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Article

Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress

by
Ravinder Kumar
1,*,
Pooja Dhansu
1,*,
Neeraj Kulshreshtha
1,
Mintu Ram Meena
1,
Mahadevaswamy Huskur Kumaraswamy
2,
Chinnaswamy Appunu
2,
Manohar Lal Chhabra
1 and
Sstish Kumar Pandey
1
1
ICAR-Sugarcane Breeding Institute Regional Centre, Karnal 132001, India
2
ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(2), 1119; https://doi.org/10.3390/su15021119
Submission received: 26 October 2022 / Revised: 13 December 2022 / Accepted: 28 December 2022 / Published: 6 January 2023
(This article belongs to the Special Issue Salt-Affected Ecosystems and Sustainable Food Production: Emerging Challenges, Management and Future Strategies)
Browse Figures
Versions Notes

Abstract

:
Additive main effects and multiplicative interaction (AMMI), as well as various AMMI-derived statistics, Genotype, and Genotype × Environment Interaction (GGE) models were employed on 24 sugarcane genotypes planted during two seasons (2017–18, 2018–19) under different induced salinity stress environments using saline water irrigation (iw) viz., E1 (Normal iw during crop season 2017–18), E2 (Normal iw during crop season 2018–19), E3 (4 dsm−1 ECiw during crop season 2017–18), E4 (4 dsm−1 ECiw during crop season 2018–19), E5 (8 dsm−1 ECiw during crop season 2017–18), E6 (8 dsm−1 ECiw during crop season 2018–19), E7 (12 dsm−1 ECiw during crop season 2017–18) and E8 (12 dsm−1 ECiw during crop season 2018–19) to assess the genotype by the environment interaction for the cane yield, commercial cane sugar (CCS) yield, number of millable cane (NMC), single cane weight (SCW), and pol % in juice. Individual and interactive effects of the genotype and environment for all the traits were significant. In the expression of total variability, the environmental contribution was higher for the cane yield (66.98%), CCS yield (67.60%), NMC (65.78%), and SCW (43.27%), whereas genotypic contribution was higher in the expression of pol% (82.48%). As per AMMI Stability Value (ASV), G14 (Co 13033), G23 (Co 15026), G7 (Co 05009), G17 (Co 13036), and G2 (Co 15025) were the most stable genotypes for the cane yield. Whereas as per GSI (genotype selection index), genotypes G24 (Co 15027), G21 (Co 15023), G23 (Co 15026), and G17 (Co 13036) were found most stable. The Sustainability Index (SI) of the cane yield (CY) and its contributing and CY-based computed traits were low for most of the genotypes, which indicates the negative impact of increased levels of irrigation-induced salinity in the expression of these traits. In the mean vs stability biplot analysis, G21 (Co 15023), G24 (Co 15027), G16 (Co 13036), G6 (Co 0238), and G20 (Co 14036) were found to be highly productive and stable genotypes for the cane yield. The superior and stable performance of early maturing notified varieties G21 (Co 15023) and G6 (Co 0238) for CY and CCS yield indicates that they will help the farmers to obtain sustainable income in saline soil conditions.

