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The effective utilization of natural variation has become essential in addressing the challenges that climate change and population growth pose to global food security. Currently adopted protracted approaches to introgress exotic alleles into elite cultivars need substantial transformation. Here, through a strategic three-way crossing scheme among diverse exotics and the best historical elites (exotic/elite1//elite2), 2,867 pre-breeding lines were developed, genotyped and screened for multiple agronomic traits in four mega-environments. A meta-genome-wide association study, selective sweeps and haplotype-block-based analyses unveiled selection footprints in the genomes of pre-breeding lines as well as exotic-specific associations with agronomic traits. A simulation with a neutrality assumption demonstrated that many pre-breeding lines had significant exotic contributions despite substantial selection bias towards elite genomes. National breeding programmes worldwide have adopted 95 lines for germplasm enhancement, and 7 additional lines are being advanced in varietal release trials. This study presents a great leap forwards in the mobilization of GenBank variation to the breeding pipelines. Ancestral wheat relatives can enrich the genetic diversity of modern cultivars and contribute to stress adaptation. A meta-genome-wide association study identifies the exotic genome footprints in a wheat pre-breeding population, as well as some exotic-specific agronomic traits.

Wheat is the second most cultivated cereal crop in the world and is an important crop in India. Leaf (brown) rust, caused by Puccinia triticina, was the most prevalent among the three rusts found in all the wheat-growing areas of India, Bhutan, and Nepal during 2016 to 2019. Leaf rust samples from wheat crops in these countries were pathotyped using the wheat differential genotypes and binomial Indian system of nomenclature. To facilitate international communication, each pathotype identified was also tested using the North American differentials. A total of 33 pathotypes were identified from 1,086 samples, including three new pathotypes: 61R47 (162-5 = KHTPM) and 93R49 (49 = NHKTN) from India and 93R57 (20-1 = NHKTN) from Nepal. Two pathotypes, 121R60-1 (77-9/52 = MHTKL) and 121R63-1 (77-5 = THTTM), accounted for 79.46% of the population. Virulence on Lr19 was identified in 0.27% of the samples from Nepal only. The proportion of pathotype 121R60-1 (77-9 = MHTKL) increased to 57.55% during these years. Virulence was not observed on Lr9, Lr24, Lr25, Lr28, Lr32, Lr39, Lr45, and Lr47 in the population of the Indian subcontinent. Eighteen polymorphic simple sequence repeat (SSR) primer pairs tested on the isolates amplified 48 alleles with an average of 2.66 alleles per primer pair. Based on SSR genotyping, these pathotypes could be grouped into two clades with another two subclades each. Many of the Lr genes present in Indian wheat germplasm (Lr1, Lr3a, Lr10, Lr11, Lr14a, Lr15, Lr16, Lr17, Lr20, Lr23, and Lr26) were ineffective for a majority of pathotypes. Most of these varieties possessed a high degree of leaf rust resistance. The field resistance of wheat varieties could be attributed to the interaction of genes, unknown resistance, or adult plant resistance.

AMMI analysis of feed barley genotypes evaluated for North Eastern Plains Zone of the country observed highly significant effects of environments (E), GxE interaction and genotypes (G). GxE Interaction effects accounted for 45.9% and 29.2% and environment effects explained 27.5% and 37.1%, during cropping seasons of 2018-19 and 2019-20, respectively. ASTAB measure achieved the desirable lower values for HUB113, DWRB137. Ranks of MASV1 and MASV measures considered HUB113, K508 genotypes would be of choice this zone. Superiority index assigned 0.65 and 0.35 weights for average yield and stability found HUB113, DWRB137 as of stable performance with high yield. Biplot graphical analysis as per 84.7 % of variation of the measures exhibited largest cluster comprised of MASV1 with ASTAB, EV, SIPC, Za, W2, WAASB and MASV measures. For the second-year Desirable lower values of ASTAB measure achieved by HUB69, Lakhan, RD3020. Ranks of composite measure MASV1 and MASV found Lakhan, HUB69, KB1830 genotypes would be of choice for these locations. Superiority index with assigned weights for yield and stability found Lakhan, DWRB213, KB1830 as of stable performance with high yield. About 79.3 % of variation of the measures under biplot analysis observed AMMI based measures were grouped together and mean yield joined hands with superiority measures of genotypes. The reliability of multi-locations trials would be increased with superiority Indexes as major features of AMMI and BLUP had been confounded. Option of assigning variable weights to the yield and stable performance help the researches to set their crop improvement targets.

AMMI analysis explained the highly significant effects of the environment, GxE interaction, and genotypes for wheat genotypes evaluated under restricted irriation timely sown multi-location trials in the Central zone of the country during 2018-19 and 2019-20. About 77.1%, 12.2% & 2.3% had been contributed by environments, interactions and genotypes of the total sum of squares due to treatments respectively in the first year.

