Evaluating Agronomic Traits of Quinoa, Millet, and Food Barley

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EVALUATING AGRONOMIC TRAITS OF QUINOA, MILLET, AND FOOD BARLEY

VARIETIES FOR ADOPTION IN RWANDA AND THE U.S. PACIFIC NORTHWEST

CEDRIC HABIYAREMYE

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

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY Department of Crop and Soil Sciences

JULY 2019

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of CEDRIC

HABIYAREMYE find it satisfactory and recommend that it be accepted.

Kevin M. Murphy, Ph.D., Chair

John P. Reganold, Ph.D.

Marcia R. Ostrom, Ph.D.

Kurtis L. Schroeder, Ph.D.

ii ACKNOWLEDGEMENT

This dissertation is the result of the labor and care of many people. I am grateful when I reflect on those who have helped me arrive at this point. Colleagues, friends, and family supported me in my journey even to arrive at graduate school, and have since enabled me to complete my Ph.D. Of these people, firstly, I would like to express my sincere gratitude to my advisor and mentor, Dr. Kevin Murphy, for his continuous support of my Ph.D. study and related research and, for his patience, motivation, and immense knowledge. I appreciate all his contributions of time, ideas, and funding to make my Ph.D. experience productive and stimulating. The joy and enthusiasm he has for his research projects were contagious and motivational for me, even during tough times in the Ph.D. pursuit. I am also thankful for the excellent example he has provided as a successful researcher. His guidance helped me throughout the time of research and writing of this dissertation. I could not have imagined having a better advisor and mentor for my Ph.D. study.

Besides my advisor, I want like to thank the rest of my dissertation committee: Drs. John

P. Reganold, Marcia R. Ostrom, Kurtis L. Schroeder, and Jade d’Alpoim Guedes not only for their insightful comments, encouragement, and advice but also for the hard question which incentivized me to widen my research from various perspectives.

I thank my fellow labmates in the Sustainable Seed Systems Lab for their help during my research, for the stimulating discussions, and for all the fun we have had since I started graduate school. Various members of the crop and soil science department and WSU community have contributed immensely to my personal and professional success at WSU. My time at WSU was made enjoyable in large part due to the many friends and groups that became a part of my life.

iii I also want to thank my friends Dr. Kimberlee Kidwell and Colleen Taugher for the incomparable love, care and support they always gave me, and for motivating me to strive for and achieve exceptionally high standards in school and life in general. In many areas, both personally and professionally, they have encouraged me to meet challenges that I had never thought possible. They showed me that I am capable and taught me never to underestimate myself or anyone else. They introduced me to the WSU community, that has later become a family. I am very grateful to Dr. Jillian Morrison for her assistance in statistical analysis, which was helpful for a complex data set that provided quite a challenge to analyze. Great thanks to

Cody Holland for his help in reviewing my dissertation.

I also thank my friends in the following institutions; UC Davis (Susan Johnson and

Andrea Carr) and CIAT Rwanda (Dr. Eliud Birachi) for their support and for making my

Borlaug LEAP fellowship experience a success. I want to thank my dear friend Olivier

Ndayiramije, for his tremendous support for quinoa and millet research projects in Rwanda. He is an integral part of this dissertation. To my uncle Jean Damascène Munyakayanza, my aunt

Justine Mukarugira and my dear friends Lama Mugabo and Joseph Kayumba, for their support and mentorship; they have encouraged me to pursue my goals with hard work and dedication and have shown me the value of honesty, sincerity, and trust in life.

I gratefully acknowledge the funding sources that made my Ph.D. work possible. I was funded by the Western SARE (Sustainable Agriculture Research & Education), and partial funds were provided by the Borlaug Leadership Enhancement in Agriculture Program (Borlaug LEAP) through a grant to the University of California-Davis by the USAID (United States Agency for

International Development).

iv I am grateful to my Rwandan community, here in Pullman for making Pullman feel like a home away from home.

Last but not least, I would like to thank my family: the splendid efforts of my beloved mother, Agnès Mukankwaya, for parental education, hard work, and patience, determination, and morale. Her love, care, encouragement, and inspiration backed up by a commitment to real self- sacrifice are highly valuable and unforgettable as they have made me the man I am today. I thank her for cultivating a sense of curiosity and determination in me from a young age. To my brother,

Rémy Pacifique Ntirenganya, for being my role model and best friend. To my beloved grandmother, who always keeps me in her prayers. Moreover, most of all, for my loving, supportive, encouraging, and patient best friend and life partner Kelly Jenson, whose faithful support during the final stages of this Ph.D. is so appreciated. Thank you for supporting me in everything, and especially I cannot thank you enough for encouraging me throughout this experience. I am additionally indebted to the rest of my family and close friends for their support.

My dedication goes to my late father who always wished to see me become a success and to everyone who have supported me every step of the way. And most importantly to God who gave me life and courage to overcome all the adversities to become the man I am today.

v EVALUATING AGRONOMIC TRAITS OF QUINOA, MILLET, AND FOOD BARLEY

VARIETIES FOR ADOPTION IN RWANDA AND THE U.S. PACIFIC NORTHWEST

Abstract

by Cedric Habiyaremye, Ph.D. Washington State University July 2019

Chair: Kevin M. Murphy

This dissertation comprises three research studies. The first study evaluated N and seeding rate effects on β-glucan protein content, grain yield, and agronomic and quality traits of hulless food barley in two no-till farms in Almota, WA, and Genesee, ID, in the Palouse region of the Pacific Northwest during 2016, 2017, and 2018. The first experiment, an agronomy trial featuring two varieties (‘Havener’ and ‘Julie’), employed N rates of 0, 62, 95, 129, and 162 kg ha-1 and three seeding rates. The second experiment, a variety trial, gauged nine food barley varieties’ suitability to no-till farming systems in the Palouse region. Nitrogen was shown to significantly increase all variables, except days to heading, test weight, percent plump kernels, percent thin kernels, and β-glucan content. In the variety trial, Genesee environments exhibited higher mean grain yield across all varieties, with ‘Kardia’ boasting the highest yield of 3,984 and

5,882 kg ha-1 in both Almota and Genesee, respectively.

The second study, conducted in 2016 and 2017, assessed the adaptability of quinoa and millet in two agro-ecological zones of Rwanda. Quinoa and millet cultivars were evaluated for their agronomic traits, including grain yield, emergence, days to heading, flowering, maturity,

vi and plant height. Results suggest that quinoa and millet have the potential as regional crops in the traditional dryland rotations in Rwanda, thereby contributing to increased cropping system diversity. However, we suggest the need to continue evaluating a diverse number of cultivars to select for genotypes adapted to specific agro-ecological zones and across seasons in Rwanda.

The third study was conducted to ascertain Rwandan smallholders’ perceptions of the government mandated crop intensification program (CIP) implemented in 2007. Data were collected over three months from 50 respondents through an ethnographic interview to assess the challenges that farmers have encountered since the implementation of CIP. The respondents asserted that their participation in the CIP was hindered by inadequate irrigation and mechanization infrastructure, lack of farmer input, and inadequate extension services, agricultural inputs, and post-harvest technologies. The CIP should weigh sustainable management practices with short-term food security needs and long-term soil fertility targets.

vii TABLE OF CONTENTS

Page

ACKNOWLEDGEMENT ...... iii

ABSTRACT ...... vi

LIST OF TABLES ...... xiii

LIST OF FIGURES ...... xvii

GLOBAL INTRODUCTION ...... 1

References ...... 8

CHAPTER ONE ...... 19

EFFECTS OF NITROGEN AND SEEDING RATE ON YIELD, PROTEIN, AND

β-GLUCAN CONTENT OF HULLESS FOOD BARLEY IN NO-TILL

CROPPING SYSTEMS IN THE PALOUSE ...... 19

Abstract ...... 19

1. Introduction ...... 20

2. MATERIALS AND METHODS ...... 23

2.1. Location ...... 23

2.2. Experimental Design and Data Collection ...... 24

2.2.1. Agronomy Trial ...... 24

2.2.2. Variety Trial ...... 24

2.2.3. Agronomy and Yield Assessment ...... 25

2.3. Statistical data analyses ...... 26

3. RESULTS ...... 26

3.1. Agronomy trial ...... 26

viii 3.1.1. Β-glucan ...... 26

3.1.2. Protein...... 27

3.1.3. Grain Yield ...... 27

3.1.4. Emergence...... 28

3.1.5. Plant Height ...... 29

3.1.6. Days to Heading ...... 30

3.1.7. Days to Maturity ...... 30

3.1.8. Test Weight...... 31

3.1.9. Percent Plump Kernels ...... 32

3.1.10. Percent Thin Kernels ...... 33

3.2. Variety trials ...... 33

3.2.1. Β-glucan ...... 33

3.2.2. Protein...... 34

3.2.3. Grain Yield ...... 34

3.2.4. Emergence...... 35

3.2.5. Plant Height ...... 35

3.2.6. Days to Heading ...... 36

3.2.7. Days to Maturity ...... 36

3.2.8. Test Weight...... 37

3.2.9. Percent Plump Kernels ...... 37

3.2.10. Percent Thin Kernels ...... 38

4. DISCUSSION ...... 39

4.1. Nitrogen effects ...... 39

ix 4.2. Seeding effects ...... 43

4.3. Variety Trials ...... 44

5. CONCLUSION ...... 44

References ...... 46

CHAPTER TWO ...... 82

ASSESSING THE ADAPTABILITY OF QUINOA AND MILLET IN TWO AGRO-

ECOLOGICAL ZONES OF RWANDA ...... 82

Abstract ...... 82

1. Introduction ...... 83

2. MATERIALS AND METHODS ...... 88

2.1. Location ...... 88

2.2. Experimental Design and Data Collection ...... 89

2.2.1. Quinoa Variety Trials ...... 89

2.2.2. Millet Variety Trials ...... 90

2.3. Statistical Analyses ...... 91

3. RESULTS ...... 91

3.1. Quinoa Variety Trials ...... 91

3.1.1. Gain Yield ...... 91

3.1.2. Emergence...... 92

3.1.3. Days to Flowering...... 93

3.1.4. Days to Maturity ...... 94

3.1.5. Plant Height ...... 94

3.2. Millet Variety Trials ...... 95

x 3.2.1. Grain Yield ...... 95

3.2.2. Emergence...... 96

3.2.3. Days to Heading ...... 96

3.2.4. Days to Maturity ...... 97

3.2.5. Plant Height ...... 98

4. DISCUSSION ...... 98

4.1. Grain Yield ...... 98

4.2. Emergence and Plant height ...... 99

4.3. Days to Flowering and Maturity ...... 100

5. CONCLUSION ...... 101

References ...... 102

CHAPTER THREE ...... 129

THE DEARTH OF DIVERSITY AND AUTONOMY: SMALLHOLDERS PERCEPTIONS OF

CROP INTENSIFICATION PROGRAM IN RWANDA ...... 129

Abstract ...... 129

1. Introduction ...... 130

2. Traditional farming in Rwanda ...... 135

3. Factors of change in agricultural reforms ...... 137

4. Methods ...... 138

5. Results ...... 139

5.1. Farmers’ perception on implementation of the National Agricultural Policy .... 139

5.2. The high cost of buying seed and fertilizers associated with growing maize ...... 141

5.3. The lack of a market for maize ...... 143

xi 5.4. Difficulty with storage ...... 143

5.5. Theft of maize in fields ...... 144

5.6. The top-down model used in projects and program implementation ...... 145

6. Conclusion ...... 146

References ...... 148

xii LIST OF TABLES

Page CHAPTER ONE

Table 1. Hulless barley variety used for variety trials to evaluate their phenotypic responses to no-till farming systems in the Palouse region of north Idaho and eastern Washington in 2016,

2017, and 2018...... 59

Table 2. Analysis of Variance with F value for β-glucan and protein for food barley grown under five nitrogen treatments on no-till farms of Almota and Genesee...... 60

Table 3. Mean data across years 2016, 2017, and 2018 for the effect of N rates on β-glucan and protein in Almota and Genesee...... 61

Table 4. Mean data across years 2016, 2017, and 2018 for varietal differences in β-glucan and protein in Almota and Genesee...... 62

Table 5. Analysis of Variance with F value for emergence rate, day to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels for food barley grown under five nitrogen treatments and three seeding rates on no-till farms of Almota and Genesee ...... 63

Table 6. Mean data across years 2016, 2017, and 2018 for nitrogen rates on agronomic traits in

Almota and Genesee...... 65

Table 7. Mean data across years 2016, 2017, and 2018 for varietal differences across N and seeding rates treatments in Almota and Genesee...... 67

Table 8. Mean data across years 2016, 2017, and 2018 for the effect of seeding rates on agronomic traits in Almota and Genesee...... 68

xiii Table 9. Pearson correlation for percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels in Almota and

Genesee ...... 69

2016, 2017, and 2018...... 70

Table 11. Mean Difference between Almota and Genesee for each variety of each trait: percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels...... 71

Table 12. Mean data across years 2016, 2017, and 2018 for β-glucan and protein in Almota and

Genesee...... 73

Table 13. Pearson correlation for percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels in Almota and

Genesee ...... 75

Table 14. Analysis of Variance with F value for percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels for food barley varieties grown in no-till farms in Almota and Genesee over three crop years

2016, 2017, and 2018...... 77

Table 15. Mean Difference between Almota and Genesee for each variety of each trait: percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels...... 78

xiv Table 16. Mean data across years 2016, 2017, and 2018 in Almota and Genesee for each trait: percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels...... 80

CHAPTER TWO Table 1. Quinoa germplasm used to assess the adaptability of quinoa in Rwanda in 2016 and

2017...... 113

Table 2. Millet germplasm used to assess the adaptability of millet species in Rwanda ...... 114

Table 3. Analysis of variance with F value for emergence rate, days to flowering, days to maturity, plant height, and grain yield for quinoa varieties grown in Musanze and Kirehe over two crop years...... 115

Table 4. Mean difference between Musanze and Kirehe for each quinoa variety of each trait: percent emergence, days to flowering, days to maturity, plant height, and grain yield...... 116

Table 5. Mean data of quinoa varieties across the years 2016 and 2017 for each trait in Musanze and Kirehe ...... 118

Table 6. Pearson correlation for percent emergence (PE), days to flowering (DH), days to maturity (DM), plant height (PH), grain yield (GY) of quinoa in both Musanze and Kirehe .....120

Table 8. Mean difference between Musanze and Kirehe for each millet variety of each trait: percent emergence, days to heading, days to maturity, plant height, and grain yield...... 122

Table 9. Mean data of millet varieties across the years 2016 and 2017 for each trait in Musanze and Kirehe ...... 123

xv Table 10. Pearson correlation for percent emergence (PE), days to heading (DH), days to maturity (DM), plant height (PH), grain yield (GY) of millet in both Musanze and Kirehe ...... 124

Table 11. Mean data comparison of millet species across the years 2016 and 2017, and locations,

Musanze and Kirehe for each trait percent emergence, days to heading, days to maturity, plant height and grain yield...... 125

xvi LIST OF FIGURES

Page CHAPTER TWO

Figure 1. Elevation from mean sea level and spatial variations of mean annual rainfall of locations where quinoa and millet trials were conducted in Rwanda (marked by black stars)... 126

Figure 2. Mean monthly temperature (°C); and total monthly precipitation (mm) (average for

1950 to 2000) in Rwanda. Monthly temperature does not change much over the year, but monthly precipitation follows seasonal pattern with two distinct dry seasons ...... 127

Figure 3. Soil organic matter (%) and soil pH of locations where quinoa and millet trials were conducted in Rwanda (marked by black stars)...... 128

xvii GLOBAL INTRODUCTION

Quinoa (Chenopodium quinoa Willd.) is a pseudocereal grain originating from Lake

Titicaca in the Peruvian and Bolivian Andes (Adolf et al. 2013). It is named “kiuna” or “kinwa” in the Quechua language and “jupha” or “jiura” in the Aymara language (Tapia, 2015). Quinoa is a broadly adapted crop with exceptional resilience to many adverse environmental and climatic conditions including nutrient-poor and saline soils, and drought-stressed marginal ecosystems

(Vacher, 1998; Aguilar & Jacobsen, 2003; Jacobsen et al. 2003; Fuentes & Bhargave, 2011).

Quinoa has excellent drought and salinity tolerance and thrives across a wide range of soil pH from acid to basic (Wilson et al. 2002; Koyro et al. 2008; Adolf et al. 2013; Peterson & Murphy,

2015). In South America quinoa grows over a broad range of latitudes (spanning nearly 4,828 km from equatorial Columbia to temperate southern Chile), a wide range of altitudes, from sea level at the coast to 4000 m above sea level (m.a.s.l.), and a diverse set of rainfall zones (Galwey,

1989; Jacobsen et al. 2003). Quinoa has great potential to contribute to food security in multitudes of regions worldwide, especially in countries where the human population has limited access to protein sources or where production conditions are limited by low humidity, reduced availability of inputs, or aridity (Rojas, 2003; Jacobsen, 2003; Wu et al., 2016). Recently, several papers have primarily reported upon salt and drought tolerance in quinoa (Wilson et al. 2002;

Koyro et al. 2008; Adolf et al. 2013; Zurita-Silva et al., 2014; Peterson & Murphy, 2015; Coral,

2015). In the last 30 years, quinoa has garnered considerable attention worldwide due to its nutritional and health benefits, as well as its flavorful and high‐quality seeds (Aluwi et al. 2017;

Wu et al. 2016; Wu et al. 2017). Quinoa possesses a well‐balanced complement of amino acids and high mineral concentration of iron, calcium, and phosphorus (Wu, 2015; Navruz‐Varli &

Sanlier, 2016; Wu et al. 2016). In 2013, quinoa was lauded by the FAO as a food with high

1 nutritive value, impressive biodiversity, and a singular role to play in attaining food security worldwide (FAO, 2013). Quinoa has been deemed one of humanity’s most promising crops for reliably providing nutritionally-dense food, while also promoting socio-economic growth in whichever country it is grown (UN, 2012; Jayne et al. 2003; Rojas, 2003). A pilot global collaborative network for quinoa (GCN‐quinoa) now exists to introduce quinoa to novel settings

(Bazile et al. 2016; Murphy et al. 2016).

Millet, a small-seeded annual cereal grain, is one of the oldest food crops known to humankind and is possibly the first domesticated cereal grain (Changmei & Dorothy, 2014).

Millet crops are a major source of energy and protein for millions of people worldwide, especially those who live in exceptionally hot, dry environs (Rachie, 1975; Fuller, 2006;

Amadou et al. 2013; Nithiyanantham et al. 2019). According to Nithiyanantham et al. (2019), millet‐based foods are considered potential prebiotics and probiotics with prospective health benefits. Grains of millet species are widely consumed for traditional medicinal purposes and holistic health remedies. Millets are unique among cereals because of their high calcium, iron, potassium, magnesium, phosphorous, zinc, dietary fiber, polyphenols, and protein content (Hulse et al. 1980; Devi et al. 2014; Gupta et al. 2014; Habiyaremye et al. 2017). Millets are important food crops in many less developed countries because of their ability to grow with limited rainfall especially in regions such as India and Sub-Saharan and West Africa, where average rainfall is often less than 500 mm and soils are sandy and slightly acidic (McDonald et al. 2003; Amadou et al. 2013; Changmei & Dorothy, 2014). Millets have been adapted to local climatic conditions by

African farmers over millennia. However, knowledge of these historic landraces is too easily lost

(Fuller, 2006; Amadou et al. 2013). Twenty different species of millet have been continuously cultivated throughout the world since their initial domestication (Fuller, 2006). The most

2 commonly cultivated millet species are proso millet (Panicum miliaceum L.), pearl millet

(Pennisetum glaucum L.R. Br.), ﬁnger millet (Eleusine coracana), kodo millet (Paspalum setaceum), foxtail millet (Setaria italica L. Beauv.), little millet (Panicum sumatrense), and barnyard millet (Echinochloa utilis) (Rachie, 1975; Bouis, 2000; Wen et al. 2014). Development of millet varieties that yield well under uncertain and extreme climatic and soil growing conditions can potentially mitigate problems of food and nutrition insecurity.

Since antiquity, barley (Hordeum vulgare L.) has been a rich source of human nutrition

(Newman & Newman, 2006). It was first domesticated in the Fertile Crescent of Asia over

10,000 years ago (Zohary & Hopf, 1993; Mohammed et al. 2016), making it among the oldest domesticated crops (Salamini et al. 2002). Barley is a principal cereal crop in the world’s temperate regions and is ubiquitous across many agro-ecological zones, from 70°N in Norway to

46°S in Chile, and at higher elevations than other cereals (Von Bothmer et al. 1995; Von

Bothmer et al. 2003; Kaso & Guben, 2015). Principal cropping regions for barley are Europe and

Russia, but it is also a valuable resilient crop in arid and semi-arid areas of Asia, the Middle East, and North Africa (FAO, 2014; FAOSTAT, 2017; USDA, 2019). Barley, boasting 140.6 million metric tons (MMT) worldwide in 2018, ranked fourth in world grain production after maize, wheat, and rice with 1,009.61 MMT, 734.74 MMT, and 495.87 MMT, respectively (STATISTA,

2019). Over half of global barley production occurred in less-developed nations (Grando &

Macpherson, 2005; Zhou, 2009; Mohammed et al. 2016).

Barley is a major staple food in areas of North Africa, the Near East, the highlands of

Central Asia, the Horn of Africa, Andean countries, and the Baltic States (Grando &

Macpherson, 2005). Food barley is generally grown in regions where other cereals grow poorly due to inadequate rainfall, high altitude, or saline soils. It remains the most viable cereal crop

3 option in arid regions (< 300 mm of rainfall) and when alternative non-cereal cropping options are limited (Grando & Macpherson, 2005). Furthermore, food barley is a popular relief crop during periods of food shortage, given its relatively short growing season—it is also used as a substitute crop for wheat when wheat market prices are prohibitively high. Therefore, food barley is valued for its contribution to improving food security in many harsh and marginal regions of the world (Grando & Macpherson, 2005; Zhou, 2009; Mohammed et al. 2016).

Of late, however, relatively little barley is consumed in human diets, but is instead grown for value-added markets, such as malting for brewing and distilling (Grando & Macpherson,

2005; Vasanthan & Hoover, 2009; Meints et al. 2015). Moreover, almost all barley used for food is pearled, which removes the hull and a significant portion of the pericarp and bran, where phytonutrients and minerals are predominantly concentrated (Grando & Macpherson, 2005;

Moreau et al. 2007; Bleidere et al. 2017). Pearling, by default, excludes barley from being considered a whole grain (Seal, 2006; O’Neil, 2010). However, hulless barley has the additional advantages of allowing the whole grain to be used without pearling the nutritious bran layers away, and this allows the hulless barley to be labeled as a whole grain (O’Neil, 2010; Bleidere et al. 2017). Besides the nutritional benefit to consumers, farmers are also saved the cost of pearling hulless barley. Interest from consumers and food companies in a nutritionally dense and β- glucan-rich food barley is high and the time is opportune for plant breeders and agronomists to capitalize on this whole foods zeitgeist (Baik & Ullrich, 2008; Thorwarth et al. 2017).