1. Introduction

Sugarcane, being a C4 crop, thrives well in tropical environments but is cultivated globally under varied climatic conditions of tropical and sub-tropical regions [1]. In the recent past, Indian sugarcane agriculture had witnessed a swift improvement in cane and sugar production. India is the second largest sugarcane producer after Brazil, with around 4.79 m ha area, 370.5 mt production, and 77.35 t ha−1 productivity [2]. Sugarcane has the ability to survive against various environmental-related limiting factors [3,4,5,6]. The theoretical maximum cane yield for sugarcane is 472 t ha−1 [7], but various environmental (biotic and abiotic) and agronomic factors (crop geometry, planting time, crop duration, rotation, water, weeds, diseases, pests and nutrients management practices, crop lodging etc.) limit the crop yield. Soil salinity is significantly coercing crop productivity, predominantly in arid and semiarid regions. In India, the great Indogangetic plane (Punjab, Haryana, Uttar Pradesh, Bihar, and some parts of Rajasthan), arid tracts of Gujarat and Rajasthan and semi-arid tracts of Gujarat, Madhya Pradesh, Maharashtra, Karnataka, and Andhra Pradesh are largely affected by soil salinity [8]. Five states [Gujarat (2.23 m ha), Uttar Pradesh (1.37 m ha), Maharashtra (0.61 m ha), West Bengal (0.44 m ha) and Rajasthan (0.38 m ha)] together account for almost 75% of the saline and sodic soils; furthermore, the area under salt-affected soils is estimated to be tripled (20 million ha) in India by 2050 [9]. The problem of fringe quality water also befalls the northwestern arid part of India, especially in the states of Rajasthan, Haryana, and Punjab [10]. Salinity inhibits plant growth through ion toxicity, nutritional imbalances, osmotic effect, and oxidative stress [11,12,13]. Although sugarcane is pondered as moderately sensitive against salinity stress [14,15], the prolonged stay of the crop (one plant + one or two ratoon crops) in the salt-affected area greatly limits the sugarcane yield [15,16]. The ill effect of salinity on the growth, development, and yield contributing traits of sugarcane have been reported by several researchers [17,18,19,20,21]. Rao et al. [22] in their study identified the threshold value for sugarcane under salinity as 1.7 dSm−1 and suggested that yield decreases by 5.9% with a per unit increase in salinity. Irrigation-induced salinity or sodicity in sugarcane is reported in several countries viz., Australia, Egypt, Iraq, the United States, India, Pakistan, Swaziland, South Africa, and Zimbabwe [23,24,25]. Increased soil salinity or sodicity are the most substantial soil chemical processes which lead to soil degradation under irrigated sugarcane [26]. The resistant and susceptible sugarcane genotypes express differential responses for various growth and yield traits against dissimilar salinity levels [27,28,29,30,31]. It indicates that there is a need to study the interaction effects of genotype, environment, and G × E thoroughly against different levels of salinity [5,32]. Plant breeders overcome the GEI (genotype by environment interaction) challenges by evaluating genotypes in a variety of distinct environments to ensure that the specific genotype with a high yield and stable performance is selected [4]. Stable performance and better adaptability are the main criteria for the selection of genotypes in any breeding program [33]. Environment plays a dominant role in the expression of the cane yield and attributed traits in sugarcane, and the importance of G × E interaction is widely recognized [34,35,36,37]. The two widely used biplot models are the AMMI biplot (the additive main effects and multiplicative interaction and the GGE biplot (genotype + genotype × environment). The analysis of variance of the genotype and environment main effects with the PCA of the GEI generates the AMMI model, whereas the AMMI2 or GEI biplot is performed based on the singular value decomposition (SVD) of a double centered G × E table [38]. The GGE biplots are based on the environment-centered SVD, provided by Yan et al. [39] and graphically represent both the genotype and genotype-by-environment based on the primary sources of variation associated with the genotype assessment. The AMMI technique is effective in identifying the discriminating genotypes that had stable performance across diverse environmental conditions [40,41]. During yield trials, an additive main effect and a multiplicative interaction (AMMI) model are widely used to analyze G × E interaction [42]. AMMI is capable of detecting GEI in a multi-dimensional environment and displaying it using a biplot. GGE biplots-based multi-environment trail, genotype evaluation, and environmental valuation have been successfully executed for varietal stability analysis by several researchers [43,44,45,46,47,48]. The GGE biplot approach for decomposing genotype plus genotype-by-environment (G + G × E) is more effective in a biplot graph compared with AMMI analysis. In addition, the GGE biplot analysis has been used by several researchers to classify the mega environment, assess genotype rankings, and decide the discriminative and representative among the tested environments [42]. The GGE biplot will aid researchers in better understanding complicated GE interactions in multi-environment breeding line trials and agronomic investigations [45]. The GGE biplot was used to determine the performance of crop cultivars in a variety of stress conditions, ideal cultivars, mega-environment, and core testing sites [49]. The direct presentation of genotype effects is not feasible in AMMI2 biplots since this approach only decomposes G × E interaction effects in the PCA. GGE biplot analysis, on the other hand, is regarded as a useful statistical technique for producing phenotypically stable and superior cultivars, identifying stable genotypes across several environments and achieving crop yield stability across multiple locations.
The AMMI model does not provide a quantitative stability measure, which is essential to quantify and rank the genotypes in terms of yield stability [50]. The AMMI stability value (ASV) is used as a selection criterion to quantify and rank the genotypes according to their yield stability [51]. Further stability per se should not be the only selection criteria, because the most stable genotypes may not necessarily give the best yield performance [34,35]. Therefore, various authors have introduced different selection criteria for the simulation selection of yield and stability [52,53,54,55,56,57,58]. Therefore, the present study aims to identify superior genotypes with stable yield performance over different salinity stress environments by evaluating the efficacy of various stability analysis methodologies.

