plant in the plot showed flowering on the main stem and the second plant from the same plot did not show flowering on the main stem, the entry for the accession was left blank. Main stem flowering data was not collected in the field during the 2015 trial. Instead, each accession was grown in the greenhouse to determine flowering pattern and leaf pubescence. However, growth pattern was irregular in the greenhouse and flowering data could not be determined for all accessions and is excluded in this analysis.

Statistical Analysis

Canonical discriminate analysis (CDA) was used to analyze the relationship between market types for phenotypic traits. The concept of the canonical discriminate function was first introduced by Fisher in 1936 to account for species relationships in iris flower data analysis and is similar to a principle component analysis (PCA). However, while both CDA and PCA transform correlated response variables into underlying uncorrelated variables, CDA accounts for the relationships between groups. In effect, both reduce the number of dimensions while maintaining the important information from the data, but the use of CDA in this experiment was justified by the higher power of discrimination between groups in CDA compared to PCA.

Analysis was performed using SAS software (SAS/Stat 14.1, SAS Institute, Cary, NC). Data for the core and mini-core collections are presented separately due to differences in replication (one per core accession per year; three per mini-core accession per year). The analysis was based on line means by year. For each market type group mean and standard error was calculated.

Additionally, the minimum, median, maximum values for lines means within group and the range among line means were calculated.

Results

The results are displayed in three sections. The first section describes the data collected for all traits across the core and mini-core collection as they currently exist. The second section describes the canonical discriminate analysis which provided insight into trends that exist within the collections. This section also highlights potential errors in the core and mini-core collection.

The third section presents a revised subset of the original data based on corrections to errors implied by the canonical discriminate analysis. Unclassified types refer to accessions that do not have an assigned market type in the GRIN database, and mixed types refer to accessions that have an unclear market type designation in the GRIN database. Data from the 2013 and 2015 growing seasons was averaged across years. Number of data points per market type may be different between tables as a result of missing data.

Yield and Grade

In the core collection, the average yield was highest in the Runner type accessions at

2283 kg/ha. Virginia types had the highest range of yields, from 40 kg/ha to 5795 kg/ha, which was the highest recorded yield in the core collection. Sound mature kernel percentage was highest in Runner types, at 71.1%. Valencia types had the lowest average SMK percentage at

64.1%. Spanish types had the highest range in SMK values, from 25.5% to 80.5%. Meats per 200 g of peanuts was highest in Spanish types at 148.4 g. Spanish types also had the highest range of meat values, from 77.6 g to 162.5 g. Valencia types had the lowest average meat value at 136.2 g

(Table 2-2).

In the mini-core collection, Virginia types yielded the highest at 2440 kg/ha. The highest yield in the mini-core was a Virginia type, which ranged from 417 kg/ha to 3694 kg/ha. SMK percentage was highest in Runner types at 69.6%. SMK content varied the most in Virginia types, from 43% to 82%. Meat content per 200 g of peanuts was highest in Spanish types at

144.7 g. Valencia types had the most variation in meat content, ranging from 83 g to 153.7 g

(Table 2-2).

The mini-core collection tended to have lower overall diversity in agronomic performances, with lower ranges across agronomic traits compared to the core collection.

However, there was a reasonable amount of variation in the mini-core, with much of the difference in range resulting from higher minimum values than the core collection.

Pod Traits

Meat to hull ratio in the core was highest in the Spanish types, averaging nearly 3:1.

Valencia types had the lowest ratio at 2.2:1. Mixed accessions had the highest range in ratios, from 1.3:1 to 6.7:1. Fancy pod percentage was highest in the hypogaea subspecies, at 26% for

Runner types and 23.6% for Virginia types. Spanish types had the lowest Fancy pod content, at

5.1%. Valencia, Virginia, and unclassified accessions had the highest average pod volume, at

62.9 mL, 58.7 mL, and 60.3 mL, respectively. Spanish types had the smallest pods on average at

38.1 mL per 20 pods. Virginia types had the most varied pod volumes, ranging from 20 mL to

160 mL per 20 pods (Table 2-3).

Pod trait trends were similar in the mini-core collection. Meat to hull ratio was highest in

Spanish types (2.75:1), and lowest in Valencia types (2.19:1). Subspecies hypogaea contained the highest average Fancy pod content in Runner (28.4%) and Virginia (30.5%). Pod volume was lowest in Spanish types at 38.6 mL, and highest in Valencia types (57.9 mL) (Table 2-3). In general, minimum values were lower and maximum values were higher in the core collection compared to the mini-core collection.

Plant Architecture

In the core collection, Valencia types were the tallest on average, at a height of 47.8 cm at

75 days after planting. Virginia and Runner types were shorter, averaging 36.9 cm and 37.7 cm,

respectively. Virginia types had the highest range of heights, from 10 cm to 96.6 cm. Plant width was similar between market types, with Valencia types as the widest at 59.9 cm and Runner types being the thinnest at 56.3 cm. Based on the data for plant height and width, it is not surprising that Valencia types had the highest height to width ratio at 0.82:1, while Virginia types had the lowest height to width ratio at 0.65:1 (Table 2-4).

The mini-core collection showed less variation among market types for plant height at 75 days after planting. The tallest market type on average was Valencia types at 44.1 cm and the shortest market type on average was Virginia types at 38.4 cm. Virginia types also contained the tallest accession at 65.8 cm tall. Plant width was very similar between market types, Spanish types averaging the lowest width at 57 cm and Runner types with the highest width at 59.2 cm.

Height to width ratio divided on subspecies lines; hypogaea varieties had lower height to width ratios at 0.68:1 for Virginia types and 0.69:1 for Runner types, while subspecies fastigiata varieties had higher height to width ratios at 0.78:1 for both Spanish and Valencia types (Table

2-3). The core collection contained a more diverse set of accessions with higher ranges in all plant architecture traits in all market types, due to notably lower maximum values in all three traits.

Leaf Traits

In the core collection, Spanish and Valencia types had the longest and widest leaves, while Runner and Virginia types had shorter and narrower leaflets. Valencia types had average leaflet length of 52.3 mm and width of 24.3 mm, Spanish types length of 51.6 mm and width of

25.1 mm, Virginia types length of 46.3 mm and width of 22.2 mm., and Runner types length of

48.7 mm and width of 23.5 mm. Spanish types had the longest leaflet internode distance in the core collection at 14.65 mm. Virginia types had the shortest internode distance at 12.31 mm. The longest internode distance was a Spanish type at 47 mm (Table 2-5).

The mini-core collection showed similar trends to the core collection. Valencia types had average leaflet length of 51.5 mm and width of 24.1 mm, Spanish types length of 50.7 mm and width of 24.7 mm, Virginia types length of 46.6 mm and width of 21.9 mm and Runner types length of 48.1 mm and width of 21.9 mm. The longest average leaflet internode distance was

Spanish types, at 14.07 mm. The shortest average leaflet internode distance was Virginia types at

12.26 mm. Spanish types contained the longest internode distance recorded at 18.33 mm (Table

2-5).

Canonical Discriminate Analysis (CDA)

First CDA

A canonical discriminate analysis was first performed on all four market types in the mini-core collection, plus all unclassified and mixed accessions, without regard for main stem flowering pattern. Correlated variables in the CDA consisted of a set of 17 commonly collected phenotypic traits, representing traits from plant architecture, leaf structure, pod characteristics, and agronomic performance. Canonical discriminant analysis assumes that the accessions in the core collection were accurately labeled with respect to market type. Based on collected phenotypic data, the pattern of segregation was not surprising. Table 2-28 shows the variable components of the canonical variables, with variables that significantly affected the canonical correlation in italics. The first canonical variate accounted for 59% of the variation. The magnitude of the phenotypic correlation – correlation between the group means of original variable and the group mean of the canonical variate – can identify the major drivers separating groups along a particular canonical axis. Yield (r = 0.94), meats (0.85), ELK (0.89), No.1s (-

0.89), others (-0.99), splits (0.87), SMK (0.97), meat to hull ratio (0.88), and leaflet length (-

0.89) were significant driving variables in the discrimination. For the second canonical variate, which accounted for 24% of observed variation, pod volume (0.91) was a significant variable.

The relative ranking of these variables is also included in the table, with 1 being the variable that most heavily influences the correlation and 17 being the trait that least impacts the correlation.

Figure 2-6A displays the plotted group centroids of this CDA. Unclassified accessions grouped closely with Valencia types, and mixed accessions grouped in between Virginia and Spanish types.

The correct flowering pattern for unclassified and mixed accessions is not known. True

Virginia and Runner types should show no mainstem flowering, and true Spanish and Valencia types should show mainstem flowering. This is not the case for all accessions in the core and mini-core collections, in fact, in the mini-core collection, 50% of Runner types, 9% of Spanish types, 25% of Valencia types, and 25% of Virginia types did not follow the correct main stem flowering pattern based on the subspecies designation. In the core collection (excluding mini- core accessions), analysis suggested that 42% of Runner types, 7% of Spanish types, 14% of

Valencia types, and 15% of Virginia types were not delineated correctly based on flowering data.

Flowering pattern was correct for all commercial standard plots, indicating that flowering pattern was accurately recorded (data not shown).

Second CDA

Market types were divided by correct and incorrect flowering pattern to determine if the incorrect flowering pattern was due to mislabeling of the market type designation of the accession. Table 2-28 shows the differences between groups based on canonical variables. In all cases, the correct and incorrect flowering patterns of the same market type were significantly different, indicating the incorrect flowering pattern corresponds to an incorrect labelling of the accession. Thus, they needed to be removed from the analysis. Figure 2-6B shows the centroid of the group clusters. The flowering pattern grouped more similarly than market type, with the

groups that did not flower on the main stem clustering closely in the upper-right, and the groups that flowered on the main stem sorting to the upper-left or center.

Using this information, it is possible to predict the correct market type designation for the incorrectly labelled accessions. The “Valencia” types that did not flower on the main stem grouped very closely to the Virginia types. “Virginia” and “Runner” types which flowered on the main stem grouped more closely to Spanish types than Valencia types. However, it is difficult to definitively sort the mislabeled accessions, and it is likely that individual accessions will sort differently. For instance, within the mislabeled Spanish types, there may be some that are Runner types and others that are Virginia types, since they do not differ significantly from either when compared as a group (Table 2-29).

Third CDA

Once the incorrect market types were removed, another CDA was performed including

Unclassified and Mixed accessions divided by flowering pattern. The canonical variables accounted for 88.9% of variation, with the first canonical variate accounting for 71% and the second canonical variate accounting for 17.7% (Table 2-28). The first canonical variate was significantly influenced by 12 of the 17 traits, while pod volume was the only significant driving variable in the second canonical variate.

Group centroids were plotted in Figure 2-6C. Unclassified and mixed accessions, which do not have a known correct flowering pattern, were grouped by their main stem flowering. The flowering pattern of unclassified types that flowered on the main stem grouped closely to

Valencia types for main stem flowering and Virginia types for non-main stem flowering. Main stem-flowering mixed types grouped closely to Spanish types, while mixed types that did not flower on the main stem did not group closely to either Runner or Virginia types (Table 2-30).

Based on the results of the canonical discriminate analysis, it is likely that accessions that did not follow the correct main stem flowering pattern based on subspecies are not correctly labeled. The correct market type labeling of these accessions will not be addressed in this paper.

In the final section of the results, these possibly mislabeled accessions are removed, producing a subset of the original data containing only correctly-flowering, refined market type groupings.

Revised Agronomic Traits

Average yield by market type in the core collection was 2588 kg/ha for Runner types,

2479 kg/ha for Spanish types, 2445 kg/ha for Valencia, and 2265 kg/ha for Virginia types (Table

2-9). Yield showed high variation in the core collection. Runners had a minimum yield of 966 kg/ha and a maximum yield of 4388 kg/ha, ranging 3422 kg/ha. Virginia types had a minimum yield of 362 kg/ha, a maximum yield of 6495 kg/ha, ranging 6133 kg/ha. Spanish types had the second highest maximum yield at 5478 kg/ha and had the lowest minimum yield at 60.4 kg/ha.

In the mini-core collection, yield average was highest in Virginia types, at 2726 kg/ha. Range in yield was lower in all market types (Table 2-10).

In the core collection, Valencia types had a far lower average SMK percent, at only

63.4% compared to 69% for Virginia, 71.2% for Spanish, and 72.1% for Runner types (Table 2-

11). Runner, Spanish, and Valencia types showed higher ranges in SMK resulting from lower minimums and higher maximums in the core compared to the mini-core. However, Virginia types ranged from 51.5% to 79.6% with a range of 28.2%, compared to a range of 38.9% in the mini-core. The mini-core collection showed significant differences in SMK percentage between market types. Valencia types had the lowest minimum SMK percent, at 36.9%, with a range of

36.9%, and had the lowest average SMK percent at 61.4% (Table 2-12).

In the core collection, Valencia types had the lowest average meat content at 135.3 g, ranging from a minimum of 77.7 g to a maximum of 156.9 g (Table 2-13). Not surprisingly, total

meats reflected percent SMK in both the core and mini-core collections. In the core collection,

Runner types were the highest (72.05% SMK and meats at 149.3 g), with Spanish types showing very similar values (71.3% SMK and meats at 148.6 g). In both traits, Valencia types were the lowest (63.4% SMK and meats at 135.3 g). Mini-core market types differed significantly by total meats. Valencia types had a high range of 70.73 g, with a minimum of 82.97 g and a maximum of 153.7 g (Table 2-14). Runner, Spanish, and Virginia types all had very similar averages at

145.08 g, 144.57 g, and 144.12 g, respectively. Meat weight is inversely related to hull weight.

Valencia types had a very low minimum and mean despite having a similar average for meat weight. One possible explanation is that Valencia types were more susceptible to pest damage than the other market types. Valencia types tended to crack more easily due to the loose packing of kernels in the pod. This would make them more vulnerable to pest damage, increasing relative hull weight and decreasing meat weight. This could also help explain the low SMK percent in

Valencia types.

Agronomic performance was useful is distinguishing Valencia types from the other three market types. Valencia types on average performed worst in every agronomic category except for yield, where the market types did not differ significantly. The core collection showed more extremes in ranges than the mini-core collection across all market types in all agronomic traits.

Revised Pod Traits

Virginia types in the core collection had a lower average meat to hull ratio than the mini- core collection; the only market type to not have a higher average ratio in the core collection.

Additionally, all market types other than the Virginia market type had a higher range in the core collection compared to the mini-core collection. Meat to hull ratio in Valencia types ranged from

0.64 to 3.65, Spanish types ranged from 0.63 to 4.33, and Virginia types ranged from 0.95 to

4.23 (Table 2-15). Meat to hull ratio in both collections reflected trends seen in meats and SMK

values; Valencia types had a low meat to hull ratio compared to the other market types, which agrees with the low SMK and meat content in Valencia types. In the mini-core collection,

Valencia types had the lowest minimum meat to hull ratio at 0.85. No other market type had a meat to hull ratio of less than 1.0 (Table 2-16).

Average percent fancy pods was lower in all market types except for Runner types in the core collection compared to the mini-core collection. However, ranges and maximums were higher across all market types: Spanish types had a range of 54.22%, Valencia types had a range of 98.08%, and Virginia types had a range of 86.8% (Table 2-17). Runner types had the highest average percent Fancy pods, at 34.58%. Virginia and Valencia types had the second and third highest Fancy pod content, at 24.66% and 20.17%, respectively. All market types had at least one accession that did not contain any pods that were fancy sized. In the mini-core collection,

Virginia types had the highest mean percent fancy pods, at 31.04%, and the highest maximum, at

83% (Table 2-18). Spanish types had the lowest average percent fancy pods, at 6.3%, ranging from 0% to 14.9%. Valencia types had an average percent fancy pods of 30%, ranging from

1.1% to 70.6%. In both collections, Spanish types had far lower Fancy pod content, averaging

4.36% in the core collection and 6.31% in the mini-core collection. (note: due to missing data, only one Runner type accession was measured for Fancy Pod percentage. The data from this accession was reported but will not be considered in this discussion.)Spanish types had the lowest average pod volume in both collections at 37.7 mL for both the mini-core and core collection (Tables 2-19 and 2-20). In the core collection, pod volume in Spanish types ranged from 15 mL to 80 mL, Valencia types ranged from 15 mL to 150 mL, and Virginia types ranged from 20 mL to 160 mL. In Spanish, Virginia and Valencia types, maximum and range were higher in the core collection than the mini-core collection. In the mini-core collection, Valencia

types had the highest average pod volume at 61.8 mL, ranging from 31.7 mL to 85 mL. Virginia types, which had an average pod volume of 54.8 mL, had the highest range, at 70 mL, and maximum, at 95 mL.

Pod traits distinctly separated Spanish types from the other three market types due to their small size (Figure 2-). Spanish types had significantly fewer fancy sized pods and significantly smaller pods overall. Valencia and Virginia types were on average the largest pods in both collections (Figure 2-). Valencia pods typically contain 3 or more seeds per pod, which results in longer pods, whereas Virginia types typically contain two large seeds per pod.