Interest in barley as a food grain is also growing due to the new research showing the presence of constituents in barley known to prevent or alleviate certain diseases (Slavin et al.,

2000; Arndt, 2006; Madhujith et al. 2006; De Angelis, 2015; Baidoo et al. 2019). Barley grain is an excellent source of soluble and insoluble dietary fiber (DF) and other bioactive constituents,

4 such as vitamin E, B-complex vitamins, minerals, and phenolic compounds (Slavin et al. 2000;

Madhujith et al. 2006). β-glucans, the major fiber constituents of barley, have been implicated in lowering plasma cholesterol, improving lipid metabolism, and low glycemic index (Delaney et al. 2003; Li et al. 2003; Behall et al. 2004, 2005, 2006; Keenan et al. 2007; Garcia et al. 2018). In

2005 the US Food and Drug Administration (FDA) allowed whole grain barley and barley- containing products to carry a claim that they reduce the risk of coronary heart disease (Wellness

Foods. 2005; FDA News Release, 2006). Some anticipate that the health benefits of barley will stimulate interest among food producers and consumers in using barley for food purposes

(Quinde et al. 2004; Baik & Ullrich, 2008; Thorwarth et al. 2017). The development of hulless food barley varieties with high protein and β-glucan content would provide incentive for production of non-malt, non-feed crops.

Historically, barley has been an important rotational crop in the Palouse region of

Washington State and Idaho in the Pacific Northwest (PNW) for its agronomic properties

(Juergens et al. 2004; McCoy, 2014; Murphy et al. 2015; Brouwer et al. 2016a). Rotations with barley have been shown to improve yield in its cereal crop counterpart, wheat, and its fibrous root system enhance nutrient cycling, benefit soil structure, reduce erosion, improve water infiltration, and build soil organic matter in the winter wheat-centric cropping systems of the

PNW (Guy & Gareau, 1998; Paulitz et al. 2002; Juergens et al. 2004). There are documented economic and environmental benefits of incorporating spring barley into the prevailing winter wheat-summer fallow (WW-SF) cropping systems in the PNW (Juergens et al. 2004). Those benefits include higher annual income, reduction of wind erosion, and suppression of weed, plant pathogens, and insect pests (Smiley et al. 1994; Young et al. 1994; Juergens et al. 2004). WW-

SF is the dominant cropping system in the low-precipitation (< 300 mm annual) region of the

5 PNW. In east-central Washington and north-central Oregon, where annual precipitation ranges from 150 to 300 mm, WW-SF cropping is practiced on 1.5 million ha (Juergens et al. 2004).

There are environmental disadvantages of WW-SF, including recurrent wind erosion, especially during drought cycles, when straw production is low (Juergens et al. 2004). Research in the

PNW and elsewhere has shown that no-till cropping mitigates soil erosion, and builds soil quality, by leaving residue on the field after harvest, where it acts as a mulch to protect the soil from erosion and to foster soil productivity (Guy & Cox, 2002; Juergens et al. 2004; Huggins &

Reganold, 2008; Pittelkow et al. 2015).

Considering the nutritional promise of quinoa, millets, and barley, their propensity for agronomic adaptability, and their resilience to climate variability, development of these crops should be a priority in global food security efforts. To combat impending and ongoing climate change, quinoa, millet and barley should be seriously considered for integration into suitable marginal cropping systems.

Chapter 1 of this dissertation discusses the effects of nitrogen and seeding rates on β- glucan content, protein content, grain yield, and agronomic and quality traits of hulless food barley in no-till cropping systems in the Palouse region of Washington State and Idaho in the

Pacific Northwest (PNW). This three-year field experiment was conducted at two no-till farms in

Almota, WA, and Genesee, ID, in the Palouse region of the PNW during 2016, 2017, and 2018.

The first experiment tested the effect of N fertilization and seeding rate on ß-glucan content, protein content, grain yield, and agronomic and quality traits. The second experiment was a variety trial to identify the best food barley breeding lines and varieties for no-till farming systems in the Palouse region. Varieties were evaluated for positive agronomic traits and high ß- glucan, and protein content.

6 Chapter 2 discusses the adaptability of quinoa, proso millet, and African millets in two agro-ecological zones of Rwanda. In this chapter, the adaptability and potential of quinoa as a novel crop in Rwanda is discussed. This study was conducted to evaluate quinoa and millet genotypes and to assess their agronomic performance via plant growth, and grain yield performance in two contrasting environments in Rwanda: Musanze (Highland) and Kirehe

(Lowland).

Chapter 3 discusses smallholders’ perception of the 2007 crop intensification program

(CIP) enacted in Rwanda. In this study, we investigated how these agricultural policies affected the supply of provision services, how they affected farming system resilience, and if they exposed farmers to risks associated with narrowly defined crop choice in the Nyamugari sector, the Kirehe District in the Eastern Province, and the Nyamagumba Sector, the Musanze District in the Northern Province of Rwanda. We also considered the implications of transitioning from a subsistence-based agricultural system to a market-based system in marginalized regions of

Rwanda from farmers perspective and how farmers perceived the associated risks. This study is based on literature review as well as 50 interviews, most of them with farmers and local administrators, conducted between October 2016 and January 2017 in the Musanze and Kirehe

Districts of Rwanda.

7 References

Adolf, V. I., Jacobsen, S. E., & Shabala, S. (2013). Salt tolerance mechanisms in quinoa

(Chenopodium quinoa Willd.). Environmental and Experimental Botany, 92, 43-54.

doi:10.1016/j.envexpbot.2012.07.004

Aluwi, N. A., Murphy, K. M., & Ganjyal, G. M. (2017). Physicochemical characterization of

different varieties of quinoa. Cereal Chemistry, 94(5), 847-856. doi:10.1094/CCHEM-

10-16-0251-R

Amadou, I., Gounga, M. E., & Le, G. W. (2013). Millets: Nutritional composition, some health

benefits, and processing-A review. Emirates Journal of Food and Agriculture, 501-508.

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18 CHAPTER ONE

EFFECTS OF NITROGEN AND SEEDING RATE ON YIELD, PROTEIN, AND

β-GLUCAN CONTENT OF HULLESS FOOD BARLEY IN NO-TILL

CROPPING SYSTEMS IN THE PALOUSE

Abstract

Barley (Hordeum vulgare L.) has a storied history as a food crop, and since antiquity, has been a dietary staple of peoples in temperate climates. However, contemporary studies have focused mostly on malting and feed barley. As such, nitrogen (N) and seeding agronomic data for food barley are lacking. In this study, we evaluated N and seeding rate effects on phenotypic characteristics of hulless food barley, including grain yield, stand establishment, plant height, days to heading, days to maturity, test weight, percent plump kernels, and percent thin kernels. In addition to phenotypic responses, we evaluated the effect of N on ß-glucan and protein content of hulless food barley. Both experiments were conducted at two no-till farms in Almota, WA, and

Genesee, ID, in the Palouse region of the Pacific Northwest during 2016, 2017, and 2018. The first experiment, an agronomy trial featuring two varieties (‘Havener’ and ‘Julie’), employed N rates of 0, 62, 95, 129, and 162 kg ha-1, and seeding rates of 2.5 mil ha-1, 3.1mil ha-1, and 3.75mil ha-1. The second experiment, a variety trial, tested nine food barley varieties to identify the best varieties for no-till farming systems in the Palouse region. N was shown to significantly increase all the variables, except days to heading, test weight, percent plump kernels, percent thin kernels, and β-glucan content. In the variety trial, Genesee produced higher mean grain yield across all varieties, with ‘Kardia’ having higher grain yields in both Almota and Genesee, averaging 3,984 kg ha-1 and 5,882 kg ha-1, respectively. Our results suggest that hulless food barley is

19 agronomically well-adapted to the Palouse. N rate recommendation for farmers growing food barley in this regions should be approximately 95kg/ha, and seeding rate should be approximately 3.75 million seed/ha to optimize grain yield and protein content.

1. Introduction

Since antiquity, barley (Hordeum vulgare L.) has been a rich source of human nutrition

(Newman & Newman, 2006). It was first domesticated in the Fertile Crescent of Asia over

46°S in Chile, and found at higher elevations than other cereals (Von Bothmer et al. 1995; Von

Bothmer et al. 2003; Kaso & Guben, 2015). Principal cropping regions for barley are Europe and

Russia, but it is also a valuable resilient crop in arid and semi-arid areas of Asia, the Middle East, and North Africa (FAO, 2014; FAOSTAT, 2017; USDA, 2019). Barley, boasting 140.6 million metric tons (MMT) globally in 2018, ranked fourth in worldwide grain production after maize, wheat, and rice with 1,009.61 MMT, 734.74 MMT, and 495.87 MMT, respectively (STATISTA,

2019). Over half of global barley production occurred in less-developed nations (Grando &

Macpherson, 2005; Zhou, 2009; Mohammed et al. 2016).

Barley is a major staple food in areas of North Africa, the Near East, the highlands of

Central Asia, the Horn of Africa, Andean countries, and the Baltic States (Grando &

Macpherson, 2005). Food barley is generally grown in regions where other cereals grow poorly due to inadequate rainfall, high altitude, or saline soils. It remains the most viable cereal crop option in arid regions (< 300 mm of rainfall) and when alternative non-cereal cropping options

20 are limited (Grando & Macpherson, 2005). Furthermore, food barley is a popular relief crop during periods of food shortage, given its relatively short growing season—it is also used as a substitute crop for wheat when wheat market prices are too high. Therefore, food barley holds an esteemed position in the food security of harsh and marginal regions of the world (Grando &

Macpherson, 2005; Zhou, 2009; Mohammed et al. 2016).

Of late, however, relatively little barley is consumed in human diets, but is instead grown for value-added markets, such as malting for brewing and distilling (Grando & Macpherson,

Interest in barley as a food grain is increasing due to the presence of constituents in barley known to prevent or alleviate certain diseases (Slavin et al. 2000; Arndt, 2006; Madhujith et al. 2006; De Angelis, 2015; Baidoo et al. 2019). Barley grain is an excellent source of soluble and insoluble dietary fiber (DF) and other bioactive constituents, such as vitamin E, B-complex vitamins, minerals, and phenolic compounds (Slavin et al. 2000; Madhujith et al. 2006). β-

21 glucans, the major fiber constituents of barley, have been implicated in lowering plasma cholesterol, improving lipid metabolism, and low glycemic index (Delaney et al. 2003; Li et al.

2003; Behall et al. 2004, 2005, 2006; Keenan et al. 2007; Garcia et al. 2018). In 2005 the US

Food and Drug Administration (FDA) allowed whole grain barley and barley-containing products to carry a claim that they reduce the risk of coronary heart disease (Wellness Foods.

2005; FDA News Release, 2006). Some anticipate that the health benefits of barley will stimulate interest among food producers and consumers in using barley for food purposes

Historically, barley has been an important rotational crop in the Palouse region of

Washington State and Idaho in the US Pacific Northwest (PNW) (Juergens et al. 2004; McCoy,

2014; Murphy et al. 2015; Brouwer et al. 2016a). Rotations with barley have been shown to improve yield in its cereal crop counterpart, wheat, and its fibrous root system enhances nutrient cycling, benefits soil structure, helps reduce erosion, improves water infiltration, and helps build soil organic matter in the winter wheat-centric cropping systems of the PNW (Guy & Gareau,

1998; Paulitz et al. 2002; Juergens et al. 2004). There are demonstrated economic and environmental benefits of incorporating spring barley into the prevailing winter wheat-summer fallow (WW-SF) cropping systems in the PNW (Juergens et al. 2004). Those benefits include higher annual income, reduction of wind erosion, and suppression of weed, plant pathogens, and insect pests (Smiley et al. 1994; Young et al. 1994; Juergens et al. 2004). WW-SF is the dominant cropping system in the low-precipitation (< 300 mm annual) region of the PNW. In east-central Washington and north-central Oregon, where annual precipitation ranges from 150

22 to 300 mm, WW-SF cropping is practiced on 1.5 million ha (Juergens et al. 2004). There are environmental disadvantages of WW-SF that include recurrent wind erosion, especially during drought cycles when straw production is low (Juergens et al. 2004). Research in the PNW and elsewhere has shown that no-till cropping mitigates soil erosion, builds soil quality, by leaving residue on the field after harvest, where it acts as a mulch to protect the soil from erosion and foster soil productivity compared with tillage-based systems (Guy & Cox, 2002; Juergens et al.

2004; Huggins & Reganold, 2008; Pittelkow et al. 2015).

Alternative cropping systems are needed to reinvigorate dryland crop production of the

US PNW. To determine the potential for production of hulless food barley in no-till cropping systems in the Palouse region, we conducted two experiments: i) an agronomic trial, with the objective to evaluate the effect of N fertilization and seeding rate on ß-glucan content, protein content, grain yield, and agronomic and quality traits; and ii) variety trials, with the objective to identify optimal food barley breeding lines and varieties with a combination of positive agronomic traits and high ß-glucan and protein content.

2. MATERIALS AND METHODS

2.1.Location

A three-year study (2016-2018) was conducted on two no-till farms; one in Genesee, ID

(Jensen farm) (46.611134oN Lat., -117.009925oW Long.) and the other in Almota, WA

(Aeschliman farm) (46.791288oN Lat., -117.438473oW Long.). Both Genesee and Almota soils are part of the Palouse series soil and are classified as silt loams (Donaldson, 1980; Frazier &

Cheng, 1989). Genesee and Almota receive mean annual precipitation of 571 mm and 467 mm, respectively (Weather Underground, 2019). Previous crops grown in the field where experiments

23 were conducted are as follows: in Genesee, winter wheat (WW), winter peas, and WW in 2016,

2017, and 2018 respectively. In Almota, WW in both years 2016, 2017, and 2018.

2.2.Experimental Design and Data Collection

2.2.1. Agronomy Trial

The agronomy trial consisted of two food barley varieties (‘Havener’ and ‘Julie’) (Obert et al. 2013). In this study, we measured the effects of N fertilization and seeding rates on agronomic traits of varieties ‘Havener’ and ‘Julie’, including grain yield, percent emergence, plant height, days to heading, days to maturity, test weight, and percent plump kernels. We also evaluated the effect of N on ß‐glucan and protein content of the two varieties, ‘Havener’ and

‘Julie’. The experiments were arranged in a randomized complete block design with five replications. Main plot treatments were varieties ‘Julie’ and ‘Havener’ and sub-plots were N application rates (0, 62, 95, 129 and 162 kg/ha), and seeding rates (2.5, 3.1 and 3.75 mil/ha).

2.2.2. Variety Trial

Nine food barley varieties were tested to identify the varieties best adapted for no-till cropping systems across and within the diverse Palouse agroecological region (Table 1). In this study, we evaluated the agronomic traits of the nine varieties, including grain yield, percent emergence, plant height, days to heading, days to maturity, test weight, and percent plump kernels. We also evaluated the ß‐glucan and protein content of the nine varieties. The experiment was designed in a randomized complete block design with four replicates. The main, and only treatment was the nine varieties of food barley.

24 2.2.3. Agronomy and Yield Assessment

Plots were planted using a no-till drill equipped with Flexi-Coil Stealth openers that allow fertilizer to be banded below and between paired rows. The openers are spaced 25 cm apart and seed is placed in paired rows 7.6 cm apart. Fertilizer was banded below the seed at time of planting. For both barley agronomy and variety trials, agronomic data were recorded as follows: percent emergence was recorded as a visual observation on a scale of 1 to 5 with 1 (0% emergence) and 5 (100 % emergence). Days to heading was recorded as number of days from sowing to the time when 50% ear had emerged from the flag leaf sheath (stage 10.3 on the

Feekes scale) according to Wise et al. (2011) and Miller (1992). Days to maturity was recorded as the number of days from sowing to the time when kernels could not be split with a fingernail

(stage 11.4 on the Feekes scale) according to Wise et al. (2011) and Miller (1992). Plant height was measured from soil surface to tip of the spike (excluding awns). Grain yield was measured as the weight of the grain harvested from the whole plot.

Seed harvesting was conducted using a 1999 Wintersteiger Nursery Master Elite plot combine (Wintersteiger Inc., Austria). Seed cleaning was conducted using an electrical seed thresher (Midwest Industry Inc., Bozeman, MT) for one 30 second interval. Test weight and seed size were recorded for each sample. Test weight was measured using Pint Cup 104 Seedburo

Equipment Co. (Chicago, IL). Seed size was measured by taking 250g sub-samples from each test-weight sample and shaking them using a grain sizer model G-2 (Swenko, Minneapolis, MN) with screen sizes of 2.38 mm and 2.18 mm for one 30 second interval. Each experimental unit was analyzed for its contents of β-glucan and protein. All nutritional analysis was conducted on dry basis using near-infrared instrumentation (Perten DA 7250, Hägersten, Sweden).

25 2.3.Statistical data analyses

Statistical analysis was performed using the statistical software SAS 9.4 University

Edition (SAS Institute IN., Cary, NC, USA). The mixed-effects methodology was used to analyze data and was performed using a mixed model with PROC GLIMIX. Model assumptions were verified using marginal and conditional studentized residuals from PROC MIXED and studentized residuals from PROC GLIMMIX. A logarithmic transformation was used for yield and plant height to satisfy the homogeneity of variance assumption. Contrasts were calculated to show which of the two varieties differed by N and seeding rates. Means and least significant differences for all the data in the table were reported. Statistical significance level was set at α =

0.05. For agronomy data, ANOVA was performed with all factors using a mixed model with

PROC GLIMIX. Pearson’s correlation coefficients were calculated based on mean trait values and used to estimate phenotypic relationships between traits of interest. Fisher's LSD test (P '

0.05) was used to compare treatment means.

3. RESULTS

3.1.Agronomy trial

3.1.1. Β-glucan

Nitrogen did not have a significant effect on β-glucan across all three years and at both locations (Table 2). However, there was a significant N × variety interaction for β-glucan (Table

2). While not significant, increased N rates increased grain β-glucan content of both varieties.

The highest grain β-glucan contents were generally observed in response to 162 kg N ha-1 (Table

3); however, β-glucan appeared to be more responsive to environmental factors than N, with

26 Genesee being 0.4% higher than Almota in β-glucan content across all varieties and years (Table

3).

There was a significant difference (p < 0.05) between varieties in β-glucan content across all five N rates in Almota and Genesee in both years of the study (Table 2) and grain β-glucan content in the varieties varied by year (Table 4). When comparing varieties’ β-glucan content,

‘Julie’ consistently had significantly higher β-glucan content than ‘Havener’ in both locations—

1.6% higher in Almota and 1.5% higher in Genesee (Table 4).

3.1.2. Protein Nitrogen had a significant effect (p < 0.001) on protein in Almota in 2017 and 2018 (but not 2016) and in Genesee in 2016 and 2018 (but not 2017) (Table 2). Grain protein responded positively to increasing N rates in both varieties, where the highest protein content was observed in response to 162 kg N ha-1 (Table 3). The average protein content across all N rates, varieties, and years was 10.9 % at both Almota and Genesee (Table 3). There was a significant difference

(p < 0.05) in protein content between varieties across the N rates in Almota and Genesee (Table

4). When comparing varieties’ protein content, ‘Julie’ had an average of 3.5 % higher protein content than ‘Havener’ in all years and locations (Table 4).

3.1.3. Grain Yield

In all three years, there was a significant N × variety interaction for yield in Almota and

Genesee (p < 0.0001) (Table 5). N rates caused a difference in grain yield across both ‘Havener’ and ‘Julie’ and three years in Almota and Genesee (p < 0.05) (Table 5). In all years at both locations, there was a significant increase in grain yield with increasing N rates across both

27 ‘Havener’ and ‘Julie’ (p < 0.05) (Table 5). The increased N rates increased grain yield of both varieties with the highest grain yield being observed in response to 129 kg N ha-1 in half of the six site-years, while the maximum yield occurred with 162 kg N ha-1 at both locations in 2016 and the highest yield at the 2018 Almota site was with 95 kg N ha-1 (Table 6). There was a decrease of 20 kg ha-1 in yield as the N rates increased to 162 kg N ha-1 (Table 6). Across the five

N treatments in all three years, the grain yield in Genesee was 376 kg ha-1 higher than the grain yield in Almota (Table 6).

When comparing varietal grain yield, across years, location, and N treatments, the results showed a significant difference (p < 0.05) in grain yield between ‘Havener’ and ‘Julie’ (Table 5).

‘Havener’ was consistently higher yielding than ‘Julie’ and had an average grain yield 906 kg ha-

1 higher than ‘Julie’. (Table 7).

In Almota, there was a significant seeding rate × variety interaction for yield in 2016 and

2018 (p < 0.0001) (Table 5). In Genesee, there was a seeding rate × variety interaction for yield in 2017 and 2018 (p < 0.05) (Table 5). Increased seeding rates increased grain yield in all the varieties in both years and locations, with the highest grain yield observed in response to 3.75 million seed ha-1 (Table 8).

No correlation was found between grain yield and other measured traits in Almota, but in

Genesee, grain yield had a strong negative correlation with test-weight (r = -0.94; p = 0.02) and a strong positive correlation with plant height (r = 0.90; p = 0.04) (Table 9).

3.1.4. Emergence

Nitrogen did not have an impact on plant emergence in Almota or Genesee in 2016 and

2018 (Table 5). However, in 2017, N had a significant impact on plant emergence in Almota and

28 Genesee (p < 0.05) (Table 5). Across all N treatments in both years, the plant emergence in

Almota was 3% higher than the plant emergence in Genesee (Table 6). In Almota, there was a significant difference (p < 0.0001) in emergence rates between varieties in 2016 and 2017, but not in 2018 (Table 5). In Genesee, there was a significant difference (p < 0.0001) in varieties’ emergence rates in 2016 and 2018 (Table 5). Across all N rates and two locations, ‘Havener’ had, on average, 11% higher emergence rate than ‘Julie’ (Table 7). Seeding rate only had a significant effect on emergence at Genesee in 2016 and 2017 with a higher percent emergence with greater seeding rate. In both Almota and Genesee, there was no correlation between emergence rate and other measured traits (Table 9).

3.1.5. Plant Height

Nitrogen positively affected plant height in 2016, 2017, and 2018 in Almota (p < 0.0001), however, in Genesee N positively affected plant height only in 2016 and 2018 (p < 0.05) (Table

5). Increasing N rates increased plant height across both varieties in all years and locations; the maximum plant height of 88 cm was recorded at both 129 kg N ha-1 and 162 kg N ha-1 (Table 6).

Plant height did not differ between varieties with the exception of Almota in 2017 where

‘Havener’ was significantly taller than ‘Julie’ (Table 7). When comparing the plant height of varieties across all N rates, years and locations, both ‘Havener’ and ‘Julie’ had on average plant height of 85 cm (Table 7).

Seeding rate did not have a significant effect on plant height (Table 5). In Almota, there was a strong negative correlation between plant height and test weight (r = -0.90; p = 0.03), and a strong positive correlation between plant height and days to maturity (r = 0.99; p = 0.0001)

(Table 9). In Genesee, plant height had a strong negative correlation with percent plump kernels

29 (r = -0.94; p = 0.02) and a strong positive correlation with grain yield (r = 0.90; p = 0.04) (Table

9).

