Increase in wheat production through management of abiotic stresses : A review

About 9% of area on earth is under crops out of which 91% is under various stresses. On an average, about 50% yield losses are due to abiotic stresses mostly due to high temperature (20%), low temperature (7%), salinity (10%), drought (9%) and other abiotic stresses (4%). As there is no scope for increasing area under agriculture, the increased productivity from these stressed land is a must to meet the ever increasing demand. Further, the severity of abiotic stresses is likely to increase due to changing climate leading to adverse effect on crops. Therefore, abiotic stresses like drought, salinity, sodicity, acidity, water logging, heat, nutrient toxicities/ deficiencies etc need to be effectively addressed through adoption of management practices like tillage and planting options, residue management, sowing time, stress tolerant cultivars, irrigation scheduling and integrated nutrient management to conserve natural resources, mitigating their adverse effect and sustainable wheat production

Characterising variation in wheat traits under hostile soil conditions in India

Intensive crop breeding has increased wheat yields and production in India. Wheat improvement in India typically involves selecting yield and component traits under non-hostile soil conditions at regional scales. The aim of this study is to quantify G*E interactions on yield and component traits to further explore site-specific trait selection for hostile soils. Field experiments were conducted at six sites (pH range 4.5-9.5) in 2013-14 and 2014-15, in three agro-climatic regions of India. At each site, yield and component traits were measured on 36 genotypes, representing elite varieties from a wide genetic background developed for different regions. Mean grain yields ranged from 1.0 to 5.5 t ha⁻¹ at hostile and non-hostile sites, respectively. Site (E) had the largest effect on yield and component traits, however, interactions between genotype and site (G*E) affected most traits to a greater extent than genotype alone. Within each agro-climatic region, yield and component traits correlated positively between hostile and non-hostile sites. However, some genotypes performed better under hostile soils, with site-specific relationships between yield and component traits, which supports the value of ongoing site-specific selection activities

Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). A set of thirty-six wheat cultivars were grown for two consecutive years under low and high nitrogen conditions. The interactions of cultivars with different environmental factors were shown to be highly significant for most of the studied traits, suggesting the presence of wider genetic variability which may be utilized for the genetic improvement of desired trait(s). Three cultivars, i.e., RAJ 4037, DBW 39 and GW 322, were selected based on three selection indices, i.e., tolerance index (TOL), stress susceptibility index (SSI), and yield stability index (YSI), while two cultivars, HD 2967 and MACS 6478, were selected based on all four selection indices which were common in both of the study years. According to Kendall’s concordance coefficient, the consistency of geometric mean productivity (GMP) was found to be highest (0.778), followed by YSI (0.556), SSI (0.472), and TOL (0.200). Due to the high consistency of GMP followed by YSI and SSI, the three selection indices could be utilized as a selection tool in the identification of high-yielding genotypes under low nitrogen conditions. The GMP and YSI selection indices had a positive and significant correlation with grain yield, whereas TOL and SSI exhibited a significant but negative correlation with grain yield under both high and low nitrogen conditions in both years. The common tolerant genotypes identified through different selection indices could be utilized as potential donors in active breeding programs to incorporate the low nitrogen tolerant genes to develop high-yielding wheat varieties for low nitrogen conditions. The study also helps in understanding the physiological basis of tolerance in high-yielding wheat genotypes under low nitrogen conditions

Novel sources of variation in grain Zinc (Zn) concentration in bread wheat germplasm derived from Watkins landraces

A diverse panel of 245 wheat genotypes, derived from crosses between landraces from the Watkins collection representing global diversity in the early 20th century and the modern wheat cultivar Paragon, was grown at two field sites in the UK in 2015-16 and the concentrations of zinc and iron determined in wholegrain using inductively coupled plasma-mass spectrometry (ICP-MS). Zinc concentrations in wholegrain varied from 24-49 mg kg-1 and were correlated with iron concentration (r = 0.64) and grain protein content (r = 0.14). However, the correlation with yield was low (r = -0.16) indicating little yield dilution. A sub-set of 24 wheat lines were selected from 245 wheat genotypes and characterised for Zn and Fe concentrations in wholegrain and white flour over two sites and years. White flours from 24 selected lines contained 8-15 mg kg-1 of zinc, which was positively correlated with the wholegrain Zn concentration (r = 0.79, averaged across sites and years). This demonstrates the potential to exploit the diversity in landraces to increase the concentration of Zn in wholegrain and flour of modern high yielding bread wheat cultivars

Harnessing the Wild Relatives and Landraces for Fe and Zn Biofortification in Wheat through Genetic Interventions—A Review