Revised Plant Traits

In the core collection, average plant height was similar to the mini-core average plant height, except that Runner types were on average nearly 5 cm shorter. The range of plant heights was larger in all market types in the core collection compared to the mini-core collection. The shortest (10 cm) and tallest (96.55 cm) plants in the core collection were both Virginia types

(Table 2-21). Virginia types were shorter on average than the other three market types in the core collection, with an average height of 35.9 cm. In the mini-core, Virginia types again had the highest range of heights (49.17 cm), from 16.67 cm to 65.83 cm (Table 2-22). Valencia types were the tallest, with an average height of 48.3 cm while Virginia types were the shortest at 36.4 cm.

Average plant width differed by only 4cm between market types in the core collection

(Table 2-23) and by 2 cm in the mini-core collection (Table 2-24). In both the mini-core and core collections, Virginia types had the highest range in widths, at 51.67 cm and 55 cm, respectively.

In the core and mini-core collection, Virginia types had a lower average height to width ratio than Spanish and Valencia types. This means that Virginia plants, with an average height- width ratio of 0.63 in the core (Table 2-25) and 0.65 in the mini-core (Table 2-26), tended to be

shorter and wider, whereas Valencia and Spanish types, which had an average height-width ratio of 0.84 and 0.78 in the mini-core, respectively, tended to be taller and more columnar.

Plant traits separated the Virginia types from Spanish and Valencia types. Despite not showing any significant differences in plant width, Virginia types were significantly shorter and had significantly lower height to width ratios than Spanish and Valencia types on average.

Further, plant traits may show distinct differences along subspecies type. Virginia and Runner types, which together form subspecies hypogaea, were the shorter than Valencia and Spanish types, which form subspecies fastigiata, although in the Runner type this difference was not significant.

Revised Leaf Traits

In the core collection, Valencia types had the longest average leaflets at 53.7 mm, and

Virginia types had the shortest average leaflets at 45.6 mm (Table 2-27). Spanish types had the highest range of leaflet lengths at 122 mm, from a minimum of 21.75 mm to a maximum of

143.75 mm. All market types had a higher maximum width and a higher range in widths in the core collection compared to the mini-core collection. In the mini-core collection, Virginia types had the shortest leaflets, averaging 45 mm, while Valencia types had the longest leaflets on average at 54.6 mm (Table 2-28). There were no significant differences between market types for leaflet width. Valencia types had the highest mean leaflet width at 25.32 mm. Valencia types had the largest leaflets on average.

The core collection and the mini-core had similar mean internode distance, but the core collection had lower minimum distances, higher maximum distances, and higher ranges than the mini-core in all market types (Tables 2-31 and 2-32). Leaflet internode distance was significantly higher in Valencia types than Virginia types in the mini-core collection (p=0.012). In the mini- core, internode distance was very similar within the two subspecies: averages for hypogaea types

differed by only 0.02 mm, and fastigiata types had averages that differed by only 0.06 mm.

Leaflet traits can distinguish Valencia types and Virginia types in the mini-core collection. On average, Valencia types have significantly longer leaflets that are significantly more spread out than Virginia types.

Discussion

Phenotypic trait characterization is critical for effective utilization of the USDA germplasm collection for several reasons. Firstly, phenotypic characterization is necessary to assess the adequacy of the collection. Secondly, phenotypic characterization can be used to assess the diversity represented in the collection. Thirdly, phenotypic characterization can identify superior lines within the collection, or reveal potential breeding challenges in utilizing the accessions.

Although morphological characteristics are often muddied in between varieties (Stalker and Simpson, 1995), market type designation is useful in categorizing a large and cumbersome collection like the USDA peanut germplasm collection into more manageable pieces. One example of the advantage of the market type system is the creation of the Valencia collection, which contains 630 accessions. A 77-accession core subset of this collection was also developed

(Dwivedi et al., 2008). For breeders interested in breeding Valencia type cultivars, it would be most useful to begin their trait mining with the Valencia genetics available in this collection.

However, market type designation, not unlike the often confused and error-prone subspecies and variety labelling (Stalker and Simpson, 1995), is both incomplete and inadequate. Most immediately problematic is that over 10% of the core collection accessions have not been assigned to a market type on the GRIN database (although variety designation may have been assigned), and approximately 4% of accessions have two or more conflicting classifications assigned to them. Market type designation is an important way to understand the larger

collection. Using phenotypic characterization, we can potentially place these Unclassified and

Mixed accessions into their correct market type designation. Market type is one of the most useful ways to group peanut germplasm, which is necessary because of the large and often unmanageable number of accessions. Market types have their basis in the subspecies and botanical varieties within A. hypogaea. However, there is evidence that the taxonomy of the botanical varieties is inaccurate based on genetic analysis (He & Prakash, 2001). Further, because peanut is a relatively newly evolved species (Moretzsohn et al., 2013), and has been under intense selection pressure, it is possible that high levels of phenotypic diversity are not reflected in the genome, which displays a relatively low amount of genetic diversity; this phenomenon has been observed in other highly self-pollinating crops (Shattuck-Eidens, Bell,

Neuhausen, & Helentjaris, 1990). Because of this difference, phenotypic traits will likely continue to be important in distinguishing botanical varieties in peanut in spite of increasingly robust genomic analysis.

Incorrect main stem flowering pattern revealed incorrectly grouped accessions. Correct identification and classification is necessary for the proper functioning of the USDA core germplasm collection (Steiner et al., 2001). The issues discovered in the mini-core and core collection most likely extend to the larger collection. In the mini-core collection, half of the

Runner types, 9% of Spanish types, 25% of Valencia types, and 25% of Virginia types were mislabeled. In the combined core collection, a confirmed 42% of Runner types were mislabeled,

7% of Spanish types were mislabeled, 14% of Valencia types were mislabeled, and 15% of

Virginia types were mislabeled. This level of error can alter critical measurements of the capacity of the collections to represent the diversity and utility contained in the collection.

Further, the handling of new additions to the GRIN database, as well as to the core collection, must be based on robust and complete information. 17% of the core collection either did not contain an explicit market type, or this designation was confused in some way. Although many of the accessions labeled “Unclassified” did have the label for var. fastigiata, many did not follow the expected main stem flowering pattern and grouped closely with subspecies hypogaea.

Even excluding these accessions, over 7% of accessions did not have a market type label. This suggests improvements should be made to ensure a complete profile of an accession before it is admitted into the database.

Genetic diversity is an important breeding tool in all crops, but is especially important in crops like peanut which have a low level of diversity. Maintaining all available diversity and being able to manage and access the diversity to contribute to breeding efforts is the central dilemma in the germplasm maintenance community, as these two concepts are often at odds with each other; maintaining all diversity necessitates the collection and subsequent regeneration of a huge number of unique lines. The concept of the core collection (Holbrook et al., 1993) and the mini-core collection (Holbrook and Dong, 2005) is to represent the available genetic diversity in a smaller number of accessions. However, while the core collection has been shown to represent the genetic variation expressed in the USDA germplasm collection, the mini-core may not show the same level of diversity across phenotypic traits as the core collection. Not including Runner types, in every single trait measured, except for percent SMK and meat to hull ratio, the core collection had a higher range of values than the mini-core. This indicates that the genetic variation expressed in the core collection is greater than the mini-core. This is not surprising; the mini-core collection contains only 10% of the accessions found in the core collection. The degree to which the reduction in accession number should impact the reduction in diversity of

expressed characteristics is a separate discussion, but so long as the ability to trait mine and identify useful genetic material is not seriously hindered, the mini-core will continue to be an invaluable tool.

Trait mining in the core and mini-core collections has yielded positive results; accessions that have genetic contributions to disease resistance, abiotic stress resistance, and high- performance have been identified (Barkley et al., 2016). A two-stage screening process is often used. The core collection was created by organizing the USDA collection into clusters of accessions that are similar. A small number of each cluster was selected to serve as a reference for their cluster. When the core collection is screened for a trait of interest, the accessions that display the desired trait can be used to narrow the search in the larger collection to a specific cluster, where accessions are more likely to show characteristics comparable to the screened core accession (Holbrook and Anderson, 1995). This screening process is heavily dependent on availability of phenotypic data. Without robust data with which to assess the genetic merits of individual accessions, the screening process would not be possible.

This dataset in its entirety can be useful to suggest a likely market type for accessions that are either unclassified or are of mixed type. The first step in this analysis is to identify all accessions that have been correctly identified. As pointed out earlier, the flowering pattern is a useful trait to detect misclassified accessions. Once this is done, robust predictive equations are developed using discriminant analysis, based on the training/validation set concept through repeated subsampling of the data using, e.g., the leave-one-out approach. Once a robust predictive equation has been developed, the likely group membership of the unknown or misclassified accessions can be performed. This information, added to the GRIN database as supplemental tables, can then be the basis to further confirm or reassign group membership.

Table 2-1. Accession type within experimental design per year Accession Type # of accessions # of reps per block # of reps per year Commercial Standard Group 1 8 3 72 Commercial Standard Group 2 6 1 18 Mini Core Collection 107 1 321 Non-Mini Core, Core Collection 687 N/A 687 Total per year 808 N/A 1098

Table 2-2. Mean, minimum, maximum, and standard error of peanut agronomic traits as a function of market type grouped by collection. Yield (kg/ha) Percent Sound Mature Kernels Weight of meats from 200g sample Market Type n Mean Min Max SE Mean Min Max SE Mean Min Max SE Core Mixed 52 1828 102 3837 132 65.3 42.9 80.1 1.1 139.3 111.6 174.1 1.7 Runner 24 2283 861 3915 183 71.1 64.0 78.8 0.8 146.6 132.7 161.2 1.4 Spanish 300 2158 54 4888 58 70.8 25.5 80.5 0.4 148.4 77.6 162.5 0.6 Unclassified 182 2103 248 4295 73 65.6 31.7 81.0 0.5 138.8 94.5 164.3 0.8 Valencia 360 2145 138 4520 49 64.1 32.6 79.8 0.4 136.2 77.7 157.0 0.6 Virginia 456 2011 40 5795 43 69.1 51.5 80.5 0.2 142.8 97.3 161.8 0.4 Mini-Core Mixed 10 2151 1406 3460 213 65.9 50.7 73.9 2.2 139.2 116.0 153.6 3.8 Runner 4 2298 1695 3352 386 69.6 65.5 72.0 1.6 131.2 98.4 145.9 11.2 Spanish 42 2365 712 4066 128 68.8 50.5 75.9 0.9 144.7 121.8 155.3 1.2 Unclassified 32 2139 690 3815 134 62.2 40.9 73.2 1.5 134.5 97.3 151.1 2.3 Valencia 48 2115 505 3728 118 62.5 36.9 73.8 1.1 134.7 83.0 153.7 2.0 Virginia 78 2440 417 4111 88 68.9 43.0 82.0 0.7 143.6 113.7 165.1 1.0 Commercial Standards Runner 8 3499 2791 4282 179 76.7 71.8 79.8 0.9 156.9 147.3 162.7 1.8 Spanish 2 3107 2759 3455 348 70.1 66.8 73.4 3.3 147.2 143.3 151.2 4.0 Valencia 6 3034 2317 4046 304 66.2 59.0 71.0 2.1 139.2 127.0 146.9 3.3 Virginia 6 3489 2844 3916 143 74.4 71.7 77.1 0.8 151.0 145.8 156.1 1.6

Table 2-3. Mean, minimum, maximum, and standard error of pod characteristics as a function of market type grouped by collection. Meat to Hull Ratio Percent Fancy Pods Pod Volume (mL) Market Type n Mean Min Max SE Mean Min Max SE Mean Min Max SE Core Mixed 52 2.5 1.3 6.7 0.1 12.8 0 54.1 2.8 49 15 90 3 Runner 24 2.8 2.0 4.3 0.1 26.0 0 94.9 9.3 52 35 70 2 Spanish 300 3.0 0.6 4.3 0.0 5.1 0 62.6 0.9 38 15 80 1 Unclassified 182 2.4 0.9 4.6 0.0 22.6 0 84.8 2.3 60 25 120 1 Valencia 360 2.2 0.6 3.7 0.0 19.4 0 98.1 1.5 63 15 150 1 Virginia 456 2.6 1.0 4.2 0.0 23.6 0 86.8 1.7 59 20 160 1 Mini-Core Mixed 10 2.4 1.4 3.4 0.2 10.3 1.7 26.3 4.5 43 30 57 3 Runner 4 2.3 1.8 2.7 0.2 28.4 24.7 32.1 3.7 56 53 60 1 Spanish 42 2.8 1.6 4.6 0.1 7.4 0 29.2 1.5 39 18 57 1 Unclassified 32 2.2 1.0 3.1 0.1 24.6 0 76.2 6.3 56 25 83 3 Valencia 48 2.2 0.9 3.3 0.1 23.6 1.1 70.6 4.3 58 32 85 2 Virginia 78 2.6 1.4 4.7 0.1 30.5 0 83.0 4.2 55 25 95 2 Commercial Standards Runner 8 3.7 2.8 4.4 0.2 90.1 82.9 96.2 2.9 51 45 56 1 Spanish 2 2.8 2.5 3.1 0.3 4.8 4.8 4.8 . 38 38 38 0 Valencia 6 2.4 1.8 2.8 0.2 69.1 53.9 79.3 7.8 62 56 67 1 Virginia 6 3.1 2.7 3.6 0.1 81.7 76.0 87.4 3.3 84 80 90 2

Table 2-4. Mean, minimum, maximum, and standard error of plant architecture as a function of market type grouped by collection. Plant Height (cm) Plant Width (cm) Height to Width Ratio Market Type n Mean Min Max SE Mean Min Max SE Mean Min Max SE Mixed 52 41.0 10.0 95.0 2.1 55.3 20.0 75.0 2.5 0.76 0.29 1.27 0.03 Runner 24 37.7 15.0 56.3 2.3 56.3 30.0 75.0 3.7 0.7 0.38 1.17 0.04 Spanish 300 42.5 15.0 68.8 0.7 56.9 20.0 75.0 1.0 0.77 0.36 1.2 0.01 Core Unclassified 182 46.5 10.0 75.0 1.1 58.0 20.0 75.0 1.3 0.82 0.33 2.25 0.01 Valencia 360 47.8 15.0 76.3 0.7 59.9 25.0 75.0 0.9 0.82 0.27 1.75 0.01 Virginia 456 36.9 10.0 96.6 0.6 57.7 20.0 75.0 0.8 0.65 0.22 1.29 0.01 Mixed 10 44.1 31.7 60.4 3.3 57.7 35.0 75.0 5.8 0.79 0.64 1 0.04 Runner 4 40.9 25.0 52.5 6.6 59.2 41.7 75.0 9.2 0.69 0.6 0.79 0.04 Spanish 42 43.3 28.3 59.2 1.5 57.0 35.0 75.0 2.5 0.78 0.57 0.98 0.02 Mini-Core Unclassified 32 44.2 15.0 62.5 2.1 57.4 26.7 75.0 2.9 0.79 0.39 1.08 0.03 Valencia 48 44.1 16.7 65.0 1.7 58.4 26.7 75.0 2.2 0.78 0.4 1.15 0.02 Virginia 78 38.4 16.7 65.8 1.3 58.3 23.3 75.0 1.8 0.68 0.35 1.1 0.02 Runner 8 24.6 19.4 33.2 1.7 60.5 0.4 0.5 5.2 0.42 42.22 75 0.02 Spanish 2 46.0 36.1 56.0 9.9 57.5 0.8 0.9 15.3 0.82 42.22 72.78 0.05 Commercial Standards Valencia 6 51.3 43.3 58.3 2.4 62.1 0.7 1.0 5.8 0.85 48.33 75 0.05 Virginia 6 30.9 21.1 42.1 3.6 63.2 0.4 0.6 5.0 0.49 48.33 75 0.04

Table 2-5. Mean, minimum, maximum, and standard error of leaflet characteristics as a function of market type grouped by collection, Leaflet Length to Width Leaflet Internode Distance Leaflet Length (mm) Leaflet Width (mm) Ratio (mm)

Market Type n Mean Min Max SE Mean Min Max SE Mean Min Max SE Mean Min Max SE Mixed 52 49.7 26.0 69.3 1.5 24.0 14.3 33.3 0.7 2.1 1.5 2.3 0.0 13.3 5.5 20.5 0.5 Runner 24 48.7 31.1 77.0 2.1 23.5 14.2 33.1 0.9 2.1 1.6 2.9 0.1 12.6 8.0 24.0 0.7 Spanish 300 51.6 21.8 143.8 0.7 25.1 13.9 58.4 0.3 2.1 0.9 4.5 0.0 14.7 6.4 47.0 0.2 Core Unclassified 182 52.0 27.5 86.6 0.9 24.4 14.3 49.2 0.4 2.1 1.0 2.9 0.0 14.5 6.0 24.5 0.3 Valencia 360 52.3 25.2 77.8 0.5 24.3 14.6 65.8 0.2 2.2 0.7 2.9 0.0 14.1 5.1 24.5 0.2 Virginia 456 46.3 23.0 86.1 0.5 22.2 13.7 48.5 0.2 2.1 1.0 4.4 0.0 12.3 5.5 24.0 0.2 Mixed 10 48.5 37.3 59.0 2.2 22.6 16.2 28.5 1.1 2.2 2.0 2.4 0.0 12.3 9.9 14.3 0.5 Runner 4 48.1 42.8 57.3 3.4 21.9 19.5 24.8 1.2 2.2 2.1 2.3 0.1 12.6 10.7 13.8 0.7 Spanish 42 50.7 35.7 68.3 1.3 24.7 18.4 31.5 0.6 2.1 1.8 2.4 0.0 14.1 9.7 18.3 0.4 Mini-Core Unclassified 32 51.7 32.4 71.9 1.7 23.9 17.6 30.8 0.6 2.2 1.8 2.4 0.0 13.8 8.4 17.0 0.4 Valencia 48 51.5 33.4 66.2 1.2 24.1 15.6 39.8 0.6 2.2 1.7 2.5 0.0 13.4 8.5 17.5 0.3 Virginia 78 46.6 20.5 68.4 1.0 21.9 9.5 31.4 0.4 2.1 1.7 2.5 0.0 12.3 6.8 17.5 0.3 Runner 8 43.8 39.3 50.4 1.2 19.8 16.6 23.2 0.8 2.2 1.8 2.5 0.1 11.3 10.0 12.1 0.2 Commercial Spanish 2 53.7 47.8 59.6 5.9 23.8 22.5 25.0 1.3 2.3 2.1 2.4 0.1 15.6 14.1 17.1 1.5 Standards Valencia 6 53.8 45.9 62.5 3.1 25.6 21.7 28.8 1.3 2.1 2.0 2.3 0.1 14.4 12.2 17.3 0.8 Virginia 6 47.0 35.9 62.5 4.1 20.8 17.6 26.0 1.5 2.3 2.0 2.5 0.1 12.4 9.5 16.3 0.9

