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Article Contents

Introduction, origin and evolution of wheat, cultivated wheats today, why has wheat been so successful, wheat gluten proteins and processing properties, wheat in nutrition and health, adverse reactions to wheat, the future for wheat.

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P. R. Shewry, Wheat, Journal of Experimental Botany , Volume 60, Issue 6, April 2009, Pages 1537–1553, https://doi.org/10.1093/jxb/erp058

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Wheat is the dominant crop in temperate countries being used for human food and livestock feed. Its success depends partly on its adaptability and high yield potential but also on the gluten protein fraction which confers the viscoelastic properties that allow dough to be processed into bread, pasta, noodles, and other food products. Wheat also contributes essential amino acids, minerals, and vitamins, and beneficial phytochemicals and dietary fibre components to the human diet, and these are particularly enriched in whole-grain products. However, wheat products are also known or suggested to be responsible for a number of adverse reactions in humans, including intolerances (notably coeliac disease) and allergies (respiratory and food). Current and future concerns include sustaining wheat production and quality with reduced inputs of agrochemicals and developing lines with enhanced quality for specific end-uses, notably for biofuels and human nutrition.

Wheat is counted among the ‘big three’ cereal crops, with over 600 million tonnes being harvested annually. For example, in 2007, the total world harvest was about 607 m tonnes compared with 652 m tonnes of rice and 785 m tonnes of maize ( http://faostat.fao.org/ ). However, wheat is unrivalled in its range of cultivation, from 67º N in Scandinavia and Russia to 45º S in Argentina, including elevated regions in the tropics and sub-tropics ( Feldman, 1995 ). It is also unrivalled in its range of diversity and the extent to which it has become embedded in the culture and even the religion of diverse societies.

Most readers will be aware of the significance of bread in the Judaeo-Christian tradition including the use of matzo (hard flat bread) at the Jewish Passover and of bread to represent the ‘host’ at the Christian Eucharist (Holy Communion). The latter may be a thin unleavened wafer, similar to the Jewish matzo, in the Roman Catholic Church and some Protestant denominations, or leavened in other Protestant denominations and the Eastern Orthodox Church. But how many readers are aware that bread is treated as sacred in everyday life in the largely Muslim communities of Central Asia, such as Uzbekistan and Kyrgyzstan? In this culture, the leavened round breads (nan) are stamped before baking and must be treated with respect, including being kept upright and never left on the ground or thrown away in public. These customs almost certainly originate from earlier indigenous religions in the Middle East in which wheat played a similar role and was sometimes equated with the sun and its god.

Although such cultural and religious traditions are fascinating and will certainly reward further study, they are essentially outside the scope of this article which will examine why wheat has developed and continues to be so successful as a crop and food source.

The first cultivation of wheat occurred about 10 000 years ago, as part of the ‘Neolithic Revolution’, which saw a transition from hunting and gathering of food to settled agriculture. These earliest cultivated forms were diploid (genome AA) (einkorn) and tetraploid (genome AABB) (emmer) wheats and their genetic relationships indicate that they originated from the south-eastern part of Turkey ( Heun et al. , 1997 ; Nesbitt, 1998 ; Dubcovsky and Dvorak, 2007 ). Cultivation spread to the Near East by about 9000 years ago when hexaploid bread wheat made its first appearance ( Feldman, 2001 ).

The earliest cultivated forms of wheat were essentially landraces selected by farmers from wild populations, presumably because of their superior yield and other characteristics, an early and clearly non-scientific form of plant breeding! However, domestication was also associated with the selection of genetic traits that separated them from their wild relatives. This domestication syndrome has been discussed in detail by others, but two traits are of sufficient importance to mention here. The first is the loss of shattering of the spike at maturity, which results in seed loss at harvesting. This is clearly an important trait for ensuring seed dispersal in natural populations and the non-shattering trait is determined by mutations at the Br ( brittle rachis ) locus ( Nalam et al. , 2006 ).

The second important trait is the change from hulled forms, in which the glumes adhere tightly to the grain, to free-threshing naked forms. The free forms arose by a dominant mutant at the Q locus which modified the effects of recessive mutations at the Tg ( tenacious glume ) locus ( Jantasuriyarat et al. , 2004 ; Simons et al. , 2006 ; Dubkovsky and Dvorak, 2007 ).

Cultivated forms of diploid, tetraploid, and hexaploid wheat all have a tough rachis apart from the spelt form of bread wheat. Similarly, the early domesticated forms of einkorn, emmer, and spelt are all hulled, whereas modern forms of tetraploid and hexaploid wheat are free-threshing.

Whereas einkorn and emmer clearly developed from the domestication of natural populations, bread wheat has only existed in cultivation, having arisen by hybridization of cultivated emmer with the unrelated wild grass Triticum tauschii (also called Aegilops tauschii and Ae . squarosa ). This hybridization probably occurred several times independently with the novel hexaploid (genome AABBDD) being selected by farmers for its superior properties. The evolution of modern wheats is illustrated in Fig. 1 which also shows examples of spikes and grain.

The evolutionary and genome relationships between cultivated bread and durum wheats and related wild diploid grasses, showing examples of spikes and grain. Modified from Snape and Pánková (2006) , and reproduced by kind permission of Wiley-Blackwell.

The genetic changes during domestication mean that modern wheats are unable to survive wild in competition with better adapted species. This was elegantly demonstrated by John Bennet Lawes in the 1880s when he decided to allow part of the famous long-term Broadbalk experiment at Rothamsted to return to its natural state ( Dyke, 1993 ). He therefore left part of the wheat crop unharvested in 1882 and monitored the growth in successive years. After a good crop in 1883 the weeds dominated and in 1885 the few remaining wheat plants (which were spindly with small ears) were collected and photographed.

The A genomes of tetraploid and hexaploid wheats are clearly related to the A genomes of wild and cultivated einkorn, while the D genome of hexaploid wheat is clearly derived from that of T . tauschii . In fact, the formation of hexaploid wheat occurred so recently that little divergence has occurred between the D genomes present in the hexaploid and diploid species. By contrast, the B genome of tetraploid and hexaploid wheats is probably derived from the S genome present in the Sitopsis section of Aegilops , with Ae . speltoides being the closest extant species. The S genome of Ae . speltoides is also closest to the G genome of T . timopheevi , a tetraploid species with the A and G genomes ( Feldman, 2001 ).

The spread of wheat from its site of origin across the world has been elegantly described by Feldman (2001) and is only summarized here. The main route into Europe was via Anatolia to Greece (8000 BP) and then both northwards through the Balkans to the Danube (7000 BP) and across to Italy, France and Spain (7000 BP), finally reaching the UK and Scandanavia by about 5000 BP. Similarly, wheat spread via Iran into central Asia reaching China by about 3000 BP and to Africa, initially via Egypt. It was introduced by the Spaniards to Mexico in 1529 and to Australia in 1788.

Currently, about 95% of the wheat grown worldwide is hexaploid bread wheat, with most of the remaining 5% being tetraploid durum wheat. The latter is more adapted to the dry Mediterranean climate than bread wheat and is often called pasta wheat to reflect its major end-use. However, it may also be used to bake bread and is used to make regional foods such as couscous and bulgar in North Africa. Small amounts of other wheat species (einkorn, emmer, spelt) are still grown in some regions including Spain, Turkey, the Balkans, and the Indian subcontinent. In Italy, these hulled wheats are together called faro ( Szabó and Hammer, 1996 ) while spelt continues to be grown in Europe, particularly in Alpine areas ( Fossati and Ingold, 2001 ).

The recent interest in spelt and other ancient wheats (including kamut, a tetraploid wheat of uncertain taxonomy, related to durum wheat) as healthy alternatives to bread wheat ( Abdel-Aal et al. , 1998 ) may also lead to wider growth for high value niche markets in the future.

Despite its relatively recent origin, bread wheat shows sufficient genetic diversity to allow the development of over 25 000 types ( Feldman et al. , 1995 ) which are adapted to a wide range of temperate environments. Provided sufficient water and mineral nutrients are available and effective control of pests and pathogens is ensured, yields can exceed 10 tonnes ha −1 , comparing well with other temperate crops. However, deficiencies in water and nutrients and the effects of pests and pathogens cause the global average yield to be low, at about 2.8 tonnes ha −1 . Wheat is also readily harvested using mechanical combine harvesters or traditional methods and can be stored effectively indefinitely before consumption, provided the water content is below about 15% dry weight and pests are controlled.

There is no doubt that the adaptability and high yields of wheat have contributed to its success, but these alone are not sufficient to account for its current dominance over much of the temperate world. The key characteristic which has given it an advantage over other temperate crops is the unique properties of doughs formed from wheat flours, which allow it to be processed into a range of breads and other baked products (including cakes and biscuits), pasta and noodles, and other processed foods. These properties depend on the structures and interactions of the grain storage proteins, which together form the ‘gluten’ protein fraction.

Transcriptomic studies have shown that over 30 000 genes are expressed in the developing wheat grain ( Wan et al. , 2008 ) while proteomic analysis of mature grain has revealed the presence of about 1125 individual components ( Skylas et al. , 2000 ). However, many of these components are present in small amounts and have little or no impact on the utilization of the grain, with one protein fraction being dominant in terms of amount and impact. This fraction is the prolamin storage proteins, which correspond to the gluten proteins. The precise number of individual gluten protein components has not been determined, but 2D gel analyses suggest that about 100 is a reasonable estimate. Together they have been estimated to account for about 80% of the total grain protein in European wheats ( Seilmeier et al. , 1991 ).

Gluten was one of the earliest protein fractions to be described by chemists, being first described by Beccari in 1728 (see translation by Bailey, 1941 ). It is traditionally prepared by gently washing wheat dough in water or dilute salt solution, leaving a cohesive mass which comprises about 80% protein, the remainder being mainly starch granules which are trapped in the protein matrix.

The ability to prepare the gluten proteins in an essentially pure state by such a simple procedure depends on their unusual properties. Firstly, they are insoluble in water or dilute salt solutions but are soluble in alcohol/water mixtures (as discussed below) and were hence defined as ‘prolamins’ by TB Osborne in his classic studies of plant proteins carried out at the end of the 19th century and the start of the 20th century ( Osborne, 1924 ). Secondly, the individual gluten proteins are associated by strong covalent and non-covalent forces which allow the whole fraction to be isolated as a cohesive mass.

What is the origin of gluten?

In common with other seed storage proteins, the gluten proteins are secretory proteins, being synthesized on the rough endoplasmic reticulum and co-translationally transported into the lumen of the ER. Once within the ER lumen, cereal seed storage proteins may follow two routes: a Golgi-dependent route leading to deposition within protein bodies of vacuolar origin or a Golgi-independent route in which protein deposits formed within the ER lumen may ultimately fuse with protein bodies of vacuolar origin (see Kumamaru et al. , 2007 , for a review).

Work carried out by Galili and colleagues ( Levanany et al. , 1992 ; Galili et al. , 1995 ; Galili, 1997 ) indicated that wheat gluten proteins may follow both routes, and this has recently been confirmed using epitope tags and specific antibodies to follow individual proteins and groups of proteins in cells of developing grain ( Tosi et al. , 2009 ). It is also clear that the protein deposits fuse to form a continuous matrix as the cells of the starchy endosperm dry and die during the later stages of grain maturation ( Fig. 2A ). Thus a proteinaceous network is present in each endosperm cell ( Fig. 2B ) and these networks are brought together when flour is mixed with water to form a continuous network in the dough. Washing the dough to remove non-gluten components therefore allows the network to be recovered as the cohesive mass which is called gluten ( Fig. 2C ).

The origin of wheat gluten. (A) Transmission electron microscopy of the developing starchy endosperm cells at 46 d after anthesis shows that the individual protein bodies have fused to form a continuous proteinaceous matrix. Taken from Shewry et al. , 1995 , ( Biotechnology 13, 1185–1190) and provided by Dr M Parker (IFR, Norwich, UK). (B) Digestion of a flour particle with amylases to remove starch reveals a continuous proteinaceous network. Taken from Amend and Beauvais (1995) and reproduced by kind permission of Getreidetechnologie. (C) After kneading, dough can be washed to recover the gluten network as a cohesive mass which is stretched in the photograph to demonstrate its viscoelastic properties.

The biochemical and molecular basis for gluten functionality

Humankind has been aware for many centuries that wheat dough has unusual properties which are shared to a limited extent by doughs made from rye flour but not by those from other cereal flours. These properties, which are usually described as ‘viscoelasticity’, are particularly important in making leavened bread, as they allow the entrapment of carbon dioxide released during leavening. However, they also underpin a range of other uses including making unleavened breads, cakes, and biscuits, pasta (from durum wheat), and noodles (from bread wheat). They are also exploited in the food industry where gluten proteins may be used as a binder in processed foods.

The volume of research carried out on wheat gluten is vast, with a simple search of the Web of Science database showing almost 20 000 papers since 1945. This volume not only reflects the commercial importance of wheat processing, but also the complexity of the system which remains incompletely understood. They include studies at the genetic, biochemical, biophysical, and functional (ie processing) levels.

Genetic studies have exploited the extensive polymorphism which exists between the gluten protein fractions present in different genotypes to establish genetic linkages between either groups of gluten proteins, or allelic forms of these, and aspects of processing quality. Similarly, studies at the biochemical and biophysical levels have demonstrated a relationship between dough strength and the ability of the gluten proteins to form polymeric complexes (called glutenins). Combining results from these two approaches highlighted the importance of a specific group of gluten proteins, called the high molecular weight (HMW) subunits of glutenin.

Cultivars of bread wheat express between three and five HMW subunit genes, with the encoded proteins accounting for up to about 12% of the total grain protein ( Seilmeier et al. , 1991 ; Halford et al. , 1992 ). The HMW subunits are only present in high molecular mass polymers and allelic variation in both the number of expressed genes and the properties of the encoded proteins results in effects on the amount and size of the polymers and hence dough strength (reviewed by Payne, 1987 ; Shewry et al. , 2003 b ). These glutenin polymers are known to be stabilized by inter-chain disulphide bonds, but it is apparent that non-covalent hydrogen bonds are also important in stabilizing the interactions between glutenin polymers and monomeric gluten proteins (called gliadins) ( Belton, 2005 ). Hence, the individual gliadins and glutenin polymers can be separated using solvents which disrupt hydrogen bonding (such as urea) but reducing agents (such as 2-mercaptoethanol or dithiothreitol) are required to break down the glutenin polymers to release the individual subunits.

Although the HMW subunits are the main determinants of glutenin elasticity relationships between other gluten proteins and functional properties have also been reported (reviewed by Shewry et al. , 2003 a ).

The relationship between the HMW subunits and dough strength was first established over 25 years ago ( Payne et al. , 1979 ) and allelic forms associated with good processing quality have been selected by plant breeders for over two decades, using simple SDS-PAGE separations. The established relationships between the number of expressed HMW subunit genes, the total amount of HMW subunit protein and dough strength have also resulted in the HMW subunit genes being an attractive target for genetic transformation, in order to increase their gene copy number and hence dough strength.

The first studies of this type were reported over 10 years ago ( Altpeter et al. , 1996 ; Blechl and Anderson, 1996 ; Barro et al. , 1997 ) and many studies have since been reported (reviewed by Shewry and Jones, 2005 ; Jones et al. , 2009 ). It is perhaps not surprizing that the results have been ‘mixed’, but some conclusions can be drawn. Firstly, expression of an additional HMW subunit gene can lead to increased dough strength, even when a modern good quality wheat cultivar is used as the recipient (see Field et al. , 2008 ; Rakszegi et al. , 2008 , as recent examples, and reviews of earlier work cited above). However, the effect depends on the precise HMW subunit gene which is used and on the expression level, with the transgenes resulting in over-strong (ie too elastic) gluten properties in some studies. Thus, although transgenesis is a realistic strategy to increase dough strength in wheat, it is also necessary to have an understanding of the underlying mechanisms in order to optimize the experimental design.

Wheat is widely consumed by humans, in the countries of primary production (which number over 100 in the FAO production statistics for 2004) and in other countries where wheat cannot be grown. For example, imported wheat is used to meet consumer demands for bread and other food products in the humid tropics, particularly those with a culinary tradition dating back to colonial occupation. Statistics are not available for the total volume of wheat which is consumed directly by humans as opposed to feeding livestock, although figures for the UK indicate about one-third of the total production (approximately 5.7 m tonnes per annum are milled with home production being 15–16 m tonnes). Globally there is no doubt that the number of people who rely on wheat for a substantial part of their diet amounts to several billions.

The high content of starch, about 60–70% of the whole grain and 65–75% of white flour, means that wheat is often considered to be little more than a source of calories, and this is certainly true for animal feed production, with high-yielding, low-protein feed varieties being supplemented by other protein-rich crops (notably soybeans and oilseed residues).

However, despite its relatively low protein content (usually 8–15%) wheat still provides as much protein for human and livestock nutrition as the total soybean crop, estimated at about 60 m tonnes per annum (calculated by Shewry, 2000 ). Therefore, the nutritional importance of wheat proteins should not be underestimated, particularly in less developed countries where bread, noodles and other products (eg bulgar, couscous) may provide a substantial proportion of the diet.

Protein content

Although wheat breeders routinely select for protein content in their breeding programmes (high protein for breadmaking and low protein for feed and other uses), the current range of variation in this parameter in commercial cultivars is limited. For example, Snape et al. (1993) estimated that typical UK breadmaking and feed wheats differed in their protein content by about 2% dry weight (eg from about 12–14% protein) when grown under the same conditions, which is significantly less than the 2-fold differences which can result from high and low levels of nitrogen fertilizer application. This limited variation in conventional wheat lines has led to searches for ‘high protein genes’ in more exotic germplasm.

Early studies of the USDA World Wheat Collection showed approximately 3-fold variation in protein content (from 7–22%), with about one-third of this being under genetic control ( Vogel et al. , 1978 ). However, the strong environmental impact on protein content (accounting for two-thirds of the variation) underpins the difficulty of breeding for this trait. Nevertheless, some success has been achieved by incorporating sources of variation from exotic bread wheat lines or related wild species.

