Elucidating the G X E Interaction Using AMMI, AMMI Stability Parameters and GGE for Cane Yield and Quality in Sugarcane 

Elucidating the G X E Interaction Using AMMI, AMMI Stability Parameters and GGE for Cane Yield and Quality in Sugarcane | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elucidating the G X E Interaction Using AMMI, AMMI Stability Parameters and GGE for Cane Yield and Quality in Sugarcane A.ANNA DURAI, Amaresh ., Arun kumar R, Hemaprabha G This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4471951/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2024 Read the published version in Tropical Plant Biology → Version 1 posted 4 You are reading this latest preprint version Abstract Stable, and high yielding genotype with superior quality across the spatial and temporal variation are to be identified due to changing weather conditions which largely influences the true genotypic performance. The present experiment was conducted with 13 clones including seven test entries along with six recently released varieties as first plant, second plant and ratoon in RBD with three replications during the year 2022-23 and 2023-24 at ICAR-SBI, Coimbatore. Combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits, cane diameter and single cane weight for which environment main effects were not significant. The AMMI ANOVA for the sucrose, CCS percent, cane yield and CCS yield showed that significant individual effects of Genotypes, Environments and genotype × environment interaction. AMMI biplot analysis revealed that the genotypes Co 17001, CoC 13339 and Co 86032 for cane yield and Co 86032 and CoC 13339 for CCS yield were stable. AMMI stability parameters such as ASV, MASV identified Co 86032, Co 15003, and CoC 13339 were stable for cane and CCS yield. The GSI, EV, SIPC showed Co 17001, Co 15003, Co 86032 and Co 11015 were stable for cane and CCS yield. Multi-trait stability analysis considering the traits like sucrose, CCS percent, cane yield, CCS yield revealed that the genotypes Co 15003 and Co 86032 were highly stable. GGE analysis such as mean vs stability, ranking of genotypes, which won where biplots pinpointed that the genotype Co 17001 is highly stable than the standards Co 11015 and Co 86032 for sucrose content, cane and CCS yield. Thus, the genotypes Co 17001 and Co 15003 were stable and superior than the commercial varieties like Co 11015 and Co 86032 according to the AMMI, AMMI stability parameters and GGE for the cane yield and CCS yield and they may be promoted for commercial cultivation in target environment. Sugarcane G x E Interaction Stability AMMI biplot GGE biplot Figures Figure 1 Figure 2 Figure 3 Introduction Sugarcane is an important commercial crop contributing tremendously to the world economy and provides nearly 70% of global sugar and is the second largest feedstock for ethanol production (Pan et al. 2022 ) accounting around 40% global biofuel (Talukdar et al. 2017 ). In India, sugarcane is grown in several states having diverse agro-ecological conditions in tropical and subtropical situations. Though the area and production of sugarcane has increased at a significant annual growth rate in major sugarcane growing states, yield of sugarcane has not witnessed a significant growth (Upreti and Singh 2017 ) due to distinct and diverse nature of sugarcane cultivation. Though India with productivity of 84 tons /ha for the second time in last five year managed to take the top spot from Brazil producing 36 million tonnes in 2021-22, it is much lower than the production potential of 280 t/ha/year (James 1983 ). The efficiency of sugar improvement programme is judged based on improvement made in sugar yield through enhanced sugar content in juice rather than through cane yield (Jackson 2005 ). Two prominent varieties namely Co 0238 and Co 86032 of sub-tropical and tropical region, respectively, have been instrumental in raising sugar production in the country. Stability of Co 86032 which was identified during the year 1986; for which the crosses were affected during 1970s giving higher yield withstanding the spatial and temporal variation in tropical India is tremendous. Similarly, Co 0238 developed for North West Zone showed its superiority over entire subtropical belt of the country. Hence, the Commission for Agricultural Costs and Prices recommended that more high yielding varieties of such kind need to be developed and efforts be made to familiarize farmers with new seed varieties and making seeds / planting material available to the farmers (GOI 2023 ). At this juncture, evaluation of large number of genotypes developed at the breeding centres for their stable performance in target environment is felt as the present requirement to improve the cane and sugar yield of sugarcane. Viewing in this direction, an experiment at Indian Council of Agricultural Research - Sugarcane Breeding Institute, Coimbatore, India was conducted to identify the superior varieties alternative to those varieties in cultivation. Sugar yield in sugarcane is improved either by increasing the sugar content or by increasing the cane yield. However, the cane yield is quantitative character and subjected largely to genotype and environment interaction. The varied environmental factors limit the ability of the breeders to select / reject the genotypes for the particular environment (Van Eeuwijk et al. 2016 .) Stable performance over the environmental variations is the expectation from a variety while selecting right genotypes for right environment is the task of the breeders. Identifying genotypes performing stably in a variety of environmental conditions is accomplished using yield stability analysis (Doehlert et al. 2001 ). Researchers encounter two sorts of issues in multilocational yield studies that try to identify superior stable high yielding genotypes: genotype differences and target site/environment variations. Therefore, there are two approaches to the issue: one that targets the genotype and the other that targets the environment. Creating a highly productive, broadly adaptable or stable genotype that can be grown in a variety of environments is one approach. If this approach doesn't work, the alternative is to combine environments into a mega-environment that is comparatively homogeneous and then suggest genotypes for each mega-environment. A variety of statistical tools, both parametric and nonparametric, are available to study genotype environment interactions (GEI), including coefficients of determination ( Ri 2 ) (Pinthus 1973 ), coefficients of variability (CVi) (Francis and Kannenbaerg 1978), genotypic variances across environments ( S 2 i ) (Roemer 1917 ), Wricke’s ecovalence (Wricke 1962 ), regression coefficient ( bi ) and deviation from regression ( S 2 di ) (Eberhart and Russel 1966 ; Perkins and Jinks 1968 ), and Shukla's stability variance (Shukla 1972 ). As the genotypic response to an environment is multivariate, the most significant problem with these stability measures is that they are univariate in nature. In order to accurately depict genotype environment interactions (GEI) based on genotype, stability statistics should also be multivariate (Gauch 1988 ; Mehareb et al. 2022 ). Multivariate methods such as principal component analysis, cluster analysis, pattern analysis, and biplots can be used to identify GE interaction patterns (Myint et al. 2019 ; Hashim et al. 2021 ; Tanin et al. 2022 ). To visualise G × E interactions (GEI), two biplot forms that have been used extensively are the AMMI (Gauch 1988 ; Gauch and Zobel 1996 ; Gauch and Zobel 1997 ) and the GGE biplots (Yan et al. 2000 ; Gauch et al. 2008 ). The AMMI model was developed by Gauch ( 1992 ) as a multivariate method that uses analysis of variance (ANOVA) and principal component analysis (PCA) to characterise the G × E interactions (GEI) in several dimensions. The GGE biplot was suggested by Yan et al. ( 2000 ) and accounts for both genotype main effects and G × E interactions (GEI) effects in the analysis. The AMMI stability parameters enable yield stability after the noise reduction from the impacts of the GE interaction (Ajay et al. 2020 ; Anuradha et al. 2022 ). The current investigation was undertaken to identify the superior, stable and high yielding clone (s) from seven test entries and six recently released varieties which were evaluated as first plant, second plant and ratoon in during the year 2022-23 and 2023-24 at ICAR-SBI, Coimbatore using multivariate statistical tools. Material and Methods Experimental Material The experiment was conducted during the year 2022-23 and 2023-24; the first plant and ratoon crop experiments were at East Chithirai Chavadi (ECC) farm (Vedapatti) and second plant crop trial was at additional land farm (Seeranaickenpalaya) of ICAR-Sugarcane Breeding Institute, Coimbatore, India located at 11 0 N, 77 0 E, 427 MSL altitude. The soil of ECC farm was clay loam with a pH of 8.1, EC of 0.1 dSm − 1 , and organic carbon of 0.5. It is medium in N with 232 kg ha -1 and low in P (25 kg ha − 1 ) and high in K (818 kg ha − 1 ). The additional land farm soil was a calcareous type with a pH of 8.7, EC of 0.32 dSm − 1 , and organic carbon of 0.3. This soil was medium in N with 210 kg ha − 1 and low in P (22 kg ha -1) and high in K (721 kg ha -1). The initial pH was determined by mixing thoroughly10g of soil in 25 ml of water and the supernatant solution was taken for the pH reading in the pH meter. The electrical conductivity was determined in the same supernatant solution using EC meter viz. Hanna instrument. Thirteen genotype / clones (Table 1 ) including seven test entries and six recently released varieties from different Sugarcane Research Stations (SRSs) located in Tamil Nadu were planted in randomised complete block design (RBD) with three replications in plot size of 6 m x 6 rows x 1.2 m. The seed rate followed was 12 buds per metre and the crop raised as per the standards followed in All India coordinated Research Programme on Sugarcane. Table 1 Particulars of the clones included in the study S. No. Genotypes (code) Developed / released by Parenatge Remarks Test entries 1 C 16337 (G1) SRS, Cuddalore Co 775 GC Moderately resistance to red rot 2 C 16338 (G2) SRS, Cuddalore Co 775 GC Moderately resistance to red rot 3 Co 13003 (G4) ICAR-SBI, Coimbatore Co 86011 x CoT 8201 Resistance to red rot 4 Co 15003 (G5) ICAR-SBI, Coimbatore CoM 0265 x Co 89003 Resistance to red rot 5 Co 17001 (G6) ICAR-SBI, Coimbatore Co 0327 x Co 0218 Moderately resistance to red rot 6 G 2014-036 (G11) SRS, Gudiyatham G 08007 GC Moderately resistance to red rot 7 Si 2014-049 (G12) SRS, Sirugamani CoSI (SC) 6 GC Moderately resistance to red rot Standards 1 Co 86032 (G7) ICAR-SBI, Coimbatore Co 62198 x CoC 671 Notified for cultivation in Peninsular Zone of India in the year 2020 2 Co 11015 (G3) ICAR-SBI, Coimbatore CoC 671 x Co 86011 Notified for cultivation in Peninsular Zone of India in the year 2023 3 CoC 13339 (G8) SRS, Cuddalore Co 86032 GC Notified for cultivation in East Coast Zone of India in the year 2020 4 CoG 6 (G9) SRS, Gudiyatham HR 83–144 x CoH 119 Released in the year 2009 5 TNAU Si (SC) 7 (G13) SRS, Sirugamani Co 99043 x CoG 93076 Released in the year 2010 for Tamil Nadu 6 CoG 7 (G10) SRS, Gudiyatham 89 V 74 GC Released for Tamil Nadu 2021 Observations recorded Data were collected on ten quantitative traits including viz., five cane yield and its contributing viz., number of millable canes (NMC), cane height (CH), cane diameter (CD), single cane weight (SCW) and cane yield (CY) and five juice related characters viz., Pol % at 10th month (ESUC) and juice brix % (Brix), Pol% (SUC), commercial