Stability analysis of wheat genotypes under rainfed timely sown trials in Northern Hills Zone of India by Additive main & multiplicative interactions (AMMI) analysis observed highly significant effects of the environment, GxE interaction and genotypes during 2018-19 and 2019-20. The ranking of genotypes had altered with utilization of more number of IPCA’s in AMMI and WAASB measures. Environments contributed about 53%, GxE interaction accounted for 30.5% and Genotypes explained only 5.4% of the total sum of squares due to treatments in the first year. Wheat genotypes HS668, VL2035, VL2036 , HS562 had been selected by Analytic measures of adaptability and Superiority indexes. Different quadrants comprised of a cluster of arithmetic, geometric, harmonic means along with corresponding adaptability measures. Superiority Indexes considering averages grouped separately. This group maintained the right angles with a group of MASV & MASV1 measures. Clustering of Adaptability measures as per arithmetic, geometric and harmonic means placed in a quadrant. Second-year reflected VL2041, HS675, HS676 & HS562, HPW471 genotypes selected by adaptability and superiority indexes. About 68% of the total variation with 38.4% and 30.2% contributions by PC1 & PC2. Adaptability measures maintained the right angle with other stability measures, with the exception of Superiority indexes. There is an additional advantage with these measures to assign variable weights to the yield and stability as per the goal of breeding trials. These indexes have the potential to provide reliable estimates of genotypes in future studies as they are considered more number of significant IPCA’s in biplots.

Highly significant effects of environment (E), GxE interaction and genotypes (G) were observed by AMMI analysis during 2018-19 and 2019-20 study years for wheat genotypes evaluated at major locations of mega zone of the country. WAASB measure observed suitability of HD3237, WH1080 and PBW644 genotypes. Superiority index while weighting 0.65 and 0.35 for yield and stability found HI1620, HD3237 and HI1628 as of stable performance with high yield. PRVG measure observed suitability of HI1620, HI1628 and BRW3806 while MHPRVG measure identified HI1620, HI1628 and HD3237 wheat genotypes. More over the average yield of genotypes ranked HI1620, HI1628 and NIAW3170 as of order of choice. SI had expressed all direct relations of moderate to high degree of correlations except with WAASB and weak relations with yield, PRVG and MHPRVG values. Only negative correlations had expressed by WAASB measure while positively correlated with yield, PRVG and MHPRVG. Second year of study observed suitability of NIAW3170, DBW296 and PBW644 genotypes as far as WAASB values were concerned. Superiority index found DBW296, HUW838 and NIAW3170 as of stable performance with high yield. More over the average yield of genotypes ranked DBW296, HUW838 and JAUW672 as of order of choice. Mean yield showed a highly significant positive correlation with SI, MHPRVG, PRVG and negative values of correlation with AMMI based measures. SI had expressed all inverse relations with measures WAASB, EV, ASV, MASV as only negative values were seen. Positive correlations were maintained by WAASB measure with Za, SIPC, ASTAB, ASV1.

Bael [Aegle marmelos (L.) Correa ex Roxb.], belongs to family Rutaceae, is one of the oldest known indigenous fruit. Its wide distribution reflecting its adaptation to wide range of edaphoclimatic conditions. Bael has ability to withstand harsh climate and tolerate heat, drought and moisture deficit situations.The efforts have been made to present the current status of bael growing in India, and discuss recent technologies adopted for bael fruit production i.e. improved varieties, propagation techniques, planting systems, canopy management, water and nutrient management, quality management, pest and disease management, physiological disorders, marketing and export scenario.

Highly significant effects of environment (E), GxE interaction and genotypes (G) observed by AMMI analysis during 2018-19 and 2019-20 study years. WAASB measure ranked suitability of UP 3039, VL 2035 and VL 2036 genotypes. Superiority index while weighting 0.65 and 0.35 for yield and stability found VL 2036, HS 668 and UP 3039 as of stable performance with high yield. PRVG and MHPRVG measures observed suitability of HS 668, HS 562 and HS 669 wheat genotypes. More over the average yield of genotypes ranked HS 668, VL 2036 and HS 669 as of order of choice. Mostly indirect relations of SI measure were observed with stability measures along with positive values for MHPRVG, PRVG and yield. WAASB measure exhibited significant indirect relationships with other measures except of moderate positive with SI, yield, MHPRVG and PRVG measures. For the second year of study WAASB measure ranked suitability of HS676, UP3064 and HS677 genotypes. Superiority index while weighting 0.65 and 0.35 for yield and stability found VL2041, HS675 and HS562 as of stable performance with high yield. PRVG and MHPRVG measures observed suitability of VL2041, HS675 and HPW470 wheat genotypes. More over the average yield of genotypes ranked VL2041, HS675 and HS507 as of order of choice. Mostly negative values were exhibited by SI measure with stability measures apart of direct with MHPRVG, PRVG and yield. WAASB measure exhibited direct relationships with other stability measures except of indirect relations with SI, yield, MHPRVG and PRVG.

