In any breeding program, it is necessary to screen and identify phenotypically stable genotypes that could perform uniformly under different environmental conditions. Such a breeding effort requires basic information of genotype × environment (G × E) interaction. Twenty genotypes including hybrids and aromatic rice were evaluated in 8 environments in two production systems viz; System of Rice Intensification (SRI) and normal cultivation environments during kharif season (May–October) 2009. The experiment was laid down in RBD with two replications in a plot of 1 m2. Pooled analysis for G × E interaction and stability revealed that the genotypes and environments were highly significant (p < 0.01) for all twelve characters studied. The G × E interaction was significant for six traits including all key components of SRI except tillers no. Both linear and non-linear components contributed towards G × E interaction. Stability parameters identified genotypes PR-114 and HKR-47 as stable for grain yield per plant and HKR-127, HKR-120, CSR-30, Pusa-1121 and IR-64 for test grain weight. Genotypes identified suitable for favourable environments were HKR-126, HSD-1, PAU-201and Govind while for unfavourable environment were HSD-1, HKRH-1094, HKR-48 and PAU-201 for different traits. IR 64 and Pusa 1121 registered 24.31 and 12.54% increase in yield in SRI over normal production system. These genotypes need to be tested in macro environments over space and time and could be utilized for direct cultivation as well as for improvements of other cultivars.
Authors:S. L. Krishnamurthy, S. K. Sharma, D. K. Sharma, P. C. Sharma, Y. P. Singh, V. K. Mishra, D. Burman, B. Maji, B. K. Bandyopadhyay, S. Mandal, S. K. Sarangi, R. K. Gautam, P. K. Singh, K. K. Manohara, B. C. Marandi, D. P. Singh, G. Padmavathi, P. B. Vanve, K. D. Patil, S. Thirumeni, O. P. Verma, A. H. Khan, S. Tiwari, M. Shakila, A. M. Ismail, G. B. Gregorio, and R. K. Singh
Genotype × environment (G × E) interaction effects are of special interest for identifying the most suitable genotypes with respect to target environments, representative locations and other specific stresses. Twenty-two advanced breeding lines contributed by the national partners of the Salinity Tolerance Breeding Network (STBN) along with four checks were evaluated across 12 different salt affected sites comprising five coastal saline and seven alkaline environments in India. The study was conducted to assess the G × E interaction and stability of advanced breeding lines for yield and yield components using additive main effects and multiplicative interaction (AMMI) model. In the AMMI1 biplot, there were two mega-environments (ME) includes ME-A as CARI, KARAIKAL, TRICHY and NDUAT with winning genotype CSR 2K 262; and ME-B as KARSO, LUCKN, KARSA, GOA, CRRI, DRR, BIHAR and PANVE with winning genotypes CSR 36. Genotypes CSR 2K 262, CSR 27, NDRK 11-4, NDRK 11-3, NDRK 11-2, CSR 2K 255 and PNL 1-1-1-6-7-1 were identified as specifically adapted to favorable locations. The stability and adaptability of AMMI indicated that the best yielding genotypes were CSR 2K 262 for both coastal saline and alkaline environments and CSR 36 for alkaline environment. CARI and PANVEL were found as the most discernible environments for genotypic performance because of the greatest GE interaction. The genotype CSR 36 is specifically adapted to coastal saline environments GOA, KARSO, DRR, CRRI and BIHAR and while genotype CSR 2K 262 adapted to alkaline environments LUCKN, NDUAT, TRICH and KARAI. Use of most adapted lines could be used directly as varieties. Using them as donors for wide or specific adaptability with selection in the target environment offers the best opportunity for widening the genetic base of coastal salinity and alkalinity stress tolerance and development of adapted genotypes. Highly stable genotypes can improve the rice productivity in salt-affected areas and ensure livelihood of the resource poor farming communities.
Authors:A. Anandan, T. Sabesan, R. Eswaran, G. Rajiv, N. Muthalagan, and R. Suresh
Superior grain quality is the main goal of rice breeders because of its high commercial value. Progress in selection for grain quality with yield in harsh environments is markedly affected by environmental variation. The genotype by environmental (G × E) interaction influence on grain quality was analyzed in this study, comprised of 17 rice hybrids grown in six location- year environments. The objective of this study was to examine the influence of G × E interaction for grain quality in hybrid rice by using AMMI model. Results of the trial revealed that grain quality was highly influenced by environmental factors and brings out the suitability of specific genotype to specific location/season through the biplot. On the other hand, external environmental variables can be regressed on the environmental scores to lead to a useful biological interpretation of the interaction effects, which is not possible in additive effect models. The implications of these results for rice hybrids on grain quality in varied environmental location are discussed.