cane sugar percentage (CCSP), commercial cane sugar yield (CCSY) at the time of harvest. The NMC (000/ha) was the population of matured canes that were ready to be crushed. On the other hand, the CH was measured in centimetres, was the vertical growth of cane from the base to the top of the stem, where it breaks easily with the hand. CD was measured from stalk portion representing the cane thickness and SCW was the average weight in kg of five mature sugarcane stalks chosen at random. The total dissolved solids content of a sugarcane juice is measured as juice brix % (Brix). The percentage of sucrose in the sugarcane juice is measured as pol % at 10 month (ESUC) and 12th month (SUC). The quantity of sugarcane collected from a particular plot is weighed and converted to cane yield t/ha (CY). The formula for computing CCSP was CCSP (%) = [(1.022 × Pol %)–( Brix % × 0.292)]. The formula for calculating CCSY (t/ha) was (CCS % × CY)/100. Statistical Analysis Combined ANOVA and Correlation analysis were performed in Statistical Tool for Agricultural Research (STAR) Version: 2.0.1. All other statistical analyses were conducted in the statistical software R version 4.1.1. The “metan” package (Olivoto et al. 2020 ) was employed to conduct the AMMI analysis of variance. Prediction assessment was carried out utilising the AMMI approach for all the above-mentioned traits across environment trials (Gabriel 1978 ). This was the AMMI model: where, Y ij was the yield of i th genotype in j th environment over all replications, µ was the grand mean, α i was the i th genotype mean deviation (genotype mean minus grand mean), β j was the j th environment mean deviation, λ k was the singular value for IPC axis k , γ ik was the i th genotype eigenvector value for IPC axis k , δ jk was the j th environment eigenvector value for IPC axis k , and ɛ ij was the error term. AMMI Stability Value (ASV) As described by Purchase et al. ( 2000 ), the AMMI stability value (ASV), which was used to compare the stability of genotypes, was calculated as follows: ASV=√[SSIPC1/SSIPC2 (IPC1) 2 ] + (IPC2) 2 By dividing the IPC1 sum of squares by the IPC2 sum of squares, SSIPC1/SSIPC2 represented the weight applied to the IPC1 value. A genotype was more particularly adapted to a given environment the higher the IPC score, whether negative or positive. Greater genetic stability across contexts was indicated by lower ASV scores. Genotype Selection Index (GSI) Each genotype's genotype selection index (GSI), which combines the mean of the characteristic and the ASV index into a single criterion, was determined. GSI i =RM i + RASV i where GSI i was the i th genotype's genotype selection index, RM i was the rank of that genotype's based on trait mean, and RASV i was the rank of that genotype's based on AMMI stability value. This parameter's low value indicated genotypes with high mean and stability that were desirable. Other AMMI parameters including the averages of the squared eigenvector values (EV) stability statistic, the sums of the absolute value of the IPC scores (SIPC), and modified AMMI stability value (MASV) were estimated as per formula given by Zobel et al. (1994), Sneller et al. ( 1997 ), and Zali et al. ( 2012 ) respectively using ‘ammistablity’ package (Ajay et al. 2023) in R version 4.1.1.. Multi-trait stability index (MTSI) Multi-trait stability index was computed using the “metan” package (Olivoto et al. 2020 ) in the statistical software R version 4.1.1. Genotype plus genotype by environment (GGE) biplot analysis The “metan” package (Olivoto et al. 2020 ) was employed to conduct the GGE (genotype plus genotype by environment) biplot analysis in the statistical software R version 4.1.1. Results Combined ANOVA for yield and quality parameters Combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits cane diameter (CD) and single cane weight (SCW) for which environment main effects were not significant (Table 2 ) Table 2 Combined ANOVA for yield and quality traits of sugarcane Source Df Brix SUC CCSP NMC CH CD SCW CY CCSY GEN 12 13.21** 17.54** 10.18** 385.03** 4584.59** 0.35** 0.36** 1866.16** 55.96** ENV 2 2.39* 6.88** 4.32** 877.70** 15705.54** 0.111 0.02 1194.75** 21.93** GEN X ENV 24 1.92** 2.21** 1.43** 192.70** 1805.67** 0.10** 0.013** 232.68** 4.77** Pooled error 72 0.56 0.58 0.43 12.51 372.94 0.238 0.005 31.78 0.75 Total 116 AMMI analysis for yield and quality parameters The analysis of variance (ANOVA) showed that significant individual effects of Genotypes (G), Environments (E) and genotype × environment interaction (G × E) for the SUC, CCSP, CY and CCSY (Table 3 ). Among these traits, the environmental main effects were higher for the CY (7.87%) and lower for SUC (4.96%). The genotype main effect for CCSY (80.90%) was higher than CY (73.74%). The G × E interaction effects were greater for CCSP (20.84%) and the lowest for CCSY (13.82%). In the principal components (IPCA), the first 2 components were significant for these traits, in which the first PCA explained maximum variance about 80.47% for SUC, 78.04% for CCSP, 70.18% for CY, and about 66.19% for CCSY. Table 3 AMMI Analysis of variance of main effects and interactions for sucrose (SUC), CCS percent (CCSP), cane yield (CY) and CCS yield (CCSY) for the genotypes under study. SUC CCSP CY CCSY Source df MS VE (%) MS VE (%) MS VE (%) MS VE (%) ENV 2 6.88 ** 4.96 4.32 ** 5.23 1194.76 ** 7.87 21.93 ** 5.28 GEN 12 17.55 ** 75.95 10.19 ** 73.93 1866.17 ** 73.74 55.96 ** 80.90 GEN X ENV 24 2.21 ** 19.09 1.44 ** 20.84 232.68 ** 18.39 4.78 ** 13.82 IPCA 1 13 3.28 ** 80.47 2.07 ** 78.04 301.46 ** 70.18 5.84 ** 66.19 IPCA 2 11 0.94 ** 19.53 0.69 21.96 151.40 ** 29.82 ** 3.52 ** 33.81 Residuals 78 0.54 0 0.40 0 31.19 0 0.74 0 Biplot Analysis for Determination of Main Effect and Environment Influence From the PC1 Vs CY (Cane yield), environments E1 and E2 established lower average main effects, whereas environment E3 expressed the highest main effects and was gainful for most of the genotypes. Genotypes G1, G4, G3, G5 and G6, expressed higher main effects; for the cane yield, on the contrary, genotypes G8, G11 and G13 exhibited lower main effects. (Fig. 1 a). The AMMI biplot using PC1 and PC2 scores for the CY reveals the genotypes G6, G8 and G7 are close to the origin and so have greater environmental adaption and stable across the environments (Fig. 1 b). From the PC1 Vs CCSY, environments E2 showed lower average main effects, whereas environments E1 and E3, expressed the highest main effects and were gainful for most of the genotypes. Genotypes G3, G4, G5, G6, and G7 expressed higher main effects; for the CCSY, in contrast, genotypes G1, G8, and G11 exhibited lower main effects (Fig. 1 c). Genotypes G7 and G8 are located adjacent to the origin in the PC1 Vs PC2 biplot and are stable performers (Fig. 1 d). From the PC1 Vs ESUC, environments E1 showed lower average main effects, whereas environments E2 and E3, expressed the highest main effects and were gainful for most of the genotypes. Genotypes G3, G6, G9, G7, G4 and G8 expressed higher main effects; for the ESUC, in contrast, genotypes G1, G2, G5, G11, and G12 exhibited lower main effects (Fig. 1 e). Genotypes G1, G9, G7 and G13 are located adjacent to the origin in the PC1 Vs PC2 biplot and are stable performers (Fig. 1 f). From the PC1 Vs SUC, environments E2 and E3 showed lower average main effects, whereas environments E1 expressed the highest main effects and was gainful for most of the genotypes. Genotypes G3, G6, G7, G8, G4, G2 and G9 expressed higher main effects; for the SUC, in contrast, genotypes G12, G13, G1, and G10 exhibited lower main effects (Fig. 1 g). Genotypes G1, G5, and G2 are located adjacent to the origin in the PC1 Vs PC2 biplot and are stable performers (Fig. 1 h). Different stability parameters The AMMI stability value ( ASV) explained G7, G5 and G8 as the top ranked genotypes for the cane yield due to a lower ASV, hence they are considered as the most stable genotypes for the cane yield. For CCSY, the genotypes G7, G5, G8 and G3 are the most stable (Table 4 ). Similar results were found with Modified AMMI stability value (MASV). According to genotypic stability index (GSI), the genotypes G5, G6 and G4 were ranked higher for cane yield whereas for CCS yield G6, G3 and G5 were ranked higher and are most stable (Table 4 ). According to averages of the squared eigenvector values (EV) stability statistic, the genotype G7 stable followed by G6, G3 and G8 (Table 4 ). As per the sums of the absolute value of the IPC scores (SIPC) the genotype G7 was stable followed by G5 and G6 for cane and CCS yield (Table 4 ). Table 4 Mean values, AMMI stability value (ASV), Mean AMMI stability value, EV, SIPC, and genotype selection index (GSI) for the genotypes studied for the trait Cane Yield (CY) and CCS Yield (CCSY). CY CCSY Gen ASV GSI EV MASV SIPC means ASV GSI EV MASV SIPC means G1 6.26 11 17 6 0.16 6.26 4.31 93.85 1.72 11 18 7 0.08 1.72 1.12 11.09 G2 7.75 13 22 9 0.27 7.75 5.65 79.14 1.98 12 21 9 0.29 1.98 2.05 10.48 G3 2.73 8 12 4 0.02 2.73 1.67 95.51 0.81 4 6 2 0.02 0.81 0.52 14.35 G4 5.35 10 13 3 0.07 5.35 2.46 95.67 1.14 6 11 5 0.04 1.14 0.75 12.79 G5 1.41 2 3 1 0.04 1.41 1.46 109.78 0.59 2 5 3 0.04 0.59 0.66 14.31 G6 2.37 6 8 2 0.02 2.37 1.65 101.57 1.28 8 9 1 0.09 1.28 1.22 14.77 G7 0.92 1 6 5 0.00 0.92 0.72 93.99 0.29 1 5 4 0.00 0.29 0.28 12.91 G8 1.61 3 10 7 0.02 1.61 1.35 82.94 0.61 3 9 6 0.01 0.61 0.43 11.11 G9 4.45 9 17 8 0.07 4.45 2.78 81.03 1.54 9 17 8 0.07 1.54 1.08 10.61 G10 2.55 7 18 11 0.13 2.55 2.75 74.28 0.86 5 16 11 0.05 0.86 0.87 8.75 G11 2.11 5 18 13 0.03 2.11 1.73 60.86 1.68 10 23 13 0.13 1.68 1.48 7.26 G12 6.60 12 24 12 0.11 6.60 2.92 64.61 2.08 13 25 12 0.12 2.08 1.25 7.57 G13 1.75 4 14 10 0.06 1.75 1.86 77.86 1.14 7 17 10 0.06 1.14 1.03 10.08 Multi-trait stability index (MTSI) Recently, the multi-trait stability index has been used as a reliable technique to help pick elite genotypes based on the stability and mean performance of different variables (Zuffo et al. 2020 ). On analysis of multi-trait stability analysis considering the traits SUC, CCSP, CY and CCSY that the genotypes G5 and G7 were highly stable and were selected having low MTSI value (Table 4 ) (Fig. 2 a). Considering the all the traits for multi-trait stability index (MTSI) it was identified that the genotype G10 and G7 were stable and were selected (Table 5 ) (Fig. 2 b). Table 5 Multitriat stability index (MTSI) for the genotypes considering yield and quality traits. Gen MTSI (SUC, CCSP, CY, CCSY) MTSI (All traits) G1 2.51 2.33 G2 3.58 3.8 G3 1.17 2.08 G4 1.69 2.04 G5 0.42 4.18 G6 1.59 2.67 G7 0.55 1.11 G8 0.663 1.77 G9 1.97 2.17 G10 1.07 1.55 G11 4.06 4.66 G12 2.72 3.94 G13 2.37 2.32 GGE Biplot analysis for yield and quality parameters A highly helpful statistical method for analysing the genotype by environment (GE) interaction and determining superior genotypes and mega environments is the GGE biplot analysis. Mean vs. Stability The genotypes G6 was found to be stable with higher CY and CCSY compared to checks G3 and G7 (Fig. 3 a and 3 b). The genotypes G3, G9, and G7 were found stable for ESUC (Fig. 3 c). For sucrose accumulation at 12th month (SUC), the genotype G3 and G6 were stable (Fig. 3 d). Ranking of genotypes and environment The genotype G6, G3, G7 and G5 was ranked higher and the environment E3 was ranked higher for the CY (Fig. 3 e and 3 f). Whereas, for CCSY, the genotypes G6, G3, G7, G8, G4, and G5 and the environment, E1 had higher rank (Fig. 3 g and 3 h). The genotype G3 ranked higher followed by G6 and G7 and the environment E2 and E3 was ranked higher for the early sucrose (Fig. 3 i and 