Multi environment and spatial analysis of early maturing sorghum [Sorghum bicolor (L.) Moench] genotypes in dry lowland areas of Ethiopia

Abstract

In Ethiopia, drought usually occur due to delay in onset, dry spell after sowing, drought during critical crop stage (flowering and grain filling stage) and too early cessation of rainfall. These situations can be addressed by developing improved sorghum varieties which are resistance to drought. Developments of sorghum varieties resistant to drought and producing better grain yield while addressing the plant biomass requirement is one of the strategies in the sorghum breeding program in dry lowland environment. A total of 90 early maturing sorghum genotypes were evaluated along with two standard check varieties to estimate the grain yield, plant height, days to flowering, days to maturity and overall agronomic aspects and stability of performance across the test environments. The trial was conducted using Randomized Complete Block Design (RCBD) in row and column arrangement. Linear mixed model has been used to predict and identify stable and superior varieties across the test environment. Correlations of the trials range from positive +1 to -1 where positive correlation is an indication of similarity among the testing environments while negative correlation is an indication of non-similarity among testing environments. Moreover, using the biplot it was observed that the stability and correlation among testing site where the angle between the two lines measure the strength of correlation. Improvement in heritability has been obtained due to spatial variation using advanced statistical analysis methods without any additional cost. Three genotypes exhibited better yield advantage, higher plant biomass and overall plant aspect including drought tolerance. In addition, these genotypes were preferred by farmers in their overall agronomic desirability (drought tolerance, earliness, head exertion and compactness, grain size and shape and threshability. Also, the national variety releasing committed has evaluated the variety verification trial both on station and farmers' field condition in 2018/2019 and they decided the release of the candidate variety 14MWLSDT7114 (2005MI5060/E-36-1) for commercial production in dry lowland environment.

Multi-Environment Evaluation and Genotype × Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i> (L.) Moench] Genotypes in Highland Areas of Ethiopia

“…In Ethiopia, various researchers have analyzed the nature of G × E interaction effects of sorghum genotypes performance across different sorghum growing agro-ecologies and they reported the existence of high G × E interaction and limited the release of widely adapted sorghum varieties [10] [19] [20] [21] [22].…”

Application of factor analytic and spatial mixed models for the analysis of multi-environment trials in common bean (Phaseolus vulgaris L.) in Ethiopia

“…This improvement can be attributed to spatial analysis’ capability to effectively capture non-genetic variation linked to the variability in agricultural field plot [ 17 , 28 ]. The research conducted in varietal field trials on common bean [ 30 ], maize [ 31 ], and sorghum [ 32 ] consistently reveals compelling evidence of the profound benefits of spatial analysis. These studies, with a common focus on the method of analysis, highlight the advantages of incorporating spatial analysis techniques to disentangle non-genetic effects that may even be intertwined with the true genetic effect.…”

Evaluation of finger millet (Eleusine coracana (L.) Gaertn.) in multi-environment trials using enhanced statistical models

“…It could also be linked to the residual dependence of plots. When spatial trends were taken into account, relevant research on maize [ 36 ], sorghum [ 37 ], barely [ 38 ], potato [ 39 ], and soybean [ 40 ] likewise produced excellent results.…”

“…the residual dependence of plots. When spatial trends were taken into account, relevant research on maize [36], sorghum [37], barely [38], potato [39], and soybean [40] likewise produced excellent results. The ANOVA-based approach always assumes constant variance across environments and no correlation between environments.…”