The Sustainability Index and Other Stability Analyses for Evaluating Superior Fe-Tolerant Rice (Oryza sativa L.)

Abstract

Rice (Oryza sativa L.) is an important agricultural commodity in Indonesia. The combination of stability analysis on yields was considered accurate in selecting superior genotypes. The objectives of this study were as follows: identify the effects of genotypes, the environment, and their interactions (GEIs) on the yields of Fe-tolerant rice; select superior genotypes (stable and high yields) under diverse environment conditions in Indonesia; and determine the mega-environments (MEs) and representative environments for Fe-tolerant rice development. Fifteen genotypes of Fe-tolerant rice were used for this study. Field experiments were conducted at six experimental fields in Indonesia using a randomized block design with two replications. A combined analysis of variance (ANOVA) was used to determine the effect of genotypes, the environment, and their interactions on Fe-tolerant rice yields. Parametric, non-parametric, AMMI (additive main effects and multiplicative interaction), GGE biplot, and SI (sustainability index) measurements were used to determine the superior genotypes. GGE biplot was also used to determine MEs and representative environments. The measurement results showed that genotypes, the environment, and their interactions significantly affected rice yields with contributions of 13.30%, 35.78%, and 50.92%, respectively. One superior Fe-tolerant rice genotype (stable and high yield) was selected based on all measurements, namely G4 (B14316E-KA-4). In this experiment, two MEs were generated. Lampung was identified as a representative environment for the development of superior genotypes. The results of this study can be used as a consideration in the release and development of new superior varieties of Fe-tolerant rice in Indonesia.

Selection for representative environment to identify high yielding and sweetness levels of sweet potato (Ipomoea batatas L.) in Indonesia

“…Similar result was also shown by several researchers, where the environment with the shortest vector is unsuitable as a test environment, especially for superior plant selection (Maulana et al 2023). A representative environment is indicated by the length of the environmental vector and the resulting angle to the abscissa (Utami et al 2023). In this study, the Bandung paddy field environment is the ideal location (Type II).…”

“…Environment has an impact on changes in crop yields and also the quality of the results (Solihin et al 2018;Maulana et al 2023). In addition to sweet potatoes, it also affects other crops' yield (Ruswandi et al 2023) such as rice (Utami et al 2023) and soybeans (Wijaya et al 2022). Environmental selection needs to be done to obtain information on locations that have the potential as a development area for a variety of some environmental factors are considered very specific in influencing crop yields, such as temperature, rainfall, altitude, and soil properties (Markos et al 2022;Maulana et al 2022;Solihin et al 2023).…”

“…Therefore, to determine a representative location, environmental selection is carried out. Some researchers use the biplot approach to determine the favorable environment (Maulana et al 2022;Ruswandi et al 2023;Utami et al 2023). The use of biplots in representative environment selection is considered more informative because it provides a visualization of the environment.…”

Morphological trait variation of the immature liberica coffee (Coffea liberica) from West Java applied difference of coffee husk compost and biofertilizer

“…According to Maxiselly et al (2021), CV values above 20% when observing immature perennial plants indicate the accuracy of data collection. This was also stated by Utami et al (2023), that research with a CV value of more than 20% shows the significant influence of the environment on the data collected.…”