3 j). The genotype G6, G3, G7, G8, G4, G2, and G9 was ranked higher and the environment E3 was ranked higher for SUC (Fig. 3 k and 3 l). Discriminative vs. Representativeness The GGE Biplots' discriminativeness vs. representative explanations helps to identify the optimal environments with the strongest ability to distinguish between genotypes. The environments E1 and E3 has high discriminating power having longest environmental vectors. The environments E2 had shorter environmental vectors having in which most of the genotypes performing equally for CY (Fig. 3 m). For CCSY, E1 and E3 were representative in nature, whereas, the environments E1 were found to have greater discriminating ability (Fig. 3 n). For ESUC, E2 was representative in nature, whereas, the environments E1 were found to have greater discriminating ability (Fig. 3 o). The environments E1 and E2 has high discriminating power having longest environmental vectors. The environments E3 had shorter environmental vectors having in which most of the genotypes performing equally for SUC (Fig. 3 p). WWW The biplot polygon showed that the genotypes G5, G2, G12, G11 and G1 were vertex genotypes for CY. The 3 environments were classified grouped into single environments, in which the genotype G6, G5 and G7 had better performance (Fig. 3 q). The biplot polygons for CCS yield revealed that the vertex genotypes were G6, G5, G9, G11, G12 and G1. There were two mega environments, the mega-environment (1) includes E1 and E3, in which better performing genotypes were G6, G3, G7 and G4. The second mega environment comprises of E2 environment, in which G5 was good performing genotype (Fig. 3 r). The biplot polygon showed that the genotypes G3, G6, G12, G10, G5, G2, and G8 were vertex genotypes for ESUC. The 3 environments were grouped into single environments, in which the genotype G3, G7 and G9 had better performance (Fig. 3 s). G3, G11, G12, G13 and G10 were vertex genotypes for the SUC. Three environments clustered into single mega environment with G3 and G6 were having greater performance for SUC (Fig. t). Discussion The changing environmental conditions will influence the true genotypic performance, hence, identifying genotypes performing stably in a variety of environmental conditions is desirable and is accomplished using stability analysis (Doehlert et al. 2001 ). Combined ANOVA for cane and CCS yield for 14 clones evaluated in 7 environments of egypt showed significant genotype, environment and G X E interaction effects (Mehareb et al. 2022 ). Evaluation of 24 genotypes under different salt stresses also showed significant main and interaction effects for all the traits under study (Kumar et al. 2023 ). AMMI analysis revealed significant changes across the clones and environments under investigation (Meena et al. 2017 ). AMMI investigations revealed that genotype, G × E, and environmental variables all significantly (P < 0.01) influenced sugar yield (Tena et al. 2019 ). The highly significant GEI suggested that different genotypes responded differently to different environments (Elbasyoni 2018 ). In the current study, combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits cane diameter (CD) and single cane weight (SCW) for which environment main effects were not significant. AMMI biplot analysis for 12 advanced clones with three standards revealed the test entry CoSnk 15102 and the standards Co 86032, and CoC 671 were found to be stable for cane and CCS yield (Yadawad et al. 2023 ). The genotype G2016-129 was found to be stable for cane yield and CCS yield in study of 15 clones (Mehareb et al. 2022 ). For cane yield and CCS yield, the genotypes Co 11015, Co 09004, Co 0240, Co 13014, and Co 14016 were found to be highly adapted and more stable over all environments based on mean performance and stability; for sucrose percentage, five genotypes Co 15021, Co 11015, Co 15007, Co 13001, and Co 16001 displayed more stability (Elayaraja et al. 2022 ). The most productive genotypes among 24 in different locations of Chhattisgarh in terms of tonnes of sugar produced per hectare were CoS 8436, VSI 8005, CoC 671, and Co 86032. These genotypes were also stable and suggested for use in commercial plantations (Verma et al. 2023 ). The AMMI biplot using PC1 and PC2 scores for the CY reveals the genotypes G6, G8, G7; for CCSY G7 and G8; for ESUC the genotypes G1, G9, G7 and G13; for SUC the genotypes G1, G5, and G2 are are close to the origin and so have greater environmental adaption and stable across the environments. A cultivar is considered stable if its AMMI stability values (ASV) are near zero, as stated by Purchase et al. ( 2000 ). An environment-specific genotype was more specifically adapted to a bigger ASV value, whether it was positive or negative. A genotype that was more stable in different conditions was indicated by a reduced ASV value (Purchase 1997 ). The ASV explained G7, G5 and G8 as the top ranked genotypes for the cane yield due to a lower ASV, hence they are considered as the most stable genotypes for the cane yield. For CCSY, the genotypes G7, G5, G8 and G3 are the most stable. Similar results were found with MASV. The highest trait mean and the lowest ASV are both ranked one. These ranks are then added to create a single simultaneous selection index for traits, known as the GSI, which combines the trait mean and the ASV index into a single criterion (Farshadfar et al. 2011 ) which was also known as yield stability index (YSI) (Bose et al. 2014 ). According GSI, the genotypes G5, G6 and G4 were ranked higher for cane yield whereas for CCS yield G6, G3 and G5 were ranked higher and are most stable (Table 4 ). A genotype is considered to be more stable if EV is low (Ajay et al. 2020 ). Accordingly, the genotype G7 stable followed by G6, G3 and G8 (Table 4 ). A genotype is considered to be more stable if sums of the absolute value of the IPC scores (SIPC) is low (Ajay et al. 2020 ). As per SIPC the genotype G7 was stable followed by G5 and G6 for cane and CCS yield (Table 4 ). Recently, the multi-trait stability index has been used as a reliable technique to help pick elite genotypes based on the stability and mean performance of different variables (Zuffo et al. 2020 ). On analysis of multi-trait stability analysis considering the traits SUC, CCSP, CY and CCSY that the genotypes G5 and G7 were highly stable and were selected having low MTSI value (Table 4 ) (Fig. 2 a). Considering the all the traits for multi-trait stability index (MTSI) it was identified that the genotype G10 and G7 were stable and were selected (Table 5 ) (Fig. 2 b). From the mean vs stability biplots, higher mean yield for the genotypes is indicated by the vertical line on the right side, which reflects the "average-environment coordinates" that pass through the origin. The second axis represents stability; genotypes aligned with the AEC track are ideal and top top performers, whereas genotypes closer to the origin are more stable (Yan et al. 2007 ). In present study, genotypes G6 was found to be stable with higher CY and CCSY compared to checks G3 and G7. The genotypes G3, G9, and G7 were found stable for ESUC. For sucrose accumulation at 12th month (SUC), the genotype G3 and G6 were stable. Genotypes or environments are ranked on biplots to identify the best genotypes according to where they fall in the concentric circle (Fig. 3 g). The genotype G6, G3, G7 and G5 was ranked higher and the environment E3 was ranked higher for the CY (Fig. 3 e and 3 f). Whereas, for CCSY, the genotypes G6, G3, G7, G8, G4, and G5 and the environment, E1 had higher rank. The GGE Biplots' discriminativeness vs. representative explanations helps to identify the optimal environments with the strongest ability to distinguish between genotypes. The environments E1 and E3 has high discriminating power having longest environmental vectors. The environments E2 had shorter environmental vectors having in which most of the genotypes performing equally for CY (Fig. 3 m). For CCSY, E1 and E3 were representative in nature, whereas, the environments E1 were found to have greater discriminating ability. The bi-plot was divided into sections by lines extending from the origin, and each section had a mega-environment—a vertex or genotype that reflects the best yield performance in the environments included in that area. In some or all situations, the vertex genotypes perform the best or the worst (Yan and Kang 2003 ). In the current study, three environments grouped in single mega-environment for CY, ESUC and SUC. In a G x E interaction study, clones FG05-424, FG06-750 and the check variety NCO334 were the most productive and stable, and advised for the test locations based on AMMI and the GGE analysis (Tena et al. 2019 ). GGE biplot analysis for 12 advanced clones with three standards revealed the genotypes CoSnk 15102, Co 86032, and CoC 671 were found to be stable for cane and CCS yield (Yadawad et al. 2023 ). The genotype G2016-129 was found to be stable for cane yield and CCS yield in study of 15 clones (Mehareb et al. 2022 ). The most productive genotypes among 24 in different locations of Chhattisgarh in terms of tonnes of sugar produced per hectare were CoS 8436, VSI 8005, CoC 671, and Co 86032. These genotypes were also stable and suggested for use in different conditions (Verma et al. 2023 ). After analysing AMMI, GSI, SI, and GGE biplot analysis for the cane and CCS yield, it was determined that genotype Co 15023 was the most stable genotype in various saline conditions. Co 0238 has a greater yield for cane and CCS yield, as shown by the AMMI and GGE biplot study (Kumar et al. 2023 ). In the current investigation, the combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits, cane diameter and single cane weight for which environment main effects were not significant. Significant individual effects of genotypes, environments, and genotype × environment interaction was revealed by the AMMI ANOVA for the traits such as sucrose, CCS percent, cane yield, and CCS yield. Co 17001, CoC 13339, and Co 86032 for cane yield and Co 86032 and CoC 13339 for CCS yield were identified as stable, according to AMMI biplot analysis. For cane and CCS yield, AMMI stability parameters including ASV, MASV found the clones Co 86032, Co 15003, and CoC 13339 as stable. Co 17001, Co 15003, Co 86032, and Co 11015 were found to be stable for cane and CCS yield by the GSI, EV, and SIPC. Multi-trait stability analysis considering the traits like sucrose, CCS percent, cane yield, CCS yield revealed that the genotypes Co 15003 and Co 86032 were highly stable. GGE analysis such as mean vs stability, ranking of genotypes, which won where biplots pinpointed that the genotype Co 17001 is highly stable than the standards Co 11015 and Co 86032 for cane yield, sucrose content and CCS yield. Thus, AMMI, ASV, MASV, EV, GSI, SIPC, MTSI, and GGE analysis elucidated that the genotypes Co 17001 and Co 15003 were stable and superior to the commercial standards, Co 11015 and Co 86032 for the cane yield and CCS yield. Abbreviations AMMI-Additive Main Effect and Multiplicative Interaction, ASV-AMMI Stability Value, MASV-Modified AMMI Stability Value, EV-Averages of the squared eigenvector values stability statistic, GSI- Genotypic Stability Index, SIPC- sums of the absolute value of the IPC scores, MTSI- Multi-trait Stability Index, GGE- Genotype Main Effects and Genotype by Environment Interaction; NMC-Number of Millable Canes, CH- Cane Height, CD- Cane Diameter, SCW-Single Cane Weight, CY- Cane Yield, ESUC- Sucrose at 10 th month, Brix- Juice brix %, SUC- Sucrose at 12 th month, CCSP- Commercial Cane Sugar Percentage, CCSY- Commercial Cane Sugar Yield. GEI- Genotype x Environment Interaction, ANOVA- Analysis of Variance. Declarations Conflict of interests The authors declare no conflict or competing interests Funding No any external source of funding for the study Author Contribution A.A.D. collected the data. A. and R.A. executed statistical analyses. A.A.D., A. and G.H. prepared the manuscript Acknowledgments Authours are gratefull for ICAR-Sugarcane Breeding Institute, Coimbatore for facilitating for execution of this study. Authors acknowledges Tamil Nadu Agricultute University (TNAU), Coimbatore for supplying the few experimental material for the study. References Ajay BC, Aravind J, Abdul FR (2019) ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model. Indian J Genet Plant Breed 79(2):460–466 Ajay BC, Bera SK, Singh AL et al (2020) Evaluation of genotype× environment interaction and yield stability analysis in peanut under phosphorus stress condition using stability parameters of AMMI model. Agric Res 9:477–486. 