2. Materials and Methods

2.1. Research Materials

The experiment with 24 sugarcane genotypes consisting of recently released commercial varieties viz., Co 98014 (G3), Co 0118 (G4), Co 0237 (G5), Co 0238 (G6), Co 05009 (G7), Co 05011 (G8), Co 06034 (G9) and Co 12029 (G13), newly evolved “Co” canes viz., Co 11027 (G10), Co 12026 (G11), Co 12027 (G12), Co 13033 (G14), Co 13034 (G15), Co 13035 (G16), Co 13036 (G17), Co 14034 (G18), Co 14035 (G19), Co 14036 (G20), Co 15023 (G21), Co 15025 (G22), Co 15026 (G23), Co 15027 (G24), and old varieties which had wider adaptation in past (Co 1148 (G1) and CoS 767 (G2) were chosen for evaluating at different levels of salt concentrations under controlled field conditions at ICAR-SBI, RC, Karnal (29.68 N latitude and 76.99 E longitude at 243 m above mean sea level), Haryana, India during crop seasons 2017–18 and 2018–2019. All the studied material except CoS 767 (variety developed by UPCSR, Shahjahanpur) was developed at ICAR-SBI, RC, Karnal under the economic breeding program. The information on parentage and key characters of the studied material is given in Supplementary Table S1.

2.2. Experimental Design

To evaluate the sugarcane genotypes under salinity stress, the pit method of planting was followed. The experiment was planted in round pits of size 60 cm × 45 cm using a factorial Randomized Block Design with three replications. Black durable polysheets were properly spread in each pit before refilling them with a nursery mixture containing a 1:1:1 ratio of soil, sand, and farmyard manure. The polysheets around the pits help to stop the leaching of salts due to irrigation or rain.

2.3. Salt Treatments

The salinity (NaCl) stress was imposed to create four production environments of different ECiw (Electrical conductivity of irrigation water) salinity levels using three levels of saline irrigation water 4 ECiw (S1), 8 ECiw (S2), and 12 ECiw (S3), along with control (normal water). The pH and EC of normal water used for irrigation were 7.7 and 0.01 dSm−1, respectively. A total of 12 two-budded setts of each genotype were planted in each pit during the second fortnight of March 2017 and March 2018. The buds were allowed to germinate by providing normal irrigation water at an interval of 7–10 days during the first month. After 30 days of planting, the normal EC irrigation water (Niw), saline water of 4 EC dsm−1ECiw, 8 EC dsm−1ECiw, and 12 EC dsm−1ECiw was used for irrigation at 10-day intervals until the onset of monsoon. During monsoon season, the crop was irrigated when required with the different salinity levels of irrigation water and with the recommended package and practices. In the two consequent years, a total of eight induced production environments of irrigation water salinity viz., E1 (Niw during crop season 2017–18), E2 (Niw during crop season 2018–19), E3 (4 dsm−1ECiw during crop season 2017–18), E4 (4 dsm−1ECiw during crop season 2018–19), E5 (8 dsm−1ECiw during crop season 2017–18), E6 (8 dsm−1ECiw during crop season 2018–19), E7 (12 dsm−1ECiw during crop season 2017–18), and E8 (12 dsm−1ECiw during crop season 2018–19) were developed in the ring pits.

