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

Novel sources of variation in grain yield, components and mineral traits identified in wheat amphidiploids derived from thinopyrum bessarabicum (Savul. & rayss) Á. löve (poaceae) under saline soils in India

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Salt-affected soils constrain wheat production globally. A wild wheat species, Thinopyrum bessarabicum (Savul. & Rayss) Á. Löve (Poaceae), and its derivatives are tolerant of high external NaCl concentrations but have not been tested yet in field conditions. The aim of this study was to study the performance of amphidiploids derived from T. bessarabicum for grain yield (GYD), yield components and grain mineral composition traits under normal and saline soil conditions. Field experiments were conducted at Karnal (pH(water) = 7.3) and Hisar (pH(water) = 8.3) sites in 2014–2015 and 2015–2016 in India. Grain samples were analysed using inductively coupled plasma–mass spectrometry (ICP-MS). Yield and yield component traits of amphidiploids were typically greater at Karnal than Hisar. The GYD was greater at Karnal (1.6 t ha−1) than Hisar (1.2 t ha−1) in 2014–2015. However, GYD was greater at Hisar (1.7 t ha−1) than Karnal (1.1 t ha−1) in 2015–2016. Mean grain zinc (Zn) concentration of eight amphidiploids, averaged across sites and years, varied from 36 to 43 mg kg−1. Some amphidiploids derived from T. bessarabicum showed greater GYD and grain Zn concentration under saline soils (Hisar) than normal soils (Karnal). These might be potential new sources for the development of salt-tolerant wheat varieties with increased grain Zn concentration under salt-affected soils

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

Variation in grain Zn concentration, and the grain ionome, in field-grown Indian wheat

Wheat is an important dietary source of zinc (Zn) and other mineral elements in many countries. Dietary Zn deficiency is widespread, especially in developing countries, and breeding (genetic biofortification) through the HarvestPlus programme has recently started to deliver new wheat varieties to help alleviate this problem in South Asia. To better understand the potential of wheat to alleviate dietary Zn deficiency, this study aimed to characterise the baseline effects of genotype (G), site (E), and genotype by site interactions (GxE) on grain Zn concentration under a wide range of soil conditions in India. Field experiments were conducted on a diverse panel of 36 Indian-adapted wheat genotypes, grown on a range of soil types (pH range 4.5–9.5), in 2013–14 (five sites) and 2014–15 (six sites). Grain samples were analysed using inductively coupled plasma-mass spectrometry (ICP-MS). The mean grain Zn concentration of the genotypes ranged from 24.9–34.8 mg kg-1, averaged across site and year. Genotype and site effects were associated with 10% and 6% of the overall variation in grain Zn concentration, respectively. Whilst G x E interaction effects were evident across the panel, some genotypes had consistent rankings between sites and years. Grain Zn concentration correlated positively with grain concentrations of iron (Fe), sulphur (S), and eight other elements, but did not correlate negatively with grain yield, i.e. no yield dilution was observed. Despite a relatively small contribution of genotype to the overall variation in grain Zn concentration, due to experiments being conducted across many contrasting sites and two years, our data are consistent with reports that biofortifying wheat through breeding is likely to be effective at scale given that some genotypes performed consistently across diverse soil types. Notably, all soils in this study were probably Zn deficient and interactions between wheat genotypes and soil Zn availability/management (e.g. the use of Zn-containing fertilisers) need to be better-understood to improve Zn supply in food systems

Grain Zn concentration in a panel of 36 genotypes, averaged across five sites in 2013–14 and six sites in 2014–15.

<p>Data represent the means of two replicate plots per genotype at Karnal, Hisar and Malda, and one replicate per genotype at Kumarganj-reclaimed and Kumarganj-sodic sites in 2013–14, and the means of two replicate plots of each genotype at all six sites in 2014–15. Genotypes 1–36 are labelled in the same order in both years on the x-axis: 1) HW 2044; 2) HD 2932; (3) RW 3684; (4) WH 1021; (5) HD 2967; (6) DBW 46; (7) KRL 1–4; (8) GW 322; (9) NW 4092; (10) GW 322; (11) NW 4092; (12) PDW 314; (13) RAJ 4229; (14) MACS 6222; (15) DPW 621–50; (16) WH 1105; (17) HI 1563; (18) KRL 210; (19) DBW 71; (20) NW 1067; (21) NW 4018; (22) DBW 14; (23) KRL 213; (24) HI 8498; (25) BH 1146; (26) DBW 51; (27) KRL 19; (28) UP 262; (29) DBW 17; (30) K 0307; (31) HD 2009; (32) HD 2733; (33) RAJ 4238; (34) DBW 39; (35) KRL 3–4; (36) Kharchia 65.</p

GGE biplot for grain Zn concentration of 36 genotypes evaluated at 6 sites over two years.

<p>Genotypes 1–36 are as: 1 (BH 1146); 2 (CBW 28); 3 (DBW 14); 4 (DBW 16); 5 (DBW 17); 6 (DBW 39); 7 (DBW 46); 8 (DBW 51); 9 (DBW 71); 10 (DPW 621–50); 11 (GW 322); 12 (HD 2009); 13 (HD 2733); 14 (HD 2932); 15 (HD 2967); 16 (HI 1563); 17 (HI 8498); 18 (HW 2044); 19 (K 0307); 20 (Kharchia 65); 21 (KRL 1–4); 22 (KRL 19); 23 (KRL 210); 24 (KRL 213); 25 (KRL 3–4); 26 (MACS 6222); 27 (NW 1067); 28 (NW 4018); 29 (NW 4092); 30 (PDW 314); 31 (Raj 4229); 32 (Raj 4238); 33 (RW 3684); 34 (UP 262); 35 (WH 1021); 36 (WH 1105).</p

Grain concentrations of Zn and other mineral elements of Indian wheat.

<p>Data are in mg kg^-1, summarised across all plots (n = 719). Grain yield and yield components data are summarised across all plots (n = 864).</p

The contribution of G, E, G*E and residual factors to variation percentage (%) in grain Zn concentration and the grain ionome of a panel of 36 wheat genotypes grown at five sites in 2013–14 and at six sites in 2014–15 and in grain yield and yield components of a panel of 36 genotypes grown at six sites over two years.

<p>The contribution of G, E, G*E and residual factors to variation percentage (%) in grain Zn concentration and the grain ionome of a panel of 36 wheat genotypes grown at five sites in 2013–14 and at six sites in 2014–15 and in grain yield and yield components of a panel of 36 genotypes grown at six sites over two years.</p

The relationship of grain Zn concentration (mg kg^-1) with GYD (t ha^-1) and other grain mineral elements in a panel of 36 wheat genotypes grown at five sites in 2013–14 and six sites in 2014–15.

<p>Data are means of two replicate per plot at Karnal, Hisar and Malda and one replicate at Kumarganj-reclaimed and Kumarganj-sodic sites in 2013–14 and two replicate per plot at six sites in 2004–15 (n = 719). Colour represents strength of correlation from strongly negative (dark blue) to strongly positive (dark red).</p