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Yadawad A, Patil SB, Kongawad BY et al (2023) Multi environmental evaluation for selection of stable and high yielding sugarcane ( Saccharum officinarum L.) clones based on AMMI and GGE biplot models. Indian J Genet Plant Breed 83(03):389–397. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists and agronomists. CRC Press, Boca Raton. Yan W, Hunt LA, Sheng Q et al (2000) Cultivar evaluation and mega‐environment investigation based on the GGE biplot. Crop Sci 40(3):597–605. Yan W, Kang MS, Ma B (2007) GGE biplot vs. AMMI analysis of genotype by environment data. Crop Sci 47:643–655. doi:10.2135/cropsci2006.06.0374. Zali H, Farshadfar E, Sabaghpour SH et al (2012) Evaluation of genotype x environment interaction in chickpea using measures of stability from AMMI model. Anna Biol Res 3:3126–3136. Zobel RW (1994) Stress resistance and root systems. In: Proceedings of the workshop on adaptation of plants to soil stress. Instrom Pub 94(2):80–99. Zuffo AM, Steiner F, Aguilera JG et al (2020) Multi‐trait stability index: A tool for simultaneous selection of soya bean genotypes in drought and saline stress. J Agronomy Crop Sci 206(6):815–822. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2024 Read the published version in Tropical Plant Biology → Version 1 posted Reviewers invited by journal 03 Jun, 2024 Editor assigned by journal 29 May, 2024 Submission checks completed at journal 29 May, 2024 First submitted to journal 24 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4471951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312090042,"identity":"a74d5c71-5847-4ea7-9a2a-8181eb173cad","order_by":0,"name":"A.ANNA DURAI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYLACHgYbIMnYeIAULWkgLQ0kaTkMponTwj8jx/DD27bzdmvbDwNtqbGJJqhF4kaOseTcttvJ284kArUcS8ttIKjnRo6BNC9Qi9kBoBbGhsOEtcgDbfnN23Yu2ez8QyK1GNzIMQPacsDO7AaxthieeVZmOedccoLZDaAtCcT4Re548uYbb8rs7M3Opz988KHGhgjvC2QYgKhEsMoEgspBgP/4AxBlT5TiUTAKRsEoGJkAAM/JSdMI1umoAAAAAElFTkSuQmCC","orcid":"","institution":"Sugarcane Breeding Institute","correspondingAuthor":true,"prefix":"","firstName":"A.ANNA","middleName":"","lastName":"DURAI","suffix":""},{"id":312090043,"identity":"2361c1e3-532b-44e9-9ca0-967d9164ca91","order_by":1,"name":"Amaresh .","email":"","orcid":"","institution":"Sugarcane Breeding Institute","correspondingAuthor":false,"prefix":"","firstName":"Amaresh","middleName":"","lastName":".","suffix":""},{"id":312090044,"identity":"e9e0fd42-1d82-42db-8d8a-14edc99dba13","order_by":2,"name":"Arun kumar R","email":"","orcid":"","institution":"Sugarcane Breeding Institute","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"kumar","lastName":"R","suffix":""},{"id":312090045,"identity":"f31297ad-ea96-4fc8-b7b1-737ea8fd13ba","order_by":3,"name":"Hemaprabha G","email":"","orcid":"","institution":"Sugarcane Breeding Institute","correspondingAuthor":false,"prefix":"","firstName":"Hemaprabha","middleName":"","lastName":"G","suffix":""}],"badges":[],"createdAt":"2024-05-24 10:45:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4471951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4471951/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12042-024-09372-2","type":"published","date":"2024-10-21T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58249257,"identity":"2f5b20a4-9b51-4030-a0a5-24384ffdb4b6","added_by":"auto","created_at":"2024-06-13 02:55:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78459,"visible":true,"origin":"","legend":"\u003cp\u003eAMMI Biplots (a) PC1 vs CY (Cane yield) (b) PC1 vs PC2 CY (Cane yield) \u0026nbsp;(c) PC1vs CCSY(CCS yield) \u0026nbsp;(d) PC1 vs PC2 for CCSY(CCS yield)\u0026nbsp; \u0026nbsp;(e) PC1 vs ESUC (Sucrose at 10\u003csup\u003eth\u003c/sup\u003e month)\u0026nbsp; \u0026nbsp;(f) PC1 vs PC2 for ESUC (Sucrose at 10\u003csup\u003eth\u003c/sup\u003e month) \u0026nbsp;(g) PC1 vs SUC (Sucrose at 12\u003csup\u003eth\u003c/sup\u003e month)\u0026nbsp; \u0026nbsp;(h) PC1 vs PC2 for SUC (Sucrose at 12\u003csup\u003eth\u003c/sup\u003e month)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4471951/v1/d69fcf601d915ae59a1cb9ad.png"},{"id":58248444,"identity":"b3cdda1a-e6c8-4eb2-9443-af4658e6a557","added_by":"auto","created_at":"2024-06-13 02:47:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137824,"visible":true,"origin":"","legend":"\u003cp\u003emulti-trait stability analysis (a) Considering SUC, CCSP, CY and CCSY (b) Considering all the traits under study.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4471951/v1/1c38927072c2c959320ca9b1.png"},{"id":58248443,"identity":"cbe7e56e-8cb4-485a-92f0-16e1cf3b147e","added_by":"auto","created_at":"2024-06-13 02:47:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1016772,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Mean vs Stability for cane yield (b) CCS yield (c) sucrose at 10\u003csup\u003eth\u003c/sup\u003e month (d) sucrose at 12\u003csup\u003eth\u003c/sup\u003e month; (e) Ranking of genotype for cane yield (f) Ranking of environment for cane yield (g) Ranking of genotype for CCS yield (h) Ranking of environment CCS yield (i) Ranking of genotype for sucrose at 10\u003csup\u003eth\u003c/sup\u003e month (j) Ranking of environment sucrose at 10\u003csup\u003eth\u003c/sup\u003e month (k) Ranking of genotype for sucrose at 12\u003csup\u003eth\u003c/sup\u003e month (l) Ranking of environment for sucrose at 12\u003csup\u003eth\u003c/sup\u003e month (m) Discriminativeness vs Representativeness for cane yield (n) CCS yield (o) sucrose at 10\u003csup\u003eth\u003c/sup\u003e month (p) sucrose at 12\u003csup\u003eth\u003c/sup\u003e month; (q) WWW biplot for cane yield (r) CCS yield (s) sucrose at 10\u003csup\u003eth\u003c/sup\u003e month (t) sucrose at 12\u003csup\u003eth\u003c/sup\u003e month.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4471951/v1/bd4f0d501c3874e9933c4f5d.jpg"},{"id":67681575,"identity":"f7e37a60-fa25-486d-8f51-226f96b4c1d2","added_by":"auto","created_at":"2024-10-28 15:59:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2083641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4471951/v1/9206478b-aafd-43ec-a787-baa420524800.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating the G X E Interaction Using AMMI, AMMI Stability Parameters and GGE for Cane Yield and Quality in Sugarcane ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSugarcane is an important commercial crop contributing tremendously to the world economy and provides nearly 70% of global sugar and is the second largest feedstock for ethanol production (Pan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) accounting around 40% global biofuel (Talukdar et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In India, sugarcane is grown in several states having diverse agro-ecological conditions in tropical and subtropical situations. Though the area and production of sugarcane has increased at a significant annual growth rate in major sugarcane growing states, yield of sugarcane has not witnessed a significant growth (Upreti and Singh \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) due to distinct and diverse nature of sugarcane cultivation. Though India with productivity of 84 tons /ha for the second time in last five year managed to take the top spot from Brazil producing 36\u0026nbsp;million tonnes in 2021-22, it is much lower than the production potential of 280 t/ha/year (James \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). The efficiency of sugar improvement programme is judged based on improvement made in sugar yield through enhanced sugar content in juice rather than through cane yield (Jackson \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Two prominent varieties namely Co 0238 and Co 86032 of sub-tropical and tropical region, respectively, have been instrumental in raising sugar production in the country. Stability of Co 86032 which was identified during the year 1986; for which the crosses were affected during 1970s giving higher yield withstanding the spatial and temporal variation in tropical India is tremendous. Similarly, Co 0238 developed for North West Zone showed its superiority over entire subtropical belt of the country. Hence, the Commission for Agricultural Costs and Prices recommended that more high yielding varieties of such kind need to be developed and efforts be made to familiarize farmers with new seed varieties and making seeds / planting material available to the farmers (GOI \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At this juncture, evaluation of large number of genotypes developed at the breeding centres for their stable performance in target environment is felt as the present requirement to improve the cane and sugar yield of sugarcane. Viewing in this direction, an experiment at Indian Council of Agricultural Research - Sugarcane Breeding Institute, Coimbatore, India was conducted to identify the superior varieties alternative to those varieties in cultivation.