The former include Atlas 50 and Atlas 66, derived from the South American line Frandoso, and Nap Hal from India. These lines appear to have different ‘high protein genes’ and both were extensively used in breeding programmes in Nebraska with the Atlas 66 gene being successfully incorporated into the commercial variety Lancota ( Johnson et al. , 1985 ). Frandoso and related Brazilian lines have also been successfully exploited in other breeding programmes in the USA ( Busch and Rauch, 2001 ). The Kansas variety, Plainsman V, also contained a high protein gene(s) from a related Aegilops species ( Finney, 1978 ).

The most widely studied source of ‘high protein’ is wild emmer (tetraploid Tr . turgidum var. dicoccoides ) wheats from Israel. One accession, FA15-3, accumulates over 40% of protein when grown with sufficient nitrogen ( Avivi, 1978 ). The gene in this line was mapped to a locus on chromosome 6B (called Gpc-B1 ), which accounted for about 70% of the variation in protein content in crosses ( Chee et al. , 2001 ; Distelfeld et al. , 2004, 2006 ). More recent studies have shown that the gene Gpc-B1 encodes a transcription factor which accelerates senescence in the vegetative parts of the plant, resulting in increased mobilization and transfer to the grain of both nitrogen and minerals (notably iron and zinc) ( Uauy et al. , 2006 ). However, it remains to be shown whether this gene can be incorporated into high-yielding and commercially viable lines.

Protein composition

Of the 20 amino acids commonly present in proteins, 10 can be considered to be essential in that they cannot be synthesized by animals and must be provided in the diet. Furthermore, if only one of these is limiting the others will be broken down and excreted. There has been much debate about which amino acids are essential and the amounts that are required, with the most recent values for adult humans being shown in Table 1 . This table includes a combined value for the two aromatic amino acids, tyrosine and phenylalanine, which are biosynthetically related, and both single and combined values for the two sulphur-containing amino acids: methionine, which is truly essential, and cysteine which can be synthesized from methionine. Comparison with the values for whole wheat grain and flour shows that only lysine is deficient, with some essential amino acids being present in considerably higher amounts than the requirements. However, the lysine content of wheat also varies significantly with the values shown in Table 1 being typical of grain of high protein content and the proportion increasing to over 30 mg g −1 protein in low protein grain ( Mossé and Huet, 1990 ). This decrease in the relative lysine content of high protein grain results from proportional increases in the lysine-poor gluten proteins when excess N is available (for example, when fertilizer is applied to increase grain yield and protein content) and also accounts for the lower lysine content of the white flour (the gluten proteins being located in the starchy endosperm tissue).

Recommended levels of essential amino acids for adult humans compared with those in wheat grain and flour (expressed as mg g −1 protein)

Amino acid proteinFAO/WHO/UNU Wheat
Grain Flour
Histidine152322
Isoleucine303736
Leucine596867
Lysine452822
Methionine+cysteine223538
    methionine
    cysteine
Phenylalanine+tyrosine386463
Threonine232926
Tryptophan61111
Valine394441
Total indispensable amino acids277339326
Amino acid proteinFAO/WHO/UNU Wheat
Grain Flour
Histidine152322
Isoleucine303736
Leucine596867
Lysine452822
Methionine+cysteine223538
    methionine
    cysteine
Phenylalanine+tyrosine386463
Threonine232926
Tryptophan61111
Valine394441
Total indispensable amino acids277339326

FAO/WHO/UNU (2007) .

Calculated from literature values as described in Shewry (2007) .

The amino acid requirements for infants and children vary depending on their growth rate, being particularly high in the first year of life. Similarly, higher levels of essential amino acids are required for rapidly growing livestock such as pigs and poultry.

Wheat as a source of minerals

Iron deficiency is the most widespread nutrient deficiency in the world, estimated to affect over 2 billion people ( Stoltzfus and Dreyfuss, 1998 ). Although many of these people live in less developed countries, it is also a significant problem in the developed world. Zinc deficiency is also widespread, particularly in Sub-Saharan Africa and South Asia, and has been estimated to account for 800 000 child deaths a year (Micronutrient Initiative, 2006 ), in addition to non-lethal effects on children and adults. Wheat and other cereals are significant sources of both of these minerals, contributing 44% of the daily intake of iron (15% in bread) and 25% of the daily intake of zinc (11% in bread) in the UK ( Henderson et al. , 2007 ). There has therefore been considerable concern over the suggestion that the mineral content of modern wheat varieties is lower than that of older varieties.

This was initially suggested by Garvin et al. (2006) who grew 14 red winter wheat cultivars bred between 1873 and 2000 in replicate field experiments and determined their mineral contents. Plants were grown at two locations in Kansas and significant negative correlations were found between grain yield, variety release date, and the concentrations of zinc in material from both of these sites and of iron in materials from only one site. Similar trends were reported by Fan et al. (2008 a , b ) who took a different approach. Rather than carrying out direct comparisons of varieties in field trials, they analysed grain grown on the Rothamsted Broadbalk long-term wheat experiment. This experiment was established in 1843 and uses a single variety which is replaced by a more modern variety at regular intervals. Analysis of archived grain showed significant decreases in the contents of minerals (Zn, Fe, Cu, Mg) since semi-dwarf cultivars were introduced in 1968. A similar difference was observed between the cultivars Brimstone (semi-dwarf) and Squareheads Master (long straw) which were grown side by side in 1988–1990, the concentrations of Zn, Cu, Fe, and Mg being 18–29% lower in Brimstone. A more recent comparison of 25 lines grown also showed a decline in the concentrations of Fe and Zn since semi-dwarf wheats were introduced ( Zhao et al. , 2009 ) ( Fig. 3 ). Although the decrease in the mineral content of modern wheats is partly due to dilution, resulting from increased yield (which was negatively correlated with mineral content), it has been suggested that short-strawed varieties may be intrinsically less efficient at partitioning minerals to the grain compared with the translocation of photosynthate.

The relationship between the iron content of wholemeal flours from 25 wheat cultivars grown on six trial sites/seasons and their release dates. Taken from Zhao et al. (2009) and reproduced by kind permission of Elsevier.

Such genetic differences in mineral content are clearly relevant to international efforts to increase the mineral content of wheat to improve health in less developed countries. Thus, increasing iron, zinc, and vitamin A contents are a major focus of the HarvestPlus initiative of the Consultative Group on International Agricultural Research (CGIAR) which is using conventional plant breeding ( Ortiz-Monasterio et al. , 2007 ) while other laboratories are using genetic engineering approaches (reviewed by Brinch-Pedersen et al. , 2007 ).

These initiatives are focusing not only on contents of minerals but also on their bioavailability. Iron is predominantly located in the aleurone and as complexes with phytate ( myo -inositolphosphate 1,2,3,4,5,6-hexa-kisphosphate). These complexes are largely insoluble, restricting mineral availability to humans and livestock. The use of transgenesis to express phytase in the developing grain can result in increased mineral availability, particularly when a heat-stable form of the enzyme is used to allow hydrolysis to occur during food processing (reviewed by Brinch-Pedersen et al. , 2007 ).

Guttieri et al. (2004) also reported an EMS-induced low phytate mutant of wheat. This mutation resulted in 43% less phytic acid in the aleurone, but has not so far been incorporated into commercial cultivars. However, previous experience with low phytic acid mutants of maize, barley, and soy bean has shown that they may also have significant effects on yield and germination rates (reviewed by Brinch-Pedersen et al. , 2007 ).

Wheat as a source of selenium

Selenium is an essential micronutrient for mammals (but not plants), being present as selenocysteine in a number of enzymes. However, it is also toxic when present in excess (above about 600 μg d −1 ; Yang and Xia, 1995 ). Cereals are major dietary sources of selenium in many parts of the world, including China ( FAO/WHO, 2001 ), Russia ( Golubkina and Alfthan, 1999 ), and the UK (MAFF, 1997). However, the content of selenium in wheat varies widely from about 10 μg kg −1 to over 2000 μg kg −1 ( FAO/ WHO, 2001 ; Combs, 2001 ).

The concentration of selenium in wheat is largely determined by the availability of the element in the soil. Consequently, wheat produced in Western Europe may contain only one-tenth of the selenium that is present in wheat grown in North America. Thus, a survey of 452 grain samples grown in the UK in 1982 and 1992 showed a mean value of 27 μg Se kg −1 fresh weight ( Adams et al. , 2002 ) compared with 370 μg SE kg −1 fresh weight for 290 samples from the USA ( Wolnik et al. , 1983 ).

Because the import of wheat from North America into Europe has declined over the last 25 years, the intake of selenium in the diet has also decreased, which has resulted in concern in some European countries. One response to this is to apply selenium to the crops in fertilizer (called biofortification), which is practised in Finland ( Eurola et al. , 1991 ).

Unlike iron, selenium is not concentrated in the aleurone, being present wherever sulphur is present. The concentration of selenium in grain from the Broadbalk continuous wheat experiment also appeared to be determined principally by the sulphur availability in the soil (which competes to prevent selenium uptake), with no evidence of decreased levels over time ( Fan et al. , 2008 b ). However, sulphur fertilizer is often applied to wheat to improve the grain quality ( Zhao et al. , 1997 ) and this could clearly have negative impacts on selenium in grain.

The reader is referred to a recent review article by Hawkesford and Zhao (2007) for a detailed review of selenium in wheat.

Wholegrain wheat and health

The consumption of white flour and bread have historically been associated with prosperity and the development of sophisticated roller mills in Austro-Hungary during the second part of the 19th century allowed the production of higher volumes of whiter flour than it was possible to produce by traditional milling based on grinding between stones and sieving (see Jones, 2007 , for a fascinating account of the history of roller milling). However, the increased consumption of bread made from highly refined white flour was not accepted universally, leading to what we would today recognize as a movement to increase the consumption of wholegrain products.

In 1880, May Yates founded the Bread Reform League in London to promote a return to wholemeal bread, particularly to improve the nutrition of the children of the poor, and suggested in 1909 that an official minimum standard of 80% flour extraction rate should be adopted. This was called ‘Standard Bread’. Although we now appreciate the nutritional advantages of wholegrain products, this was not supported by the science of the time and clearly conflicted with the tastes of consumers as well as the economics of bread production. Nevertheless, the League continued to campaign and received scientific support in 1911 when Gowland Hopkins agreed that ‘Standard Bread’ may contain ‘unrecognized food substances’ which were vital for health: these were subsequently called vitamins ( Burnett, 2005 ).

By contrast, Thomas Allinson (1858–1918) had a much greater impact by marketing and vigorously promoting his own range of wholemeal products. He can therefore be regarded as the father of the wholegrain movement and remains a household name to this day in the UK ( Pepper, 1992 ).

We now know that wholegrain wheat products contain a range of components with established or proposed health benefits which are concentrated or solely located in the bran. Hence they are either present in lower amounts or absent from white flour which is derived almost exclusively from starchy endosperm cells. They vary widely in their concentrations. For example, lignans, a group of polyphenols with phytoestrogen activity, are present at levels up to about 10 μg g −1 in wholemeal wheat and twice this level in bran ( Nagy-Scholz and Ercsey, 2009 ), while total phenolic acids in wholemeal range up to almost 1200 μg g −1 ( Li et al. , 2008 ).

The most detailed study of wheat phytochemicals which has so far been reported was carried out as part of the EU Framework 6 HEALTHGRAIN programme ( Poutanen et al. , 2008 ; Ward et al. , 2008 ). This study determined a range of phytochemicals in 150 wheat lines grown on a single site in one year, meaning that the levels of the components may have been influenced by environmental as well as genetic effects. The lines were selected to represent a broad range of dates and places of origin. The choice of phytochemicals focused on those which have putative health benefits and for which cereals are recognized dietary sources. For example, cereals are considered to account for about 22% of the daily intake of folate (vitamin B12) in the UK ( Goldberg, 2003 ) and 36% and 43% of the daily intake in Finnish women and men, respectively ( Findiet Study Group, 2003 ). In the HEALTHGRAIN study the contents of folates in wholemeal varied from 364 to 774 ng g −1 dry weight in 130 winter wheats and from 323 to 741 ng g −1 dry weight in 20 spring wheats, with the content in the former being positively correlated with bran yield and negatively correlated with seed weight (indicating concentration in the bran) ( Piironen et al. , 2008 ).

The quantitatively major group of phytochemicals in the wheat grain is phenolic acids, derivatives of either hydroxybenzoic acid or hydroxycinnamic acid. Epidemiological studies indicate that phenolic acids have a number of health benefits which may relate to their antioxidant activity; the total antioxidant activities of grain extracts and their phenolic acid contents being highly correlated ( Drankham et al. , 2003 ; Beta et al. , 2005 ; Wende et al. , 2005 ).

Cereals are also significant sources of tocols (which include vitamin E) (27.6–79.7 μg g −1 in the HEALTHGRAIN study) ( Lampi et al. , 2008 ) and sterols (670–959 μg g −1 ) ( Nurmi et al. , 2008 ).

The HEALTHGRAIN study also determined the levels of dietary fibre. In wheat, this mainly derives from cell wall polymers: arabinoxylans (approximately 70%) with lower amounts of (1-3)(1-4)β- D -glucans (approximately 20%) and other components. The arabinoxylans also occur in soluble and insoluble forms, with the latter being rich in bound phenolic acids which form oxidative cross-links. These bound phenolic acids account, on average, for 77% of the total phenolic acid fraction and are predominantly ferulic acid. Soluble fibre is considered to have health benefits ( Moore et al. , 1998 ; Lewis and Heaton, 1999 ) which are not shared by insoluble fibre and these may therefore be reduced by the phenolic acid cross-linking. However, insoluble fibre may also have benefits in delivering phenolic antioxidants into the colon: these benefits may include reduction in colo-rectal cancer ( Vitaglione et al. , 2008 ).

The HEALTHGRAIN study showed wide variation in the contents of total and water-extractable arabinoxylans in both white flour and bran fractions ( Gebruers et al. , 2008 ) ( Fig. 4 ). Similarly, Ordaz-Ortiz et al. (2005) showed variation from 0.26% to 0.75% dry weight in the content of water-extractable arabinoxylan in 20 French wheat lines and from 1.66% to 2.87% dry weight in total arabinoxylans. A high proportion of the variation in water-extractable arabinoxylans is also heritable ( Martinant et al. , 1999 ).

Contents of arabinoxylan (AX) fibre in flour and bran of 150 wheat cultivars grown on a single site as part of the EU FP6 HEALTHGRAIN project. (A) Total AX in flour (mg g −1 ); (B) water-extractable AX in flour (%); (C) total AX in bran (mg g −1 ), and (D) water-extractable AX in bran. Prepared from data reported by Gebruers et al. (2008) with permission of the authors.

It is clear from these and other studies that there is sufficient genetically determined variation in the phytochemical and fibre contents of wheat to be exploited in breeding for varieties with increased nutritional benefits.

Allergy to wheat

Both respiratory and food allergies to wheat have been reported.

Respiratory allergy (bakers' asthma) has been known since Roman times (when slaves handling flour and dough were required to wear masks) and is currently one of the most important forms of occupational allergy. For example, it is the second most widespread occupational allergy in the UK and has been reported to affect over 8% of apprentice bakers in Poland after only 2 years exposure ( Walusiak et al. , 2004 ). A wide range of wheat grain proteins have been shown to react with immunoglobulin (Ig)E in sera of patients with bakers' asthma, including gliadins, glutenins, serpins (serine proteinase inhibitors), thioredoxin, agglutinin, and a number of enzymes (α- and β-amylases, peroxidase, acyl CoA oxidase, glycerinaldehyde-3-phosphate dehydrogenase and triosephosphate isomerase) (reviewed by Tatham and Shewry, 2008 ). However, it is clear that the predominant wheat proteins responsible for bakers' asthma are a class of α-amylase inhibitors, also known as CM proteins due to their solubility in chloroform:methanol mixtures ( Salcedo et al. , 2004 ). Furthermore, their activity has been demonstrated by a range of approaches including skin pricks and RAST (radioallergosorbent test) as well as immunoblotting, ELISA, and screening expression libraries with IgE fractions.

The CM proteins comprise monomeric, dimeric, and tetrameric forms, with subunit masses ranging between about 10 000 and 16 000. They differ in their spectrum of activity but all inhibit mammalian and insect α-amylases (including those in some pest organisms) rather than endogenous wheat enzymes. Hence, they are considered to be protective rather than regulatory in function. Eleven individual subunits have been shown to play a role in bakers' asthma (using one or more of the assays listed above) but they differ in their activity, with a glycosylated form of CM16 being particularly active.

Wheat is listed among the ‘big eight’ food allergens which together account for about 90% of all allergic responses. However, the incidence of true (ie IgE-mediated) food allergy is, in fact, fairly infrequent in adults, although it may affect up to 1% of children ( Poole et al. , 2006 ). A number of wheat proteins have been reported to be responsible for allergic responses to the ingestion of wheat products but only one syndrome has been studied in detail. Wheat-dependent exercise-induced anaphylaxis (WDEIA) is a well-defined syndrome in which the ingestion of a product containing wheat followed by physical exercise can result in an anaphylactic response. Work carried out by several groups has clearly established that this condition is associated with a group of ω-gliadins (called ω5-gliadins) which are encoded by genes on chromosome 1B ( Palosuo et al. , 2001 ; Morita et al. , 2003 ; Battais et al. , 2005 ). Mutational analysis has also identified immunodominant epitopes in the ω5-gliadins: short glutamine-rich and proline-rich sequences present in the repetitive domains of the proteins ( Matsuo et al. , 2004 , 2005 ; Battais et al. , 2005 ). However, a number of other proteins have also been shown to react with IgE from patients with WDEIA, including gliadins, glutenin subunits, and related proteins from barley and rye (reviewed by Tatham and Shewry, 2008 ).