Table 2-6. Between Canonical Structure for core collection accessions. Correlation coefficients and the relative ranking of each variable in both canonical variables are displayed. Values with asterisks (*) denote variables that contribute a significant weight to the correlation. The first set of canonical variables did not account for incorrect flowering data and included all four market types plus unclassified and mixed accessions. The second set of canonical variables excluded the unclassified and mixed accessions to more precisely discriminate between correct and incorrectly grouped accession based on flowering data. The third canonical variable pair used only the correctly grouped accessions for each market type based on flowering data and added unclassified and mixed types of different flowering patterns as separate classes. With flowering data, excluding With corrected flowering data, all Without Flowering Data unclassified and mixed groups Variable Can1 Rank Can2 Rank Can1 Rank Can2 Rank Can1 Rank Can2 Rank Yield 0.94* 3 -0.07 16 0.31 15 -0.19 14 0.43 15 -0.17 16 Pod Volume -0.39 15 0.91* 1 -0.17 17 0.88* 1 -0.14 17 0.97* 1 Plant Height -0.76 11 -0.53 6 -0.86* 6 -0.17 15 -0.93* 6 -0.16 17 Plant Width 0.31 16 0.48 7 -0.44 14 0.52 5 -0.42 16 0.31 10 Height:Width Ratio -0.77 10 -0.59 5 -0.87* 5 -0.26 12 -0.94* 5 -0.24 12 Meats 0.85* 9 -0.28 14 0.67 13 -0.44 8 0.81* 10 -0.46 6 ELK 0.89* 6 0.42 10 0.88* 2 0.16 16 0.96* 2 0.22 13 Mediums -0.05 17 -0.48 8 -0.73* 9 -0.46 7 -0.59 13 -0.47 5 No.1s -0.89* 4 -0.44 9 -0.87* 4 -0.14 17 -0.97* 1 -0.19 14 Others -0.99* 1 0.00 17 -0.71* 11 0.41 10 -0.94* 4 0.25 11 Splits 0.87* 8 -0.37 12 0.72* 10 -0.61 3 0.78* 11 -0.56 3 SMK 0.97* 2 -0.14 15 0.80* 8 -0.50 6 0.88* 8 -0.40 8 Meat:Hull Ratio 0.88* 7 -0.31 13 0.69 12 -0.57 4 0.76* 12 -0.51 4 Leaflet Distance -0.55 13 -0.75 3 -0.82* 7 -0.44 9 -0.88* 9 -0.41 7 Leaflet Length -0.89* 5 -0.38 11 -0.90* 1 -0.19 13 -0.95* 3 -0.19 15 Leaflet Width -0.69 12 -0.66 4 -0.87* 3 -0.37 11 -0.90* 7 -0.38 9 Leaflet Length:Width Ratio -0.44 14 0.78 2 -0.30 16 0.85* 2 -0.43 14 0.56 2 Variation Accounted For 58.9% 24.4% 53.9% 21.3% 71.2% 17.7%

Table 2-7. Canonical discriminate analysis of market type split by main stem flowering pattern. In every case, classes that have the same market type, but different flowering pattern are significantly different. These comparisons are denoted with an asterisk (*). Market Type and Runner Runner Spanish Spanish Valencia Valencia Virginia Virginia Flowering No Yes No Yes No Yes No Yes Runner No 1 0.0002* 0.71 0.01 0.06 <.0001 0.69 0.13 Runner Yes 1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Spanish No 1 0.003* 0.02 <.0001 0.19 0.08 Spanish Yes 1 <.0001 <.0001 <.0001 0.00 Valencia No 1 <.0001* 0.01 <.0001 Valencia Yes 1 <.0001 <.0001 Virginia No 1 <.0001* Virginia Yes 1

Table 2-8. Probabilities of distance based on canonical correlations for all market types plus unclassified and mixed accession split by flowering data. Mixed accessions that did not flower on the main stem were not significantly distant from Runner and Virginia types, and Mixed accessions that did flower on the main stem were not significantly different from Spanish types. The clustering of unclassified accessions that did not flower on the main stem was not significantly different than the clustering of Runner types and the clustering of unclassified accessions that did flower on the main stem was not significantly different than the clustering of Valencia types. Market Type Mixed No Mixed Yes Runner Spanish Unclassified No Unclassified Yes Valencia Virginia Mixed No 1 0.04 0.93 0.01 0.01 <.0001 <.0001 0.10 Mixed Yes 1 0.01 0.34 0.00 0.05 0.01 0.00 Runner 1 0.00 0.27 <.0001 <.0001 0.57 Spanish 1 <.0001 <.0001 <.0001 <.0001 Unclassified No 1 <.0001 <.0001 0.05 Unclassified Yes 1 0.20 <.0001 Valencia 1 <.0001 Virginia 1

Table 2-9. Yield (kg·ha-1) of peanuts as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 2587.9 965.5 2699.6 4387.9 3422.4 295.3 Spanish 273 2479.4 60.4 2467.2 5478.2 5417.7 67.0 Valencia 306 2442.6 154.3 2449.4 5066.4 4912.1 60.0 Virginia 375 2265.1 362.4 2152.0 6495.4 6133.0 52.6

Table 2-10. Yield (kg·ha-1) of peanuts as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 2292.9 1899.6 2292.9 2686.3 786.6 393.3 Spanish 40 2628.4 797.9 2575.3 4557.3 3759.4 148.1 Valencia 36 2457.7 566.1 2547.9 3860.6 3294.4 150.1 Virginia 66 2726.1 467.2 2608.0 4608.0 4140.7 111.7

Table 2-11. Percentage sound mature kernels per adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 72.05 65.34 71.88 78.8 13.46 1.04 Spanish 273 71.23 43.06 71.64 80.47 37.41 0.33 Valencia 306 63.37 32.55 63.92 79.81 47.27 0.4 Virginia 375 69.1 51.46 69.93 79.64 28.18 0.26

Table 2-12. Sound mature kernel percent by market type for the adjusted mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 70.51 68.99 70.51 72.03 3.04 1.52 Spanish 40 68.7 50.46 69.14 75.87 25.41 0.9 Valencia 36 61.37 36.91 61.81 73.83 36.93 1.3 Virginia 66 69.03 43.01 70.13 81.95 38.94 0.81

Table 2-13. Meats per 200g of peanuts as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 149.25 137.95 149.16 161.2 23.25 1.75 Spanish 273 148.61 77.59 150.36 162.47 84.87 0.59 Valencia 306 135.32 77.72 136.41 156.99 79.27 0.64 Virginia 375 142.58 97.33 143.98 161.74 64.41 0.45

Table 2-14. Meats per 200g of peanuts as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 145.08 144.25 145.08 145.9 1.65 0.82 Spanish 40 144.57 121.8 144.76 155.3 33.5 1.26 Valencia 36 132.69 82.97 135.28 153.7 70.73 2.52 Virginia 66 144.12 113.73 145.6 165.07 51.33 1.09

Table 2-15. Meat to hull ratio as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 3.02 2.22 2.93 4.3 2.07 0.15 Spanish 273 3.01 0.63 3.03 4.33 3.69 0.04 Valencia 306 2.17 0.64 2.14 3.65 3.01 0.03 Virginia 375 2.56 0.95 2.57 4.23 3.28 0.03

Table 2-16. Meat to hull ratio as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 2.66 2.61 2.66 2.71 0.1 0.05 Spanish 40 2.75 1.56 2.66 4.57 3.02 0.09 Valencia 36 2.11 0.85 2.12 3.34 2.49 0.09 Virginia 66 2.68 1.37 2.68 4.73 3.36 0.07

Table 2-17. Percent Fancy pods as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 34.58 0 20.07 94.93 94.93 15.33 Spanish 136 4.36 0 0 54.22 54.22 0.82 Valencia 152 20.17 0 12 98.08 98.08 1.65 Virginia 187 24.66 0 12.73 86.81 86.81 1.85

Table 2-18. Percent Fancy pods as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 32.11 32.11 32.11 32.11 0 . Spanish 20 6.31 0 6.68 14.86 14.86 1.13 Valencia 18 29.95 1.1 21.27 70.56 69.46 4.84 Virginia 33 31.04 0 28.44 83.03 83.03 4.82

Table 2-19. Pod volume as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 12 46.94 35 46.67 60 25 2.23 Spanish 246 37.73 15 35 80 65 0.76 Valencia 286 63.01 15 60 150 135 0.97 Virginia 346 60.34 20 55 160 140 1.2

Table 2-20. Pod volume as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 55 53.33 55 56.67 3.33 1.67 Spanish 40 37.73 18.33 38.33 56.67 38.33 1.33 Valencia 36 61.78 31.67 61.67 85 53.33 1.91 Virginia 66 54.84 25 51.67 95 70 2.15

Table 2-21. Plant height at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 33.27 15 32.29 50 35 3.18 Spanish 275 43.11 15 43.75 68.75 53.75 0.68 Valencia 308 49.89 20 50 76.25 56.25 0.63 Virginia 377 35.88 10 35 96.55 86.55 0.65

Table 2-22. Plant height at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 38.13 25 38.13 51.25 26.25 13.13 Spanish 40 43.3 28.33 42.08 59.17 30.83 1.5 Valencia 36 48.3 30 47.92 65 35 1.53 Virginia 66 36.43 16.67 34.79 65.83 49.17 1.34

Table 2-23. Plant width at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 56.22 35 50.83 75 40 4.71 Spanish 275 57.14 25 56.25 75 50 1.08 Valencia 308 60.25 25 65.63 75 50 0.91 Virginia 377 58.21 20 60 75 55 0.88

Table 2-24. Plant width at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 58.33 41.67 58.33 75 33.33 16.67 Spanish 40 56.95 35 55.08 75 40 2.54 Valencia 36 58.98 36.67 55.63 75 38.33 2.43 Virginia 66 57.75 23.33 56.46 75 51.67 2.01

Table 2-25. Plant height to width ratio at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 0.59 0.38 0.62 0.73 0.35 0.03 Spanish 275 0.78 0.46 0.77 1.2 0.74 0.01 Valencia 308 0.85 0.47 0.85 1.5 1.03 0.01 Virginia 377 0.63 0.22 0.63 1.29 1.07 0.01

Table 2-26. Plant height to width at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 0.64 0.6 0.64 0.68 0.08 0.04 Spanish 40 0.78 0.57 0.79 0.98 0.41 0.02 Valencia 36 0.84 0.6 0.83 1.15 0.55 0.02 Virginia 66 0.65 0.35 0.64 0.88 0.53 0.02

Table 2-27. Leaflet length at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 47.32 31.1 43.43 77 45.9 3.08 Spanish 276 52.34 21.75 51.47 143.75 122 0.7 Valencia 308 53.7 35.25 52.45 77.75 42.5 0.51 Virginia 378 45.56 22.99 43.93 86.11 63.13 0.53

Table 2-28. Leaflet length at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 46.03 43.38 46.03 48.67 5.28 2.64 Spanish 40 51.21 36.7 51.41 68.29 31.59 1.25 Valencia 36 54.56 42.9 56.72 66.21 23.31 1.03 Virginia 66 44.97 20.54 44.27 63.63 43.09 0.93

Table 2-29. Leaflet width at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 22.7 14.18 22.47 33.13 18.95 1.29 Spanish 276 25.41 13.88 25.38 58.44 44.56 0.29 Valencia 308 24.94 15.1 24.33 65.75 50.65 0.25 Virginia 378 21.67 13.65 20.88 48.53 34.88 0.23

Table 2-30. Leaflet width at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 20.97 19.45 20.97 22.5 3.05 1.53 Spanish 40 24.89 18.4 25.08 31.5 13.1 0.55 Valencia 36 25.32 18.82 24.96 39.79 20.97 0.61 Virginia 66 21.1 9.45 20.98 28.92 19.47 0.41

Table 2-31. Leaflet internode distance at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 12.4 8 11.96 24 16 1.04 Spanish 276 14.96 6.5 14.5 47 40.5 0.24 Valencia 308 14.57 7.15 14.28 24.5 17.35 0.16 Virginia 378 12 5.45 11.48 24 18.55 0.16

Table 2-32. Leaflet internode distance at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 11.68 10.7 11.68 12.67 1.97 0.98 Spanish 40 14.27 9.65 14.33 18.33 8.68 0.38 Valencia 36 14.21 11.02 14.17 17.5 6.48 0.27 Virginia 66 11.7 6.82 11.53 16.17 9.35 0.24

Table 2-33. Leaflet length to width ratio at 75 days after planting as a function of adjusted market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 2.1 1.64 2.11 2.92 1.28 0.09 Spanish 276 2.07 0.87 2.06 4.51 3.64 0.02 Valencia 308 2.17 0.72 2.17 2.9 2.18 0.01 Virginia 378 2.11 0.96 2.1 4.4 3.44 0.01

Table 2-34. Leaflet length to width ratio at 75 days after planting as a function of adjusted market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 2.18 2.14 2.18 2.22 0.09 0.04 Spanish 40 2.06 1.79 2.05 2.39 0.6 0.02 Valencia 36 2.18 1.69 2.19 2.5 0.81 0.03 Virginia 66 2.14 1.69 2.13 2.47 0.77 0.02

Figure 2-1. Plot arrangement denoting plots 1.5 m wide x 3 m long, with 3 m between plots within a row and 1.5 m distance between plots across rows during the 2013 field season at the University of Florida Plant Science Research and Education Unit at Citra, FL. Photo courtesy of Greg MacDonald.

2 1

3 4

Figure 2-2. Leaflet measurements were collected by selecting fully mature leaves from two randomly selected plants. Leaflet length and width were measured to the nearest millimeter (represented by the yellow and red arrows, respectively), and the distance between the leaflet pairs was recorded (represented by the blue arrow). Photo courtesy of author.

Figure 2-3. Plant height and width was determined by taking the average height and width of three plants per row. Photo courtesy of Greg MacDonald.

Figure 2-4. Apparatus for measuring pod volume. Photo courtesy of author.

Figure 2-5. A Spanish type (left) with a pod volume of 20 mL, and a Virginia type (right) with a pod volume of 90 mL. Photo courtesy of author.

Figure 2-6. A Valencia type (left) and a Virginia type (right), both with a pod volume of 80 mL. The Valencia type has 3 or more seeds per pod, making them long and thin, while the Virginia type has only 2 seeds per pod, but are rounder. Photo courtesy of author.

Figure 2-7. Plotted group centroids of canonical variables. The first CDA contained all groups without regard for correct flowering pattern (A). The second CDA contained known market types accounting for flowering pattern (B). The third CDA contained only correctly flowering known market types and unknown market types by flowering pattern (C). The third CDA accounted for 5.6% more variation than the first CDA.

CHAPTER 3 BIOCHEMICAL CHARACTERIZATION

Background

Peanut (Arachis hypogaea L.), is an oilseed crop of global economic importance. While it is commonly referred to as a nut, botanically peanut is a legume, belonging to the family

Fabaceae. Distinct in the geocarpic growth of its fruit, peanut is native to South America, evolving roughly 2.5 million years ago resulting from a chromosome-doubling hybridization event combining the closely related genomes of progenitors Arachis duranensis Krapov. and

W.C. Gregory and Arachis ipaensis Krapov. and W.C. Gregory. Peanut has relatively low genetic variation available to breeders due to its relatively recent speciation, the close relationship of the parent genomes, and the genetic bottleneck created due to intersectional infertility and ploidy differences between A. hypogaea and its wild relatives.

To help preserve the limited genetic diversity available in the peanut genome, germplasm collections have been created and maintained across the globe. In the United States, roughly

10,000 accessions of unique peanut germplasm make up the USDA peanut germplasm collection maintained at the Agricultural Research Station in Griffin, Georgia. To further assist breeders in exploring and exploiting the genetic resources in the USDA collection, the core germplasm collection was developed (Holbrook et al., 1993). This collection contains 831 of the accessions in the full collection and has been shown to contain a large portion of the genetic diversity present in the full collection. Further, a mini-core collection was later developed containing 112 accessions from the core collection specifically to be used in research where replication is needed and would not be possible with the high number of accessions in the core (Holbrook and

Dong, 2005).

