Hybrids that are stable or adaptable in a specific location for the western region of Indonesia are required to increase production of maize in Indonesia. The objectives of the study were (i) to select maize hybrids which are stable or adaptable in the western region of Indonesia and (ii) to determine the discriminant location for evaluating superior hybrids in the western region. Therefore, twelve maize hybrids were planted in different locations and seasons in the western region. Hybrids were selected based on GGE biplot analysis. The results showed that G9 and G10 were stable maize hybrids. G6 was the selected hybrid for the first megaenvironment; whereas, G3 was selected as the hybrid for the second megaenvironment. The L8 and L17 were the discriminant environment for evaluating hybrids in the western region of Indonesia. The high-yielding hybrids selected in this study should be broadly evaluated on-farm in order to disseminate for small holder farmers in Sumatera and Java islands.

Selection of high yielding and stable maize hybrid requires effective method of evaluation. Multienvironment evaluation is a critical step in plant breeding programs that is aimed at selecting the ideal genotype in a wide range of environments. A method of evaluation that combines a variety parameter of stability could provide more accurate information to select the ideal genotype. The aims of the study were (i) to identify the effect of genotype, environment, and genotype × environment interactions (GEIs) on maize hybrid yields and (ii) to select and to compare maize hybrids that have high and stable yields in diverse environments in Sumatra Island based on combined analysis, selection index, and GGE biplot. The study was conducted in five different environments in Sumatra Island, Indonesia, using a randomized complete block design repeated three times. Data were estimated using combined variance analysis, parametric and nonparametric stability, sustainability index, and GGE biplot. The results showed that the genotype had a significant effect on maize hybrid yields with a contribution of 41.797%. The environment contributed to 24.314%, and GEIs contributed 33.889% of the total variation. E1 (Karo, South Sumatra; dry season) and E3 (Tanjung Bintang, Lampung; dry season) were identified as the most ideal environments (representative) for testing the hybrids for wider adaptability. The maize hybrid with high and stable yields can be selected based on combined stability analysis and sustainability index as well as GGE biplot. These three methods are effectively selected high yielding and stable genotypes when they are used together. The three maize hybrids, namely, MH2, MH8, and MH9, are recommended as high yielding and stable genotype candidates.

Determination of grain yields of stable and high-yielding maize hybrids in a wide environment requires high accuracy. Many stability measurement methods have been used in multi-environment experiments. However, the relationships among the different methods are still difficult to understand. The objectives of this study were to 1. Identify the effect of growing season and location (Environments = E), hybrids (Genotypes = G), and their interactions (GEIs) on grain yields; 2. Select high-yielding and stable maize hybrids in a wide range of environments; 3. Determine the relationship between each stability estimation; and 4. Determine the mega-environment of maize hybrid and identify the best locations for testing. Field experiments were conducted at ten locations in Java Island, Indonesia, for two growing seasons using a randomized completed block design with three replications. The experimental results showed that the main effects of the growing season, location, hybrid, and GEIs, significantly affected maize hybrid yields. Stability estimations of TOP, S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, KR, NP⁽⁴⁾, CV_i, and b_i, belong to the concept of dynamic stability that can be used to select maize hybrids in favorable environments, while other estimations were classified as in the static stability. Three maize hybrids were successfully selected, with high and stable yields based on numerical and visual stability estimations, namely SC2, SC7, and SC9. The three hybrids can be used as candidates for sustainable maize development programs. The dry season, the rainy season, and the combination of two growing seasons produced three mega-environments. GJRS and KARS were the most discriminative environments. Both environments can be used as favorable environments for selecting the ideal maize hybrid.