Other allergic responses to wheat proteins include atopic dermatitis, urticaria, and anaphylaxis. Not surprizingly, these symptoms have been associated with a number of wheat proteins, most notably gluten proteins but also CM proteins, enzymes, and lipid transfer protein (LTP) (reviewed by Tatham and Shewry, 2008 ).

Comparison of the proteins identified as responsible for the respiratory and food allergy shows significant overlap in their functions (most being storage or protective) and identities (notably gluten proteins and CM proteins).

Intolerance to wheat

Dietary intolerance to wheat is almost certainly more widespread than allergy, notably coeliac disease (CD) which is estimated to affect 1% of the population of Western Europe ( Feighery, 1999 ), and dermatitis herpetiformis which has an incidence between about 2-fold and 5-fold lower than CD ( Fry, 1992 ).

CD is a chronic inflammation of the bowel which leads to malabsorption of nutrients. Like bakers' asthma, CD has been known since classical times but it was only defined in detail in 1887 and its relationship to wheat established by Dicke in the late 1940s ( Losowsky, 2008 ).

A series of elegant studies carried out over the past decade, particularly by Sollid, Koning and co-workers, have established that CD results from an autoimmune response which is triggered by the binding of gluten peptides to T cells of the immune system in some (but not all) individuals with the human leucocyte antigens (HLAs) DQ2 or DQ8, expressed by specialized antigen-presenting cells. The presented peptides are then recognized by specific CD4+ T cells which release inflammatory cytokines which lead to the flattening of the intestinal epithelium. It has also been demonstrated that tissue transglutaminase enzyme present in the epithelium of the intestine plays an important role, generating toxic peptides by deamidation of glutamine residues to give glutamate.

The HLA-DQ2 antigen is present in about 95% of coeliac patients ( Karell et al. , 2003 ) and detailed studies have identified the peptide sequences which are recognized by intestinal T cell lines, using either peptide fractions produced from gluten proteins or synthetic peptides. This has led to the definition of two overlapping immunodominant epitopes corresponding to residues 57–68 (α-9) and 62–75 (α-2) of A gliadin (a form of α-gliadin) ( Arentz-Hansen et al. , 2000 , 2002 ; Anderson et al. , 2000 ; Ellis et al. , 2003 ). Related epitopes were similarly defined in γ-gliadins, corresponding to residues 60–79, 102–113, 115–123, and 228–236 ( Sjöström et al. , 1998 ; Arentz-Hansen et al. , 2002 ; Vader et al. , 2002 a ). Furthermore, Vader et al. (2002 b ) showed that the spacing between glutamine and proline residues determined the specificity of glutamine deamidation and hence peptide activation, and developed algorithms to predict the presence of novel T cell stimulatory peptides in gluten proteins and in related proteins from other cereals.

Less work has been carried out on the determinants of the HLA8-DQ8 associated coeliac disease, which affects only about 6% of patients without HLA-DQ2 and 10% of patients with HLA-DQ2 ( Karell et al. , 2003 ). This has again allowed immunodominant epitopes to be identified in gliadins and glutenin subunits ( van der Wal et al. , 1998 , 1999 ; Mazzarella et al. , 2003 ; Tollefsen et al. , 2006 ) although detailed structural studies indicate that the HLA-DQ2 and HLA-DQ8-mediated forms of the disease may differ in their molecular mechanisms ( Henderson et al. , 2007 ).

The possibility of producing wheat which lacks the coeliac toxic peptides has been discussed for many years but interest in the strategy tended to decline as it became clear that most, if not all, gluten proteins are toxic to at least some susceptible individuals, rather than only the α-gliadins as initially thought. However, Spaenij-Dekking et al. (2005) and van Herpen et al. (2006) have shown that it is possible to identify natural forms of gliadin which have few or no coeliac toxic epitopes, raising the possibility of selecting for less toxic lines of wheat by classical plant breeding. RNA interference (RNAi) technology has also been used to silence the α-gliadin ( Becker et al. , 2006 ; Wieser et al. , 2006 ) and γ-gliadin ( Gil-Humanes et al. , 2008 ) gene families, although some effects on grain-processing properties were observed.

The combination of these two approaches may therefore allow the production of less toxic, if not non-toxic, wheat for coeliac patients without significant loss of the processing properties conferred by the gluten proteins.

Dermatitis herpetiformis is a skin eruption resulting from ingestion of gluten, and is associated with the deposition of IgA antibodies in dermal papillae. These include IgA antibodies to epidermal transglutaminase which is considered to be an important autoantigen in disease development ( Hull et al. , 2008 ).

Other medical conditions related to gluten proteins

There are many reports of the association of wheat, and particularly wheat proteins, with medical conditions, ranging from improbable reports in the popular press to scientific studies in the medical literature. Not surprisingly, they include autoimmune diseases such as rheumatoid arthritis which may be more prevalent in coeliac patients and relatives ( Neuhausen et al. , 2008 ). It is perhaps easier to envisage mechanisms for relationships between such diseases which have a common immunological basis ( Hvatum et al. , 2006 ) than to explain a well-established association between wheat, coeliac disease, and schizophrenia ( Singh and Roy, 1975 ; Kalaydiian et al. , 2006 ) Other reported associations include ones with sporadic idiopathic ataxia (gluten ataxia) ( Hadjivassiliou et al. , 2003 ), migraines ( Grant, 1979 ), acute psychoses ( Rix et al. , 1985 ), and a range of neurological illnesses ( Hadjivassiliou et al. , 2002 ). An association with autism has also been reported ( Lucarelli et al. , 1995 ) with some physicians recommending a gluten-free, casein-free diet ( Elder, 2008 ).

Some of these effects may be mediated via the immune system but effects which are not immune-mediated are notoriously difficult to define and diagnose. However, they could result from the release within the body of bioactive peptides, derived particularly from gluten protein. Thus, gluten has been reported to be a source of a range of such peptides including opioid peptides (exorphins) ( Takahashi et al. , 2000 ; Yoshikawa et al. , 2003 ) and an inhibitor of angiotensin I-converting enzyme ( Motoi and Kodama, 2003 ) (see also reviews by Dziuba et al. , 1999 ; Yamamoto et al. , 2003 ). However, these activities were demonstrated in vitro and their in vivo significance has not been established.

There is little doubt that wheat will retain its dominant position in UK and European agriculture due to its adaptability and consumer acceptance. However, it may also need to adapt to face changing requirements from farmers, food processors, governments, and consumers.

Reducing inputs

Currently grown wheat cultivars require high inputs of nitrogen fertilizer and agrochemicals to achieve high yields combined with the protein content required for breadmaking. For example, UK farmers currently apply 250–300 kg N ha −1 in order to achieve the 13% protein content required for the Chorleywood Breadmaking Process, which is the major process used for breadmaking in the UK. Since a 10 tonnes ha −1 crop containing 13% protein equates to about 230 kg N ha −1 , this means that 50–70 kg N ha −1 may be lost. As fertilizer N currently costs about £1 kg −1 this represents a significant financial loss as well as a loss of the energy required for fertilizer production and may also have environmental consequences.

A number of projects worldwide are therefore focusing on understanding the processes that determine the efficiency of uptake, assimilation, and utilization of nitrogen in order to improve the efficiency of nitrogen recovery in the grain (reviewed by Foulkes et al. , 2009 ).

Reducing the nitrogen requirement of wheat does not only relate to the grain protein content, as an adequate supply of nitrogen is also essential for high wheat yields in order to build a canopy and fix carbon dioxide by photosynthesis. Furthermore, a substantial proportion of this nitrogen is remobilized and redistributed to the developing grain during canopy senescence ( Dalling, 1985 ). Hawkesford and colleagues at Rothamsted Research have targeted this process in order to develop a strategy for improving the recovery of N in the grain, using a combination of biochemical analysis and metabolite and transcript profiling to identify differences in metabolites and gene expression which are associated with efficient mobilization and redistribution ( Howarth et al. , 2008 ). Some of the genes identified in these and similar studies are suitable candidates for manipulation to increase the proportion of the total nitrogen recovered in the grain.

Stability of quality

The increases in temperature and carbon dioxide concentration associated with climate change are expected to have effects on crop development and yield, although the magnitude of these is difficult to predict due to interactions with other factors which may also be affected, notably water availability and populations of pests and pathogens ( Coakley et al. , 1999 ; Semenov, 2008 ). Similarly, although it is generally accepted that higher growth temperatures result in greater dough strength, the precise effects are not clearly understood (see review by Dupont and Altenbach, 2003 ) with heat stress (ie above 30–33 °C) actually resulting in dough weakening and reduced quality ( Blumenthal et al. , 1993 ). A recent review of the effects of CO 2 concentration on grain quality also failed to draw clear conclusions ( Högy and Fangmeier, 2008 ).

Of more immediate interest to wheat breeders and grain-utilizing industries are year-to-year fluctuations in growth conditions, and the frequency and magnitude of such fluctuations are also predicted to increase in the future ( Porter and Semenov, 2005 ). Although some cultivars are generally considered to be more consistent in quality than others, this is largely anecdotal with no detailed scientific comparisons.

Given the recent advances in ‘omics’ technologies it should now be possible to dissect the effects of G×E on grain development and quality, and to establish markers suitable for use in plant breeding. However, this will require substantial resources and a multi-disciplinary approach: by growing mapped populations and lines in multi-site/multi-year trials and determining aspects of composition and quality from gene expression profiling to pilot scale breadmaking.

Wan et al. (2009) have reported the application of this approach using a limited set of seven doubled haploid lines to identify a number of transcripts whose expression profile was associated with quality traits independently of environmental conditions. Millar et al. (2008) also reported a larger study in which three doubled haploid populations of wheat were used to map novel QTLs (quantitative trait loci) for breadmaking and pastry making which were stable over two years field trials, but did not relate quality traits to gene expression profiles.

Wheat is an attractive option as a ‘first generation biofuel’ as the high content of starch is readily converted into sugars (saccharification) which can then be fermented into ethanol. Murphy and Power (2008) recently reported that the gross energy recovered in ethanol using wheat was 66 GJ ha −1 a −1 , but that this only corresponds to 50% energy conversion and that the net energy production is as low as 25 GJ ha −1 a −1 . The same authors also calculated that the net energy production could be increased to 72 GJ ha −1 a −1 if the straw was combusted and the residue after distillation, called stillage or distillers grain and solubles (DGS), was converted to biogas (biomethane).

A major concern about using wheat grain for biofuel production is the high energy requirement for crop production, including that required to produce nitrogenous fertilizer. It is therefore necessary to develop new crop management strategies to reduce inputs ( Loyce et al. , 2002 ) as well as exploiting wheats with low N input requirements combined with high starch contents ( Kindred et al. , 2008 ).

The second major concern is, of course, the impact on international grain prices which may exacerbate problems of grain supply to less affluent populations.

New benefits to consumers

The increasing awareness of the important role of wheat-based products in a healthy diet has been discussed above, focusing on the identification and exploitation of natural variation in bioactive components. However, in some cases the natural variation in a trait may be limited in extent or difficult to exploit and, in this case, other approaches may be required. Currently, the most important target of this type of approach is resistant starch.

Most of the starch consumed in the human diet, including wheat starch, is readily digested in the small intestine, resulting in a rapid increase in blood glucose which may contribute to the development of type 2 diabetes and obesity ( Sobal, 2007 ). However, a fraction of the starch may resist digestion and pass through the small intestine to the colon, where it is fermented to short chain fatty acids, notably butyrate, which may have health benefits including reduction of colo-rectal cancer (as discussed by Topping, 2007 ).

Although the proportion of resistant starch (RS) depends on a number of factors including the effects of food processing, the most widely studied form is high amylose starch. In most species, amylose accounts for 20–30% of starch and amylopectin for 70–80%. However, mutant lines have been identified in a number of species in which the proportion of amylose is increased up to about 40% (e.g. Glacier barley; Yoshimoto et al. , 2000 ).

Selection for high amylose mutants is relatively easy in a diploid species such as barley, but more painstaking approaches are required in hexaploid wheat as mutations in homoeologous genes on all three genomes may be required to have a significant effect on the phenotype. This has been demonstrated very elegantly by Yamamori et al. (2000) who combined mutations in the gene encoding the starch synthase II enzyme (also called starch granule protein 1) that catalyses the synthesis of amylopectin. The resulting triple mutant line contained about 37% amylose.

However, the complexity of starch biosynthesis means that similar high amylose phenotypes can result from changes in other enzymes, with a notable example being the use of RNA interference technology to down-regulate the gene encoding starch-branching enzyme IIa ( Regina et al. , 2006 ). The resulting transgenic lines had up to 80% amylose and increased RS as measured in rat feeding trials. This study demonstrates the power of GM technology, although it remains to be shown that lines with such high levels of amylose will have acceptable yields and properties for milling and processing.

It also remains to be shown that consumers will be prepared to eat bread and other foods produced from GM wheat. The wheat grain and its products have been treated with reverence by humans for millennia and GM wheat may just be regarded as a step too far, even in countries in which other GM crops are currently accepted.

I wish to thank all of my colleagues and collaborators who have contributed to the work discussed in this article, Professor John Snape and the John Innes Institute for providing Fig. 1 and Dr Jane Ward (Rothamsted) for preparing Fig. 4 . Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences Research Council of the UK.

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Home > Books > Wheat - Recent Advances

Introductory Chapter: Advancements in Wheat Research

Published: 16 November 2022

DOI: 10.5772/intechopen.108193

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Wheat - Recent Advances

Edited by Mahmood-ur-Rahman Ansari

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Author Information

Abdul manan.

  • Department of Bioinformatics and Biotechnology, G.C. University, Faisalabad, Pakistan

Usman Ali Ashfaq *

Mahmood-ur-rahman ansari.

*Address all correspondence to: [email protected]

1. Introduction

Wheat is the most common food crop we grow. In 2018, it was grown on 214 million hectares, which is approximately 30% of the total land area sown to cereal crops. With an average yield of about 3.4 tonnes per hectare, 734 million tonnes of wheat were produced in 2018 [ 1 ]. Wheat’s role in human nutrition is another way to show how important it is. Approximately 20% of our protein intake and 20% of our carbohydrate intake come from wheat. Also, wheat represents our most traded cereal [ 2 ]. In 2018, over 118 mt of wheat was exported, which was 40% of all cereal exports. Australia, which is a major wheat producer, has average yields of less than 2 tons/ha, whereas yields in the UK are normally somewhere around 7 tons/ha and 8 tons/ha and are well more than 10 tons/ha in several zones [ 3 ]. Early in 2020, New Zealand had the maximum wheat yield ever observed: 17.4 t/ha. In many other places, it is hard to get more than 1 t/ha. The wide range of wheat yields shows how different the places where wheat is grown [ 4 ]. Due to variable environments, breeding programs usually concentrate on specific target areas. Breeding of wheat has been done mostly by the general public around the world, with help from governmental bodies or farmer groups. However, this has gradually evolved as profit incentive in breeding has grown, particularly in the EU and countries like Australia in which there is a clear way to make money from the benefits of improved varieties [ 5 ].

2. Recent developments in wheat for drought stress tolerance

Early on, the breeders realized that making wheat mature at right time for growing season was the most important adaptation trait that had to do with yield. The most important thing that affects yield, or more accurately, yield potential, is how much water is available. To get the most out of the crop, breeders look for patterns that mature and develop throughout the growth period. There is usually, although not always a proper relationship between both grain yield and biomass, growers try to time the development of the plant to coincide with when there is enough water. In cool climates, planting in the fall gives plants time to grow roots before the cold weather of winter, and then they grow quickly in the spring and early summer. If the winters are too cold and there is a chance of freezing damage, growers need to select varieties that can be planted after the danger has receded. They should also endeavor to extend the planting season as late as possible before drought and heat stress slow growth. This is different from hot season crops, whose growing season is cut short by killing frosts in temperate zones [ 6 ]. If there is enough irrigation water, the season can be extended into the summer. If there is not enough fertigation water, early maturing lines are needed. In Mediterranean-type climates, where it rains in the winter but is hot and dry in the summer, biomass can be built by planting in the fall and letting the plants grow over the winter. However, the plants need to be mature early enough to be harvested before every dry season.

It is well known how important it is to match maturity to the growing season, and so this attribute is usually well organized in existing projects by using genes and loci for earliness, vernalization, and photoperiod response. There might still be ways to change the various growth stages to better match the environment, but generally, the way elite varieties grow and develop makes sure that the crop could indeed take benefit of the times when there is enough water and then flower and have full seed before the end of the season. Even though adjusting development to the atmosphere has been important for increasing the yield of wheat, complications stem during unusual stages when the growth trajectory of elite varieties does not match the patterns of rainfall and temperature [ 7 ]. This problem is getting worse because the weather is becoming more unpredictable. Farmers know that some periods will be severe and that they may take a loss. However, good years can make up for this. The fact that bad years are happening more and more often is a big problem, and farmers are looking for varieties that will do well in good years but cause less damage in bad ones. Breeders try to solve this problem by looking for ways to use water more efficiently and reduce the effects of things that might make yield stability less stable.

3. Recent developments in wheat for high temperature tolerance

Wheat production is in danger from many environmental factors. For example, the last 10 years (2010–2019) were the warmest on record, and the steady temperature rise is thought to have caused many changes in the way the climate system works [ 8 ]. In its Fifth Assessment Report, the Intergovernmental Panel on Climate Change said that by 2050, the average global temperature could rise by 2–5°C, or even more, and rain trends are likely to become less consistent [ 9 ]. “High confidence” says that these changes, like more frequent extreme events, are having an effect on food security. Food insecurity has effects that reach far and wide. Hagel, who used to be the head of the US Department of Defense, said that changes in climate “can add a lot to the problems of global instability, hunger, poverty, and war.” In 2018, climate was found to be a cause of “crisis-level acute food insecurity” in 26 of the 33 countries where it happened [ 10 ]. Since Russia, India, France, China, and the United States produce 50% of the world’s wheat, “any weather shock or external shock to production in these countries will have an immediate effect on global prices and price volatility.” Improving how efficiently food is made is seen as a key way to make food production more sustainable in the future. About half of the world’s wheat is affected by excessive heat, and 20 million ha or more often have too little water [ 11 ].

