Variety selection in wheat cultivation

Variety selection in wheat cultivation Arun Kumar Joshi, International Maize and Wheat Improvement Center (CIMMYT); Vinod Kumar Mishra, Banaras Hindu University, India; and Simanchal Sahu, Orissa University of Agriculture and Technology, India

1 Introduction

2 Wheat variety selection methods: natural and traditional selection

3 Wheat variety selection methods: modern molecular breeding

4 Variety selection by plant breeders

5 Variety selection by farmers

6 Conclusion

7 Where to look for further information

8 References

1 Introduction The development of new varieties of crop plants largely occurs through variation and selection. Variation, defined as the emergence of differences between individuals in a population, serves as the raw material for varietal development or plant breeding. Using important traits as the criteria, superior plants suited to particular environments, management methods or markets can then be selected. For example, if a variable population is exposed to selection in the presence of a commonly grown variety (known as a check variety), some genotypes may perform better than the check. A breeder or farmer may then be able to select a genotype with greater potential than the commonly grown variety. The superiority of an identified line is normally validated through a series of trials, which often take the form of a multi-year multi-location evaluation. Once the superiority of a new variety has been established, it is registered and released for cultivation by a competent authority. In many countries, a committee is responsible for identifying new crop varieties developed by the public sector. In India, this is the task of the Varietal Identification Committee, which usually takes decisions during an annual national workshop. Following this, the government again verifies the superiority through another committee and then issues an official notification for the release of variety for cultivation by farmers. There are typically also standard identification procedures for varieties developed by the private sector, but these differ between countries. Seeds are then produced following a defined procedure, so as to maintain the superior variety’s physical and genetic purity and to meet minimum seed standards. They are then disseminated among farmers and other stakeholders. http://dx.doi.org/10.19103/AS.2016.0004.11 © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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Variety selection in wheat cultivation

2 Wheat variety selection methods: natural and traditional selection Varieties are now selected for cultivation according to a well-organized procedure; however, even before humans started experimenting with improving native plant populations, natural selection played its role in the evolution of different crop species. During prehistoric times, selection occurred in nature and man chose desirable crops or plants. Artificial selection became a fundamental crop improvement process when crops were first domesticated for agriculture. Humans then started modifying within individual crop populations, guided by their own needs. The selection procedures adopted during prehistory were not based on sound scientific knowledge, but gradually the scientific principles of plant science were discovered. A turning point was the discovery of the laws of inheritance by Gregor Mendel in 1866. Currently, leading-edge techniques such as molecular tools have entered the selection arena to further enhance our ability to choose the desired genotype.

2.1 Natural selection Natural selection is inherent in a genetically heterogeneous population at any place or time, and is the only known process that can produce adaptive evolutionary changes (Silvertown and Doust, 1993). It manifests itself through differences in genotypic fitness, which is a complex parameter encompassing adaptation, survival ability and viability. On the Origin of Species by Charles Darwin (1859) is a classic work on the role of natural selection in the evolution of crop plants. Darwin argued that though there were morphological species types, these arose by natural selection among the variable members of the previous species. Francis Galton was the first to apply statistical methods to the study of differences in human populations and believed that nature, and not nurture, determined hereditary (Gillham, 2001). Before domestication, crop species were subject only to natural selection. The basis for natural selection was adaptation to the prevailing environment. The plant types more adapted to their environment produced more progeny than others, hence were selected by nature. However, the role of natural selection has been greater for some plant characteristics than others. Photoperiod adaptations, for example, are considered the most important plant characteristic because day neutral crops (such as wheat and rice) are more widely adapted than day sensitive crops. Other important characteristics that have influenced natural selection are tolerance to biotic and abiotic stresses, dormancy, quality and seed shattering. Natural selection generally favours heterogeneity as it has more buffering capacity. Present-day agriculture, however, has moved towards growing homogeneous populations of crops for morphological features, quality and also for disease resistance. In remote areas of many parts of the world, such as the hills of Bhutan and Nepal, landraces have emerged, which are more genetically diverse than other varieties, probably due to the larger role of natural selection in subsistence farming, compared to more industrialized agriculture.