A good maize plant breeding program must consider the effect of genotype-by-environment interactions (GEIs) and the correlation of important traits. The purpose of this study was to evaluate genetic variation, identify and investigate the implications of GEIs on breeding maize mutants in order to determine the ideal genotypes (stable and high yield), identify the best intercropping system for maize mutants, and identify the heritability and relationships of the traits tested that are important in cultivation. This research was carried out in five different intercropping systems in Upland West Java, Indonesia. A randomized block design with two replications was used in the field experiment. The measurement results revealed that the genetic diversity of maize mutants is broad, with six axes having eigenvalues ranging from 8.76 to 1.07 and a cumulative value of 76.64%. The neighbor-joining tree also showed a wide range of variation, yielding five distinct groups. The GEIs had a significant effect on the 14 traits tested, particularly yield. The environment had a significant impact on the variation of all the traits tested. The ideal cropping system for selecting the ideal maize mutant was Environment 4 (maize + rice). Superior maize mutants were successfully selected based on the GGE biplot. Thus, the sustainability index (SI) was used to successfully select maize mutants that were superior by 20.00%. These two methods selected only 15.00% of the maize mutants. One trait has a low heritability value, five traits have a moderate heritability value, and 17 traits have a high heritability value. The yields correlated positively and significantly with nine yield traits. Future maize-breeding programs can be based on data on genetic diversity, heritability, and the relationship of the traits tested. The best intercropping system can be used as an alternative for increasing maize cultivation income. Selected maize mutants can be proposed as new superior genotype candidates.

There are many local varieties of sweet potatoes which are cultivated and consumed in Indonesia. The food industry which uses sweet potato as the main raw material has been developed in West Java. Demand for orange-fleshed sweet potato is high, but the supply of demand has not been fulfilled. This is because the varieties that are widely cultivated do not meet consumer standards and preferences, so new superior genotypes are needed following demand. Currently, selection of stable and high-yielding genotypes and accordance with consumer and industry preferences is one of the focuses of sweet potato research. Orange-fleshed sweet potato multi locations testing in accordance with consumer and industry preferences, can be used as a basis for consideration in the development program. The purpose of this study were to identify genotype by environment interactions (GEIs) and t select superior genotypes and to estimate yield stability across three locations in West Java, Indonesia. Combined analysis of variance (ANOVA) was used to determine significant differences between each genotype tested in term of yield and to estimated genotype by environment interactions (GEIs). Additive Main Effects and Multiplicative Interaction (AMMI), Genotype Plus Genotype by Environment Interactions (GGE) biplots, and Parametric and non-parametric stability measurements were used to determine yield stability from genotypes tested in all locations (Sumedang Regency, Bandung Regency, Karawang Regency). Data in this article showed that the genotypes, environments, and GEIs had an effect on sweet potato yields, with influences of 35.03%, 18.87%, and 46.01%, respectively. The results in this data also indicate that some new sweet potato genotypes have stable and high yields in three environments in West Java, Indonesia. So they were can be used for development in sweet potato breeding programs.

Genetic studies on yield and yield quality are becoming benchmarks for farmers and industry in selecting and developing varieties. Evaluations that combine various stability statistics can provide more accurate information to select the ideal genotype. This study aims to identify the effect of genotype by environment interactions (GEIs) for yield and yield quality, to select high yield and stable sweet potato genotypes, as well as to select superior genotypes based on yield and yield quality. Three different environments in West Java, Indonesia, were used to test the sweet potato genotypes using a randomized block design that was repeated three times. Highly significant effects of sweet potato genotypes (G), environments (E), and GEIs were observed for yield and yield quality. The Combined ANOVA showed that GEIs effect contributed 54.88% for yield, 40.01% for sweetness, 10.46% for moisture content, 68.80% for tuber diameter, and 72.57% for tuber length from the sum of square. Five most high and stable yield on sweet potato genotypes identified by all measures, includes G4, G6, G7, G31, and G32. Genotype by yield*traits (GYT) selected seven genotypes that have superior in yield and yield quality, they were G7, G15, G4, G20, G6, G31, and G14. Based on stability measurements and GYT biplots, the genotypes G4, G6, G7, and G31 are in both slices. So that the four genotypes have high, stable yields, and have a good combination of traits for yield quality. Our findings can be used for improvement cultivation involving partner companies, partner institutions, and farmers, and the selected genotypes can be release as superior varieties candidate.