Models show that there is a chance that crops in global “breadbaskets” could fail at the same time because of heat or drought, and variation in rainfall and temperature (including drought) are indeed blamed for 40% of the variation in the production of wheat from 1 year to the next. By the end of this century, severe water shortages are predicted in approximately 60% of the world’s cereal growing regions, and each 1°C temperature rise is expected to decrease yield by a mean of 6% [ 12 ]. A few other research and modeling research shows that increasing the level of CO 2 in the atmosphere at least to some extent counteract the harmful effects of drought and heat stress, but the data are not constant [ 13 ]. Also, the models do not take into account the harmful effects of rising night-time temperatures, thermal shocks, unsteady rain patterns, and dietary components, which are not helped by higher CO 2 levels.

4. Recent developments in wheat for quality improvement

Wheat can be used for many different things, and each of these needs different qualities. The most important is GPC (grain protein content), which is a key factor how well a grain makes bread and pasta. Farmers usually get more money for grains that are high in protein. Content of protein is the most important quality trait, but the type of grain protein and a number of other qualities, such as grain hardness, also affect how the grain will be used [ 14 ]. There are several ways that heat and drought stress can change the quality. Extreme stress can stop starch from forming, which makes the grain smaller. Even mild stress can change the balance between gliadin and glutenin proteins, which changes the quality of the grain. But heat and drought have the most important effect on quality through their relationship to GPC. GPC is a complicated trait that is strongly affected by the environment but also has a clear genetic part. Breeders often choose plants based on how much protein they have since this is such a big part of how much the grain is worth [ 15 ]. Not surprisingly, the most important environmental factor that affects GPC is the amount of N fertilizer applied, but there is also a negative relationship between GCP and yield in many environments. GPD stands for “grain protein deviation,” which is a way that varieties can be different from the relationship between yield and GPC [ 16 ]. It has been suggested that breeders could use GPD as a selection target to get around or lessen the effect of the negative relationship. In a study done in Australia, the negative correlation was found to be the strongest in low-yielding environments that are stressed by heat and drought. Breeders in Australia seem to have chosen high-protein genotypes that do well in low-yield environments based on how well these genotypes limit biomass accumulation to save nitrogen for grain filling. Analysis of a large number of field trials in different environments has shown that breeders need to select under drought stress and limited N supply to maximize both yield and GCP in new varieties, and studies in Europe have shown that selecting based on GPD is also important [ 17 ].

5. Future prospects

Way forward in “Climate Change” conditions is to produce wheat cultivars having multiple favorable traits which may better mitigate the crop in stress environment. The wheat plants may be stronger to tolerate various biotic as well as abiotic stresses along with better nutritional value, as food security and safety are both equally important. What we need now is to produce wheat plants having temperature, drought, salinity tolerance as well as more protein contents so that plant may be grown healthy under adverse environments along with more nutrition in terms of protein, carbohydrates, etc. The breeders are highly recommended to include all possible factors contributing toward climate change in their breeding programs while developing new wheat cultivars.

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Wheat: From Nutrition to Cultivation and Technology

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Wheat is a main cultivated food crop and establishes a central staple food in many countries worldwide. Wheat and other cereals are experiencing a revival phenomenon because of the increasing interest in plant-based diets and the consequent demand for raw materials and versatile ingredients that can be ...

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Bread wheat: a role model for plant domestication and breeding

  • Eduardo Venske 1 ,
  • Railson Schreinert dos Santos 1 ,
  • Carlos Busanello 1 ,
  • Perry Gustafson 2 &
  • Antonio Costa de Oliveira   ORCID: orcid.org/0000-0001-8835-8071 1  

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Bread wheat is one of the most important crops in the world. Its domestication coincides with the beginning of agriculture and since then, it has been constantly under selection by humans. Its breeding has followed millennia of cultivation, sometimes with unintended selection on adaptive traits, and later by applying intentional but empirical selective pressures. For more than one century, wheat breeding has been based on science, and has been constantly evolving due to on farm agronomy and breeding program improvements. The aim of this work is to briefly review wheat breeding, with emphasis on the current advances.

Improving yield potential, resistance/tolerance to biotic and abiotic stresses, and baking quality, have been priorities for breeding this cereal, however, new objectives are arising, such as biofortification enhancement. The narrow genetic diversity and complexity of its genome have hampered the breeding progress and the application of biotechnology. Old approaches, such as the introgression from relative species, mutagenesis, and hybrid breeding are strongly reappearing, motivated by an accumulation of knowledge and new technologies. A revolution has taken place regarding the use of molecular markers whereby thousands of plants can be routinely genotyped for thousands of loci. After 13 years, the wheat reference genome sequence and annotation has finally been completed, and is currently available to the scientific community. Transgenics, an unusual approach for wheat improvement, still represents a potential tool, however it is being replaced by gene editing, whose technology along with genomic selection, speed breeding, and high-throughput phenotyping make up the most recent frontiers for future wheat improvement.

Final consideration

Agriculture and plant breeding are constantly evolving, wheat has played a major role in these processes and will continue through decades to come.

Bread wheat ( Triticum aestivum L.) is one of the most important crop species, responsible for the emergence and development of agriculture and has fed, and continues to feed, a large part of the world’s population across many centuries [ 97 , 106 ]. Wheat has been improved by man over the last 8000 to 10,000 years ago when the species first arose. Initially it happened in an unconscious way, then intentionally, but empirically, and then, for more than a century, based on scientific knowledge [ 18 , 64 ].

Wheat breeding, as for many other crops, has been evolving fast, both in terms of basic science, methods and tools. The literature on wheat breeding is vast, including countless scientific papers, reviews and even dense book collections already published. Therefore, all relevant aspects and examples cannot be covered in a single text. On the contrary, we do encourage readers to go through this bibliographic ever growing wealth for a deeper understanding on any given topic. Thus, the objective of this review is to provide a brief and valuable synthesis on some selected aspects related to the history, but especially, current advances in wheat breeding, devoted especially to students and researchers with little or even no knowledge on the theme. Through this review, the reader can have a quick and general overview on the discussed topics and, when necessary, get a direction to start searching for further literature, as we have tried to cite the most important and recent papers on each topic. Therefore, in the next sections we show the origin of this species and how it became so important with a brief history of wheat cultivation and breeding. Priorities and particularities of wheat breeding are presented. Special consideration is given to new approaches and tools that are currently under development, and the ones that lately reappeared. Finally, the promising future and perspectives are discussed.

Origin and importance

One of the fathers and lifelong ally of agriculture.

Bread or common wheat is undoubtedly one of the most important cultivated plants, in fact, in addition to its ancestry, the cereal represents a large part of the history of agriculture itself [ 8 , 18 , 44 , 58 , 93 , 97 ].

Today, wheat is the basis of a significant part of the world’s diet, being an important source of energy (providing ca. of 20% of world population demand), and protein (also providing ca. 20%), as well as vitamins and other beneficial compounds, not only for humans, but also as animal feed [ 42 , 106 ].

It is grown from 67° North to 45° South, including a wide range of altitudes, but it is less cultivated in tropical regions [ 33 ]. In 2016, more than 749 million tonnes of this cereal were produced on 220 million hectares around the world, which puts wheat in second place regarding production among the cereal crops (behind maize - Zea mays L.) but in the first position regarding area harvested amongst all crops [ 32 ]. Approximately 95% of wheat cultivated is hexaploid with the remaining 5% being durum wheat ( T. turgidium L.) and few other less important types [ 106 ].

The origin of the species

Bread wheat is an allohexaploid species (2 n  = 6× = 42, AABBDD genomes), resulting from the combination of 3 interrelated diploid genomes [ 28 , 66 , 79 , 83 ]. Donors of the A genome ( T. urartu ) and B genome (closely related to Aegilops speltoides ), diverged from a common ancestor about 7 million years ago. These two species first generated (~ 5.5 million years ago) the donor of the D genome ( Ae. tauschii ), through hybridization and homoploid speciation. Less than one million years ago emmer wheat ( T. turgidum ), an allotetraploid with AABB genomes became into existance. Finally, from 8000 to 10,000 years ago, probably in the Fertile Crescent, in a region that nowadays comprises Northern Iran, the hybridization between T. turgidum and Ae. tauschii gave rise to the hexaploid T. aestivum , which after domestication and centuries of cultivation and selection, resulted in the bread wheat that is cultivated today [ 27 , 28 , 53 , 67 , 68 , 79 , 83 , 98 ].

Unlike other cultivated species, hexaploid wheat was not selected from a wild species, but arose from the hybridization between a species already cultivated by man that time (emmer wheat), so it is possible to say that maybe there was never any T. aestivum in the wild [ 106 ]. The reasons why this cereal became so widely adopted by man include its high environmental adaptability, thanks to its allopoliploid nature, which has conferred to wheat the so-called “genomic plasticity”. Also, due to its excellent food/feed qualities, not only regarding carbohydrates, proteins and vitamin content, but also for the unique elastic property of its gluten, which provided a more diverse use for its flour [ 27 , 106 ].

The beginning and evolution of wheat cultivation and breeding

The emergence of modern T. aestivum occurred due to agriculture. Thanks to growing its ancestor (emmer) in an area with spontaneous occurrence of Ae. tauschii , the inter-specific hybridization that generated this species occurred [ 27 ]. After its emergence, cultivation gradually began to predominate around its center of origin and then expanded to several regions of the globe, improved by natural selection and man in an unintentional way [ 18 ].

The “intentional” breeding, even if empirical, began at the end of the XVIII century. The first reported attempts to allow for cross-fertilization of different types of plants was made by Knight (1787) in England. These crosses allowed for the observation of improvements especially for disease resistance [ 64 ]. At the end of the XIX century, Vilmorin, in France, and Rimpau in Germany, amongst other breeders, made important contributions in the development of superior wheat genotypes by man-made hybridization or simply selection, motivated by Darwin [ 22 , 23 ], but occurred without a clear understanding of important foundations of their work [ 64 ]. Breeding from a solid scientific base began only after the rediscovery of Mendel’s findings, at the beginning of the last century. Biffen’s classic work [ 7 ] was probably the first to validate such knowledge in wheat, once again focusing on disease resistance. Nilsson-Ehle [ 76 ] greatly contributed to the study of quantitative traits involving grain color in wheat.

Other advances took place gradually over the decades, until a major leap was made with the so-called “Green Revolution” of the mid-1960s, when wheat and rice ( Oryza sativa L.) were protagonists [ 9 , 29 , 80 , 91 ]. This revolution consisted in the development of “modern” cultivars - those of wheat mainly by CIMMYT, the International Center for Maize and Wheat Improvement, Mexico. Those were short statured (semi-dwarf), photoperiod insensitive and high yielding spring cultivars. This was only possible due to the incorporation of the genes Reduced height ( Rht ) and Photoperiod ( Ppd ), which have had extremely important effects on the adaptability of this species. Ppd-D1a, which is an insensitive allele to the photoperiod that reduces flowering time, and Rht-B1b and Rht-D1b , which makes the cereal insensitive to gibberellin, shortened plant’s stature. These genes are today widespread in the wheat elite germplasm all around the world and new alleles are still under study, with potential to contribute to this trait [ 10 , 125 , 128 ].

These new genotypes became widely adopted, especially in developing countries, and generated an impact on the reduction of hunger and poverty, with huge repercussions [ 9 , 29 , 78 , 80 ]. The Nobel Peace Prize awarded to Dr. Norman E. Borlaug deserves a special mention here, due to his decisive role in this revolution [ 9 ].

Since then, wheat breeding has advanced even further with new technologies such as molecular markers, the recent availability of a reference genome sequence and annotation, and even the recent use of techniques such as genome editing, genomic selection, speed breeding and high-throughput phenotyping. The evolution of wheat breeding accross time is briefly illustrated in Fig.  1 , highlighting phases and important events.

figure 1

Wheat breeding timeline. Three main phases can be defined in wheat breeding history: the “unconscious”, the “empirical” and the “scientific” breeding, this latter is illustrated with several important events

Wheat breeding: priorities and some general aspects

The priorities in wheat breeding.

The main objectives of wheat breeding have been similar over many decades. Increasing the yield potential has been prioritized in order to meet the food requirements of an ever increasing population [ 9 , 80 ].

Probably the second most important trait is disease resistance, as from the first breeding attempts by Knight in 1787 until today, in different countries [ 64 ]. For instance, “old diseases”, such as the rusts, are still a cause of concern for wheat cultivation, but new ones are appearing, such as wheat blast, considered one of the most recent and concerning threat for wheat cultivation worldwide [ 127 ].

Third, is tolerance to abiotic stresses, especially drought and heat – the latter is a borderline to cereal crop expansion, cold and acid soils (aluminum), and various quality traits. Finally, all the others must come, such as resistance to insects, lodging, double-purpose (forage and grain), and improved nutrient use and grain biofortification efficiency, among numerous others. This ranking is based on a general overview on the vast available literature, however this order of priority more than certainly varies within each environmental region and over time.

As already mentioned, publications on wheat breeding are vast, fortunately there has been a number of reviews already published, which summarize the most important steps already taken for different traits, ie., yield potential [ 29 , 91 ], stem rust resistance [ 107 ], drought tolerance [ 74 ] and biofortification, which should grow in importance over the next few years [ 129 ].

New priorities in wheat breeding

Most future priorities in wheat breeding should remain the same, but the need for faster development and accumulation of knowledge from different fields should provide new strategies and paths to reach these goals. Increasing photosynthetic capacity has been shown to be one of the most important barriers to improve wheat yield potential and there is theoretical evidence that it could be enhanced by the insertion of genes for C4 carbon fixation, whose strategy has merited investment [ 87 , 90 ].

Wheat grain is known to be rich in gluten, a trait that is critical for baking, but negative for consumption by celiac, and also non-celiac gluten-sensitive people has been a largely discussed topic among nutritionists [ 15 , 37 ]. This may lead to a potential reduction in wheat consumption in the coming decades, unless we can provide grain that does not possess this disadvantage. Fortunately, there is evidence of some wheats that possess a gluten, but of a chemically different type, which can be consumed by people with celiac disease, potentially becoming an important target for wheat breeding in forthcoming years [ 95 , 111 ].

Special aspects on wheat breeding

Wheat is a self-pollinated species. Therefore, the conventional structure of its breeding programs do not differ much from other autogamous plants. It includes the use of artificial hybridizations between previously selected genotypes, something already performed for more than two centuries, and different forms of selection within segregating populations [ 64 , 100 ]. It is recognized that these processes were, and will continue to be, the main responsiblity for the development of wheat cultivars worldwide. However, new tools and approaches are assisting this process, increasing its success rate and diminishing costs, time and labour.

Improving wheat may be more difficult than for many other crops, since the breeder needs to “match” quantity and quality, allying yield with grain and flour quality, which needs are not a constant concern for crops like soybean ( Glycine max L.) or maize ( Zea mays L.), which can, for the most part, focus on yield [ 106 ]. Also, it is a species with restricted genetic variability when compared to most of other crops. Moreover, its genome size, complexity and polyploid nature constitute a challenge when applying some biotechnological techniques.

The restricted genetic diversity

Wheat is recognized to have restricted genetic variability, when compared to most other crops [ 18 , 20 ]. This is due to several reasons: 1) it is an allohexaploid generated by crosses involving three highly interrelated diploid species, and poplyploidization is a force which restricts itself genetic variability; 2) another reason, suggests that few plants of the ancestral species were involved in the formation of wheat, also restricting its initial genetic variability [ 27 , 58 ]; 3) Finally, it is a young species, ca. 8000 to 10,000 years old, which is insufficient time for the species to accumulate mutations or to receive genes or alleles by natural or artificial interspecific cross-breeding processes [ 20 , 28 , 66 ].

Domestication, centuries of cultivation, and modern breeding have further restricted the genetic variability of several cultivated species, and wheat is among them [ 34 , 71 , 89 , 119 ]. It is important to remember that wheat was one of the first species to be domesticated and cultivated, further decreasing its variability due to constant selection cycles since then [ 18 , 58 , 93 ]. The impact of the narrowing of wheat variability is visible through current projections, which show that the cereal might not meet its demand in few decades [ 88 ], unless measures are taken in order to broaden its genetic base.

To broaden the genetic diversity available for wheat breeding, different techniques will need to be applied, including induce mutation, genetic transformation, genome editing, and introgressions from species of the secondary and tertiary gene pools.

Resurgent and current approaches in wheat breeding

Introgressions.

Among all crop species, wheat is probably the one in which most research has been invested regarding the use of wild and cultivated relatives as source of variability for its improvement. The attempt to incorporate traits of related species into wheat germplasm is not new. In fact, the attempts in this sense began long ago, as early as plant breeding itself [ 6 ]. If, on one hand, wheat is restricted in variability within its germplasm, there is an immeasurable richness in variation found in related species belonging to its secondary and tertiary gene pools [ 25 , 102 , 131 ].

The most important introgression to date in wheat involved a chromosomal translocation 1RS-1BL between wheat and rye ( Secale cereale L.), generated in the first third of the last century, which increased wheat yield potential and resistance/tolerance to biotic and abiotic stresses. This segment is still present in many of important cultivars currently used [ 21 , 85 , 101 ]. The researcher E.R. Sears deserves also a special mention here, due to his great contribution to this field. Today, there are several excellent chromosome manipulation studies in progress (e.g. [ 54 ]). However, there is a consensus that the practical use of introgressed genes in the development of superior cultivars has in the past been very limited and should be further explored [ 132 ].

