Effect of Genotype by Environment Interaction (GEI), Correlation, and GGE Biplot analysis for high concentration of grain Iron and Zinc biofortified lentils and their agronomic traits in multi-environment domains of Nepal

Lentil (Lens culinaris Medik. culinaris ) is a cool season food legume and "the house of essential nutrients", contains the high quality and quantity of proteins (up to 35%) and minerals calcium, phosphoros, potassium, folic acids, iron, zinc, selenium and vitamins. Lentil is the fourth most important crop grown after rice, maize, wheat & millet in terms of area (MoAD, 2016). In Nepal, it is mostly eaten as dal (Concentrated soup) with rice besides various food preparations. Rice or wheat bread and dal are the best combination in daily dish of low income people of Nepal who cannot afford the animal products. Virtually the major proportion of rural people relies on lentil and other pulses for their nutritional security. It has diverse role in farming system which adds 42-75 kg biological nitrogen fixation. Lentil seed is rich in protein for human consumption, and lentil straw is a valued animal feed. It is also known as the exportable commodity, in 2016/17 lentil was exported in the value of USA $ 10 million from Nepal. It is grown as sole crop as well as mixed crop and intercropped with sugarcane, mustard, linseed, wheat, mango-orchard etc. In general, lentil is commonly grown as relay cropping system prior to rice harvest and in Nepal, still 0.24 million hectares of lands are rice fallows and has a great scope to vertical and horizontal expansion. Inmost lentils producing areas yield seems to be not more than one-half of potential yields while improved genotypes contribute to increase lentil production (Erskine, 2009). Lentil is adapted to low rainfall and is predominantly grown in the winter in regions (Sarker et al., 2003). Selecting genotypes for high mean yield and yield stability has been a challenge for lentil breeders. Yield is a quantitative trait while GxE interaction showed the yield stability and micronutrients heritability. It is also the interplay in the effect of genetic and non-genetic on development of any genotypes. Consequently Gx E interaction helps breeder to select the desirable varieties in the process of evaluation & increase efficiency of selection (Sabaghnia et al., 2008). It is reported the main environmental effects (E) and Genotype by Environment Interaction (GEI) as the most important sources of variation for the measured yield of crops. The yield is a combined result of the effects of the genotype (G), E and GEinteraction. Environment is responsible more than 80% effects of the total yield variation, while Genotype and G x E interaction has small effect. Environmental factors include soil moisture, sowing time, fertility, temperature and day length which is strong influenced during the various stages of plant growth (Bull et al., 1992). Therefore GEI is an extremely important in the development and evaluation of plant varieties. Flores et al. (1998) compared 22 univariate and multivariate methods to analyze genotype by environment (GE) interactions. There are two possible strategies for interpreting GEinteraction with univariate parametric methods including analysis of variance and simple linear regression analysis of cultivar yield. The requirement for stable genotypes that perform well over a wide range of environments becomes increasingly important as farmers need reliable production quantity (Gauch et al.,2008). Therefore, identifying most stable genotypes is an important objective in many plant breeding programs for all crops, including lentil. The performance of a genotype is determined by three factors: genotypic main effect (G), environmental main effect (E) and their interaction (Yan et al.,2007). Understanding genotype by environment (GE) interactions is necessary to accurately determine stability in lentil genotypes and help breeding programs by increasing efficiency of selection (Sabaghnia et al.,2008). The complexities of genotype × environment interaction (GEI) make selection difficult to identify the best performing and most stable genotypes (Yau, 1995). Thus, first we need to identify the stable genotypes for their yield and yield component traits. Stability of genotypes over wide range of environments is desirable and depends upon GEI (Ali and Sawar, 2008). AMMI analysis has been shown to improve both the post-dictive and predictive success of yield trials by altering the noise (random variation) from the data pattern, thereby improving predictive accuracy (Gauch and Zobel, 1988). Understanding the structure and nature of GEI is of utmost significance in crop improvement programs because the significant GEI can seriously impair efforts in selecting the superior genotypes (Danyaliet al., 2012). The objectives of this investigation were: to apply a GGE biplot and AMMI analysis model to evaluate the magnitude of the effect of GEI on grain yield of 25 high grain Fe and Zn lentil accessions tested across the three locations over the years and to evaluate relationships between test environments for identification of favorable genotypes for lentil production areas in terai agro-domains.