Highly significant effects of the environment (E), GxE interaction and genotypes (G) observed by AMMI analysis during 2018-19 and 2019-20 study years. First year of the study observed suitability of WH1124&HD3059 wheat genotypes by WAASB measure. Superiority index while weighting 0.65 and 0.35 for yield & stability found WH1124 &PBW771 as of stable performance with high yield. Moreover the average yield of genotypes ranked PBW771 & WH 1124 as of the order of choice. PRVG and MHPRVG measures observed the suitability of PBW 771 & PBW752 wheat genotypes. The first two PCAs explained 82.3% of the variation of the original variables. SI clubbed with EV & SPIC. Values of SI for wheat genotypes expressed high direct relation with yield, MHPRVG&PRVG measures only, and negative correlation with remaining measures. WAASB measure exhibited direct relationships with most of the AMMI based stability measures and negative values of correlation with SI, PRVG, MHPRVG and yield. Second year of study wheat genotypes DBW291, WH1264 & HD3334 were selected by WAASB measure. Superiority index found PBW812, HD3334& WH1264 as of stable performance along with high yield. MHPRVG measures observed suitability of PBW812, PBW771 & DBW173 while PRVG favoured PBW812, PBW771& JKW261 while consensus observed regarding WH1021 & WH1124 as unstable wheat genotypes. Moreover the average yield of genotypes ranked PBW812, JKW261 & PBW771 as of the order of choice. Values of SI measure had expressed positive correlation of high magnitude with yield, PRVG and MHPRVG whereas indirect relations of high degree with AMMI based stability measures. Values of WAASB measure had positive relations with AMMI based stability measures along with negative values of correlation with SI, yield, PRVG, and MHPRVG.

Increasing temperature especially the High night temperature (HNT) is a major constriction for sustaining global food production under changing climate scenario. Recent climate change has resulted into events of abrupt night temperature rise either throughout the crop period or at specific growth stage. Historical temperature observations and model projections have predicted a more pronounced increase in night temperature [minimum temperature (Tmin.)] compared to that of day temperatures [maximum temperature (Tmax.)]. Hence, the present study was carried out to identify the most critical growth stage affected by HNT and their effects on physiological, growth and yield traits of wheat. A unique field-based movable temperature controlling system was designed to enhance the night temperature by 5 °C than ambient temperature. The three critical phenological stages of wheat i.e. at GS 15 (before tillering initiation), GS 45 (booting) and GS 75 (grain filling) of Zadok’s scale were separately subjected to HNT treatment. The results reveals that GS 75 (grain filling) was the most sensitive stage affected by HNT, which has recorded remarkable reduction in grain yield (GY), Harvest Index (HI), Thousand grain weight (TGW) and grain weight per spike (GWPS) compared to control treatment. Pearson correlation analysis showed that breeders can choose lines with higher TGW, GWPS and grain number per spike (GNPS) for obtaining better GY under HNT conditions and vegetative/booting stage HNT in field condition may not attribute for any significant yield loss.

Reports on biased interpretation for the stability of the genotypes under AMMI analysis, considering only the first two interaction principal components, has been observed in recent past. Simultaneous use of yield and stability of genotypes in a single measure had been advocated for identification of highly productive and broadly adapted genotypes. The performance of superiority index, allowed variable weighting mechanism for yield and stability, has been compared with AMMI based measures. For the first year (2018-19) Superiority index, weighting 0.65 and 0.35 for yield and stability, found UAS3002, MP3336 and HI1633 as of stable performance with high yield. Recent analytic measures the relative proportion of genotypic value (PRVG) and Harmonic mean of the relative proportion of genotypic value (MHPRVG) selected CG1029, HI1634 and HD2932 wheat genotypes. Indirect relations were expressed by Superiority Index (SI) with other stability measures. Superiority index saw stable performance along with high yield of HD2864 and HI1634 for the second year 2019-20. PRVG as well as MHPRVG measures observed suitability of CG1029 and HD2864 while MP3336 as unstable wheat genotypes. Values of SI measure had expressed only indirect relations of high degree with stability measures except with yield, PRVG and MHPRVG values. Stability measures by the simultaneous use of AMMI and yield would be more meaning full and useful as compared to measures consider either the AMMI or yield of genotypes only.

Breeding resistant varieties of wheat is a continuous process and empirical disease control depends on deployment of diverse sources of genetic resistance to the deadly rusts. A total of 166 exotic wheat germplasm accessions were evaluated for resistance to wheat rusts having some component of resistance to either any of the three rusts and spot blotch. The data for adult plant resistance (APR) on leaf and stem rusts, was recorded at Wellington while yellow rust epiphytotic was artificially created at Karnal for identification of resistant lines. Based on APR data 85, 96 and 16 accessions were found completely resistant to leaf, stem and stripe rusts respectively. Based on the seedling resistance data against different pathotypes (10 each for leaf and stripe and 5 for stem rust) of each rust, nine, fifteen and five accessions were found highly resistant respectively. Based on the multi-environment adult plant resistance data and race specific seedling data, we were able to identify 28, accessions highly resistant to either one or two rusts. For spot blotch the evaluation was done at hotspot locations Wadura and Coochbehar and 26 accessions were found to be highly resistant against this disease. The lines so identified in this study are being used in the hybridization programs to enhance the levels of resistance.

