Genotypic and phenotypic diversity in Ethiopian barley (Hordeum vulgare ssp hordeum) farmer varieties based on morphologic and molecular markers

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Genotypic and phenotypic diversity in Ethiopian barley (Hordeum

vulgare ssp hordeum) farmer varieties based on morphologic and

molecular markers

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Academic year

2021

Doctoral program in Agrobiodiversity

Genotypic and phenotypic diversity in Ethiopian barley (Hordeum

vulgare ssp hordeum) farmer varieties based on morphologic and

molecular markers

Candidate

Basazen Fantahun

Supervisor

Professor Mario Enrico Pè

Tutor

Professor Matteo Dell' Acqua

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Basazen Fantahun Lakew

Submitted to the faculty of the graduate school

Scuola Superiore Sant'Anna, Pisa

In the requirements for the degree of DOCTOR OF PHILOSOPHY

(Agrobiodiversity: Plant Genetic Resources)

March, 2021

Pisa, Italy

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ACKNOWLEDGEMENTS

Thanks to God, the LORD OF OUR FATHERS, the son of VIRGIN MERRY, for his salvation, unconditional love, care and nourishment all the way through my early days until where I am now. I strongly believe he will glorify the rest chapters of my life with his blessings. Regarding this Ph.D study I am too much indebted to my supervisor professor Mario Enrico Pè, for his patience, directions, sharpening critics, encouragement and guidance both during the courses and the research work. Had it not been for his support, understanding and willingness to help, I may not have this work done. You shaped me to a better personality my supervisor, my sincere gratitude. Professor Matteo Dell’ Acqua, my tutor, I will take this opportunity to thank you from the bottom tip of my heart for the all-round support you provided me with. Most importantly, you were so valuable in helping me manage my data both phenotypic and molecular. You were pretty courteous, friendly and prone to assist. I took note of quite many things for which I was new, thank you once again. Dr. Carlo Fadda it was you who paved the way to this accomplishment through Bioversity-EBI partnership projects with which I had, and off course hope to have, the opportunity to work with you. I am very much sincere to the Italian government for the provision of the Ph.D scholarship through Scuola Superiore Sant’Anna. It is now worth mentioning the contribution of Bioversity International Addis and Ethiopian Biodiversity Institute for hosting the expenses for the field work and providing me with study leave in the same order. My colleagues, who especially supported me during the execution of the field experiments both in my presence and absence, Tesfaye Woledesemayat (Bioversity-EBI project coordinator), Fitsume Sileshi, Mebet Tadele and Abebe Geberemarkos, thank you very much. I extend my deepest gratitude “Enate” (Mama), Etaferahu Bekele, the endless scarification extreme in my life, wish to have many more years of your company. My beloved wife, Meron Alemayehu, women of alert, you are pack of all the solutions for all the up and downs in our family life. I do not think I can be here without your encouragement. Nahusenay and Hawaz Basazen as I have been kept on saying it ever since the very first date I have you, you are my reason to live. May the glory of the FATHER, the love of the SON and the peace of the HOLLY SPIRIT fall up on you.

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Acronyms

ANOVA Analysis of variance

BLUPs Best Linear Unbiased Predictors

CSA Central Statistical Agency

DP Diversity panel

EBI Ethiopian Biodiversity Institute

FAO Food and Agriculture Organization

FPT Farmers preferred traits

GWAS Genome wide association study

IBD Identical by descent

IBS Identical by state

MAS Marker assisted selection

MTA Marker trait association

PCA Principal component analysis

QTL Quantitative trait loci

QTN Quantitative trait nucleotide

REML Restricted maximum likelihood

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General Introduction

Barley (Hordeum vulgare L.) was recognized to be among the founder crops together with emmer wheat, einkorn wheat, lentil, pea, flax, chick pea and bitter vetch that initiated agriculture in the old World (Zohary, 1999). It has a long standing story of domestications which dates back to early pre pottery Neolithic sites (Badr et al., 2000; Zohary et al., 2012). The domestication through which barley has gone through is an on-going debate among scientists. The founder effect, domestication traits and species diversity are the evidences taken into account to assess the domestication event(s) crops has gone through. The founder effect highlights the richer genetic polymorphism in the wild progenitor and this level of richness was largely lost during the domestication event, the crop is a result of single domestication event. Mutations that triggered the so-called domestication syndrome are considered the genetic evidence for a single domestication event in many crops, if it involves the same major genes, multiple domestication is invoked otherwise (Zohary, 1999).

In cereals the development of tough rachis (a stalk that bears the seeds), free threshing grain and non-shattering phenotype; reduced dormancy in pulses have been identified as the major crop domestication traits (Allaby, 2015; Haas et al., 2019). More specifically, three selected traits for non-brittle rachis, six rowed spike and naked caryopsis were the key traits in barley domestication (Salamini et al., 2002). Two tightly linked genes (btr1 and btr2) govern rachis non-brittleness in barley (Pourkheirandish et al., 2015). The six-row spike phenotype was revealed to be controlled by a recessive vrsl gene located on chromosome 2HL (Lundqvist et al., 1997). A single locus, nudum, harboring a single gene that controls the naked caryopsis, nud, is located on the long arm of chromosome 7H (Fedak et al., 1972; Pourkheirandish and Komatsuda, 2007; Taketa et al., 2008). A single domestication event that took place 10,500 to 10,100 calibrated years before present (calBP ) in the Fertile Crescent from the wild progenitor Hordeum vulgare spp spontaneum has been a predominant hypothesis until recent years (Harlan and Zohary, 1966). According to this hypothesis, barley has a monophyletic evolution and the wild progenitor was domesticated only once. A tightly linked marker to the recessive gene nud was subjected to molecular analysis and the results obtained

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sustained the monophyletic hypothesis (Taketa et al., 2004). A separate experiment that involved 317 wild and 57 cultivars based on 400 AFLP polymorphic loci reinforced the monophyletic evolution of barley (Badr et al., 2000). Similar results were observed on the analysis of on chloroplast DNA extracted from 11 wild barley accessions collected from Israel, Jordan and Morocco and 9 cultivated barley. Interestingly, it was found that the wild barley group exhibited polymorphism in the restriction sites and out of the three chloroplast lineages observed only one was retained in the cultivated group (Clegg et al., 1984). The monophyletic origin of barley from the wild progenitor Hordeum vulgare subsp spontaneum was also seconded by other authors (Pakniyat et al., 1997; Nevo, 2006).

Alternative to this hypothesis of monophyletic evolution, the wild progenitor of barley could have been domesticated more than once both in space and time was proposed (Zohary, 1999). Among the founder crops, following the discovery of two non-brittle rachis btr1 and btr2 (which are independent recessive genes), it was only for barley that has been argued to have undergone multiple domestication events (Takahashi et al., 1968). The finding of six rowed wild barley (Hordeum vulgare subsp. agriocrithon) (Aberg, 1938) in Tibet, supposed as an additional wild progenitor, was thought to be a turning point towards the domestication of barley outside the Fertile crescent, pointing again to a polyphyletic evolution. Subsequent scientific findings, however, reported that Hordeum agriocriton was a spontaneous hybrid between the wild progenitor Hordeum spontaneum and the cultivated six-row Hordeum vulgare (Zohary, 1959; Staudt, 1961) suggesting that cultivated six-six-row barley was domesticated from the two-rowed wild progenitor. The underlying mechanism behind this was a mutation that induces loss of function of Vrs1allele. This is a protein suppressor of the development of lateral rows and dictates the development of six-rowed phenotype from two-rowed wild progenitor (Komatsuda et al., 2007; Dai et al., 2012). As opposed to the hitherto debate on the barley monophyletic and polyphyletic evolution, a new scenario has been recently proposed. This new scenario suggest the cultivated barley as a mosaic product resulted from several wild relatives with unequal contributions, identified across the genome (Allaby, 2015; Poets et al., 2015; Pankin et al., 2018). The debate on barley’s evolution has acquired an additional reason to continue.

Cultivated barley (Hordeum vulgare ssp vulgare) belongs to the grass tribe Triticeae comprising the genus Hordeum, which is characterized by spiked inflorescence as compared to the panicle inflorescence of most grasses in the tribe. Hordeum is a medium-sized genus known to comprise 33 species and 45 taxa including the cultivated barley and is widely distributed across the World.