Micronutrient deficiencies, particularly iron (Fe) and zinc (Zn), in human diets are affecting over three billion people globally, especially in developing nations where diet is cereal-based. Wheat is one of several important cereal crops that provide food calories to nearly one-third of the population of the world. However, the bioavailability of Zn and Fe in wheat is inherently low, especially under Zn deficient soils. Although various fortification approaches are available, biofortification, i.e., development of mineral-enriched cultivars, is an efficient and sustainable approach to alleviate malnutrition. There is enormous variability in Fe and Zn in wheat germplasm, especially in wild relatives, but this is not utilized to the full extent. Grain Fe and Zn are quantitatively inherited, but high-heritability and genetic correlation at multiple locations indicate the high stability of Fe and Zn in wheat. In the last decade, pre-breeding activities have explored the potential of wild relatives to develop Fe and Zn rich wheat varieties. Furthermore, recent advances in molecular biology have improved the understanding of the uptake, storage, and bioavailability of Fe and Zn. Various transportation proteins encoding genes like YSL 2, IRT 1, OsNAS 3, VIT 1, and VIT 2 have been identified for Fe and Zn uptake, transfer, and accumulation at different developing stages. Hence, the availability of major genomic regions for Fe and Zn content and genome editing technologies are likely to result in high-yielding Fe and Zn biofortified wheat varieties. This review covers the importance of wheat wild relatives for Fe and Zn biofortification, progress in genomics-assisted breeding, and transgenic breeding for improving Fe and Zn content in wheat

Correlation coefficients among yield and component traits of a panel of elite 36 wheat genotypes grown at six sites in India in 2013/14 and 2014/15.

<p>Colour represents strength of correlation from strongly negative (dark blue) to strongly positive (dark red). Traits measured 1–10 are labelled as: (1) grain yield (GYD); (2) 1000 grain weight (TGW); (3) grain weight per spike (GWS); (4) harvest index (HI); (5) Plant height at maturity (PHT); (6) Days to maturity (DTM); (7) Days to anthesis (DTA); (8) biological yield (BYD); (9) grain number per spike (GNS); (10) Productive tillers per meter (PTM).</p

Wheat genotypes used in field trials during 2013/14 and 2014/15.

<p>Wheat genotypes used in field trials during 2013/14 and 2014/15.</p

Mean grain yield of wheat harvested in 2013/14 (a,c,e,g,i,k,m) and 2014/15 (b,d,f,h,j,l,n) at six sites.

<p>In panels (a) and (b), data are means of each genotype at all sites. For all other panels, data are means of two replicate plots per site each year. Genotypes 1–36 are labelled in the same order on the x-axis as: (1) Kharchia-65; (2) KRL 3–4; (3) KRL 19; (4) BH 1146; (5) UP 262; (6) Raj 4238; (7) DBW 14; (8) Raj 4229; (9) HD 2932; (10) NW 4092; (11) HD 2009; (12) GW 322; (13) HI 1563; (14) NW 4018; (15) HW 2044; (16) KRL 1–4; (17) HD 2733; (18) HI 8498; (19) DBW 51; (20) MACS 6222; (21) DBW 16; (22) NW 1067; (23) KRL 213; (24) PDW 314; (25) WH 1021; (26) DBW 46; (27) K 0307; (28) DBW 17; (29) WH 1105; (30) RW 3684; (31) DBW 71; (32) CBW 38; (33) DPW 621–50; (34) HD 2967; (35) KRL 210; (36) DBW 39.</p

Sowing dates and cropping pattern of field experiments.

<p>Sowing dates and cropping pattern of field experiments.</p

Biplot for grain yield of 36 genotypes evaluated at 6 sites over two years.

<p>Genotypes G1-G36 are as: G1 (BH 1146); G2 (CBW 28); G3 (DBW 14); G4 (DBW 16); G5 (DBW 17); G6 (DBW 39); G7 (DBW 46); G8 (DBW 51); G9 (DBW 71); G10 (DPW 621–50); G11 (GW 322); G12 (HD 2009); G13 (HD 2733); G14 (HD 2932); G15 (HD 2967); G16 (HI 1563); G17 (HI 8498); G18 (HW 2044); G19 (K 0307); G20 (Kharchia 65); G21 (KRL 1–4); G22 (KRL 19); G23 (KRL 210); G24 (KRL 213); G25 (KRL 3–4); G26 (MACS 6222); G27 (NW 1067); G28 (NW 4018); G29 (NW 4092); G30 (PDW 314); G31 (Raj 4229); G32 (Raj 4238); G33 (RW 3684); G34 (UP 262); G35 (WH 1021); G36 (WH 1105).</p