Another strategy in this field is the development of synthetic wheat, repeating the interspecific crosses that occurred in nature that led to the formation of hexaploid wheat [ 61 , 130 ]. In this method, different accessions of the species T. monococcum , T. turgidum, and Ae. tauschii can be used for the formation of new genetic constitutions of wheat, greatly increasing the genetic variability of the primary gene pool [ 73 ]. Numerous synthetic wheat germplasm pools have been developed by CIMMYT [ 130 ]. This illustrates an advantage that wheat possesses, as an allohexaploid, when compared to diploid species.

The use of other species in wheat pre-breeding programs has been an important field of research (for a complete review, see [ 72 ]). Recently, however, it seems to be reaching a new momentum, driven by a remarkable shortage of genetic diversity in wheat, accompanied by an increased need for improved adaptability for the crop. This adaptability is needed to counteract the unfavorable conditions brought by the ongoing climate changes. Enhanced technologies for introgression detection, such as high-throughput genotyping, have motivated investiments in this field. Other potential approaches, such as gene editing will be further discussed in a dedicated section [ 12 , 54 , 131 ].

Mutagenesis

Mutation induction, whether via chemical or physical mutagens, has been widely used in order to increase the genetic variability in several cultivated species, including wheat [ 77 ]. The polyploid nature of wheat confers a kind of buffer effect, in which mutations in one of its genomes can be compensated by homoeologous genes masking their effect making them difficult to be detected [ 77 ]. Fortunately, TILLING methods [ 108 , 114 ] and high-resolution melting analysis [ 26 ] have proven to be efficient for the detection of mutations in the different genomes of hexaploid wheat.

From 1960 to 2017, 256 wheat cultivars were generated by mutagenesis in different countries and have been registered in the FAO/IAEA database ( https://mvd.iaea.org ). In this repository [ 31 ], all cultivars are described with information about how the mutations were induced and focuses on the value-added attributes. Among the many examples of agronomically important mutations are resistance to herbicides of the imidazolinones group [ 84 ] and increases in amylose content and starch resistance [ 109 ].

Molecular markers and new genotyping approaches

The use of molecular markers for QTL mapping and marker-assisted selection (MAS), such as for resistance to fusarium head blight [ 13 ] and drought [ 39 ] has been growing and the accumulation of data generated during the past decades has allowed us to perform different meta-analyses [ 39 ]. From the 1990s to 2000s, AFLP, RFLP, and SSR were the most used markers [ 17 , 40 , 46 , 75 , 110 ]. However, recently a revolution occurred, in which science changed from the use of a few markers, from the types mentioned above, to thousands of single nucleotide polymorphism (SNP) markers using high-throughput platforms. This was initiated with DArT markers [ 1 ] and then with SNPs evaluated through genotyping arrays such as Illumina ® 9 K iSelect Beadchip Assay [ 16 ], Illumina ® iSelect 90 K SNP Assay [ 121 ] and Axiom ® 820 K SNP array [ 126 ], in which respectively 9000 to nearly 820,000 SNPs can be evaluated in a single analysis. Also, using genotyping by sequencing (GBS), thanks to the arrival of next generation sequencing technologies, maps containing 20 to 450 K loci have already been generated for wheat [ 82 , 96 ].

Similarly to other crops, genetic mapping also evolved from mapping populations generated from crosses between only two contrasting parents to genome-wide association studies (GWAS), in which hundreds of diverse accesses are evaluated on each study, thus allowing the capture of a larger genetic diversity and a deeper look in the causal variation between agronomically interesting phenotypes [ 3 , 14 , 38 , 56 , 60 , 81 ].

Genomic selection

Although Marker Assisted Selection (MAS) has proven to be useful in a number of situations in wheat breeding, it has the limitation of being only able to aid the selection for a few genes or alleles at a time. However, it is well known in crop breeding that most agronomic traits present a quantitative nature, are governed by numerous genes, most of these with very small effect on the phenotype. In this regard, genomic selection (GS) came as a revolutionizing ally, also in animal breeding [ 69 ]. The approach aims ultimately to perform selection and prediction of breeding values based only on genotyping, within a model calibrated with phenotypic values, and with a whole genome perspective, i.e., taking into account genomic polymorphisms in linkage disequilibrium with as many as possible genes with effect on a given trait [ 51 ].

The number of studies applying GS in wheat breeding are at an increasing rate. One of the main measures to assay the effectiveness of GS is its accuracy, i.e., how much the prediction compares with the real phenotypes. Applying genotyping by sequencing, GS for wheat yield under irrigated and drought conditions showed accuracies of 0.28 and 0.45, respectively, which are low to moderate values [ 81 ]. On the other hand, GS for fusarium head blight resistance showed moderate to high accuracies, being 0.82 the highest value found, for fusarium damaged kernels trait [ 4 ]. High accuracies are pursued in this approach, and many factors affect its value, such as the heritability of the trait, the number and quality of the markers, the GS statistical model adopted, among others [ 43 ]. In this regard, Bassi et al. [ 5 ] proposed different schemes dedicated to the implementation of GS in wheat breeding.

The reference genome sequence and annotation

In 2005, efforts to generate a reference genome of wheat for the scientific community began, with the establishment of the International Wheat Genome Sequencing Consortium (IWGSC). Nine years latter, in 2014, the first version of this sequence, still considered as a draft, was published for the hexaploid wheat cultivar Chinese Spring [ 47 ]. This huge and complex sequence, estimated in 16 to 17 Gb in total, has been gradually assembled, improved and made available through the repository of the consortium ( https://www.wheatgenome.org ). Finally, after another 3 years, a first version of the annotation has been made available [ 48 ], which has also been continuosly improved [ 49 ]. In addition to IWGSC, another research group was responsible for the first near-complete assembly of the hexaploid bread wheat genome, with a total of 96% of its sequence, also of Chinese Spring [ 136 ].

Now these reference genomes, especialy the one made available by IWGSC, through its platform for public access, are a powerful tool for breeding and other genetic studies on this crop, being used to better understand wheat evolution [ 28 , 66 ] and for genome wide association studies [ 3 ], among many other examples of use.

The completion of the first wheat reference genome of the Chinese Spring cultivar has been considered a step-change by researchers. However, it is obvious that more representatives from the species should also be sequenced, for a more effective use of genomics in breeding. It motivated the establishment of 10+ Wheat Genomes Project \ ( http://www.10wheatgenomes.com/ ). This global partnership aims to characterize the wheat ‘pan genome’, and will generate at high quality wheat genome assemblies and develop strategies and resources to compare multiple wheat genome sequences from around the world.

Hybrid breeding

In some crops, such as maize and rice, the development and cultivation of hybrid cultivars is common, not recent and with clear advantages over the cultivation of open pollinated populations or inbred lines. For wheat, however, less than 1% of the area is cultivated with hybrids [ 52 , 63 ]. After unsuccessful attempts during the past decades, research in the development and cultivation of hybrids seems to be becoming one priority in wheat breeding [ 63 , 124 ].

This is due to a huge accumulation of knowledge and new technologies, and recent results are promising. The use of genomic tools to analyze the heterotic pattern among large groups of lines has proved to be efficient in obtaining highly productive hybrids [ 135 ], with genome wide selection being the most advantageous method of prediction [ 60 ]. In this sense, several hybrids have shown to be highly advantageous regarding yield [ 62 ] and resistant to diseases [ 70 ], while several difficulties associated with seed production are being overcome [ 124 ].

Genetic transformation (transgenics)

The cultivation of transgenics is still a debate topic in our society. Its acceptance is not unanimous around the world, either because of social or religious reasons [ 106 ]. The scientific results have not been able to overcome the fear on its potential effects on human health [ 45 , 65 ]. This is why there are not many records of the use of transgenic wheat cultivars [ 116 ], not allowing its comparison with crops such as soybean, maize or cotton, even after 27 years of the first transformed wheat [ 117 ]. Indeed, authors have termed wheat as the cereal abandoned by GM [ 127 ]. Research results, however, have been encouraging, generating genotypes with improved resistance to powdery mildew ( Blumeria graminis ) [ 134 ], leaf spot caused by Bipolaris sorokiniana [ 50 ] and fusarium head blight (caused mainly by Fusarium graminearum ) [ 59 ]. Also, tolerance to drought [ 118 ], salinity and freezing [ 35 ] and even improvement in baking traits [ 86 ] have been achieved, among other traits [ 116 ]. Another alternative tool is the creation of cisgenic plants, where transferred genes come from the same species, something that has proven to be more easily accepted by society [ 113 ]. Despite these considerations, genetic transformation has been quickly replaced by genome editing, a very powefull approach, as presented in the next topic.

Genome editing

Among the most recent and promising innovations in terms of biotechnology and plant breeding involves genome or gene editing [ 11 , 99 ]. This technique can accurately target segments of the genome for modification, either by deletion, insertion or substitution of nucleotides [ 99 ]. In wheat, despite the great complexity of its extensive, redundant, and polyploid genome, several attempts have proven to be successful [ 105 , 115 , 122 , 133 ]. Even a specific protocol for this species has already been established using the CRISPR/Cas9 system [ 104 ]. Among the most exciting results obtained with this technique is the simultaneous modification of three homoeo-alleles of the same gene, i.e., being capable of modifying this gene in all three different genomes, demonstrating the precision that these methods have been able to reach [ 122 ].

Gene editing can also be applied as a tool for gene introgression from wild relatives into wheat background, in which the linkage drag can be mitigated by precise gene replacement [ 120 ].

Meiotic recombination manipulation

Crop breeding relies largely on meiotic recombination, which allows for recombination of genes/alleles in different new genetic compositions, thus allowing selecting new improved cultivars [ 57 ]. Controlling this process would be of high interest for breeders. In bread wheat, the Ph1 locus is a well-characterized regulator of this process, whose main role is allowing only homologous chromosomes (belonging to the same genome) to pair and recombine during meiosis [ 57 , 94 , 103 ]. In this regard, there are mutant lines that harbor an alternative allele for this locus, for instance ph1 , which is not functional, thus allowing homoeologous chromosomes to pair and recombine [ 132 ]. These homoeologous chromosomes include the ones from wheat, but also chromosomes from species from the secondary and tertiary gene pools of the cereal, during the process of gene introgression, being this a powerfull mechanism for this approach [ 132 ]. Since other genes appear to contribuite on this mechanism, other studies are being carried out to better elucidate it.

Speed breeding

Crop breeding is, or, has been, a process which requires considerable time, usualy several years - as for wheat - until a new improved cultivar can be released. The current increasing demand for food added to a number of other factors, such as the ongoing climate change, put pressure on breeding to accelerate the process. Growing segregating lines out of season, at different locations, and the double haploid method have contribuited in this regard, but speed breeding has come as a game-changer to accelerate the plant improvement. It is a very recent approach which ultimately aims to shorten plant’s generation time, accelerating breeding and research programmes, in which wheat has been protagonist, among few other crops [ 123 ]. It is basically based on photoperiod, light and temperature manipulation (artificially), in growth chambers and glasshouses, and allows one to achieve up to six generations per year - from seed to seed, for spring wheat [ 36 , 123 ]. The method not only allows for generation advancing, but also for faster phenotyping for numerous traits, such as flowering time, plant height and disease resistance in wheat [ 36 ].

High-throughput phenotyping

The use of high-throughput phenotyping, aims to evaluate several traits in a large number of plants over a short period of time. This technique is comprised of several highly optimized and automated steps, and emerged also in an attempt to follow the performance achieved through genotyping towards the increasing demands of breeding [ 2 , 14 , 24 ].

This can be done under controlled conditions, such as in growth chambers or greenhouses, using plant-manipulating robots and photographic cameras with temperature sensors, CO 2 meters and scales for weighing live plants [ 30 , 92 ]. At field level, tractor-coupled or self-propelled platforms, drones or even satellite imagery can perform the tasks [ 19 , 41 , 55 , 112 ]. After data collection, analysis is also differentiated, requiring specific software, such as for image processing [ 30 , 55 ].

Final considerations and future perspectives

Agriculture has the challenge of meeting the increasing demand for food by an ever growing world population, and these days in an adverse scenario of climate change, restricted availability of arable land and water and constant evolution of pathogens, among other obstacles. Moreover, the demand for food goes beyond quantity, as quality is also required, especially regarding nutritional aspects. Bread wheat and plant breeding have a crucial role on this task.

Breeding has been responsible for increasing wheat yields and improving many other traits, such grain quality, resistance to biotic stresses, etc. However, the cereal mean genetic gain has to be doubled in the next few decades, in order to meet its global demand. Thus, efforts in the development and implementation of improved strategies must continuously take place in wheat breeding programs.

Classical breeding, which is largelly based on crosses and phenotypic selection has been the most used plant breeding method around the globe for more than one century and is still the main approach these days, responsible for the release of the largest number of cultivars. This approach will still be applied as the main or even unique strategy for several years to come, specially in developing countries. It will be gradually replaced to a certain extent by improved methods, again firstly in developed countries, next, in developing ones. Crosses may be replaced by direct insertion of a gene of interest through gene editing and phenotypic selection by GS. However, the complete extinction of the classical breeding cannot be even conceived. Instead, combined approaches will probably predominate in breeding programs. Crosses followed by speed breeding practices and high-throughput phenotyping for selection or GS is a simple example of a combined scheme.

Gene editing and GS are the current cutting-edge approaches in plant breeding. Both can still be improved to deliver more effective results, which will probably happen within the next decade. However, the most important “improvement” required from these methods resides on the reduction of their costs, which is especially true for GS, as genotyping is still considerably expensive. As science and technology continue to move towards., it is difficult to even predict which advance will become available for breeders in two or three decades.

Plant breeding has experienced innovations and revolutions throughout its existence and wheat has been witness to most, if not all, of these transformations and probably will continue as an ally of the transformations to come.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Amplified Fragment Length Polymorphism

Centro Internacional de Mejoramiento de Maíz Y Trigo (International Center for Maize and Wheat Improvement)

Clustered Regularly Interspaced Short Palindromic Repeats / CRISPR-associated protein 9

Diversity Arrays Technology

Joint: Food and Agriculture Organization of the United Nations / International Atomic Energy Agency

Genetically modified

Genomic Selection

Pairing homoeologous 1 (gene)

Photoperiod (gene)

Quantitative Trait Loci

Restriction Fragment Length Polymorphism

Reduced height (gene)

Single Nucleotide Polymorphism

Simple Sequence Repeats

Targeting Induced Local Lesions IN Genomes

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Acknowledgements

We would like to thank the Brazilian funding agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (CAPES) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS).

Grants and fellowships were received from the Brazilian Institutes: Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul - FAPERGS.

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Venske, E., dos Santos, R.S., Busanello, C. et al. Bread wheat: a role model for plant domestication and breeding. Hereditas 156 , 16 (2019). https://doi.org/10.1186/s41065-019-0093-9

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Meeting the challenges facing wheat production: the strategic research agenda of the global wheat initiative.

wheat introduction research paper

1. Introduction

2. background, 2.1. why wheat, 2.2. impact of climate change, 2.3. the wheat initiative, 2.4. global wheat research, 3. existing strategic research agenda—work in progress.

A fully assembled and aligned wheat genome sequenceComplete and pan genome also developedTranscript databases and germplasm collection sequenced
Wheat data availability via an open information exchange frameworkWheatIS developedExpand databases linked to WheatIS and increase functionality
The ability to build new combinations of allelesContinuing workImprove access to germplasm with complex allele combinations

3.1. Objective 1: To Increase Yield Potential

3.2. objective 2: to protect ‘on farm’ yield, 3.3. objective 3: ensuring the supply of high-quality safe wheat, 3.4. objective 4: enabling technologies and the sharing of resources, 3.5. objective 5: germplasm accessibility, 3.6. objective 6: knowledge exchange, education and training, 4. major issues and challenges facing wheat production and research, 4.1. inconsistencies in regulatory environment, 4.2. access to staff with the necessary skills in both new and old technologies, 4.3. data access and standards, 4.4. support for multinational research and public–private partnerships, 5. research priorities, 5.1. strengthen existing research activities, 5.2. enhance agronomy in its broadest definition (crop production and soil management), 5.3. increase genetic diversity.

  • A broad series of activities can be undertaken to address this research priority:
  • Revise and update the Global Wheat Conservation Strategy prepared in 2007 [ 26 ].
  • Encourage the large-scale genotyping and phenotypic characterisation of germplasm held in the major genebanks.
  • Advocate for the free and open exchange of germplasm and associated data.
  • Encourage the utilisation of existing specialist germplasm collections collated by EWGs and share the outcomes: ◦ Tetraploid collections developed by the Durum EWG ▪ Durum elite and landrace collection in conjunction with a tetraploid core collection (GDP: Global Durum wheat Panel) capturing about 80% of the AABB haplotypes [ 27 ] of the collection (TGC: Tetraploid wheat Global Collection) described in [ 28 ]. ◦ Heat and drought tolerant germplasm collections developed by HeDWIC. ◦ Wheat quality assessment panels developed by the Quality EWG.
  • Support research aimed at the enhanced utilisation of unadapted germplasm: ◦ Development of introgression populations. ◦ Re-domestication. ◦ Exploration of novel germplasm evaluation strategies. ◦ Development of efficient methods for gene editing.

5.4. Understanding Root and Soil Biology

  • Continuing improvement of root phenotyping techniques, particularly in the field.
  • Expand information of soil–microbe–plant interactions.
  • Integration of data and information on roots and the microbiome in the analysis of wheat production with the full cropping system. It will also be important to emphasise the differences between low and high input systems and organic farming.

6. Wheat Initiative Structure and Organisation

6.1. develop educational and training programs.

  • Ensure the full and rapid implementation of the postgraduate and ECR plan for involvement in the EWGs.
  • Establish an exchange program that provides partial funding for students to work in other laboratories.
  • Encourage EWGs to deliver training workshops and courses, and link to existing options offered by other organisations, such as universities, CIMMYT and ICARDA.
  • Develop an online Wheat Initiative seminar program.
  • Develop mentoring programs to support students and link to industry.