3.1.6. Days to Heading

Analysis of variance revealed that nitrogen rates affected days to heading in 2017 at both

Almota (p = 0.02) and Genesee (p = <0.0001) (Table 5). However, there was not a clear trend to suggest that nitrogen rate has an influence in days to heading. Over the course of the study,

Almota was two days earlier than Genesee for days to heading (Table 6). In five of the six site- years, there was a significant difference in days to heading between varieties (Table 5). When comparing varieties’ days to heading across all N and seeding rates rates in both Almota and

Genesee, ‘Havener’ was three days earlier to heading than ‘Julie’ (Table 7). Seeding rates did not have any effect on days to heading (Table 8). Days to heading did not correlate with any other measured traits (Table 9).

3.1.7. Days to Maturity

According to ANOVA, nitrogen rate affected days to maturity at both Almota and

Genesee in 2016, 2017, and 2018 (p < 0.05) (Table 5). Increased N rates increased days to maturity of all the varieties; across varieties in both years and locations, the highest number of days to maturity was observed in response to 162 kg N ha-1 (Table 6). Across all N treatments in all 3 years, Almota was 4 to 5 days earlier than Genesee in days to maturity (Table 6). When comparing varieties’ days to maturity across all five N rates in both Almota and Genesee,

‘‘Havener’’ was 3 to 8 days earlier in maturity than ‘Julie’ (Table 7).

30 In Almota, there was a strong negative correlation between days to maturity and test weight (r = -0.95; p = 0.01) and a strong positive correlation between days to heading and plant height (r = 0.99; p = 0.0001). However, in Genesee, there was a strong negative correlation between days to maturity and percent plump kernels (r = -0.90; p = 0.04) (Table 9). Seeding rates did not have any effect on days to maturity (Table 8).

3.1.8. Test Weight

The effect of different N rates on test weight was significant in Almota 2017 (p < 0.0001) and at Genesee in 2016 (p = 0.01) and 2018 (p < 0.0001) (Table 5). Across all N treatments in both years, Almota was 3 kg hL-1 higher in test weight than Genesee, at a statistically significant level (p < 0.05) (Table 6). Increasing N rates usually resulted in decreased test weight; across all locations and years the 0 N treatment resulted in highest test weight, and the 162 N treatment resulted in the lowest test weight (Table 6). There was a significant difference (p < 0.05) between

‘Havener’ and ‘Julie’ in test weight across all five N rates in both Almota and Genesee, and

‘Havener’ was 3 kg hL-1 higher in test weight than ‘Julie’ (Table 7). There was no difference in test weight caused by seeding rates across all three years at Almota and Genesee (Table 5).

For all 3 years in Almota, there was a significant N × variety interaction for test weight (p

< 0.05) (Table 5). In Genesee there was a significant N × variety interaction for test weight in

2017 (p = 0.04) and 2018 (p < 0.0001), but not in 2016 (Table 5). In Almota there was a strong negative correlation between test weight and days to maturity (r = -0.95; p = 0.01) and plant height (r = -0.91; p = 0.03). In Genesee, there was a strong negative correlation between test weight and grain yield (r = -0.94; p = 0.02) (Table 9).

31 3.1.9. Percent Plump Kernels

Nitrogen did not have a significant impact on percent plump kernels at Almota in any of the 3 years (Table 5). However, N had a significant impact on percent plump kernels 2017 at

Genesee (p < 0.0001) but not in 2016 or 2017 (p > 0.05) (Table 5). Increased N rates resulted in decreased percent plump kernels; looking across both locations and years the 0 N treatment resulted in the highest percent plump kernels and the 162 N treatment resulted in the lowest percent plump kernels (Table 5). Across all N treatments in both years, Almota was 10% higher in percent plump kernels than Genesee, at a statistically significant level (p < 0.05) (Table 6).

There was a significant difference between varieties in percent plump kernels across all 3 years

(p < 0.05) (Table 5). When comparing varieties’ percent plump kernels across all five N rates in both Almota and Genesee, ‘Julie’ was 14% higher in percent plump kernels than ‘Havener’

(Table 7).

Seeding rates had a significant effect on percent plump kernels in 2018 at both Almota (p

< 0.0001) and Genesee (p = 0.03). However, seeding rates did not affect a significant difference in percent plump kernels in either location in 2016 or 2017 (Table 5). Increased seeding rates resulted in decreased percent plump kernels; looking across all locations and years, the 2.5 million seeds ha-1 treatment resulted in the highest percent plump kernels and the 3.75 million seeds ha-1 treatment resulted in the lowest percent plump kernels (Table 8).

No correlation was found between percent plump kernels and other measured traits in

Almota but in Genesee percent plump kernels had a strong negative correlation with days to maturity (r = -0.90; p = 0.04) and plant height (r = 0.94; p = 0.02) (Table 9).

32 3.1.10. Percent Thin Kernels

Nitrogen did not affect percent thin kernels in Almota 2016 or 2108, nor in Genesee in

2016, 2017, or 2018. However, N had a significant impact on percent thin kernels in Almota in

2017 (p = 0.03) (Table 5). There was a significant difference (p < 0.05) between locations in percent thin kernels across all N treatments in 3 years and Genesee was 2 % higher percent thin kernels than Almota (Table 6). There was a significant difference between varieties in percent plump kernels across all 3 years in Almota and Genesee (p < 0.05) (Table 5). When comparing varieties’ percent thin kernels, ‘Havener’ was 6% higher in percent thin kernels than ‘Julie’

(Table 7).

Seeding rates had a significant effect in percent thin kernels in 2018 at Almota (p = 0.01), but not in 2016 or 2017. There was no significant difference in percent thin kernels associated with seeding rate at Genesee (Table 5). Increased seeding rates resulted in increased percent thin kernels; across all locations and years the 3.75 million seeds ha-1 treatment resulted in the highest percent thin kernels and the 2.5 million seeds ha-1 treatment resulted in the lowest percent thin kernels (Table 8). No correlation was found between percent thin kernels and other traits in either location (Table 9).

3.2.Variety trials

3.2.1. Β-glucan

There was a significant difference between varieties in all 3 years of the study with regard to β-glucan content. There was also a significant genetic × environment interaction for β- glucan content each year (p < 0.05) (Table 10). In 2016, location had a significant effect on β- glucan of all varieties except 10WA-130.5, ‘Julie’, and ‘Kardia’ (Table 11). In 2017 location had

33 a significant effect on β-glucan for all varieties except 10WA-118.13, ‘Julie’, ‘Kardia’, and

X07G31-T120 (Table 11). In 2018, location had a significant effect on β-glucan for all varieties except for 10WA-130.5, ‘Kardia’, and ‘Transit’ (Table 11).

Mean β-glucan across all varieties and across all 3 years was 8.2% at Almota and 8.5% at

Genesee (Table 12). The variety with the highest β-glucan content across all 3 years was

‘Transit’ in Almota (10.9%) and ‘Julie’ (11.3%) in Genesee (Table 12). In Almota there was no correlation between β-glucan and other traits, however, in Genesee, there was a strong negative correlation between β-glucan and grain yield (r = -0.81; p = 0.008) (Table 13).

3.2.2. Protein

In all three years, there was a significant genetic × environment interaction for protein content (p < 0.05) (Table 10). In 2016 location had a significant effect on the protein content of all varieties except for ‘Julie’ (Table 11). In 2017, location had a significant effect on the protein content of all varieties except for Ab09BG10HL-85 and X07G31-T120 (Table 11). In 2018, location had a significant effect on the protein content of all varieties except for Ab09BG10HL-

85, ‘Goldenhart’, and X07G31-T120 (Table 11). Mean protein content across all nine varieties and 3 years was 11.9% at both Almota and Genesee (Table 12). The variety with the highest protein content was ‘Julie’ in both locations, with an average of 13.3% in each location (Table

12). There was no correlation between protein and any other trait in either location (Table 13).

3.2.3. Grain Yield

There was a significant genetic × environment interaction for yield (p < 0.05) across

2016, 2017, and 2018 (Table 14). When comparing the response of varieties across Almota and

34 Genesee, the results indicated that all nine varieties were significantly affected by locations (p <

0.05) (Table 15). The average grain yield at Almota and Genesee was 3,402 kg ha-1 and 5,013 kg ha-1, respectively. ‘Kardia’ was the highest yielding variety in both locations, with an average of

3,984 kg ha-1 and 5,882 kg ha-1 at Almota and Genesee, respectively (Table 16)

3.2.4. Emergence

In 2016 there was a significant genetic × environment interaction for plant emergence (p

= 0.0001) (Table 10). Mean emergence rates were 90% at Almota and 79% at Genesee across all

3 years. In 2016, the emergence of all nine varieties was significantly affected by location (p <

0.05) (Table 11). In 2017, all varieties were significantly affected by location except for 10WA-

107.8, Ab09BG10HL-85, ‘Goldenhart’, ‘Kardia’, and X07G31-T120 (Table 11). However, in

2018, none of the varieties’ emergence was affected by location (Table 11). Varieties with the highest emergence rate at Almota were ‘Kardia’ and Ab09BG10HL-85, each averaging 96 %. At

Genesee, the variety with the highest emergence rate was 10WA-118.13, with an average of 88%

(Table 12)

3.2.5. Plant Height

In all 3 years, there was a significant genetic × environment interaction for plant height (p

< 0.05) (Table 10). In both 2016 and 2017 location had a significant effect on plant height across all nine varieties (p < 0.05) (Table 11). However, in 2018, the location had a significant effect on all varieties except for 10WA-118.13 (Table 11). Mean plant height across all 3 years was 80 cm in Almota and 94 cm in Genesee. The tallest variety in Almota was ‘Julie’ with an average 83 cm, and in Genesee, ‘Transit’, with an average of 100 cm (Table 12). In both Almota and

35 Genesee there was a strong negative correlation between emergence rate and plant height (r = -

0.81; p = 0.008) in Almota and (r = -0.77; p = 0.015) in Genesee (Table 13).

3.2.6. Days to Heading

In all 3 years, there was a significant genotype × environment interaction for days to heading (p < 0.05) (Table 10). Location had a significant effect on days to heading of all varieties except for Ab09BG10HL-85 and X07G31-T120 in 2016; 10WA-107.8 and

Ab09BG10HL-85 in 2017; and 10WA-107.8, 10WA-130.5, and Ab09BG10HL-85 in 2018

(Table 11). Across all three years Mean days to heading was 55 days in Almota and 57 days in

Genesee. The earliest heading variety was 10WA-118.13 in both Almota and Genesee, with an average of 53 days and 49 days, respectively (Table 12). In Almota, there was no correlation between days to heading and any other traits (Table 13). However, in Genesee, there was a moderate positive correlation between days to heading and days to maturity (r = 0.69; p = 0.04)

(Table 13).

3.2.7. Days to Maturity

In all three years, there was a significant genotype × environment interaction for days to maturity (p < 0.05) (Table 10). In 2016, location had a significant effect on days to maturity of all nine varieties (p < 0.05) (Table 11). However, in 2017, location had a significant effect on days to maturity for all varieties except for 10WA-118.13, 10WA-130.5, Ab09BG10HL-85,

‘Transit’, and X07G31-T120 (Table 11); in 2018 location had a significant effect on days to maturity for all varieties except for ‘Julie’, ‘Kardia’, ‘Transit’, and X07G31-T120 (Table 11).

Mean days to maturity was 94 days in Almota and 98 days in Genesee across all three years. The

36 earliest maturing variety in Almota was Ab09BG10HL-85, with an average of 90 days to maturity, and 10WA-118.13 and X07G31-T120 in Genesee, with each averaging 96 days (Table

12). In both Almota and Genesee there was a strong positive correlation between days to maturity and percent plump kernels (r = 0.74; p = 0.024) and (r = 0.75; p = 0.019) (Table 13).

There was also a strong negative correlation between days to maturity and percent thin kernels (r

= -0.73; p = 0.027) in Almota and (r = -0.87; p = 0.002) in Genesee. There was also a strong positive correlation between days to maturity and percent plump kernels (r = 0.74; p = 0.02) in

Almota and (r = 0.75; p = 0.01) (Table 13).

3.2.8. Test Weight

In all three years, there was a significant genotype × environment interaction for test weight (p < 0.05) (Table 10). Location had a significant effect on the test weight of all nine varieties in 2016. Location also had a significant effect on the test weight of all varieties in 2017 except for ‘Goldenhart’, 10WA-118.13, ‘Julie’, and ‘Transit’ in 2018 (Table 11).

Mean test weight was 72 kg hL-1 in Almota and 70 kg hL-1 in Genesee across all three years. The varieties with the highest test weight were 10WA-130.5, ‘Goldenhart’, and ‘Julie’ in

Almota with an average of 74 kg hL-1 each. In Genesee, the variety with the highest test weight was ‘Goldenhart’, with an average of 73 kg hL-1 (Table 12).

3.2.9. Percent Plump Kernels

In all three years, there was a significant genotype × environment interaction for percent plump kernels (p < 0.05) (Table 10). Location had a significant effect on percent plump kernels for all the varieties in 2016 except for 10WA-118.13, 10WA-130.5, Ab09BG10HL-85, ‘Transit’,

37 and X07G31-T120. In 2017 location had a significant effect on percent plump kernels of all varieties except for 10WA-118.13, 10WA-130.5, Ab09BG10HL-85, and ‘Transit’. In 2018, location had a significant effect on percent plump kernels for all nine varieties (Table 11). Mean percent plump kernels were 80% in Almota and 84% in Genesee (Table 12). The varieties with the highest percent plump kernels in Almota were ‘Goldenhart’ and ‘Kardia’, with an average of

89% each. In Genesee, the variety with the highest percent plump kernels was ‘Kardia’, with an average of 91% (Table 12). In both Almota and Genesee there was a strong positive correlation between percent plump kernels and days to maturity (r = 0.74; p = 0.024) and (r = 0.75; p =

0.019) (Table 13).

3.2.10. Percent Thin Kernels

There was a significant genotype × environment interaction for percent thin kernels in all years (p < 0.05) (Table 10). The location had a significant effect on percent thin kernels of most of the varieties in 2016 and 2017 except for 10WA-118.13, 10WA-130.5, and ‘Transit’ (Table

11). In 2018, location had a significant effect on percent thin kernels for all nine varieties (Table

11). Mean percent thin kernels were 5% in Almota and 3% in Genesee. The varieties with the highest percent thin kernels in Almota were ‘Transit’ and X07G31-T120 with an average of 7% each. In Genesee, the variety with the highest percent thin kernels was X07G31-T120 with an average of 5% (Table 12). In both Almota and Genesee there was a strong negative correlation between percent plump kernels and percent thin kernels (r = -0.98; p = 0.0001) and (r = -0.87; p

= 0.002) (Table 13).

38 4. DISCUSSION

4.1.Nitrogen effects

Cultivar, environmental factors, and nitrogen fertilization have been reported to affect yield, chemical composition, and grain quality of barley (Widdowson et al. 1982; Åman &

Newman 1986; Bach-Knudsen et al. 1987; Conry 1994). β-glucan and protein are the major components of barley (Åman et al. 1985). β-glucan is an important component of water-soluble dietary fiber, which is found only in plants. Given its unique functional and nutritional properties, β-glucan is the most important fiber component in barley. It is found throughout the grain endosperm and germ, but germ β-glucan has higher dietetic value (Fincher 1975; McNeil et al. 1975; Bacic & Stone 1981; Salomonsson et al. 1984). β-glucan content is associated with both genotype and environmental factors (Stuart et al. 1988; Güler, 2003). In this study there were significant differences among varieties in grain β-glucan content; ‘Julie’ had higher β- glucan content than ‘Havener’ in both years, with 1.6% higher in Almota and 1.5% higher in

Genesee (Table 4). Our results are in agreement with those of Stuart, (1998) who reported that the β-glucan content of barley varieties is dependent on both variety and environmental factors.

N fertilizers are usually used to increase grain yield. However, Brummer & Feed (1994) reported that N levels in soil and N fertilization are major factors which affect β-glucan content.

The grain β-glucan content reported in our study varied according to variety, nitrogen treatment, and location. Increased N rates increased grain β-glucan content of all varieties, and the highest grain β-glucan content was generally observed in response to 162 kg N ha-1 (Table 3). When

Almota and Genesee data were combined, increase in N rates (0, 62, 95, 129, 162 kg N ha-1) corresponded with increased β-glucan content (7.3%, 7.3%, 7.4%, 7.4%, and 7.6%) (Table 3).

Our findings are in agreement with those of Sorensen and Truelsen 1985; Henry (1986), and

39 Oscarsson et al. (1998) who reported that barley β-glucan content increased with increasing nitrogen rate. Brummer & Feed, (1994); Thomason et al. (2012); Aghdam & Samadiyan, (2014) also found that higher nitrogen levels generally increased grain β-glucan in barley and oats. Our results also found differences in β-glucan contents between Almota and Genesee, where Genesee was 0.4% higher than Almota in β-glucan content across all varieties and years (Table 3). This is in agreement with Aman, 1986; Lehtonen & Aikasalo, 1987; Åman et al. 1989) who reported significant differences in barley β-glucan content between locations. This is also in line with

Tiwari & Cummins, (2009), who reported that temperature and precipitation are the most important climatic factors affecting β-glucan content. Because β-glucan accumulates late in grain development, stress from temperature and precipitation factors can cause the early end of grain development, and therefore, reduced β-glucan concentration in grains (Tiwari & Cummins,

2009).

Dubetz (1961) and Hutcheon & Paul (1966) found that moisture and N availability were two important factors affecting the protein content of wheat. Soper & Huang (1963) and Nuttall et al. (1971) also demonstrated that percent protein and total protein per unit area could be predicted by measuring available soil N plus added N fertilizer. In our study, grain protein responded positively to increasing N rates in all varieties, where the highest protein content was observed in response to 162 kg N ha-1 (Table 3). With both Almota and Genesee locations combined, increase in N rates (0, 62, 95, 129, 162 kg N ha-1) corresponded with increased protein content (9.7%, 9.9%, 10.8%, 11.5%, and 12.4%) (Table 3). However, there were no differences in grain protein between locations. Janković et al. (2011) also found that increased nitrogen rates significantly affected the total protein content in malting barley grain. With an increase in the total nitrogen rate (50, 70, 90, and 110 kg ha-1), the protein content also increased

40 (12.57, 12.84, 12.88, and 13.46%). Generally, protein content increases with increasing nitrogen supply (Sorensen & Truelsen 1985; Conry 1994; Therrien et al. 1994).

Grain yield and quality traits of spring barley can vary greatly due to growing conditions

(Leistrumaite & Paplauskiene, 2005). Mouchova et al. (1996) found that the efficiency of applied nitrogen fertilizers and their loss are influenced by the interaction of many factors, such as type of soil, crop, agricultural practice, including the rate, the form, the timing of fertilization and year-to-year weather patterns. The amount of nitrogen that a barley crop needs to maximize yield and quality depend on seasonal conditions, soil type, rotational history of the soil, and yield potential. Nitrogen is needed for early tiller development, without which the yield potential of the crop is limited. As expected, increased N rates increased grain yield of all the barley varieties in both years and locations; the highest grain yield was observed in response to the 129 kg N ha-1 treatment. However, there was a decrease of 20 kg ha-1 in yield as the N rates increased to 162 kg

N ha-1 (Table 6). With both Almota and Genesee locations combined, increasing N rates (0, 62,

95, 129, 162 kg N ha-1) corresponded with increased grain yields (2,774, 4,014, 4242, 4,305, and

4,283 kg ha-1) (Table 6). These results are consistent with previous reports by Singh & Uttam

(1992) and Alazmani (2014) who reported increased grain yield resulting from increased nitrogen fertilization. Ryan et al. (2009) reported that nitrogen increased leaf area, tiller formation, leaf area index and leaf area duration, and that this increase led to much greater production of dry matter and grain yield (Ryan et al. 2009). However, Singh & Uttam (1992) reported that increasing N fertility beyond a certain limit ultimately decreased grain yield.

According to our study, an N rate of 129 kg N ha-1 is optimal for achieving maximum yield. In this study, we were able to identify the variety that yielded well regardless of N rates. On average ‘Havener’ had a grain yield of 906 kg ha-1 higher than ‘Julie’ across all five N rates and

41 two locations (Table 7). It is possible to select barley cultivars and different growing locations, but environmental factors are much more difficult to control. The interaction between predetermined and environmental factors could play an important role in the yield and quality of barley.

Nitrogen fertilizer application, while often required to increase grain yield and protein composition of the barley grain may also reduce physical grain quality traits. Increased N rates resulted in decreased test weight and percent plump kernels. Across all locations and years, the 0

N treatment resulted in the highest test weight and percent plump kernels, and the 162 N treatment resulted in the lowest test weight and percent plump kernels (Table 6). However, percent thin kernels remained constant despite change in N rates. Jackson et al. (1994) and

Bleidere et al. (2013) also found that decline in test weight, 1000 kernel weight, and percent plump kernels, resulted from increasing N rates. Our results also indicated no correlation between days to heading and days to maturity. Tsenow (2009), found that the growing conditions significantly affected the time to heading and physiological maturity, especially under heat stress. Tsenow also reported that the period between heading and physiological maturity varied according to the temperatures in respective years. Higher temperature sums should, in principle, accelerate the date to heading and maturity. Moreover, in warmer years, highest degree maturation is accelerated, which increase the correlation between days to heading and days to maturity, while in cooler years combined with more available soil moisture during grain filling, maturation is retarded, i.e., the correlation between days to heading and days to maturity decreases. Bullrich et al. (2002) and Blake et al. (2009) reported a lack of correlation between days heading and days to physiological maturity, and that the period of grain filling was

42 markedly less affected by the temperature, moisture, and light than time to heading, but rather, was dictated exclusively by genotype.

4.2.Seeding effects

The seeding rates currently used for barley vary between agro-climatic zones. In the drier areas, the recommended rate 257 plants m-2 (Turk, 1998) is much lower than for wetter areas

(Munir, 2002). Higher seeding rates are recommended in wetter areas because they promote greater crop competition against weeds (Kirkland, 1993). In this study, increased seeding rates increased grain yield in all the varieties in both years and locations; the highest grain yield was observed in response to the 3.75 million seed ha-1 treatment (Table 8). Once aggregated Almota and Genesee data indicated that increase in seeding rates (2.5, 3.1, and 3.75 million seed ha-1) corresponded with increased grain yield (3,853, 3,868, and 4, 051 kg ha-1), respectively (Table

8). According to Kirkland, (1993), yield increase observed alongside increased seeding rates is a function of more spikes being produced as a result of more plants being established. In Kirkland

(1993) grain yields increased as seeding rates increased, with maximum yields obtained at 400 plants m-2. This argues that the effects of seeding rate on grain yield are derived from increased production of spikes per unit area, not through the increased production of fertile tillers per plant.

In general, fertility and seeding rate were directly associated with the yield of barley. Similarly, our study revealed that a seeding rate of 3.75 million seed ha-1 combined with 129 kg N ha-1 is necessary for the maximum grain yield.

43 4.3.Variety Trials

Selection of plant varieties specifically adapted to regional production and end-use is an important component of building a resilient food system (Brouwer et al. 2016b). Quality of barley is paramount its use as human food, and many studies have shown that quality is inextricable from growing conditions and cultivar selection (Bhatty 1995; Oscarsson et al. 1998).