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Inflorescence with fertile central and sterile lateral floret in the two-rowed, both central and lateral fertile floret in the six-rowed comprising three single flowered spikelet at each rachis node, uniquely characterizes the floral part of the genus (Graner et al., 2003; Blattner, 2009, 2018). Hordeum is known to exhibit high level of diversity and it comprises both annual species, with pronounced inbreeding, and a majority of perennials, with self-incompatibility system. The genus is based on X=7 chromosome and cultivated barley including many other species are diploid with 2n=2x=14. Tetraploid (2n=4x=28) and hexaploid (2n=6x=42) species exist in the genus (Bothmer et al., 2003; Brassac and Blattner, 2015; Blattner, 2018). The barley gene pool is a cradle of valuable alleles in light of the improvement of the crop (Bothmer et al., 1995). Cultivated barley including breeding lines, cultivars, and farmers’ varieties and the wild progenitor Hordeum vulgare ssp spontaneum constitute the primary gene pool. The closest relative of the primary gene pool is Hordeum vulgare ssp bulbosum, the only species in the secondary gene pool. Lower fertility rate was observed in crosses between H. bulbosum and cultivated barley (Blattner, 2018). Higher fertility rate between these two species can be achieved by keeping the hybrid at lower temperature (Pickering, 1984) or using embryo rescue technique (Pickering, 1983). All the other wild Hordeum species are considered as the tertiary gene pool. Species in the tertiary gene pool holds traits for pathogen resistances and adaptations to extreme environmental conditions, which are of high value, if they can be transferred into cultivated barley. However, strong hybridization barriers and low chromosome pairing in hybrids with the cultivated barley restricted the utilization of these species in the barley breeding programs (Bothmer et al., 1995; Blattner, 2018).

Barley is an annual diploid 2n = 2x = 14 whose haploid genome has a complexity of about 5.3 Gb (Dai et al., 2014; Hisano et al., 2016) of which 80% is composed by repetitive elements (Sreenivasulu et al., 2008; Mayer et al., 2012; Mascher et al., 2017). The presence of genetic diversity for yield and yield components, resistance against diseases and abiotic stress tolerance, is crucial for the maintenance and enhancement of productivity. Genetic diversity also gives the chance to exploit the genetic resources depending on their adaptability to a wide range of microenvironments. These environments may vary in such characteristics as edaphic, climatic and geographic features (Ellis et al., 2000; Govindaraj et al., 2015). Adequate knowledge of the available genetic diversity and its distribution significantly affects parental choice with diverse genetic background and which in turn affects crop improvement strategies (Dávila et al., 1998). The barley genome evolution has been

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shaped by a prolonged gene flow between the wild progenitor and the cultivated species for thousands of years and this determines the high genetic diversity generally observed in barley genetic resources (Poets et al., 2015).

Barley is classified in spring or winter types, depending on its ecological requirement (Baik and Ullrich, 2008). Kernel row number, which is governed by the Vrsl gene, located on chromosome 2H, is a major source of variation among barley genotypes. The dominant Vrsl allele produces the two-rowed phenotype and the recessive vrsl produces the six-two-rowed phenotype and hence responsible for about a threefold yield advantage over the two row-phenotype (Pourkheirandish and Komatsuda, 2007). There also exist irregular variants for kernel row-type, which are characterized by randomly missing fertile lateral spikelet (Graner et al., 2003). The most exclusive variation among the barley genotypes, not observed in any species of the tribe Triticeae, is hulled versus naked caryopsis. The naked caryopsis is a character governed by a single recessive allele of the nud gene, located on the long arm of chromosome 7H (Fedak et al., 1972; Pourkheirandish and Komatsuda, 2007). Barley is also characterized by variation in quality traits such as malting quality, lysine and β-glucan content and other quality parameters (Baik and Ullrich, 2008).

a b c

Figure 1a.two-rowed barley spike b. six-rowed barley, c. seeds of naked barley.

Barley is known for its versatility in terms of adaptation though it tends to perform best in the highland areas. It is adapted to a wide range of agro-ecologies, from the arid regions of the Mediterranean to altitudes up to 4,500 m in the Himalayas (Bothmer et al., 1991). Barley is the fourth most important cereal grown in the World, after wheat, rice and maize, both in area coverage and production. Data referring to 2017 indicate that barley is grown over more than 47 million hectares, with a total production of more than 147 mil tones (FAOSTAT: http://www.fao.org/faostat/en/#data/QC accessed June, 25, 2019). More than one hundred countries produce barley all over the World and in the past

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ten years the production from the European countries takes the largest share (FAOSTAT: http://www.fao.org/faostat/en/#data/QC accessed June, 25, 2019). According the USDA estimate for the year 2019/20 barley’s production is expected to reach 156.12 million metric tons, with 12.8% increase (World Agricultural production.com, 2020).

According to Kislev et al. (1992) archaeological evidences suggests that the consumption of wild barley dates back to 17,000 BC, making barley among the first crops used in the human diet. Barley is a staple food in many regions of the World, it is being used as an alternative source of animal feed and it is the main cereal for malting and brewing industries (Baik and Ullrich, 2008). There is a growing interest on barley for its health benefits that stems from the fact that it is rich in β-glucan. This important bio-chemical lowers blood cholesterol, has an anti-oxidant property and prevents heart failure(Agostini et al., 2015). Barley is also an alternative food for type II diabetes (Sullivan et al., 2013; Yadav et al., 2015).

The cultivation of barley in Ethiopia is believed to commence some 5000 years ago (Gamst, 1984). Both in area of production and quantity in Ethiopia barley is the fifth most important cereal crop after tef (Eragrostis tef), maize (Zea mays L.), wheat (Triticum aesvivum L. and Triticum turgidum) and sorghum (Sorghum bicolar L.). As a cool-season crop barley is well suited to high altitudes despite its versatility to adapt from <2000 to above 3000 m.a.s.l (Lakew et al., 1997), but barley fields can be found at as low as 1400 m.a.s.l and as high 4000 m.a.s.l. showing wide ecological plasticity. However, at altitudes above 3000 m.a.s.l, barley becomes the predominant source of food, beverages, animal feed, roof thatching and other necessities. Barley also grows best on well-drained soils and can tolerate higher levels of soil salinity than most other crops (Mulatu and Grando, 2011). Until the recent past, barley was being produced in two cropping seasons in one year, the short rainy season Bleg, between February and April, and the main rainy season Meher that spans between June and September. However, the recent climatic changes in Ethiopia limit the growing season to the main rainy season only in most barley growing areas of the country.

The introduction of new cultivars with higher yield has always been meant to boost production and therefore contribute significantly to the global food security. The dichotomy here is that a rapid and progressively higher coverage of these varieties has led to the replacement of the traditional varieties at an alarming rate, causing the reduction of the natural reservoirs of useful alleles the new varieties may not carry. In fact, even if this is not the only reason, it is the key factor for the replacement of old

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varieties and therefore emitting genetic erosion (van de Wouw et al., 2010; Van De Wouw et al., 2010; Sonnino, 2017). Hence, it becomes evident, the need to conserve genetic resources. Genetic resources are not conserved for conservation per se rather it encompasses their preservation, enhancement and sustainable utilization (Kasso and Balakrishnan, 2013). To this end, two main conservation strategies are in use Worldwide these days, ex situ and in situ conservation strategies (Dulloo et al., 2010; Kasso and Balakrishnan, 2013). Ex situ conservation mainly involves the conservation of biodiversity outside the natural habitat and comprises gene banks, field gene banks and collections maintained via in vitro or cryo-preservation. Whereas in situ conservation refers to the conservation of genetic resources within their habitat and includes on-farm conservation and conservation in protected areas. These two strategies are complementary in a sense that the in-situ conservation is dynamic and allows the evolutionary process to continue but is less accessible to breeders and other users. Ex situ is more of static but it allows easy access to different users (Jarvis et al., 2000; Kasso and Balakrishnan, 2013; Borner et al., 2014). According to FAO (2010) more than 7.4 million accessions of more than 165,000 plant species (Fu, 2017) are currently conserved in 1750 gene banks across the World of which the majority being duplicate. Only 1.9 to 2.2 million are reported to be distinct. The safety duplicate of the World crop genetic resources, the Svalbard Global Seed vault, which is managed by the Global Crop Diversity Trust and the government of Norway, holds more than 774,600 samples from 53 gene banks proportional to one-third of the distinct accessions of crops conserved in the gene banks globally (Westengen et al., 2013). Among cereals barley stand third for the number of accessions (466531) stored in gene banks after wheat and rice (FAO, 2010). In the Ethiopian national gene bank >16,000 barley accessions are currently stored. Ethiopian barley farmers’ varieties are reservoirs of high valued traits of global importance. This can be exemplified by the majority of north western European spring barley varieties having broad spectrum powdery mildew resistance locus that originated from Ethiopian farmers’ varieties (Jørgensen, 1992; Piffanelli et al., 2004). Besides, barley farmers’ varieties from Ethiopia were found to be the source of barley yellow dwarf virus resistance gene (Qualset et al., 1977; Beoni et al., 2016).