The intercropping of legumes with cereals help to achieve sustainable intensification by their mutual complementarity at efficiently using radiation, nutrients, etc. Several studies indicated such beneficial effects on the other component crop however, little research has been conducted to quantify their effects on the subsequent crop in a cropping system. In this study, the effect of the legume intercropping on the entire cropping system, particularly the maize + legume-wheat system was studied. Four legumes intercropped to maize followed by wheat crop were studied for intensification measures such as wheat equivalent yield (WEY), land equivalent ratio (LER), sustainable value index (SVI), and economic returns. N saving effect of legumes on the subsequent wheat crop was quantified with two N levels. Maize + cowpea-wheat combination was the most productive and economic intercrop combination (LER = 1.71, SVI = 0.96) with an increase in net economic return (43.63%) with a B:C ratio of 1.94. An additional 25% N (37.5 kg ha−1) was saved in the wheat crop when the legume intercropping was undertaken with maize. The results suggest that intercropping is the key to diversification and reduces the risk of crop failures by enhancing land-use efficiency, soil fertility, and economic returns under weather vagaries. This will be beneficial to small and marginal farmers of many countries.

A new bread wheat variety HI 1633 (Pusa Vani) has been released and notified by the Central Sub-Committee on Crop Standards, Notification and Release of Varieties for Agricultural Crops, Government of India for commercial cultivation under irrigated and late sown conditions of Peninsular Zone of India. HI 1633 has average yield of 41.7 q ha-1 and showed superiority over checks. The potential yield of HI 1633 is 65.8 q ha-1 and found resistant to black and brown rusts. HI 1633 found to have excellent chapati quality (7.63), biscuit quality (7.08), high grain hardness (>80.0), test weight (80.3 kg hl-1) and sedimentation value (45.0 ml). It has high protein content (12.4 %) and presence of 5+10 subunit of Glu-D1 reflecting higher gluten strength. It has good amount of micronutrients viz., iron (41.6 ppm) and zinc (41.1 ppm) content making it rich in nutritional qualities. This variety has been recommended for irrigated late sown conditions and would contribute to increasing wheat production and alleviate the socio-economic status of farmers of Peninsular zone in India.

Understanding the physiological mechanism of tolerance under stress conditions is an imperative aspect of the crop improvement programme. The role of plant hormones is well-established in abiotic stress tolerance. However, the information on the role of gibberellic acid (GA) in abiotic stress tolerance in late sown wheat is still not thoroughly explored. Thus, we aimed to investigate the role of endogenous GA3 level in stress tolerance in contrasting wheat cultivars, viz., temperature-tolerant (HD 2643 and DBW 14) and susceptible (HD 2189 and HD 2833) cultivars under timely and late sown conditions. We created the variation in endogenous GA3 level by exogenous spray of GA3 and its biosynthesis inhibitor paclobutrazol (PBZ). Tolerant genotypes had higher antioxidant enzyme activity, membrane stability, and photosynthesis rate, lower lipid peroxidase activity, and better growth and yield traits under late sown conditions attributed to H2O2 content. Application of PBZ escalated antioxidant enzymes activity and photosynthesis rate, and reduced the lipid peroxidation and ion leakage in stress, leading to improved thermotolerance. GA3 had a non-significant effect on antioxidant enzyme activity, lipid peroxidation, and membrane stability. However, GA3 application increased the test weight in HD 2643 and HD 2833 under timely and late sown conditions. GA3 upregulated GA biosynthesis and degradation pathway genes, and PBZ downregulated kaurene oxidase and GA2ox gene expression. GA3 also upregulated the expression of the cell expansins gene under both timely and late sown conditions. Exogenous GA3 did not increase thermotolerance but positively affected test weight and cell expansins gene expression. No direct relationship existed between endogenous GA3 content and stress tolerance traits, indicating that PBZ could have conferred thermotolerance through an alternative mechanism instead of inhibiting GA3biosynthesis.