Contemporary plant breeders have developed novel barley genotypes which need to be vetted for their competitiveness and quality. This study examined nine genetically diverse food barley cultivars, which were subjected to experiments carried out over 3 years at two locations—

Almota, WA and Genesee, ID—to identify superior food barley cultivars with a suite of positive agronomic traits and high ß‐glucan and protein content. The top three barley cultivars in β- glucan content across all three years and locations were ‘Transit’, ‘Goldenhart’, and

Ab09BG10HL-85 with 11.1%, 10.3%, and 9.8%, respectively. The top three barley cultivars in protein content across all three years and locations were ‘Transit’, ‘Goldenhart’, and

Ab09BG10HL-85 with 13.2%, 12.7%, and 12.4% respectively. The top three barley cultivars for grain yield across all three years and locations were ‘Kardia’, 10WA-118.13, and 10WA-107.8 with 13.2%, 12.7%, and 12.4% respectively.

5. CONCLUSION

N had a significant effect on protein content but did not have effect on β-glucan content.

Julie had higher β-glucan (8.2%) and protein (12.6%) content compared to Havener which had β- glucan (6.6%) and protein (9.1%). Location had a significant effect on β-glucan but not on protein content. Genesee resulted in higher β-glucan (7.6%) and protein (10.9%) content compared to Almota. This is likely because Genesee received more precipitation and had cooler

44 temperatures compared to Almota. Increased nitrogen fertilization resulted in higher mean grain yield of Havener and Julie in both Almota and Genesee up to 95 kg/ha, but no difference in yield was found between 95, 129 and 162 kg/ha N. Havener had higher yields (3,908 kg/ha) than Julie

(3,099 kg/ha) across locations and years. Based on our results, N rate recommendation for farmers growing food barley in these regions should be approximately 95kg/ha with seeding rate of approximately 3.75 million seed/ha to optimize grain yield and protein content.

For the variety trials, a significant variation was found among varieties for β-glucan, protein content and grain yield. Transit was the variety with the highest β-glucan content (11%) and Julie had the highest protein content (13.2%) Kardia had the highest yields with average grain yield of 3,984 kg/ha in Almota and 5,882 kg/ha in Genesse, respectively β-glucan content was strongly influenced by genotype and location. Mean protein content across all nine varieties and three years was 11.9% in each location. β-glucan appeared to be more related to environmental factors, such as precipitation, than to fertility. Therefore, the selection of food barley varieties with high β-glucan content should be conducted, with environmental adaptation in mind.

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58 Table 1. Hulless barley variety used for variety trials to evaluate their phenotypic responses to no-till farming systems in the Palouse region of north Idaho and eastern Washington in 2016, 2017, and 2018. Entry # Seed Source Entry name 1 UI ‘Transit’ 2 UI ‘Julie’ 3 UI 2Ab09-X06F058HL-31 4 UI ‘Kardia’ 5 UI Ab09BG10HL-85 6 WSU X07G31-T120 7 WSU 10WA-107.8 8 WSU 10WA-118.13 9 WSU 10WA-130.5 UI: University of Idaho; WSU: Washington State University

59 Table 2. Analysis of Variance with F value for β-glucan and protein for food barley grown under five nitrogen treatments on no-till farms of Almota and Genesee. Locations/Year Effect β-glucan (%) Protein (%) Almota

2016 N Var 388.52*** 267.49*** N × Var. 34.39*** 56.03*** 2017 N 5.03*** Var 121.17*** 85.72*** N × Var. 20.18*** 52.93*** 2018 N 4.63*** Var 340.96*** 67.15*** N × Var. 32.65*** 40.08*** Genesee

2016 N 4.53*** Var 242.71*** 78.79 N × Var. 35.78*** 68.00*** 2017 N Var 126.95*** 182.04*** N × Var. 18.19*** 42.35*** 2018 N 8.45*** Var 197.08*** 41.12*** N × Var. 19.70*** 75.40*** Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001. PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: Grain Yield; TW: Test Weight.

60 Table 3. Mean data across years 2016, 2017, and 2018 for the effect of N rates on β-glucan and protein in Almota and Genesee. β-glucan (%) Protein (%) N (kg/ha) Almota Genesee Almota Genesee 2016

0 6.8 7.3 10.7 9.2 62 6.6 7.2 10.9 9.2 95 6.7 7.4 11.7 10.4 129 6.7 7.3 12.1 10.8 162 6.8 7.7 12.6 12.2 Mean 6.7 7.4 11.6 10.4 LSD (p < 0.05) ns ns ns 1.69 2017

0 6.8 7.6 9.4 11.4 62 7.0 7.8 9.5 11.6 95 7.2 7.9 10.4 12.6 129 7.2 7.8 11.6 13.5 162 7.4 7.9 12.5 13.6 Mean 7.1 7.8 10.7 12.6 LSD (p < 0.05) ns ns 1.71 ns 2018

0 7.7 7.4 9.5 8.0 62 7.5 7.5 9.1 9.0 95 7.6 7.6 10.0 9.8 129 8.0 7.6 11.0 10.1 162 8.0 7.7 11.8 11.9 Mean 7.8 7.5 10.3 9.8 LSD (p < 0.05) ns ns 1 1.41 ns: not significant; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

61 Table 4. Mean data across years 2016, 2017, and 2018 for varietal differences in β-glucan and protein in Almota and Genesee. β-glucan (%) Protein (%) Varieties Almota Genesee Almota Genesee 2016

‘Havener’ 5.8 6.5 9.5 8.7 ‘Julie’ 7.6 8.2 13.7 12.0 Mean 6.7 7.4 11.6 10.4 LSD (p < 0.05) 0.18 0.21 0.52 0.75 2017

‘Havener’ 6.5 7.1 8.9 10.3 ‘Julie’ 7.7 8.5 12.4 14.8 Mean 7.1 7.8 10.7 12.6 LSD (p < 0.05) 0.22 0.25 0.75 0.66 2018

‘Havener’ 6.9 6.8 8.9 8.4 ‘Julie’ 8.6 8.2 11.7 11.1 Mean 7.8 7.5 10.3 9.8 LSD (p < 0.05) 0.14 0.2 0.54 0.84 LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

Almota

2016 N 20.62*** 7.86*** 17.03***

Var. 126.70*** 319.30*** 116.46*** 97.23*** 91.64*** 1147.25*** 197.01***

N × Var. 32.59*** 126.93*** 105.94*** 95.71*** 22.30*** 106.40*** 35.57***

SR × Var. 63.69*** 150.79*** 56.55*** 44.28*** 44.49*** 587.31*** 109.91***

2017 N 3.48** 3.19* 13.32*** 6.36*** 99.90*** 6.60*** 2.81*

63 Var. 14.21*** 88.18*** 6.69** 27.29*** 5.71* 246.78*** 144.04***

N × Var. 8.81*** 60.74*** 4.39** 146.57*** 11.54*** 45.21*** 28.14***

SR × Var. 7.36*** 42.24*** 13.18*** 139.44*** 74.75***

2018 N 21.17*** 51.30*** 35.70***

SR 5.94*** 5.48**

Var. 250.74*** 152.76*** 13.28*** 129.36*** 83.18*** 83.20***

N × Var. 77.81*** 92.41*** 97.68*** 64.92*** 52.84*** 14.64*** 18.51***

SR × Var. 117.21*** 71.72*** 7.95*** 56.76*** 44.19*** 45.64*** Genesee

2016 N 21.86*** 15.74*** 41.81*** 3.81**

SR 4.21*

Var. 93.47*** 1232.14*** 84.68*** 48.57*** 150.54*** 144.36***

N × Var. 28.03*** 107.18*** 130.35*** 32.29*** 175.53*** 32.59*** 29.65***

SR × Var. 53.07*** 513.40*** 40.57*** 23.10*** 73.85*** 72.54***

2017 N 5.28*** 14.67*** 9.17*** 6.39*** 4.70***

SR 15.72*** 8.03***

Var. 61.00*** 134.99*** 38.32*** 27.93*** 210.45*** 261.55***

N × Var. 8.30*** 11.32*** 156.87*** 11.62*** 3.29* 66.53*** 54.61***

SR × Var. 16.88*** 37.56*** 63.52*** 32.50*** 13.98*** 108.45*** 141.56***

2018 N 18.16*** 10.04*** 7.70*** 4.78***

SR 3.57* 3.30*

Var. 14.48*** 983.76*** 106.15*** 37.81*** 26.41*** 32.58*** 39.43***

N × Var. 3.87* 112.44*** 59.79*** 15.25*** 29.78*** 18.07*** 9.42*** 8.99***

SR × Var. 7.25*** 400.56*** 48.78*** 19.14*** 13.11*** 20.81*** 25.05***

Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001. PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels.

Table 6. Mean data across years 2016, 2017, and 2018 for nitrogen rates on agronomic traits in Almota and Genesee.

N (kg/ha) PE (%) DH (day) DM (day) PH (cm) GY (kg/ha) TW (kg/hL) PPK (%) PTK (%)

Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee

2016

0 79 71 57 59 94 99 85 80 2944 2937 77 73 85 78 4 5

62 83 72 57 59 94 99 81 85 4030 4138 75 70 85 80 3 4

95 80 77 57 59 94 99 80 87 4010 4601 76 72 85 81 4 3

129 81 80 58 59 100 103 85 90 4218 4745 76 72 86 79 3 4

162 73 76 58 59 102 106 84 92 4301 4979 76 72 84 79 4 3

Mean 79 75 57 59 97 101 83 87 3901 4280 76 72 85 80 4 4

LSD (p < ns ns ns ns 2.36 1.85 2.49 3.10 331.79 313.06 ns 1.02 ns ns ns ns 0.05)

2017

0 79 87 56 61 95 97 82 84 1927 2720 78 74 84 74 5 5

65 62 88 90 56 58 95 99 81 84 3774 3826 77 73 87 71 4 5

95 88 85 55 59 96 99 80 88 4107 3539 77 73 85 67 3 6

129 88 77 56 60 98 102 85 87 4291 3347 75 73 87 66 3 6

162 93 74 55 60 98 103 85 86 4192 3217 68 74 85 67 4 6

Mean 87 83 56 60 96 100 83 86 3658 3330 75 73 86 69 4 6

LSD (p < 7.25 8.03 0.49 0.89 1.21 2.48 2.64 ns 246.41 405.51 3.51 ns ns 4.37 1.25 ns 0.05)

2018

0 84 87 58 58 94 99 76 83 2432 3686 77 76 68 62 13 14

62 88 82 57 58 94 99 82 86 3625 4690 76 75 65 63 14 15

95 91 87 57 58 94 99 84 87 4125 5068 76 74 71 60 10 16

129 84 88 57 57 97 103 90 90 4079 5150 75 73 71 59 10 17

162 83 85 56 57 100 104 94 88 3977 5035 76 73 68 58 11 17

Mean 86 86 57 57 96 101 85 87 3648 4726 76 74 69 60 12 16

LSD (p < ns ns ns ns 1.77 1.86 2.67 2.56 295.42 545.9 ns 1.45 ns ns ns ns 0.05) PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels, PTK: percent thin kernels; ns: not significant; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

Table 7. Mean data across years 2016, 2017, and 2018 for varietal differences across N and seeding rates treatments in Almota and Genesee. PE (%) DH (day) DM (day) PH (cm) GY (kg/ ha) TW (kg/ hL) PPK (%) PTK (%)

Varieties Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee

2016

‘Havener’ 93 85 55 57 93 99 83 87 4447 4773 77 72 77 73 6 6

‘Julie’ 66 65 59 61 100 104 83 86 3354 3787 74 71 93 86 2 2

Mean 79 75 57 59 97 101 83 87 3901 4280 76 72 85 80 4 4

LSD (p < 4.74 4.08 0.46 0.21 1.38 1.17 ns ns 195.59 249.5 0.45 ns 0.91 2.23 0.59 0.81 0.05)

2017

‘Havener’ 91 81 56 58 95 96 84 86 4065 3760 77 74 79 62 5 8

‘Julie’ 83 84 56 61 98 104 82 85 3252 2899 73 73 92 76 2 4

Mean 87 83 56 60 96 100 83 86 3658 3330 75 73 86 69 4 6

67 LSD (p < 4.54 ns ns 0.56 0.7 1.25 1.75 ns 274.64 245.35 2.34 0.52 1.62 1.87 0.58 0.51 0.05)

2018

‘Havener’ 88 90 55 55 92 98 85 87 3905 5311 77 76 62 52 15 22

‘Julie’ 84 81 59 60 99 103 85 87 3390 4141 75 73 75 69 8 10

Mean 86 86 57 57 96 101 85 87 3648 4726 76 74 69 60 12 16

LSD (p < ns 4.67 0.42 0.33 0.98 1.09 ns ns 249.55 335.78 0.38 0.89 2.68 6.03 1.58 3.73 0.05) PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels, PTK: percent thin kernels; ns: not significant; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

Table 8. Mean data across years 2016, 2017, and 2018 for the effect of seeding rates on agronomic traits in Almota and Genesee. PE (%) DH (day) DM (day) PH (cm) GY (kg/ha) TW (kg/hL) PPK (%) PTK (%) SR (x1 million) Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee

2016

2.5 77 71 57 59 96 101 83 86 3845 4294 76 71 86 80 3 4

3.1 79 74 57 59 97 101 83 87 3873 4196 76 72 86 80 4 4

3.75 82 80 57 59 97 101 83 87 3984 4349 76 71 84 78 4 4

Mean 79 75 57 59 97 101 83 87 3901 4280 76 72 85 80 4 4

LSD (p < ns 6.24 ns ns ns ns ns ns ns ns ns ns ns ns ns ns 0.05

2017

2.5 88 75 56 60 96 100 83 86 3611 2978 75 73 87 69 3 6

3.1 85 82 56 60 96 100 83 87 3584 3305 76 74 86 70 4 5

3.75 89 92 56 59 96 100 83 85 3779 3706 75 73 85 69 4 6

Mean 87 83 56 60 96 100 83 86 3658 3330 75 73 86 69 4 6

LSD (p < ns 6 ns ns ns ns ns ns ns 321.22 ns ns ns ns ns ns 0.05)

2018

2.5 84 87 57 57 95 101 84 87 3549 4839 76 75 72 65 9 13

3.1 89 85 57 57 96 101 85 87 3525 4723 76 74 65 61 13 15

3.75 85 85 57 57 95 100 86 86 3869 4615 76 74 68 55 12 19

Mean 86 86 57 57 96 101 85 87 3648 4726 76 74 69 60 12 16

LSD (p < ns ns ns ns ns ns ns ns ns ns ns ns 3.97 8 2.53 5.05 0.05) PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels; ns: not significant; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

Table 9. Pearson correlation for percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels in Almota and Genesee Almota PE DH DM PH GY TW PPK PTK Variables (%) (day) (day) (cm) (kg/ha) (kg/hL) (%) (%) PE

DH -0.17

DM -0.22 -0.06

PH -0.22 0.05 0.99***

GY 0.69 -0.19 0.55 0.54

TW 0.04 0.34 -0.95* -0.91* -0.67

PPK 0.34 0.67 0.23 0.31 0.48 -0.07

PTK -0.28 -0.29 -0.61 -0.65 -0.73 0.53 -0.86

Genesee PE DH DM PH GY TW PPK PTK Variables (%) (day) (day) (cm) (kg/ha) (kg/hL) (%) (%) PE

DH -0.08

DM -0.67 -0.03

PH -0.23 -0.39 0.81

GY -0.12 -0.74 0.59 0.90*

TW -0.05 0.82 -0.45 -0.78 -0.94*

PPK 0.34 0.05 -0.90* -0.94* -0.71 0.54

PTK -0.30 0.39 0.82 0.67 0.30 -0.18 -0.87

*p < 0.05, *** p < 0.001 PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels.

Table 10. Analysis of Variance with F value for percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels for food barley varieties grown in no-till farms in Almota and Genesee over three crop years 2016, 2017, and 2018. DH DM GY TW PPK PTK β-glucan Protein Year Effect PE (%) PH (cm) (day) (day) (kg/ha) (kg/hL) (%) (%) (%) (%)

2016 Location 31.71*** 4.99* 46.29*** 371.95*** 41.13*** 31.16*** 1.86 0.61 0.05 25.81*** Var. 1.16 12.70*** 1.8 0.21 8.72*** 7.66*** 8.80*** 23.32*** 165.32*** 6.87*** G × E 5.12*** 36.73*** 6.19*** 44.19*** 37.13*** 16.35*** 5.08*** 10.10*** 66.60*** 13.53*** 2017 Location 5.81* 4.35* 9.59** 248.76*** 42.85*** 9.03** 1.56 1.37 2.74 9.91** Var. 1.19 7.79*** 4.62*** 0.36 4.90*** 14.15*** 49.09*** 34.57*** 78.10*** 14.62*** G × E 1.92 17.96*** 5.83*** 33.24*** 16.39*** 18.97*** 20.60*** 12.82*** 39.71*** 17.68*** 2018 Location 0.04 2.65 25.89*** 13.72** 248.78*** 2.13 44.01*** 57.26*** 0.07 4.91*

Var. 1.45 7.21 5.43*** 1.36 0.97 20.25*** 2.65* 2.31* 158.17*** 19.03***

G × E 1.2 14.31*** 12.11*** 3.09** 55.15*** 12.84*** 8.02*** 9.14*** 37.00*** 14.44*** Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001. PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels; G × E: genetic × environment interaction

Variety DH DM GY TW PPK β-glucan Protein (%) PE (%) PH (cm) PTK (%) Name/Year (day) (day) (kg/ha) (kg/hL) (%) (%)

2016 10WA-107.8 - 3.68** 3.54** 5.27*** 14.60*** 14.19*** -7.10*** 2.82* -3.27** -8.46*** -7.42*** 10WA-118.13 - 2.39* - 5.97*** 5.27*** 13.25*** 12.60*** -7.39*** -0.45 0.22 -5.08*** -6.07*** 10WA-130.5 - 3.46** 2.35* 4.08** 15.91*** 9.11*** -4.00** 0.38 -0.69 -1.52 -6.53*** Ab09BG10HL-85 - 4.11** 1.16 3.49** 13.97*** 5.50*** - 8.13*** 0.05 2.64* 6.87*** -5.68*** ‘Goldenhart’ -4.75*** 7.10*** 5.57*** 15.39*** 7.55*** -6.07*** 2.82* -2.23* 9.20*** -2.37* ‘Julie’ -5.17*** 8.29*** 6.76*** 15.47*** 5.36*** -3.23** 2.44* -3.16** 0.79 -1.14 ‘Kardia’ -4.11** 9.48*** 4.98*** 13.36*** 13.98*** -10.36*** 3.23** -3.30** -0.44 -6.50***

71 ‘Transit’ -5.60*** 5.91*** 5.27*** 16.33*** 4.55*** -4.88*** -0.12 -0.29 10.02*** -2.76*

X07G31-T120 - 5.39*** 1.16 5.27*** 15.62*** 7.22*** -5.28*** -1.36 3.50** -3.81** -6.05***

2017 10WA-107.8 - 1.53 1.6 2.58* 13.09*** 9.36*** -6.05*** 4.71*** -4.11** -4.21*** 2.57* 10WA-118.13 - 2.72* - 5.23*** 0.78 10.23*** 8.07*** -3.54** -0.03 0.5 -0.3 3.55** 10WA-130.5 - 2.12* 2.15* 1.98 13.54*** 5.78*** -2.32* -0.24 -1.34 2.13* 3.41** Ab09BG10HL-85 - 1.23 1.96 1.98 11.91*** 3.77** -3.85** -0.37 2.80* 7.83*** 0.46 ‘Goldenhart’ -1.53 3.44** 5.39*** 12.68*** 6.20*** -0.75 5.02*** -3.31** 10.45*** 7.55*** ‘Julie’ -3.02** 4.91*** 5.26*** 12.23*** 3.90** -2.76* 4.20*** -3.89** 1.89 7.94*** ‘Kardia’ -1.23 5.84*** 3.77** 12.46*** 7.88*** -11.27*** 5.35*** -4.24*** 1.7 5.34*** ‘Transit’ -2.42* 5.10*** 1.68 14.74*** 3.88** -4.04** -1.57 -0.13 9.18*** 6.49***

X07G31-T120 - 0.03 2.52* 1.08 13.96*** 8.89*** -2.40* - 2.69* -0.3 -0.58 4.46***

2018

10WA-107.8 -0.85 0.82 5.68*** 2.57* 15.64*** 2.29* 4.35*** -4.91*** -3.45** 3.18** 10WA-118.13 0.67 -4.87*** 3.15** 0.99 17.61*** 1.07 5.33*** -5.82*** -3.52** 3.57** 10WA-130.5 0.06 0.6 4.63*** 3.21** 14.93*** 3.28** 3.85** -4.88*** 0.11 3.44** Ab09BG10HL-85 -0.85 1.7 3.47** 2.95** 16.83*** 2.65* 6.59*** -6.86*** -3.17** 0.79 ‘Goldenhart’ 0.37 4.10** 7.78*** 3.34** 12.77*** 3.57** 6.37*** -7.44*** 6.09*** 1.03 ‘Julie’ 0.06 4.32*** 8.62 3.42** 14.24*** 1.88 7.23*** -7.72*** -4.38*** -3.07** ‘Kardia’ 0.97 4.54*** 6.62 2.74* 18.85*** -4.96*** 6.41*** -6.86*** 0.62 6.67*** ‘Transit’ -1.77 4.10** 3.84 4.37*** 13.01*** 1.02 4.29*** -5.25*** -1.82 3.14** X07G31-T120 -0.55 2.35* 3.42 2.95** 15.25*** 0.74 3.92** -4.34*** -4.20*** 1.18

72 Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001. PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant

height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels

Table 12. Mean data across years 2016, 2017, and 2018 for β-glucan and protein in Almota and Genesee. Variety Name/Year β-glucan (%) Protein (%)

Almota Genesee Almota Genesee 2016 10WA-107.8 6.0 5.3 11.7 10.9 10WA-118.13 6.2 6.7 12.9 11.2 10WA-130.5 7.5 7.7 12.2 11.2 Ab09BG10HL-85 10.7 11.1 14.4 12.5 ‘Goldenhart’ 9.9 10.4 12.4 11.6 ‘Julie’ 7.9 8.6 14.7 13.1 ‘Kardia’ 8.4 7.8 12.8 11.0 ‘Transit’ 10.8 11.4 13.0 13.0 X07G31-T120 6.8 7.0 12.3 11.4 Mean 8.2 8.4 12.9 11.8 LSD (p < 0.05) 0.44 0.51 0.47 1.33 2017

10WA-107.8 5.0 5.6 10.9 11.9 10WA-118.13 6.4 7.1 11.8 11.9 10WA-130.5 7.3 8.1 11.2 12.1 Ab09BG10HL-85 10.5 11.3 13.1 13.3 ‘Goldenhart’ 9.5 10.3 10.1 11.2 ‘Julie’ 7.2 8.0 12.5 13.8 ‘Kardia’ 6.9 8.0 11.5 13.0 ‘Transit’ 10.3 10.6 12.3 13.1 X07G31-T120 6.5 7.1 10.3 10.5 Mean 7.7 8.5 11.5 12.3 LSD (p < 0.05) 0.64 0.94 0.68 0.78 2018

10WA-107.8 7.4 7.5 10.7 11.9 10WA-118.13 7.2 7.6 11.2 11.8 10WA-130.5 8.7 8.6 11.2 11.7 Ab09BG10HL-85 7.5 7.6 10.4 10.7 ‘Goldenhart’ 11.1 10.4 10.4 10.9 ‘Julie’ 9.1 8.7 12.6 12.7 ‘Kardia’ 8.3 7.8 11.1 11.6 ‘Transit’ 11.5 11.8 12.1 12.6 X07G31-T120 7.2 7.2 10.6 10.9 Mean 8.7 8.6 11.1 11.6 LSD (p < 0.05) 0.5 0.49 1.02 0.57 ns: not significant; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

Table 13. Pearson correlation for percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels in Almota and Genesee Almota

PE DH DM PH GY TW PTK β-glucan Variables (%) (day) (day) (cm) (kg/ha) (kg/hL) PPK (%) (%) (%) PE DH 0.18

DM -0.34 -0.01

PH -0.81* -0.58 0.17

GY 0.56 -0.28 0.28 -0.34

TW -0.45 -0.11 0.12 0.54 -0.50

PPK -0.22 0.16 0.74* 0.06 0.38 -0.23

PTK 0.20 -0.15 -0.73* -0.05 -0.46 0.22 -0.98***

Β-glucan -0.17 0.40 -0.10 -0.09 -0.57 0.19 -0.03 0.10 Protein -0.48 0.10 -0.48 0.43 -0.59 0.07 -0.02 0.06 0.32 Genesee PE DH DM PH GY TW PTK β-glucan Variables (%) (day) (day) (cm) (kg/ha) (kg/hL) PPK (%) (%) (%) PE DH -0.45

DM 0.02 0.69*

PH -0.77* 0.63 0.04

GY 0.63 -0.38 -0.31 -0.59

TW -0.23 -0.14 -0.04 0.32 -0.72

PPK 0.31 0.37 0.75* -0.34 0.19 -0.41

PTK 0.01 -0.60 -0.87* -0.04 0.10 0.22 -0.87* Β-glucan -0.63 0.41 0.25 0.47 -0.81** 0.25 0.00 -0.16 Protein -0.52 0.35 0.52 0.17 -0.46 0.02 0.23 -0.47 0.44 *p < 0.05, *** p < 0.001 PE: percent emergence; DH: days to heading; DH: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels.