2.2 Selection by humans The selection of varieties by humans, termed selective breeding, is the result of a conscious decision by a farmer or plant breeder to keep the progeny of a particular parent © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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in preference to others in order to retain or reject a particular plant characteristic. For example, in wheat, selection may be for large heads and seeds, attractive grain colour, synchronous flowering, early maturity, pleasing taste or superior chapati or bread quality. In fact, selection by humans has brought about changes in almost all of the characteristics of wheat. Arnold (1985) has suggested that all crops were developed by humans through conscious selection, whereas Jenkins (1966) had argued the opposite. Different phases in the human improvement of crops by variety selection can be distinguished: the era of domestication, the era of pre-Mendelian selection, the post-Mendelian era and the modern era of crop breeding. The rate of change has accelerated recently due to rapid advances in knowledge in agricultural science.

Role of domestication Domestication, the process of bringing wild species under human management, was the first step in crop improvement by humans, which led to the beginnings of agriculture and the development of cultivated plants. The domestication of crop plants began around 10 000 years ago (Murray, 1970; Renfrew, 1973). Over years of cultivation, man has selected, knowingly or unknowingly for the characteristics that make plant populations more suited to his needs. Hence, there are now large differences between cultivated plants and wild populations surviving in nature. For wheat, domestication is believed to have occurred about 10 000 years ago in Asia, in the Fertile Crescent of the Middle East. The staple crops in this region used to be einkorn (Triticum monococcum L.) and emmer (T. turgidum sp. Dicoccum L.) wheat, both non-brittle types. Natural crossing among different species followed by allopolyploidization and mutations in genes governing threshability and other traits, however, led to the formation of today’s economically important bread wheat Triticum aestivum (Faris, 2014).

Variety selection in the pre-Mendelian era Choosing the right kind of wheat planting material has long been a significant agricultural practice. There are references, for example, to the selection during Roman times of the biggest ears and the largest kernels (Jenkins, 1966). Prior to 1800, very little plant research could be considered plant breeding; however, some of the botanical studies of that time laid the foundations for modern plant breeding. The Vilmorin Company, established in France in 1727, contributed extensively to the development of plant breeding knowledge and the production of improved varieties. Then, in 1819, Patrick Shirreff began his experiments into wheat and oat hybridization and selection with the purpose of obtaining superior cultivars (Stoskopf et al., 1993), and successfully developed a new oat variety in 1824 and a new wheat variety in 1832. He was among the first cereal breeders to develop the principle of selecting pure lines. His work was based on selecting individual plants from heterogeneous populations, in which pure line selection had never previously been practised (Stoskopf et al., 1993). Red May, dating from 1830, was perhaps the first wheat variety produced in the United States by selection. Then in 1837, Henry Zemmerman was one of the earliest individuals in North America to select a pure line wheat cultivar (Ball, 1930). In the same year, Le Couteur, a farmer from the Isle of Jersey, selected wheat based on form difference and subjected the isolates to progeny tests (catalogue.nla.gov. au/Record/813341). Red Fife wheat, a selection made in 1842, set a new performance standard for yield and quality. The pedigree method of selection was developed in 1890 and was in use in Great Britain, Sweden and other European countries before the turn of © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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the century. It was developed when it was realized that genetically superior plants are lost when selected plants are cultivated in bulk. The rediscovery of Mendel’s laws of inheritance (dating from 1866) in 1900 by Correns (Germany), De Vries (Holland) and Tschermak (Austria) led people to understand the laws of heredity and especially how hybridization and mutation could create totally new variations for developing and selecting a variety.

