Research Article

GGE Biplot Analysis for Stability in Diverse Maturity Groups of Rice (Oryza sativa L.) Advanced Lines

Sreedhar Siddi, D. Anil and N. Lingaiah

  • Page No:  114 - 121
  • Published online: 31 Jan 2022
  • DOI : HTTPS://DOI.ORG/10.23910/1.2022.2597

  • Abstract
  •  siddu.35@gmail.com

The experiment was carried out under three seasons with 15 genotypes at Agricultural Research Station, Kunaram, Telangana state, India during rabi season (December to April) 2014–15 (E1), kharif season (July to November) 2015 (E2) and rabi season(December to April) 2015–16 (E3). The objective of the study was to assess the stability and adaptability of 15 rice genotypes of the various maturity groups over three seasons. The GGE biplot tool of these 15 rice genotypes of various maturity durations expressed a significant genotype, environment and G×E interaction for yield and days to 50% flowering. Genotype and environment interaction effect was responsible for the greatest part of the variation, followed by genotypes and environment effects for grain yield. Days to 50% flowering of genotypes was highly affected by environments followed by genotypes, and genotype and environment interaction. It also detected that rabi season 2014–15 (E1) was identified as the best suited season for the potential expression of the grain yield, while kharif season 2015 (E2) was the right season for the expression of reduced days to 50% flowering. Further, the what–won–where model indicated that short duration rice genotype G14 (KNM 1690) and medium duration genotype G9 (KNM 1632) in the environments rabi season 2014–15 (E1) and kharif season 2015 (E2), respectively and the early line G11 (KNM 1684) in the environment rabi season 2015–16 (E3) were the winning genotypes and suitable for their respective environments for grain yield. G7 (KNM 1616) was the vertex early genotype and closer to the ideal genotype expressed high yield and stability for all the environments. G13 (KNM 1689) and G14 (KNM 1690) were found to be stable for earliness across all the seasons and could be utilized for the development of early duration varieties. The rice genotype, G15 (BPT 5204) was found to be stable for lateness for all the seasons.

Keywords :   G×E interaction, GGE Biplot, polygon, rice, stability, yield

  • INTRODUCTION

    Breeding rice varieties with genotype by environment interaction studies play an important role in exercising stable varieties for yield and its contributing traits to improve rice productivity. Plant breeders conduct multi-environment trials (MET) primarily to identify the superior cultivars for a target region and secondarily to determine if the target region can be subdivided into different mega environments (Yan et al., 2000; Crossa et al., 2002). Most breeding programs face complex mega–environments with unpredictable genotype-environment interaction and genotype evaluation based on mean performance and stability has been a perennial problem and challenge (Yan and Kang, 2003). Hence, genotype-environment interaction has been a research focus among the breeders and geneticists which would help to get the information on the adaptability and stability performance and may complement the selection process and recommendation of a genotype for a target environment (Ebdon and Gauch, 2002; Gauch, 2006; Ahmadi et al., 2012; Jeberson et al., 2017). Breeders must therefore use tools to efficiently and accurately measure the response of the lines in multiple test environments (Yan et al., 2007). There are several biometric models proposed to analyze the GEI and explore adaptability and stability. However, multiplicative models that look at the response of genotypes to specific environments or to different environments have more accurate criteria to analyze this phenomenon in different crops (Goncalves et al., 2020). Various statistical models such as AMMI (Gauch, 2006) and GGE biplot models (Yan et al., 2000) are widely used across the seasons to assess their stability and to quantify the effect of genotype x environment (GxE) interaction on the yield of genotypes (Balakrishnan et al., 2016; Rasul et al., 2017).

    Wider adaptability and stability are the prime consideration in formulating effective breeding programs and selecting varieties (Dewi et al., 2014; Worku et al., 2016). The sustainability of rice production depends on the development of new rice cultivars with high yields and stable performance across diverse environments (Akter et al., 2014). It is therefore essential to apply new approaches to increase rice yield in already cultivated areas (Khush, 2005).