GY TW PPK PTK β-glucan Protein Year Effect PE (%) DH (day) DM (day) PH (cm) (kg/ha) (kg/hL) (%) (%) (%) (%) 2016 Location 31.71*** 4.99* 46.29*** 371.95*** 41.13*** 31.16*** 1.86 0.61 0.05 25.81***

Var. 1.16 12.70*** 1.8 0.21 8.72*** 7.66*** 8.80*** 23.32*** 165.32*** 6.87*** G × E 5.12*** 36.73*** 6.19*** 44.19*** 37.13*** 16.35*** 5.08*** 10.10*** 66.60*** 13.53*** 2017 Location 5.81* 4.35* 9.59** 248.76*** 42.85*** 9.03** 1.56 1.37 2.74 9.91** Var. 1.19 7.79*** 4.62*** 0.36 4.90*** 14.15*** 49.09*** 34.57*** 78.10*** 14.62*** G × E 1.92 17.96*** 5.83*** 33.24*** 16.39*** 18.97*** 20.60*** 12.82*** 39.71*** 17.68*** 2018 Location 0.04 2.65 25.89*** 13.72** 248.78*** 2.13 44.01*** 57.26*** 0.07 4.91*

77 Var. 1.45 7.21 5.43*** 1.36 0.97 20.25*** 2.65* 2.31* 158.17*** 19.03***

Variety DM GY TW PPK β-glucan Protein PE (%) DH (day) PH (cm) PTK (%) Name/Year (day) (kg/ha) (kg/hL) (%) (%) (%)

2016

10WA-107.8 - 3.68** 3.54** 5.27*** 14.60*** 14.19*** -7.10*** 2.82* -3.27** -8.46*** -7.42***

10WA-118.13 - 2.39* - 5.97*** 5.27*** 13.25*** 12.60*** -7.39*** -0.45 0.22 -5.08*** -6.07***

10WA-130.5 - 3. 46** 2.35* 4.08** 15.91*** 9.11*** -4.00** 0.38 -0.69 -1.52 -6.53***

Ab09BG10HL-85 - 4.11** 1.16 3.49** 13.97*** 5.50*** -8.13*** 0.05 2.64* 6.87*** -5.68***

‘Goldenhart’ -4.75*** 7.10*** 5.57*** 15.39*** 7.55*** -6.07*** 2.82* -2.23* 9.20*** -2.37*

‘Julie’ -5.17*** 8.29*** 6.76*** 15.47*** 5.36*** -3.23** 2.44* -3.16** 0.79 -1.14

‘Kardia’ -4.11** 9.48*** 4.98*** 13.36*** 13.98*** -10.36*** 3.23** -3.30** -0.44 -6.50***

‘Transit’ -5.60*** 5.91*** 5.27*** 16.33*** 4.55*** -4.88*** -0.12 -0.29 10.02*** -2.76*

X07G31-T120 - 5.39*** 1.1 6 5.27*** 15.62*** 7.22*** -5.28*** -1.36 3.50** -3.81** -6.05***

2017

10WA-107.8 - 1.53 1.6 2.58* 13.09*** 9.36*** -6.05*** 4.71*** -4.11** -4.21*** 2.57*

10WA-118.13 - 2.72* - 5.23*** 0.78 10.23*** 8.07*** -3.54** -0.03 0.5 -0.3 3.55**

10WA-130.5 - 2.12* 2.15* 1.98 13.54*** 5.78*** -2.32* -0.24 -1.34 2.13* 3.41**

Ab09BG10HL-85 - 1.23 1.96 1.98 11.91*** 3.77** - 3.85** -0.37 2.80* 7.83*** 0.46

‘Goldenhart’ -1.53 3.44** 5.39*** 12.68*** 6.20*** -0.75 5.02*** -3.31** 10.45*** 7.55***

‘Julie’ -3.02** 4.91*** 5.26*** 12.23*** 3.90** -2.76* 4.20*** -3.89** 1.89 7.94***

‘Kardia’ -1.23 5.84*** 3.77** 12.46*** 7.88*** -11.27*** 5.35*** -4.24*** 1.7 5.34***

‘Transit’ -2.42* 5.10*** 1.68 14.74*** 3.88** -4.04** -1.57 -0.13 9.18*** 6.49***

X07G31-T120 - 0.03 2.52* 1.08 13.96*** 8.89*** -2.40* -4.46*** 2.69* -0.3 -0.58

2018

10WA-107.8 -0.85 0.82 5.68*** 2.57* 15.64*** 2.29* 4.35*** -4.91*** -3.45** 3.18**

10WA-118.13 0.67 -4.87*** 3.15** 0.99 17.61*** 1.07 5.33*** -5.82*** -3.52** 3.57**

10WA-130.5 0.06 0.6 4.63*** 3.21** 14.93*** 3.28** 3.85** -4.88*** 0.11 3.44**

Ab09BG10HL-85 -0.85 1.7 3.47** 2.95** 16.83*** 2.65* 6.59*** -6.86*** -3.17** 0.79

‘Goldenhart’ 0.37 4.10** 7.78*** 3.34** 12.77*** 3.57** 6.37*** -7.44*** 6.09*** 1.03

‘Julie’ 0.06 4.32*** 8.62 3.42** 14.24*** 1.88 7.23*** -7.72*** -4.38*** -3.07**

‘Kardia’ 0.97 4.54*** 6.62 2.74* 18.85*** -4.96*** 6.41*** -6.86*** 0.62 6.67***

‘Transit’ -1.77 4.10** 3.84 4.37*** 13.01*** 1.02 4.29*** -5.25*** -1.82 3.14**

X07G31-T120 -0.55 2.35* 3.42 2.95** 15.25*** 0.74 3.92** -4.34*** -4.20*** 1.18

79 Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001. PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels.

Table 16. Mean data across years 2016, 2017, and 2018 in Almota and Genesee for each trait: percent emergence, days to heading, days to maturity, plant height, grain yield, test weight, percent plump kernels, and percent thin kernels.

Varieties/Year % Emergence DH (day) DM (day) PH (cm) GY (kg/ha) TW (kg/hL) PPK(%) PTK (%)

Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee Almota Genesee

2016

10WA-107.8 100 69 55 57 97 97 78 93 4,489 5,770 69 66 90 90 1 2

10WA-118.13 88 94 53 50 95 98 78 89 4,185 5,381 71 65 74 80 4 4

10WA-130.5 94 75 57 55 92 97 75 99 3,393 4,594 74 69 76 77 3 4

Ab09BG10HL-85 100 63 55 55 87 98 77 92 2,408 3,861 69 65 68 78 6 5

‘Goldenhart’ 94 56 57 59 96 98 75 97 3,296 4,113 72 67 89 90 3 2

‘Julie’ 56 69 55 61 96 101 78 96 2,661 3,686 73 71 90 87 2 2

‘Kardia’ 100 63 57 61 96 97 72 92 4,114 5,885 64 64 91 93 2 2

‘Transit’ 81 50 55 59 92 100 76 100 2,488 3,498 71 69 72 75 4 3

80 X07G31-T120 100 44 55 55 97 97 76 97 2,778 4,261 71 68 64 69 7 5

Mean 90 65 56 57 94 98 76 95 3,347 4,561 71 67 80 83 4 3

LSD (p < 0.05) ns ns 1.2 1.12 3.12 ns ns 4.69 416.75 485.11 1.78 2.6 4.25 5.33 1.03 1.11

2017

10WA-107.8 100 81 56 55 97 97 80 94 3,516 4,674 69 67 91 90 2 2

10WA-118.13 88 75 53 47 90 95 79 85 3,620 4,238 71 70 74 82 5 4

10WA-130.5 88 81 57 55 97 95 76 98 2,996 3,866 74 71 78 79 3 4

Ab09BG10HL-85 94 88 57 55 94 97 77 91 2,543 3,494 72 70 75 80 7 5

‘Goldenhart’ 75 94 53 59 103 102 76 94 3,333 3,824 73 74 90 92 3 2

‘Julie’ 81 75 56 60 95 106 79 91 2,633 3,488 75 70 91 88 2 2

‘Kardia’ 94 88 55 61 94 102 72 96 3,695 4,144 65 61 89 93 2 2

‘Transit’ 94 75 57 59 92 97 77 102 2,705 3,446 73 69 75 76 5 4

X07G31-T120 94 100 54 57 92 95 77 99 3,479 4,553 72 72 66 70 7 5

Mean 91 83 55 57 95 98 76 94 3,214 4,000 72 70 82 84 4 3

LSD (p < 0.05) ns ns 1.87 0.87 4.47 5.26 ns 5.86 519.68 557.33 3.33 2.01 4.34 4.14 1.07 1.01

2018

10WA-107.8 81 88 57 55 97 99 83 93 3,274 6,608 75 75 76 84 7 5

10WA-118.13 100 94 54 50 92 95 88 82 3,826 7,215 74 74 76 88 7 4

10WA-130.5 100 88 56 55 92 99 86 95 3,822 6,016 75 77 74 83 8 4

Ab09BG10HL-85 94 81 57 56 90 97 84 94 3,915 6,820 75 76 82 91 5 3

‘Goldenhart’ 94 94 56 59 100 102 90 93 3,790 5,060 77 77 89 87 3 3

‘Julie’ 88 94 56 60 97 106 91 93 3,094 6,068 74 75 82 94 6 1

‘Kardia’ 94 100 56 60 95 102 90 90 4,143 7,616 65 67 86 89 4 3

‘Transit’ 88 75 56 59 92 97 88 99 3,019 5,554 72 75 66 89 11 2

X07G31-T120 75 94 56 57 93 95 87 93 3,234 6,454 72 74 74 83 8 5

Mean 91 90 56 57 94 99 86 92 3,646 6,479 74 75 78 88 7 3

81 LSD (p < 0.05) ns ns ns 1.95 2.5 5.69 ns 7.83 799.33 850.3 3.3 2.62 5.61 ns 1.86 2.07

PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; TW: test weight; PPK: percent plump kernels; PTK: percent thin kernels; ns: not significant; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

CHAPTER TWO

ASSESSING THE ADAPTABILITY OF QUINOA AND MILLET IN TWO AGRO-

ECOLOGICAL ZONES OF RWANDA

Abstract

Quinoa and several millet species are nutritionally valuable seed crops with versatile applications in food production and consumption. Both quinoa and millet have the potential to provide a drought tolerant, nutritious complementary crop to maize that is predominantly cultivated in Rwanda. The objective of this study was to evaluate quinoa and millet genotypes and assess their agronomic performance in two agroecological zones of Rwanda. Twenty quinoa and fourteen millet cultivars were grown in 2016 and 2017 in Musanze, located in the highland region, and Kirehe, located in the Eastern lowland region of Rwanda. Quinoa and millet cultivars were evaluated for their agronomic traits including, grain yield, emergence, days to heading, flowering, and maturity, and plant height. Across both years, quinoa yield ranged from 189 to

1,855 kg/ha in Musanze and from 140 to 1,259 kg/ha in Kirehe. Millet yield ranged from 16 to

1,536 kg/ha in Musanze and from 21 to 159 kg/ha in Kirehe. Differences for genotype, environment, and G × E were found for grain yield of both quinoa and millet. Plant height in quinoa and millet were affected by location. Mean cultivar plant height was shorter in Kirehe

(µ=73 cm and 58 cm for quinoa and millets, respectively) than Musanze (µ=93 cm and 76 cm for quinoa and millets, respectively). There was G × E interaction for maturity in quinoa and millet in both 2016 and 2017. In both Musanze and Kirehe, Titicaca was the earliest maturing quinoa variety with an average of 91 days and Earlybird was the earliest maturing millet variety with an average of 91 days. The results suggest that quinoa and millet have the potential as regional

crops for inclusion in the traditional dryland cropping rotations in Rwanda, thereby contributing to increased cropping system diversity. We suggest the need to continue evaluating a diverse number of cultivars to select for genotypes adapted to specific agro-ecological zones and across seasons in Rwanda. We also recommend complementary research on local farmer interest in these crops from both agronomic, market and dietary perspectives as well as training of local farmers on quinoa production, post-harvest and handling, and cooking.

1. Introduction

Agriculture is the main economic activity in Rwanda with 70% of the total population, and around 72% of the working population, engaged in the sector (RDB, 2015; FAO, 2019). Tea and coffee are the principal exports, while plantains, cassava, potatoes, sweet potatoes, maize, and beans are the most commonly grown staple food crops (FAO, 2019). There has been a strong decline in poverty in Rwanda over the past decade, but despite this 63% of the population still live in extreme poverty defined by the World Bank as less than $1.25 a day and is food and nutritionally insecure (NISR, 2012; World Bank, 2013). Thus, increasing agricultural productivity and diversity are key to the success of Rwanda’s economy and the well-being of its population (FAO, 2015).

There are ample opportunities to increase agricultural productivity in Rwanda (Cantore,

2011). For one, the yield gap (the difference between attainable and actual yields) is very high

(70%) for most cereal and legume crops, and still considerable for the root and tuber crops

(Niyitanga, 2015). While there are theoretically many opportunities to improve farm productivity and income of smallholder households, making those opportunities manifest is fraught with difficulties. Most agricultural land in Rwanda is on hillsides and is therefore susceptible to nutrient deficiency and soil erosion (Clay et al. 1998; FAO, 2019). Moreover, all agriculture in Rwanda is

rainfed and subject to the caprice of meteorological patterns (SEI, 2009; Cantore, 2011; Rwanyiziri

& Rugema, 2013). Climate change and concomitant increases in climate variability, make agricultural production unpredictable from one season to another (SEI, 2009; FAO, 2019).

Droughts and extreme temperatures plague the Eastern province of Rwanda, whereas in the

Northern province, floods are commonplace (SEI, 2009). As such, these regions are afflicted with food shortages whenever dramatic changes in climate occur (MINITERE, 2006; SEI, 2009;

Muhire et al. 2014). Given that future climates are likely to be more variable, and that climatic events will become more extreme, the vulnerability of agriculture to such fluctuations is likely to increase in kind. It is imperative, therefore, that Rwanda would increase its capacity to cope with extreme adverse climatic events (SEI, 2009; FAO, 2019).

The practice of increasing crop diversity via sustainable crop intensification can aid in producing more food, more efficiently, while protecting the environment and promoting positive social and economic outcomes (Prasad, 2016; USAID, 2016). Sustainable crop intensification and increased cropping system diversity will contribute to food security and a range of developmental goals such as eradicating extreme poverty and hunger (FNSMS, 2013; McMahon & Flowers,

2016). Crop diversity is among the key tenets of sustainable agriculture (McMahon & Flowers,

2016). Utilizing multiple crops that fill distinct niches in an agroecosystem improves farmers’ ability to manage weeds, diseases, and insect pests as well as potentially improving the environmental performance of the cropping system (Lin, 2011; McMahon & Flowers, 2016).

Fundamental agronomic research can help overcome production and market obstacles by mitigating the uncertainty of growing novel crops, which ultimately, can also enhance food and nutrition security.

The Rwandan diet is highly diverse in plant foods but consists primarily of plantains, cassava, beans, potatoes, and sweet potatoes (FAO, 2015; USAID, 2016). Protein consumption is insufficient for about a quarter of the population (FNSMS, 2013; FAO, 2015). Micronutrient deficiency—known as ‘hidden hunger’—is also prevalent for iron, calcium, and vitamin B12, which may lead to blindness, stunting, impaired cognitive development in children, increased susceptibility to infectious diseases, and even premature death (FNSMS, 2013; USAID 2016). In

Rwanda, of the 1.74 million persons under five years of age, 38% are stunted, and 9% are underweight (USAID, 2016). The most pronounced micronutrient deficiencies are for calcium, vitamin A, vitamin B12, vitamin D, selenium, vitamin E, Fe, Zn, and Ca (FNSMS, 2013; USAID

2016; CSIS, 2016). The primary dietary source of calcium in Rwanda are beans, milk, cassava, and sweet potatoes; and the primary source of vitamin A comes from plantains, vegetables, palm oil, offal, and sweet potatoes (CSIS, 2016). The massive consumption of cassava and sweet potatoes—that is, carotenoid-biofortified cassava and orange-fleshed sweet potatoes—could have a significant impact on concentration of vitamin A status in Rwandan diets (FNSMS, 2013; FAO,

2013).

An increase in grain and cereal production is necessary to improve and sustain nutritional security (FNSMS, 2013). Introducing quinoa and millet crops in the existing diet and cropping system will assuage issues of nutritional insecurity and the ability to cope with extreme climatic events, especially drought. Quinoa and millets are nutritious grains that may be consumed by humans with unique agronomic virtues such as drought and heat tolerance (Jacobsen, 2003; Wu et al. 2015; Devi et al. 2014; Nithiyanantham et., 2019).

Quinoa (Chenopodium quinoa Willd.) is a pseudocereal grain originating from Lake

Titicaca in the Peruvian and Bolivian Andes (Adolf et al. 2013). Quinoa is a broadly adapted

crop with exceptional resilience to many adverse environmental and climatic conditions including nutrient-poor and saline soils, and drought-stressed marginal ecosystems (Vacher,

1998; Aguilar & Jacobsen, 2003; Jacobsen et al. 2003; Fuentes & Bhargave, 2011). Quinoa has excellent drought and salinity tolerance and thrives across a wide range of soil pH (Wilson et al.

2002; Koyro et al. 2008; Adolf et al. 2013; Peterson & Murphy, 2015). In South America quinoa grows over a broad range of latitudes (spanning nearly 4,828 km from equatorial Columbia to temperate southern Chile), a wide range of altitudes, from sea level at the coast to 4000 m above sea level (m.a.s.l.), and a diverse set of rainfall zones (Galwey, 1989; Jacobsen et al. 2003).

Quinoa has excellent potential to contribute to food security in multitudes of regions worldwide, especially in countries where the human population has limited access to protein sources or where production conditions are limited by low humidity, reduced availability of inputs, or aridity (Rojas, 2003; Jacobsen, 2003; Wu et al. 2016). Recently, several papers have primarily reported upon salt and drought tolerance in quinoa (Wilson et al. 2002; Koyro et al. 2008; Adolf et al. 2013; Zurita-Silva et al. 2014; Peterson & Murphy, 2015; Coral, 2015). In the last 30 years, quinoa has garnered considerable attention worldwide due to its nutritional and health benefits, as well as its flavorful and high‐quality seeds (Aluwi et al. 2017; Wu et al. 2016; Wu et al.

2017). Quinoa possesses a well‐balanced complement of amino acids and high mineral concentration of iron, calcium, and phosphorus (Wu, 2015; Navruz‐Varli & Sanlier, 2016; Wu et al. 2016). In 2013, quinoa was lauded by the FAO as a food with high nutritive value, impressive biodiversity, and a singular role to play in the achievement of food security worldwide (FAO,

2013). Quinoa has been deemed one of humanity’s most promising crops for reliably providing nutritionally-dense food, while also instigating socio-economic growth in whichever country it is grown (UN, 2012; Jayne et al. 2003; Rojas, 2003).

Millet is one of the oldest food crops known to humankind and possibly the first domesticated cereal grain (Changmei & Dorothy, 2014). Millet crops are a major source of energy and protein for millions of people worldwide, especially those who live in exceptionally hot, dry environs (Rachie, 1975; Fuller, 2006; Amadou et al. 2013; Nithiyanantham et al. 2019). According to Nithiyanantham et al. (2019), millet‐based foods are considered potential prebiotics and probiotics with prospective health benefits. Grains of millet species are widely consumed for traditional medicinal purposes and holistic health remedies. Millets are unique among the cereals because of their high calcium, iron, potassium, magnesium, phosphorous, zinc, dietary fiber, polyphenols, and protein content (Hulse et al. 1980; Devi et al. 2014; Gupta et al. 2014;

Habiyaremye et al. 2017). Millets are important food crops in many underdeveloped countries because of their ability to grow with limited rainfall (Amadou et al. 2013). Millets have been adapted to local climatic conditions by African farmers over millennia. However, knowledge of these landraces is too easily lost (Fuller, 2006; Amadou et al. 2013). Twenty different species of millet have been cultivated throughout the world since their initial domestication. (Fuller, 2006).

The most commonly cultivated millet species are proso millet (Panicum miliaceum L.), pearl millet

(Pennisetum glaucum L.R. Br.), ﬁnger millet (Eleusine coracana), kodo millet (Paspalum setaceum), foxtail millet (Setaria italica L. Beauv.), little millet (Panicum sumatrense), and barnyard millet (Echinochloa utilis) (Rachie, 1975; Bouis, 2000; Wen et al. 2014). Development of crops that yield well under uncertain and extreme climatic and soil growing conditions will mitigate problems of food and nutrition insecurity.

Considering the nutritional promise of quinoa and millets, their propensity for agronomic adaptability, and resilience to weather variability, this research was conducted to generate data to aid in Rwanda’s crop intensification/diversity efforts. Furthermore, the objective of this study was

to introduce quinoa to Rwanda at large, and to evaluate its—and millets’—overall adaptability, and agronomic qualities such as plant growth and grain yield in two contrasting environments

Rwandan environments: Musanze (highland) and Kirehe (lowland). We wanted to identify quinoa and millet varieties with high yields and valuable agronomic characteristics in each location and gather information on the adaptability and genotype x environment interactions of the leading quinoa varieties worldwide; beneficial information as quinoa and millet continues to push past their traditional boundaries into the peripheries of the agricultural world.