6.2. The Wheat Initiative as an Advocacy and Lobby Organisation

  • Produce public explanatory documents and videos covering the Wheat Initiative activities, major topics and issues affecting wheat production, such as the role of germplasm exchange, gene editing, hybrid wheat, and crop protection.
  • Participate in relevant G20 workshops and meetings and develop links to government agencies and international organisations.
  • Advocate and lobby for the support of transnational research.
  • Develop links to the wheat grower and processing industry organisations.
  • Promote wheat resources such as WheatIS and WheatVIVO.

6.3. Expand Engagement

  • The Institutions’ Coordination Committee has established a sub-committee to work through the options to build membership.
  • Develop and distribute documentation explaining the value to industry from joining the WI—Industry.
  • Increase industry participation in WI activities, particularly in training and mentorship: a component would be to identify platforms and capabilities that could be used by industry.
  • Identify and target government and institutional organisations in major wheat producing and wheat-importing countries to seek greater engagement in the WI.
  • Target early career researchers in under-represented countries to encourage the membership of EWGs. In addition, provide support to allow key people from these regions to participate in WI activities.

6.4. Supporting Multinational Research

  • Stage 1 —Coordination across existing research to capture synergies, prevent duplication and identify gaps—low incremental costs but a proactive coordination is instrumental and essential.
  • Stage 2 —Project alignment and leverage of existing investments: initially focus on the twinning of existing projects or building on a call(s) for proposals by one or more national funders joining (e.g., recent AAFC (Canada)/BBSRC (UK) IWYP-aligned call-linked consecutive calls for proposals in each country).
  • Stage 3 —Scaling-up joint investment: under the key areas of interest to all funders, funding can be allocated to a common/centrally managed pot/program or managed nationally by a lead funder, still aligned under a broad umbrella theme.

7. Conclusions

  • Boost research and technology delivery capabilities by investing in staff and student training and encourage and support the exchange of personnel between research organisations and building research infrastructure. This can be achieved if national research programmes place priority on activities with strong international linkages. Financial or organisational support from national agencies to research groups seeking participation in international partnerships would be beneficial.
  • Provide support, both financial and organisational, to international activities aiming to facilitate the exchange of resources, particularly germplasm, and support the evaluation and delivery of research outcomes.
  • Actively participate in Wheat Initiative research alliances that gather the capabilities and resources targeting global research challenges. These include the work of the Expert Working Groups and the three current alliances: The International Wheat Yield Partnership (boosting wheat yield potential), the Alliance for Wheat Adaptation to Heat and Drought (producing heat- and drought-tolerant germplasm) and the Wheat Initiative Crop Health Alliance (diagnosis and monitoring of wheat diseases).

Author Contributions

Data availability statement, acknowledgments, conflicts of interest, abbreviations.

AAFCAgriculture and Agri-Food Canada
AHEADAlliance for Wheat Adaptation to Heat and Drought
BBSRCBiotechnology and Biological Sciences Research Council
CIMMYTInternational Maize and Wheat Improvement Centre
EWGExpert Working Group(s)
FEWGFunding Expert Working Group
HeDWICHeat and Drought Wheat Improvement Consortium
ICARDAInternational Centre for Agricultural Research in the Dry Areas
IWGSCInternational Wheat Genome Sequencing Consortium
IWYPInternational Wheat Yield Partnership
SRAStrategic Research Agenda
UKUnited Kingdom
WATCH-AWheat Initiative Crop Health Alliance
WheatISWheat Information System
WIWheat Initiative
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Click here to enlarge figure

Annual Average for 2011–2020 DataMaizeRiceWheat
Area sownMillion hectares191162219
ProductionMillion tonnes1057739733
ImportMillion tonnes14942189
Value (USD billion)3.82.55.3
ExportMillion tonnes15343192
Value (USD billion)3.42.44.9
% Production traded14626
Annual average for 2010–2019 data
Food quantityMillion tonnes139584499
kg/capita/year198066
CaloriesKcal/capita/day159542540
Proteing/capita/day3.89.916.4
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Langridge, P.; Alaux, M.; Almeida, N.F.; Ammar, K.; Baum, M.; Bekkaoui, F.; Bentley, A.R.; Beres, B.L.; Berger, B.; Braun, H.-J.; et al. Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative. Agronomy 2022 , 12 , 2767. https://doi.org/10.3390/agronomy12112767

Langridge P, Alaux M, Almeida NF, Ammar K, Baum M, Bekkaoui F, Bentley AR, Beres BL, Berger B, Braun H-J, et al. Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative. Agronomy . 2022; 12(11):2767. https://doi.org/10.3390/agronomy12112767

Langridge, Peter, Michael Alaux, Nuno Felipe Almeida, Karim Ammar, Michael Baum, Faouzi Bekkaoui, Alison R. Bentley, Brian L. Beres, Bettina Berger, Hans-Joachim Braun, and et al. 2022. "Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative" Agronomy 12, no. 11: 2767. https://doi.org/10.3390/agronomy12112767

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Introduction to Wheat.pdf

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Wheat is widely known as common wheat (T. aestivum), a grass widely cultivated for its seed and also a cereal grain which is a worldwide staple food. This content is a short introduction to the wheat (origin, evolution, production technology, and prominently breeding technologies).

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M.A Mannan Munna

Wheat cereal plant of the genus Triticum, especially, T. aestivum, of the family Graminae. The grain constitutes a major food item and an important commodity on the world grain market. Wheat is one of the first of the grains domesticated by human. Bread wheat is known to have been grown in the Nile Valley by 5000 BC and it is believed that the Mediterranean region was the centre of domestication. The civilization of West Asia and of the European peoples have been largely based on wheat, while rice has been more important in the East Asia. Since agriculture began, wheat has been the chief source of bread for Europe and the Middle East. It was introduced into Mexico by the Spaniards in the early part of 16th century and into Virginia, USA by English colonists early in the 17th century. Although it is one of the oldest of the cereal crops, it was introduced in Bengal in 1930-31. Its importance as a food crop was recognised around 1942-43. The plants bear this edible grain in dense spikes. The culm of the mature wheat plant is a hollow, jointed cylinder that comprises 3-6 nodes and internodes.

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Wasim Akram

Wheat is the dominant crop in temperate countries being used for human food and livestock feed. Its success depends partly on its adaptability and high yield potential but also on the gluten protein fraction which confers the viscoelastic properties that allow dough to be processed into bread, pasta, noodles, and other food products. Wheat also contributes essential amino acids, minerals, and vitamins, and beneficial phytochemicals and dietary fibre components to the human diet, and these are particularly enriched in whole-grain products. However, wheat products are also known or suggested to be responsible for a number of adverse reactions in humans, including intolerances (notably coeliac disease) and allergies (respiratory and food). Current and future concerns include sustaining wheat production and quality with reduced inputs of agrochemicals and developing lines with enhanced quality for specific end-uses, notably for biofuels and human nutrition.

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Wheat is the world’s most popular crop. It is grown over a large area and under a wide range of conditions and provides more nutrition to humans than any other species. Wheat is the leading source of vegetable protein in human food, having higher protein content than either maize (corn) or rice, the other major starchy crops. Wheat is the primary food staple in North Africa, south Asia and the Middle East, and is growing in popularity in Asia. In world different types of species are grown according to their geographical conditions. Some species are Einkorn (T. monococcum), Emmer (T. dicoccon), Durum (T. durum), Kamut (T. polonicum), Bread wheat (T. aestivum) and Spelt (T. spelta). These species are used for production of different cultural foods based on religion or customs including different breads. Various types of breads are made in different cultures that are consumed as staple or at special festivals. In addition breeding wheat for quality was discussed and possibilities of increasing yield through breeding products such as synthetics, hybrids and biotechnology product i.e. transgenics wheats was reviewed.

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Modern bread wheat, Triticum aestivum, is a hybrid hexaploid which emerged in the Middle-East region about 8-9000 years ago … usually considered to be the result of natural hybridisation and multiple allopolyploid speciation. Contrariwise, the abrupt, late appearance of a singular cultivar with traits favouring human utilisation suggests the possibility of a directed process, and this paper identifies an ancient Coptic [Egyptian] record specifically detailing the origin of the cultivated [polyploid] wheat plant by human intervention probably facilitated by radiation.

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Wheat is an excellent health-building food and is considered as good source of protein, minerals, B-group vitamins and dietary fiber. The quality and quantity characteristic of protein called gluten has make wheat as the principal cereal, being more widely used for the making of bread than any other cereal. Gluten makes bread dough stick together and gives it the ability to retain gas. Wheat has several medicinal virtues; starch and gluten in wheat provide heat and energy; the inner bran coats, phosphates and other mineral salts; the outer bran, the much-needed roughage the indigestible portion that helps easy movement of bowels; the germ, vitamins B and E; and protein of wheat helps build and repair muscular tissue. The whole wheat provides protection against diseases such as constipation, ischaemic, heart disease, disease of the colon called diverticulum, appendicitis, obesity and diabetes. However, the wheat germ, which is removed in the process of refining, is also rich in essential vitamin E, the lack of which can lead to heart disease. The loss of vitamins and minerals in the refined wheat flour has led to widespread prevalence of constipation and other digestive disturbances and nutritional disorders.

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  • v.8(8); 2007 Aug

Science Letters

Nutritional composition of pakistani wheat varieties *, khan ikhtiar.

1 Institute of Chemical Sciences, University of Peshawar, Peshawar 25120, Pakistan

2 Department of Biotechnology, University of Malakand, Chakdara 23020, Pakistan

Pakistani wheat varieties are grown over a wide agro-climatic range and as such are anticipated to exhibit yield and quality differences. It is therefore necessary to investigate the nutritional status of wheat varieties in terms of biochemical and physiochemical characteristics available for food and nutritional purposes in Pakistan. The result shows that wheat grains of different varieties contain a net protein level of 9.15%~10.27%, 2.15%~2.55% total fats, 1.72%~1.85% dietary fibers, 77.65×10 −6 ~84.25×10 −6 of potassium and 7.70×10 −6 ~35.90×10 −6 of sodium ions concentration, 0.24×10 −6 ~0.84×10 −6 of phosphorus, 1.44%~2.10% ash, 31.108~43.602 g of thousand grain mass (TGM) and 8.38%~9.67% moisture contents. This study is significant in providing an opportunity to explore the available wheat varieties and to further improve their nutritional excellence and also essential for setting nutritional regulations for domestic and export purposes.

INTRODUCTION

Wheat is one of the most important domesticated crops grown around the world. Bread wheat plays a major role among the few crop species being extensively grown as food sources, and was likely a central point to the beginning of agriculture (Harlan, 1981 ). Wheat is considered as utmost among the cereals largely due to the fact, that its grain contains protein with unique chemical and physical properties. Besides being a rich source of carbohydrates, wheat contains other valuable components such as protein, minerals (P, Mg, Fe, Cu and Zn) and vitamins like thiamine, riboflavin, niacin and vitamin E. However, wheat proteins are deficient in essential amino acids such as lysine and threonine (Adsule and Kadam, 1986 ).

Global wheat production is concentrated mainly in Australia, Canada, China, European Union, India, Pakistan, Russia, Turkey, Ukraine and the United States, accounting for over 80% of world wheat production. Pakistan is the 8th largest wheat producer, contributing about 3.17% of the world wheat production from 3.72% of the wheat growing area. Wheat in Pakistan is a leading food grain and occupies a central position in agriculture and its economy (Shuaib et al., 2007 ). The wheat breeders in Pakistan presently are paying more attention to evolve new varieties possessing an improved yield potential coupled with superior quality. However, Pakistan has been a food deficit country for long. The breeding efforts in the past remained focused mainly on increasing the per hectare yield of wheat, thus the potential of grain quality improvement remained unexploited. Production was geared up to local market, which is neither quality conscious nor sufficiently diversified to demand exacting standards. Pakistani wheat varieties are grown over a wide agro-climatic range and as such are expected to exhibit yield and quality differences (Chowdhry et al., 1995 ). It is therefore necessary to investigate the biochemical composition of wheat varieties available for food and nutritional purposes in Pakistan, which would provide an opportunity to explore the available wheat varieties for greater excellence in their nutritional quality.

MATERIALS AND METHODS

Wheat grains of twelve varieties were collected from different ecological regions of Pakistan during August to December 2005. The samples were stored in labeled glass bottle to ensure preserve integrity. The analysis was carried out at the Department of Biotechnology, University of Malakand Pakistan during February to June 2006.

Kjeldahl method was used to determine percent nitrogen (%N) as described by American Association of Cereal Chemists (AACC, 1995 ). The calculated %N was multiplied by a protein factor of 5.70.

Wheat samples of different varieties were placed in cuvettes (3 cm diameter) sealed with aluminum and plastic foils. The absorbance spectra [log(1/ R )] were recorded on a near infrared spectrometer model 6500 (NIR System Inc., Silver Springs, MD, USA) equipped with computer. The spectra were obtained with the help of computer software WinISI-II version 1.02 (Foss NIR Systems, Infrasoft International). The values of protein, fats and fiber were calculated directly from the system. Samples of each wheat variety were analyzed twice and the results were stored in computer for statistical analysis.

Physiochemical parameters like moisture, ash, total fat and fiber contents were determined according to the standard AOAC ( 1990 ) methods (AOAC methods Nos. 925.10, 923.03, 2003.05 and 993.19 respectively) and by the procedure described recently by Zeb et al.( 2006 ). Wet digestion was performed for mineral quantification. The mineral composition (Na + and K + ) was determined with the help of flame photometer (Jenway PFP7, Barloworld Scientific, England). The phosphorus content of wheat grain was determined by the reaction of acidified solution of ammonium molybdate containing ascorbic acid and antimony (Chapman and Pratt, 1978 ). The phosphate in plant sample reacts to form an ammonium molydiphosphate complex, which is reduced to blue color solution by ascorbic acid. The amount of light absorbed by the solution was measured at 660 nm with UV-visible spectrophotometer (Shimadzu UV-1700, Shimadzu, Japan). The sample reading was measured from the standard calibration curve.

Statistical analysis like standard deviation and mean values was carried out for duplicate and triplicate measurements of individual analysis and overall readings of NIRS (near infrared reflectance spectroscopy) and AOAC methods using computer software SPSS 12.0 for windows (released on Sept. 4, 2003, Lead Technologies Inc., USA).

RESULTS AND DISCUSSION

Protein contents.

Grain protein percentage is an important component of grain quality. Protein contents measured by standard Kjeldahl method show a higher level than protein contents calculated from NIRS as given in Table ​ Table1. 1 . However the highest mean value from both methods are (mean± SD ): (10.27±1.161)%, (10.25±1.061)% and (10.20±1.414)% for varieties Gandam-711, Watan and Bakhtawar-92 respectively. Low values were observed in Saleem-2000 (9.15±0.212)%, and Fakhre-Sarhad (9.57±0.460)%. Generally grain protein contents in wheat varies between 8% and 17%, depending on genetic make-up and on external factors associated with the crop, however the protein contents in wheat measured by NIRS have been shown to be in the range of 10%~19% (Hruschka and Norris, 1982 ). Thus our values for protein contents on both NIRS and Kjeldahl method are in this range, but mostly on the lower side of the scale.

Protein, fats and fiber contents of different Pakistani wheat varieties determined by different methods

Variety name Protein (%) Fat (%) Fiber (%)
Kjeldahl NIRS Mean± AOAC NIRSMean± AOAC NIRSMean±
Tatara11.09.2510.12±1.2372.72.202.45±0.3541.91.651.77±0.177
Watan11.09.5010.25±1.0612.42.252.32±0.1062.01.601.80±0.283
Gandam-71110.99.2510.27±1.1612.22.202.20±0.0001.91.701.80±0.141
Bakhtawar-9211.29.2010.20±1.4142.82.202.50±0.4241.91.751.82±0.106
Fakhre-Sarhad9.99.259.57±0.4602.62.202.40±0.2831.81.751.77±0.035
Bhakkar-0111.09.3010.15±1.2022.72.202.45±0.3541.91.801.85±0.071
Ghaznawy10.89.109.95±1.2022.22.202.20±0.0001.91.751.82±0.106
Saleem-20009.09.309.15±0.2122.12.202.15±0.0711.91.751.82±0.106
Wafaq-0111.09.2010.10±1.2712.92.202.55±0.4951.91.701.80±0.141
Gandam-200211.09.2010.10±1.2712.52.202.35±0.2122.01.701.85±0.212
Chudry-9711.09.3010.15±1.2022.62.202.40±0.2831.71.801.75±0.071
Inqilab-9110.29.409.80±0.5652.42.202.30±0.1411.81.651.72±0.101

Results regarding standard Kjeldahl analysis of protein reveals highest level of 11.2% protein in variety Bakhtawar-92, while Tatara, Watan, Bhakkar-01, Wafaq-01, Gandam-2002 and Chudry-97 contain 11.0% protein. The lowest value is present in Saleem-2000 (9.0%). This trend is close to the mean values from both methods. However our value correlates with the recently reported value of 8.6% for variety Inqilab-91 (Hussain et al., 2006 ).

Fats contents

The Fats values are higher for AOAC method than NIRS value (Table ​ (Table1). 1 ). Highest total mean fat content are present in Wafaq-01, Bakhtawar-92 with values of (2.55±0.495)%, (2.50±0.424)%, and (2.45±0.354)% shown by Tatara and Bhakkar-01. The most probable reason for the low level may be the presence of lipase or lipase activity, which is responsible for the hydrolysis of lipids in dormant wheat during storage (Rose and Pike, 2006 ).

Dietary fiber

Dietary fiber measurement is essential for the assessment of potential therapeutic and preventive effect of fiber intake (Anderson and Bridges, 1988 ). The values of fiber content for each variety are given in Table ​ Table1. 1 . The highest mean values of fiber are: (1.85±0.071)%, (1.85±0.212)% for Bhakkar-01, Gandam-2002, and a same value of (1.82±0.106)% is shown by Ghaznawy, Bakhtawar-92 and Saleem-2000 respectively. It has been shown that wheat is among cereals containing lowest level of fiber (Anderson and Bridges, 1988 ).