    Rice has been widely consumed as an essential food for human beings and grown around the world. Over half of the world’s population constantly include rice in their diet (Rao et al., 2016; Nili et al., 2017; Sharifi et al., 2017; Poli et al., 2018; Suman et al., 2021) and Asian countries produce nearly 80% of rice in the world. Among rice growing countries in the world, India has the largest area under rice crop of about 44.1 million hectares with a production of 165.3 million tons; however, its productivity per unit area is low i.e 3.78 t ha-1 (Kesh et al., 2021).  Due to escalating population, declining arable land and climate change, demands for higher productivity have become a critical issue in all over the world (Oladosu et al., 2017). Earliness is an important agronomic trait, has the advantage of varieties to suit various cropping situations, especially where the water supply is a limited period of time (Bueno and Lafarge, 2017). It also helps in the escape of crops from various pests and disease incidence and reduces crop loss leads to enhancing rice productivity with profitability and input use efficiency. Even though more than 1000 rice varieties have been released in India, most of the varieties were not accepted due to inconsistent performance in diverse environments.  For these reasons, the present study was aimed with objectives to evaluate rice genotypes with varying yield levels and maturity durations for the stability and adaptability in a selected set of rice advanced lines across growing seasons by using GGE biplot analysis.


  • MATERIALS AND METHODS

    The experiment was carried out under three seasons with 15 genotypes during rabi season(December to April) 2014–15 (E1), kharif season(July to November) 2015 (E2) and rabi season (December to April) 2015–16 (E3) at Agricultural Research Station, Kunaram. The details of the experimental material and environments are presented in Table 1.


    The farm is geographically situated at 18.6oN Latitude, 79oE Longitude and an elevation of 231m AMSL. The soil is silty loam with pH 7.43 and EC 0.26 dS m–1. Based on the imperative trait i.e days to 50% flowering which was recorded in kharif season 2015 (E2), the genotypes included in the present study were classified into different maturity groups. The experiment was carried out using a randomized complete block design with two replications in three environments. Grain yield was recorded at the time of maturity with 13% grain moisture and then plot yield in kg plot–1 was converted to kg ha–1. Days to 50% flowering were recorded on the day 50% of plants flowering in an experimental plot. Trial in each season was conducted as one environment for the multi–environment analysis. Data obtained from each season was analyzed separately by running a single analysis of variance and thereafter data from all the three seasons was pooled for analysis of variance to perform the combined analysis of advanced lines across the seasons to test the presence of significant genotype, environment and genotype-environment variation. In general, yield and days to 50% flowering are complex traits, dependent on several contributing characters and highly influenced by genetics as well as environmental factors. Thus, there is a need to identify the high–yielding stable genotypes in various maturity groups of rice suitable for a wide range of environments. Analysis of variance was significant for genotypes, environments and genotype×environment (G×E) components for yield as well as for days to 50% flowering indicating the use of GGE biplot analysis in identifying the stable genotypes.

    The term “GGE” emphasizes the understanding that G and GE are the two sources of variation that are relevant to genotype variation and must be considered simultaneously for appropriate genotype and the test environment evaluation. GGE biplot analysis has evolved into a comprehensive analysis system whereby most of the questions that may be asked of genotype by environment table can be graphically addressed (Yan et al., 2000; Yan, 2001; Yan and Kang, 2003) and facilitates the comparison of genotypes and their interaction among the environments (Gauch, 2006) for plant breeders, quantitative geneticists and agronomists.

    Here the GGE biplot methodology was deployed to investigate the evaluation of environments related to ideal environments, evaluation of genotypes related to ideal genotypes and identification of winning genotypes and their mega environments based on polygon view by which–won–where pattern for grain yield and days to 50% flowering.  ANOVA and stability analysis for yield trait and days to flowering was carried out by using the AMMI model and GGE model R–packages 1.5, PB Tools 1.4 version IRRI.  