Peanut oil is composed of 8 major fatty acids, although only oleic (C18:1), linoleic

(C18:2) and palmitic acid (C16:0) are present at levels over 5% in most accessions. Oleic acid alone accounts for around 50-60% of total oil. Linoleic acid is normally observed at levels around 20-30%, with palmitic acid accounting for approximately 10% of total oil. Stearic

(C18:0), behenic (C22:0), arachidic (C20:0), lignoceric (24:0), and gadoleic acid (C20:1) combine to make up the remaining 10% of total oil. Oleic, linoleic, and gadoleic acid are unsaturated fatty acids, while palmitic, stearic, behenic, arachidic, and lignoceric acid are saturated fatty acids. Fatty acid composition has an impact on peanut flavor and the development of rancidity. Oxidative rancidity is common in nuts, which tend to have high oil content. Rancid peanuts have an “off” flavor considered unfit for human consumption, with roasting, a common step in peanut processing, contributing and accelerating the process of rancidity (Maté, Saltveit,

& Krochta, 1996). Because rancidity has the highest impact on polyunsaturated fatty acids, linoleic acid content is the primary contributor to rancidity in peanut since it is the only polyunsaturated fatty acid; oleic and gadoleic acid are mono-unsaturated fatty acids.

The quality and quantity of oil in peanut are important traits to the peanut industry. The oil content in peanut is approximately 40-50% by dry weight (Dean et al., 2009). Peanut oil is a major use of peanut in the world market, although oil processing is less common in the U.S.; in fact, there are only four facilities domestically that do so, utilizing expeller crushing technology.

While only around 10-15% of peanuts are crushed for oil domestically, globally around 40% of peanut is used for oil (USDA-FAS, 2017). Peanut oil is popular as an alternative frying oil in specialty potato products and is a preferred cooking oil for fish. Peanut oil has a high smoke point, a highly sought-after quality in cooking and frying oils, and is relatively expensive compared to more common oils such as cotton and soybean oil. The quality of peanut oil is

partially determined by the fatty acid composition. Behenic acid, present in peanut oil but only found in trace amounts in other plant oils, is used to detect peanut oil contamination. Palmitic acid, the only saturated fat found in relatively high quantities, can affect the viscosity of the oil, making the oil thicker at higher palmitic acid concentrations. Further, higher levels of linoleic acid, the only polyunsaturated fatty acid present in peanut oil, makes peanut oil less viscous

(List, 2016).

A relatively recent breakthrough in peanut breeding was the discovery and successful breeding of the Hi-Oleic trait. Oleic and linoleic acid content are inversely related due to the conversion of oleic acid to linoleic acid during seed development; thus, early harvest can result in higher oleic acid content. The occurrence of peanut lines with extremely high levels of oleic acid (80%), and as a result extremely low levels of linoleic acid (2%), was first seen in experimental University of Florida breeding line F435 (Norden, Gorbet, Knauft, & Young,

1987). Although the genetic mechanism was not yet fully understood, the Hi-Oleic trait was further developed and successfully bred into commercially available cultivars by Gorbet and

Knauft, beginning with the Sun Oleic 95R cultivar, and later the Sun Oleic 97R cultivar, both of which had been shown to possess extremely high levels of oleic acid (>80%) (Gorbet & Knauft,

2000). It was later discovered that Hi-Oleic peanuts have a higher oleic to linoleic acid ratio due to a mutation in the FAD2B gene (Burton et al., 2004), which controls the conversion of oleic acid to linoleic acid during seed development. The trait was confirmed to be tightly controlled by two recessive alleles of the FAD2 gene, ahFAD2A and ahFAD2B (Chu, Holbrook, & Ozias-

Akins, 2009). Hi-Oleic varieties display several beneficial qualities, including improved shelf life due to reduced rancidity due to the reduced polyunsaturated fatty acids which are prone to break down into undesired chemical compounds (Moore & Knauft, 1989) (Mugendi et al., 1998)

and possible health benefits (Derbyshire, 2014). The market demand for Hi-Oleic peanuts has resulted in the release of Hi-Oleic varieties for all market types (Holbrook et al., 2016), and identification of lines possessing the Hi-Oleic trait have been greatly expediated with the development of real-time PCR techniques (Barkley et al., 2010).

Although the core and mini-core collections have been hugely beneficial in the advancement of breeding efforts, they have not been adequately characterized across biochemical traits. Because of the increased emphasis on oil composition in peanut oil, as well as the potential for even more breakthroughs in peanut biochemistry, a fully comprehensive biochemical profile must be accessible for every accession in the core collection. This experiment will help fill in gaps in data to better understand the oil and protein composition of individual accessions as well as trends within and between the collections.

Research Objectives

1. Provide robust biochemical data for use in breeding and genetic research.

A) Biochemically profile the core collection (including the mini-core) of the USDA germplasm collection across traits representing diverse characteristics including: flowering pattern, oil content, fatty acid composition, and protein content.

B) Assess the level of diversity among traits represented by the core and mini-core subsections of the USDA germplasm collection.

2. To analyze the core and mini-core collections for any inconsistencies, redundancies, or documentation errors that could jeopardize the utility and function of the USDA germplasm collection.

3. To replenish seed for the continued maintenance of the germplasm repository in Griffin, GA.

Experimental Background

The University of Florida Plant Science Research and Education Unit was chosen for the study, located in Citra, Florida. Fields consisted of Candler fine sand, and peanuts were grown according to University of Florida recommended practices for irrigation, fertility, and pest

management (Wright et al., 2000). The experiment was arranged in an augmented randomized block design with three blocks. In each block, fourteen check varieties of commonly grown commercial cultivars from each USDA GRIN market type (Valencia, Virginia, Spanish, and

Runner), called “commercial standards”, were planted. Eight of the check varieties were replicated three times in each block, and six of the check varieties were replicated once in each block due to lack of seed availability (Table 3-1). Replicated three times per block were Georgia-

06G (Runner), FL-107 (Hi-Oleic Runner), Bailey (Virginia), Florida Fancy (Hi-Oleic Virginia),

H&W-136 (Valencia), NM Valencia A (Valencia), OLin (Hi-Oleic Spanish), and Tamnut OL-06

(Hi-Oleic Spanish). Replicated once per block were NM 309-2 (Hi-Oleic Valencia), Florida-07

(Hi-Oleic Runner), Tifguard (Runner), Tamrun OL-11 (Hi-Oleic Runner), Red River (Hi-Oleic

Runner), and Jupiter (Virginia). Overall there were six Runner check varieties, three Virginia check varieties, two Spanish check varieties, and three Valencia check varieties. Eight of the fourteen check varieties contained the Hi-Oleic trait. Hi-Oleic peanuts have a higher oleic to linoleic acid ratio due to a mutation in the FAD2B gene (Burton et al., 2004). Hi-Oleic varieties display several beneficial qualities, including improved shelf life (Mugendi et al., 1998) and possible health benefits (Derbyshire, 2014). The market demand for Hi-Oleic peanuts has resulted in the release of Hi-Oleic varieties for all market types (Holbrook et al., 2016).

107 mini-core lines were replicated once in each block. Additionally, the experiment was augmented with 687 core lines randomly distributed between the blocks (Table 3-1). The three blocks were arranged against the slope of the field, running north-south.

Planting

Seeds were obtained from the USDA Germplasm Repository in Griffin, Georgia. Seeds were planted using the standard planting procedures used by the USDA Germplasm Repository.

Each accession was planted in a two-row plot 3 m in length with 75 cm row spacing (Figure 3-

1). There was a 3 m space between each plot in the planting direction, and a 1.5 m gap between rows to reduce the risk of cross contamination. Seeds were planted 3.5 cm deep at a density of 50 seeds per row (5 cm seed spacing). In 2013, fields were planted May 12-15, and in 2015 fields were planted in the last week of April.

Harvest

Virginia types were harvested 135-140 days after planting. After digging, one healthy plant from each plot was selected and photographed. After 2-3 days of drying, plants were thrashed using commercial plot thrashers and the pods bagged in mesh sacks. Sacks were temporarily stored in a semi-truck trailer, where they were fumigated with phostoxin (aluminum phosphide) to control moth populations. Samples were then transported to the University of Florida Weed Science

Building for processing.

Processing

Samples were cleaned, removing any stems and leaves left from the thrashing process.

The pods were then placed in brown paper bags and labelled. Approximately 10 mL of spinosad

((2R,3aS,5aR,5bS,9S,13S,14R,16aS,16bR)-2-(6-deoxy-2,3,4-tri-O-methyl-αL- mannopyranosyloxy)-13-(4-dimethylamino-2,3,4,6-tetradeoxy-β-D-erythropyranosyloxy)-9- ethyl-2,3,3a,5a,5b,6,7,9,10,11,12,13,14,15,16a,16b-hexadecahydro-14-methyl-1H-as- indaceno[3,2-d]oxacyclododecine-7,15-dione and 50-5%

indaceno[3,2-d]oxacyclododecine-7,15-dione) insecticide (Tracer1) was applied at a rate of at 1.0 mg-ai/kg individually to each bag for further insect control. Peanuts were stored in shell under ambient conditions.

Sample Preparation

Three seeds from a previously selected subsample of mature peanuts were flash frozen with liquid nitrogen and ground to a fine powder using a mortar and pestle. Ground tissue was placed into a plastic bag and stored frozen at -20°C prior to analysis.

Raw Protein Content

For the 2013 harvest, a Bradford protein assay using a Bovine Serum Albumin standard was used to calculate raw protein content in the seed (Bradford, 1976). This method proved problematic, with protein content averaging over 50% across accessions, while literature indicates that protein content should be approximately 26% on average (Dean et al., 2009).

Additionally, there was large inconsistency between replications of mini-core and commercial standard cultivars, indicating the test was performed incorrectly. As a result, this data was not reported in this analysis.

For the 2015 harvest, protein content in seed was calculated using total nitrogen detection via Kjeldahl digestion. The standard conversion factor of 5.46 was used for raw protein content calculation. Samples were digested using a modified version of the aluminum block digestion procedure used in Gallaher et al. (1975). Approximately 0.25 g of frozen ground tissue was used, and the exact weight recorded. This ground tissue was then placed into a glass tube and 3.2 g of a

9:1 potassium sulfate and cobalt sulfate catalyst mixture was added. Digestion was conducted for

4 hours at 400°C using 4.5 mL of sulfuric acid and 2 mL hydrogen peroxide. Solution was then

1Dow AgroScience, 2016

filtered into autosampler vials, and ammonia content in the resulting solution was calculated using a semi-automated colorimeter.

Total Oil Content

Total oil was calculated using an extraction procedure from (Jean Thomas, unpublished).

Approximately 0.2 g of ground tissue was used, and the exact weight recorded. Tissue was placed into 16x125 mm disposable glass tubes to which 2 mL of 50-50 hexane:tert-butyl ether was added. The tube was vortexed, capped, and placed in a fume hood for 10 hours. The tube was then centrifuged to settle solids and the liquid layer removed and placed in a separate, pre- weighed 16x125 mm tube. The extraction of the solids for oil was repeated three times, resulting in approximately 6 mL of extraction liquid in the pre-weighed tube. This tube was then placed in a heated evaporator unit2. Water below the tubes was heated to 40°C and nitrogen gas was pumped into to chamber to remove the hexane:tert-butyl ether extractants, resulting in only pure oil remaining in the tube. The tube, now containing pure oil, was then weighed again, and the original tube weight was subtracted from the final weight, leaving the raw weight of the extracted oil. The weight of the extracted oil was divided by the total weight of ground tissue used to calculate percent oil by weight.

Fatty Acid Composition

One drop (approximately 0.025 g) of the extracted oil was placed into a 12x32 mm amber autosampler vial. 200 µL of hexane were added to dissolve the oil and 200 µL of an esterification mixture containing one-part sodium methoxide, four parts petroleum ether, and two parts ethyl ether was added to the vial, and vortexed. An additional 600 µL of hexane were added to the vial, vortexed, and allowed to sit for 30 minutes at room temperature prior to analysis.

2 Zymark Turbo-Vap LV evaporator, Sotax Ag, Aesch, Switzerland

An Agilent 7890 gas chromatographer unit equipped with a flame ionization detector

(FID) was used for fatty acid determination. A 15 m Agilent/J&W DB-225 narrow-bore column

(0.25 mm) with a 60:1 split inset was set to an internal temperature of 280°C. A 1 µL injection volume was used and the carrier gas was helium set at a flow rate of 1 mL/minute. The detector temperature was set to 300°C and total run time was 17 minutes per sample. The retention time in minutes for the fatty acids are as follows: palmitic (1.619), stearic (2.465), oleic (2.638), linoleic (2.878), arachidic (3.500), gadoleic (4.154), behenic (6.419) and lignoceric (9.328). The resulting peak heights were recorded, and the height of individual peaks were divided by the combined height of all peaks to calculate percentage of total oil for each fatty acid.

Statistical Analysis

Canonical discriminate analysis (CDA) was used to analyze the relationship between market types for biochemical traits. The concept of the canonical discriminate function was first introduced by Fisher in 1936 to account for species relationships in iris flower data analysis and is similar to a principle component analysis (PCA). However, while both CDA and PCA transform correlated response variables into underlying uncorrelated variables, CDA accounts for the relationships between groups. In effect, both reduce the number of dimensions while maintaining the important information from the data, but the use of CDA in this experiment was justified by the higher power of discrimination between groups in CDA compared to PCA.

Additionally, the minimum, median, maximum values for lines means within group and the range among line means were calculated.

Results

The results are displayed in three sections. The first section discusses the data collected for all traits across the core and mini-core collection as they currently exist. The second section describes the canonical discriminate analysis which provided insight into trends that exist within the collections. This section also highlights potential errors in the core and mini-core collection.

The third section presents a revised subset of the original data based on corrections to errors implied by the canonical discriminate analysis. Unclassified types refer to accessions that do not have an assigned market type in the GRIN database, and mixed types refer to accessions that have an unclear market type designation in the GRIN database. Data from the 2013 and 2015 growing season was averaged across years. Number of data points per market type may be different between tables as a result of missing data.

Biochemical Analysis

Protein content was similar between the core and mini-core collections, making up approximately 30% of dry matter. In the mini-core, Runner types had the highest average protein content at 30.53%, while Valencia types had the lowest average protein content at 28.18% (Table

3-2).

Total oil content was similar among the market types in the core collection, averaging around 50% oil (Table 3-2). Valencia types had the lowest oil content on average at 48.86%, and

Virginia types had the highest average oil content at 51.37%. Virginia types also had the highest maximum oil content at 74.22%, as well as the highest range in oil contents at 49.42%.

Component fatty acids varied across market types. In all cases, the three most prevalent fatty acids were, in order, oleic acid, linoleic acid, and palmitic acid. Stearic acid and behenic acid made up roughly 2-3% of the oil profile each in most accessions. Gadoleic acid and lignoceric acid each accounted for roughly 1% of total oil on average.

Starting with the least abundant fatty acid, gadoleic acid showed differences between market types, but because of the low total percentage, this difference accounted for less than

0.2% difference in content. Virginia types had the highest gadoleic acid content at 0.9%, and

Spanish types had the lowest average gadoleic acid content at 0.72% (Table 3-3). Total lignoceric acid was similar between market types, with a low average of 0.87% in Spanish types to a high average of 1.05% in Valencia types. The highest recorded lignoceric acid value was

2.63%, a Virginia type (Table 3-3).

Runner and Virginia types had lower behenic acid content than Valencia and Spanish types. Runner types had the lowest average behenic acid content at 2.07%, while Valencia types had the highest average behenic acid content at 2.47% (Table 3-4). The highest behenic acid content recorded was 4.34% and belonged to a Valencia type. Stearic acid accounted for roughly

3% of total oil on average. Spanish types had the highest average stearic acid content at 3.23%.

Stearic acid content also varied more than behenic acid content despite similar average content.

Valencia types had a range of 7.66% and Virginia types had a range of 6.59% for stearic acid content (Table 3-4).

Average palmitic acid content varied by market type from 9.27% in Virginia types to

11.21% in Spanish types (Table 3-5). Valencia types had the highest maximum palmitic acid content at 56.6%. The two most abundant oils, oleic and linoleic acid, combined account for roughly 75-80% of total oil. These two oils are also directly negatively related; oleic acid is converted to linoleic acid during seed development, so higher relative linoleic acid results in lower relative oleic acid content. This trend is reflected on a broad scale across subspecies.

Runner and Virginia types had lower average linoleic acid content at 24.41% and 24.11% respectively, while Spanish and Valencia had relatively higher average linoleic acid content at

31% and 30.46%, respectively (Table 3-5). Alternately, Runner and Virginia types had higher oleic acid content on average, Runner types averaging 58.18% oleic acid and Virginia types averaging 38.89% oleic acid. Spanish and Valencia types had lower oleic acid content, at 49.27% for Spanish and 49.87% for Valencia.

Protein content was calculated as a percentage of dry weight. Trends in protein content were opposite of oil content; Spanish and Valencia types, which had lower average oil content, had higher average protein content, at 29.36% and 29.82% respectively. Virginia types had the lowest average protein content at 27.24% (Table 3-6).

