Modern high yielding rice varieties have replaced most of the traditional cultivars in recent past. Mushk Budji, is one such short grained landrace known for its aroma and exquisite quality, however, is highly susceptible to blast disease that has led to considerable decline in its area. Mushk Budji was crossed to a triple-gene donor line, DHMAS 70Q 164-1b and followed through marker-assisted foreground and background selection in first and second backcross generations that helped to incorporate blast resistance genes Pi54, Pi1 and Pita. Marker-assisted background selection was carried out using 78 SSR and STS markers that helped to reduce linkage drag around the genes Pi54, Pi1 and Pita to 2.74, 4.60 and 2.03 Mb, respectively. The three-gene lines in BC2F2:3 were genotyped using 50 K SNP chip and revealed more than 92% genome similarity to the RP. 2-D gel assay detected differentially expressing 171 protein spots among a set of backcross derived lines, of which 38 spots showing match score of 4 helped us to calculate the proteome recovery. MALDI-TOF analysis helped to detect four significant proteins that were linked to quality and disease resistance. The improved lines expressed resistance to blast under artificial and natural field conditions.

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Crop yield varies considerably within agroecology depending on the genetic potential of crop cultivars and various edaphic and climatic variables. Understanding site-specific changes in crop yield and genotype × environment interaction are crucial and needs exceptional consideration in strategic breeding programs. Further, genotypic response to diverse agro-ecologies offers identification of strategic locations for evaluating traits of interest to strengthen and accelerate the national variety release program. In this study, multi-location field trial data have been used to investigate the impact of environmental conditions on crop phenological dynamics and their influence on the yield of mungbean in different agroecological regions of the Indian subcontinent. The present attempt is also intended to identify the strategic location(s) favoring higher yield and distinctiveness within mungbean genotypes. In the field trial, a total of 34 different mungbean genotypes were grown in 39 locations covering the north hill zone (n = 4), northeastern plain zone (n = 6), northwestern plain zone (n = 7), central zone (n = 11) and south zone (n = 11). The results revealed that the effect of the environment was prominent on both the phenological dynamics and productivity of the mungbean. Noticeable variations (expressed as coefficient of variation) were observed for the parameters of days to 50% flowering (13%), days to maturity (12%), reproductive period (21%), grain yield (33%), and 1000-grain weight (14%) across the environments. The genotype, environment, and genotype × environment accounted for 3.0, 54.2, and 29.7% of the total variation in mungbean yield, respectively (p < 0.001), suggesting an oversized significance of site-specific responses of the genotypes. Results demonstrated that a lower ambient temperature extended both flowering time and the crop period. Linear mixed model results revealed that the changes in phenological events (days to 50 % flowering, days to maturity, and reproductive period) with response to contrasting environments had no direct influence on crop yields (p > 0.05) for all the genotypes except PM 14-11. Results revealed that the south zone environment initiated early flowering and an extended reproductive period, thus sustaining yield with good seed size. While in low rainfall areas viz., Sriganganagar, New Delhi, Durgapura, and Sagar, the yield was comparatively low irrespective of genotypes. Correlation results and PCA indicated that rainfall during the crop season and relative humidity significantly and positively influenced grain yield. Hence, the present study suggests that the yield potential of mungbean is independent of crop phenological dynamics; rather, climatic variables like rainfall and relative humidity have considerable influence on yield. Further, HA-GGE biplot analysis identified Sagar, New Delhi, Sriganganagar, Durgapura, Warangal, Srinagar, Kanpur, and Mohanpur as the ideal testing environments, which demonstrated high efficiency in the selection of new genotypes with wider adaptability.

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