\u003c/p\u003e \u003cp\u003eSugar yield in sugarcane is improved either by increasing the sugar content or by increasing the cane yield. However, the cane yield is quantitative character and subjected largely to genotype and environment interaction. The varied environmental factors limit the ability of the breeders to select / reject the genotypes for the particular environment (Van Eeuwijk et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e.) Stable performance over the environmental variations is the expectation from a variety while selecting right genotypes for right environment is the task of the breeders. Identifying genotypes performing stably in a variety of environmental conditions is accomplished using yield stability analysis (Doehlert et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Researchers encounter two sorts of issues in multilocational yield studies that try to identify superior stable high yielding genotypes: genotype differences and target site/environment variations. Therefore, there are two approaches to the issue: one that targets the genotype and the other that targets the environment. Creating a highly productive, broadly adaptable or stable genotype that can be grown in a variety of environments is one approach. If this approach doesn't work, the alternative is to combine environments into a mega-environment that is comparatively homogeneous and then suggest genotypes for each mega-environment. A variety of statistical tools, both parametric and nonparametric, are available to study genotype environment interactions (GEI), including coefficients of determination (\u003cem\u003eRi\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) (Pinthus \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), coefficients of variability (CVi) (Francis and Kannenbaerg 1978), genotypic variances across environments (\u003cem\u003eS\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ei\u003c/em\u003e) (Roemer \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1917\u003c/span\u003e), Wricke\u0026rsquo;s ecovalence (Wricke \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1962\u003c/span\u003e), regression coefficient (\u003cem\u003ebi\u003c/em\u003e) and deviation from regression (\u003cem\u003eS\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003edi\u003c/em\u003e) (Eberhart and Russel \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Perkins and Jinks \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), and Shukla's stability variance (Shukla \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). As the genotypic response to an environment is multivariate, the most significant problem with these stability measures is that they are univariate in nature. In order to accurately depict genotype environment interactions (GEI) based on genotype, stability statistics should also be multivariate (Gauch \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Mehareb et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Multivariate methods such as principal component analysis, cluster analysis, pattern analysis, and biplots can be used to identify GE interaction patterns (Myint et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hashim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tanin et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To visualise G \u0026times; E interactions (GEI), two biplot forms that have been used extensively are the AMMI (Gauch \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Gauch and Zobel \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Gauch and Zobel \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and the GGE biplots (Yan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Gauch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The AMMI model was developed by Gauch (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) as a multivariate method that uses analysis of variance (ANOVA) and principal component analysis (PCA) to characterise the G \u0026times; E interactions (GEI) in several dimensions. The GGE biplot was suggested by Yan et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and accounts for both genotype main effects and G \u0026times; E interactions (GEI) effects in the analysis. The AMMI stability parameters enable yield stability after the noise reduction from the impacts of the GE interaction (Ajay et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Anuradha et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The current investigation was undertaken to identify the superior, stable and high yielding clone (s) from seven test entries and six recently released varieties which were evaluated as first plant, second plant and ratoon in during the year 2022-23 and 2023-24 at ICAR-SBI, Coimbatore using multivariate statistical tools.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Material\u003c/h2\u003e \u003cp\u003eThe experiment was conducted during the year 2022-23 and 2023-24; the first plant and ratoon crop experiments were at East Chithirai Chavadi (ECC) farm (Vedapatti) and second plant crop trial was at additional land farm (Seeranaickenpalaya) of ICAR-Sugarcane Breeding Institute, Coimbatore, India located at 11\u003csup\u003e0\u003c/sup\u003e N, 77 \u003csup\u003e0\u003c/sup\u003e E, 427 MSL altitude. The soil of ECC farm was clay loam with a pH of 8.1, EC of 0.1 dSm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and organic carbon of 0.5. It is medium in N with 232 kg ha -1 and low in P (25 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and high in K (818 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The additional land farm soil was a calcareous type with a pH of 8.7, EC of 0.32 dSm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and organic carbon of 0.3. This soil was medium in N with 210 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and low in P (22 kg ha -1) and high in K (721 kg ha -1). The initial pH was determined by mixing thoroughly10g of soil in 25 ml of water and the supernatant solution was taken for the pH reading in the pH meter. The electrical conductivity was determined in the same supernatant solution using EC meter viz. Hanna instrument. Thirteen genotype / clones (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) including seven test entries and six recently released varieties from different Sugarcane Research Stations (SRSs) located in Tamil Nadu were planted in randomised complete block design (RBD) with three replications in plot size of 6 m x 6 rows x 1.2 m. The seed rate followed was 12 buds per metre and the crop raised as per the standards followed in All India coordinated Research Programme on Sugarcane.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticulars of the clones included in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes (code)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloped / released by\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParenatge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest entries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC 16337 (G1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Cuddalore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 775 GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately resistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC 16338 (G2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Cuddalore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 775 GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately resistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo 13003 (G4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICAR-SBI, Coimbatore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 86011 x CoT 8201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo 15003 (G5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICAR-SBI, Coimbatore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoM 0265 x Co 89003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo 17001 (G6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICAR-SBI, Coimbatore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 0327 x Co 0218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately resistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG 2014-036 (G11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Gudiyatham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG 08007 GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately resistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSi 2014-049 (G12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Sirugamani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoSI (SC) 6 GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately resistance to red rot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo 86032 (G7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICAR-SBI, Coimbatore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 62198 x CoC 671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNotified for cultivation in Peninsular Zone of India in the year 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo 11015 (G3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICAR-SBI, Coimbatore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoC 671 x Co 86011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNotified for cultivation in Peninsular Zone of India in the year 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoC 13339 (G8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Cuddalore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 86032 GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNotified for cultivation in East Coast Zone of India in the year 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoG 6 (G9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Gudiyatham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR 83\u0026ndash;144 x CoH 119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReleased in the year 2009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNAU Si (SC) 7 (G13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Sirugamani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo 99043 x CoG 93076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReleased in the year 2010 for Tamil Nadu\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoG 7 (G10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRS, Gudiyatham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 V 74 GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReleased for Tamil Nadu 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eObservations recorded\u003c/h3\u003e\n\u003cp\u003eData were collected on ten quantitative traits including viz., five cane yield and its contributing viz., number of millable canes (NMC), cane height (CH), cane diameter (CD), single cane weight (SCW) and cane yield (CY) and five juice related characters viz., Pol % at 10th month (ESUC) and juice brix % (Brix), Pol% (SUC), commercial cane sugar percentage (CCSP), commercial cane sugar yield (CCSY) at the time of harvest. The NMC (000/ha) was the population of matured canes that were ready to be crushed. On the other hand, the CH was measured in centimetres, was the vertical growth of cane from the base to the top of the stem, where it breaks easily with the hand. CD was measured from stalk portion representing the cane thickness and SCW was the average weight in kg of five mature sugarcane stalks chosen at random. The total dissolved solids content of a sugarcane juice is measured as juice brix % (Brix). The percentage of sucrose in the sugarcane juice is measured as pol % at 10 month (ESUC) and 12th month (SUC). The quantity of sugarcane collected from a particular plot is weighed and converted to cane yield t/ha (CY). The formula for computing CCSP was CCSP (%) = [(1.022 \u0026times; Pol %)\u0026ndash;( Brix % \u0026times; 0.292)]. The formula for calculating CCSY (t/ha) was (CCS % \u0026times; CY)/100.