Vol. 12, Issue 7 (2023)

Author(s):
RP Meena, SC Tripathi, SC Gill, A Khipal, N Kumar and Ajay Verma

Abstract:
Analysis of variance for twelve sea weed formulations treatments evaluated at fifteen major locations of coordinated wheat and barley locations across the country during 2021-22 cropping seasons observed highly significant variations due to locations, Treatments x Locations interactions effects and treatments for thousands grains weight of wheat crop. The contributions of the significant components were of 84.1%, 4.5% and 0.2% of total sum of squares. First significant component of AMMI1analysis contributed for 38.6% whereas AMMI2, AMMI3, AMMI4, AMMI5 accounted for 24.4%, 13.5%, 10.1%, 5.3% respectively of TxL interactions effects for grains per ear heads. The Total contributions of significant components were of 92.1% while first two significant components accounted for 63.2%. First two interaction principal components in ASV1 and ASV measures utilized 55.1% of T×L interaction sum of squares for thousands grains weight recommended (T₅, T₁₀, T₇) and (T₅, T₁₀, T₆) as of stable performance. The analytic adaptability measures MASV and MASV1considered all significant components and based on 90.1% of interactions sum of squares had identified T₁₁, T₁₀, T₅ treatments. Measures PRVG and PRVG*Mean for grains per ear heads settled for T₈, T₃, T₄ whereas HMPRVG along with HMPRVG*Mean measures also identified the same treatments and large average values expressed by T₈, T₃, T₄ treatments only. Average values for thousands grains weight based on BLUP effects of sea weed formulations achieved by T₁₂, T₁₁, T₆ and measures GAIu and HMu observed the more values for the same treatments T₁₂, T₁₁, T₆ treatments. The superiority index as weighted average of trait value and stable performance in 65 to 35 ratios found T₈, T₅ T₂ as suitable treatments for grains per ear heads. Biplot analysis for thousands grains weight found tight direct association of MASV, MASV1 measures observed with IPC1 and W5, W6 and WAASB values. Values of W3 along with CVu expressed direct relationship with IPC3, IPC7, W1, W2, ASV, ASV1 measures on left side whereas also associated with stdev, stdevu, CV values o the right-hand side. Direct strong relations had observed among adaptability measures PRVG, PRVG*G, PRVG*Gu, mean, HMu, GAIu, meanu, HMPRVGu, HMPRVG values. The multivariate hierarchical clustering analysis based on Ward’s method for grains per ear heads observed Superiority index measure while considering average values separated W1, W2, W3, W4, W5, W6, WAASB, ASV1, ASV IPC6, IPC4, MASV, MASV1 Stdev, Stdevu CV, IPC2, IPC3, SiG measures from IPC1, IPC5, IPC7, SiHm, SiHmu, Meanu, Adaptability measures, GAI, HM at the first node of classification.