  • RESULTS AND DISCUSSION

    Phenotypically stable varieties are usually sought for the commercial production of field crops. The present study was carried out to collect the information on 15 rice genotypes for their stability in three seasons. Pooled analysis of variance revealed that genotypes (G), environments (E) and genotypexenvironment (GE) were significantly different among the rice lines tested (Table 2 and 3) indicating the differential response of genotypes to the environments and their role in the phenotypic expression for yield and days to 50% flowering.


    The highly significant GxE effects suggest that genotypes may be selected for adaption to specific environments which is in accordance with the findings of XU et al. (2014). GGE biplot analysis is widely used for the analysis of GGE interaction in multi-environment yield trials (Yan, 2014; Kaplan et al., 2017).

    The ANOVA results for grain yield revealed that genotype and environment interaction was the principal source of variation explained 52.66% and the genotypes were contributed to 16.85% of the total variation. A low contribution of 14.87% to the grain yield was observed for environments. These results were superior to those presented by Ponnuswamy et al. (2018) in rice studied by the same model. On the other hand, environment contributed for 76.57% of the total variation, whereas genotype and genotype and environment interaction recorded low variations 14.19% and 8.84%, respectively for days to 50% flowering. High variation of environments for days to 50% flowering could be due to the low night temperatures (10–15oC) at nursery to initial tillering stage which results in prolonged duration of varieties during the rabi season 2014–15 (E1) and rabi season 2015–16 (E3). Low night temperatures are usually observed in November, December and January months with the temperature range of 10–15oC in Telangana State where the rice nursery and initial tillering stages are exposed. Low temperatures can cause physiological alternations in rice (De Los Reyes et al., 2003).

    For grain yield and days to 50% flowering, all the 15 genotypes significantly differed from each other across the testing seasons indicating thereby substantial variation due to GE. The results also indicated the presence of significant cross-over’s. Hence, identification of genotypes based on mean performance would be misleading (Table 4).


    The performance of genotypes, instead, should be on the basis of their performance in the respective environments. GGE biplot analysis revealed that high grain yield variability was observed in the first two principal components (PC, s) PC1 and PC2, which explained 63% and 22% of the total variation, respectively. Similarly, days to 50% flowering had a total of 95% variation explained by PC1 (75%) and PC2 (20%). PC 1 values were higher than PC2 for grain yield and days to 50% flowering explaining a higher contribution of genotype in the total sum of squares.

    Among the environments, rabi season 2014–15 (E1) was found to be the most suitable environment for the potential expression of grain yield and the most ideal environment for testing general adoption as it made a small angle with the Average Environment Axis (AEA) and had large PC1 score and small PC2 score, and representative of all the three environments. This season will help in selecting cultivars that are widely adopted and bear general adoption. It was observed that grain yield was significantly higher in the dry season (rabi) than wet season (kharif) under irrigated rice production in tropical conditions and the variation was observed for the ideotype suitability for different seasons (Bueno and Lafarge, 2017). On the other hand, rabi season2015–16 (E3) being distant from other testing environments produced the longest environment vector with a large PC2 score. Thus, this environment was regarded as the highly discriminating and desirable testing season for examining special adoption for grain yield (Figure 1). The season, rabi 2015–16 (E3) was the most representative and ideal environment for deciphering the general adaptability of the cultivars since it demonstrated a small angle with AEA (Average Environment Axis) for days to 50% flowering (Figure 2).


    An interesting application of GGE biplot is the evaluation of a genotype relative to an ideal genotype. Although such an ideal genotype may not exist in reality, it could be used as a reference for genotype evaluation (Mitrovic et al., 2012). An ideal genotype is one with large PC1 scores representing the high yielding ability and small PC2 scores representing high stability (Yan et al., 2000).  The concentric circles help to rank the genotypes based on their distances to the ideal genotype, and the genotypes evaluated in multi–environmental trials, shifts in the relative ranking of genotype by environment interactions often occur (Alam et al., 2014; Parihar et al., 2017). Thus, Figure 3 revealed that early genotype G7 (KNM 1616) fell into the center of concentric circles in the positive direction, was nearer to the ideal genotype in terms of high yielding ability and stability, compared with the rest of the genotypes. The second and third most desirable early genotypes would be G6 (KNM 1610) and G4 (KNM 1600), respectively which were near to G7 (KNM 1616).  Similar kinds of observations were earlier reported by Poli et al. (2018).