Variety selection in the post-Mendelian era Natural populations of wheat are heterogeneous mixtures of several homozygous genotypes, thus several pure lines can be selected from such populations. In 1910, W. H. Nilsson was the first to realize that a pure line can be produced from a population through descent from the self-fertilization of an individual homozygous plant (Stoskopf et al., 1993). Earlier, in 1903 Johannsen has proposed the concept of pure line based on his studies with beans (Phaseolus vulgaris). He explained the genetic basis of pure lines, which was subsequently popularized by Vilmorin in France and Nilsson Ehle in Sweden. In pure line selection, a large number of plants are selected from a wheat population (naturally available or created through hybridization or any other means) and are harvested individually. Their individual progenies are evaluated, and the best progeny is released as a pure line variety. In this method, selection is based on progeny testing. It is used for improvement of local varieties and also for handling segregating populations derived from an organized crossing programme. Mass selection, considered as the oldest method of breeding, has played a significant role in variety selection. This is a simple approach in which a large number of plants of similar phenotype are selected and seeds bulked to derive a new variety. Selection is based on the appearance of visible superiority of the plants and the selected plants are not subjected to progeny testing. This method had been used in wheat for improving land races or purification of a mixed variety (Walker, 1969). There are studies indicating its use to improve yield and tillering in wheat (Derera and Bhatt, 1972; Redden and Jensen, 1974). Combination breeding involves making crosses to transfer one or more characteristics, usually governed by few genes, from one variety into another. In making a cross, one can develop a hybrid (F1) or a pure line variety. To develop pure line varieties, the F1 is selfed and selection is made in subsequent segregating generations. Three important selection procedures for selection in the segregating generation are pedigree, bulk and backcross selection. Pedigree selection, which predated the rediscovery of Mendel’s laws, involves the maintenance of detailed pedigree records. In other words records of progenies are kept, so that each progeny can be traced back to the F2 plant from which it originated (Allard, 1960). When applying the method to wheat, the population size in segregating generations should be large because genes for traits like yield are located in almost all chromosomes (Shebeski, 1967). It is often used to correct weaknesses in an established variety through combination breeding and is also useful in the selection of new superior recombinant types (transgressive breeding) (Jenson, 1988). However, this method suffers from the disadvantage of the ineffectiveness of single plant selection in an early generation. It is also expensive, limits the amount of material carried (Harrington, 1937) and involves detailed and precise record keeping. Various modifications have been proposed to increase the efficiency of the pedigree method. Several experts began to think of handling segregating generations as bulks and then handling a larger number of crosses. Nilsson-Ehle (1908) at Swalof was the first © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.


