GENETIC VARIABILITY AND ASSOCIATION AMONG YIELD AND YIELD COMPONENTS OF WHEAT GENOTYPES (Triticum Aestivum L.)

Abstract

The demand for wheat will be increased 60 % compared to existing by 2050 and the wheat productivity of Nepal is lower than world’s average productivity. Therefore, it is important to increase the yield potentiality of wheat. This research was conducted for the estimation of variability and association among yield and yield components of wheat. The experimental field was designed in alpha lattice design with 20 diverse wheat genotypes considering 12 important traits. In variability analysis, thousand kernel weight (TKW) had maximum values for the genotypic coefficient of variance (GCV) (11.83%) and phenotypic coefficient of variance (PCV) (12.14%) indicating the presence of high variation in TKW. The number of spikes/m2 (NSPM) and grain yield (GY) showed higher values for heritability (82.62% and 58.95%) and GA (73.36 and 456.25). These traits have additive gene action and are less affected by the environmental condition. Plant height (PH) had significant positive correlation with GY (0.346) and chlorophyll showed highly significant negative correlation with GY (-0.448). NSPM and PH had a maximum positive direct effect (0.375 and 0.347), chlorophyll content had maximum negative direct effect (-0.234) and days to heading (DTH) and days to anthesis (DTA) showed a positive indirect effect on GY. The selection of genotypes with higher values of PH and NSPM along with moderate or low chlorophyll content is a prerequisite for attaining a higher yield in wheat. Genotypes having longer DTH, DTA and days to maturity (DTM) should be considered during selection of genotypes to obtain higher yield.

Agro-morphological analysis of yield and yield attributing traits of wheat under heat stress condition

“…It showed a negative correlation with TKW, and HI. A similar relationship of NSPS with SW and NGPS was found by Thapa, et al [25].…”

“…It showed a significant negative correlation with TKW (-0.33) and a non-significant negative correlation with PH, GY, and HI. It was found that NGPS showed a positive and highly significant correlation with SW and NSPS [25]. Number of spikelets per spike showed a significantly high positive correlation with SL (0.44), SW (0.46), and NGPS (0.68) and a non-significant positive correlation with PH, GY, and SPMS.…”

“…It exhibited a negative correlation with GY, SPMS, and HI. It was shown that SL had a positive and highly significant correlation with SW but in contrast, it had a negative and highly significant correlation with NSPS [25].…”