    For days to 50% flowering, the genotype G15 (BPT 5204) had the highest mean across the environments followed by G9 placed on the center of the concentric circles which represents the medium and longer duration, respectively.  On the contrary, genotypes notably G14 (KNM 1690) and G10 (KNM 1638) had lower means for days to 50% flowering across the environments placed in the negative direction represented the stable early duration. The present results are in conformity with the earlier reports and this confirmed that days to 50% flowering is a stable character (Dushyanth Kumar et al., 2020). However, genotype G13 (KNM 1689) was the least stable genotype and had the lowest mean for days to flowering (Figure 4).


    The adaptability of the genotypes across the environments and best–suited genotypes for specific environments was identified based on the GGE biplot polygon viewgraph (Yan and Kang, 2003). One of the most attractive features of a GGE biplot polygon is its ability to show the which–won–where pattern of a genotype by environment data set (Poli et al., 2018). It divided the biplot into five sections, and three environments fell into two of them as two mega environments for grain yield.  Vertex early and medium duration genotypes G14 (KNM 1690) and G9 (KNM 1632) were the winning genotypes in mega environment 1 consisting of kharif season 2015 (E2) and rabi season2014–15 (E1), respectively.  While the early line G11 (KNM 1684) was the winner in mega environment 2 i.e rabi season2015–16 (E3).  Similarly, early genotype G13 (KNM 1689) and late cultivar G15 (BPT 5204) were better in kharif season 2015 (E2) and rabi season2014–15 (E1) environments. It concludes that different cultivars should be selected and deployed for each different environment. Similar results were reported by the rice workers (Akter et al., 2015). The other vertex early genotype, G2 (KNM 1592) showed low yield and was poorly adapted to three environments (Figure 5).



    Out of 15 genotypes tested, early line G7 (KNM 1616) was not only high yielding, but also exhibited stable yield across all the seasons. This was evident from Table 4 and Figure 5 where it falls on the vertex of the polygon, and also appears near the biplot origin. Other early genotypes such as G6 (KNM 1610) and G4 (KNM 1600) recorded high yield next to G7 (KNM 1616) with more stability being nearer to biplot origin. These results are in close correspondence with the results reported by Susanto et al. (2015) in rice while studying G×E interaction for Fe content.

    With regard to days to 50% flowering,cultivar G15 (BPT 5204) recorded its lateness in kharif season 2015 (E2) and rabi season2015–16 (E3) since it was located on the vertex of the polygon falling on E2 and E3 in a positive direction. Similarly, in kharif season2015 (E2) and rabi season 2015–16 (E3) environments, G9 (KNM 1632) was regarded as medium duration variety.  In rabi season 2014–15 (E1), G6 (KNM 1610) and G5 (KNM 1604) were late in duration. In comparison with all the lines, G13 (KNM 1689) and G14 (KNM 1690) were found to be early lines (Figure 6).


  • CONCLUSION

    Genotypes G7 (KNM 1616) and G6 (KNM 1610) are the potential donors for grain yield, and the genotypes G13 (KNM 1689) and G14 (KNM 1690) forearliness improvement. In the current scenario, crossing programme between these genotypes would be a prospective approach with high demand and low inputs for developing short duration high yielding varieties. Winning genotypes G14 (KNM 1690), G11 (KNM 1684) and G13 (KNM 1689) for yield and earliness required further testing in multilocation and multi-environmental trials.


  • ACKNOWLEDGEMENT

    The authors sincerely thank to Regional Agricultural Research Station, Jagtial for providing the support.


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Cite

1.
Siddi S, Anil D, Lingaiah N. GGE Biplot Analysis for Stability in Diverse Maturity Groups of Rice (Oryza sativa L.) Advanced Lines IJBSM [Internet]. 31Jan.2022[cited 8Feb.2022];13(1):114-121. Available from: http://www.pphouse.org/ijbsm-article-details.php?article=1568

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