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to use the bulk method for selection in segregating populations to screen winter wheat progeny for high yield and winter hardiness (Newman, 1912; Allard, 1960). Harlan et al. (1940) proposed the bulk method to handle selection in a large number of crosses. In this method, which has the considerable merit of simplicity, the F2 and subsequent generations are harvested in mass or as bulks, while the semi-natural selection imposed by environment changes gene/allele frequencies (Walker, 1969). At the end of the bulking period, individual plants are selected and evaluated in the same manner as in the pedigree method. Large periods of bulk handling are required for character fixation because homozygous types are fixed by the F6–F7 generations (Jain, 1961; Allard and Jain, 1962). The bulk method is advantageous for maintaining substantial variability over an extended period during which selection of desirable types can be made (Suneson and Wiebe, 1962). Though natural selection in bulk breeding may be useful in selecting subtle differences in different traits (Suneson and Stevens, 1953), in many cases it might eliminate desirable plants such as dwarf genotypes in crosses involving tall and dwarf (Sharma, 1989; Jennings and Aquino, 1968). Therefore, artificial selection may be used as a support at suitable stages (Jain, 1961). Several modifications of the bulk method have been suggested, among which the mass pedigree method (Harrington, 1937) and single seed descent (SSD) (Johnson and Bernard, 1962; Grafius, 1965; Brim, 1966) are important. The mass pedigree method is based on bulking the populations when the environment is unfavourable for selection and doing single plant selection when environment becomes favourable. SSD, which involves the quick advancement of generation by taking one seed from each of the 200–250 F2 plants and carrying out selection when lines become homozygous, was first suggested by Goulden (1939, 1941). SSD is quite convenient, time saving and advantageous from the point of maintaining considerable variability (Brim, 1966; Empig and Fehr, 1971; Qualset, 1975). Singh et al. (1998) investigated the crossing and selection schemes (pedigree, modified bulk, selected bulk and non-selected bulk) in wheat at CIMMYT, Mexico, and established that there was little difference among schemes in terms of grain yield and other traits. The selection of parents was the most important feature for yield potential and other agronomic traits irrespective of cross type and selection scheme (Singh et al., 1998). According to these authors, owing to genetic gains and cost efficiency, selected bulks is the most attractive selection method (Singh et al., 1998). In CIMMYT, Mexico, pedigree selection was primarily used from 1944 to 1985. From the mid-1980s to the mid-1990s the main selection method was modified pedigree/bulk and this was replaced in the late 1990s by the selected bulk method. Due to genetic gains and cost efficiency (Singh et al., 1998), selected bulk is regarded as the most attractive selection scheme for CIMMYT’s wheat breeding. The backcross method, proposed by Harlan and Pope (1922) and studied in detail by Briggs (1930, 1935, 1938, 1941, 1958), was designed to transfer a desirable allele in place of an undesirable one through repeated backcrossing with the recipient (recurrent) parent. The donor parent is used only once in the hybridization. This method requires a good recurrent parent, a donor parent and proper expression of the character (Briggs and Allard, 1953), and is more practicable in the case of simply inherited traits. For polygenic traits, each backcross progeny needs to be selfed for 2–3 generations with simultaneous selection. Selection followed by single backcrossing has been found very useful in obtaining higher yielding genotypes at CIMMYT and is commonly used in their global wheat programme (Singh et al., 2008). © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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3 Wheat variety selection methods: modern molecular breeding 3.1 Marker-assisted selection From the 1980s onwards, there has been considerable research into various molecularbased selection tools. Marker-assisted selection (MAS), in particular, is having a growing impact on wheat improvement programmes (Gupta et al., 2008, 2014; Tyagi et al., 2014). While MAS for selection on major genes proved straightforward, it has been more challenging to apply it for selection on quantitative traits. It has been estimated that by the end of first decade of this century, at least 60 genes/quantitative trait loci (QTL) had been handled using MAS around the world, mainly in developed countries (Gupta et al., 2010). Among the developing countries, there has been remarkably successful implementation of MAS in India to improve several cultivars, proving the potential of the tool (Gupta et al., 2011). Backcrossing strategies are particularly suited to a MAS approach, with markers providing the means for either foreground selection, or a combination of foreground and background selection. The latter allows an accelerated recovery of the recipient parent’s genome, which is the purpose of backcrossing. Some of the molecular markers developed for various traits are listed in Table 1. Table 1 Some successful examples of marker-assisted selection and gene pyramiding in wheat Marker type

Effect of selection

3 genes combinations (Pm1+Pm3a, Pm1+Pm10, Pm3a+Pm10)

RFLP

Powdery mildew resistance in introgression lines

Liu et al. (2000)

HMW-glutenins

Major genes (Glu-A1 and Glu-D1)

AS-PCR

Improvement in glutenin quality

de Bustos et al. (2001)

3

Scab resistance

One QTL

SSR

Scab resistance in F2:3

Zhou et al. (2003)

4

Leaf rust resistance

2 genes (Lr19, Lr24)

STS

Successful pyramiding in F3 lines

Singh et al. (2004)

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FHB resistance, orange blossom wheat midge resistance and leaf rust resistance

6 QTL for FHB, Sm1 and Lr21

SSR

Successful introduction of FHB, Sm1 and Lr21 resistance genes

Somers et al. (2005)

6

Increased GPC

Gpc-B1 gene

SSR

Improved GPC in BC2F4 plants

Davies et al. (2006)

7

Pre-harvest sprouting (PHS)

2 QTL

SSR

Increased grain dormancy in white-grained wheat

Kottearachchi et al. (2006)

No.

Target trait(s)

Target loci

1

Powdery mildew resistance

2

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Reference

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Variety selection in wheat cultivation Table 1 (Continued) No.