Before the advent of genetics and genomics, breeding was solely based on morphologic evaluations and recurrent selection on plant genetic materials and derived crosses. Despite the advantage of a relatively straightforward implementation of a field evaluation, selection cycle requiring no special equipment, breeding efforts based on phenotyping alone have been hampered by the limited specificity and resolution of morphological markers and by the genotype by environment interactions rendering

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plant performance inconsistent across seasons and locations (Jonah et al., 2011). Furthermore, especially with the identification of quantitative trait loci (QTL), the application of molecular markers improves the efficiency and precision of selection (Al-Abdallat et al., 2017). Molecular markers are of different groups and mainly categorized as low throughput markers, such as restriction fragment length polymorphism (RFLP), medium throughput markers, which includes random amplification of polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) and high throughput markers, which are basically SNP markers (Mammadov et al., 2012). Alternatively they are categorized as hybridization based, PCR based and DNA-sequence based markers (Govindaraj et al., 2015; Nadeem et al., 2018). In barley the application of molecular markers, particularly in genetic diversity studies, has been consistent in recent years see for instance (Muñoz-Amatriaín et al., 2014; Pasam et al., 2014; Bengtsson. et al., 2017).

The coupling of phenotypic information with sequence information has a positive impact on the capacity to develop superior varieties. Indeed, the characterization of allelic diversity of plant genetic resources undergoing field characterization enables the discovery of genes and molecular markers associated with desirable agronomic traits through QTL analyses (Mora et al., 2016; Wang et al., 2016) and genome wide association studies (GWAS) (Alqudah et al., 2020). The deriving information is then incorporated in breeding efforts to speed up the development of improved varieties via marker assisted selection (MAS) (Tanaka et al., 2019) or even biotechnological approaches. Following recent advances in DNA sequencing technologies, the degree of identifying high-density SNP genotypes meant for association mapping is increasing linearly. In this regard, GWAS, can be considered as a powerful tool to re-join the complex quantitative traits with their corresponding genes (Muñoz-Amatriaín et al., 2014). This tool has been used in mapping QTL for agronomic traits (Tondelli et al., 2013), drought tolerance (Jabbari et al., 2018), resistance to foliar diseases (Daba et al., 2019). Recent studies demonstrated that the traditional knowledge of smallholder farming communities may be integrated in GWAS efforts, increasing the capacity to identify genomic loci supporting varietal uptake in marginal farming systems (Kidane et al., 2017). An integrated system targeting both agronomic traits and farmer preference traits may speed up the production of new varieties addressing local farmer needs (Mancini et al., 2017). The determination of the genetic basis of agriculturally important traits, however, is not a straightforward task due to the complexity of the underlying molecular mechanisms and of their manifold interactions. Moreover, GWAS efforts rely on the faithful

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characterization of the genomic features of the genetic populations under study, as kinship and structure may confound allelic variation identical by state (IBS) from that identical by descent (IBD) (Falush et al., 2007), inflating the chance to identify false positives. Factors such as human or environmentally driven selection, genetic drift, mating system and growth habit can have an effect on the population structure (Flint-Garcia et al., 2003) and must be accounted for using genomic data. Despite the vast richness of barley genetic resources of Ethiopia, they are little characterized both with regards to their agronomic potential, their appreciation by local farming communities and their genetic diversity. The detailed characterization of Ethiopian barley farmer varieties both in molecular and phenotypic terms is fundamental for assessing the genetic diversity available in the country. This knowledge in turn would allow a rationalization of conservation strategies, and the consequent valorization of crop genetic resources in modern breeding strategies based on genomics. Altogether, these characterizations may lay the basis for a GWAS approach providing molecular tools to current and future breeding efforts targeting Ethiopian barely cultivation, but not only. With the current study we wish to contribute to this strategic aim by a thorough characterization of the genotypic and phenotypic diversity of a large collection of the Ethiopian barley genetic resources, mostly farmer varieties, representative of the different agro-ecological conditions of the country. In particular, the objectives of this study were the following:

1) to select and purify a core collection of Ethiopian barley genotypes;

2) to phenotypically characterize the core collection for phenology and agronomic traits; 3) to identify farmer’s preferences by means of participatory selection approaches;

4) to identify farmers’ varieties with good performance to be either directly distributed to farmers or to be used in breeding programs.

5) to identify marker trait association with the measured phenotypes and farmer scores using GWAS approaches.

Thesis organization

The thesis is organized in four chapters written in the form of the draft of a scientific article. The first chapter gives the background information about the subject under study and including the specific objectives. In the second chapter, I provided the detail phenotypic characterization of a barley diversity panel comprising 320 barley accessions, for 11 pheno-agronomic traits and traits based on farmers’

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preference, through participatory evaluation approaches. The third chapter discusses the field performance of the diversity panel for two major disease of barley leaf scald and powdery mildew. The fourth chapter deals with genome wide association studies that combines the phenotypic and genotypic data generated. Finally, the fifth and last chapter focuses on the general discussion of the hitherto provided chapters and future perspectives.

References

Aberg, E. (1938) , Hordeum agriocrithon nova sp., a wild six-rowed barley. Ann. Agric. Coll. Sweden, 6: 159-212 -, Ann. Agric. Coll. Sweden, 6: 159-212 . Available at: https://www.google.com.et/search?hl=en&biw=1366&bih=608&tbm=bks&ei=rRZVXueoLuno rgSc_rD4Ag&q=Aberg+%2C+1938%2C++Hordeum+agriocrithon+nova+sp.%2C+a+wwild+si

x-rowed+barley.+Ann.+Agric.+Coll.+Sweden%2C+6%3A+159-212&oq=Aberg+%2C+1938%2C++Hordeum+agriocrithon+n (Accessed: 25 February 2020). Agostini, S., Chiavacci, E., Matteucci, M., Torelli, M. and Pitto, L. (2015) ‘Barley beta-glucan

promotes MnSOD expression and enhances angiogenesis under oxidative microenvironment, J. Cell. Mol.Med.’, 19(1), pp. 227–238. doi: 10.1111/jcmm.12442.

Al-Abdallat, A. M., Karadsheh, A., Hadadd, N. I., Akash, M. W., Ceccarelli, S., Baum, M., Hasan, M., Jighly, A. and Abu Elenein, J. M. (2017) ‘Assessment of genetic diversity and yield performance in Jordanian barley (Hordeum vulgare L.) landraces grown under Rainfed conditions’, BMC Plant Biology. BMC Plant Biology, 17(1), pp. 1–13. doi: 10.1186/s12870-017-1140-1.

Allaby, R. G. (2015) ‘Barley domestication: The end of a central dogma?’, Genome Biology. Genome Biology, 16(1), pp. 15–17. doi: 10.1186/s13059-015-0743-9.

Alqudah, A. M., Sallam, A., Stephen Baenziger, P. and Börner, A. (2020) ‘GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley – A review’, Journal of Advanced Research. Cairo University, 22, pp. 119–135. doi: 10.1016/j.jare.2019.10.013.

Badr, A., Müller, K., Schäfer-Pregl, R., El Rabey, H., Effgen, S., Ibrahim, H. H., Pozzi, C., Rohde, W. and Salamini, F. (2000) ‘On the origin and domestication history of barley (Hordeum vulgare)’, Molecular Biology and Evolution, 17(4), pp. 499–510. doi: 10.1093/oxfordjournals.molbev.a026330.

Baik, B. and Ullrich, S. (2008) ‘Barley for food: characteristics, improvement, and renewed interest’, Journal of cereal science, 48, pp. 233–242.

Bengtsson., Åhman, I., Manninen, O., Veteläinen, M., Reitan, L., Alsheikh, M., Gertsson, B., Tuvesson, S., Jalli, M., Jensen, J., Backes, G., Krusell, L., Hjortshøj, R., Helgadóttir, Á., Göransson, M., Sveinsson, S., Jahoor, A. and Orabi, J. (2017) ‘Genetic diversity, population structure and linkage disequilibrium in Nordic spring barley (Hordeum vulgare L. subsp. vulgare)’, Genetic Resources and Crop Evolution, 64(8), pp. 2021–2033. doi:

(17)

10.1007/s10722-017-0493-5.