2. MATERIALS AND METHODS

2.1.Location

A two-year study (2016 and 2017) was conducted in two different agroclimatic zones of

Rwanda: the Eastern lowland region, Gahara Sector, Kirehe District, Eastern Province in a farm situated at 2.2168° S Lat., 30.7580° E Long.; and the highlands region, Muhoza Sector, Musanze

District, Northern Province, situated at 1.5035° S Lat., 29.6325° E Long. The Eastern lowlands are 1,000 to 1,500 m.a.s.l., receive 740 to 1,000 mm annual rainfall, and mean annual temperatures range between 19 and 22°C. The highlands—which include the Congo-Nile Ridge and volcanic chains of Birunga—are 2,000 to 4,500 m.a.s.l., receive 1,300 to 1,550 mm annual rainfall, and mean annual temperature range between 10 and 14°C (Gotanegre et al. 1974; Ilunga et al. 2004;

REMA, 2015; Ilunga & Muhire, 2010; David et al. 2011; Muhire et al. 2015) (Figure 1 & 2).

Rwanda has four climatic seasons: the long rainy season (late February-late May) and the short rainy season (end September-early December) are interspersed between the long dry (June–

September) and short dry (mid-December–mid-February) seasons (Kizza, 2009; Muhire et al.

2015). The two rainy seasons correspond with crop production seasons—season B, and season A,

respectively—the latter marking the beginning of the agricultural year (FAO GIEWS, 2018). Our field trials were conducted in season A. These seasons are dictated mainly by regional atmospheric circulation (Gotanegre et al. 1974; Ilunga et al. 2004; Kizza, 2009), i.e. the position and intensity of anticyclones (a weather system with high atmospheric pressure at its center, around which air slowly circulates in a clockwise (northern hemisphere) or counterclockwise (southern hemisphere) direction), namely: Mascarene, Saint Helena, Azores and Siberian (Ilunga et al. 2004; Anyah &

Semazzi, 2007; Kizza et al. 2009). The soil in Musanze developed from volcanic parent materials, and is therefore comparatively more rich in soil organic matter than that of Kirehe (Nzeyimana et al. 2014; Uwitonze et al. 2016) (Figure 3).

2.2.Experimental Design and Data Collection

2.2.1. Quinoa Variety Trials

Fifteen quinoa varieties and five breeding lines were grown in a randomized complete block design (RCBD) with four replicates (Table 1). Quinoa varieties were obtained from the

Sustainable Seed Systems Lab at Washington State University (Pullman, Washington, U.S.) and the USDA- Germplasm Resources Information Network (Table 1) and chosen based on their diversity in morphological and agronomic traits and their potential for adaptation to the climate of

Rwanda. Each plot was hand planted into two rows, in 4m x 1.2 m plots, using 5g seed per/plot.

The phenotypic data were recorded according to Sosa-Zuniga et al. (2017); percent emergence was recorded on a scale of 1 to 5 with 1 (0% emergence) and 5 (100 % emergence). Days to flowering was recorded as the number of days from when 50% of leaves surrounding the inflorescence separated, leaving the inflorescence visible from above. Days to maturity was recorded as the number of days from sowing to the time when 50% of the head had senesced. Plant height was

measured from soil surface to the tip of the panicle at maturity using three randomly selected plants per plot. Grain yield was measured as the weight of the grain harvested from the whole plot. The plots were harvested individually using sickles to cut stems of the plants. All plants were then bundled, and hand-threshed. The seeds were then processed by winnowing, using the wind to separate smaller particles and immature seeds from the mature seeds and for ﬁnal removal of any foreign plant material.

2.2.2. Millet Variety Trials

Fourteen millet varieties were grown in a RCBD with four replicates (Table 2). The fourteen millet varieties tested were classified into three species; proso millet, foxtail millet, and finger millet (Table 2). The millet seeds were obtained from USDA-ARS, Plant Genetic Resources

Conservation Unit (Griffin, GA, USA) and National Resource Program, Iowa State University

Regional Plant Introduction Station (Ames, IA, USA) (Table 2). Percent emergence was recorded as an observation on a scale of 1 to 5 with 1 denoting 0% emergence and 5, 100 % emergence.

Days to heading was recorded as the number of days from sowing to the time when in 50% of the ear had emerged from the flag leaf sheath. Days to maturity was measured as the number of days from sowing to the time when 50% of the ear had ripened (stage 11.4 on the Feekes scale, according to Wise et al. (2011) and Miller (1992). Plant height was also measured at stage 11.4 on the Feekes scale using three randomly selected plants per plot. GY was measured as the weight of the grain harvested from the whole plot. Millet was planted, harvested, threshed, and cleaned similarly to quinoa.

2.3.Statistical Analyses

Statistical analysis was performed using the statistical software SAS 9.4 SAS University

Edition (SAS Institute IN., Cary, NC, USA). The data were analyzed using mixed-effect methodology. Analysis of variance, correlation, and regression analysis were conducted, and

ANOVA was performed with all factors using a mixed model with PROC GLIMIX. Due to yearly discrepancies (a heavy rain that occurred in Musanze in 2017 that caused flooding resulted in the whole trial needing replanting) analyses were performed for each respective year.

Fisher's LSD test (P ' 0.05) was used to compare means. Pearson correlation analysis was used to assess the relationship between the agronomic traits including percent emergence, days to flowering (quinoa), day to heading (millet), days to maturity, plant height, and grain yield across all two years. Statistical signiﬁcance level was set at α =0.05.

3. RESULTS

3.1.Quinoa Variety Trials

3.1.1. Gain Yield

A significant genotype × environment interaction for grain yield was observed in 2016 (p

< 0.0001) (Table 3). When comparing responses of varieties across location, all the varieties were affected by location for grain yield except for Cahuil, Linares, Temuko, and QuF9P39-72

(p > 0.05) (Table 4). In Musanze and Kirehe, mean quinoa yield was 812 kg/ha and 422 kg/ha, respectively (Table 5). QQ74 was the highest yielding variety in Musanze and Kirehe with 1,580 kg/ha and 832 kg/ha, respectively (Table 5).

In 2017, grain yield was affected by location (p < 0.0001), and there was a significant genotype × environment interaction (p < 0.0001) (Table 3). When comparing responses of varieties

across locations, all varieties were affected by locations for grain yields except for Cahuil, Linares, and QuF9P39-72 (Table 4). Mean yield in Musanze and Kirehe was 1,118 kg/ha and 780 kg/ha, respectively (Table 5). NL-6 (3,004 kg/ha), and QuF19P39-51 (2,089 kg/ha) were the highest yielding varieties in Musanze and Kirehe, respectively (Table 5).

In both 2016 and 2017grain yield was strongly correlated with percent emergence (r = 0.71; p = 0.001) and moderately correlated to plant height (r = 0.56; p = 0.007) but showed no relationship with other traits (Table 6).

3.1.2. Emergence

In 2016, location affected plant emergence (p = 0.002) (Table 3 & 4). Mean emergence rates were 59% in Musanze and 73% in Kirehe (Table 5). There was a significant genotype × environment interaction for plant emergence (p < 0.0001) (Table 3). Location significantly affected the emergence rates of all varieties (Table 4). The varieties with the highest plant emergence in Musanze were QuF9P1-20, QQ74 and Kaslaea, with 85%, 81%, and 80% emergence respectively, whereas QQ74, Cahuil, Cherry Vanilla, and Titicaca had 100%, 99%, 99%, and 96% emergence, respectively, in Kirehe (Table 5).

In 2017 location affected plant emergence (p < 0.0001) (Table 3). Mean emergence rates were 69% in Musanze, and 66% in Kirehe; the differences were not significant (Table 5). There was a significant genotype × environment interaction for plant emergence (p < 0.0001) (Table 3).

Location significantly affected the emergence rates of all varieties (Table 4). The varieties with the highest plant emergence in Musanze were Kaslaea, Titicaca, and NL-6 with 89%, 87%, and

84% emergence, respectively, whereas QQ74, KU-2, and Cherry Vanilla had 91%, 89%, and 85% emergence, respectively, in Kirehe (Table 5).

Percent emergence was strongly correlated with grain yield (r = 0.71; p = 0.001) and negatively correlated to days to flowering (r = -0.58; p = 0.009) and days to maturity (r = -0.57; p

= 0.01), but was not related to plant height (Table 6).

3.1.3. Days to Flowering

In 2016, location affected days to flowering (0.0001)—genotype × environment interaction was significant (p <0.0001) (Table 3). Days to flowering differed between locations; days to flowering were 48 days and 43 days in Musanze and Kirehe, respectively (Table 5). Location significantly affected the days to flowering of all varieties (p < 0.0001) (Table 4). CO407 Dave,

Titicaca, and NL-6 had the earliest flowering dates in Musanze with 38 days, 43 days, and 45 days;

Titicaca, Cahuil, and QQ 74 had the earliest flowering dates in Kirehe with 41 days each (Table

5).

In 2017, location had an effect on days to flowering (p < 0.0001), and there was a significant genotype × environment interaction (p < 0.0001) (Table 3). Days to flowering differed between locations: Musanze was 48 days and Kirehe 41 days to flowering (Table 5). Location significantly affected the days to flowering of all varieties (p < 0.0001) (Table 4). NL-6, Titicaca, and CO407 Dave flowered earliest at 42 days, 43 days, and 43 days, in Musanze; in Kirehe, varieties NL-6, Titicaca, and Cahuil were the earliest to flower at 40 days each (Table 5).

Days to flowering was moderately correlated with days to maturity (r = 0.46, p = 0.04) and negatively correlated to percent emergence (r = -0.58; p = 0.009) (Table 6).

3.1.4. Days to Maturity

Analysis of variance showed a significant genotype × environment interaction for days to maturity in 2016 (Table 3). Location significantly affected the days to maturity of all varieties (p

< 0.0001) (Table 4). The early maturing varieties in Musanze included Titicaca, QQ 74, and

CO407 Dave with 96, 98, and 110 days respectively, while Black, QuF19P39-51, and QuF9P39-

72 were the late maturing varieties with 133, 135, and 138 days to maturity respectively. In Kirehe,

Titicaca, QQ 74, and CO407 Dave were there earliest maturing varieties with 85, 90, and 91 days to maturity, respectively, while Linares, QuF19P39-51, and Blanca were the latest maturing varieties at133, 135 and 141 days to maturity, respectively (Table 5).

In 2017, there was a significant genotype × environment interaction for days to maturity

(Table 3). Location significantly affected the days to maturity of all varieties (p < 0.0001) (Table

4). Titicaca, QQ 74, and Cahuil were the earliest maturing varieties in Musanze with 96, 98, 100 days to maturity respectively while Black, QuF9P39-72, and Blanca were the latest maturing varieties with 135, 138, 140 days to maturity, respectively. In Kirehe, Titicaca, QQ 74, and

QuF9839-64 were the earliest maturing varieties with 85, 88, 90 days to maturity respectively, while Linares, QuF9P39-72, and QuF19P39-51 were the late maturing varieties with 130, 130 and

135 days to maturity, respectively (Table 5).

Days to maturity were moderately correlated with days to flowering (r = 0.46, p = 0.04) and negatively correlated to percent emergence (r = -0.57; p = 0.01) (Table 6).

3.1.5. Plant Height

There was a significant genotype × environment interaction for plant height in both 2016 and 2017 (Table 3). Location significantly affected the plant height of all varieties (p < 0.0001)

(Table 4). In 2016, KU-2, Cahuil, and Temuko were the shortest varieties; in Musanze with 68,

69, and 69 cm respectively while QQ 74, QuF9P1-20, and Black were the tallest varieties with

112, 115, and 117 cm respectively (Table 5). In Kirehe, Temuko, KU-2, and Puno were the shortest varieties at 35, 36, and 43 cm, respectively, while QuF9840-29, QQ 74, and QuF9P1-20 were the tallest varieties at 71,77, and 98 cm tall (Table 5).

In 2017, Blanca, Cahuil, and Titicaca were the shortest in Musanze at 69, 73, and 75 cm, respectively, while varieties Black, Cherry Vanilla, QQ 74, QuF9P1-20 were the tallest at 122,

118, 117, and 117 cm, respectively. In Kirehe, Linares, QuF9P39-72, and Cahuil were the shortest at 58, 58, and 62 cm, respectively, while QuF19P39-51, QuF9840-29, and Black were the tallest at 123, 112, and 99 cm, respectively (Table 5).

Plant height was moderately correlated with grain yield (r = 0.59, p = 0.007) but not correlated with any other traits (Table 6).

3.2.Millet Variety Trials

3.2.1. Grain Yield

In both 2016 and 2017, there was a significant genotype × environment interaction for grain yield (p < 0.05) (Table 7). When comparing responses of varieties across location, results indicated that in both 2016 and 2017, all the varieties’ grain yield were affected by environment except variety USSR, in 2017 (Table 8). In 2016 Musanze and Kirehe yielded an average of 699 kg/ha and 23 kg/ha, respectively (Table 9). Variety GR 664 was the highest yielding variety in Musanze at 1,748 kg/ha, and variety 4535, the highest yielding variety in Kirehe, at 127 kg/ha (Table 9).

Grain yield was not correlated with any other traits (Table 10).

In 2017, the millet in Musanze and Kirehe yielded an average of 521kg/ha and 110 kg/ha, respectively (Table 9). Variety GR 664 was the highest yielding variety in Musanze at 1,329 kg/ha, and variety Sunup was the highest yielding variety in Kirehe at 229 kg/ha (Table 9). Grain yield did not correlate with any other traits (Table 10).

3.2.2. Emergence

In 2016, environment had as significant interaction with plant emergence (p = 0.002)

(Table 7). Mean emergence rates were 97% in Musanze and 89% in Kirehe (Table 9). There was a significant genotype × environment interaction for plant emergence (p = 0.001) (Table 7).

Environment significantly affected the emergence rates of all varieties except 4535, I.Se.410-A, and Long Gu (Table 8). The varieties with the highest plant emergence in Musanze were GR 664,

Huntsman, Minsum, and Turghai with 100% emergence rate each, whereas 4535 and Long Gu demonstrated the greatest percent emergence at 99%, in Kirehe (Table 9).

In 2017 location did not have an impact on plant emergence (Table 7). The mean emergence rates were 90% in Musanze, and 92% in Kirehe, and the differences were not significant (Table 9). There was no genotype × environment interaction for plant emergence (Table

7). The varieties with the highest plant emergence in Musanze were GR 664 and USSR with 100% emergence rate each, whereas Turghai had higher emergence rate in Kirehe of 98 % (Table 9).

Percent emergence did not correlate with any other traits (Table 10).

3.2.3. Days to Heading

In both 2016 and 2017, environment did not affect days to heading. However, there was a significant genotype × environment interaction (p <0.0001) (Table 7). In 2016, mean days to

heading were 56 in Musanze and 53 in Kirehe; differences were not significant (Table 9).

Environment significantly affected the days to heading of all varieties except I.Se.410-A and Long

Gu (Table 8). Earlybird was earliest to heading in both Musanze and Kirehe at 36 and 34 days to heading, respectively (Table 9).

In 2017, the mean days to heading were 123 in Musanze and 121 in Kirehe; the differences were not significant (Table 9). Environment did not affect days to heading of the varieties GR 658,

GR 664, GR 665, Huntsman, Minsum, Turghai, or USSR (Table 8). Earlybird was earliest to head in both Musanze and Kirehe at 34 and 40 days to heading, respectively (Table 9).

There was a strong positive correlation between days to heading and days to maturity (r =

0.88, p = 0.0001) and plant height (r = 0.69, p = 0.007) (Table 10).

3.2.4. Days to Maturity

There was a significant genotype × environment interaction for days to maturity in both

2016 and 2017 (p < 0.0001) (Table 7). Environment significantly affected the days to maturity of all varieties (p < 0.05) (Table 8). In 2016, mean days to maturity was 121 in Musanze and 118 in

Kirehe; the differences were not significant (Table 9). Earlybird was earliest maturing in both

Musanze and Kirehe at 93 and 87 days to maturity, respectively (Table 9). In 2017, varieties averaged 123 and 121 days to maturity in Musanze and Kirehe, respectively; differences were not significant (Table 9). Earlybird was earliest maturing in both Musanze and Kirehe at 93 and 87 days to maturity, respectively (Table 9).

There was a strong positive correlation between days to maturity and days to heading (r =

0.88, p = 0.0001) and days to maturity and plant height (r = 0.88, p = 0.0001) (Table 10).

3.2.5. Plant Height

Across all two years, there was a significant genotype × environment interaction for plant height (p < 0.05) (Table 7). Both genotype and environment effects were significantly different (P

< 0.0001). In 2016, varieties in Musanze and Kirehe were on average 70, and 53 cm tall, respectively; these differences were significant (Table 9). Earlybird was the shortest variety in both

Musanze and Kirehe at 38 cm and 22 cm tall, respectively (Table 9). In 2017, environment affected the plant height of all varieties except 4535, Bei Huong, and Long Gu (Table 8). Plant height differed between Musanze and Kirehe; varieties in Musanze and Kirehe were, on average, 81 cm and was 63 cm tall, respectively (Table 9). Earlybird was the shortest variety in both Musanze and

Kirehe at 53 cm and 26 cm tall, respectively (Table 9).

Plant height was strongly correlated with days to heading (r = 0.69, p = 0.007) and strongly correlated with days to maturity (r = 0.88, p = 0.0001) (Table 10).

4. DISCUSSION

4.1.Grain Yield

Across all two years, higher yields for both quinoa and millet were obtained in Musanze than in Kirehe. Quinoa yield in Musanze ranged between 189 and 1,855 kg/ha, with Kaslaea being the highest yielding variety, while in Kirehe, yield ranged between 140 and 1,259 kg/ ha, with QuF19P39-51 being the highest yield variety. Millet yield in Musanze ranged between 16 and 1,536 kg/ha, with G664 being the highest yielding variety, while in Kirehe, yield ranged between 21 and 159 kg/ha, with 4535 being the highest yield variety. The higher yields in

Musanze compared to Kirehe are likely the result of greater moisture in Musanze compared to

Kirehe during the time of the experiments. Musanze receives, on average, 550 mm more

precipitation per year than Kirehe (Ilunga et al. 2004; REMA, 2015) (Figure 1 & 2). Musanze is also known for its rich volcanic soil (Figure 3). Umar (2006) and Shwabe et al. (2013) reported that abiotic stressors such as extreme temperature and low water availability are often the most important restricting factors in the growth and productivity of major crop species. Drought reduces leaf size, stem extension, and root proliferation, which causes disruption of photosynthetic pigments and reduced gas exchange, ultimately leading to reduction in plant growth and productivity (Richards et al. 2002; Anjum et al. 2011). This study’s results are consistent with previous reports, which stated that water stress in millet reduces seed yield

(Mahalakshmi et al. 1985). When comparing the yield of millet species across locations in both

2016 and 2017, proso millet species yielded 410 kg/ha on average, followed by foxtail millet at

184 kg/ha, and finger millet at 87 kg/ha (Table 11). We would be remiss to not also mention severe yield loss, mainly in proso millet and finger millet species, inflicted by birds.

4.2.Emergence and Plant height

Both quinoa and millet varieties’ height were affected by location (p < 0.0001) (Table 3

& 7). Plants were significantly shorter in Kirehe, with an average height of 73 cm compared to

93 cm in Musanze for quinoa, and an average height of 76 cm and 58 cm in Musanze and

Kirehe, respectively, for millet. Emergence rate was 5.5% higher in Musanze than in Kirehe for quinoa, and 3% for millet. Less moisture in Kirehe was likely responsible for the shorter plant height and low percent emergence. Water deficit is known to cause impaired germination and poor crop emergence (Harris et al. 2002). Kaya, (2009) reported that water deficit in sunflower severely reduced germination and seedling stand, and delayed germination by one to two days.

Drought stress and water scarcity have been reported to reduce plant height in switchgrass

(Panicum virgatum), channel millet (Echinochloa turneriana), barnyard millet, and pearl millet

(Conover & Sovonick-Dunford, 1989; Madakadze, 1999). Adams et al. (2001) reported that drought stress and water scarcity slow enzymatic activity, and by extension, plant growth.

Nonami (1998) also reported that cell elongation of higher plants could be inhibited by interruption of water flow from xylem to surrounding elongating cells under water deficit conditions. Plant height among millet species revealed that proso millet species are shorter compared to other millet species with an average of 53 cm (proso millet), 82 cm (finger millet), and 110 cm (foxtail millet) (Table 11).

4.3.Days to Flowering and Maturity

The most significant factors for heat stress-related yield loss in cereals include high- temperature-induced shortening of development of vegetative phases, reduced light perception over the shortened life cycle, and perturbation of the processes associated with carbon assimilation (transpiration, photosynthesis, and respiration) (Stone, 2001). The process of grain filling—the accumulation of reserve nutrients in the developing and maturing grain—is also sensitive to environmental conditions, and thus, strongly affect yield (Yang & Zhang, 2006).

When comparing the effect of location on maturity across all varieties, plants in Kirehe were five days earlier to maturation for quinoa, and two days earlier for millet, than in Musanze. Days to maturity for quinoa in Musanze ranged between 96 and 138 days, with Titicaca being the earliest maturing variety, while in Kirehe days to maturity ranged between 85 and 135 days, with

Titicaca being the earliest maturing variety. However, across all millet varieties, days to maturity in Musanze ranged between 95 and 180 days with ‘Earlybird’ being the earliest maturing variety, while in Kirehe days to maturity ranged between 86 and 173 days, with ‘Earlybird’ being the

100

earliest maturing variety. The difference between millet species in heading and maturity were pronounced; days to heading for proso millet ranged between 36 and 52 day, followed by finger millet at 102 days, and foxtail millet at 63 to 75 days. Similarly, days to maturity for proso millet species were the most expedient at 91 to 109 days, followed by foxtail millet at166 to 175 days, and finger millet at172 days. The results are in agreement with what was reported Baltensperger,

(2002) and Williams et al. (2007) who found that proso millet was the earliest maturing of all millet species.

5. CONCLUSION

These experiments have demonstrated that quinoa and millet can grow well in a variety of agroecological zones in Rwanda, from highland to lowlands. Yield data indicates that for both quinoa and millet, Musanze-like environs promote greater yield than Kirehe-like environs. The results of this study also underscore the need to continue evaluating quinoa and millet cultivars to identify genotypes adapted to specific agro-ecological zones and growing seasons in Rwanda. The variation in yield data in this study merit more research, regional variety trials, and establishment of quinoa and millet breeding programs in Rwanda, so as to effectively optimize seed yield in target environments across the country. Efforts like these will further elucidate the potential for quinoa and millet inclusion in the traditional dryland cropping rotations in Rwanda.