The mini-core collection reflected some of the trends seen in the core collection in the biochemical profile, while some of the minor fatty acids were different. Total oil content was similar to the core accessions, with Runner types recording the highest average oil content at

52.17%, and Valencia types the lowest at 49.98% (Table 3-3). The two least abundant fatty acids, gadoleic and lignoceric acid, were less abundant in the core collection compared to the mini-core collection, with the exception of gadoleic acid content in Runner types. Virginia types had an average gadoleic acid content of 1.03%, the highest of any market type, while Runner types had the lowest at 0.83%. Lignoceric acid content averaged from Runner types at 1.13% to

Valencia types at 1.32% (Table 3-3).

Behenic and stearic acid content was higher in all market types in the mini-core collection compared to the core collection. Average behenic acid content ranged from 2.49% in

Runner types to 2.85% in Spanish types (Table 3-4). Across market types, behenic acid content was between 15-21% higher in the mini-core than the core collection. Stearic acid content was on average higher in the mini-core collection; average stearic acid content was 33% higher for

Runner types, 12.1% higher for Spanish types, and 7% higher for Virginia and Valencia types

(Table 3-4).

Palmitic acid content was roughly the same between the core and mini-core collections.

Palmitic acid content was highest in Spanish types at 10.96% and lowest in Virginia types at

9.58% (Table 3-5). Linoleic acid content was slightly higher in the core collection than the mini- core collection. As in the core collection, Spanish and Valencia had higher linoleic acid contents

(28.34% and 27.8%) compared to Virginia and Runner types (23.69% and 22.94%) (Table 3-5).

However, oleic acid contents were comparable between the core and mini-core collection.

A comparison of the fatty acid makeup of the market types can be seen in Figure 3-2 through Figure 3-7.

Canonical Discriminate Analysis (CDA)

Three canonical discriminate analyses were conducted with differing underlying assumptions.

First CDA

The first CDA performed did not account for main stem flowering pattern and included mixed and unclassified accessions. Total oil content, total protein content, and the fatty acid profile were considered. In the first section of Table 3-7, the component variables that significantly affected the discrimination are bolded and italicized. The first canonical variate accounted for 56% of variation. The magnitude of the biochemical correlation (the correlation of the canonical variate group mean and the group means of the original biochemical variable) enables the identification of the most influential traits separating the market types across either axis. On the x-axis, palmitic acid content, oleic acid content, linoleic acid content, behenic acid content, unsaturated fat content, and oleic to linoleic acid ratio were significant drivers of discrimination. The second canonical variate, which accounted for 24.8% of variation, was not

significantly impacted by any component variable. There is discrimination seen between the plotted market type centroids: subspecies fastigiata clustered on the right side of the graph, while subspecies hypogaea clustered on the left side of the graph (Figure 3-8A). Unclassified accessions grouped more closely with subspecies fastigiata, while mixed accessions grouped into the middle of the figure.

Second CDA

The correct main stem flowering pattern for unclassified and mixed accessions is not known. True Virginia and Runner types should show no mainstem flowering, and true Spanish and Valencia types should show mainstem flowering. This is not the case for all accessions in the core and mini-core collection, in fact, in the mini-core collection, 50% of Runner types, 9% of

Spanish types, 25% of Valencia types, and 25% of Virginia types did not follow the correct main stem flowering pattern based on the subspecies designation. In the core collection (excluding mini-core accessions), analysis suggested that 42% of Runner types, 7% of Spanish types, 14% of Valencia types, and 15% of Virginia types were not delineated correctly based on flowering data. Flowering pattern was correct for all commercial standard plots, indicating that flowering pattern was accurately recorded (data not shown).

The second CDA accounted for correct and incorrect main stem flowering pattern for known market types. Runner, Virginia, Spanish, and Valencia types were sorted by their main stem flowering pattern, with “yes” indicating that the accession flowered on the main stem and

“no” indicating that the accession did not flower on the main stem. The CDA was again performed with the same variables as the first CDA. The first canonical variate accounted for

71.1% of variation, with palmitic acid content, stearic acid content, oleic acid content, linoleic acid content, gadoleic acid content, behenic acid content, and oleic to linoleic acid ratio acting as significant driving forces in the discrimination. For the second canonical variate, which

accounted for 14% of variation, total oil content and lignoceric acid content were significant variables. When comparing groups that had the same market type but different flowering patterns, Virginia and Valencia types with different flowering patterns were significantly different. Groups that did not flower on the main stem clustered closely to the bottom left, while main stem flowering groups clustered to the right of the graph (Figure 3-8B).

Third CDA

The third CDA kept only the correctly flowering known market types and separated the unclassified and mixed accessions by their flowering pattern. The first canonical variable accounted for 68% of all variation, while the second canonical variable accounted for 17.4%. All groups that flowered on the main stem grouped significantly differently compared to Virginia types, which did not flower on the main stem. Interestingly, Spanish and Valencia types sorted significantly differently as well (p=0.01). The centroids of the group means are plotted on Figure

3-8C.

Revised Biochemical Traits

Total oil content was highest in Runner types at 52.35%, followed by Virginia types

(51.86%), Spanish types (50.37%), and Valencia types (48.54%) (Table 3-8). There is a high range of values for oil content in the core collection; however, oil content had a standard error of

0.25-0.28 for Virginia, Valencia, and Spanish types.

Gadoleic acid content was highest in Runner and Virginia types at 1.08% and 0.94% respectively (Table 3-9). Spanish types had the lowest gadoleic acid content at only 0.69%.

Lignoceric acid content was similar in Runner, Virginia, and Valencia types, differing by only

0.04% (Table 3-10). Spanish types had the lowest lignoceric acid content at 0.84%, the only market type to average below 1%. The highest recorded lignoceric acid content in the core collection was a core accession at 2.51%.

Arachidic acid content was highest in Valencia types at 1.41% and lowest in Runner types at 1.1% (Table 3-11). Valencia types had the highest range in arachidic acid content, from

0.48% to 8.06%. Behenic acid content was higher in Valencia and Spanish types, 2.52% and

2.35%, than in Runner and Virginia types, 2.01% and 2.14% (Table 3-12). The highest recorded behenic acid percentage was 3.94%, a Virginia type. Average stearic acid content by market type ranged from 2.29% in Runner types to 3.28% in Spanish types (Table 3-13).

Palmitic acid content was higher in subspecies fastigiata, with Valencia and Spanish types averaging 11.1% and 11.29%, respectively, compared to 8.92% and 8.47% in Virginia and

Runner types (Table 3-14). Linoleic acid content was also greater in Valencia and Spanish types, at 31.67% and 31.27% respectively (Table 3-15). Runner and Virginia types had averages of

19.34% an 22.42%. Based on this result, it is not surprising that Runner and Virginia types had a much higher oleic acid content (>60%), while Spanish and Valencia types had lower oleic acid content (<50%) (Table 3-16). Runner types had the highest oleic to linoleic acid ratio on average at 6.76:1, followed by Virginia (2.88:1), Spanish (1.74:1) and Valencia (1.56:1) (Table 3-17).

Runner and Virginia types also had higher unsaturated fat content compared to Spanish and

Valencia types (Table 3-18).

The mini-core collection showed many similar trends in biochemical traits as the core collection, although not always as distinct, likely due to the smaller number of replications.

Additionally, extremes in specific fatty acids are not present in the mini-core. Oil content and fatty acid data is presented in Tables 3-19 through 3-31.

Discussion

Biochemical characterization is necessary for the effective utilization of the core germplasm collection. Biochemical characteristics are important traits in the peanut industry, and depending on the market, certain biochemical traits may be favored over others. In Asian

markets, most peanuts are crushed for oil. In the United States, fewer peanuts are crushed for oil and most are instead used in peanut products like peanut butter. Fatty acid composition of oil has become increasingly more important with the emergence of the Hi-Oleic trait, which reduces rancidity in peanut products. As more researched is performed on the beneficial and detrimental qualities of certain component fatty acids on human health, the quality and content of peanut oil could once again become a leading breeding objective.

Other than the FAD2B gene, additional QTLs have been developed for oleic acid and linoleic acid content (Pandey et al., 2014) and minor fatty acids (Wang et al., 2015). Biochemical data can help further identify regions of the genome that control or effect the fatty acid makeup of peanut oil. This data also shows differences among the market types, usually along subspecies lines. Additionally, differences in oil composition was observed between the core and mini-core collection. However, both the core and mini-core collection show high amounts of variability in fatty acid composition across all market types, presenting genetic potential to further improve peanut oil quality.

Protein is a basic component of the human diet but is quickly becoming one of the scarcest. High protein foods can be derived from animal or plant sources, but animal proteins are costly and inefficient compared to plant proteins. Compared to the most commonly consumed

“nuts”, peanut had the highest protein content, at approximately 26% protein content on average.

However, previous reports indicate relatively high rates of protein content diversity available in current peanut germplasm (Dean et al., 2009). This study supports this finding; protein content in the core collection reached a maximum of around 35-39% depending on market type, with minimum values in the low to high teens. This supports the high breeding potential available in the germplasm collection to improve and modify protein content in commercial peanuts.

Average protein content was higher than previously reported, however this may be due to the total nitrogen detection methodology.

Specific fatty acid content was effective at discriminating between market types.

Palmitic, oleic, and linoleic acid all significantly contributed to the first canonical variable in the analysis. These three fatty acids combined account for around 90% of total oil and have the largest influence on oil quality and properties. For instance, palmitic acid accounts for approximately half of the saturated fat content in peanut oil; reducing palmitic acid content in favor of oleic or linoleic acid content could improve the healthfulness of peanut oil, as saturated fats are considered less healthy than unsaturated fats. Additionally, oleic to linoleic acid ratio was significantly higher in Runner and Virginia types compared to Valencia and Spanish types, despite no accession in the core or mini-core collection possessing the Hi-Oleic characteristic.

There were also observed differences in some minor fatty acids, such as stearic acid, gadoleic acid, and behenic acid. Although these fatty acids are not currently considered to be of high importance, further research may uncover flavor, health, and oil quality benefits of even these minor fatty acids.

Table 3-1. Accession type within experimental design per year Accession Type # of accessions # of reps per block # of reps per year Commercial Standard Group 1 8 3 72 Commercial Standard Group 2 6 1 18 Mini Core Collection 107 1 321 Non-Mini Core, Core Collection 687 N/A 687 Total per year 808 N/A 1098

Table 3-2. Total protein and oil content as a function of market type grouped by core collection, mini-core collection, and commercial standards. Note that total oil was collected both years and thus has twice as many replications Protein Content Total Oil Content Market Type n Mean Min Max SE. n Mean Min Max SE Core Mixed 27 28.75 19.46 36.2 0.55 54 50.36 41.93 57.1 0.52 Runner 12 28.42 17.23 35.7 1.03 24 50.86 35.46 58.5 0.97 Spanish 138 29.36 14.39 39.6 0.24 276 50.39 39.04 74.08 0.27 Unclassified 88 29.5 18.36 36.8 0.29 176 49.37 29.51 60 0.36 Valencia 173 29.82 13.19 37.5 0.21 346 48.86 23.81 60 0.25 Virginia 213 27.24 13.03 38.1 0.27 426 51.37 24.8 74.22 0.22 Mini-core Mixed 5 28.61 22.6 35.97 1.54 10 52.59 50.3 57.07 0.7 Runner 2 30.53 21.05 36.8 3.49 4 52.17 45.43 56.12 2.37 Spanish 21 28.87 20.46 35.6 0.63 42 51.43 44.06 57.36 0.48 Unclassified 16 29.13 23.56 37.43 0.63 32 49.69 43.41 57.17 0.66 Valencia 24 29.49 19.46 38.1 0.63 48 49.98 42.84 57.1 0.5 Virginia 39 28.18 18.08 37.17 0.59 78 51.69 40.02 61.27 0.43 Commercial Runner 4 26.94 18.17 36.2 2.79 8 54.3 51.21 56.67 0.62 Standards Spanish 1 28.62 26.37 30.87 2.25 2 49.72 49.2 50.24 0.52 Valencia 3 29.29 24.13 33.4 1.64 6 46.32 41.36 50.22 1.39 Virginia 3 28.02 21.98 35.57 2.66 6 53.96 50.7 57.33 0.99

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Table 3-3. Gadoleic acid and lignoceric acid content as a function of market type grouped by core collection, mini-core collection, and commercial standards Gadoleic Acid Lignoceric Acid Market Type n Mean Min Max SE Mean Min Max SE. Core Mixed 27 0.92 0.44 1.56 0.06 1.1 0.48 1.79 0.08 Runner 12 0.89 0.22 1.54 0.1 0.93 0.25 1.44 0.09 Spanish 138 0.72 0.27 1.62 0.02 0.87 0.25 1.91 0.03 Unclassified 88 0.75 0.08 1.33 0.02 0.89 0.24 1.58 0.04 Valencia 173 0.85 0.32 1.63 0.02 1.05 0.3 1.84 0.03 Virginia 213 0.9 0.22 1.54 0.02 1 0.12 2.63 0.03 Mini-core Mixed 5 0.88 0.73 1.13 0.07 1.09 0.91 1.34 0.08 Runner 2 0.83 0.65 1.02 0.18 1.13 0.87 1.38 0.26 Spanish 21 0.87 0.58 1.13 0.03 1.25 0.85 1.55 0.04 Unclassified 16 0.96 0.77 1.28 0.04 1.34 1.1 1.78 0.05 Valencia 24 0.97 0.65 1.29 0.04 1.32 0.84 1.68 0.04 Virginia 39 1.03 0.61 1.92 0.04 1.29 0.82 1.83 0.04 Commercial Standards Runner 4 1.39 0.9 1.72 0.18 1.39 1.09 1.65 0.12 Spanish 1 0.99 0.99 0.99 . 1.2 1.2 1.2 . Valencia 3 0.92 0.89 0.97 0.03 1.29 1.16 1.37 0.07 Virginia 3 1.11 0.96 1.29 0.1 1.18 1.09 1.26 0.05

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Table 3-4. Peanut arachidic acid, behenic acid, and stearic acid content as a function of market type grouped by core collection, mini- core collection, and commercial standards. Arachidic Acid Behenic Acid Stearic Acid Market Type n Mean Min Max SE Mean Min Max SE Mean Min Max SE Core Mixed 27 1.32 0.86 1.98 0.05 2.47 1.55 3.58 0.11 2.86 1.9 4.86 0.13 Runner 12 1.21 0.52 1.67 0.11 2.07 0.7 2.88 0.17 2.82 1.45 4.64 0.28 Spanish 138 1.33 0.51 2.14 0.03 2.37 0.94 3.72 0.05 3.23 1.43 5.16 0.06 Unclassified 88 1.27 0.52 2.08 0.04 2.21 0.63 3.37 0.07 2.96 1.12 5.04 0.09 Valencia 173 1.37 0.48 8.06 0.04 2.47 0.85 4.34 0.05 3.02 1.22 8.88 0.06 Virginia 213 1.27 0.5 2.44 0.02 2.15 0.57 3.94 0.04 2.86 0.2 6.79 0.07 Mini-core Mixed 5 1.4 1.13 1.65 0.1 2.51 2.09 3.04 0.16 3.17 2.22 3.94 0.33 Runner 2 1.55 1.48 1.62 0.07 2.49 2.28 2.69 0.21 3.75 3.57 3.94 0.19 Spanish 21 1.59 1.28 1.91 0.04 2.85 2.17 3.46 0.08 3.62 2.7 4.55 0.11 Unclassified 16 1.52 1.11 2.27 0.07 3 2.23 5.07 0.15 3.37 2.14 5.1 0.2 Valencia 24 1.5 1.12 1.82 0.04 2.84 2.09 3.49 0.07 3.23 2.36 4.3 0.12 Virginia 39 1.51 0.9 5.36 0.11 2.51 1.88 2.95 0.05 3.04 1.51 4.32 0.11 Commercial Standards Runner 4 1.34 1.28 1.38 0.02 2.46 2.4 2.52 0.03 2.64 2.3 2.93 0.13 Spanish 1 1.65 1.65 1.65 . 2.65 2.65 2.65 . 3.87 3.87 3.87 . Valencia 3 1.55 1.54 1.57 0.01 2.7 2.58 2.79 0.06 3.58 3.51 3.69 0.06 Virginia 3 1.32 1.26 1.38 0.04 2.23 2.13 2.4 0.08 2.78 2.77 2.8 0.01

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Table 3-5. Peanut palmitic acid, linoleic acid, and oleic acid content as a function of market type grouped by core collection, mini-core collection, and commercial standards. Palmitic Acid Linoleic Acid Oleic Acid