Beoni, E., Chrpová, J., Jarošová, J. and Kundu, J. K. (2016) ‘Survey of Barley yellow dwarf virus incidence in winter cereal crops, and assessment of wheat and barley resistance to the virus’, Crop and Pasture Science, 67(10), pp. 1054–1063. doi: 10.1071/CP16167.

Blattner, F. R. (2009) ‘Progress in phylogenetic analysis and a new infrageneric classification of the barley genus Hordeum (Poaceae: Triticeae)’, Breeding Science, 59(5), pp. 471–480. doi: 10.1270/jsbbs.59.471.

Blattner, F. R. (2018) Taxonomy of the Genus Hordeum and barley (Hordeum vulgare In: N.Stein and G.J. Muehlbauer(eds.). The Barley Genome, Compendium of Plant Genomes, The Barley Genome. Edited by N. Stein and G. J. Muehlbauer. Springer International Publishing. doi: 10.1007/978-3-319-92528-8.

Borner, A., Landjeva, S., Nagel, M., Rehman Arif, M., Allam, M., Agacka, M., Doroszewska, T. and Lohwasser, U. (2014) ‘Plant genetic resources for food and agriculture’, Genetics and plant physiology, 4(September), pp. 13–21. doi: 10.4337/9781781001660.00011.

Bothmer, R., Jacobsen, N., Baden, C. and Jorgensen, R. (1991) An ecogeographical study of the genus

Hordeum. 131 p, ISBN:9290432071. Available at:

https://www.cabdirect.org/cabdirect/abstract/19916779537 (Accessed: 13 December 2019). Bothmer, R. von., Jacobse, N., Baden, C., Jorgensen, R. and Linde-Laursen, I. (1995) An

ecogeographical study of the genus Hordeum (2nd edition) 129 p, ISBN: 978-92-9043-229-6. Bothmer, R. von, Sato, K., Knüpffer, H. and Hintum, T. van (2003) ‘Chapter 1 Barley diversity-an

introduction’, Developments in Plant Genetics and Breeding, 7(C), pp. 3–8. doi: 10.1016/S0168-7972(03)80003-8.

Brassac, J. and Blattner, F. R. (2015) ‘Species-level phylogeny and polyploid relationships in Hordeum (Poaceae) inferred by next-generation sequencing and in silico cloning of multiple nuclear loci’, Systematic Biology, 64(5), pp. 792–808. doi: 10.1093/sysbio/syv035.

Clegg, M. T., Brown, A. H. D. and Whitfeld, P. R. (1984) ‘Chloroplast DNA diversity in wild and cultivated barley: Implications for genetic conservation’, Genetical Research, 43(3), pp. 339– 343. doi: 10.1017/S0016672300026112.

Daba, S. D., Horsley, R., Brueggeman, R., Chao, S. and Mohammadi, M. (2019) ‘Genome-wide association studies and candidate gene identification for leaf scald and net blotch in barley (Hordeum vulgare L.)’, Plant Disease. American Phytopathological Society, 103(5), pp. 880– 889. doi: 10.1094/PDIS-07-18-1190-RE.

Dai, F., Chen, Z. H., Wang, X., Li, Z., Jin, G., Wu, D., Cai, S., Wang, N., Wu, F., Nevo, E. and Zhang, G. (2014) ‘Transcriptome profiling reveals mosaic genomic origins of modern cultivated barley’, Proceedings of the National Academy of Sciences of the United States of America, 111(37), pp. 13403–13408. doi: 10.1073/pnas.1414335111.

Dai, F., Nevo, E., Wu, D., Comadran, J., Zhou, M., Qiu, L., Chen, Z., Beiles, A., Chen, G. and Zhang, G. (2012) ‘Tibet is one of the centers of domestication of cultivated barley’, Proceedings of the

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National Academy of Sciences of the United States of America, 109(42), pp. 16969–16973. doi: 10.1073/pnas.1215265109.

Dávila, J. A., Sánchez De La Hoz, M. P., Loarce, Y. and Ferrer, E. (1998) ‘The use of random amplified microsatellite polymorphic DNA and coefficients of parentage to determine genetic relationships in barley’, Genome, 41(4), pp. 477–486. doi: 10.1139/g98-044.

Dulloo, M. E., Hunter, D. and Borelli, T. (2010) ‘Ex situ and in situ conservation of agricultural biodiversity: Major advances and research needs’, Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 38(2), pp. 123–135. doi: 10.15835/nbha3824878.

Ellis, R. P., Forster, B. P., Robinson, D., Handley, L. L., Gordon, D. C., Russell, J. R. and Powell, W. (2000) ‘Wild barley: A source of genes for crop improvement in the 21st century?’, Journal of Experimental Botany, 51(342), pp. 9–17. doi: 10.1093/jxb/51.342.9.

Falush, D., Stephens, M. and Pritchard, J. K. (2007) ‘Inference of population structure using multilocus genotype data: Dominant markers and null alleles’, Molecular Ecology Notes, 7(4), pp. 574–578. doi: 10.1111/j.1471-8286.2007.01758.x.

FAO (2010) ‘Chapter 3 The state of ex situ conservation’, The second report on the state of the world’s plant genetic resources for food and agriculture, pp. 54–90.

Fedak, G., Tsuchiya, T. and Helgason, S. B. (1972) ‘Use of Monotelotrisomics for Linkage Mapping in Barley’, Canadian Journal of Genetics and Cytology, 14(4), pp. 949–957. doi: 10.1139/g72-117.

Flint-Garcia, S. A., Thornsberry, J. M. and Buckler, E. S. (2003) ‘Structure of Linkage Disequilibrium in Plants’, Annual Review of Plant Biology, 54(1), pp. 357–374. doi: 10.1146/annurev.arplant.54.031902.134907.

Fu, Y. B. (2017) ‘The vulnerability of plant genetic resources conserved ex situ’, Crop Science, 57(5), pp. 2314–2328. doi: 10.2135/cropsci2017.01.0014.

Gamst, F. C. (1984) The Qemant : a pagan-Hebraic peasantry of Ethiopia. Waveland Press.

Govindaraj, M., Vetriventhan, M. and Srinivasan, M. (2015) ‘Importance of genetic diversity assessment in crop plants and its recent advances: An overview of its analytical perspectives’, Genetics Research International, 2015, p. 14. doi: 10.1155/2015/431487.

Graner, A., Bjørnstad, A., Konishi, T. and Ordon, F. (2003) The domestication of cultivated barley In: von Bothmer et al(eds) Diversity in barley Hordeum, ISBN: 0 444 505857. Amesterdam, Boston, Heidelber, London, New York, Oxford, Paris, San Diego, San Francisco, Singapor, Sydeny, Tokyo: Elsevier B.V.

Haas, M., Schreiber, M. and Mascher, M. (2019) ‘Domestication and crop evolution of wheat and barley: Genes, genomics, and future directions’, Journal of Integrative Plant Biology, 61(3), pp. 204–225. doi: 10.1111/jipb.12737.

Harlan, J. R. and Zohary, D. (1966) ‘Distribution of Wild Wheats and Barley’, Science, 153(3740), pp. 1074–1080, DOI: 10.2307/1719142.

(19)

Hisano, H., Tsujimura, M., Yoshida, H., Terachi, T. and Sato, K. (2016) ‘Mitochondrial genome sequences from wild and cultivated barley (Hordeum vulgare)’, BMC Genomics, 17(1). doi: 10.1186/s12864-016-3159-3.

Jabbari, M., Fakheri, B. A., Aghnoum, R., Nezhad, N. M. and Ataei, R. (2018) ‘GWAS analysis in spring barley (Hordeum vulgare L.) for morphological traits exposed to drought’, PLoS ONE, 13(9), pp. 1–22. doi: 10.1371/journal.pone.0204952.

Jarvis, D. I., Myer, L., Klemick, H., Guarino, L., Smale, M., Brown, a H. D., Sadiki, M., Sthapit, B. and Hodgkin, T. (2000) A Training Guide for In Situ Conservation On-farm Version 1. Internationala Plant Genetic Resources Institute, Rome, Italy. ISBN 92-9043-452-x. Available at: http://www.planttreaty.org/sites/default/files/training_guide.pdf.

Jonah, P. M., Bello, L. L., Lucky, O., Midau, A. and Moruppa, S. M. (2011) ‘Review : The Importance of Molecular Markers in Plant Breeding Programmes, ISSN: 0975-5896. Global Journals Inc. (USA)’, 11(5).

Jørgensen, I. H. (1992) ‘Discovery, characterization and exploitation of Mlo powdery mildew resistance in barley’, Euphytica, 63(1–2), pp. 141–152. doi: 10.1007/BF00023919.