101

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Table 1. Quinoa germplasm used to assess the adaptability of quinoa in Rwanda in 2016 and 2017 Seed Accession Variety Name Origin Collection site source Name WSU N/A Titicaca Denmark N/A GRIN Ames 13745 Kaslaea NM-USA N/A GRIN PI 614886 QQ74 Maule-Chile Talca WSU N/A Black N/A San Luis Valley in Colorado WSU N/A Blanca N/A San Luis Valley in Colorado WSU N/A KU-2 Netherland N/A GRIN Ames 13743 Isluga Chile N/A Backyard Beans and Grains WSU N/A Linares N/A Project WSU N/A Puno Denmark N/A WSU N/A Cahuil N/A San Luis Valley in Colorado Frank Morton of Wild Garden WSU N/A Red Head N/A Seeds WSU N/A NL-6 Netherland N/A GRIN PI 596293 CO 407 Dave CO-USA N/A WSU N/A Temuco N/A Bountiful Garden Seeds Frank Morton of Wild Garden WSU N/A Cherry Vanilla N/A Seeds WSU N/A QuF9P1-20 BYU-USA BYU population WSU N/A QUF9P40-29 BYU-USA BYU population WSU N/A QUF9P39-72 BYU-USA BYU population WSU N/A QUF9P39-64 BYU-USA BYU population WSU N/A QUF9P39-51 BYU-USA BYU population WSU: Washington State University; GRIN: Germplasm Resources Information Network; BYU: Brigham Young University; N/A: Not available

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Table 2. Millet germplasm used to assess the adaptability of millet species in Rwanda Entry Accession Scientific name Accession Year Origin Latitude Longitude Number Number Name Collected 1 PI 318897 Eleusine coracana 4535 1967 Ethiopia N/A N/A 2 PI 578074 Panicum miliaceum Huntsman 1994 Nebraska, USA 41.23240N 98.41600W 3 PI 517017 Panicum miliaceum GR 658 1986 Ouarzazate, Morocco 30.91670N 6.91670W 4 PI 291363 Panicum miliaceum USSR 1963 China N/A N/A 5 PI 649385 Panicum miliaceum Minsum 1980 Minnesota, USA 46.00000N 94.00000W 6 PI 536011 Panicum miliaceum Sunup 1989 Nebraska, USA 41.23240N 98.41600W 7 PI 649382 Panicum miliaceum Turghai 1961 North Dakota, USA 47.00000N 100.00000W 8 PI 517018 Panicum miliaceum GR 664 1986 Ouarzazate, Morocco 30.93350N 6.93700W 9 PI 578073 Panicum miliaceum Earlybird 1994 Nebraska, USA 41.23240N 98.41600W 10 PI 531431 Panicum miliaceum Unikum 1989 Czechoslovakia 50.08330N 14.41670E 11 PI 517019 Panicum miliaceum GR 665 1986 Ouarzazate, Morocco 30.93350N 6.93700W 12 PI 458626 Setaria italica Bei Huong 1 1981 China N/A N/A

114 13 PI 458645 Setaria italica Long Gu 14 1981 China N/A N/A

14 PI 464544 Setaria italica I.Se.410-A 1981 Ethiopia N/A N/A

Table 3. Analysis of variance with F value for emergence rate, days to flowering, days to maturity, plant height, and grain yield for quinoa varieties grown in Musanze and Kirehe over two crop years.

Years Effect DF PE (%) DF (day) DM (day) PH (cm) GY (kg/ha) Location 1 10.07** 64.06*** 1.31 71.31*** 17.10*** 2016 Variety 18 5.47*** 1.77* 37.16*** 2.38** 2.01*

G × E 19 23.80*** 88.24*** 65.78*** 93.46*** 15.28*** Location 1 113.47*** 115.66*** 129.87*** 114.46*** 39.86*** 2017 Variety 18 53.27*** 140.99*** 194.00*** 85.84*** 17.31***

G × E 19 37.46*** 98.07*** 71.79*** 58.32*** 23.00*** DF: degrees of freedom; PE: percent emergence; DF: days to flowering; DM: days to maturity; PH: plant height; GY: grain yield; G × E: genetic × environment interaction. Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001.

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Table 4. Mean difference between Musanze and Kirehe for each quinoa variety of each trait: percent emergence, days to flowering, days to maturity, plant height, and grain yield. Variety PE (%) DF (day) DM (day) PH (cm) GY (kg/ha) Name/Year 2016 Black 4.35*** 9.44*** 8.99*** 12.10*** 4.44*** Blanca 4.48*** 9.24*** 9.18*** 8.10*** 2.47* C0407 Dave 5.58*** 8.00*** 6.68*** 10.81*** 5.02*** Cahuil 6.16*** 9.05*** 7.06*** 7.24*** 1.19 Cherry Vanilla 6.10*** 9.44*** 7.62*** 11.15*** 5.50*** Isluga 3.53** 9.65*** 8.25*** 9.03*** 2.85** KU-2 5.73*** 9.17*** 8.71*** 6.83*** 2.90** Kaslaea 5.85*** 9.39*** 8.03*** 9.94*** 5.57*** Linares 2.12* 9.75*** 8.95*** 8.21*** 1.02 NL-6 5.55*** 8.85*** 8.55*** 8.84*** 3.49** 116 Puno 5.06*** 9.73*** 7.53*** 9.33*** 4.48*** QQ 74 6.44*** 9.26*** 6.50*** 11.97*** 6.87*** QuF19P39-51 2.91** 10.02*** 9.22*** 9.32*** 2.34* QuF9839-64 5.27*** 9.71*** 6.68*** 11.19*** 5.70*** QuF9839-72 2.94** 9.75*** 9.22*** 8.64*** 1 QuF9840-29 4.48*** 9.48*** 8.81*** 9.78*** 2.50* QuF9P1-20 4.97*** 9.93*** 7.87*** 13.05*** 4.33*** Temuko 2.45* 9.78*** 8.83*** 6.90*** 1.56 Titicaca 5.43*** 8.58*** 6.28*** 8.22*** 3.65*** 2017 Black 5.76*** 10.03*** 9.45*** 9.26*** 4.52*** Blanca 6.35*** 10.12*** 9.42*** 5.91*** 3.27** C0407 Dave 6.68*** 9.19*** 7.17*** 8.02*** 5.02*** Cahuil 6.06*** 9.12*** 7.15*** 5.60*** 1.54 Cherry Vanilla 6.87*** 9.97*** 7.87*** 8.69*** 4.43*** Isluga 4.36*** 9.69*** 8.88*** 8.90*** 3.09**

KU-2 7.35*** 9.72*** 9.18*** 6.02*** 4.83*** Kaslaea 7.20*** 9.63*** 8.20*** 7.85*** 8.11*** Linares 3.25** 10.83*** 9.30*** 6.33*** 1.39 NL-6 7.09*** 8.83*** 9.30*** 7.48*** 9.38*** Puno 6.68*** 10.12*** 7.89*** 7.64*** 5.22*** QQ 74 7.50*** 9.76*** 6.76*** 8.81*** 5.80*** QuF19P39-51 5.58*** 10.63*** 9.65*** 8.00*** 4.25*** QuF9839-64 6.39*** 10.26*** 6.97*** 8.83*** 4.80*** QuF9839-72 4.10*** 10.08*** 9.65*** 6.02*** 0.73 QuF9840-29 5.32*** 9.90*** 9.11*** 7.52*** 3.29** QuF9P1-20 6.20*** 10.79*** 8.06*** 8.94*** 5.54*** Temuko 4.80*** 10.22*** 9.19*** 6.92*** 2.71** Titicaca 6.68*** 8.98*** 6.58*** 6.66*** 4.30*** PE: percent emergence; DF: days to flowering; DM: days to maturity; PH: plant height; GY: grain yield. Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001.

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Table 5. Mean data of quinoa varieties across the years 2016 and 2017 for each trait in Musanze and Kirehe Variety PE (%) DF (day) DM (day) PH (cm) GY (kg/ha) Name/Year Musanze Kirehe Musanze Kirehe Musanze Kirehe Musanze Kirehe Musanze Kirehe 2016 Black 53 72 48 43 133 129 117 70 1136 307 Blanca 53 78 47 42 131 141 75 54 479 479 C0407 Dave 69 90 38 43 101 91 100 71 1186 545 Cahuil 76 99 47 41 107 96 69 44 180 332 Cherry Vanilla 75 99 49 42 111 114 108 63 1308 582 Isluga 43 59 49 44 126 110 90 47 701 254 Kaslaea 80 79 48 43 118 118 98 53 1317 604 KU-2 73 89 47 42 128 127 68 36 667 351 Linares 20 46 50 44 131 133 80 46 227 142 NL-6 71 84 45 42 123 130 84 55 679 672 Puno 62 81 51 42 111 110 95 43 1036 532

118 QQ 74 81 100 48 41 98 90 112 77 1580 832 QuF19P39-51 36 46 52 45 135 135 89 55 467 429 QuF9839-64 73 70 50 44 101 91 110 60 1408 498 QuF9839-72 35 50 50 44 138 130 80 57 167 245 QuF9840-29 53 78 49 43 131 125 87 71 529 392 QuF9P1-20 85 33 51 45 111 125 115 98 1233 50 Temuko 30 40 51 43 133 123 69 35 385 136 Titicaca 62 96 43 41 96 85 75 57 745 629 Mean 59 73 48 43 119 116 90 57 812 422 LSD (p < 0.05) 8 1 5 7 187 2017 Black 60 75 48 44 135 126 122 99 1308 535 Blanca 68 80 50 42 140 115 69 81 694 891 C0407 Dave 74 79 43 43 104 94 100 97 1404 691 Cahuil 76 53 44 40 100 100 73 62 470 133 Cherry Vanilla 74 85 50 40 110 110 118 85 1283 521

Isluga 50 48 47 42 131 110 116 98 804 546 Kaslaea 89 66 47 41 114 116 98 94 2392 868 KU-2 80 89 47 43 130 126 76 70 1038 1288 Linares 40 30 55 42 130 130 88 57 414 138 NL-6 84 73 42 40 130 130 93 91 3004 528 Puno 76 74 51 40 111 110 101 82 1286 1065 QQ 74 81 91 48 42 98 88 117 92 1700 642 QuF19P39-51 55 79 52 46 135 135 86 123 434 2089 QuF9839-64 74 69 50 45 101 90 115 97 1412 521 QuF9839-72 54 31 50 42 138 130 82 58 210 89 QuF9840-29 53 75 49 42 131 120 83 112 607 1078 QuF9P1-20 76 58 51 50 111 117 117 97 1326 1208 Temuko 55 53 51 42 133 120 88 80 358 1171 Titicaca 87 51 43 40 96 85 75 97 1091 815 Mean 69 66 48 42 120 113 96 88 1118 780

119 LSD (p < 0.05) 8 1 5 7 234 PE: percent emergence; DF: days to flowering; DM: days to maturity; PH: plant height; GY: grain yield; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

Table 6. Pearson correlation for percent emergence (PE), days to flowering (DH), days to maturity (DM), plant height (PH), grain yield (GY) of quinoa in both Musanze and Kirehe Variables PE DF DM PH PE DF -0.58* DM -0.57* 0.46* PH 0.25 0.22 -0.27 GY 0.71*** -0.21 -0.34 0.59* *p < 0.05, *** p < 0.001

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Table 7. Analysis of variance with F value for emergence rate, days to heading, days to maturity, plant height, and grain yield for millet varieties grown in Musanze and Kirehe over two crop years 2016 and 2017. Years Effect DF PE (%) DH (day) DM (day) PH (cm) GY (kg/ha) Location 1 18.31*** 0.89 0.18 8.31** 36.22*** 2016 Variety 13 0.8 283.56*** 1392.68*** 57.73*** 1.69 G × E 14 2.94** 44.07*** 55.02*** 51.38*** 3.27*** Species 2 2.08 510.34*** 1764.45*** 174.79*** 1.97* Location 2.19 0.46 0.14 12.99*** 27.47*** 2017 Variety 13 1.13 106.42*** 778.31*** 15.74*** 2.24* G × E 14 1.12 46.76*** 50.18*** 22.51*** 2.92** Species 2 0.88 245.67*** 458.36*** 47.28*** 2.9* DF: degrees of freedom; PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; G × E: genetic × environment interaction. Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001.

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Table 8. Mean difference between Musanze and Kirehe for each millet variety of each trait: percent emergence, days to heading, days to maturity, plant height, and grain yield. Variety PE (%) DH (day) DM (day) PH (cm) GY (kg/ha) Name/Year 2016 4535 -1.84 14.05*** 8.99*** -2.16* -4.47*** Bei Huong -2.48* 3.77*** 8.87*** 2.79** -3.66*** Earlybird -5.30*** -7.90*** -6.18*** -10.60*** -4.68*** GR 658 -3.66*** -2.16* -3.76*** -6.97*** -3.22** GR 664 -3.66*** -3.18** -2.78** -4.48*** -2.68** GR 665 -3.21** -3.12** -3.15** -6.17*** -3.12** Huntsman -2.94** -3.87*** -3.33** -9.92*** -3.90*** I.Se.410-A -1.66 1.03 7.11*** 6.69*** -4.70*** Long Gu -1.66 0.58 7.96*** 3.01** -4.55*** Minsum -3.48** -3.87*** -4.79*** -10.23*** -4.76*** Sunup -2.48* -4.22*** -4.79*** -9.29*** -4.03*** Turghai -2.02* -4.52*** -5.39*** -7.17*** -3.84*** USSR -3.12** -3.66*** -4.91*** -5.83*** -3.00** Unikum -3.48** -5.06*** -4.79*** -9.58*** -4.27*** 2017 4535 0.7 14.33*** 8.62*** -0.98 -4.04*** Bei Huong -1.2 5.04*** 8.56*** 0.14 -3.47** Earlybird 0.59 -4.36*** -6.27*** -9.36*** -4.42*** GR 658 1.12 -1.03 -2.78** -6.25*** -2.79** GR 664 1.54 -1.91 -2.78** -3.38** -2.28* GR 665 0.8 -1.18 -3.46** -5.70*** -3.01** Huntsman 1.01 -1.84 -3.46** -7.00*** -3.75*** I.Se.410-A 1.54 11.40*** 7.27*** 2.79** -4.48*** Long Gu 0.7 3.62*** 7.30*** -0.03 -4.26*** Minsum 0.7 -1.03 -4.38*** -8.13*** -4.09*** Sunup 1.33 -2.14* -4.47*** -7.34*** -3.22** Turghai 1.54 -1.33 -4.72*** -5.15*** -3.23** USSR 2.38 -1.23 -4.55*** -4.92*** -1.87 Unikum 0.91 -2.04* -4.32*** -5.61*** -3.56** PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield. Significant level at (p < 0.05) while *p < 0.05, ** p < 0.01, and *** p < 0.001.

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Table 9. Mean data of millet varieties across the years 2016 and 2017 for each trait in Musanze and Kirehe Variety PE (%) DH (day) DM (day) PH (cm) GY (kg/ha) Name/Year Musanze Kirehe Musanze Kirehe Musanze Kirehe Musanze Kirehe Musanze Kirehe 2016 4535 93 99 106 101 178 170 75 74 15 127 Bei Huong 96 93 72 70 173 172 108 99 917 25 Earlybird 98 73 36 34 93 87 38 22 81 5 GR 658 95 85 57 50 108 100 60 42 1296 25 GR 664 100 83 48 50 108 108 77 54 1748 31 GR 665 94 89 51 49 108 105 60 48 1418 11 Huntsman 100 88 50 46 105 105 45 25 742 12 I.Se.410-A 100 96 71 58 167 160 138 116 35 19 Long Gu 95 99 63 60 167 167 111 99 177 12 Minsum 100 84 47 47 97 97 44 23 18 2 Sunup 96 93 48 45 97 97 47 29 628 11 Turghai 100 94 47 44 97 92 57 42 774 22 USSR 95 89 47 48 95 97 73 45 1504 19 Unikum 98 85 44 43 97 97 46 27 428 8 Mean 97 89 56 53 121 118 70 53 699 23 LSD (p < 0.05) 4 6 12 11 222 2017 4535 85 93 105 97 181 173 76 101 16 190 Bei Huong 88 80 70 69 180 173 102 99 634 51 Earlybird 91 89 34 40 97 85 53 26 101 36 GR 658 88 94 53 47 112 108 70 49 1062 40 GR 664 100 90 44 47 112 108 108 60 1329 55 GR 665 84 94 52 47 100 108 70 55 916 48 Huntsman 89 93 45 47 100 108 68 42 501 36 I.Se.410-A 88 96 70 100 170 167 132 112 57 41 Long Gu 88 91 63 65 170 167 106 95 202 31 Minsum 93 89 50 49 100 100 56 37 24 173 Sunup 90 94 42 47 99 100 67 39 423 229 Turghai 85 98 50 47 100 97 67 62 441 217 USSR 100 95 45 50 97 100 88 54 1278 205 Unikum 88 93 43 47 107 97 74 54 309 186 Mean 90 92 55 57 123 121 81 63 521 110 LSD (p < 0.05) 3 7 12 10 155 PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

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Table 10. Pearson correlation for percent emergence (PE), days to heading (DH), days to maturity (DM), plant height (PH), grain yield (GY) of millet in both Musanze and Kirehe Variables PE DH DM PH PE DH 0.20 DM 0.13 0.88*** PH 0.38 0.69* 0.88*** GY 0.12 -0.32 -0.35 -0.12 *p < 0.05, *** p < 0.001

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Table 11. Mean data comparison of millet species across the years 2016 and 2017, and locations, Musanze and Kirehe for each trait percent emergence, days to heading, days to maturity, plant height and grain yield PE DH DM PH Scientific Name Common Name (%) (days) (days) (cm) GY (kg/ha) 2016 Eleusine coracana Finger millet 96 a 103 a 174 a 74 b 71 b Panicum miliaceum Proso millet 92 a 46 c 100 c 45 c 439 a Setaria italica Foxtail millet 97 a 66 b 168 b 112 a 197 b LSD (p < 0.05) 6 2 2 5 212 2017 Eleusine coracana Finger millet 89 a 101 a 177 a 88 b 103 b Panicum miliaceum Proso millet 91 a 47 c 103 b 61 c 380 a Setaria italica Foxtail millet 89 a 72 b 168 a 106 a 171 b LSD (p < 0.05) 4 3 4 7 143 PE: percent emergence; DH: days to heading; DM: days to maturity; PH: plant height; GY: grain yield; LSD: least significant difference. LSD comparisons are significant at the 0.05 level.

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Figure 1. Elevation from mean sea level and spatial variations of mean annual rainfall of locations where quinoa and millet trials were conducted in Rwanda (marked by black stars). Source (Muhire et al. 2015)

126

Figure 2. Mean monthly temperature (°C); and total monthly precipitation (mm) (average for 1950 to 2000) in Rwanda. Monthly temperature does not change much over the year, but monthly precipitation follows seasonal pattern with two distinct dry seasons Source: www.worldclim.org

127

Figure 3. Soil organic matter (%) and soil pH of locations where quinoa and millet trials were conducted in Rwanda (marked by black stars). Source: www.worldclim.org

128

CHAPTER THREE

THE DEARTH OF DIVERSITY AND AUTONOMY: SMALLHOLDERS

PERCEPTIONS OF CROP INTENSIFICATION PROGRAM

IN RWANDA

Abstract

The crop intensification program (CIP) was introduced in Rwanda in 2007 by the

Ministry of Agriculture and Animal Resources (MINAGRI), as a solution to land fragmentation, low use of agricultural inputs and low access to extension services. The aim was to move from subsistence agriculture to market-oriented agriculture. However, the implementation of the CIP fails to draw lessons from previous Green Revolution experiences in terms of its effects on social inequality, on ecological sustainability, and knowledge exchange and creation. Due to the voluntary nature of farmers' participation and their reluctance to participate, this study aimed at assessing the factors that influence their participation and the challenges they have encountered since the implementation of CIP. Data were collected from 50 in-depth interviews with farmers and local leaders in Nyamaguba Sector, Musanze District Northern Province and Nyamugari

Sector, Kirehe District, Eastern Province. The respondents argue that the participation in the CIP was hindered by inadequate irrigation and mechanization facilities, lack of farmers' participation in the CIP planning process, inadequate extension services, and lack of access to agricultural inputs and post-harvest technologies. The respondents also reported several financial and socio- economic concerns associated with growing crops under the CIP model: specifically, maize.

They reported difficulties with 1) the high cost of buying seed and fertilizers associated with growing maize 2) the lack of a market for maize, 3) difficulty with storage and 4) theft of maize

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in fields leading to conflict in communities. Based on these results, it appears that closer collaboration between farmers, local leaders, extension agents, and agricultural service providers could enhance participation in the program. Empowering farmers should not only be done by top-down mechanisms (one-way transfers of technologies and management systems) but also requires the active involvement of the community (bottom-up) including the incorporation of local farming knowledge, the environmental aspects of agriculture, and recognition of the social and cultural rights of farmers.

1. Introduction

Over the past decade, development policies across Sub-Saharan Africa (SSA) re-centered smallholder agriculture as a potential engine of economic growth. In hopes of catalyzing an

African Green Revolution (World Bank, 2007), major development actors (e.g., the World Bank, the United States Agency for International Development (USAID), and the United Nations

(UN)) and national governments have made substantial investments in agricultural intensification, (Jayne et al., 2010; Moseley et al., 2015). As in earlier Green Revolutions in

Mexico and Asia, contemporary agricultural intensification programs throughout SSA prioritize smallholders, aiming to encourage farmers to adopt high-yielding varieties of maize, rice, and wheat and to increase the application of agrochemicals, which these varieties often require to achieve optimal productivity (Jayne et al., 2010; Moseley et al., 2015). Africa’s Green

Revolution has thus far taken shape as a top-down enterprise, with governments and development agencies incentivizing and sometimes enforcing the increased adoption of novel agricultural technologies (Moseley et al., 2015; Clay, 2017; Gengenbach et al., 2018; Moseley et

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al., 2015). While multilateral research organizations, African regional non-governmental institutions, and African political figures may appear to be driving these changes, some critics consider the main impetus for the African Green Revolution to come from multinational companies, who stand to enrich themselves by supplying many of the inputs purchased by governments and farmers (Dano, 2007). Chambers (1989) also argues that the top-down approach to setting agendas and developing agricultural technologies, commonly practiced by the international research and development community, does not benefit the poor farmer. One of the arguments against this top-down model of agricultural development is that the conditions that poor farmers operate within put limits on the extent to which they can benefit from the transfer of modern technologies and management practices, such as those associated with the Green

Revolution (Chambers, 1989; Jayne et al., 2010; Gengenbach et al., 2018). The Green

Revolution did indeed raise productivity for specific, high-yielding crop varieties, but, arguably, not for the benefit of the poor who farm under very different conditions from middle and large- sized farmers and may have depended on traditional crop varieties due to dietary and environmental considerations (Chambers, 1989; Clay, 2017; Gengenbach et al., 2018).

Advocates of the new ‘Green Revolution for Africa’ argue that models of agricultural intensification based on off-farm industrial inputs such as improved seeds and chemical fertilizers may strengthen smallholders’ ability to increase yields and participate in national and international agricultural markets (World Bank, 2007). In short, they argue that African countries must catch up with the Green Revolution in other parts of the world, in order to both boost their productivity and address rural poverty. It is the World Bank’s opinion, that ensuring food security and the profitability of agriculture for African farmers will require a ‘revolution in

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smallholder farming’ (World Bank, 2007) based on the introduction of industrialized inputs such as improved seeds and chemical fertilizers and pesticides, distributed through private-friendly state interventions in input markets.

The stated goals of these intensification policies are to sustainably end hunger and malnourishment and develop agriculture as an engine of overall economic growth (Rashid,

Dorosh, & Malek, 2013; World Bank, 2007). However, Huggins, (2014) and Clay, (2017) argue that these policies can encourage patterns of land acquisition (‘land grabbing’) as well as contract farming arrangements. This can involve coalitions of foreign and domestic capital; and market- based accumulation of land by wealthy and politically-connected elites (land concentration).