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCombined ANOVA and Correlation analysis were performed in Statistical Tool for Agricultural Research (STAR) Version: 2.0.1. All other statistical analyses were conducted in the statistical software R version 4.1.1. The \u0026ldquo;metan\u0026rdquo; package (Olivoto et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was employed to conduct the AMMI analysis of variance. Prediction assessment was carried out utilising the AMMI approach for all the above-mentioned traits across environment trials (Gabriel \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). This was the AMMI model:\u003c/p\u003e \n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"195\" height=\"69\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere, \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e was the yield of \u003cem\u003ei\u003c/em\u003eth genotype in \u003cem\u003ej\u003c/em\u003eth environment over all replications, \u003cem\u003e\u0026micro;\u003c/em\u003e was the grand mean, \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e was the \u003cem\u003ei\u003c/em\u003eth genotype mean deviation (genotype mean minus grand mean), \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e was the \u003cem\u003ej\u003c/em\u003eth environment mean deviation, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e was the singular value for IPC axis \u003cem\u003ek\u003c/em\u003e, \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003eik\u003c/em\u003e\u003c/sub\u003e was the \u003cem\u003ei\u003c/em\u003eth genotype eigenvector value for IPC axis \u003cem\u003ek\u003c/em\u003e, \u003cem\u003eδ\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e was the \u003cem\u003ej\u003c/em\u003eth environment eigenvector value for IPC axis \u003cem\u003ek\u003c/em\u003e, and \u003cem\u003eɛ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e was the error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAMMI Stability Value (ASV)\u003c/h2\u003e \u003cp\u003eAs described by Purchase et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the AMMI stability value (ASV), which was used to compare the stability of genotypes, was calculated as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eASV=\u0026radic;[SSIPC1/SSIPC2 (IPC1)\u003csup\u003e2\u003c/sup\u003e] + (IPC2)\u003csup\u003e2\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003eBy dividing the IPC1 sum of squares by the IPC2 sum of squares, SSIPC1/SSIPC2 represented the weight applied to the IPC1 value. A genotype was more particularly adapted to a given environment the higher the IPC score, whether negative or positive. Greater genetic stability across contexts was indicated by lower ASV scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenotype Selection Index (GSI)\u003c/h2\u003e \u003cp\u003eEach genotype's genotype selection index (GSI), which combines the mean of the characteristic and the ASV index into a single criterion, was determined.\u003c/p\u003e \u003cp\u003eGSI\u003csub\u003ei\u003c/sub\u003e=RM\u003csub\u003ei\u003c/sub\u003e + RASV\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere GSI\u003csub\u003ei\u003c/sub\u003e was the i\u003csup\u003eth\u003c/sup\u003e genotype's genotype selection index, RM\u003csub\u003ei\u003c/sub\u003e was the rank of that genotype's based on trait mean, and RASV\u003csub\u003ei\u003c/sub\u003e was the rank of that genotype's based on AMMI stability value. This parameter's low value indicated genotypes with high mean and stability that were desirable.\u003c/p\u003e \u003cp\u003eOther AMMI parameters including the averages of the squared eigenvector values (EV) stability statistic, the sums of the absolute value of the IPC scores (SIPC), and modified AMMI stability value (MASV) were estimated as per formula given by Zobel et al. (1994), Sneller et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and Zali et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) respectively using \u0026lsquo;ammistablity\u0026rsquo; package (Ajay et al. 2023) in R version 4.1.1..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMulti-trait stability index (MTSI)\u003c/h2\u003e \u003cp\u003eMulti-trait stability index was computed using the \u0026ldquo;metan\u0026rdquo; package (Olivoto et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in the statistical software R version 4.1.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenotype plus genotype by environment (GGE) biplot analysis\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;metan\u0026rdquo; package (Olivoto et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was employed to conduct the GGE (genotype plus genotype by environment) biplot analysis in the statistical software R version 4.1.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCombined ANOVA for yield and quality parameters\u003c/h2\u003e \u003cp\u003eCombined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits cane diameter (CD) and single cane weight (SCW) for which environment main effects were not significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombined ANOVA for yield and quality traits of sugarcane\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCCSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNMC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSCW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCCSY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.18**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e385.03**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4584.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1866.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e55.96**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.32**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e877.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15705.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1194.75**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e21.93**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEN X ENV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.92**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e192.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1805.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e232.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.77**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePooled error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e372.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAMMI analysis for yield and quality parameters\u003c/h2\u003e \u003cp\u003eThe analysis of variance (ANOVA) showed that significant individual effects of Genotypes (G), Environments (E) and genotype \u0026times; environment interaction (G \u0026times; E) for the SUC, CCSP, CY and CCSY (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these traits, the environmental main effects were higher for the CY (7.87%) and lower for SUC (4.96%). The genotype main effect for CCSY (80.90%) was higher than CY (73.74%). The G \u0026times; E interaction effects were greater for CCSP (20.84%) and the lowest for CCSY (13.82%). In the principal components (IPCA), the first 2 components were significant for these traits, in which the first PCA explained maximum variance about 80.47% for SUC, 78.04% for CCSP, 70.18% for CY, and about 66.19% for CCSY.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAMMI Analysis of variance of main effects and interactions for sucrose (SUC), CCS percent (CCSP), cane yield (CY) and CCS yield (CCSY) for the genotypes under study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCCSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCCSY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVE (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.88\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1194.76\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.93\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.55\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1866.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.96\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEN X ENV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e232.68\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.78\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPCA 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e301.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.84\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPCA 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e151.40\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.82\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiplot Analysis for Determination of Main Effect and Environment Influence\u003c/h2\u003e \u003cp\u003eFrom the PC1 Vs CY (Cane yield), environments E1 and E2 established lower average main effects, whereas environment E3 expressed the highest main effects and was gainful for most of the genotypes. Genotypes G1, G4, G3, G5 and G6, expressed higher main effects; for the cane yield, on the contrary, genotypes G8, G11 and G13 exhibited lower main effects. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The AMMI biplot using PC1 and PC2 scores for the CY reveals the genotypes G6, G8 and G7 are close to the origin and so have greater environmental adaption and stable across the environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). From the PC1 Vs CCSY, environments E2 showed lower average main effects, whereas environments E1 and E3, expressed the highest main effects and were gainful for most of the genotypes. Genotypes G3, G4, G5, G6, and G7 expressed higher main effects; for the CCSY, in contrast, genotypes G1, G8, and G11 exhibited lower main effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Genotypes G7 and G8 are located adjacent to the origin in the PC1 Vs PC2 biplot and are stable performers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eFrom the PC1 Vs ESUC, environments E1 showed lower average main effects, whereas environments E2 and E3, expressed the highest main effects and were gainful for most of the genotypes. Genotypes G3, G6, G9, G7, G4 and G8 expressed higher main effects; for the ESUC, in contrast, genotypes G1, G2, G5, G11, and G12 exhibited lower main effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Genotypes G1, G9, G7 and G13 are located adjacent to the origin in the PC1 Vs PC2 biplot and are stable performers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). From the PC1 Vs SUC, environments E2 and E3 showed lower average main effects, whereas environments E1 expressed the highest main effects and was gainful for most of the genotypes. Genotypes G3, G6, G7, G8, G4, G2 and G9 expressed higher main effects; for the SUC, in contrast, genotypes G12, G13, G1, and G10 exhibited lower main effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Genotypes G1, G5, and G2 are located adjacent to the origin in the PC1 Vs PC2 biplot and are stable performers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferent stability parameters\u003c/h2\u003e \u003cp\u003eThe AMMI stability value \u003cb\u003e(\u003c/b\u003eASV) explained G7, G5 and G8 as the top ranked genotypes for the cane yield due to a lower ASV, hence they are considered as the most stable genotypes for the cane yield. For CCSY, the genotypes G7, G5, G8 and G3 are the most stable (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similar results were found with Modified AMMI stability value (MASV). According to genotypic stability index (GSI), the genotypes G5, G6 and G4 were ranked higher for cane yield whereas for CCS yield G6, G3 and G5 were ranked higher and are most stable (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to averages of the squared eigenvector values (EV) stability statistic, the genotype G7 stable followed by G6, G3 and G8 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As per the sums of the absolute value of the IPC scores (SIPC) the genotype G7 was stable followed by G5 and G6 for cane and CCS yield (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values, AMMI stability value (ASV), Mean AMMI stability value, EV, SIPC, and genotype selection index (GSI) for the genotypes studied for the trait Cane Yield (CY) and CCS Yield (CCSY).