Kasso, M. and Balakrishnan, M. (2013) ‘Ex Situ Conservation of Biodiversity with Particular Emphasis to Ethiopia ’, ISRN Biodiversity, 2013, pp. 1–11. doi: 10.1155/2013/985037.

Kidane, Y. G., Mancini, C., Mengistu, D. K., Frascaroli, E., Fadda, C., Pè, M. E. and Dell’Acqua, M. (2017) ‘Genome wide association study to identify the genetic base of smallholder farmer preferences of durum wheat traits’, Frontiers in Plant Science, 8(July), pp. 1–11. doi: 10.3389/fpls.2017.01230.

Kislev, M. E., Nadel, D. and Carmi, I. (1992) ‘Epipalaeolithic (19,000 BP) cereal and fruit diet at Ohalo II, Sea of Galilee, Israel’, Review of Palaeobotany and Palynology, 73(1–4), pp. 161–166. doi: 10.1016/0034-6667(92)90054-K.

Komatsuda, T., Pourkheirandish, M., He, C., Azhaguvel, P., Kanamori, K., Perovic, D., Stein, N., Graner, A., Wicker, T., Tagiri, A., Lundqvist, U., Fujimura, T., Matsuoka, M., Matsumoto, T. and Yano, M. (2007) ‘Six-rowed barley originated from a mutation in a homeodomain-leucine zipper I-class homeobox gene’, Proceedings of the National Academy of Sciences of the United States of America, 104(4), pp. 1424–1429. doi: 10.1073/pnas.0608580104.

Lakew, B., Semeane, Y., Alemayehu, F., Gebre, H., Grando, S., Van Leur, J. A. G. and Ceccarelli, S. (1997) ‘Exploiting the diversity of barley landraces in Ethiopia’, Genetic Resources and Crop Evolution, 44(2), pp. 109–116. doi: 10.1023/A:1008644901982.

Lundqvist, U., Franckowiak, J. D. and Konishi, T. (1997) ‘New and revised descriptions of barley genes’, Barley Genet. Newsl, pp. 22–516.

Mammadov, J., Aggarwal, R., Buyyarapu, R. and Kumpatla, S. (2012) ‘SNP markers and their impact on plant breeding’, International Journal of Plant Genomics, 2012(May, 2014). doi: 10.1155/2012/728398.

(20)

Tsedal, Tinasu, B., Letekiros, Hadash, Letemedhin, Tinasu, A., Kiros, Leteberhan, Letekidan, Hareya, Letecheal, Hiritu, Ametezigu, Amlak, Y. G., Slassie, P. G. G., Girmay, P. G. S., Gebre, G. M., Mesfin, G. S., Desta, K., Teklay, S., Kidan, H. G., Egziabher, T. G., Desalegn, P. W., Gebre, H., Micheal, H. G., Mebrahtu, G., Hailemichael, Teklay, A., Tesfay, E., Gitehun, A., Tadesse, E., Asfaw, M., Aragaw, K., Brku, T., Tadasse, Y., Hailu, M., Kassun, A., Gel Aw, G., Emagn, M., Mitku, F., Mengste, A., Yigzaw, B., Tesfaw, E., Belay, T., Yirga, W., Sisay, P. A., Hailu, B., Wale, P. T., Setegn, M., Tesfie, T., Meretie, B., Kassie, L., Biset, T., Hailu, Y., Agazu, L., Yigzaw, M., Yigzaw, A. and Abate, G. (2017) ‘Joining smallholder farmers’ traditional knowledge with metric traits to select better varieties of Ethiopian wheat’, Scientific Reports, 7(1), pp. 1–11. doi: 10.1038/s41598-017-07628-4.

Mascher, M., Gundlach, H., Himmelbach, A., Beier, S., Twardziok, S. O., Wicker, T., Radchuk, V., Dockter, C., Hedley, P. E., Russell, J., Bayer, M., Ramsay, L., Liu, H., Haberer, G., Zhang, X. Q., Zhang, Q., Barrero, R. A., Li, L., Taudien, S., Groth, M., Felder, M., Hastie, A., Šimková, H., Stanková, H., Vrána, J., Chan, S., Munõz-Amatriaín, M., Ounit, R., Wanamaker, S., Bolser, D., Colmsee, C., Schmutzer, T., Aliyeva-Schnorr, L., Grasso, S., Tanskanen, J., Chailyan, A., Sampath, D., Heavens, D., Clissold, L., Cao, S., Chapman, B., Dai, F., Han, Y., Li, H., Li, X., Lin, C., McCooke, J. K., Tan, C., Wang, P., Wang, S., Yin, S., Zhou, G., Poland, J. A., Bellgard, M. I., Borisjuk, L., Houben, A., Doleael, J., Ayling, S., Lonardi, S., Kersey, P., Langridge, P., Muehlbauer, G. J., Clark, M. D., Caccamo, M., Schulman, A. H., Mayer, K. F. X., Platzer, M., Close, T. J., Scholz, U., Hansson, M., Zhang, G., Braumann, I., Spannagl, M., Li, C., Waugh, R. and Stein, N. (2017) ‘A chromosome conformation capture ordered sequence of the barley genome’, Nature, 544(7651). doi: 10.1038/nature22043.

Mayer, K. F. X., Waugh, R., Langridge, P., Close, T. J., Wise, R. P., Graner, A., Matsumoto, T., Sato, K., Schulman, A., Ariyadasa, R., Schulte, D., Poursarebani, N., Zhou, R., Steuernagel, B., Mascher, M., Scholz, U., Shi, B., Madishetty, K., Svensson, J. T., Bhat, P., Moscou, M., Resnik, J., Muehlbauer, G. J., Hedley, P., Liu, H., Morris, J., Frenkel, Z., Korol, A., Bergès, H., Taudien, S., Felder, M., Groth, M., Platzer, M., Himmelbach, A., Lonardi, S., Duma, D., Alpert, M., Cordero, F., Beccuti, M., Ciardo, G., Ma, Y., Wanamaker, S., Cattonaro, F., Vendramin, V., Scalabrin, S., Radovic, S., Wing, R., Morgante, M., Nussbaumer, T., Gundlach, H., Martis, M., Poland, J., Pfeifer, M., Moisy, C., Tanskanen, J., Zuccolo, A., Spannagl, M., Russell, J., Druka, A., Marshall, D., Bayer, M., Swarbreck, D., Sampath, D., Ayling, S., Febrer, M., Caccamo, M., Tanaka, T., Wannamaker, S., Schmutzer, T., Brown, J. W. S., Fincher, G. B. and Stein, N. (2012) ‘A physical, genetic and functional sequence assembly of the barley genome’, Nature. Nature Publishing Group, 491(7426), pp. 711–716. doi: 10.1038/nature11543.

Mora, F., Quitral, Y. A., Matus, I., Russell, J., Waugh, R. and Del-Pozo, A. (2016) ‘SNP-Based QTL mapping of 15 complex traits in barley under rain-fed and well-watered conditions by a mixed modeling approach’, Frontiers in Plant Science, 7. doi: 10.3389/fpls.2016.00909.

Mulatu, B. and Grando, S. (2011) Barley Research and Development in Ethiopia. Proceedings of the 2nd Nationa Barley Research and Development Review Workshop. 28-30 November 2006, HARC, Holettat, Ethiopia.

Muñoz-Amatriaín, M., Cuesta-Marcos, A., Hayes, P. M. and Muehlbauer, G. J. (2014) ‘Barley genetic variation: Implications for crop improvement’, Briefings in Functional Genomics and Proteomics, 13(4), pp. 341–350. doi: 10.1093/bfgp/elu006.

(21)

Nadeem, M. A., Nawaz, M. A., Shahid, M. Q., Doğan, Y., Comertpay, G., Yıldız, M., Hatipoğlu, R., Ahmad, F., Alsaleh, A., Labhane, N., Özkan, H., Chung, G. and Baloch, F. S. (2018) ‘DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing’, Biotechnology and Biotechnological Equipment, 32(2), pp. 261–285. doi: 10.1080/13102818.2017.1400401.

Nevo, E. (2006) ‘Genome evolution of wild cereal diversity and prospects for crop improvement’, Plant Genetic Resources, 4(1), pp. 36–46. doi: 10.1079/pgr2006108.

Pakniyat, H., Powell, W., Baird, E., Handley, L. L., Robinson, D., Scrimgeour, C. M., Nevo, E., Hackett, C. A., Caligari, P. D. S. and Forster, B. P. (1997) ‘AFLP variation in wild barley (Hordeum spontaneum c. Koch) with reference to salt tolerance and associated ecogeography’, Genome, 40(3), pp. 332–341. doi: 10.1139/g97-046.