Target trait(s)

Target loci

Marker type

Effect of selection

8

Fusarium head blight

3 QTL

SSR

Maximum grain fill phenotypic selection following the marker-based selection

Miedaner et al. (2006)

9

Powdery mildew resistance

3 QTL

SSR

Effective selection for powdery mildew resistance in both greenhouse and field experiments

Tucker et al. (2006)

10

Cereal cyst nematode resistance

2 genes (CreX, CreY)

SCAR

Higher resistance in pyramided line

Barloy et al. (2007)

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Dough properties, durable rust resistance and height

Rht-B1, Rht-D1, Rht8, Lr24/Sr24, Lr34/Yr18, GluA3, Glu-B3

SSR

Increased genetic improvement for specific target genes, particularly at the early stage of a breeding programme

Kuchel et al. (2007)

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4 genes (Lr1, Lr9, Lr24, Lr47)

STS, SCAR, CAPS

Effective selection for resistance gene

Nocente et al. (2007)

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FHB resistance and DON content

3 QTL

SSR

Increased gain for major QTL only

Wilde et al. (2007)

14

Spot blotch resistance

4QTLs

SSR

Enhanced spot blotch resistance

Kumar et al. (2009)

15


4QTLs

SSR

Enhanced spot blotch resistance

Kumar et al. (2010)

16


Lr24 + Lr28

SCAR

Better yield potential than recipient parent

Chhuneja et al. (2011)

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Adult plant powdery mildew resistance

QPm.caas-1A + QPm.caas-4DL + QPm.caas-2BS + QPm.caas-2BL + QPm.caas-2DL

–

Expressed better APR to powdery mildew than the more resistant parent

Bai et al. (2012)

Reference

(Continued)

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Table 1 (Continued) Marker type

Effect of selection

Glu-A1x, GluB1x and Glu-D1x

SSR

Improved baking quality

Izadi-Darbandi and Yazdi-Samadi (2012)

Terminal heat tolerance

2QTLs

SSR

Enhanced heat tolerance

Paliwal et al. (2012)

20

Heat tolerance

14 QTL

SSRs

Validation of QTL

Sadat et al. (2013)

21


Co-location with leaf rust resistance gene

SSR

Spot blotch resistant genes was co-located with Lr34 and Lr46

Lillemo et al. (2013)

22

Terminal heat tolerance

7 QTLs

SSR

Enhanced heat tolerance

Tiwari et al. (2013)

23

Zn and Fe content in grain

10 QTLs

SNP and DArT

Increased Zn and Fe content in grain

Srinivasa et al. (2014)

24

Grain protein content

Gpc-B1 gene

SSR

Enhanced grain protein content in a welladopted variety HUW234

Vishwakarama et al. (2014)

25

Grain protein content

Gpc-B1gene

SSR

Enhanced >3% grain protein content as compared to recipient variety

Mishra et al. (2015)

26

Stem rust resistant genes

Sr25, SrWeb, Sr50

SSR

All these genes pyramided in HUW234, a mega variety of NEPZ, India

Yadav et al. (2015)

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2 QTLs

SSR

Pyramided 2 QTLs for spot blotch resistance in to welladopted variety

Vasistha et al. (2015)

No.

Target trait(s)

