Additive Main Effect and Multiplicative Interactions (AMMI) and Regression Analysis in Sorghum [<i>Sorghum bicolor</i> (L). Moench] Varieties

Abstract

Background and justification: Ethiopia is the third largest sorghum producer in Africa next to Nigeria and Sudan. Shortage of widely adapted and stable high yielding variety is one of the major bottlenecks for production and productivity of sorghum in the country. Grain yield performance is not the only parameter for selection as a genotype with the highest grain yield would not necessarily mean stable and adaptable across location and years. Eberhart and Russell (1996) and AMMI model could be the preferable tools to identify stable, high yielding and adaptable genotype (s) for wider or specific environments. Objectives: To identify stable high yielding sorghum varieties that could be adapted for wider and/or specific environments and make recommendations for further demonstration and production in the test environments and similar agro ecologies. Material and methods: A total of 21 released sorghum varieties and a local check were evaluated at three locations in western Ethiopia (Bako, Gute, Biloboshe) and two locations in eastern Ethiopia (Mechara and Mieso) in 2017 main cropping seasons. The trial was arranged in a randomized complete block design (RCBD) in three replications. Summary of results and application of the study: The combined analysis of variance revealed highly significant effect of environment and genotype by environment interactions for grain yield. This indicated that the tested varieties showed inconsistent grain yield performance across locations. Birmash variety gave the highest grain yield with average yield of 3.5 ton ha-1 but specifically adapted to Gute, Biloboshe and Mechara. Baji was the second high yielding variety with mean grain yield of 3.3 ton ha-1 and relatively with wider adaptability. The first two IPCAs accounted for a total of 88.64% of the interaction sum square. In general, deviation from regression coefficient, AMMI stability value and genotype selection index revealed that Baji, Birmash, Emahoy, IS9302 and Gambella-1107 were relatively stable varieties with optimum grain yield and therefore recommended for further demonstration and popularization in the test locations and areas with similar agro-ecologies.

AMMI analysis of genotype × environment interaction on grain yield of sesame (Sesamum indicum L.) genotypes in Iran

“…According to Kindeya et al [25], the AMMI model and GGE biplot are the best methods to assay the GEI. In this study, our analysis of 15 sesame genotypes on four test locations in two years for the grain yield trait gave similar F-test results to the AMMI analysis performed by Gebeyehu et al [16]. Each of the AMMI stability parameters relates to a different concept of yield stability and may be useful to plant breeders attempting to select genotypes with high, stable and predictable yield across environments [26].…”

“…The distance from the zero point in a two-dimensional scatter plot between IPC1 (interaction PCA 1) vs. IPC2 is the ASV. IPC1 has a stronger effect than IPC2 in the sum of square G Â E; therefore, a factor must be added to IPC1 for the symmetrical discrepancy with IPC2, in such a way that, the role of IPC1 and IPC2 fit in the sum of square G Â E [16]. In this equation, the most stable genotype and test environment have minimum ASV [17].…”

Response of African Sorghum Genotypes for Drought Tolerance under Variable Environments

“…This agrees with the mean yield varying from 1.75 t/ha at environment E5 to 4.05 t/ha at environment E4 (Table 2), indicating a significant variance in yield productivity of the 225 sorghum genotypes in the six selected environments. Several studies have also reported that grain yield in multi-environment trials is highly affected by environmental influence [16,22,30,31]. Therefore, the selection of genotypes suited for specific environments is critical to improving grain production.…”

“…Of the two methods, AMMI analysis is recommended as the most effective due to its ability to illustrate the complex interaction between genotypes and environments accurately and graphically [27]. AMMI has been applied successfully to understand genotype-by-environment reactions in sorghum which allowed the identification of suitable genotypes for a wide range of environments [16,30,31]. The GEI of African sorghum genotypes under drought-stress environments can aid the efficient selection and recommendation of the best-suited genotypes for multiple purposes.…”

Genotype by environment interactions and stability for grain yield and other agronomic traits of sorghum in Ethiopia

“…Overall, AMMI 2 biplot revealed the relative performance of IS38326, Merera, ETSL100587, and ETSL100152 genotypes either as best or least in some or all environments in GY because they were farthest from the origin. High-yielding genotypes under speci c environments have been previously reported in sorghum (Gebeyehu et al, 2019). On the other hand, based on AMMI2, genotypes such as ETSL101712, IS25531, ETSL101312, and ETSL100771 were insensitive to environmental interaction in grain yield as they were close to the origin.…”