Authors:S. Sareen, R. Munjal, N. Singh, B. Singh, R. Verma, B. Meena, J. Shoran, A. Sarial, and S. Singh
Terminal heat, which is referred as increase in temperature during grain filling, is one of the important stress factors for wheat production. Current estimates indicate that wheat crop grown on around 13.5mha in India is affected by heat stress. In order to meet the challenges of high temperature ahead of global warming, concerted efforts are needed to evaluate germplasm for heat tolerance and identify and develop genotypes suitable for such stressed environments. The advanced wheat genotypes developed for stress and normal environments by different research centers were evaluated across 7 locations representing varied agroclimatic zones during 2007–08 and 2008–09 to study their adaptability for heat stress and non-stress environments. The additive main effects and multiplicative interaction analysis for G × E interactions revealed differences amongst locations to phenology and grain yield. Genotype RAJ 4083 developed for cultivation under late sown conditions in peninsular zone was also found adaptable to timely sown conditions. Similarly, HD 2733 a cultivar of NEPZ timely sown conditions and PBW 574 an advanced breeding line of NWPZ late sown conditions was found adapted to Peninsular zone. The cultivar RAJ 3765 showed specific adaptability to Pantnagar in NWPZ. Genotype NW 3069 developed for NEPZ timely sown conditions have shown adaptability to number of locations; timely sown conditions at Karnal and Hisar in NWPZ and Niphad in PZ. Likewise, WH 1022 developed for NEPZ late sown conditions exhibited specific adaptability to all timely sown locations in NWPZ.
Authors:R. Ponnuswamy, A. Rathore, A. Vemula, R.R. Das, A.K. Singh, D. Balakrishnan, H.S. Arremsetty, R.B. Vemuri, and T. Ram
The All India Coordinated Rice Improvement Project of ICAR-Indian Institute of Rice Research, Hyderabad organizes multi-location testing of elite lines and hybrids to test and identify new rice cultivars for the release of commercial cultivation in India. Data obtained from Initial Hybrid Rice Trials of three years were utilized to understand the genotype × environment interaction (GEI) patterns among the test locations of five different agro-ecological regions of India using GGE and AMMI biplot analysis. The combined analysis of variance and AMMI ANOVA for a yield of rice hybrids were highly significant for GEI. The GGE biplots first two PC explained 54.71%, 51.54% and 59.95% of total G + GEI variation during 2010, 2011 and 2012, respectively, whereas AMMI biplot PC1 and PC2 explained 46.62% in 2010, 36.07% in 2011 and 38.33% in 2012 of the total GEI variation. Crossover interactions, i.e. genotype rank changes across locations were observed. GGE biplot identified hybrids, viz. PAN1919, TNRH193, DRH005, VRH639, 26P29, Signet5051, KPH385, VRH667, NIPH101, SPH497, RH664 Plus and TNRH222 as stable rice hybrids. The discriminative locations identified in different test years were Coimbatore, Maruteru, VNR, Jammu, Raipur, Ludhiana, Karjat and Dabhoi. The AMMI1 biplot identified the adaptable rice hybrids viz., CNRH102, DRH005, NK6303, NK6320, DRRH78, NIPH101, Signet5050, BPH115, Bio452, NPSH2003, and DRRH83. The present study demonstrated that AMMI and GGE biplots analyses were successful in assessing genotype by environment interaction in hybrid rice trials and aided in the identification of stable and adaptable rice hybrids with higher mean and stable yields.
GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.
Authors:N. Pržulj, M. Mirosavljević, P. Čanak, M. Zorić, and J. Boćanski
Unpredictable environmental conditions lead to occurrence of large genotype by environment (G × E) interaction. It reduces the correlation between genotypic and phenotypic values and complicates selection of superior genotypes. The objective of this study was to estimate genotype by year (G × Y) interaction using AMMI model, to identify spring barley genotypes with stable and high yield performance and to observe association of different meteorological variables with tested growing seasons. The trials with 15 spring barley genotypes were conducted during seven years (1999–2005) at the location of Rimski Šančevi. The results showed that the influence of year (Y), genotype (G) and G × Y interaction on barley grain yield were significant (p < 0.01). Meteorological variables varied significantly from year to year and Y explained the highest percent of treatment variation (81%). The first three IPCA were significant and explained 83% of interaction variation. According to this study, it could be concluded that AMMI analysis provided an enhanced understanding of G × Y interaction in barley multi-years trials. Among the tested genotypes, LAV and NS 477 could be separated as highest yielding genotypes, however LAV could be recommended for further breeding program and large-scale production due to its stable and high yielding performance. It also provided better insight in specific association between spring barley grain yield and meteorological variables.