Target loci

18

Glutenin

19

Reference

3.2 Genomic selection Genomic selection is an emerging technique of selection (Meuwissen et al., 2001), in which joint merit of a large number of mapped markers across the genome is used (de Koning and McIntyre, 2012). The major difference between conventional breeding and genomic selection approaches is that in the former, selection is based on the phenotypic performance, whereas in the latter, genetic makeup of plants/lines is used as the deciding © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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factor. The successful utilization of this approach became possible due to major advances in the development of statistical tools such as BayesB and the GBLUP (Gianola et al., 2009; Gianola, 2013; de los Campos et al., 2013; Daetwyler et al., 2013). Burgueño et al. (2012) first used GBLUP for assessing G × E under genomic selection, while Heslot et al. (2014) used crop-modelling data for same assessment. Jarquin et al. (2014) proposed a random effect GBLUP model where random variance–covariance structures of markers and environmental covariables were used to determine the effects of markers and environmental covariates. To perform genomic selection, a population (referred to as the training population), is genotyped and phenotyped, and used to produce a statistical model. This model is then used on another population (called the breeding population) which is genotyped, but not phenotyped. The performances for various traits of the breeding population are predicted using allelic identity with loci that were found to be associated with the phenotype in the training population (Würschum et al., 2013). Selections of new breeding parents are made based on the genomic estimated breeding value (GEBV) which is derived from the combination of useful loci that occur in the genome of each individual in the breeding population. Compared to conventional breeding approach, genomic selection is much faster as it is not necessary to wait for late filial generations (F6 or even beyond) to phenotype quantitative traits such as grain yield. In GS approach, GEBV of a third set of individuals, called validation population (VP), is also determined whose correlation with phenotypic evaluation is used to estimate the ‘accuracy’ of the genomic selection model. The recent experiences on wheat breeding suggest that genomic selection is a promising approach to improve complex traits like grain yield (Massman et al., 2013). Major advances bring ‘genomic selection’ closer to its inclusion in the standard variety selection toolbox; however, a higher level of success has been achieved in animal breeding than in plant breeding (de Koning and McIntyre, 2012; Jonas and de Koning, 2013). Dairy cattle improvement per generation by genomic selection is double that of those achieved by traditional breeding (de Koning, 2016), but results for plant breeding have not been as impressive. Nevertheless, GBS is under development for wheat as a powerful genotyping approach capitalizing on ‘next-generation’ DNA sequencing technology advancements (Poland et al., 2012a). It has been successfully applied to CIMMYT breeding germplasm to develop genomic selection (GS) prediction models (Poland et al., 2012a). In initial studies, the cross-validation prediction accuracy with genotype by sequencing (GBS) was greater than that achieved with an established marker platform (DArT) and sufficiently high that GS could be applied in breeding programmes (Poland et al., 2012b).

3.3 Next-generation phenotyping Selection based on physical appearance of the plant or characteristics of its traits (phenotype) used to be considered an easy method, and has been utilized extensively for the development of varieties. It is interesting to note that with increasing use of genomic tools for high-resolution linkage mapping, the necessity of precision phenotyping is in no way less than the conventional breeding (Cobb et al., 2013). The experiences drawn over past few years suggest that designing effective next-generation phenotyping is not simple and requires coordination among different subjects such as biologists, computer scientists, statisticians and engineers (Houle et al., 2010). In fact precise phenotyping is being seen as a major limitation in adopting genomic selection approach. It is believed that in the © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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next two decades, phenotyping will depend more and more on increasing automation and throughput for improved precision, fast data recording and genetic analysis (Weber and Broman, 2001; Spalding and Miller, 2013). Advances in phenotyping technology can provide us with tools to accurately measure plants’ characteristics on a much larger scale and thus will narrow the divisions between genomics, plant function and agricultural traits (Breccia and Nestares, 2014). A new area which combines precision with automation in the recording of phenotypic observations is known as ‘phenomics’. Phenomics entails phenotyping at multiple levels of organization (ranging from cellular components to whole plants and canopy) and encompasses structural, physiological and performance-related traits (Dhondt et al., 2013). High-throughput techniques for whole plant-level as well as at organ-level phenotyping are being developed. Data collection is now very fast, and the precision of the data has been enhanced manifold using advanced imaging technologies, including visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). A useful list of available image-analysing tools can be found at www. plant-image-analysis.org (Lobet, 2013). There are various examples of successful automated systems in controlled conditions for measuring aboveground traits (Pereyra-Irujo et al., 2012; Tisne et al., 2013), root architecture (Famoso et al., 2010; Clark et al., 2011) and both shoot and root growth (Ruts et al., 2013; Camargo et al., 2014; Yang et al., 2014). Field-phenotyping systems have also been developed using proximal sensing devices (Busemeyer et al., 2013). Infrared imagery, stereo image analysis, acoustic-based distance sensing, non-contact measurement of chlorophyll fluorescence, laser distance sensing and near-infrared spectroscopy are potential tools to obtain phenomic data in field conditions (White et al., 2012). There are imaging techniques that are used to characterize the plant temperature responses to the water status and transpiration rate and detect difference in stomatal conductance (Chen et al., 2014b). Large-scale platforms for tissue or cell-level phenotyping are still undeveloped, however. Advances in the robotic sampling of plants grown in field or greenhouse platforms combined with automatic analysis and proper conservation of the sample for further analysis will facilitate biochemical and histological characterization in large-scale phenotyping.