Genetic diversity is crucial for successful adaptation and sustained improvement in crops. India is bestowed with diverse agro-climatic conditions which makes it rich in wheat germplasm adapted to various niches. Germplasm repository consists of local landraces, trait specific genetic stocks including introgressions from wild relatives, exotic collections, released varieties, and improved germplasm. Characterization of genetic diversity is done using morpho-physiological characters as well as by analyzing variations at DNA level. However, there are not many reports on array based high throughput SNP markers having characteristics of genome wide coverage employed in Indian spring wheat germplasm. Amongst wheat SNP arrays, 35K Axiom Wheat Breeder's Array has the highest SNP polymorphism efficiency suitable for genetic mapping and genetic diversity characterization. Therefore, genotyping was done using 35K in 483 wheat genotypes resulting in 14,650 quality filtered SNPs, that were distributed across the B (~ 50%), A (~ 39%), and D (~ 10%) genomes. The total genetic distance coverage was 4477.85 cM with 3.27 SNP/cM and 0.49 cM/SNP as average marker density and average inter-marker distance, respectively. The PIC ranged from 0.09 to 0.38 with an average of 0.29 across genomes. Population structure and Principal Coordinate Analysis resulted in two subpopulations (SP1 and SP2). The analysis of molecular variance revealed the genetic variation of 2% among and 98% within subpopulations indicating high gene flow between SP1 and SP2. The subpopulation SP2 showed high level of genetic diversity based on genetic diversity indices viz. Shannon's information index (I) = 0.648, expected heterozygosity (He) = 0.456 and unbiased expected heterozygosity (uHe) = 0.456. To the best of our knowledge, this study is the first to include the largest set of Indian wheat genotypes studied exclusively for genetic diversity. These findings may serve as a potential source for the identification of uncharacterized QTL/gene using genome wide association studies and marker assisted selection in wheat breeding programs.

Fifty wheat genotypes were evaluated at nine diverse locations in India to identify high-yielding and stable genotypes. The analysis of variance based on additive main effects and multiplicative interaction (AMMI) indicated significant genotype, environment and genotype - environment (GE) interactions, with a total variation of 5.99, 20.23 and 73.77%, respectively. A biplot-AMMI analysis and yield stability index incorporating the AMMI stability value and yield in a single non-parametric index were used to discriminate the genotypes with highest and stable yield; the genotypes G135, G125, G104, G112 and G144 were found to be promising. Two mega environments (ME) were identified based on GGE (genotype and GE interaction) biplot analysis and the genotypes G119 and G120 and G107, G148 and G146 performed best in the mega-environments ME I and ME II, respectively. Both approaches allowed the identification of stable genotypes (G112 and G135), which can be included in the national testing program, with a view to release a new variety.

MicroRNA are 20–24 nt, non-coding, single stranded molecule regulating traits and stress response. Tissue and time specific expression limits its detection, thus is major challenge in their discovery. Wheat has limited 119 miRNAs in MiRBase due to limitation of conservation based methodology where old and new miRNA genes gets excluded. This is due to origin of hexaploid wheat by three successive hybridization, older AA, BB and younger DD subgenome. Species specific miRNA prediction (SMIRP concept) based on 152 thermodynamic features of training dataset using support vector machine learning approach has improved prediction accuracy to 97.7%. This has been implemented in TamiRPred (http://webtom.cabgrid.res.in/tamirpred). We also report highest number of putative miRNA genes (4464) of wheat from whole genome sequence populated in database developed in PHP and MySQL. TamiRPred has predicted 2092 (>45.10%) additional miRNA which was not predicted by miRLocator. Predicted miRNAs have been validated by miRBase, small RNA libraries, secondary structure, degradome dataset, star miRNA and binding sites in wheat coding region. This tool can accelerate miRNA polymorphism discovery to be used in wheat trait improvement. Since it predicts chromosome-wise miRNA genes with their respective physical location thus can be transferred using linked SSR markers. This prediction approach can be used as model even in other polyploid crops.

Production and productivity of wheat crop is hampered by the biotic and abiotic stresses putting the world food security at risk. Among the biotic stresses, rusts are the major threat throughout the wheat growing areas in the world whereas Karnal bunt, powdery mildew, Fusarium head blight and loose smut are of minor importance. These diseases severely affect the yield and quality of wheat grain. Till date these diseases have been contained by the use of host resistance alongwith the chemical fungicides. However, the host resistance is overcome with the evolution of new virulent pathotypes. Deployment of diverse and durable sources of resistance in breeding programmes is the most economic and effective means of reducing yield losses caused by these diseases.With an aim to identify novel sources of resistance to these pathogens, 247 Indian wheat accessions were evaluated for multiple disease resistance at six different hot spots in order to identify potentially new sources of resistance. Of the 247 accessions of wheat, 14 accessions highly resistant to stripe rust, 142 for Leaf rust and 146 for stem rustwere identified. Thirty-two accessions were found resistant to all the three rusts. Thirty accessions showed immune reaction to powdery mildew and 61 were recorded resistant. Data obtained from spot blotch experiment revealed one accession immune to spot blotch infection while two accessions were recorded resistant with 10% infection. Karnal bunt (KB) resistance (infection level