Micronutrients composition

Micronutrients compositions especially potassium and sodium are essential cations abundantly present in plants (Mäser et al., 2002 ). Data regarding mineral composition of different Pakistani wheat varieties are presented in Table ​ Table2. 2 . The highest potassium contents are observed in Chudry-97, Saleem-2000 and Gandam-2002 with value (mean± SD ) of 84.25×10 −6 ±0.353×10 −6 , 84.15×10 −6 ±2.192×10 −6 and 83.50×10 −6 ±1.272×10 −6 respectively. While lowest value is there for variety Ghaznawy with a value of 77.65×10 −6 ±2.050×10 −6 . The highest sodium value was obtained for Tatara, Gandam-2002 and Fakhre-Sarhad with values of 35.90×10 −6 ±3.676×10 −6 , 33.80×10 −6 ±2.828×10 −6 and 32.05×10 −6 ±3.747×10 −6 respectively, while the lowest value was observed in Bhakkar-01 with 7.70×10 −6 ±1.555×10 −6 of sodium. Regarding Na + /K + ratio, high value variety is Tatara with value of 0.461 and was positioned first. The lowest value was observed in Bhakkar-01 with 0.098. Highest level of phosphorus is present in varieties Watan and Saleem-2000 with a mean value of 0.84×10 −6 , while Bhakkar-01 and Chudry-97 are placed on the second position having 0.68×10 −6 of phosphorus. It has been observed that lower phosphorus concentration in higher yielding variety is necessarily associated with success in selection for dry matter yield, where uptake of nutrient is limited (Lipsett, 1964 ).

Mineral compositions of different Pakistani wheat varieties

Wheat variety Na (×10 ) K (×10 )Na /K Phosphorus (×10 )
Tatara35.90±3.67677.80±3.1110.4610.36
Watan30.60±3.25283.10±4.1010.3680.84
Gandam-71130.95±3.04080.15±3.0400.3860.60
Bakhtawar-9229.40±4.52581.75±2.4740.3590.42
Fakhre-Sarhad32.05±3.74781.55±2.7570.3930.53
Bhakkar-017.70±1.55578.60±2.2620.0980.68
Ghaznawy31.25±1.06077.65±2.0500.4020.44
Saleem-200031.45±3.46584.15±2.1920.3730.84
Wafaq-0131.25±3.60681.15±2.0500.3850.66
Gandam-200233.80±2.82883.50±1.2720.4040.24
Chudry-9731.20±3.81884.25±0.3530.3700.68
Inqilab-9131.45±3.32381.15±1.2020.3870.26

Physiochemical characteristics

Physiochemical characteristics like ash, total grain mass and moisture are important parameters in the study of nutritional and agricultural aspect. The value of ash content is given in Table ​ Table3. 3 . The highest level is present in Gandam-2002, with a value (mean± SD ) of (2.16±0.3804)% and Watan (2.15±0.3535)%. The lowest level is observed in variety Fakhre-Sarhad with (1.44±0.2100)% ashes. Our values are in some cases higher than already obtained for some other Pakistani wheat varieties (1.3%~1.95%) (Kamal et al., 2003 ). However the variation and negative relationships of ash with yield and all the other parameters may be attributable to the finding that ash is higher in those genotypes, which are more affected by drought during grain filling (Araus et al., 1998 ). Thousand grain mass (TGM) is an important parameter for determining productivity of wheat. TGM of different varieties is accessible in Table ​ Table3. 3 . Data represent the high mass of Wafaq-01 and Tatara with 43.602 g and 41.484 g respectively. Bhakkar-01, Inqilab-91 and Bakhtawar-92 have less TGM value of 34.025 g, 33.495 g and 31.108 g respectively. Jamil and Khan ( 2002 ) reported a TGM value of 36.29 g for Bakhtawar-92, which is 5.182 g higher than our value of 31.108 g. The probable reason for this high value may be the fresh grains they used. The result of TGM for Inqilab-91 is lower than the means value reported by Hussain et al.( 2006 ).

Physiochemical components of different Pakistani wheat varieties

Wheat variety Ash (%) TGM (g) Moisture (%)
Tatara1.58±0.167541.4849.42±1.527
Watan2.15±0.353535.7248.38±2.291
Gandam-7111.83±0.091939.5318.70±2.128
Bakhtawar-922.10±0.435531.1089.27±1.605
Fakhre-Sarhad1.44±0.210036.8609.08±2.156
Bhakkar-011.63±0.381134.0259.36±2.184
Ghaznawy1.80±0.154135.1339.03±1.796
Saleem-20002.04±0.344336.0718.76±1.329
Wafaq-011.69±0.293443.6029.67±2.029
Gandam-20022.16±0.380439.2979.16±2.602
Chudry-971.58±0.306840.2109.64±1.923
Inqilab-911.77±0.270833.4959.00±1.838

Data regarding moisture content are present in Table ​ Table3. 3 . It is evident from the table that highest level of moisture is present in Wafaq-01, Chudry-97 and Tatara with values of (9.67±2.029)%, (9.64±1.923)% and (9.42±1.527)% respectively. Our value is higher than moisture contents of wheat varieties determined in D.I. Khan, Pakistan (6.06%) (Jamil and Khan, 2002 ). The main reason for this difference is the large drought area of D.I. Khan. Thus moisture content appears to affect the grain hardness and is important parameter for meal as well (Gaines and Windham, 1998 ).

It is concluded from the present study that wheat grains of different varieties in Pakistan contain total protein of 9.15%~10.27%, 2.15%~2.55% total fats, 1.72%~1.85% dietary fibers, 77.65×10 −6 ~84.25×10 −6 of potassium and 7.70×10 −6 ~35.90×10 −6 of sodium ions concentration, 0.24×10 −6 ~0.84×10 −6 of phosphorus, 1.44%~2.10% ash, 31.108~43.602 g of TGM and 8.38%~9.67% moisture contents. This study is important in providing an opportunity to improve the nutritional quality of wheat and for setting up of nutritional and export regulations in Pakistan.

* Project (No. HEC/FD/2007/670) supported by the Higher Education Commission (HEC), Pakistan

Production efficiency of wheat farmers in the Arsi Zone, Oromia Region of Ethiopia

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  • Published: 04 July 2024
  • Volume 4 , article number  51 , ( 2024 )

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wheat introduction research paper

  • Zenaye Degefu Agazhi   ORCID: orcid.org/0000-0002-2870-2157 1 ,
  • Melkamu Mada 1 &
  • Mebratu Alemu 1  

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Wheat is a cereal crop that contributes to food security; thus, Ethiopia must boost the production efficiency of wheat to meet the sustainable development goal of eradicating hunger and poverty. Consequently, a significant revolution is occurring in the Ethiopian wheat industry to improve production and productivity. Therefore, it is critical to understand the current level of wheat farmers’ efficiency, as its production is highly influenced by existing agricultural technologies and climate change, which makes it dynamic. Accordingly, this study employed the parametric Cobb–Douglas stochastic frontier and two-limit Tobit models to evaluate wheat farmers’ efficiency and determine their drivers in Ethiopia’s largest wheat-producing area, the Arsi Zone. A multistage sampling strategy was applied to obtain a representative sample of 422 wheat farmers. The model’s output suggested that the average technical, allocative, and economic efficiency scores were 80.8%, 88.1%, and 71.3%, respectively. It is confirmed that wheat farmers’ efficiencies can increase with household head age, education level, livestock ownership, contact with extension agents, wheat mechanization, and involvement in non/off-farm activities but decrease with household distance from the main market and total land holdings. To realize the potential gains from wheat cultivation in Ethiopia, the government needs to develop policies and strategies that enhance farmers’ education, livestock production, and extension contact, facilitate infrastructure, market development, and wheat mechanization, and promote non/off-farm activities.

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1 Introduction

Agriculture remains the foundation of the Ethiopian economy which makes up more than 32.5% of the country’s GDP in 2020–2021 [ 1 ] and employs nearly 77.2% of the population, even though it is giving way to the industry and manufacturing sectors. A total of 65.1% of Ethiopian agricultural GDP is derived from crop production [ 1 ], and cereal crops are the main source of caloric food for people’s daily food consumption [ 2 ]. However, this sector in Ethiopia is highly subsistence due to its reliance on rain, low marketing facilities, poor infrastructural development, insufficient farm input, and climate change-induced natural disasters [ 3 , 4 ]. Wheat is classified as a cereal crop, so its production contributes significantly to the country’s economic development [ 5 ]. Ethiopia has enormous wheat production potential, which makes it the world’s 18 th and Africa’s 2 nd largest wheat producer. It accounts for approximately 15.31% of the Ethiopian total grain farmland (1.87 million hectares) and 17.71% of the total grain production (58.08 million quintals) [ 6 ] in the 2021/2022 main production season. The demand for wheat in Ethiopia is rapidly expanding due to population growth and urbanization, which have shifted dietary preferences toward wheat-based convenience foods [ 7 ].

For a country such as Ethiopia to ensure food security and meet the nutritional needs of its rapidly expanding population, increasing wheat production efficiency is essential. In light of this, Ethiopia’s wheat sector is currently undergoing a massive revolution, intended to swiftly expand wheat output, significantly close the supply–demand imbalance, and achieve wheat self-sufficiency as a national goal [ 8 ]. However, Ethiopia’s wheat productivity in 2022–2023 was 3 tons/ha, which is below both the global average (3.55 tons/ha) and that of Egypt (6.4 tons/ha) [ 9 ] due to biotic and abiotic constraints and technical, socioeconomic, and institutional factors. This research was undertaken in the Arsi zone, Ethiopia’s largest wheat producer. Wheat production in the Arsi zone accounts for 11.03% of the total wheat land (approximately 0.209 million hectares) and 12.45% of the total wheat production (approximately 7.2 million quintals) of the country [ 6 ].

Ethiopian wheat production is predominantly carried out by subsistence farmers, which results in low productivity and inefficient wheat production [ 5 , 10 , 11 ], mostly associated with problems of inadequate supply and high costs of critical farm inputs [ 8 , 12 ]. It is critical to improve Ethiopia’s wheat production efficiency to address the issues caused by an expanding population, climate change, and shifting demands. Understanding the current level of wheat production efficiency will result in better resource management and optimization that eventually increases the productivity and profitability of wheat production, which contributes to overall agricultural development and helps to achieve the national objective of ending hunger and poverty.

Numerous studies on the production efficiency of wheat in Ethiopia confirm the existing wheat production inefficiency, even though there is an opportunity to boost output from the current level of input and production technologies. However, the majority of these studies [ 11 , 13 , 14 ] focused on estimating technical efficiency, with only a few looking at all efficiency estimates simultaneously. Furthermore, Ethiopian wheat production is undergoing a tremendous revolution [ 8 ], and wheat production is dynamic similar to that in other agricultural sectors, and fluctuates with existing production technology and climate unpredictability. Therefore, recurrent studies on the production efficiency of wheat are needed. Accordingly, this study was undertaken in the Arsi zone of the Oromia regional state of Ethiopia to assess wheat farmers’ technical efficiency (TE), allocative efficiency (AE), and economic efficiency (EE) and to identify their drivers.

2 Research methodologies

2.1 description of the study area.

The Arsi zone is located in the Oromia region of Ethiopia in the southeastern part of the country. It has a total area of 19,825.22 km 2 and is divided into 26 districts [ 6 ]. According to the Arsi Zone Office of Agriculture (2024), the rainy season begins in June and peaks in July and August, while the Belg season runs from February to April. The zone receives a yearly average rainfall of 1020 mm and a temperature that ranges between 20 and 25 °C. The zone has an altitude that ranges from 500 to 4377 m above sea level (m.a.s.l.) with four agroecological zones, namely, extreme highlands (above 3200 m.a.s.l.), the highlands (2300–3200 m.a.s.l.), midlands (1500–2300 m.a.s.l.), and the lowlands (below 1500 m.a.s.l.), which cover 6%, 34%, 40%, and 20%, respectively.

The population was officially estimated to be 3,980,967 people in mid-2022, with 49.92% males and 50.07% females [ 15 ]. The total population is composed of 88.14% rural residents, 11.59% urban residents, and 0.27% pastoralists. Wheat, barley, maize, tef, sorghum, and coffee are among the most important crops grown in the region. Wheat is the most important crop grown by 360,697 holders, accounting for 43.7% of the zone’s total cereal production from its 39.8% zonally cultivated lands. The study sites are the Digeluna–Tijo, Dodota, and Ludehetosa districts, which are indicated in Fig.  1 below.

figure 1

Map of the study area

2.2 Data source and sampling techniques

This study was based on cross-sectional data collected by using a semi structured questionnaire and interviews with farm households that were selected randomly using a multistage sampling technique. In the 1 st stage, the Arsi zone was purposively selected due to its wheat production potential. In the second stage, accessible major wheat-producing districts in the zone were identified for each agroecological zone with the help of key informants who knew the study area well. Then, the highland districts of Digeluna–Tijo, the lowland district of Dodota, and the midland district of Ludehetosa were chosen randomly by the lottery method. In the 3 rd stage, the total number of kebeles Footnote 1 in each district that corresponded to the district’s agroecological class was listed. Then, based on the proportion of kebeles in each district, three kebeles from Ludehetosa, three from Digeluna-Tijo, and two from Dodota were randomly selected. Then, from the kebele-level administrative office, a list of households that produced wheat during the main agricultural production season of 2022/2023 was obtained. Finally, a total of 422 wheat producers were selected and distributed for each district (Table  1 ) based on the proportion of their total wheat producers using a simple random sampling technique.

2.3 Method of data analysis

2.3.1 estimation of wheat farmer efficiencies.

The two methods employed to evaluate smallholder farm efficiency levels are the parametric stochastic frontier model (SFM) and nonparametric data envelope analysis (DEA). The parametric SFM is based on an econometric approach with a specific functional form, while the nonparametric DEA relies on mathematical techniques [ 16 ]. To estimate efficiency, both techniques use distinct approaches that consider random noise and flexibility in the structure of the production technology. SFM typically has only one output, whereas DEA can include multiple outputs from a producer [ 17 ].

Due to their intrinsic differences, the parametric SFM and nonparametric DEA approaches have been debated. Studies on measuring efficiency indicate that researchers can use either approach because there is little difference in the estimated results [ 18 , 19 ]. According to [ 20 ], the SFM forecasts firm-specific inefficiencies and random errors that cause deviations from the frontier. Parametric SFM is still a commonly used approach for measuring farmers’ production efficiency, despite its limitations in explicitly assuming a functional form for production technology and inefficiency term distribution. Since the SFM accounts for stochastic noise and enables hypothesis testing about production technology structure, the existing level of inefficiency, and overall model performance, it is regarded as a more accurate method for measuring efficiency. The objective of the study, data type, and functional forms used to represent the technology of production determine the model used in efficiency analysis. When there is a significant level of disturbance in the data due to measurement error, SFM is better than DEA for calculating the total factor productivity growth rate [ 16 ].

In this study, the SFM, which captures statistical noise that the decision-making unit cannot control, such as errors in measurement and changes in the environment, was used to estimate the coefficients of the production/cost function and the levels of wheat farmers’ efficiencies (TE, AE, and EE) [ 20 ]. The functional forms that are most frequently utilized in SFM are translog and Cobb‒Douglas (CD). To select the best-fit functional form for the data at hand, null hypotheses were tested using the generalized likelihood ratio (GLR), and CD was selected, as discussed in Sect.  3.2 , which assumes unitary substitution elasticity, continuous production elasticity, and constant factor demand [ 20 ]. Using the CD over translog production function for smallholder farmers is endorsed, as the changes in returns to scale are unlikely to have a significant impact on production technology [ 21 ].

Accordingly, the CD-Stochastic Frontier Production Function (CD-SFPF) is specified as:

where ln represents the natural logarithm, \({y}_{i}\) is the wheat yield in quintals; \({X}_{1}\) is the land under wheat (hectares); \({X}_{2}\) is the labor (family/hired) (man/day); \({X}_{3}\) is the chemical fertilizer (kg); \({X}_{4}\) represents the seed (kg); \({X}_{5}\) is the oxen power (oxen-day); \({X}_{6}\) is the chemical (pesticide and herbicide) (liters); \({\beta }_{0}\) is the intercept; \({\beta }_{i}\) are the coefficients of the production function; and \({v}_{i}\) is a random error that is assumed to be independently and identically distributed N (0, \({\upsigma }^{2}\) ). \({u}_{i}\) is an error term that is a one-sided nonnegative variable that indicates the household’s technical inefficiency, which reflects how far the observed output falls below the potential yield from a given level of technology and inputs.

By assuming that the production function in Eq. ( 1 ) is self-dual CD, the respective cost function of the CD production function can be stated as:

where \({TC}_{i}\) is the minimum cost of wheat production for the \({i}{\text{th}}\) household; \({Y}_{i}^{*}\) is the respective wheat yield adjusted for noise; P represents the prices of inputs; and \({\alpha }_{\text{s}}\) are their coefficients. Then, the input price and adjusted level of the output were substituted by using Shephard’s Lemma technique into the subsequent input demand equations, and the economically efficient input vector of the \({i}{\text{th}}\) farmer, \({X}_{ie}\) , can be expressed as

where n = 6 indicates the number of inputs used for wheat production.

The explained cost measure enables us to determine the level of AE and then EE, which can explain the technical efficiency of each wheat farmer in terms of observed output ( \({Y}_{i}\) ) and the corresponding frontier output ( \({Y}_{i}^{*}\) ) using the existing production technology [ 22 ].

Then, the economic efficiency of each farmer is defined as the ratio of the minimum observed total production cost (TC*) to the actual total production cost (TC).