Pankin, A., Altmüller, J., Becker, C. and von Korff, M. (2018) ‘Targeted resequencing reveals genomic signatures of barley domestication’, New Phytologist, 218(3). doi: 10.1111/nph.15077. Pasam, R. K., Sharma, R., Walther, A., Özkan, H., Graner, A. and Kilian, B. (2014) ‘Genetic diversity

and population structure in a legacy collection of spring barley landraces adapted to a wide range of climates’, PLoS ONE, 9(12), pp. 1–29. doi: 10.1371/journal.pone.0116164.

Pickering, R. A. (1983) ‘The location of a gene for incompatibility between Hordeum vulgare L. and H. bulbosum L.’, Heredity, 51(2), pp. 455–459. doi: 10.1038/hdy.1983.55.

Pickering, R. A. (1984) ‘The influence of genotype and environment on chromosome elimination in crosses between Hordeum vulgare L. × Hordeum bulbosum L.’, Plant Science Letters, 34(1–2), pp. 153–164. doi: 10.1016/0304-4211(84)90138-X.

Piffanelli, P., Ramsay, L., Waugh, R., Benabdelmouna, A., D’Hont, A., Hollricher, K., Jørgensen, J. H., Schulze-Lefert, P. and Panstruga, R. (2004) ‘A barley cultivation-associated polymorphism conveys resistance to powdery mildew’, Nature, 430(7002), pp. 887–891. doi: 10.1038/nature02781.

Poets, A. M., Fang, Z., Clegg, M. T. and Morrell, P. L. (2015) ‘Barley landraces are characterized by geographically heterogeneous genomic origins’, Genome Biology, 16(1), pp. 1–11. doi: 10.1186/s13059-015-0712-3.

Pourkheirandish, M., Hensel, G., Kilian, B., Senthil, N., Chen, G., Sameri, M., Azhaguvel, P., Sakuma, S., Dhanagond, S., Sharma, R., Mascher, M., Himmelbach, A., Gottwald, S., Nair, S. K., Tagiri, A., Yukuhiro, F., Nagamura, Y., Kanamori, H., Matsumoto, T., Willcox, G., Middleton, C. P., Wicker, T., Walther, A., Waugh, R., Fincher, G. B., Stein, N., Kumlehn, J., Sato, K. and Komatsuda, T. (2015) ‘Evolution of the Grain Dispersal System in Barley’, Cell. Cell Press, 162(3), pp. 527–539. doi: 10.1016/j.cell.2015.07.002.

Pourkheirandish, M. and Komatsuda, T. (2007) ‘The importance of barley genetics and domestication in a global perspective’, Annals of Botany, 100(5), pp. 999–1008. doi: 10.1093/aob/mcm139. Qualset, C. O., McGuire, P. E., Vogt, H. E. and Topcu, M. A. (1977) ‘ Ethiopia as a Source of

(22)

527–529. doi: 10.2135/cropsci1977.0011183x001700040011x.

Salamini, F., Özkan, H., Brandolini, A., Schäfer-Pregl, R. and Martin, W. (2002) ‘Genetics and geography of wild cereal domestication in the near east’, Nature Reviews Genetics, 3(6), pp. 429– 441. doi: 10.1038/nrg817.

Sonnino, A. (2017) ‘International instruments for conservation and sustainable use of plant genetic resources for food and agriculture: An historical appraisal’, Diversity, 9(4). doi: 10.3390/d9040050.

Sreenivasulu, N., Graner, A. and Wobus, U. (2008) ‘Barley genomics: An overview’, International Journal of Plant Genomics, 2008. doi: 10.1155/2008/486258.

Staudt, G. (1961) ‘The origin of cultivated barleys: A discussion’, Economic Botany, 15(3), pp. 205– 212. doi: 10.1007/BF02862158.

Sullivan, P., Arendt, E. and Gallagher, E. (2013) ‘The increasing use of barley and barley by-products in the production of healthier baked goods’, Trends in Food Science and Technology. Elsevier Ltd, 29(2), pp. 124–134. doi: 10.1016/j.tifs.2012.10.005.

Takahashi, R., Hayashi, J., Hiura, U. and Yasuda, S. (1968) ‘A study of cultivated barleys from nepal himalaya and north india with special reference to their phylogenetic differentiation’, in Bericht des Ohara Instituts fu¨r Landwirtschaftliche Biologie, Okayama 14:, pp. 85–122.

Taketa, S., Amano, S., Tsujino, Y., Sato, T., Saisho, D., Kakeda, K., Nomura, M., Suzuki, T., Matsumoto, T., Sato, K., Kanamori, H., Kawasaki, S. and Takeda, K. (2008) ‘Barley grain with adhering hulls is controlled by an ERF family transcription factor gene regulating a lipid biosynthesis pathway’, Proceedings of the National Academy of Sciences of the United States of America, 105(10), pp. 4062–4067. doi: 10.1073/pnas.0711034105.

Taketa, S., Kikuchi, S., Awayama, T., Yamamoto, S., Ichii, M. and Kawasaki, S. (2004) ‘Monophyletic origin of naked barley inferred from molecular analyses of a marker closely linked to the naked caryopsis gene (nud)’, Theoretical and Applied Genetics, 108(7), pp. 1236–1242. doi: 10.1007/s00122-003-1560-1.

Tanaka, T., Ishikawa, G., Ogiso-Tanaka, E., Yanagisawa, T. and Sato, K. (2019) ‘Development of genome-wide SNP markers for barley via reference- Based RNA-seq analysis’, Frontiers in Plant Science, 10(May), pp. 1–9. doi: 10.3389/fpls.2019.00577.

Tondelli, A., Xu, X., Moragues, M., Sharma, R., Schnaithmann, F., Ingvardsen, C., Manninen, O., Comadran, J., Russell, J., Waugh, R., Schulman, A. H., Pillen, K., Rasmussen, S. K., Kilian, B., Cattivelli, L., Thomas, W. T. B. and Flavell, A. J. (2013) ‘Structural and Temporal Variation in Genetic Diversity of European Spring Two-Row Barley Cultivars and Association Mapping of Quantitative Traits’, The Plant Genome, 6(2), p. plantgenome2013.03.0007. doi: 10.3835/plantgenome2013.03.0007.

Wang, J., Sun, G., Ren, X., Li, C., Liu, L., Wang, Q., Du, B. and Sun, D. (2016) ‘QTL underlying some agronomic traits in barley detected by SNP markers’, BMC Genetics. BMC Genetics, pp. 1–13. doi: 10.1186/s12863-016-0409-y.

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Westengen, O. T., Jeppson, S. and Guarino, L. (2013) ‘Global Ex-Situ Crop Diversity Conservation and the Svalbard Global Seed Vault: Assessing the Current Status’, PLoS ONE, 8(5). doi: 10.1371/journal.pone.0064146.

World Agricultural production.com (2020) Barley Production by Country | World Agricultural

Production 2019/2020. Available at:

http://www.worldagriculturalproduction.com/crops/barley.aspx (Accessed: 5 March 2020). van de Wouw, M., van Hintum, T., Kik, C., van Treuren, R. and Visser, B. (2010) ‘Genetic diversity

trends in twentieth century crop cultivars: A meta analysis’, Theoretical and Applied Genetics, 120(6), pp. 1241–1252. doi: 10.1007/s00122-009-1252-6.

Van De Wouw, M., Kik, C., Van Hintum, T., Van Treuren, R. and Visser, B. (2010) ‘Genetic erosion in crops: Concept, research results and challenges’, Plant Genetic Resources: Characterisation and Utilisation, 8(1), pp. 1–15. doi: 10.1017/S1479262109990062.

Yadav, S. K., Singh, A. K., Pandey, P. and Singh, S. (2015) ‘Genetic Variability and Direct Selection Criterion for Seed Yield in Segregating Generations of Barley (<i>Hordeum vulgare</i> L.)’, American Journal of Plant Sciences, 06(09), pp. 1543–1549. doi: 10.4236/ajps.2015.69153.

Zohary, D. (1959) ‘Is Hordeum agriocrithon the ancestor of six- rowed barley’, Evolution, 13, pp. 279–280.

Zohary, D. (1999) ‘Monophyletic vs. polyphyletic origin of the crops on which agriculture was founded in the Near East’, Genetic Resources and Crop Evolution, 46(2), pp. 133–142. doi: 10.1023/A:1008692912820.