Heat stress (HS) drastically reduces the yield and quality of wheat grains. High temperature during critical stages (pollination and grain-filling) causes improper fertilization and formation of defragmented granules and shriveled seeds. Hormones and signaling molecules modulates the tolerance potential of the plants under stress. Nitric oxide (NO) is an important signaling molecule involved in triggering diverse physiological and biochemical processes under adverse conditions. Here, we studied the effect of sodium nitroprusside (SNP) (150 μM set based on pilot experiment) on heat stress-tolerance and grain quality related traits of two contrasting wheat cultivars Raj3765 as thermotolerant and HD2932 as thermosusceptible under differential HS (T1 - 30 °C, 1 h; T2 - 38 °C, 1 h), as compared to control (22 ± 2 °C) at different stages of growth. The expression of many important stress-associated genes (previously identified through Transcriptome sequencing) was observed upregulated in response to NO and HS; small HSP17 showed maximum fold increase in Raj3765. Similarly, the networks of antioxidant enzymes (SOD, CAT, and GPX) were observed triggered in response to NO, HS and NO + HS treatment in Raj3765 than HD2932. The accumulation of proline and free amino acid in cytoplasm were also reported higher under NO and HS treatment respectively in Raj3765, as compared to HD2932. Grain quality related traits like carbohydrate (starch) and proteins (gliadin) were observed higher in response to NO under HS in Raj3765. Heat stress was observed to increase the activities of α/β amylases in developing grains involved in degrading the starch quality. NO was observed to decrease the amylolytic activity in both the cultivars; very low amylolytic activity was observed in thermotolerant, as compared to thermosusceptible cultivars. Exogenous application of NO (150 μM) at different stages can be used as inexpensive technology for mitigating the problem of terminal HS in wheat – a farmer friendly approach for maintaining the quality of grains under present threat of global climate change.

We report here the genome-wide changes resulting from low N (N-W+), low water (N+W-)) and dual stresses (N-W-) in root and shoot tissues of two rice genotypes, namely, IR 64 (IR64) and Nagina 22 (N22), and their association with the QTLs for nitrogen use efficiency. For all the root parameters, except for root length under N-W+, N22 performed better than IR64. Chlorophyll a, b and carotenoid content were higher in IR64 under N+W+ treatment and N-W+ and N+W- stresses; however, under dual stress, N22 had higher chlorophyll b content. While nitrite reductase, glutamate synthase (GS) and citrate synthase assays showed better specific activity in IR64, glutamate dehydrogenase showed better specific activity in N22 under dual stress (N-W-); the other N and C assimilating enzymes showed similar but low specific activities in both the genotypes. A total of 8926 differentially expressed genes (DEGs) were identified compared to optimal (N+W+) condition from across all treatments. While 1174, 698 and 903 DEGs in IR64 roots and 1197, 187 and 781 in N22 roots were identified, nearly double the number of DEGs were found in the shoot tissues; 3357, 1006 and 4005 in IR64 and 4004, 990 and 2143 in N22, under N-W+, N+W- and N-W- treatments, respectively. IR64 and N22 showed differential expression in 15 and 11 N-transporter genes respectively, under one or more stress treatments, out of which four showed differential expression also in N+W- condition. The negative regulators of N- stress, e.g., NIGT1, OsACTPK1 and OsBT were downregulated in IR64 while in N22, OsBT was not downregulated. Overall, N22 performed better under dual stress conditions owing to its better root architecture, chlorophyll and porphyrin synthesis and oxidative stress management. We identified 12 QTLs for seed and straw N content using 253 recombinant inbred lines derived from IR64 and N22 and a 5K SNP array. The QTL hotspot region on chromosome 6 comprised of 61 genes, of which, five were DEGs encoding for UDP-glucuronosyltransferase, serine threonine kinase, anthocyanidin 3-O-glucosyltransferase, and nitrate induced proteins. The DEGs, QTLs and candidate genes reported in this study can serve as a major resource for both rice improvement and functional biology.

HI 1628 has average yield of 50.4 q/ha over the zone and showed significant superiority over the checks; with a potential yield of 65.1 q/ha. It has shown excellent and wider adaptation and significantly superior performance across different irrigation regimes over the checks with an average yield of 45.5q/ha. It showed high levels of field resistance to yellow and brown rusts. It is a good quality bread wheat genotype with excellent chapati quality (7.56), bread quality (7.64), biscuit spread factor (8.27) and high sedimentation value (56.6 ml). It has high protein content (~11 %) and protein quality (Glu score of 8/10) for high molecular weight subunits and presence of 5+10 subunit of Glu-D1 reflecting higher gluten strength in this genotype.

HI 8805 has average yield of 30.4 q/ha over the zone and showed significant superiority over the checks; with a potential yield of 35.4 q/ha. It has shown excellent and wider adaptation and significantly superior performance across different irrigation regimes over the checks with an average yield of 30.7q/ha. It showed high levels of field resistance to black and brown rusts. It has high protein content (12.8%), yellow pigment content (4.9 PPM), test weight (83.7 kg/hl), sedimentation value (42 ml), iron content (40.4 ppm), zinc content (33.9 ppm) with an overall pasta acceptability (5.7).

