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

Assessment of terminal heat tolerance based on agro-morphological and stress selection indices in wheat

A study was conducted to quantify the effects of terminal heat stress on yield and component traits for two consecutive years under normal and late sown environments. Analysis of variance indicated significant differences among genotypes for most of the studied traits. Tiller/meter (T/M) and grain yield were the most affected traits (>30% reduction), whereas traits like grain filling rate (GFR), grain number per spike (GNPS) and thousand kernel weight (TKW) were less affected (<15%) under heat stress. The traits, viz. GFR, GNPS, TKW, T/M and spike weight, as well as the relative change in these traits exhibited positive and significant correlation with yield and yield stability index (YSI), while negative and significant correlation with heat susceptibility index (HSI) and kernel weight reduction percentage (KWR) under heat stress. Based on HSI, KWR, and YSI indices, genotypes WH 1021, NW 1014 and NW 2036 were identified as the heat tolerant, while HD 3086, HD 2967 and HD 3059 were identified to be highly productive under both normal and heat stress environments based on STI and mean yield across the environments. The above selected genotypes also showed high mean performance for GFR and TKW under heat stress and could be used for breeding wheat genotypes for heat tolerance

High density digital recording

In bread wheat, QTL interval mapping was conducted for nine important drought responsive agronomic traits. For this purpose, a doubled haploid (DH) mapping population derived from Kukri/Excalibur was grown over three years at four separate locations in India, both under irrigated and rain-fed environments. Single locus analysis using composite interval mapping (CIM) allowed detection of 98 QTL, which included 66 QTL for nine individual agronomic traits and 32 QTL, which affected drought sensitivity index (DSI) for the same nine traits. Two-locus analysis allowed detection of 19 main effect QTL (M-QTL) for four traits (days to anthesis, days to maturity, grain filling duration and thousand grain weight) and 19 pairs of epistatic QTL (E-QTL) for two traits (days to anthesis and thousand grain weight). Eight QTL were common in single locus analysis and two locus analysis. These QTL (identified both in single- and two-locus analysis) were distributed on 20 different chromosomes (except 4D). Important genomic regions on chromosomes 5A and 7A were also identified (5A carried QTL for seven traits and 7A carried QTL for six traits). Marker-assisted recurrent selection (MARS) involving pyramiding of important QTL reported in the present study, together with important QTL reported earlier, may be used for improvement of drought tolerance in wheat. In future, more closely linked markers for the QTL reported here may be developed through fine mapping, and the candidate genes may be identified and used for developing a better understanding of the genetic basis of drought tolerance in wheat

Epistatic QTL (QQ) and their interaction with environment (QQE) detected by two-locus analysis using Kukri/Excalibur DH mapping population.

<p>Epistatic QTL (QQ) and their interaction with environment (QQE) detected by two-locus analysis using Kukri/Excalibur DH mapping population.</p

Details of major and stable QTL for different traits identified during the present study using Kukri/Excalibur DH mapping population.

<p>Details of major and stable QTL for different traits identified during the present study using Kukri/Excalibur DH mapping population.</p

Pearson’s correlation coefficients (r values) among different agronomic traits based on pooled data of Kukri/Excalibur DH mapping population in IR and RF environments.

<p>Pearson’s correlation coefficients (r values) among different agronomic traits based on pooled data of Kukri/Excalibur DH mapping population in IR and RF environments.</p

Details of 22 environments used for phenotyping of the Kukri/Excalibur DH population.

<p>Details of 22 environments used for phenotyping of the Kukri/Excalibur DH population.</p

Main effect QTL detected by two-locus analysis with additive effects and additive × environments interactions (Q_A×E) for DTA, DTM, and GFD in a Kukri/Excalibur DH mapping population.

<p>Main effect QTL detected by two-locus analysis with additive effects and additive × environments interactions (Q_A×E) for DTA, DTM, and GFD in a Kukri/Excalibur DH mapping population.</p

QTL for different agronomic traits identified following composite interval mapping (CIM) using Kukri/Excalibur DH population.

<p>The details of phenotypic variation explained (R²) and additive effect are also included.</p