Zohary, D., Hopf, M. and Weiss, E. (2012) Domestication of Plants in the Old World: The origin and spread of domesticated plants in Southwest Asia, Europe, and the Mediterranean Basin. Available at: https://books.google.com/books?hl=en&lr=&id=1hHSYoqY-AwC&oi=fnd&pg=PP1&dq=zohary+and+hopf+2012&ots=Iyb8oB3OlM&sig=a_XFkZKK3A0 o7g78GkGJq7qgV1c (Accessed: 13 December 2019).

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2

Genetic diversity of Ethiopian cultivated barley (Hordeum vulgare ssp vulgare L.) genotypes as revealed by pheno-agronomic and farmers’ preferred traits.

Abstract

Efficient conservation and subsequent utilization of the available genetic resource is primarily dependent on the strength in the assessment of variation among genotypes. An experiment was carried out with two objectives aiming at 1) determining the extent of phenotypic variability present in a panel of 293 lines from farmer varieties and 27 cultivars 2) identifying candidate lines for further evaluation in improvement programs and successive utilization. It was conducted at two locations in Ethiopia, Aris Negelle and Holleta in 2017/18 and 2018/19 cropping seasons. Among the best 30 lines for grain yield across all the environments, lines from farmers’ varieties constitute 73%. Based on the spike row number, the best performing lines combined across all the environments were six-rowed types, whereas based on the two years data at Arsi Negelle the two-rowed spike type dominates and at Holleta the six-rowed types. In this experiment lines that mature in less than 85 days were identified and these lines were found to be consistent across all the environments. It was also observed that phenologic traits had higher heritability than the agronomic traits. After principal component analysis, the first two PCs explained 56.97% of the variation whereas cluster analysis grouped the lines into ten clusters. Correlation coefficient between grain and biomass yield was significant and comparatively high (r=0.38***). Significant, high and negative correlation coefficient (-0.72***) was observed between 1000 kernel weight and number of seeds per spike. Considering farmers’ evaluation, it was observed that grain yield (metric) had a low correlation coefficient with the farmers’ preferred traits suggesting the importance of involving the end users (farmers) in variety development activities. The two farming communities had differential preferences.

Key words: Diversity panel, barley farmer variety, principal component analysis, farmers’ evaluation Introduction

Barley is widely grown in the World, being the fourth most important cereal crop after wheat, rice and maize. In 2017 barley was grown over more than 47 million hectares for a total production of more than 147 mil tons. Russia, Australia and Germany were the largest producers (FAOSTAT: http://www.fao.org/faostat/en/#data/QC accessed June, 25, 2019). It is a diploid (2n=2x=14) and one of the largest cereals with a genome size 4.79 billion letters of genetic code which is twice the size of the human genome (Mascher et al., 2017). The crop is known for its versatility and natural stands can

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be found in wider range of agro-ecologies, from the arid regions of the Mediterranean to altitudes up to 4,500 m in the Himalayas (Bothmer et al., 1991)

Barley is staple food in many regions of the World, is used as an alternative source of animal feed and is the main cereal for malting and brewing industries (Baik and Ullrich, 2008). The interest towards barley cultivation increased because it has become an important component of healthy food that can minimize susceptibility to heavy weight, type II diabetes, cardiac disease (Abumweis et al., 2010; Chutimanitsakun et al., 2013; Sullivan et al., 2013) and cancer (Grando Stefania, 2005; Dykes and Rooney, 2007; Holtekjølen et al., 2011; Kumar et al., 2014). The presence of beta-glucan which is an anti-cholesterol compound and acetylcholine, which improves the recovery after memory loss makes barley a cereal with high nutritional quality (Yadav et al., 2015).

Ethiopia being field and horticultural crops biodiversity hotspot, regarded as one of the twelve centers of origin of crops (Vavilov, 1992). Especially for barley the diversity was found to be immense to the extent that there are plenty of forms which are unique to Ethiopia including the deficient and irregular barley types (Vavilov, 1992). All the con-varieties identified across the World: vulgare, distichion, intermedium and labile (Asfaw, 2000) are currently under production in the country. Barley is the fifth most important crop (CSA, 2018) after tef, maize, wheat and sorghum both in area of production and quantity produced. Since recent past produced only in the main growing season, however, there are very few places growing barley in the short rainy season as well. Barley is being conserved in Ethiopia both in situ in community seed banks located on the different agro-ecologies of the country and ex situ. It makes the largest collection of all the crops conserved in the Ethiopian gene bank with more than 16,000 farmers’ varieties. Barley farmers’ varieties, cover approximately 90% of the land devoted to barley in Ethiopia, thus significantly contributing to food security in the country (Tanto, 2009). Different researchers have reported on the level of diversity on agro-morphological traits of barely (Asfaw, 1988, 1989; Demissie and Bjørnstad, 1996; Kebebew et al., 2001). Diversity for yield and yield related traits were also reported by (Ren et al., 2013; Dorostkar et al., 2015; Zeng, 2015; Al-Abdallat et al., 2017; Al-Sayaydeh et al., 2019; Matin et al., 2019). Multivariate analysis technique in studying the divergence between genotypes and the degree of association between the traits has become increasingly important. Hence different authors reported the application of cluster and principal component analysis in barley (Žáková and Benková, 2006; Eticha et al., 2010; Enyew et al., 2019).

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However, the research efforts made so far to uncover the diversity in barley focusing on the barley genetic resources of Ethiopia were not much compared to the huge genetic resource in the country. Even among those done, the majority were reflecting the breeders view which may be considered as one of the challenges for being out of production for some of the varieties shortly after their release. The involvement of farmers in varietal development to identify varieties that suit their needs therefore can be considered as a remedial action for such and related limitations of the conventional breeding strategies (Ceccarelli and Grando, 2007). In addition, despite large number of barley accessions conserved in ex situ gene bank little has been characterized so far and hence no core collection of these accessions was developed. Owing to the change in climate, production of barley twice a year is now reduced to once/year causing significant decrease in yearly production in Ethiopia. Furthermore, there is a considerable gap between the World average productivity of barley (3.0 t/ha) compared to that of Ethiopia (1.9 t/ha). Therefore, the current research which was based on a panel of barley lines, of which 293 were lines derived from farmers’ varieties and 27 were improved varieties, will contribute in narrowing the existing gap, with the final objective of identification of candidate lines for further evaluation in improvement programs and successive distribution and utilization.

Material and Methods Phenotyping

Experimental sites

The barley panel of lines was sown at two locations, Arsi Negelle and Holleta for two years 2017/18 and 2018/19 during the main cropping season. Arsi Negelle is situated in the central rift valley of Ethiopia at a latitude of 7°21′N, longitude of 38°42′E and an elevation of 2043 meters above sea level (m.a.s.l.). The annual rainfall ranges from 500-1000mm and the average annual temperature varies from 10-25°C with Andosol type of soil (Mekonnen et al., 2018). Holleta Agricultural Research Center located at a latitude of 9°00'N and a longitude of 38°30'E with an altitude of 2400 m.a.s.l. The temperature at this location varies between 6°C to 22°C with annual average rainfall of 1144 mm. The soil type is classified as Eutric Nitisol with a pH of 4.92 (http://www.eiar.gov.et/holetta/.)

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Genetic Materials

For this experiment 249 barley accessions were obtained from Ethiopian Biodiversity Institute (EBI). These accessions were representative of four regions, 17 different administrative zones and altitude ranging from <1,350 to >3,550 m.a.s.l). From these accessions, through ear-to-row purification, 501 lines were developed. Out of these lines, based on morphological characteristics, avoiding sister lines to maximize both genotypic and phenotypic diversity, 293 lines were retained to be included in the phenotyping experiment (Table S9). In addition to those lines, 27 improved varieties developed and maintained by the national agricultural system were also included for a total of 320 lines. The cultivars were obtained from the respective maintainer agricultural research center. These genetic materials comprise 6-rowed, 2-rowed (both deficiens and male fertile) and irregular barley variants. Food and malt barley types, hulled and hulless barley were also included.

Field Experiment

The experiment was laid according to alpha lattice design with two replications in four rows of 2.5m length where each plot of 2m2 was sown with 17g of seeds. Since it was conducted at two locations, two years and two replications the total number of plots managed were 2560.The plots were fertilized with DAP and Urea fertilizers as per the recommended rate of applications for the two sites.

