Stability of fruit and bunch of mapanget tall and Indonesia coconut hybrid with several planting distance using AMMI model and GGE biplot analysis

Abstract

Coconut plantations with new system of planting distance allow wider use of distance under the coconut than the conventional planting distance. Evaluation stability ofMapanget coconut (DMT) and KHINA hybrid coconut at various planting distance systems can be used as a recommendation for the development of coconuts with new planting distance in the most suitable environmental conditions. The purpose of this study was to determine the stability of the yields of DMT and KHINA grown with various distance models for five years observation. The experiment was arranged in a factorial experiment design consistsof 8 treatments with 2 replications, in the Mapanget Experimental Garden, North Minahasa Regency, North Sulawesi Province, for 5 years from 2014 to 2018. The results showed that the combined analysis variance was significantly different (F < 0.05) both GXE interaction for the number of fruits, while the character number of bunches is not different. The results of AMMI Biplot showed that Khina coconut with the most stable distance of 9 x 9 m followed by Khina coconut with a distance of 5x16 for land use under coconut plants with intercropping suggested using a distance of 5x16 m.

Genotype by Environment Interaction by AMMI and GGE Bi-Plot Analysis for Maize (Zea Mays L.) for Transitional High Land Agroecology of Ethiopia.

“…Only one test environment (Bako) is both representative and discriminating, making it a good place to nd genotypes that are generally adapted. Similar results as many authors Y Matana [4]; SALEEM [26], Dagnechew [27], Ndlala [28], Dao [29] and Moges [30], who used GGE biplot to identify high representativeness and discriminating for different crops.…”

“…The multiplicative effects analysis (Table 4) revealed that the AMMI model with test environment contributed 55.78 percent of the total variation of sum square for grain yield, while G and G x E sources of variation accounted for 7.88 percent and 14.55 percent, respectively. Other research has found that in multi-environment trials, location accounts for the vast majority of total variation, with genotype and G x E accounting for only a minor portion [4,5,15]. In Table 4, AMMI revealed that the rst two hybrids (G11, G12) were the best performers in each environment, while CK1 was the worst performer.…”

“…As a result, yield stability and superiority of maize genotypes evaluated in different environments can help breeders develop superior hybrid. The additive main effects and multiplicative interaction models are frequently used for data analysis to sort out statically signi cant different within genotype, Environment, and their interaction (G x E) to select superior and stable genotype [4,9]. The primary goals of this study were to (1) evaluate the yield performance and stability of maize genotypes for Ethiopia's transitional high land, and (2) investigate the in uence of genotype by environment interaction on genotype yield.…”

“…As a result, multi-environment trials are required to identify a dependable and superior hybrid. Therefore, the general strategy of the testing trial in year-by-locations representing agroecological conditions rather than multiple tests combined year after year [4,5]. When genotypes rank differently in different environments (locations and/or years), G x E interaction is important from the standpoint of breeders.…”