Market Type n Mean Min Max SE Mean Min Max SE Mean Min Max SE Core Mixed 27 10.51 8.15 12.35 0.27 27.3 2.81 36.74 1.37 53.51 41.66 80.91 1.64 Runner 12 9.48 6.66 11.62 0.48 24.41 2.79 35.48 2.65 58.18 44.67 81.5 3.06 Spanish 138 11.21 7.26 13.1 0.09 31 3.99 39.13 0.37 49.27 40.35 78.26 0.44 Unclassified 88 10.34 6.89 12.35 0.12 30.3 17.45 40.13 0.51 51.28 43.31 67.14 0.67 Valencia 173 10.79 6.04 56.64 0.28 30.46 11.08 40.02 0.38 49.87 23.43 75.87 0.47 Virginia 213 9.27 2.19 12.75 0.1 24.11 10.81 39.32 0.41 58.45 38.89 70.98 0.5 Mini- core Mixed 5 10.32 8.19 12.9 0.87 26.6 21.42 35.47 2.63 54.06 43.13 62.1 3.65 Runner 2 9.85 8.54 11.16 1.31 22.94 16.7 29.18 6.24 57.46 50.45 64.47 7.01 Spanish 21 10.96 7.95 12.25 0.27 28.34 14.54 33.75 1.13 50.52 44.51 66.57 1.39 Unclassified 16 10.64 8.62 13.3 0.28 28.43 21.02 33.6 1 50.74 43.43 61.76 1.27 Valencia 24 10.48 8.02 12.87 0.25 27.8 17.42 35.33 1.12 51.85 42.46 66.74 1.4 Virginia 39 9.58 6.96 12.1 0.21 23.69 11.51 34.64 0.9 57.33 44.32 73.02 1.1 Commercial Standards Runner 4 7.26 6.07 9.91 0.89 8.16 2.31 23.83 5.23 75.35 57.54 82.17 5.94 Spanish 1 9 9 9 . 14.91 14.91 14.91 . 65.72 65.72 65.72 . Valencia 3 9.79 7.4 11.06 1.2 24.1 9.99 31.35 7.05 56.02 47.33 72.66 8.33 Virginia 3 8.29 6.2 9.98 1.11 15.11 3.51 21.76 5.82 67.97 61.25 81.27 6.65

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Table 3-6. Peanut oleic to linoleic acid ratio and unsaturated fat content as a function of market type grouped by core collection, mini- core collection, and commercial standards. Oleic:Linoleic Acid Ratio Unsaturated Fat Content Market Type n Mean Min Max SE Mean Min Max SE Core Mixed 27 2.97 1.13 28.79 1 81.7 78.9 85.9 0.41 Runner 12 4.59 1.26 29.28 2.26 83.5 78.6 86.9 0.88 Spanish 138 1.76 1.08 21.98 0.15 81 76.2 88.9 0.18 Unclassified 88 1.78 1.19 3.78 0.06 82.3 77.6 90.2 0.28 Valencia 173 1.74 0.94 6.29 0.05 81.2 39.4 88.5 0.32 Virginia 213 2.65 1.02 6.37 0.06 83.5 77.6 90.2 0.17 Mini- core Mixed 5 2.16 1.22 2.9 0.32 81.5 78.6 84.4 1.19 Runner 2 2.84 1.75 3.94 1.1 81.2 80.3 82.2 0.96 Spanish 21 1.95 1.38 4.6 0.19 79.7 77.6 84 0.34 Unclassified 16 1.86 1.3 2.94 0.12 80.1 76.3 84 0.5 Valencia 24 2.34 1.2 10.16 0.37 79.5 53.1 85.3 1.2 Virginia 39 2.68 1.28 6.38 0.17 82.1 75.5 85.5 0.31 Commercial Standards Runner 4 22.76 2.5 35.77 7.12 84.9 82.3 86.2 0.89 Spanish 1 5.03 5.03 5.03 . 81.6 81.6 81.6 . Valencia 3 3.5 1.52 7.41 1.96 81 79.6 83.6 1.3 Virginia 3 10.97 2.84 26.99 8.01 84.2 82.5 86.1 1.02

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Table 3-7. Between Canonical Structure for core collection accessions. Correlation coefficients and the relative ranking of each variable in both canonical variables are displayed. Values with an asterisk (*) denote variables that contribute a significant weight to the correlation. The first set of canonical variables did not account for incorrect flowering data and included all four market types plus unclassified and mixed accessions. The second set of canonical variables excluded the unclassified and mixed accessions to more precisely discriminate between correct and incorrectly grouped accession based on flowering data. The third canonical variable pair used only the correctly grouped accessions for each market type based on flowering data and added unclassified and mixed types of different flowering patterns as separate classes. With flowering data, excluding Without Flowering Data unclassified and mixed With corrected flowering data, all groups Variable Can1 Rank Can2 Rank Can1 Rank Can2 Rank Can1 Rank Can2 Rank Protein Content -0.63 7 0.05 9 -0.33 10 -0.04 11 -0.52 9 -0.19 8 Oil Content -0.54 9 0.75 3 -0.30 11 0.84* 2 -0.27 11 0.86* 1 Palmitic Acid 0.91* 4 0.34 6 0.93* 4 0.35 7 0.96* 4 0.22 6 Stearic Acid 0.58 8 0.78 2 0.73* 8 0.56 5 0.80* 8 0.51 3 Oleic Acid -0.98* 3 -0.11 8 -0.96* 2 -0.23 9 -0.99* 1 -0.05 11 Linoleic Acid 0.98* 1 0.00 12 0.95* 3 0.21 10 0.98* 2 0.01 12 Arachidic Acid -0.48 11 -0.43 5 0.41 9 0.61 4 0.40 10 0.39 5 Gadoleic Acid -0.48 10 -0.79 1 -0.75* 7 -0.62 3 -0.84* 7 -0.45 4 Behenic Acid 0.98* 2 0.04 11 0.88* 5 -0.26 8 0.86* 6 -0.15 9 Lignoceric Acid 0.41 12 -0.58 4 -0.13 12 -0.87* 1 -0.17 12 -0.73 2 Unsaturated Oil Content -0.90* 5 -0.04 10 -1.00 1 -0.02 12 -0.97* 3 0.07 10 Oleic:Linoleic Acid Ratio -0.89* 6 -0.25 7 -0.86* 6 -0.44 6 -0.93* 5 -0.21 7 Variation Accounted For 56.4% 24.8% 71.1% 14.0% 68.0% 17.4%

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Table 3-8. Total oil content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 52.35 43.59 52.36 58.5 14.91 1 Spanish 263 50.37 39.04 50 74.08 35.05 0.28 Valencia 302 48.54 23.81 47.6 60 36.19 0.27 Virginia 356 51.86 24.8 52.4 74.22 49.42 0.25

Table 3-9. Gadoleic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 1.08 0.83 1.1 1.54 0.71 0.09 Spanish 126 0.69 0.27 0.66 1.41 1.14 0.02 Valencia 147 0.82 0.32 0.84 1.54 1.22 0.02 Virginia 174 0.94 0.28 0.97 1.54 1.26 0.02

Table 3-10. Lignoceric acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 1.01 0.62 1.07 1.44 0.82 0.11 Spanish 126 0.84 0.28 0.74 1.91 1.63 0.03 Valencia 147 1.05 0.3 1.03 1.65 1.35 0.03 Virginia 174 1.03 0.12 1.04 2.63 2.51 0.03

Table 3-11. Arachidic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 1.1 0.73 1.1 1.33 0.6 0.08 Spanish 126 1.33 0.51 1.35 2.14 1.63 0.03 Valencia 147 1.41 0.48 1.43 8.06 7.58 0.05 Virginia 174 1.26 0.57 1.26 2.2 1.63 0.03

Table 3-12. Behenic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 2.01 1.58 2.07 2.53 0.95 0.13 Spanish 126 2.35 0.99 2.37 3.66 2.67 0.05 Valencia 147 2.52 0.85 2.57 3.47 2.62 0.05 Virginia 174 2.14 0.67 2.22 3.94 3.27 0.05

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Table 3-13. Stearic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 2.29 1.45 2.22 2.98 1.53 0.21 Spanish 126 3.28 1.43 3.36 5.16 3.73 0.06 Valencia 147 3.11 1.22 3.19 8.88 7.66 0.07 Virginia 174 2.75 0.2 2.61 6.44 6.24 0.07

Table 3-14. Palmitic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 8.47 6.66 8.35 10.2 3.54 0.5 Spanish 126 11.29 7.39 11.49 13.1 5.71 0.08 Valencia 147 11.1 8.15 10.7 56.64 48.49 0.32 Virginia 174 8.92 2.19 8.71 12.39 10.2 0.09

Table 3-15. Linoleic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 19.34 2.79 21.44 29.03 26.24 3.23 Spanish 126 31.27 3.99 31.49 39.13 35.14 0.35 Valencia 147 31.67 11.08 32.25 40.02 28.94 0.32 Virginia 174 22.42 10.81 21.71 39.32 28.51 0.38

Table 3-16. Oleic acid content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 64.7 55.37 62.86 81.5 26.13 3.37 Spanish 126 48.96 40.6 48.33 78.26 37.66 0.41 Valencia 147 48.17 23.43 47.86 63.58 40.15 0.35 Virginia 174 60.55 42.41 60.93 70.98 28.57 0.44

Table 3-17. Oleic to linoleic acid ratio as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 6.74 1.91 2.93 29.28 27.37 3.77 Spanish 126 1.74 1.09 1.53 21.98 20.89 0.16 Valencia 147 1.56 0.94 1.49 4.31 3.37 0.03 Virginia 174 2.88 1.12 2.85 6.37 5.24 0.07

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Table 3-18. Unsaturated fat content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 7 85.12 81.92 85.51 86.26 4.34 0.57 Spanish 126 80.92 76.21 80.68 86.85 10.64 0.18 Valencia 147 80.67 39.4 80.69 87.46 48.06 0.35 Virginia 174 83.91 78.11 83.87 90.18 12.07 0.17

Table 3-19. Protein content as a function of market type in the USDA core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 14 27.35 17.23 27.91 35.7 18.47 1.61 Spanish 261 29.42 14.39 29.6 39.6 25.21 0.24 Valencia 300 30.31 20.62 30.7 37.5 16.88 0.19 Virginia 361 26.87 13.03 27.48 38.1 25.07 0.31

Table 3-20. Total oil content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 55.39 54.67 55.39 56.12 1.45 0.73 Spanish 40 51.51 44.06 51.76 57.36 13.3 0.5 Valencia 36 49.57 42.84 48.92 57.1 14.26 0.6 Virginia 66 51.66 40.02 51.58 61.27 21.25 0.49

Table 3-21. Gadoleic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 1.02 1.02 1.02 1.02 0 . Spanish 20 0.86 0.58 0.88 1.11 0.52 0.03 Valencia 18 0.93 0.65 0.95 1.13 0.49 0.03 Virginia 33 1.07 0.7 1.04 1.92 1.21 0.04

Table 3-22. Lignoceric acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 1.38 1.38 1.38 1.38 0 . Spanish 20 1.25 0.85 1.28 1.55 0.7 0.04 Valencia 18 1.34 0.95 1.33 1.68 0.74 0.04 Virginia 33 1.32 0.96 1.28 1.83 0.87 0.04

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Table 3-23. Arachidic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 1.62 1.62 1.62 1.62 0 . Spanish 20 1.6 1.28 1.63 1.91 0.63 0.03 Valencia 18 1.54 1.18 1.56 1.82 0.65 0.04 Virginia 33 1.52 0.9 1.42 5.36 4.46 0.13

Table 3-24. Behenic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 2.69 2.69 2.69 2.69 0 . Spanish 20 2.88 2.17 2.8 3.46 1.3 0.08 Valencia 18 2.96 2.26 2.97 3.49 1.23 0.07 Virginia 33 2.5 1.88 2.46 2.95 1.08 0.05

Table 3-25. Stearic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 3.57 3.57 3.57 3.57 0 . Spanish 20 3.66 3 3.61 4.55 1.55 0.1 Valencia 18 3.3 2.36 3.33 4.23 1.86 0.12 Virginia 33 2.98 1.51 3.13 4.32 2.81 0.12

Table 3-26. Palmitic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 8.54 8.54 8.54 8.54 0 . Spanish 20 11.09 7.95 11.48 12.25 4.3 0.25 Valencia 18 10.96 9.69 10.9 12.87 3.17 0.23 Virginia 33 9.32 6.96 9.12 12 5.04 0.2

Table 3-27. Linoleic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 16.7 16.7 16.7 16.7 0 . Spanish 20 28.89 14.54 29.76 33.75 19.22 1.04 Valencia 18 30.43 22.41 30.72 35.33 12.92 0.73 Virginia 33 22.53 11.51 22.75 34.64 23.13 0.87

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Table 3-28. Oleic acid content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 64.47 64.47 64.47 64.47 0 . Spanish 20 49.77 44.51 48.86 66.57 22.06 1.24 Valencia 18 48.53 42.46 47.55 57.76 15.3 0.89 Virginia 33 58.74 44.32 58.08 73.02 28.7 1.08

Table 3-29. Oleic to linoleic acid ratio as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 3.94 3.94 3.94 3.94 0 . Spanish 20 1.86 1.38 1.66 4.6 3.22 0.18 Valencia 18 2.06 1.2 1.57 10.16 8.96 0.48 Virginia 33 2.85 1.28 2.61 6.38 5.09 0.18

Table 3-30. Unsaturated fat content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 1 82.19 82.19 82.19 82.19 0 . Spanish 20 79.53 77.6 79.37 82.64 5.04 0.28 Valencia 18 78.45 53.11 79.66 83.32 30.21 1.51 Virginia 33 82.34 75.46 82.43 85.46 10 0.34

Table 3-31. Protein content as a function of market type in the USDA mini-core peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Runner 2 27.79 21.05 27.79 34.53 13.48 6.74 Spanish 40 28.79 20.46 28.45 35.6 15.14 0.65 Valencia 36 30.07 21.74 30.42 38.1 16.36 0.64 Virginia 66 28.08 18.08 27.19 37.17 19.09 0.68

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Figure 3-1. Plot arrangement denoting plots 1.5 m wide x 3 m long, with 3 m between plots within a row and 1.5 m distance between plots across rows during the 2013 field season at the University of Florida Plant Science Research and Education Unit at Citra, FL. Photo courtesy of Greg MacDonald.

RUNNER Linoleic 24.41%

Palmitic 9.48% Stearic 2.82%

Other Behenic 7.92% 2.07% Arachidic 1.21% Lignoceric 0.93% Gadoleic 0.89%

Oleic 58.19%

Figure 3-2. Average fatty acid profile of Runner accessions in the core of the USDA peanut germplasm collection grown in Citra, FL.

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SPANISH

Linoleic 31.00%

Palmitic 11.21% Stearic 3.23%

Other Behenic 8.52% 2.37%

Arachidic 1.33% Lignoceric 0.87% Gadoleic 0.72%

Oleic 49.27% Figure 3-3. Average fatty acid profile of Spanish accessions in the core of the USDA peanut germplasm collection grown in Citra, FL.

VALENCIA Linoleic 30.50%

Palmitic 10.80% Stearic 3.02%

Other Behenic 8.77% 2.47%

Arachidic 1.37% Lignoceric 1.05% Gadoleic 0.85%

Oleic 49.93% Figure 3-4. Average fatty acid profile of Valencia accessions in the core of the USDA peanut germplasm collection grown in Citra, FL.

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VIRGINIA Linoleic 24.11%

Palmitic 9.27% Stearic 2.86%

Other Behenic 8.18% 2.15% Arachidic 1.27% Lignoceric 1.00% Gadoleic 0.90%

Oleic 58.44%

Figure 3-5. Average fatty acid profile of Virginia accessions in the core of the USDA peanut germplasm collection grown in Citra, FL.

UNCLASSIFIED Linoleic 30.30%

Palmitic 10.34% Stearic 2.96%

Other Behenic 8.08% 2.21%

Arachidic 1.27% Lignoceric 0.89% Gadoleic 0.75%

Oleic 51.28% Figure 3-6. Average fatty acid profile of unclassified accessions in the core of the USDA peanut germplasm collection grown in Citra, FL.

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MIXED Linoleic 27.30%

Palmitic 10.51% Stearic 2.86%

Other Behenic 8.67% 2.47% Arachidic 1.32% Lignoceric 1.10% Gadoleic 0.92%

Oleic 53.52%

Figure 3-7. Average fatty acid profile of mixed accessions in the core of the USDA peanut germplasm collection grown in Citra, FL.

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Figure 3-8. Plotted group centroids of canonical variables. The first CDA contained all groups without regard for correct flowering pattern (A). The second CDA contained known market types accounting for flowering pattern (B). The third CDA contained only correctly flowering known market types and unknown market types by flowering pattern (C).

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CHAPTER 4 CONCLUSIONS

Core collections of germplasm resources are of immense importance in plant breeding.

Many crops have core collections, which represent nearly all the available genetic diversity in the complete collection in roughly one-tenth the number of accessions. The USDA peanut core germplasm collection consists of 831 lines, which act as a road map into the larger complete collection, which contains approximately 10,000 unique accessions. Like many core collections, the USDA peanut core collection was developed based on geographic and morphological data and has been shown to represent a vast majority of the genetic diversity of the complete USDA collection. However, core collections require constant adjustment as new data becomes available and breeding goals shift; the peanut core collection is no exception. In this study, morphological and biochemical data were collected and analyzed for the core collection, the smaller mini-core collection, and commercial standard cultivars to assess the variation contained in each collection, identify potentially useful characteristics displayed by accessions, and to better understand the relationships and trends within and between each collection. Our findings confirm that the core and mini-core collections contain high levels of exploitable morphological and biochemical variation. There are also inconsistencies in accession market type groupings which could be corrected to better represent the available germplasm for breeders interested in a specific market type.

Phenotypically there is a large amount of diversity in the core and mini-core collection.

Morphological traits can be broken down into plant architecture, leaf characteristics, pod and seed traits, and agronomic performance. Each group of traits was useful in distinguishing one or more market types. For instance, Spanish type accessions had much smaller pods on average compared to the other market types, so pod volume is a distinguishing trait for Spanish types. In

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most cases, two or more market types cannot be distinguished using a single trait. Biochemical data also showed high variation in oil content, protein content, and fatty acid composition.