Background: Efficient estimation of main and interaction effects under multi environment trials had been carried out by AMMI. Studies with low proportion of the variance explained by first interaction principal component IPCA1 under AMMI analysis had been reported biased interpretation for the stability of the genotypes.Methods: Weighted Average of Absolute scores (WAASB), quantitative stability measure had been recommended for selection of productive genotypes with broad adaptation. The selection of promising genotypes had been assisted by use of superiority index (WAASBY) that considered simultaneous use of yield and stability by allowing variable weighting mechanism for yield and stability.Result: Wheat GW509 and HI1633 genotypes had been identified by stability measure WAASB for the first year. While Superiority index considered stability and high yield settled for HI1633 and Raj4083 genotypes. Analytic measures of adaptability PRVG and MHPRVG observed suitability of HI1633 and Raj 4083. SI expressed positive linear behaviour with yield, MHPRVG and PRVG measures. While stability measure WAASB maintained moderate negative correlation with yield, SI, MHPRVG and PRVG. Wheat genotypes HI1633, HI1641 and HI1646 identified by stability measure for the next year of study. Superiority index selected HI1641, HI1633 and MACS6752 genotypes. SI expressed direct linear relation with yield, MHPRVG and PRVG. Stability measure exhibited indirect relationships with SI, MHPRVG, PRVG and yield Wheat genotypes HI1633, Raj 4083 for first year and HI1641, MACS6752 for the second year of study.

In plants, iron (Fe) & zinc (Zn) uptake and transportation from the rhizosphere to the grain is a critical process regulated by complex transcriptional regulatory networks. However, understanding the combined effect of Fe & Zn starvation on their uptake and transportation and the molecular regulatory networks that control them lack in wheat. Here, we performed a comprehensive physiological, biochemical and transcriptome analysis in two bread wheat genotypes,i.e.Narmada 195 and PBW 502, differing in inherent Fe & Zn content to understand the mechanism of Fe & Zn homeostasis. Compared to PBW 502, Narmada 195 exhibited increased tolerance to Fe & Zn withdrawal by an increased level of antioxidant enzymes and DPPH radical scavenging activity along with less malondialdehyde (MDA), H2O2level, increased PS accumulation and lower reduction of root and shoot Fe & Zn content and length, leaf chlorosis, and leaf area. By integrating physiological and biochemical data along with co-expression & functional genome annotation and gene expression analysis, we identified 25 core genes associated with four key pathways,i.e.Met cycle (10), PS biosynthesis (4), antioxidant (3) and transport system (8) that were significantly modulated by Fe & Zn withdrawal in both the genotypes. Genes of these four pathways were more considerably up-regulated in Narmada 195, allowing better tolerance to Fe & Zn withdrawal and efficient uptake and transportation of Fe & Zn. Chromosomal distribution and sub-genome wise mapping of these genes showed a contribution from all the chromosomes except group 5 chromosomes with the highest number of genes mapped to chromosome 4 (24%) and sub-genome D (40%). Besides, we also identified 26 miRNAs targeting 14 core genes across the four pathways. Together, our work provides a crucial angle for an in-depth understanding of regulatory cross-talk among physiological, biochemical and transcriptional reprogramming underlying Fe & Zn withdrawal in wheat. Core genes identified can serve as valuable resources for further functional research for genetic improvement of Fe & Zn content in wheat grain.HighlightOur work provides a crucial angle for a comprehensive understanding of the regulatory mechanism underlying Fe & Zn withdrawal associated with physiological, biochemical and transcriptional reprogramming in wheat.

Background: Rapeseed (Brassica napus L.) also known as ‘rape’ is bright yellow flowering member of family Brassicaceae, cultivated for its oil rich seed. It is third most important oilseed crop after soybean and oil palm. Despite an important oilseeds crop there is little advancement in suitable crop management strategies for improving crop productivity. The SRI has set of principles such as use of young seedlings, one seedling per hill, square planting, mechanical weeding, addition of organic matter. Recently the SRI is being successfully experimented in other crops such as wheat, maize, soybean, blackgram, kidney bean, potato in the name of System of Crop Intensification. Methods: This research experiment was carried out at the Student’s Research Farm, Khalsa College, Amritsar during the rabi season of 2018-19. The Split plot design with three replications was carried under age of seedling in main plot treatments A1 [20 day seedling], A2 [30 day seedling] and A3 [40 day seedling] and four intra row spacing treatments in sub plots S1 [15cm], S2 [30 cm], S3 [45 cm] and S4 [60 cm]. Result: Seed yield decreased progressively with increase in age of seedling. Wider spacing between plants (45 cm) observed high number of siliquae per plant than closer spacing (15 cm). Seed yield was significantly highest when crop was transplanted with 45 cm intra row spacing. The percent increase in seed yield in different SMI (System of Mustard Intensification) spacing treatments over closer spacing S1 (15 cm) were 21.62, 6.36, 4.89, per cent in S3, S4 and S2 respectively.

