AMMI and GGE biplots for G×E analysis of wheat genotypes under rain fed conditions in central zone of India

The highly significant environments, genotypes and G×E interaction observed by AMMI analysis of 17 wheat genotypes evaluated at 8 locations in the central zone of the country. Environments(E), genotypes -environment interaction(GE) and genotypes explained 68.8%, 17.6% and 3.2% of the total sum of squares respectively. First four interaction principal components accounted 33.7%, 30.2%, 14.6% and 12.6% of the G×E interaction variation, respectively. The highest positive IPCA1 score of genotype G8 followed by G11 and G10 supported by yield higher than the grand mean 21.8q/ha. Environments E4 (Jabalpur) and E8 (Partapgarh) recorded maximum yield 32.6q/ha and 28.4q/ha while lowest yield was realized in E1 (Arnej). GGE biplot analysis under polygon view indicated that G13 was better in E6 (Sagar), whereas G1 was better in E7 (Bilaspur) and E8 (Partapgarh). The genotype G1, at the centre of concentric circles, was the ideal genotype in terms of yield performance as compared to the other genotypes. In addition, G15 and G12, located on the next consecutive concentric circle, may be regarded as desirable genotypes

Evaluation of biofortified spring wheat genotypes for yield and micronutrients

Advanced wheat genotypes were tested for agronomic as well as grain iron and zinc content traits. The analysis of variance indicated variation for all traits except iron (Fe) and zinc (Zn). The grain Fe content ranged from 39-58 mg/kg whereas grain Zn ranged from 32-47 mg/kg among the tested lines. A significant positive correlation (0.45) was observed between grain Fe and Zn content. There was no association between yield and grain Fe and Zn content indicating that improvement in these micronutrients will not have any undesirable affect on yield. The data was further analysed for principal component analysis and genotype by trait association. The first five principal components viz., PC1 (0.3149), PC2 (0.2198), PC3 (0.1461), PC4 (0.10) and PC5 (0.0923) accounted for 0.87 of the total variation. The major traits contributing to the PC1 are days to heading, days to maturity, grain iron content and yield. The cluster analysis revealed significant variation among the tested germplasm thus providing opportunities for increasing the micronutrient content along with yield through hybridization with high micronutrient content lines

Emerging Trends in Agri-Bioinformatics -A meeting report

ABSTRACT Bioinformatics in agriculture is viewed as impending thrust areas that have opened new avenues for agribioinformatics developments. 'Omics' technologies have produced huge amount of sequence data from several crops, animals and microorganisms. Efficient computational tools comprising intelligent data query, retrieval analysis and visualization tools have been developed for data mining and accelerating the process of gene discovery. This paper highlights the frontier research work in Agri-Bioinformatics

QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, North Africa, and South Asia regions

Heat and drought adaptive quantitative trait loci (QTL) in a spring bread wheat population resulting from the Seri/Babax cross designed to minimize confounding agronomic traits have been identified previously in trials conducted in Mexico. The same population was grown across a wide range of environments where heat and drought stress are naturally experienced including environments in Mexico, West Asia, North Africa (WANA), and South Asia regions. A molecular genetic linkage map including 475 marker loci associated to 29 linkage groups was used for QTL analysis of yield, days to heading (DH) and to maturity (DM), grain number (GM2), thousand kernel weight (TKW), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover. A QTL for yield on chromosome 4A was confirmed across several environments, in subsets of lines with uniform allelic expression of a major phenology QTL, but not independently from PH. With terminal stress, TKW QTL was linked or pleiotropic to DH and DM. The link between phenology and TKW suggested that early maturity would favor the post-anthesis grain growth periods resulting in increased grain size and yields under terminal stress. GM2 and TKW were partially associated with markers at different positions suggesting different genetic regulation and room for improvement of both traits. Prediction accuracy of yield was improved by 5 % when using marker scores of component traits (GM2 and DH) together with yield in multiple regression. This procedure may provide accumulation of more favorable alleles during selection