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eCY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eCCSY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMASV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eASV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMASV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSIPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003emeans\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.26\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.72\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.75\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.98\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.45\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.60\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.08\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMulti-trait stability index (MTSI)\u003c/h2\u003e \u003cp\u003eRecently, the multi-trait stability index has been used as a reliable technique to help pick elite genotypes based on the stability and mean performance of different variables (Zuffo et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On analysis of multi-trait stability analysis considering the traits SUC, CCSP, CY and CCSY that the genotypes G5 and G7 were highly stable and were selected having low MTSI value (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Considering the all the traits for multi-trait stability index (MTSI) it was identified that the genotype G10 and G7 were stable and were selected (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultitriat stability index (MTSI) for the genotypes considering yield and quality traits.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTSI (SUC, CCSP, CY, CCSY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTSI (All traits)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGGE Biplot analysis for yield and quality parameters\u003c/h2\u003e \u003cp\u003eA highly helpful statistical method for analysing the genotype by environment (GE) interaction and determining superior genotypes and mega environments is the GGE biplot analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMean vs. Stability\u003c/h2\u003e \u003cp\u003eThe genotypes G6 was found to be stable with higher CY and CCSY compared to checks G3 and G7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The genotypes G3, G9, and G7 were found stable for ESUC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). For sucrose accumulation at 12th month (SUC), the genotype G3 and G6 were stable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRanking of genotypes and environment\u003c/h2\u003e \u003cp\u003eThe genotype G6, G3, G7 and G5 was ranked higher and the environment E3 was ranked higher for the CY (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Whereas, for CCSY, the genotypes G6, G3, G7, G8, G4, and G5 and the environment, E1 had higher rank (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003eThe genotype G3 ranked higher followed by G6 and G7 and the environment E2 and E3 was ranked higher for the early sucrose (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej). The genotype G6, G3, G7, G8, G4, G2, and G9 was ranked higher and the environment E3 was ranked higher for SUC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003el).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDiscriminative vs. Representativeness\u003c/h2\u003e \u003cp\u003eThe GGE Biplots' discriminativeness vs. representative explanations helps to identify the optimal environments with the strongest ability to distinguish between genotypes. The environments E1 and E3 has high discriminating power having longest environmental vectors. The environments E2 had shorter environmental vectors having in which most of the genotypes performing equally for CY (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em). For CCSY, E1 and E3 were representative in nature, whereas, the environments E1 were found to have greater discriminating ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003en). For ESUC, E2 was representative in nature, whereas, the environments E1 were found to have greater discriminating ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eo). The environments E1 and E2 has high discriminating power having longest environmental vectors. The environments E3 had shorter environmental vectors having in which most of the genotypes performing equally for SUC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ep).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eWWW\u003c/h2\u003e \u003cp\u003eThe biplot polygon showed that the genotypes G5, G2, G12, G11 and G1 were vertex genotypes for CY. The 3 environments were classified grouped into single environments, in which the genotype G6, G5 and G7 had better performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eq). The biplot polygons for CCS yield revealed that the vertex genotypes were G6, G5, G9, G11, G12 and G1. There were two mega environments, the mega-environment (1) includes E1 and E3, in which better performing genotypes were G6, G3, G7 and G4. The second mega environment comprises of E2 environment, in which G5 was good performing genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003er). The biplot polygon showed that the genotypes G3, G6, G12, G10, G5, G2, and G8 were vertex genotypes for ESUC. The 3 environments were grouped into single environments, in which the genotype G3, G7 and G9 had better performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003es). G3, G11, G12, G13 and G10 were vertex genotypes for the SUC. Three environments clustered into single mega environment with G3 and G6 were having greater performance for SUC (Fig. t).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe changing environmental conditions will influence the true genotypic performance, hence, identifying genotypes performing stably in a variety of environmental conditions is desirable and is accomplished using stability analysis (Doehlert et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Combined ANOVA for cane and CCS yield for 14 clones evaluated in 7 environments of egypt showed significant genotype, environment and G X E interaction effects (Mehareb et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Evaluation of 24 genotypes under different salt stresses also showed significant main and interaction effects for all the traits under study (Kumar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AMMI analysis revealed significant changes across the clones and environments under investigation (Meena et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). AMMI investigations revealed that genotype, G \u0026times; E, and environmental variables all significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) influenced sugar yield (Tena et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The highly significant GEI suggested that different genotypes responded differently to different environments (Elbasyoni \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the current study, combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits cane diameter (CD) and single cane weight (SCW) for which environment main effects were not significant. AMMI biplot analysis for 12 advanced clones with three standards revealed the test entry CoSnk 15102 and the standards Co 86032, and CoC 671 were found to be stable for cane and CCS yield (Yadawad et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The genotype G2016-129 was found to be stable for cane yield and CCS yield in study of 15 clones (Mehareb et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For cane yield and CCS yield, the genotypes Co 11015, Co 09004, Co 0240, Co 13014, and Co 14016 were found to be highly adapted and more stable over all environments based on mean performance and stability; for sucrose percentage, five genotypes Co 15021, Co 11015, Co 15007, Co 13001, and Co 16001 displayed more stability (Elayaraja et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The most productive genotypes among 24 in different locations of Chhattisgarh in terms of tonnes of sugar produced per hectare were CoS 8436, VSI 8005, CoC 671, and Co 86032. These genotypes were also stable and suggested for use in commercial plantations (Verma et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The AMMI biplot using PC1 and PC2 scores for the CY reveals the genotypes G6, G8, G7; for CCSY G7 and G8; for ESUC the genotypes G1, G9, G7 and G13; for SUC the genotypes G1, G5, and G2 are are close to the origin and so have greater environmental adaption and stable across the environments. A cultivar is considered stable if its AMMI stability values (ASV) are near zero, as stated by Purchase et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). An environment-specific genotype was more specifically adapted to a bigger ASV value, whether it was positive or negative. A genotype that was more stable in different conditions was indicated by a reduced ASV value (Purchase \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The ASV explained G7, G5 and G8 as the top ranked genotypes for the cane yield due to a lower ASV, hence they are considered as the most stable genotypes for the cane yield. For CCSY, the genotypes G7, G5, G8 and G3 are the most stable. Similar results were found with MASV. The highest trait mean and the lowest ASV are both ranked one. These ranks are then added to create a single simultaneous selection index for traits, known as the GSI, which combines the trait mean and the ASV index into a single criterion (Farshadfar et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) which was also known as yield stability index (YSI) (Bose et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). According GSI, the genotypes G5, G6 and G4 were ranked higher for cane yield whereas for CCS yield G6, G3 and G5 were ranked higher and are most stable (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A genotype is considered to be more stable if EV is low (Ajay et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Accordingly, the genotype G7 stable followed by G6, G3 and G8 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A genotype is considered to be more stable if sums of the absolute value of the IPC scores (SIPC) is low (Ajay et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As per SIPC the genotype G7 was stable followed by G5 and G6 for cane and CCS yield (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Recently, the multi-trait stability index has been used as a reliable technique to help pick elite genotypes based on the stability and mean performance of different variables (Zuffo et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On analysis of multi-trait stability analysis considering the traits SUC, CCSP, CY and CCSY that the genotypes G5 and G7 were highly stable and were selected having low MTSI value (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Considering the all the traits for multi-trait stability index (MTSI) it was identified that the genotype G10 and G7 were stable and were selected (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eFrom the mean vs stability biplots, higher mean yield for the genotypes is indicated by the vertical line on the right side, which reflects the \"average-environment coordinates\" that pass through the origin. The second axis represents stability; genotypes aligned with the AEC track are ideal and top top performers, whereas genotypes closer to the origin are more stable (Yan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In present study, genotypes G6 was found to be stable with higher CY and CCSY compared to checks G3 and G7. The genotypes G3, G9, and G7 were found stable for ESUC. For sucrose accumulation at 12th month (SUC), the genotype G3 and G6 were stable. Genotypes or environments are ranked on biplots to identify the best genotypes according to where they fall in the concentric circle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). The genotype G6, G3, G7 and G5 was ranked higher and the environment E3 was ranked higher for the CY (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Whereas, for CCSY, the genotypes G6, G3, G7, G8, G4, and G5 and the environment, E1 had higher rank. The GGE Biplots' discriminativeness vs. representative explanations