Market type distinction was most obvious in specific fatty acid content, notably oleic and linoleic acid content.

Main stem flowering data was not consistent within the designated market types in

GRIN. There are two possible explanations: one, main stem flowering is not consistent within subspecies or market type, and thus should not be used as the definitive measure of subspecies designation; or two, accessions within the core collection are assigned to an incorrect subspecies and market type. Our results support the latter; canonical discriminate analysis of market types separated by different main stem flowering patterns for both morphological and biochemical traits showed that accessions with the same market type designation, but different main stem flowering pattern did not cluster together, whereas accessions that shared the same main stem flowering pattern grouped closely together. When accessions that were either not yet assigned a market type or had two or more market types assigned were grouped by their main stem flowering pattern, in most cases there was close clustering to accessions with known market types; this could help correctly label these accessions in the future. This result suggests that main stem flowering pattern needs to be confirmed before market type is assigned and can be used to accurately distinguish subspecies.

While our data suggests errors within the current system of market type classification, reassigning these accessions to the correct market type is more difficult. However, early attempts to create an internally-verified model to sort accessions based on phenotypic traits were promising. The model, using only the six most highly discriminating phenotypic traits for market type, was developed using a random two-thirds of the core collection. The market type of the

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remaining third was hidden, and the model applied, allowing comparison between the predicted market type and the actual market type. This attempt did not account for flowering pattern and did not include any biochemical data; inclusion of both factors could greatly improve the accuracy of the model. The market type system is a useful tool, however, because the market type classification is essentially tying a historic genetic structure to an industry standard based on decades of breeding, certain problems have developed. Due to the muddied subspecies classification system on which the market type classification is based, market type assignment is not always clear and precise. Botanical varieties have been shown to be generally distinct genetically, but a notable amount of exceptions occur in genomic studies, and the subspecies designation and groupings of entire botanical varieties is disputed (Ferguson et al., 2004)(Xiong et al., 2011)(Barkley et al., 2007)(Tang et al., 2008). It is in the exceptions that the problems lie.

Germplasm collections are inherently diverse, which makes any grouping of accessions based on observed traits problematic. Going back to the previous example of the small pod volume typically seen in Spanish types, the classification of specific accessions becomes difficult. The core and mini-core collections, even when accounting for corrected main stem flowering pattern, contain correctly flowering Spanish types with pod volumes well over the average pod volumes of Virginia and Valencia types, as well as Virginia type accessions with a pod volume smaller than the average Spanish type. Because there is uncertainty in the genetic basis of the taxonomic system of botanical variety designation, and there are characteristics displayed by individual accessions which do not match closely to the expected characteristic in that market type, it is difficult to definitively classify these accessions with confidence. Further, from a taxonomical perspective genetic relatedness is a very important metric on which to organize peanut

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accessions, however to breeders and the larger peanut industry, the expressed traits may ultimately be more important.

Morphological and biochemical analysis is needed to compliment genomic studies. It has been observed that self-pollinating crops can possess large amounts of phenotypic variation in relatively low amounts of genetic variation (Shattuck-Eidens et al., 1990). The mechanism of this phenomenon is not fully understood but could help explain the diversity seen in the core collection despite relatively meager exploitable polymorphic genetic material in the peanut genome compared to other major crops which are cross-pollinating, such as maize.

Phenotyping studies must be repeated multiple years over diverse environments in order to accurately characterize accessions. In a comprehensive review of soybean breeding and phenotyping efforts, it was concluded that in order to accurately predict genetic composition based phenotypic data, diverse sites should be considered across multiple years. Additionally, important traits such as yield were disproportionately affected by GxE interactions (Robinson and Comstock, 1995). While this study was effective in describing the core collection in Citra,

Florida, results may vary elsewhere, and this experiment should be repeated in multiple locations to enhance our understanding of the collection as a whole.

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APPENDIX A SUPPLEMENTAL TABLES

Table A-1. Yield (kg·ha-1) of peanuts as a function of market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 53 1828.4 102.5 1725.3 3836.5 3734.1 132.4 Runner 24 2283.1 861.4 2362.2 3914.8 3053.4 183.4 Spanish 300 2158.1 53.9 2153.7 4887.5 4833.6 58.5 Unclassified 182 2102.7 248.3 2095.2 4294.8 4046.5 73.3 Valencia 360 2144.7 137.7 2124.3 4520.2 4382.5 48.9 Virginia 456 2011.4 40.1 1920.0 5795.1 5755.0 43.2

Table A-2. Sound mature kernel percent by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 53 65.3 42.9 65.5 80.1 37.2 1.1 Runner 24 71.1 64.0 70.7 78.8 14.8 0.8 Spanish 298 70.8 25.5 71.4 80.5 55.0 0.4 Unclassified 182 65.6 31.7 66.9 81.0 49.3 0.5 Valencia 357 64.1 32.6 65.1 79.8 47.3 0.4 Virginia 454 69.1 51.5 69.9 80.5 29.0 0.2

Table A-3. Meat content per 200g of peanuts by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 53 139.3 111.6 141.5 174.1 62.5 1.7 Runner 24 146.6 132.7 145.6 161.2 28.5 1.4 Spanish 298 148.4 77.6 150.2 162.5 84.9 0.6 Unclassified 182 138.8 94.5 140.3 164.3 69.8 0.8 Valencia 357 136.2 77.7 137.7 157.0 79.3 0.6 Virginia 454 142.8 97.3 144.0 161.8 64.5 0.4

Table A-4. Yield (kg·ha-1) of peanuts by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 2151.3 1406.1 2136.7 3460.1 2054.0 213.5 Runner 4 2298.3 1694.8 2073.0 3352.3 1657.5 385.8 Spanish 42 2365.0 711.9 2297.6 4066.0 3354.1 127.9 Unclassified 32 2139.0 690.4 2311.8 3815.4 3124.9 133.5 Valencia 48 2115.2 505.1 2201.8 3728.3 3223.1 118.4 Virginia 78 2439.9 416.9 2384.0 4111.2 3694.3 88.0

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Table A-5. Meat content per 200g of peanuts by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 139.2 116.0 141.8 153.6 37.6 3.8 Runner 4 131.2 98.4 140.4 145.9 47.5 11.2 Spanish 42 144.7 121.8 144.8 155.3 33.5 1.2 Unclassified 32 134.5 97.3 134.3 151.1 53.9 2.3 Valencia 48 134.7 83.0 138.0 153.7 70.7 2.0 Virginia 78 143.6 113.7 144.8 165.1 51.3 1.0

Table A-6. Sound mature kernel percentage by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 65.9 50.7 67.5 73.9 23.2 2.2 Runner 4 69.6 65.5 70.5 72.0 6.5 1.6 Spanish 42 68.8 50.5 69.1 75.9 25.4 0.9 Unclassified 32 62.2 40.9 61.9 73.2 32.3 1.5 Valencia 48 62.5 36.9 64.0 73.8 36.9 1.1 Virginia 78 68.9 43.0 69.9 82.0 38.9 0.7

Table A-7. Meat to hull ratio by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 53 2.45 1.26 2.42 6.73 5.47 0.12 Runner 24 2.82 1.97 2.68 4.30 2.32 0.11 Spanish 298 2.99 0.63 3.02 4.33 3.69 0.04 Unclassified 182 2.36 0.90 2.35 4.61 3.71 0.04 Valencia 357 2.22 0.64 2.21 3.65 3.01 0.03 Virginia 454 2.58 0.95 2.57 4.24 3.29 0.02

Table A-8. Fancy pod percentage by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 26 12.8 0.0 9.7 54.1 54.1 2.8 Runner 12 26.0 0.0 14.4 94.9 94.9 9.3 Spanish 148 5.1 0.0 0.0 62.6 62.6 0.9 Unclassified 91 22.6 0.0 16.7 84.8 84.8 2.3 Valencia 177 19.4 0.0 11.6 98.1 98.1 1.5 Virginia 226 23.6 0.0 12.0 86.8 86.8 1.7

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Table A-9. Pod volume by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 46 49.1 15.0 47.5 90.0 75.0 2.7 Runner 22 52.0 35.0 50.8 70.0 35.0 2.1 Spanish 267 38.1 15.0 35.0 80.0 65.0 0.8 Unclassified 166 60.3 25.0 60.0 120.0 95.0 1.5 Valencia 330 62.9 15.0 60.0 150.0 135.0 0.9 Virginia 414 58.7 20.0 50.0 160.0 140.0 1.1

Table A-10. Meat to hull ratio by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 2.41 1.38 2.45 3.35 1.97 0.19 Runner 4 2.32 1.79 2.39 2.71 0.92 0.21 Spanish 42 2.75 1.56 2.66 4.57 3.02 0.09 Unclassified 32 2.20 0.95 2.10 3.11 2.16 0.10 Valencia 48 2.19 0.85 2.25 3.34 2.49 0.07 Virginia 78 2.64 1.37 2.63 4.73 3.36 0.06

Table A-11. Percent Fancy pods by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 5 10.3 1.7 6.2 26.3 24.6 4.5 Runner 2 28.4 24.7 28.4 32.1 7.4 3.7 Spanish 21 7.4 0.0 7.4 29.2 29.2 1.5 Unclassified 16 24.6 0.0 21.0 76.2 76.2 6.3 Valencia 24 23.6 1.1 18.2 70.6 69.5 4.3 Virginia 39 30.5 0.0 26.8 83.0 83.0 4.2

Table A-12. Pod volume by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 42.6 30.0 41.3 56.7 26.7 2.9 Runner 4 56.3 53.3 55.8 60.0 6.7 1.4 Spanish 42 38.6 18.3 39.2 56.7 38.3 1.4 Unclassified 32 55.9 25.0 55.0 83.3 58.3 2.9 Valencia 48 57.9 31.7 60.0 85.0 53.3 1.8 Virginia 78 55.1 25.0 52.5 95.0 70.0 1.9

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Table A-13. Plant height at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 41.0 10.0 40.0 95.0 85.0 2.1 Runner 24 37.7 15.0 40.0 56.3 41.3 2.3 Spanish 301 42.5 15.0 42.5 68.8 53.8 0.7 Unclassified 182 46.5 10.0 46.3 75.0 65.0 1.1 Valencia 360 47.8 15.0 48.8 76.3 61.3 0.7 Virginia 457 36.9 10.0 35.0 96.6 86.6 0.6

Table A-14. Plant width at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 55.3 20.0 55.0 75.0 55.0 2.5 Runner 24 56.3 30.0 50.8 75.0 45.0 3.7 Spanish 301 56.9 20.0 55.0 75.0 55.0 1.0 Unclassified 182 58.0 20.0 60.0 75.0 55.0 1.3 Valencia 360 59.9 25.0 65.0 75.0 50.0 0.9 Virginia 457 57.7 20.0 58.8 75.0 55.0 0.8

Table A-15. Plant height-width ratio at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 0.76 0.29 0.74 1.27 0.98 0.03 Runner 24 0.70 0.38 0.66 1.17 0.79 0.04 Spanish 301 0.77 0.36 0.75 1.20 0.84 0.01 Unclassified 182 0.82 0.33 0.83 2.25 1.92 0.01 Valencia 360 0.82 0.27 0.83 1.75 1.48 0.01 Virginia 457 0.65 0.22 0.65 1.29 1.07 0.01

Table A-16. Plant height at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 44.1 31.7 44.2 60.4 28.8 3.3 Runner 4 40.9 25.0 43.1 52.5 27.5 6.6 Spanish 42 43.3 28.3 42.1 59.2 30.8 1.5 Unclassified 32 44.2 15.0 44.4 62.5 47.5 2.1 Valencia 48 44.1 16.7 42.9 65.0 48.3 1.7 Virginia 78 38.4 16.7 36.7 65.8 49.2 1.3

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Table A-17. Plant width at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 57.7 35.0 59.2 75.0 40.0 5.8 Runner 4 59.2 41.7 60.0 75.0 33.3 9.2 Spanish 42 57.0 35.0 55.1 75.0 40.0 2.5 Unclassified 32 57.4 26.7 51.7 75.0 48.3 2.9 Valencia 48 58.4 26.7 55.6 75.0 48.3 2.2 Virginia 78 58.3 23.3 56.5 75.0 51.7 1.8

Table A-18. Plant height-width ratio at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 0.79 0.64 0.79 1.00 0.36 0.04 Runner 4 0.69 0.60 0.69 0.79 0.19 0.04 Spanish 42 0.78 0.57 0.79 0.98 0.41 0.02 Unclassified 32 0.79 0.39 0.81 1.08 0.70 0.03 Valencia 48 0.78 0.40 0.79 1.15 0.75 0.02 Virginia 78 0.68 0.35 0.69 1.10 0.75 0.02

Table A-19. Leaflet length at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 49.66 25.95 49.38 69.25 43.30 1.54 Runner 24 48.69 31.10 48.04 77.00 45.90 2.11 Spanish 302 51.61 21.75 50.76 143.75 122.00 0.68 Unclassified 182 51.98 27.50 52.23 86.63 59.13 0.87 Valencia 360 52.26 25.19 51.18 77.75 52.56 0.50 Virginia 458 46.31 22.99 45.06 86.11 63.13 0.49

Table A-20. Leaflet width at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 23.95 14.29 24.29 33.29 19.00 0.66 Runner 24 23.45 14.18 24.06 33.13 18.95 0.91 Spanish 302 25.05 13.88 25.03 58.44 44.56 0.29 Unclassified 182 24.44 14.26 24.01 49.21 34.95 0.39 Valencia 360 24.34 14.60 23.80 65.75 51.15 0.24 Virginia 458 22.16 13.65 21.39 48.53 34.88 0.22

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Table A-21. Leaflet length to width ratio at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 2.07 1.54 2.13 2.34 0.80 0.03 Runner 24 2.09 1.64 2.11 2.92 1.28 0.06 Spanish 302 2.07 0.87 2.06 4.51 3.64 0.02 Unclassified 182 2.14 1.00 2.15 2.88 1.87 0.02 Valencia 360 2.16 0.72 2.16 2.90 2.18 0.01 Virginia 458 2.10 0.96 2.09 4.40 3.44 0.01

Table A-22. Leaflet internode distance at 75 days after planting by market type for the core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 54 13.30 5.50 13.08 20.50 15.00 0.47 Runner 24 12.60 8.00 12.08 24.00 16.00 0.66 Spanish 302 14.65 6.40 14.38 47.00 40.60 0.23 Unclassified 182 14.46 6.00 14.08 24.50 18.50 0.29 Valencia 360 14.07 5.05 13.90 24.50 19.45 0.16 Virginia 458 12.31 5.45 11.65 24.00 18.55 0.15

Table A-23. Leaflet length at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 48.54 37.34 49.04 58.96 21.62 2.18 Runner 4 48.06 42.84 46.03 57.33 14.50 3.36 Spanish 42 50.65 35.73 51.25 68.29 32.56 1.25 Unclassified 32 51.71 32.44 51.29 71.92 39.48 1.66 Valencia 48 51.52 33.37 53.21 66.21 32.84 1.24 Virginia 78 46.62 20.54 46.15 68.42 47.88 0.95

Table A-24. Leaflet width at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 22.60 16.17 21.48 28.46 12.29 1.10 Runner 4 21.88 19.45 21.61 24.83 5.39 1.17 Spanish 42 24.67 18.40 25.01 31.50 13.10 0.55 Unclassified 32 23.93 17.60 24.02 30.83 13.23 0.61 Valencia 48 24.09 15.64 24.30 39.79 24.15 0.60 Virginia 78 21.90 9.45 21.64 31.38 21.93 0.42

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Table A-25. Leaflet length to width ratio at 75 days after planting by market type for the mini- core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 2.17 1.99 2.12 2.40 0.42 0.04 Runner 4 2.18 2.06 2.18 2.30 0.24 0.05 Spanish 42 2.06 1.79 2.05 2.39 0.60 0.02 Unclassified 32 2.15 1.77 2.18 2.42 0.65 0.03 Valencia 48 2.16 1.69 2.18 2.50 0.81 0.03 Virginia 78 2.13 1.69 2.13 2.47 0.77 0.02

Table A-26. Leaflet internode distance at 75 days after planting by market type for the mini-core collection of the USDA peanut germplasm collection grown in Citra, FL. Market Type n Mean Min Median Max Range Std. Err. Mixed 10 12.28 9.85 12.50 14.33 4.48 0.47 Runner 4 12.60 10.70 12.94 13.83 3.13 0.68 Spanish 42 14.07 9.65 14.08 18.33 8.68 0.39 Unclassified 32 13.84 8.35 14.25 17.00 8.65 0.42 Valencia 48 13.37 8.52 13.50 17.50 8.98 0.33 Virginia 78 12.26 6.82 11.83 17.50 10.68 0.26

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APPENDIX B DESCRIPTOR GUIDE

United States

Peanut Descriptors

Stanley W. Dezern, Agronomy Department,

University of Florida, Gainesville FL, 2018

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ABSTRACT

Dezern, Stanley W., 2018. United States Peanut Descriptors. University of Florida, Gainesville FL.