Following [ 23 ], the AE index can be derived from Eqs. ( 4 ) and ( 5 ) as indicated below:

2.3.2 Determinants of wheat farmers’ efficiency

There are two methods for analyzing the source of efficiency of SFPF [ 24 ]. The 1 st technique is a one-stage procedure in which the technical efficiency level and its driver are estimated simultaneously. The 2 nd technique is a two-step procedure where in the 1 st stage, the efficiency scores are computed; then, in the 2 nd stage, the resulting efficiency scores are regressed using either the OLS method or Tobit regression. In this study, a two-stage estimation procedure with two-limit Tobit regression was employed to identify the determinants of efficiency since it allows the estimation of AE and EE in addition to TE [ 10 , 25 , 26 , 27 ], unlike the one-stage approach, which permits the estimation of only the determinants of TE. Understanding the factors that influence wheat farmers’ efficiency is an important segment of research that aims at tackling the problems encountered by farmers, promoting sustainable agriculture, and increasing the agricultural sector’s competitiveness and resilience.

Accordingly, the two-limit Tobit model is formulated by following [ 28 ] as:

where i refers to the \({i}{\text{th}}\) farmer and \({y}_{i}^{*}\) is the latent variable that indicates the level of TE, AE, or EE of the \({i}{\text{th}}\) farmer. \(\beta_{0}\)  is the intercept while \(\beta_{j}\) are coefficients and \({\mu }_{i}\) is a random error term that is independently and normally distributed. \({X}_{ij}\) are variables that affect the TE, AE, and EE of wheat producers and are presented in Table  2 below.

After denoting the observed dependent variable as \({y}_{i}\) , the Tobit model can be written as

where \({y}_{i}^{*}\) represents the efficiency score of the \({i}{\text{th}}\) household. The likelihood function of the Tobit model is specified as:

where \({L}_{1j}\) and \({L}_{2j}\) are the lower and upper bounds, respectively, while \(\omega\) (.) = the cumulative normal distribution, φ (.) = the normal density function.

As indicated by [ 29 , 30 ], the overall marginal effect (ME) of the Tobit model in Eq. ( 9 ) is divided into the following three MEs.

The unconditional expected value of wheat farmers’ efficiencies

The expected value of wheat farmers’ efficiencies, which is conditional upon being between the limits

The probability of being between the mean value (A) and upper limit (U)

where \({Z}_{L/A}=\frac{{-\beta }^{\prime}X}{\sigma }\) and \({Z}_{U}=\frac{(1-\beta X)}{\sigma }\) are standardized variables that come from the likelihood function given the limits of \({y}^{*}\) , and \(\sigma\) is the standard deviation of the model.

3 Results and discussion

3.1 descriptive statistics.

This study comprised 88.9% male-headed households of the 422 randomly selected wheat-producing farmers, with an average age of 46.22 years (Table  3 ). The mean household size in the study area is 4.88 persons (4.38 adult equivalent), which is consistent with the national average family size (4.6 person) [ 35 ], with an average education level of 7.01 years. The mean livestock holdings were 6.47 TLU, while the average land holdings were 2.45 hectares, which is greater than the national average livestock holdings of 2.7 TLU [ 36 ] and land holdings of 1.1 hectares [ 37 ]. Nearly 59.4% of the respondents used mechanization for wheat production, while 31.3% utilized credit. The mean distance from the main market was 7.08 km, and the average frequency of contact with the extension agent was 4.44 days per year. Nearly 66% of the respondents participated in off/nonfarm activities. The farmers produced 40.12 quintals of wheat, using, on average, 188.1 kg of seed, 13.79 man-days of labor, 8.97 oxen days, 236.14 kg of fertilizer, and 3.76 litters of agrochemicals from an average of 1.41 hectares of wheat land.

3.2 Efficiency of wheat producers

Before estimating the SFM, it is important to test the three-model specification-related hypotheses. The first test involved selecting either the CD or translog functional form, and a GLR test was performed. The results indicated that the coefficients of the translog production function were almost zero ( \({{\varvec{H}}}_{{\varvec{o}}}\) : \({{\varvec{\beta}}}_{{\varvec{i}}{\varvec{j}}}\)  = 0), which confirmed that the CD functional form best fit the data at hand ( \({{\varvec{H}}}_{1}\) : \({{\varvec{\beta}}}_{{\varvec{i}}{\varvec{j}}}\)  ≠ 0) (Table  4 ).

Then, the null hypothesis argues that the inefficiency component of the total error term is equal to zero ( \({H}_{o}:\gamma\)  = 0), and an alternative hypothesis ( \({H}_{1}:\gamma\)  ≠ 0) was tested by calculating the GLR. The findings demonstrate that the null hypothesis that state wheat farmers in the study area are 100% efficient is not accepted. Another hypothesis to be tested was that all coefficients of the inefficiency model are simultaneously equal to zero (i.e., \({H}_{o}\) : \({\delta }_{1}\)  =  \({\delta }_{2}\)  =  \({\delta }_{3} . . . . . {\delta }_{10}\)  = 0) against the alternative hypothesis, and a GLR test was performed. The findings confirmed the rejection of the null hypothesis, favoring the alternative hypothesis, which argues that the explanatory variables that affect wheat farmers’ efficiency levels are not all equal to zero (Table  4 ).

The maximum likelihood of CD-SFPF was estimated after testing the above hypothesis. Here, the input variables were land under wheat (in hectares), the labor force in man-equivalent for wheat production (in man/days), oxen (oxen-days), the quantity of seed (kg), chemicals (herbicides and pesticides in liters), and fertilizer (both NPS and urea in kg), where the output variable was wheat yield (quintals) in the 2022/23 production year. For the estimate of the CD-SFPF, the model specification result is significant ( wald \({chi}^{2}\)  = 3056.26, prob >  \({chi}^{2}\)  = 0.0000), suggesting that at the 1% level of statistical error, the null hypothesis that states all slope coefficients are zero is rejected. Furthermore, sigma squared ( \({\upsigma }^{2}\) ), which is a diagnostic measure of the inefficiency component that confirms the quality of model estimation and the validity of the distributional form assumed for the composite error term, was statistically significant. The Gamma coefficient (γ =  \(\sigma_u^2\) / \({\upsigma }^{2}\) ) is 0.766, indicating that technical inefficiency accounts for 76.6% of the wheat yield disparity at the frontier, while 23.4% is attributed to factors outside farmers’ control (Table  5 ).

Table 5 presents the maximum likelihood parameter estimates of the CD-SFPF, and the results confirm that all inputs except labor were found to positively and significantly affect wheat output at a 1% level of statistical error. The model output indicated that a 1% increase in land, seed, fertilizer, chemical, and oxen days resulted in increases in wheat yields of 0.477, 0.088, 0.311, 0.130, and 0.003%, respectively, while all other factors remained constant .

The coefficients of inputs indicate the elasticity of output, which is summed to be 1.022, but this value does not guarantee the existing return to scale unless the hypothesis is tested. Accordingly, the hypothesis that farmers follow a constant return-to-scale was tested, which states that the sum of parameter coefficients is equal to 1 ( \({H}_{o}\) : \({\beta }_{1}+{\beta }_{2}+{\beta }_{3}+{\beta }_{4}+{\beta }_{5}+{\beta }_{6}\)  = 1) against the alternative hypothesis ( \({H}_{1}\) : \({\beta }_{1}+{\beta }_{2}+{\beta }_{3}+{\beta }_{4}+{\beta }_{5}+{\beta }_{6}\)  ≠ 1). The test results confirm the acceptance of the null hypothesis, indicating the presence of constant returns to scale among wheat farmers in the research area. This means that a 1% increase in all inputs would boost the overall wheat yield by approximately 1%.

The associated dual cost frontier function parameters are subsequently determined using the CD production function parameter listed below:

where P stands for the price of inputs which is measured in Birr Footnote 2 P Land is the rental value of land per hectare for one year (Ludehetosa = 10,000 Birr, Digeluna Tijo = 15,000 Birr, and Dodota = 8000 Birr on average), and P oxen is the price of paired oxen power that is estimated at 400 Birr/day. P Labor is the wage rate of labor estimated at 200 Birr/day, P Fertilizer is the price of chemical fertilizer per kg estimated at Birr (urea = 36 Birr/kg and NPS = 42 Birr/kg), P Seed is the price of seed (improved = 55 Birr/kg and local = 40 Birr/kg), and P Chemical is estimated at 1800 Birr/liter.

3.3 Wheat production efficiency scores and distributions

Table 6 summarizes the SFM outputs and indicates that the mean TE, AE, and EE of the wheat farmers were approximately 80.8%, 88.1%, and 71.3%, respectively. Of the total number of respondents, approximately 41.47%, 36.26%, and 42.18% had TE, AE, and EE scores below the average level, respectively. The highest mean TE (83.8%) and AE (87.2%) were recorded in the Ludehetosa District, while the lowest mean TE (74.9%) and AE (88.3%) were recorded in Dodota. On the other hand, the highest mean EE (73.3%) was recorded in Digeluna Tijo District, where the lowest was recorded again in Dodota (66.5%). The disparity in wheat efficiency among districts could be attributed to restricted water availability and erratic or insufficient rainfall in lowland (Dodota) areas compared to those in midland (Ludehetosa) and highland areas (Digeluna Tijo), as wheat requires appropriate moisture for proper cultivation.

The mean TE was 80.8%, with the lowest value of 31.8% and the highest value of 96.4%. This means that if farmers can acquire essential managerial and technical skills, on average, they can increase their output by 19.2% (100–80.8). This suggests that if the average wheat farmer maintains the same TE level as its most efficient farmer, it could reduce 16.18% (100–80.8/96.4) of the inputs required to produce the maximum possible output.

The average AE was 88.1%, with the lowest value of 44.2% and the highest value of 99.1%, suggesting that wheat farmers may save 11.9% (100–88.1) of their present input costs if resources are used efficiently. This demonstrates that wheat farmers with an average AE score may reduce production costs by 11.09% (100–88.1/99.1), which is necessary to achieve the level of the most allocatively efficient wheat farmers by cost-effectively reallocating resources.

The mean EE score is 71.3%, with the lowest value of 31.0% and the highest value of 94.1%. This implies that wheat producers with average EE can reduce the existing average costs by 29.4% (100–71.3) to obtain the lowest possible production cost while maintaining the same level of wheat yield. The results also suggested that wheat farmers with an average EE would reduce their average costs of production by 24.22% (100–71.3/94.1) to achieve the most economically efficient production.

3.4 Determinants of wheat farmers’ efficiency

In this study, to identify the factors that affect wheat farmers’ TE, AE, and EE, a two-limit Tobit model was estimated. Before estimation, diagnostic tests were carried out, and the findings verified that there were no serious econometric problems with the data at hand. Tobit regression was used (Table  7 ), and the results revealed that livestock ownership, household head age, education level, engagement in off/nonfarm activity, and contact with agricultural extension agents had a positive and significant effect on TE, whereas total farm size and distance from the main market had a negative impact. Similarly, wheat producers’ AE increased considerably with family size, frequency of extension contacts, and wheat mechanization, and decreased with distance from the main market. It has also been specified that education level, off/nonfarm activity engagement, extension contact, and wheat mechanization have a positive and substantial effect on EE, while total farm size and distance from the main market have a negative impact.

The model output indicates that farmers’ age, which is a good predictor of agricultural experience, enhances the TE of wheat producers at a 1% level of statistical error. The ME estimation indicates that a 1-year increase in the age of the household head results in a 0.10%, 0.10%, and 0.50% increase in the level of TE, the expected value of TE, and the likelihood of a household scoring above the mean TE, respectively. This result is in line with the findings of [ 10 , 13 ], who identified the age of households as a key determinant of agricultural production efficiency. This may be because as the household head’s age increases, farm knowledge accumulated through experience also increases. This helps to make better agricultural decisions and enhances resource management practices in wheat production through better on-farm risk mitigation strategies, which improves wheat production efficiency.

The education level of the household head was found to have a positive and significant effect on the TE and EE of wheat farmers at the 10% level of statistical error. As education level increases by 1 year of schooling, the level, expected value, and likelihood of scoring above the mean value of TE increase by 0.20%, 0.19%, and 0.98%, respectively, while the overall level of EE, the expected value of EE, and the probability of scoring above the mean value of EE increase by 0.24%, 0.23%, and 0.98%, respectively. The same result was also reported by [ 10 , 13 , 14 ], who confirmed the positive role of education in enhancing agricultural production efficiency. This could be because well-educated farmers have greater access to information and can easily comprehend farm instructions since education increases farmers’ consciousness over the need to acquire, analyze, and apply knowledge that increases wheat production efficiency.

Livestock holding in TLU was among the positively significant variables that determined the TE of wheat farmers at a 5% level of statistical error. This implies that households with higher livestock ownership are expected to have better wheat TE. As livestock holdings increased by 1 TLU, the overall level of TE increased by 0.32%, while the expected value of TE increased by 0.29%, and the probability of a household scoring above the mean TE increased by 1.53%. The positive role of holding livestock was expected since it is a good proxy for the wealth of farm households. A similar result was reported by [ 11 , 25 ], as livestock provide additional income, and traction power for crop cultivation, which is expected to enhance wheat production efficiency.

The model results also confirm that farmers’ residence distance from the major market was inversely correlated with all efficiency estimates (TE, AE, and EE) at the 1% level of statistical error. This implies that as farmers get closer to the main market, from where they purchase farm inputs and sell their products, they will be more efficient than other farmers. This result is consistent with the findings of [ 10 , 25 , 32 ], who reveal a negative role for farm residence distances from the main market. This may be because, as farmers are closer to the market, they are more likely to experience lower expenses associated with marketing transactions.

The coefficient of the total farm size was negative and significantly affected both TE and EE at a 5% level of statistical error. This indicates that as total land holdings increase, the level of wheat farmers’ TE and EE decreases. A study by [ 13 , 25 ] also confirmed the negative association between landholding and production efficiency, even if the land is an important economic variable that enhances household income by increasing production volume. However, farm size is inversely related to the efficiency level of particular crops since it challenges farm management practices and resource distribution, and smaller landholdings tend to have better management practices and ease the administration of production inputs, which can result in a more effective wheat production system.

The model results confirm that farmers’ contact was positively associated with TE, AE, and EE at the 10%, 5%, and 5% levels of statistical error, respectively. This implies that wheat producers with a greater frequency of contact with agricultural extensions are expected to have better wheat production efficiency. A study by [ 10 , 11 ] also confirmed the positive role of agricultural extension contact on the efficiency of agricultural production. This may be because agricultural extension centers are the primary source of agricultural information that helps farmers become more informed and proficient, which enhances their farming methods and productivity. In addition, extension agents facilitate and promote the adoption of improved agricultural practices that contribute to the efficiency of agricultural production.

Family size was found to have a positive and significant effect on wheat farmers’ AE at the 1% level of statistical error. This suggests that households with larger families are more likely to have better AE than others. As family size increased by one adult equivalent, the overall level of AE, the predicted value of AE, and the likelihood of scoring above the mean of AE increased by 0.56%, 0.45%, and 1.72%, respectively. This outcome was expected given that family size is a significant source of family labor, which is consistent with previous research [ 11 , 25 ]. As labor availability increases, agricultural activities that require an intensive labor force will perform well, and the adoption of agricultural technology will also be enhanced [ 38 , 39 ], which is expected to increase the production efficiency of wheat farmers.

Wheat mechanization is another important factor that positively and significantly affects both AE and EE at the 1% level of statistical error. This implies that households that utilize machinery for wheat production are expected to have greater wheat production efficiency than nonusers. The findings of [ 33 , 34 ] also support the positive role of mechanization in agricultural development through enhancing production and productivity. This may be because mechanizing wheat production improves farmers’ efficiency by lowering labor costs, shortening the length of plowing and harvesting, and reducing postharvest losses.

Household head participation in off/nonfarm activities was also found to significantly and positively affect both TE and EE at the 1% level of statistical error. This suggests that households engaged in off/nonfarm activities are predicted to have greater TE and AE levels than their counterparts (those who have not participated). The same result was reported by [ 10 , 25 ], which may be because off/nonfarm activity increases household income and secures diversified income sources that reduce risks. This contributes to the purchase of farm inputs such as improved seeds and fertilizer and enhances the adoption of improved agricultural technologies that increase production efficiencies.

4 Conclusion and policy implications

As Ethiopian wheat production is undergoing a revolution and agricultural production is dynamic, understanding the current level of wheat production efficiency and knowing its determinants are important. Accordingly, the SFM and a two-limit Tobit model were employed to identify wheat farmers’ efficiencies and their drivers, respectively. The results indicated that the average TE, AE, and EE values were 80.8%, 88.1%, and 71.3%, respectively, indicating that wheat farmers are working below the maximum attainable efficiency levels. These data imply that there is space for improving the TE, AE, and EE of wheat farmers in the Arsi zone at given levels of inputs and production technologies. The two-limit Tobit model results show that age, education, livestock holdings, contact with agricultural extension agents, family size, wheat mechanization, and non/off-farm participation enhance the efficiency of wheat producers, while the distance from the main market and total land holdings reduce their efficiency.

Finally, to improve wheat farmers’ efficiency and attain the national objective of wheat self-sufficiency, it is critical to encourage policies and strategies that increase access to education through skilled educators and an appropriate learning environment. It is also essential to promote livestock production, which may be achieved by encouraging consistent animal feed production and providing efficient veterinarian services. Furthermore, it is necessary to boost wheat mechanization through subsidies and financial assistance. In addition, to enhance wheat production efficiency, market development and an environment that encourages farmers’ diverse income streams must be promoted through improved infrastructure and credit provisions.

Data availability

The data will be made available upon request.

Code availability

The code will be made available upon request.

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Zenaye Degefu Agazhi, Melkamu Mada & Mebratu Alemu

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All authors contributed to the conception and design of the study. ZDA: developed the proposal, collected, analyzed, interpreted the data, and wrote the paper. MM and MA have supervised this paper’s data collection, analysis, and manuscript preparation. All authors read and approved the final manuscript.

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Agazhi, Z.D., Mada, M. & Alemu, M. Production efficiency of wheat farmers in the Arsi Zone, Oromia Region of Ethiopia. Discov Food 4 , 51 (2024). https://doi.org/10.1007/s44187-024-00134-3

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