Data collection

Data collection was based on the descriptor list developed by Bioversity International formerly named as International Plant Genetic Resource Institute(IPGRI, 1994) and International Union for the Protection of New Varieties (UPOV, 1994). Four phenological and seven agronomic traits were collected in the current experiment. Phenological characters were days to booting (number of days from sowing to 50% of the plants show swollen boot, number), days to heading (number of days from sowing to heads of 50% of plants fully emerged, number), days to mature (number of days from sowing to 75% physiological maturity, number) and grain filling period (number of days from heading to maturity, number); agronomic traits were plant height (cm), number of effective tiller (spike bearing branches of a plant, number), spike length(cm), grain yield (ton ha-1), biomass yield (ton ha-1), number of kernel per spike (number) and thousand kernel weight (gm). All the phenological traits, grain yield, biomass yield and thousand kernel weight were collected on plot basis. Data on plant height, number

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5 randomly sampled plants. The data on grain yield and thousand kernels were adjusted to 12.5% moisture content.

Phenotypic data analysis

Restricted maximum likelihood (REML) algorithm was used to produce best linear unbiased prediction (BLUP) mean, in which case the genotype and the environments were fitted as a random. The BLUP means combined over the four environments were calculated using the META- R statistical software version 6.04 (Alvarado et al., 2019). BLUP means were used in subsequent statistical analyses (principal component analysis and cluster analysis) and the model used to produce the means was as follows:

Yijk = µ+ Envi + Repj(Envi) + Blockk(EnviRepj) + Genl + Envi x Genl + Ꜫijkl

where Yijk is the trait of interest, µ is the general mean, Envi is the effect of the ith environment,

Repj(Envi) is the effect of the jth replicate with in the ith environment, Blockk(EnviRepj) is the effect

of the kth incomplete block within the ith environment and jth replicate, Genl is the effect of the lth

genotype, Envi x Genl is genotype by environment interaction and Ꜫijkl is the error associated with the ith environment, jth replication, kth incomplete block and the lth genotype. Broad-sense heritability of

a given trait combined over the environments on the other hand was calculated as:

ℎ2 = 𝜎 2 g 𝜎2 g+ 𝜎2 ge 𝑛𝐸𝑛𝑣𝑠 + 𝜎2e/(𝑛𝐸𝑛𝑣𝑠 𝑥 𝑛𝑟𝑒𝑝𝑠)

where 𝜎2g, 𝜎2e, 𝜎2ge were the genotypic, error and genotype x error interaction variance components,

respectively, and nEnvs was number of environments, nreps was the number of replicates. (Alvarado et al., 2019)

Principal component analysis was carried out using the prcomp function of R statistical software version R.3.6.1.(R core team, 2019). The combined BLUP means of the characters standardized to mean of zero and a variance of one in order to reduce differences in measurement scale of the collected data (Sneath and Sokal, 1973) were used for this analysis. Clustering of the observations was performed by Ward amalgamation steps (Ward, 1963) using MINITAB vr.14 statistical package based on squared Euclidian distance (D2). The D2 values obtained for pairs of clusters were considered as

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the calculated values of Chi-square (χ2_{) and were tested for significance both at 1 and 5% probability} levels against the tabulated values of χ2_{for ‘P’ degree of freedom, where P is the number of characters} considered (Singh RK, 1985). R version 3.6.1 was also applied to produce boxplot using ggplot 2 function and correlation between variables using performance analytics function.

Farmers’ evaluation

Five women and five men farmers evaluated each plot at each location. Prior to evaluation, a focus group discussion was held to discuss traits important in determining the quality of a genotype. The evaluation was done on trait basis and the traits emerged following focus group discussion were optimum maturity, grain yield, biomass and overall performance. The farmers evaluated the genotypes as excellent, very good, average, weak, and poor with a score value of 5, 4, 3, 2 and 1 respectively. While scoring the overall performance, farmers in the participatory evaluation considered all the other three traits together and gave their scoring. To avoid peer influence, each farmer gave her/his own ranking for all the plots independently and at a time using their fingers to indicate a particular score. From the farmers evaluation we collected 51,200 data points over the two locations making the total data points collected for metric and farmers preferred traits 160,640.

Farmers’ data analysis

The scoring by individual farmers were averaged for the women and men farmers separately. The average score was used to calculate best linear unbiased predictor values for all the farmer preferred traits. META- R statistical software version 6.04 (Alvarado et al., 2019) was used to calculate BLUP means for each of the genotypes based on REML algorithm. Pearson correlation was calculated for both farmer preferred and related metric trait using corrplot and ggplot2 functions of R-programming software for the two locations independently. The correlation between FPT at the different location had also been calculated.

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Results Phenotyping

Variance component analysis

Diversity assessment between the barley lines through REML variance component analysis revealed that there was a highly significant difference (p<0.001) among lines for all the traits under consideration combined over environments (Table 1). The effect of the environment was also found to be significant (p<0.01) for all characters except spike length. The genotype by environment interaction effect also showed a similar trend to that of the genotype. The genotype by year interaction effects at each location were found to be significant (p<0.01) for all the traits, suggesting differential response of the lines over the seasons (Table S3). Comparing the grain yield over the environments (two locations over two years) it was found that the highest grain yield was observed at Holleta year II (Fig 1) and the lowest at Arsi Negelle year II.

Table 1. Variance component analysis and heritability estimate of barley lines combined over four environments.

DB (days to booting), DH (days to heading), DM (days to maturity) and GFP (grain filling period), PH (plant height), TIL (fertile tiller), SPS (seeds per spike), SPL (spike length), BM (biological yield), GY (grain yield), TKW (thousand kernel weight), h2_{(broad sense heritability)}*** _{= significant at (P<0.001) and}ns_{= not significant}

Traits Source of variation Grand

mean h2 _LSD _CV (%) Environment variance Genotype variance Genotype x Env. variance Error variance DB 2.97*** _46.16*** _4.25*** _2.97 _58.54 _0.97 _2.44 _2.94 DH 3.31*** _62.19*** _4.04*** _3.31 _66.31 _0.98 _2.48 _2.74 DM 5.25*** _70.01*** _7.94*** _5.25 _100.66 _0.96 _3.28 _2.28 GFP 7.55*** _1.90*** _5.76*** _7.55 _34.30 _0.44 _2.03 _8.01 PH (cm) 16.22*** _34.93*** _34.47*** _16.22 _97.72 _0.77 _5.66 _4.12 NET 0.35*** _0.17*** _0.37*** _0.35 _3.61 _0.55 _0.54 _16.39 SPL (cm) 0.33ns _0.43*** _0.34*** _0.33 _7.78 _0.77 _0.62 _7.37 SPS 18.1*** _82.35*** _31.91*** _18.1 _34.77 _0.89 _6.01 _12.24 BM (t ha-1₎ _0.64*** _0.54*** _3.38*** _0.64 _8.75 _0.37 _1.15 _9.17 GY (t ha-1₎ _0.01*** _0.06*** _0.35*** _0.01 _2.22 _0.39 _0.37 _5.40 TKW (gm) 5.25*** _21.13*** _9.34*** _5.25 _43.43 _0.88 _3.22 _5.28

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HB-1307, with a grain yield of 2.79 t ha-1_{, was found to be the highest yielding line and the line}

Accn#16862-A with grain yield of 1.85 t ha-1_{was the lowest yielding line. Among the top 30 lines}

combined over the test environments for grain yield (Table S1), six lines (HB-1307, Accn#235551-B, CROSS41/98, Accn#208841-B, Accn#16726-A and Ibon 174/03) were also in the top 30 when the analysis was based on combined data over years for each location separately. This indicated that the mentioned lines were consistent over years and adapted to both locations. However, some of the lines showed specific adaptation in which case the farmer variety Accn# 24970 was the highest yielding line over the two years at Holleta (3.43 t ha-1) but not in the top 30 at Arsi Negelle. Likewise, the farmer variety Accn#1826 was the highest yielding across years at Arsi Negelle (2.03 t ha-1) but not in the top 30 at Holleta. Among the lines in top 30 combined over all the environments although there were differences in grain yield, those differences were not statistically significant. The only statistically significant difference was between HB-1307 and Gobe.

Figure 1. Grain yield with the respect to the four test environments. The x-axis represents, the environments Arsi Negelle year I (ARSINE1) and year II (ARSINEG2); Holleta year I (HOLLETA1) and year II (HOLLETA2). The y-axis represents the estimated grain yield in t ha-1_.

In the top 30 lines across all environments for grain yield 73% were derived from farmers’ varieties (Fig 2, panel a). In addition, 60% of the top 30 lines were six-rowed spike type and the remaining were 2-rowed, none of them were irregular spike type. Based on individual locations two years average,