Detection of Superior Rice Genotypes and Yield Stability under Different Nitrogen Levels Using AMMI Model and Stability Statistics

Abstract

Sustainable agriculture is a prerequisite for food and environmental security. Chemical fertilization, especially nitrogenous fertilization, is considered the most consumed for field crops. In rice crops, plants consume much less than half of the applied N-fertilizer. In the current investigation, multiple N environments were generated by applying different N doses of urea fertilizer to a permanent transplanted field for two successive summer growing seasons at a rice research and training center, Kafrelsheikh, Egypt. A set of 55 genotypes consisting of 25 Jabonica, 4 Tropical Japonica, 20 Indica, and 6 Indica/Japonica were transplanted under no N (0N), Low N (LN), medium N (MN), and High N (HN) (i.e., 0, 48, 96, and 165 Kg N ha−1, respectively). Highly significant differences were detected among the tested genotypes. AMMI analysis of variance revealed the existence of the genotype via nitrogen interaction (GNI) on yield performance. The GNI principal components (IPCA) IPCA1 and IPCA2 scores were significant and contributed values of 71.1 and 21.7, respectively. The highest-ranked genotypes were MTU1010, IR22, SK2046, SK2058, IR66, and Yabani LuLu based on their grain yield means (30.7, 29.9, 29.5, 29.3, 28.8, and 28.5 g plant−1). These genotypes were grouped into the same SCL according to the stability analysis ranking matrix. Based on AMMI analysis and biplots, MTU1010 and Yabani LuLu showed yield stability across environments. Meanwhile, the which-won-where biplot showed that IR22 was superior under unfavorable N-levels and MTU1010 was stable across the different environments. These findings are considered to be of great importance to breeders for initiating low-nitrogen-input breeding programs for sustainable agriculture.

Exploring Genetics by Environment Interactions in Some Rice Genotypes across Varied Environmental Conditions

“…Consequently, all the interaction information could be speculated by plotting these two PCs. Our findings provide proofing for the results of several earlier investigations obtained by others that support the idea that the first two IPCs of the AMMI model are the most important during quantifying GEI [ 14 , 34 , 35 , 36 , 37 ].…”

“…Meanwhile, other investigations reported genotypes’ effects to be the most effective impacts. In these investigations, the differences between environments were mainly due to one effect, such as identifying the most stable rice genotype across different fertilizer levels [ 14 ].…”

“…Furthermore, this approach allows for tailored genotype selection tuned to distinct natural environments [ 13 ]. Additionally, such multilocation preliminary yield trials facilitate the selection of promising varieties by demonstrating their yield potential and stability across diverse environments [ 14 ]. So long as the environment differs in terms of climatic conditions, the varieties that exhibit stability across these environments demonstrate greater resilience to the effects of climate change.…”

“…The metan package provides a comprehensive set of tools for MET data management, manipulating, analyzing, and visualizing the MET data. It has been successfully employed to quantify yield stability in various crops, including rice [ 14 ], lentil [ 22 ], wheat [ 23 ], soybean [ 24 , 25 ], sugarcane [ 26 ], and others [ 27 , 28 , 29 , 30 , 31 ].…”

Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset

“…Traditionally, analysis methods of the genotype x environment interaction have been used for the positioning of cultivars, however, the interpretations obtained after obtaining data and generalizations about the environment do not allow the application or extrapolation of results for all regions [2][3]. However, when the environments are well characterized, conclusions on cultivar performance are extrapolatable, as used nitrogen levels in rice [4] or used water deficit environments in maize [5]. The simulation models of plant growth in response to climate, soil, and cultivars allow identifying the best sowing times, which are made available annually for different cultures and locations, such as the Agricultural Zoning of Climatic Risk, and have helped Brazilian farmers in all grain-producing locations [6][7].…”