Such contracts may be encouraged by the states, which sometimes use coercive mechanisms and interventionist strategies to incentivize agricultural investment, which may be explicitly linked to corporate interests (Dano, 2007; Huggins, 2014). Activities of international development agencies and corporations may become intertwined with those of the states and foreign capital, leading to changing relations between land and labor (Huggins, 2013; Huggins, 2014). Land grabbing’ is only one aspect of broader patterns of reconfiguration of control over land, labor, and markets in SSA. It is the state agencies that facilitate the integration of commercial actors into government-approved and state-controlled commodity chains (Jayne et al., 2010; Huggins,

2014). As with earlier Green Revolutions, land, water, and technology are identified as the limiting factors (Jarosz, 2014). Increased inputs (such as hybrid seed, chemical fertilizers, and pesticides) and agroengineering (terracing, draining wetlands, and irrigating fields) are thus positioned as essential to shore up ‘yield gaps’ and improve environmental sustainability by curtailing further expansion of agricultural land into biodiverse areas (Foley et al., 2011; Lobell et al., 2009; Tilman et al., 2002).

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With its Crop Intensification Program (CIP), Rwanda is an archetype of the New Green

Revolution in SSA. In 2007, the country was the first to sign a compact with the Comprehensive

Africa Agriculture Development Program (CAADP), a pan-African framework of agriculture-led growth. As one of the most densely populated countries in SSA, Rwanda faces high population pressure with more than 60% of farm households cultivating less than 0.5 ha (MINAGRI 2012).

Approximately 82% of the population is engaged in small-scale food production (NIS, 2010;

WFP, 2012). To address these issues, the Rwandan government (GoR) began to implement the

Strategic Plan for the Transformation of Agriculture (PSTA I&II) in 2004. Since 2004, PSTAs have systematically transformed rural landscapes and livelihoods through land reform and technology adoption (Clay, 2017; GoR, 2004; MINAGRI, 2012). This strategy aimed to stimulate the intensification of agricultural production to support economic growth and improve the livelihoods of rural Rwandans through the commoditization of agricultural production for regional and international export markets (MINAGRI 2004–2012).

Striving to become a middle-income country by 2020, the GoR aims to convert largely subsistence-based smallholders to professional farmers. With its Land Policy (since 2006) and

CIP (implemented since 2007 in pilot phases and countrywide since 2010), the GoR and development partners have formalized land tenure, drained marshlands, and constructed terraces throughout the country (Kathiresan, 2012). The CIP aims to increase the productivity of export- oriented crops by increasing access to hybrid seed and fertilizer, by augmenting agricultural extension services, and by compelling smallholders to consolidate landholdings with neighbors and cultivate crops selected by government authorities (MINAGRI, 2011). Crops under this

‘crop regionalization’ mandate (maize, rice, wheat, bean, soybean, Irish potato, and cassava)

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were selected via a national-level exercise that considered agroecological zones and the crops’ export potential (MINAGRI, 2012; Huggins, 2014). Contracts (imihigo) with higher administrative levels outline hectare targets on which to consolidate land and plant selected crops, assuring that national-level targets are enacted at local levels (Ingelaere, 2014). Routine audits at various levels further solidify the top-down nature of this program. This policy was enforced at the household level by the local authorities, and non-compliance could have serious consequences, including fines (Huggins 2013). To ensure that they meet government quotas for land in the CIP: local administrators threaten noncompliant farmers with fines, uprooting crops, or appropriating land (Cioffo, Ansoms, & Murison, 2016; Clay, 2017). In Kirehe District,

Eastern Province, and Musanze District, Northern Province, those who tried to continue their traditional practice of growing ‘banned’ crops were fined and had the ‘prohibited’ crops uprooted (Personal communication 2016). Farmers were required to plant monocrops of beans in one season and maize the other season according to a schedule defined by the government

(Huggins, 2014; Clay, 2017).

As a CIP report states, the program ‘‘encourages crop specialization to realize economies of scale and to orient the agricultural sector more towards the commercial market” (MINAGRI,

2012: 30). While increases in agricultural production and commercialization are seen to have played a role in driving Rwanda’s impressive economic growth (8 percent yearly from 2000 to

2012) and poverty reduction (EICV, 2015; World Bank, 2014), research also shows that these policies may exacerbate uneven land access (Dawson, Martin, & Sikor, 2016) and food insecurity (Clay, 2018b). Studies on the initial phases of the CIP found that smallholder autonomy has been constrained, especially for socioeconomically disadvantaged households

(Ansoms, Walker, & Murison, 2014; Huggins, 2014; Pritchard, 2013). Studies also demonstrate

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that smallholders’ ability to succeed with CIP-prescribed land-use practices is highly uneven varying according to agroecological conditions and pre-existing resource access inequalities

(Cioffo et al., 2016; Clay, 2017). In this study, we explored how these agricultural policies affected the supply of provision services, how they might affect system resilience, and if they expose farmers to higher risks associated with narrowly defined crop choice in Nyamugari sector, Kirehe District in Eastern Province, and Nyamagumba Sector, Musanze District in

Northern Province. Finally, we considered the impact of transitioning from a subsistence-based agricultural system to a market-based system in marginalized regions of Rwanda and how farmers perceived risks. This study is based on literature review as well as 50 interviews, most of them with farmers and local administrators, conducted between October 2016 and January 2017, and participant observations in Musanze and Kirehe Districts of Rwanda.

2. Traditional farming in Rwanda

Traditional agriculture in Rwanda combines farming and animal husbandry which largely depend not only on rain-fed agriculture but also on traditional practices using simple tools such as hoes and machetes (Clay et al. 1998; Sana engineering Plc, 2010). Traditional farming systems found in the Rwanda highlands are representative of highland agriculture in East Africa, where over 65 million people employ mixed cropping systems (Garrity et al. 2012). The diversity found within these systems is potentially more resilient to socio-ecological fluctuations

(Lin, 2011). Rwandan farmers have used their traditional knowledge to conserve their land resources and settled along the upper ridges of hillsides where soils are more fertile, and cultivation was a simpler task than on steeper slopes and in marshy valleys (Nwafor, 1979).

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European colonizers have long argued that Rwanda faces “overpopulation” and sought to introduce measures to counter the perceived high population densities in the country and reduce challenges affecting the agricultural system. In particular, emigration policies to neighboring countries were put into place under Belgian rule after World War II, and those policies were continued following Rwanda’s independence in 1962 (Mukamanzi, 1982). The redistribution of large populations under cash crop projects (the “paysannats” policy) in 1963, mostly in the southern part of the country was also developed. A major policy of dealing with population issues was to promote the increase of agricultural production, which was achieved during the

1970s through the expansion of traditional agriculture, namely the system of familial landholding and farming. Following the 1980s, other efforts have focused on family planning with the creation of the National Office of Population (ONAPO) in 1981(ONAPO, 1990a).

Rapid population growth in recent decades has caused several changes in traditional agricultural systems. In the past, Rwandan farmers could migrate in response to growing demographic pressure; they tended to move to the drier, eastern provinces, once the exclusive domain of the pastoralists. Today, however, in the absence of unoccupied lands, farmers cultivate the same holdings year after year and in increasingly intensive ways. According to May

(1993), Rwandan authorities have largely focused on agricultural intensification as a means of increasing production. However, this policy has led to a process of environmental degradation.

Key indicators such as the ratios of persons per cultivated and cultivable hectare, the size of farm holdings, and the numbers of plots per holding have all increased since 1970. According to

MINAGRI (2009a; b), in Rwanda food crops account for 92% of the total land cultivated while cash crops coffee and tea account for 6.3 and 1.6%, respectively. Farm households traditionally grow a diverse range of crops in both polycultures and sole crops, with bananas (Musa spp.),

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common beans (Phaseolus vulgaris L.), sorghum (Sorghum bicolor), buro (Eleusine coracana), maize (Zeas mays L.), sweet potatoes (Ipomoea batatas), and cassava (Manihot esculenta) as dominant crops (Voss, 1992). Varietal mixtures, or multiple varieties of a crop planted in the field together, were frequently used in Rwanda and allowed farmers to adapt to the unique ecological particularities of their farms (Voss, 1992).

3. Factors of change in agricultural reforms

The agricultural changes in recent years in Rwanda appear to be largely driven by three dynamics. 1) the ambitious development targets in the country’s ‘Vision 2020’ document necessitate that economic growth increases by an average of 13% per year for Rwanda to reach middle-income country status by 2020 (MINECOFIN, 2000). Agricultural commercialization and growth are identified as key elements of economic expansion. 2) Authorities are concerned with a perception of land scarcity, referring to the latter as a ‘time bomb’ (Sommers, 2006). The

GoR is particularly concerned about the negative effects of land fragmentation (MINITERE,

2004). Average population density is now around 519 people per square km (Worldometers,

2019), the highest in Africa. Per hectare yields are low, and the population growth rate remains very high. Landholdings are very small with more than 60 percent of households cultivating less than 0.7 ha, 50 percent cultivating less than 0.5 ha, and more than 25 percent cultivating less than

0.2 ha and food production does not keep pace with demand, requiring food import (ROR 2008;

MINAGRI, 2009). 3) The GoR maintains a discourse of national economic ‘self-sufficiency’ and intensely dislikes the various implicit and explicit ‘strings’ attached to international aid. In line with President Kagame’s exhortation for Rwandans to become ‘entrepreneurial’ (e.g., Kagame,

2009), the agricultural reform is designed to increase domestic and international investment in

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the farming sector to stimulate commercial activities. These concerns have been used to justify consolidation of farms and the multiplication of agricultural cooperatives through which the land-poor may access land. The agricultural sector has been given a high priority in the government’s planning for development. The current national thrust is for the sector to move from subsistence agriculture to commercial mode of production thought CIP. (IFAD, 2014,

MINAGRI, 2010).

4. Methods

This study uses qualitative social science methods to explore the on-the-ground impacts and perspectives related to these policies. In-depth, personal interviews were conducted in rural areas of Rwanda; Nyamugari sector, Kirehe District in Eastern Province, and Nyamagumba

Sector, Musanze District in Northern Province. These regions were selected because they were reported to have faced serious challenges during the implementation of CIP. Purposive sampling

(Patton, 1990) was utilized to identify informants willing to converse about their experiences with agricultural practices and its history. In order to hear a range of perspectives, this project focused on a sample of 25 informants in each location. The respondents were a mix of relatively elderly and middle-aged individuals. A semi-structured interview was carried out and was part of a survey that lasted approximately 45 min. Interviews were carried out during the timeframe from October 2016 to January 2017 by Cedric Habiyaremye. The interviews were conducted face to face in Kinyarwanda. A digital voice recorder was used during the interview, and both the confidentiality of the recording and the whole study were assured, and individual names were not recorded. Brief notes were also taken in addition to recordings, and key words were registered in the notes to ensure that any ambiguity or disturbance in the recording would not lead to

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misinterpretation of the information caused by incomplete or missing data. After the completion of interviews, recorded interviews were transcribed into Microsoft Word documents. No real names or any personal details were included in the final paper. The interviews lasted for about 30 min to 45 min. The respondents were fully aware at the whole time during the interview that their participation was voluntary and that they could terminate the interview at any time. The confidentiality and anonymity of the respondents’ identities and responses were guaranteed by the researcher. We asked farmers specifically about the challenges they had encountered since the implementation of CIP, their engagement in the decision-making process behind what to cultivate. Specifically, we queried farmers on the differences they felt existed between maize (a recently promoted crop) and millets which were traditionally grown in the region.

Data analysis included the interview notes (or transcriptions), field notes, and secondary printed materials. The particular focus was on the ways that respondents perceived the risks and choices of subsistence cropping strategies in Rwanda from 2007 to the present. The data was hand-coded according to themes and patterns in the manner elaborated by (Strauss & Corbin) with an eye towards identifying emerging trends, the full range of perspectives, and any other notable issues. As this study uses a qualitative methodological approach, the findings are framed around the different themes that emerged throughout the interviews and observations.

5. Results

5.1.Farmers’ perception on implementation of the National Agricultural Policy

Farmers argue that the GoR claims that policy was implemented in a participatory way and, it was not. 100% of farmers reported that they were not involved in the decision-making

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process to grow certain types of crops. According to the respondents of the 2016 interview, they all argue that there was a striking lack of field testing regarding the agricultural policy. The selection of crops for the regional crop specialization program was done following a short pilot program (Ansoms, 2009; Personal communication, 2016). The government claims that

‘approved’ crops were selected according to ‘the preferences of farmers’ (Republic of

Rwanda/United Nations, 2007). However, farmers argue that the crop intensification program was developed by state agencies, not organizations directly representing the interests of farmers

(Anonymous, 2007; Personal communication 2016). Our interviewees also argued that

Policymakers have few institutional or personal connections to rural development issues, and many tend to have a condescending or even disdainful attitude to poor smallholders practicing

‘traditional’ forms of agriculture (Ansoms, 2009; Personal communication, 2016). For example, farmers reported that since the program started, failed crops of maize took place in the Eastern province, primarily due to water scarcity exacerbated by rainfall shortages. We argue that understanding how water scarcity affects maize production can strengthen the adaptation capabilities to policy makers and farmers. Drought tolerant and /or resistant crops can generate higher yield in these areas than maize.

Rwandan civil society, which includes many rural development and farmers’ organizations, has little capacity to influence policy and has been largely reduced (through state practices of cooptation and control) to a role of ‘policy implementation. During the interview, farmers stated that agronomists working for one of the largest farmers’ organizations sometimes describe themselves as working ‘for’ government programs such as the CIP. Their methods are completely intertwined with state designs and activities. Smaller organizations have offered some criticism of policy implementation, but are constrained by the authoritarian context

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(Gready, 2010; personal communication 2016). The results from the interviews indicated that in

Rwanda, CIP is criticized for not having considered the fact that most of the small-scale farmers do not have enough means to diversify the source of income to buy other needed foodstuffs not produced under the regional crop specialization program and land use consolidation scheme. In addition, its integration with other relevant policies appears not to have been fully considered to allow an evaluation of possible side effects of the new farming approach before its implementation concerning the environment and livelihoods of farmers. Farmers also reported several financial and socio-economic concerns associated with growing crops under the CIP model: specifically, maize. In particular, they reported several difficulties with regards to cultivating maize: 1) the high cost of buying seed and fertilizers associated with growing maize

2) the lack of a market for maize, 3) difficulty with storage and 4) the theft of maize in fields that lead to conflict in the communities.

5.2.The high cost of buying seed and fertilizers associated with growing maize

Input use involves the disbursement of cash by the farmers. This may be the explanation behind the introduction of subsidies by the government. Nonetheless, the sustainability of these subsidies on inputs is questionable. Farmers argued that the government specifies hybrid seed varieties to be planted by farmers who are in some cases provided for free by state and para- statal organizations but must usually be purchased. This represents an increased investment by farmers and a potential reduction in local agri-diversity. Farmers interviewed complained about the cost of fertilizer. Increased use of inorganic fertilizers is also a key element of intensification.

The government aimed to increase national annual fertilizer use from less than 5,000 metric tons

(MT) in 2005 to 56,000 MT by 2012 (Morris et al., 2009). Initially, the GoR subsidized

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inorganic fertilizer so that the cost to farmers is about 50% of market value; but will phase out subsidies from 2012 onwards (MINAGRI 2010a: 14; MINAGRI, 2011). In parts of Rwanda such as Kirehe and Musanze Districts where this interview was conducted, local officials oblige farmers to purchase fertilizer, whether they want to or not. Farmers are forbidden to sell fertilizer or apply to crops other than those approved by the government and have been arrested for selling it (Personal communication, 2016). Farmers also reported that since CIP, they are so dependent on fertilizer application for yield. One farmer reported: “These days nothing grows in the soil without applying fertilizer. Chemical fertilizers have destroyed our soils, and it is not easy to fix the damage” (Personal communication 2016).

The seven crops selected by the government since 2006 for regional crop specialization were maize, rice, wheat, beans, soybean, Irish potato, and cassava (MINAGRI, 2012). Each administrative sector (an administrative unit smaller than the district, larger than the cellule) typically specializes in only three of these. Households must dedicate a proportion of their land – often all of it – to these crops and those who do not may be punished (Personal communication,

2016). The interviewed farmers reported that in Kirehe District, Eastern Province, and Musanze

District Northern Province those who tried to continue their traditional practices of growing

’banned’ crops were fined and had the ‘prohibited’ crops uprooted (Personal communication,

2016). Farmers discussed the benefits of growing traditional millets. Informants point out that millets were easier to grow with less water and less need for fertilizer input than maize. This meant it was a cheaper crop. They could also save and re-use seed. They also reported feeling hungrier after eating maize compared the traditional millet and complained that the less diverse diet introduced following CIP reforms led to worse nutrition.

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5.3.The lack of a market for maize

This had impacts on other parts of life, and many farmers reported that they could no longer afford school fees due to lack of return investments in growing maize. Farmers also discussed that there was an in-country market for millet produce and that many secondary products could be made from millets such as beer and alcohol, which helped farmers market it as a commodity. The farmers reported that, even though they were asked to grow maize intensively, despite the investments they make in buying inputs to grow maize, they struggle to get access to the market and sometimes the yield end up being lost due to lack of appropriate post-harvest and handling facilities and equipment. The interviewed farmers reported that they do not have a ready market; they sell their produce to middlemen who buy at prices as they set, which are not profitable to the farmers. The results from our interview indicated that smallholder farmers do not have bargaining power in this scenario. Even though the national thrust is to move from subsistence agriculture to market-oriented agriculture, based on the results from our interviews, we argue that rural-based agricultural financing, contract farming, insurance, storage, processing, and marketing have been overlooked. These constraints if not addressed, they will continue to make many smallholders suffer due to the accumulation of debts associated with input purchasing, and these debts are a big burden for farmers to overcome. Farmers should be given the right to grow crops of their choice, and there should be a development of the well-intended market; an equitable market that fit the needs of the poor.

5.4.Difficulty with storage

We queried farmers on the differences they felt existed between maize (a recently introduced crop) and millets which were traditionally grown in the region after the

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implementation of CIP. 100% of farmers reported that maize requires a lot of work and time commitment in post-harvest and handling compared to millet. Official statistics also suggest that between 20 percent and 30 percent of the farm produce is being lost during the post-harvest period. According to the numbers stated by farmers, around or more than 60% of the harvest for some cooperatives can be lost in one season due to the lack of drying and storage facilities.

Farmers added that maize grains are difficult to store; the produce does not stay longer without getting harmed, which makes it hard for them to have enough to eat at their homes and to meet the quality required by agro-processing factories. For example, the quality of maize needed by most of the agro-processing firms including Africa Improved Food (AIF) and MINIMEX, two major local maize procession companies the quality required is grade one (Aflatoxin-free). With the lack of proper handling of maize grain, farmers produce will continue to be too substandard for the market and household consumption. This can lead to the development of health problems associated with aflatoxin in maize.

5.5.Theft of maize in fields

The interviewed farmers reported alarming cases of maize theft in their community, which led to many conflicts, and they had requested that local leaders assist them in combatting that community crime. During the interviews, farmers reported that thieves had formed organized teams that raid maize fields at night, cutting down the entire field or snapping the ears.

As one of the precautionary measures, farmers in the community tended to guard their fields in the night from the time maize kernels are fully developed until harvest. Farmers argued that they have never experienced such crimes in other crops they grow or used to grow, potentially because stealing small-grained millets are far more difficult than snapping off ears of maize.

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5.6.The top-down model used in projects and program implementation

The results of the study refer to relevant issues and claims in the literature about

Rwanda’s agricultural policy and implementation. The results from our interview characterized the relationship between authorities and farmers in Rwanda as a largely top-down, state-centered governance approach, especially in regard to policy implementation. Farmers reported that they could not challenge the implementation of the program because they could face serious consequences. Our research suggests that a range of actions that could be taken to improve the situation. Above-all, the implementation of agriculture-related policies needs the full involvement of the farmers. This would require prior consultation with them to hear their concerns and problems, seek their consent and take into account (to some extent) their wishes and local contexts before designing solutions and action steps. Farmers need to know and understand that they are first stakeholders and primary beneficiaries rather than being viewed as

'always ready-actors' often requested to put into practice whatever is decided by authorities. All stakeholders who participate in agricultural policy development and implementation should reconsider externally imposed policy implementation in order to enable farmers to understand and act in their best interests and the interests of their communities and the larger civil society.

Most studies of the Rwandan agricultural sector overlook this coercive element (such as Booth &

Golooba-Mutebi, 2012), even though concerns have been raised over several years (see, e.g., Des

Forges, 2006; Musahara and Huggins, 2005; Ansoms, 2009; Huggins 2009). An institutional culture of paternalism and ‘enforcement’ in agricultural extension dates back to colonial practices of forced commodification of agricultural labor (notably the coffee sector) and,

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arguably, in pre-colonial practices (Newbury, 1988) and appears to be one of the largest obstacles to achieving a healthy and sustainable agricultural sector.

6. Conclusion

The government of Rwanda has launched an ambitious and well-financed agricultural reform in response to structural constraints which made a business-as-usual approach untenable.

Despite some initial technical and logistical problems in many areas, the reform has already led to significant increases in agricultural yields for government-approved crops. Based on our interviews with farmers in key regions, the question remains whether or not an increase in yield has led to an improvement in farmer livelihoods and well-being. These reforms may cause highly differentiated socioeconomic and socio-political effects at the local level. The GoR’s efforts to intervene in commodity chains by ‘linking’ agribusiness interests to producer cooperatives could under some circumstances provide mutual benefits to producers and those higher up the value chain. However, we argue that the CIP fails to draw lessons from previous Green Revolution experiences in terms of its effects on social differentiation, on ecological sustainability, and knowledge exchange and creation. The policy of regional crop specialization, in tandem with a policy of ‘encouraging’ smallholders to join producer cooperatives that are often implemented coercively, restricts the ability of farmers to make their own decisions regarding crop choice, investment in inputs, and marketing. Interviews with farmers indicated significant degrees of dissatisfaction and an emerging counter-government narrative of smallholders being reduced to

‘working for the state.’ This has also raised concerns on the sustainability of CIP in the long run.

In the long run, intensification driven by chemical fertilizers and pesticides could strongly reduce soil fertility and organic matter. Like some farmers reported that: “These days nothing grows in the soil without applying chemical fertilizer. Chemical fertilizers have destroyed our soils, and it

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is not easy to fix the damage” (Personal communication 2016). CIP programs explicitly recognize soil protection as an important measure to preserve soil fertility, but the GoR is much more ambiguous in recognizing the importance of sustainable agriculture for long run land productivity. A CIP target mentions the use of the fertilizer but not the use of organic fertilizers.

MINAGRI is currently implementing programs to strengthen sustainable agriculture, but there is not yet a clear national strategy for sustainable agriculture. MINAGRI estimations say that sustainable practices within the land husbandry, water harvesting, and hillside irrigation project would be very profitable. The CIP should incorporate sustainable management practices to balance short term food security needs and long-term soil fertility targets: many of these targets might be better met by allowing farmers the autonomy to return to traditional cultivation systems like millet. A revision of the CIP to include the support of measures to preserve soil organic matter would reconcile the Rwanda short term needs of food security and long-term need for soil productivity. Therefore, it is time for policymakers to listen to farmers who have developed cultivation systems that have sustained them for millennia in this environment and to listen to their concerns.

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