An analysis of wheat yield and adaptation in India

Multi-environment wheat trials provide valuable information on the extent of genotype x environment interaction, the stability of genotypes and define and confirm agro-ecological regions through associations among sites. The All India Coordinated Crop Improvement Project on wheat evaluates candidates for release across the wheat growing regions of India. To facilitate this process the wheat area is divided into six agro-ecological zones; the northwestern plains zone (NWPZ), the northeastern plains zone (NEPZ), the central zone (CZ), the peninsular zone (PZ), the northern hills zone (NHZ) and the southern hills zone (SHZ). Factor analytic (FA) models were used to analyze the genotype x environment interaction for yield of 813 wheat genotypes evaluated at 136 locations across the six agro-ecological zones in 1307 individual advanced variety trials between 2008/09 and 2012/13. Genotype x environment interaction was firstly assessed separately within each of the six established agro-ecological zones. Key locations with a high genetic correlation with all other locations within each zone were identified. Predicted genetic values of important cultivars that were represented in a wider range of environments within each zone were estimated and highly stable genotypes were found. Genotype x environment interaction was subsequently assessed across agro-ecological zones. Only those environments where the models accounted for \u3e 99% of the genetic variance were retained for further analysis and two smaller zones (NHZ and SHZ) with little or no genotype congruence with other agro-ecological zones were removed. Thus 476 genotypes from 488 environments were included in the analysis. Fifteen clusters of environments with similar patterns of adaptation were found. These clusters were then characterized based on zonal classification, sowing time, irrigation regime, latitude and year and three regions broadly representing the main wheat growing areas of India were identified. These regions represent a combination of the NWPZ and NEPZ defined by latitude, a central region that combines CZ locations with northern PZ locations and a southern region comprised of southern PZ sites. Further stratification of these zones was then possible based on sowing time and irrigation practice. One cluster of 29 environments had a high average genetic correlation (r = 0.75) with most other environments and production zones. These represent key locations where larger number s of entries might be grown in future seasons as they are the best predictors of yield across cropping zones

An analysis of wheat yield and adaptation in India

Multi-environment wheat trials provide valuable information on the extent of genotype x environment interaction, the stability of genotypes and define and confirm agro-ecological regions through associations among sites. The All India Coordinated Crop Improvement Project on wheat evaluates candidates for release across the wheat growing regions of India. To facilitate this process the wheat area is divided into six agro-ecological zones; the northwestern plains zone (NWPZ), the northeastern plains zone (NEPZ), the central zone (CZ), the peninsular zone (PZ), the northern hills zone (NHZ) and the southern hills zone (SHZ). Factor analytic (FA) models were used to analyze the genotype x environment interaction for yield of 813 wheat genotypes evaluated at 136 locations across the six agro-ecological zones in 1307 individual advanced variety trials between 2008/09 and 2012/13. Genotype x environment interaction was firstly assessed separately within each of the six established agro-ecological zones. Key locations with a high genetic correlation with all other locations within each zone were identified. Predicted genetic values of important cultivars that were represented in a wider range of environments within each zone were estimated and highly stable genotypes were found. Genotype x environment interaction was subsequently assessed across agro-ecological zones. Only those environments where the models accounted for \u3e 99% of the genetic variance were retained for further analysis and two smaller zones (NHZ and SHZ) with little or no genotype congruence with other agro-ecological zones were removed. Thus 476 genotypes from 488 environments were included in the analysis. Fifteen clusters of environments with similar patterns of adaptation were found. These clusters were then characterized based on zonal classification, sowing time, irrigation regime, latitude and year and three regions broadly representing the main wheat growing areas of India were identified. These regions represent a combination of the NWPZ and NEPZ defined by latitude, a central region that combines CZ locations with northern PZ locations and a southern region comprised of southern PZ sites. Further stratification of these zones was then possible based on sowing time and irrigation practice. One cluster of 29 environments had a high average genetic correlation (r = 0.75) with most other environments and production zones. These represent key locations where larger number s of entries might be grown in future seasons as they are the best predictors of yield across cropping zones