4 Variety selection by plant breeders There is no one ideal wheat variety for all situations: different varieties are required depending on the selection objectives. Breeders and farmers may have the same end goals in mind; however, farming conditions vary across countries and regions and due to differences in management practices and level of industrialization. Breeders therefore have to develop varieties for a range of conditions, while farmers only require varieties suitable for their own environment or market.

4.1 Environment and location The growing environment or location and size of the breeding operation influence the traits a breeder desires for a new or improved variety. Wheat is grown from the equator to 67o N in Scandinavia and 45o S in Argentina, Chile and New Zealand (Trethowan et al., 2005), so many different varieties are required due to the large-scale variations in conditions © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.


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over this huge area. The chosen varieties must be able to cope with the prevailing biotic and abiotic constraints of the intended region, have the resilience to tolerate the relevant abiotic stresses and harness the positive potential of the given environment. In addition, different parts of the world have different cropping patterns and different end users have different food habits. For example, some parts of world have fallow wheat cropping systems, some rice–wheat, and others potato–wheat. While chapati is popular in South Asia, noodles in China and South East Asia, people from Europe prefer bread. No one wants red wheat in South Asia while it is very popular in Europe and America. Based on the diversity of soil, temperature, rainfall and other environmental factors, CIMMYT wheat breeders categorized the global wheat growing areas into 12 megaenvironments (MEs; Rajaram et al., 1994). A ME was defined as a broad, not necessarily contiguous area, occurring in more than one country and frequently transcontinental, defined by similar biotic and abiotic stresses, cropping system requirements, consumer preferences and, for convenience, by volume of production (Rajaram et al., 1994). MEs were defined for all three kinds of cultivated wheats – spring, winter and facultative. The first six MEs (ME1–ME6) were defined for spring wheat, next three (ME7–ME9) for facultative and the remaining three (ME10–ME12) for winter wheats. The location of MEs are given in Fig. 1 while the characteristic features in Table 2. Selection of variety for a given ME takes care of major stresses, although there may be variation in secondary stresses. Some of the varieties are referred to as mega varieties since they have covered a large area after their release. The Green Revolution variety Sonalika was the first such mega variety, covering more than 14 million hectares in South Asia. Subsequently, several mega varieties were released, the most recent being PBW343 and HD 2967 in India; the first one covered around 7–8 million ha during early part of this century while the latter was grown in around 7 million hectares in the just concluded 2016–17 crop cycle.

Figure 1 Global distribution of mega-environments of wheat (Source: http://wheatatlas.org/ megaenvironments). © Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.

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Table 2 Summary of characteristic features of 12 mega-environments of wheat (Source: http:// wheatatlas.org/megaenvironments) Wheat types and temperature range Spring wheat

Rainfall/Irrigation

16°C >Tmin* > 11°C

16°C >Tmin* ≥ 3°C

11°C >Tmin* ≥ 3°C

−13°C >Tmin*

Irrigated¹ Low/irrigated¹ 4C

Moderate/low High

Winter wheat

3°C >Tmin* ≥ −2°C

−2°C >Tmin* ≥ −13°C

7

10

9

12

8

11

1

Low High/irrigated¹

Facultative wheat

4A, 4B 6B

5 2A, 2B, 3

6A

*Average minimum for 3 consecutive coolest months); ¹ >5% of 5 arc min grid cell equipped for irrigation; ME2A Tmin judged in wettest quarter (3 consecutive wettest months) with = 250 mm precipitation, elevation = 1400 m; ME2B coolest quarter precipitation = 150 mm, elevation