The purpose of this document is to update and modify the descriptor guide created by Pittman in 1995, which itself was a revised copy of the descriptors outlined in the peanut germplasm catalogs published by the S-9 Plant Germplasm Collection and Utilization Regional Project in 1963, 1976, and 1985. Although many of the descriptors are unchanged, changes in the use of this guide and the data produced by this guide justify a revision. Furthermore, many references in the 1995 guide are outdated or unavailable. This revised guide outlines the descriptors used by breeders and includes relevant pictures as visual aids. Accurate and efficient description of germplasm is critical to the management and identification of robust genetic resources. This guide is divided into four descriptor sections: Whole plant traits, Pod traits, Seed traits, and Stress traits. This guide is not definitive but has been utilized to effectively phenotype the core of the USDA peanut germplasm collection.

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Table of Contents

I. Overview Pg. 4

II. Whole Plant Traits Pg. 5

a. Growth Habit Pg. 5

b. Plant Size Pg. 6

c. Main Stem Prominence Pg. 8

d. Flowering on Main Stem Pg. 9

e. Leaf Color Pg. 10

f. Stem Pigmentation Pg. 11

g. Maturity Pg. 11

III. Pod Traits Pg. 12

a. Pod Shape Pg. 12

b. Pod Beak Pg. 15

c. Pod Constriction Pg. 16

d. Pod Reticulation Pg. 19

e. Seeds per Pod Pg. 20

f. Pod Weight Pg. 20

g. Market Type Pg. 20

IV. Seed Traits Pg. 21

a. Seed Coat Pattern Pg. 21

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b. Seed Coat Color Pg. 22

c. Seed Weight Pg. 24

V. Stress Traits Pg. 24

VI. Reference Accessions Pg. 25

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I. Overview

How to Use This Guide

This guide outlines the standard descriptors for peanuts in the United States. Descriptors help categorize and identify accessions, which enables breeders to use germplasm resources more efficiently. Accurate description is often tedious but is an essential part of the breeding process. Use this guide to direct you through the description process. Description of peanuts can be roughly divided into three major sections: pre-harvest measurements, at- harvest measurements, and post-harvest measurements. In this guide, the timing of the measurements will be color coded in the colors seen above. It is important to collect your data at the correct time to ensure uniformity of data in large databases. Most categories allow for the selection of only one “type” per plot or accession. Pick the description that most closely describes the accession. Do not select more than one “type” unless otherwise noted. If no type adequately describes the accession, or the accession contains two or more distinct “types”, select “Other” or “Mixed”, respectively, and make a note of why this was selected. This guide is most useful if viewed on a screen as a pdf. Colors can become distorted or less distinct if printed. If a printed copy is required, use a high-quality printer to maximum utility. Schedule of Measurements

Timing of Trait Measurement Trait 60-70 Days After Planting Growth Habit 60-90 Days After Planting Main Stem Flowers on Axis Leaf Color Stem Pigmentation At Harvest Plant Size Main Stem Maturity Post-Harvest Pod Shape Pod Beak Pod Constriction Pod Reticulation Seed per Pod Pod Weight Market Type Seed Coat Color/Pattern Seed Weight

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II. Whole Plant Traits

A. Growth Habit

Figure B-1. Prostrate growth has an erect or creeping main stem growth habit. Pictured above is accession number 118474 displaying the prostrate growth habit.

Figure B-2. Spreading growth is characterized by an erect mainstem with creeping lateral branches which tend to curve upwards at the tip. Pictured is accession number 493717 displaying the spreading growth pattern.

Figure B-3. Bunch growth is described as having partially erect stems that curve upward from the base, typically with a notably taller mainstem. Growth is compact. Pictured above is accession number 403742 displaying the bunch growth pattern.

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Figure B-4. Erect growth is characterized as having straight branches that extend at least at a 45° vertical angle, with a slightly taller mainstem. Pictured above is accession number 356008 displaying the erect growth habit.

Categories (select one): 1. Prostrate (Fig. B-1) 2. Spreading (Fig. B-2) 3. Spreading and Bunch 4. Bunch (Fig. B-3) 5. Erect (Fig. B-4) 6. Mixed (describe) Growth Habit is classified 60-70 days after planting, before plants touch between rows.

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B. Plant Size

Fig. B-5 Dwarf plant size seen in PI Fig. B-6 Small plant size seen in PI 362129 565455

Fig. B-7 Medium plant size seen in PI Fig. B-8 FloriGiant displaying Large 565443 plant size (PI 565455)

Categories (select one): 1. Dwarf (Fig. B-5) 2. Small (Fig. B-6) 3. Medium (Fig. B-7) 4. Large (Fig. B-8)

5. Extra Large (Fig. B-9)

6. Mixed (describe)

Plant Size is determined at Harvest.

Fig. B-9 Extra Large plant size seen in PI 468248

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C. Main Stem Prominence

Fig. B-10 Virginia 81 Bunch (PI 565474) displaying a “Not Apparent” main stem. Note the difficulty in distinguishing the main stem from auxiliary stems.

Main Stem

Secondary Stems

Fig. B-11 Starr (PI 565443) showing a “somewhat apparent” main

stem. Note the nearly vertical mainstem on the plant on the right.

Main stem is approximately the same length as some auxiliary

stems, but more erect.

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Main Stem

Secondary Stems

Fig. B-12 PI 221056 displaying an “prominent” main stem. This photo displays the at-harvest measurement, as opposed to 60-90 days after planting.

Categories (Select one):

1. Not apparent 2. Somewhat apparent 3. Prominent 4. Mixed (describe)

Main Stem prominence is measured twice: once at 60-90 Days After Planting and again at Harvest.

D. Flowering on Main Stem

Categories (select one): 1. No 2. Yes (Fig. 13)

3. Mixed (describe) Main Stem Flowering on Main Stem is determined at 60-90 Days After Flower Planting.

Fig. B-13 Flowering on the main stem of PI 288198 136

E. Leaf Color

Fig. B-14 Very Light Green (PI Fig. B-15 Light Green (PI 403772) 261904)

Fig. B-16 Green (PI 264156) Fig. B-17 Dark Green (PI 468219)

Categories (Select one): 1. Very Light Green (Fig. B-14) 2. Light Green (Fig. B-15) 3. Green (Fig. B-16) 4. Dark Green (Fig. B-17) 5. Very Dark Green (Fig. B-18) 6. Mixed (Describe) 7. Other (Golden, Variegated) Fig. B-18 Very Dark Green (PI Leaf Color is determined at 60-90 442579) Days After Planting.

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F. Stem Pigmentation

Fig. B-19 Green stem pigmentation seen Fig. B-20 Purple stem pigmentation seen in PI 407648 in PI 261940

Categories (select one): 1. Green (Fig. B-19) 2. Purple (Fig. B-20) 3. Mixed (describe) 4. Other (white, golden, etc.) Stem Pigmentation is measured at 60-90 Days After Planting. Measurement should be taken on the main stem close to the ground.

G. Maturity

Categories (select one): 1. Very Early 2. Early 3. Medium 4. Late 5. Very Late 6. Mixed (describe)

Maturity is measured at Harvest.

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III. Pod Traits

A. Pod Shape

Fig. B-21 Vulgaris pod shape seen

in PI 210833. Vulgaris types are characterized as having: -Two seeds per pod -Absent to Slight Beak -Slight to Moderate Constriction -Slight Reticulation

Fig. B-22 Fastigiata pod shape seen in PI 288178. Fastigiata types are characterized as having: -Three or more seeds per pod -Absent to Slight Beak -Slight Constriction -Slight Reticulation

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Fig. B-23 Peruviana pod shape seen in PI 502024. Peruviana types are characterized as having: -Three or more seeds per pod -Slight to Moderate Beak -Slight Constriction -Prominent to Very Prominent Reticulation

Fig. B-24 Hypogaea pod shape Hypogaea types are characterized as having: -Two seeds per pod -Absent to Slight Beak -Slight to Moderate Constriction -Sight to Moderate Reticulation

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Fig. B-25 Hirsuta pod shape. Hirsuta types are characterized as having:

-Two or Three seeds per pod -Moderate to Prominent Beak -Moderate to Very Deep Constriction -Very Prominent Reticulation

Categories (select one): 1. Vulgaris (Fig. B-21) 2. Fastigiata (Fig. B-22) 3. Peruviana (Fig. B-23) 4. Hypogaea (Fig. B-24) 5. Hirsuta (Fig. B-25) 6. Mixed (describe) 7. Other (describe) Pod Shape is determined post-Harvest.

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B. Pod Beak

Fig. B-26 Absent pod beak Fig. B-27 Slight pod beak

Fig. B-28 Moderate pod beak Fig. B-29 Prominent pod beak

Categories (select one): 1. Absent (Fig. B-26) 2. Slight (Fig. B-27) 3. Moderate (Fig. B-28) 4. Prominent (Fig. B-29) Pod Beak is measured post- Harvest. 142

C. Pod Constriction

Fig. B-30 Pod constriction of 0. Both Fig. B-31 Pod constriction of 1. One side sides are nearly flat along the middle. No is nearly flat, and the other shows slight distinction can be made between each ridges. kernel.

Fig. B-32 Pod constriction of 1. One side Fig. B-33 Pod constriction of 2. Both is nearly flat, and the other shows slight sides show slight constriction. constriction.

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Fig. B-34 Pod constriction of 3. Both Fig. B-35 Pod constriction of 3. Both sides show moderate constriction. sides show moderate constriction.

Fig. B-36 Pod constriction of 4. Both Fig. B-37 Pod constriction of 4. Both sides show deep constriction. sides show deep constriction.

Categories (select one): 0. No Constriction (Fig. B-30) 1. Slight Constriction (Fig. B-31, Fig. B-32) 2. Moderate Constriction (Fig. B-33) 3. Deep Constriction (Fig. B-34, Fig. B-35) 4. Very Deep Constriction (Fig. B-36, Fig. B-37) Pod Constriction is measured post-Harvest.

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3 4 2 2 3 4 4 3 3 3 2 3 2 4 2 2 4 4 3

Fig. 38 Example of a completed 20-sample pod constriction measurement.

Plot 0 1 2 3 4 Total Fig. 39 Data entry for a 1 5 10 5 20 completed 20-sample pod 2 5 12 3 20 constriction measurement. 3 2 11 4 3 20 Fig. 38 above shows Plot 8 4 1 19 20 on the chart. This method 5 4 5 11 20 of data collection can also 6 1 15 4 20 be used for pod 7 3 3 14 20 reticulation, pod beak, 8 6 7 7 20 seeds per pod, seed coat 9 14 6 20 pattern, and seed coat 10 4 10 5 1 20 color. 145

D. Pod Reticulation

Fig. B-40 Pod Reticulation of 0. Surface Fig. B-41 Pod Reticulation of 1. Visible nearly smooth. reticulation but surface nearly smooth.

Fig. B-42 Pod Reticulation of 2. Visible Fig. B-43 Pod Reticulation of 3. Visible reticulation with moderate depth. reticulation with deep depth.

Categories (select one): 0. Smooth (Fig. B-40) 1. Slight (Fig. B-41) 2. Moderate (Fig. B-42) 3. Rough (Fig. B-43) Pod Reticulation is measured post-Harvest.

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E. Seeds Per Pod Number of Seeds per Pod Plot 1 2 3 4 5 Total 1 2 18 20 2 12 3 5 20 3 1 19 20 4 4 12 4 20 5 12 8 20 6 20 20 7 3 17 20 8 6 7 7 20 9 6 14 20 10 4 13 3 20

Fig. 44 Seeds per pod is measured by randomly selecting 20 pods and grouping them based on the number of seeds within each pod. A four-digit code is used to describe the groups in decreasing frequency. For example, the code for Plot 5 would be 3400, since 3 is the most frequent number of seeds per pod, 4 is the second most frequent number of seeds per pod, and there were no other numbers of seeds per pod recorded. F. Pod Weight

Pod Weight is the combined mass of 100 randomly selected mature pods, measured to the nearest gram.

G. U.S. Pod Market Type

U.S. Pod Market Type is a term used to classify cultivated peanuts into groups based on similar traits. Three of the four market types roughly correspond to a subspecies designation from the Pod Shape section: Spanish types correspond to vulgaris (Fig. 21); Valencia types correspond to fastigiata (Fig. 22); and Virginia types correspond to hypogaea (Fig. 24). The Runner type is a hybrid of Virginia and Spanish types and does not have a variety designation.

Categories (select one): 1. Spanish (Fig. B-21) 2. Valencia (Fig. B-22) 3. Runner 4. Virginia (Fig. B-24) 5. Mixed (describe)

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IV. Seed Traits

A. Seed Coat Pattern

Fig. B-45 Striped or “flecked” pattern. Note that the seed coat has one dominant color but has small bands or spots of a different color.

Fig. B-46 Bicolor pattern. Notice that there is not a dominant color; the seed coat is nearly evenly split between two colors.

Categories (select one): 1. Single testa color 2. Striped/Flecked (Fig. 45) 3. Bicolor (Fig. 46) 4. Mixed (describe) 5. Other (describe)

Seed Coat Pattern can be recorded using the method described in the Pod Constriction section. Additionally, a

five-digit code is described in the Seed Coat Color section (Fig. 48).

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White Tan Pink

Red Purple Dark

Fig. B-47 Seed coat colors found in peanut

Categories: 1. White 2. Tan 3. Pink 4. Red 5. Purple 6. Dark Seed Coat Color can be recorded using the same method described in the Pod Constriction section. The five-digit system for recording seed coat color and pattern is described below (Fig. B-48).

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10200 30420 Tan Red and Tan Single seed coat color Bicolor seed coat pattern

30510 10100

Purple and White White Bicolor seed coat Single seed coat pattern color

Fig. B-48 The five-digit code system is the preferred method of seed coat color and pattern data entry in many databases. The first two digits describe the seed pattern, and the last three digits describe the seed color. In the top left image, there is a single testa color. The seed coat pattern designation for single testa color is 1, so the code for single testa color is 10. The top left image displays a tan seed coat color, which is assigned a 2. Because there is no secondary color to consider, the code is 200. Combined, the code becomes 10200. The bottom left image shows a bicolor seed coat pattern, which is assigned a 3 in the seed coat pattern designation. The primary color is purple, which is scored a 5; the secondary color is white, which is scored a 1. Combined, the code becomes 30510.

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B. Seed Weight Seed weight is the combined mass of 100 seeds, measured to the nearest gram.

V. Stress Traits

Disease, Pest, or Stress Resistance Factors

Categories (select one): 1. Immune 2. Highly resistant 3. Moderately resistant 4. Slightly resistant 5. Intermediate 6. Slightly susceptible 7. Moderately susceptible 8. Highly susceptible 9. Dead Disease, Pest, or Stress Resistance Factors are rated during the growing season.

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VI. Reference Accessions

Variety PI # Seed Available Plant Picture Pod Picture Seed Picture Flower Picture BPZ 53 468248 Yes Yes Yes Yes No BPZHa 706-7 497631 No Yes Yes Yes Yes Bayo Americana 497365 Yes Yes Yes Yes No Chico 565455 Yes Yes No No No Chriollo 331334 Yes Yes No Yes Yes Early Bunch 565458 Yes Yes Yes Yes No Florigiant 565445 Yes Yes No No No Florunner 565448 Yes Yes Yes Yes No GKBSPSc 2 468190 Yes Yes Yes Yes No GKBSPSc 27 468222 Yes Yes Yes Yes No GKBSPSc 224 475871 Yes Yes No No No Guanajuato-2 280688 No Yes No No Yes Gujarat Dwarf 362129 Yes Yes No Yes No IN59-31 269114 Yes Yes Yes Yes No KSSc 812 497374 Yes Yes No Yes No KSScCo 828-2 497415 Yes Yes Yes Yes No Mount Makulu Red 371965 Yes Yes No Yes No NC 3033 565460 Yes Yes Yes Yes No NC 7 565459 Yes Yes Yes Yes No New Mexico Val A 565452 No Yes No No No Pza 614-3 497302 Yes Yes Yes Yes No Peru No. 9 262129 Yes Yes Yes Yes No Pronto 565475 Yes Yes Yes Yes No RCM 384 274191 Yes Yes Yes Yes No Rosando Grande 468242 Yes Yes Yes Yes No S 540 476063 Yes Yes Yes Yes No SPZ 454-1 502014 No Yes No Yes No SPZ 471-1 502045 Yes Yes Yes Yes Yes SPZ 487-2 502089 Yes Yes No Yes No SPZ 489 476181 Yes Yes Yes Yes No Southern Runner 506419 Yes Yes No No No Starr 565443 Yes Yes Yes Yes No Tifton 8 565463 Yes Yes Yes Yes No US 1359 590455 Yes Yes Yes Yes No Virginia 81 Bunch 565474 Yes Yes Yes Yes No

Table B-1. Accessions used as standards. Seeds availability and the presence of plant, pod, seed, and flower pictures on the USDA GRIN database is noted. In addition, all reference accessions have a complete or nearly complete set of descriptor data available.

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BIOGRAPHICAL SKETCH

Will Dezern was born and raised in Orlando, Florida, graduating from Freedom High

School in 2012. He majored in plant science with a specialization in plant genetics at the

University of Florida from 2012-2016, where he was involved with many campus organizations such as CALS ambassadors and the CALS Leadership Institute. Will was the president of the

Agronomy-Soils club for two years and was involved in ecology research throughout the duration of his undergraduate degree. Will received his master’s degree in agronomy in the spring of 2018, and afterwards hopes to work in agricultural extension.

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