Barley is one of the oldest cultivated crops in world that has been cultivated across diverse ecological regions since centuries. This varied agro-climatic cultivation of barley makes it vulnerable to various biotic stresses like fungal, viral, bacterial diseases and insect pest infestations. In order to reduce yield losses due to biotic stresses, breeding for disease resistance became most important in barley improvement. Disease resistance breeding has been successfully implemented in barley with release of disease resistant and insect-pest tolerant varieties using conventional breeding methods in past. With the advancement of molecular technologies like, QTL mapping, genome wide association mapping, gene editing, genomic selection and embryo rescue, identification of resistant gene from wild-type/secondary gene pool and their incorporation in cultivated barley has become an achievable target in recent years. More than hundred major genes and QTLs have been identified against wide range of pathogens and insect-pests. Recently available high-throughput genotyping platforms have promoted development of high-density genetic maps that will further support marker assisted selection with refined disease breeding strategies in barley. This chapter reviews the biotic stresses, germplasm variability and traditional disease breeding in past and the status and scope of present day molecular marker and next generation technologies for developing biotic stress resistance in barley.

Background: Wheat improvement program of the country identify genotypes with specific and general adaptations to ensure sustain yield for food security of the people. Yield behavior of promising wheat genotypes were studied at twelve locations of Central Zone of the country under restricted irrigation timely sown conditions. Methods: Recent analytic measures of adaptability viz., Relative Performance of Genetic Values (PRVG), Harmonic Mean of Genetic Values (MHVG) and Harmonic Mean of the Relative Performance of Genotypes (MHPRVG) were compared by considering Best Linear Unbiased Predictors (BLUP) of genotypes based on mixed model approach. Result: Genotypes MP3288, BRW3775 and DBW110 had been marked by analytic measures PRVG, MHVG, MHPRVG, HM for high yield and better adaptability across locations as per first year (2016-17) findings. HI8791 and DBW110 genotypes occupied places near to the origin in Biplot graphical analysis. Specific adapta­tions of genotype HI8791 for Sagar and Dhanduka locations were observed. DBW110 would be suitable for Jabalpur, Gwalior and Udaipur locations, whereas BRW3775 identified for Kota, Sanosora and Vijapur. Genotypes GW495, GW322, HI8713 and GW1339 had achieved high yield and better adaptability during the year (2017-18). The genotype UAS466 had expressed specific adapta­tions to Jabalpur and Gwalior, whereas HI8627 for Bhopal and Udaipur, NIAW3170 were identified for Indore and Vijapur, DDW47 for Sansora, Dhanduka and Pratapgarh. The recent analytic measures based on harmonic means of the relative performance of predicted genetic values have been observed as an appropriate to identify the better adaptive genotypes with higher yield.

Drought stress is one of the major yield limiting factors in realising the full genetic potential of wheat. In the present study, backcross inbred line population derived from GW322*2/ HI1500 was characterized for different morpho-physiological traits and grain yield under rainfed and irrigated conditions. Principal component analysis revealed that first five components explained over 66.35 per cent of variation. Grain yield showed significant correlation with biomass and physiological traits viz., NDVI2, NDVI3, NDVI5, CT2, CT3 and CT4. The analysis of variance on grain yield data showed that mean squares of environments, genotypes and GEI were highly significant (p 0.01). To determine effects of GEI on grain yield, data were subjected to AMMI and GGE biplot analysis, which identified BILs G20, G2, G254, G214, G44 and G64 as the most stable and high yielding. Based on Smith selection index expected genetic gain for grain yield at 5% was 131.01gm/plot. Hence, selecting genotypes based on morpho-physiological traits will be rewarding under moisture depleting environment.

Present study aimed to compare parametric with non parametric measures for the adaptability bevaiour of fifteen dual purpose barley genotypes evaluated at ten locations under coordinated system of Barley network. Regression coefficients less than one attained by five genotypes while more than one by the six genotypes. Lower environmental variance exhibited by G14 followed by G03 and G15 whereas higher by G11 G10 and G5. Higher values of W2 i by G11, G10, and G5 pointed out higher contribution to G x E interaction. Adaptable genotype would be G5, G7 and G8 as claimed by lower Pi values. GAI identified G20 genotype, followed by G7, G5, and G6, better in terms of yield and adaptability. Si (1) and Si (2) measures pointed G14 & G8 while Si (3) and Si (6) selected G14 followed by G4 and G10. NPi (1), NPi (2) and NPi (3) considered G1 and G14 as stable genotypes whereas as per NPi (4) G5 & G6 were of unstable types. Grain yield expressed significant negative relation with Si (6), NPi (2), NPi (3), NPi (4) while positive with Kang sum rank. bi was negatively correlated with Wi 2 , S2 xi , CVi, and 2 i. Superiority index expressed significant negative association with NPi (2), NPi (3), NPi (4) and direct positive relation with Si (2), NPi (1). Biplot analysis based on PCA1 versus PCA2 clustered measures in 3 major groups. Larger clubbed NPi (1), Si (1), Si (2), Wi 2, 2 i, S2 xi along with Kang whereas separate group comprised of yield, with GAI & Pi.