Genotype X Environment Interaction for Storage Root Yield in Sweetpotato Under Managed Drought Stress Conditions

Abstract

Most crops, including sweetpotato vary widely in yield performance across different agro-ecological environments. This study was set up to determine the genotype x environment interaction (GEI) for storage root yield performance of 24 sweetpotato genotypes in eight environments; two locations -Kiboko and Thika, two moisture stress conditions -drought stress and no drought stress, and two years -2011 and 2012 in Kenya. Plots of three rows each of 10 plants per genotype, at density of 0.9 m × 0.30 m were panted during dry season under split plot design replicated twice. Managed irrigation was applied to control moisture stress during the growth period. Fresh storage root yield (FSR) data was collected, and drought susceptibility indices (DSI) determined. Additive Main Effects and Multiplicative Interactions (AMMI) genotype main effect by genotype-environment (GGE) interaction biplots and regression analyses were done using Genstat 14 th edition to determine GEI effects. The environment, genotype main effects, and the GEI were all significant (P < 0.001). The DSI showed significant variation of genotypes in different environments. The highest yielding genotypes across the environments were G7, G14, G15, and G10 while most stable genotypes were G5, G22 and G2 and the least stable was G24. Thus, genotypes G5, G22 and G2 may be used across environments, while genotypes G7 and G14 could be used, 1) in specific locations to boost production under unpredictable rainfall conditions, 2) incorporated into drought screening breeding programmes to develop a new generation of drought tolerant sweetpotato varieties to meet changing climatic conditions.

Genotype × environment interaction and selection for drought adaptation in sweetpotato (Ipomoea batatas [L.] Lam.) in Mozambique

“…The genotype 9 environment interaction (G 9 E) significantly affect sweetpotato growth and productivity, as noted when testing bred-germplasm or cultivars under managed drought across sites in Kenya (Kivuva et al 2014), across locations in Mozambique (Henderson et al 1997) and Rwanda (Jannsens 1983), varying eco-geography in Perú (Grüneberg et al 2005), or across sites and over years in South Africa (Tekalign 2007). The G 9 E causes difficulty to selection of clones with wide adaptation, which may delay the cultivar release (Rukundo et al 2013).…”

The analysis of usefulness of locations on the basis of spring barley trials

“…The mega-environments (Kandus et al, 2010) contained three locations (two in two years and one in one year) considered as five environments. The same analysis was conducted on sweet potato root (Ipomoea batatas) yield in Kenya (Kivuva et al, 2014), but only in two locations (although there were eight environments), and on wheat (Triticum aestivum L.) in Serbia in five locations (Zečević et al, 2009).…”

Genotype environment interactions for grain yield in rice under no drought and drought conditions

“…The use of GGE biplots has been appreciated by many researchers in rice and other crops (Hagos and Abay, 2013;Kivuva et al, 2014;Lakew et al, 2014;Muthoni et al, 2015) as it graphically displays Musila et al 291 general pattern of genotype responses across environments in multi-environmental trials data usually concealed in the general ANOVA. In this study, the GGE biplot results revealed that there was no correlation between environments E2 and E4, indicating that these two environments discriminated the genotypes differently.…”

“…Environment E1 fell into the second sector and the winning genotype was G37, while E4 fell into the third sector with genotype G38 winning in this environment. Other researchers in subSaharan Africa have also appreciated the use of the polygon view of GGE biplot in identification of the best genotypes in different environments and revealing possible mega environments among the test environments (Kivuva et al, 2014;Lakew et al, 2014;Muthoni et al, 2015). The biplot view of mean yield and stability revealed that the average grain yield of G37, G41 and G38 was higher than that of the average (ideal) genotype across the test environments.…”