helps to identify the optimal environments with the strongest ability to distinguish between genotypes. The environments E1 and E3 has high discriminating power having longest environmental vectors. The environments E2 had shorter environmental vectors having in which most of the genotypes performing equally for CY (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em). For CCSY, E1 and E3 were representative in nature, whereas, the environments E1 were found to have greater discriminating ability. The bi-plot was divided into sections by lines extending from the origin, and each section had a mega-environment\u0026mdash;a vertex or genotype that reflects the best yield performance in the environments included in that area. In some or all situations, the vertex genotypes perform the best or the worst (Yan and Kang \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In the current study, three environments grouped in single mega-environment for CY, ESUC and SUC. In a G x E interaction study, clones FG05-424, FG06-750 and the check variety NCO334 were the most productive and stable, and advised for the test locations based on AMMI and the GGE analysis (Tena et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). GGE biplot analysis for 12 advanced clones with three standards revealed the genotypes CoSnk 15102, Co 86032, and CoC 671 were found to be stable for cane and CCS yield (Yadawad et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The genotype G2016-129 was found to be stable for cane yield and CCS yield in study of 15 clones (Mehareb et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The most productive genotypes among 24 in different locations of Chhattisgarh in terms of tonnes of sugar produced per hectare were CoS 8436, VSI 8005, CoC 671, and Co 86032. These genotypes were also stable and suggested for use in different conditions (Verma et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). After analysing AMMI, GSI, SI, and GGE biplot analysis for the cane and CCS yield, it was determined that genotype Co 15023 was the most stable genotype in various saline conditions. Co 0238 has a greater yield for cane and CCS yield, as shown by the AMMI and GGE biplot study (Kumar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current investigation, the combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits, cane diameter and single cane weight for which environment main effects were not significant. Significant individual effects of genotypes, environments, and genotype \u0026times; environment interaction was revealed by the AMMI ANOVA for the traits such as sucrose, CCS percent, cane yield, and CCS yield. Co 17001, CoC 13339, and Co 86032 for cane yield and Co 86032 and CoC 13339 for CCS yield were identified as stable, according to AMMI biplot analysis. For cane and CCS yield, AMMI stability parameters including ASV, MASV found the clones Co 86032, Co 15003, and CoC 13339 as stable. Co 17001, Co 15003, Co 86032, and Co 11015 were found to be stable for cane and CCS yield by the GSI, EV, and SIPC. Multi-trait stability analysis considering the traits like sucrose, CCS percent, cane yield, CCS yield revealed that the genotypes Co 15003 and Co 86032 were highly stable. GGE analysis such as mean vs stability, ranking of genotypes, which won where biplots pinpointed that the genotype Co 17001 is highly stable than the standards Co 11015 and Co 86032 for cane yield, sucrose content and CCS yield. Thus, AMMI, ASV, MASV, EV, GSI, SIPC, MTSI, and GGE analysis elucidated that the genotypes Co 17001 and Co 15003 were stable and superior to the commercial standards, Co 11015 and Co 86032 for the cane yield and CCS yield.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMMI-Additive Main Effect and Multiplicative Interaction, ASV-AMMI Stability Value, MASV-Modified AMMI Stability Value, EV-Averages of the squared eigenvector values stability statistic, GSI- Genotypic Stability Index, SIPC-\u0026nbsp;sums of the absolute value of the IPC scores, MTSI- Multi-trait Stability Index, GGE- Genotype Main Effects and Genotype by Environment Interaction; NMC-Number of Millable Canes, CH- Cane Height, CD- Cane Diameter, SCW-Single Cane Weight, CY- Cane Yield, ESUC- Sucrose at 10\u003csup\u003eth\u003c/sup\u003e month, Brix- Juice brix %, SUC- Sucrose at 12\u003csup\u003eth\u003c/sup\u003e month, CCSP- Commercial Cane Sugar Percentage, CCSY- Commercial Cane Sugar Yield. GEI- Genotype x Environment Interaction, ANOVA- Analysis of Variance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict or competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo any external source of funding for the study\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.A.D. collected the data. A. and R.A. executed statistical analyses. A.A.D., A. and G.H. prepared the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eAuthours are gratefull for ICAR-Sugarcane Breeding Institute, Coimbatore for facilitating for execution of this study. Authors acknowledges Tamil Nadu Agricultute University (TNAU), Coimbatore for supplying the few experimental material for the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjay BC, Aravind J, Abdul FR (2019) ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model. Indian J Genet Plant Breed\u003cem\u003e \u003c/em\u003e79(2):460\u0026ndash;466\u003c/li\u003e\n\u003cli\u003eAjay BC, Bera SK, Singh AL et al (2020) Evaluation of genotype\u0026times; environment interaction and yield stability analysis in peanut under phosphorus stress condition using stability parameters of AMMI model. Agric Res 9:477\u0026ndash;486.\u003c/li\u003e\n\u003cli\u003eAnuradha N, Patro TSSK, Singamsetti AY et al (2022) Comparative study of AMMI-and BLUP-based simultaneous selection for grain yield and stability of finger millet [\u003cem\u003eEleusine coracana\u003c/em\u003e (L.) Gaertn.] genotypes. Front Plant Sci 12:786839.\u003c/li\u003e\n\u003cli\u003eBose, LK, Jambhulkar NN, Pande K et al (2014) Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions. Chilean J Agri Res 74:3\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eDoehlert DC, McMullen MS, Hammond JJ (2001) Genotypic and environmental effects on grain yield and quality of oat grown in North Dakota. Crop Sci 41: 1066\u0026ndash;1072.\u003c/li\u003e\n\u003cli\u003eEberhart SA, Russel WA (1966) Stability parameters for com paring varieties. Crop Sci 6:36\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eElayaraja K, P Govindaraj, Mahadevwamy HK et al (2022) Stability Analysis for Identification of Stable Genotypes of Sugarcane (\u003cem\u003eSaccharum\u003c/em\u003e \u003cem\u003espp.)\u003c/em\u003e through AMMI Model. Indian J Genetic Plant Breed 82(04):480\u0026ndash;489.\u003c/li\u003e\n\u003cli\u003eElbasyoni IS (2018) Performance and stability of commercial wheat cultivars under terminal heat stress. Agronomy 8 (4):37. https://doi.org/10.3390/agronomy8040037.\u003c/li\u003e\n\u003cli\u003eFarshadfar E, Mahmodi N, Yaghotipoor A (2011) AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Australian J Crop Sci 5:1837\u0026ndash;1844.\u003c/li\u003e\n\u003cli\u003eFrancis TR, Kannenberg LW (1978) Yield stability studies in short-season maize. 1. A descriptive method for grouping geno types. Canad J Plant Sci 58:1029\u0026ndash;1034.\u003c/li\u003e\n\u003cli\u003eGabriel KR (1978) Analysis of meteorological data by means of canonical decomposition and biplots. 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J Agronomy Crop Sci 206(6):815\u0026ndash;822.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trpb","sideBox":"Learn more about [Tropical Plant Biology](http://link.springer.com/journal/12042)","snPcode":"12042","submissionUrl":"https://submission.nature.com/new-submission/12042/3","title":"Tropical Plant Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sugarcane, G x E Interaction, Stability, AMMI biplot, GGE biplot","lastPublishedDoi":"10.21203/rs.3.rs-4471951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4471951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStable, and high yielding genotype with superior quality across the spatial and temporal variation are to be identified due to changing weather conditions which largely influences the true genotypic performance. The present experiment was conducted with 13 clones including seven test entries along with six recently released varieties as first plant, second plant and ratoon in RBD with three replications during the year 2022-23 and 2023-24 at ICAR-SBI, Coimbatore. Combined ANOVA revealed that there was a significant genotype main effects, environment main effects and G X E interaction effect for all the traits under study except for the traits, cane diameter and single cane weight for which environment main effects were not significant. The AMMI ANOVA for the sucrose, CCS percent, cane yield and CCS yield showed that significant individual effects of Genotypes, Environments and genotype \u0026times; environment interaction. AMMI biplot analysis revealed that the genotypes Co 17001, CoC 13339 and Co 86032 for cane yield and Co 86032 and CoC 13339 for CCS yield were stable. AMMI stability parameters such as ASV, MASV identified Co 86032, Co 15003, and CoC 13339 were stable for cane and CCS yield. The GSI, EV, SIPC showed Co 17001, Co 15003, Co 86032 and Co 11015 were stable for cane and CCS yield. Multi-trait stability analysis considering the traits like sucrose, CCS percent, cane yield, CCS yield revealed that the genotypes Co 15003 and Co 86032 were highly stable. GGE analysis such as mean vs stability, ranking of genotypes, which won where biplots pinpointed that the genotype Co 17001 is highly stable than the standards Co 11015 and Co 86032 for sucrose content, cane and CCS yield. Thus, the genotypes Co 17001 and Co 15003 were stable and superior than the commercial varieties like Co 11015 and Co 86032 according to the AMMI, AMMI stability parameters and GGE for the cane yield and CCS yield and they may be promoted for commercial cultivation in target environment.\u003c/p\u003e","manuscriptTitle":"Elucidating the G X E Interaction Using AMMI, AMMI Stability Parameters and GGE for Cane Yield and Quality in Sugarcane ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 02:47:06","doi":"10.21203/rs.3.rs-4471951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2024-06-03T13:21:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-29T19:15:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-29T14:50:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Plant Biology","date":"2024-05-24T10:44:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trpb","sideBox":"Learn more about [Tropical Plant Biology](http://link.springer.com/journal/12042)","snPcode":"12042","submissionUrl":"https://submission.nature.com/new-submission/12042/3","title":"Tropical Plant Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eeb0c30d-06b4-4311-a583-cfbcc191465d","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T15:58:33+00:00","versionOfRecord":{"articleIdentity":"rs-4471951","link":"https://doi.org/10.1007/s12042-024-09372-2","journal":{"identity":"tropical-plant-biology","isVorOnly":false,"title":"Tropical Plant Biology"},"publishedOn":"2024-10-21 15:56:54","publishedOnDateReadable":"October 21st, 2024"},"versionCreatedAt":"2024-06-13 02:47:06","video":"","vorDoi":"10.1007/s12042-024-09372-2","vorDoiUrl":"https://doi.org/10.1007/s12042-024-09372-2","workflowStages":[]},"version":"v1","identity":"rs-4471951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4471951","identity":"rs-4471951","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}