2.4. Determination of Salinity Stress on Genotype Performance

The crop was harvested after 240 days, and data on the cane yield (t ha−1), NMC (numbers) and SCW (kg) was recorded. At the same time, juice quality traits viz., brix%, pol%, purity% and CCS% were estimated in a laboratory as per ICUMSA methods [59]. CCS (t ha−1) was worked out with the following equation:
CCS   t   h a − 1 = Cane yield × CCS % 100
Different salinity levels were considered as an environment, and G × E interaction was analyzed using additive main effects and multiplicative interaction (AMMI) analysis, as per Gauch and Zobel [50], and GGE biplot analysis as per Yan et al. [60] was performed graphically, based on AMMI and GGE biplot using R studio (a simplified version of R statistical software) developed by the R Core Team [61]. The metan package of R studio was used for AMMI and GGE biplots analysis [62]. The GGE biplots and AMMI are graphical images to exemplify G × E interaction and genotype ranking based on mean and stability. The graph generated is based on multi-environment evaluation (which-won-where pattern), genotype evaluation (mean versus stability), and tested environment ranking (discriminative versus representative). The ranking of genotypes was allocated in increasing order of each stability parameter.

2.5. AMMI Stability Value (ASV)

The AMMI stability value (ASV), used to compare the stability of genotypes as described by Purchase et al. [51], was calculated as follows:
A S V = S S I P C A 1 S S I P C A 2 × I P C A 1 s c o r e + ( I P C A 2 s c o r e ) 2
where SSIPCA1/SSIPCA2 is the weight given to the IPCA1-value by dividing the IPCA1 sum of squares by the IPCA2 sum of squares. The larger the IPCA score, either negative or positive, the more specifically adapted a genotype is to certain environments. Smaller ASV scores indicate a more stable genotype across environments.

2.6. Genotype Selection Index (GSI)

The genotype selection index (GSI) was calculated for each genotype, which incorporates both the mean of the trait and the ASV index in a single criterion [52].
G S I i = R M i + R A S V i
where GSIi is the genotype selection index for the ith genotype, RMi is the rank of the trait mean of the ith genotype, and RASVi is the rank of the AMMI stability value for the ith genotype. GSI incorporate both the mean of the trait and stability in a single criterion. The low value of this parameter shows desirable genotypes with high mean and stability.

2.7. Sustainability Index (SI)

The sustainability index was estimated by the following formula as described by Manjoor et al. [53].
S I = Y − σ n Y M × 100
where Y = Average performance of a genotype, σn = Standard deviation, and YM = Best performance of a genotype in any environment. The values of the sustainability index were divided arbitrarily into five groups viz. very low (up to 20%), low (21–40%), moderate (41–60%), high (61–80%), and very high (above 80%).

3. Results

3.1. Effect of Salinity on Sugarcane Genotypes—AMMI Analysis

The analysis of variance (ANOVA) showed that individual and interactive effects of Genotypes (G), Environments (E), and genotype × environment interaction (G × E) were significant for all the studied traits viz., the cane yield, CCS yield, NMC population, SCW and Pol%. The environmental main effect represented 66.98%, the genotype explained 25.41%, and the G × E interaction explained 7.61% of the total variation for the cane yield. Similarly for the CCS yield (t ha−1), the contribution of environment, genotype, and interaction effects on the total variation was 67.60%, 24.88%, and 8.12%, respectively. In addition, significant variations for NMC (represented 65.78% environmental variation, 21.81% genotypic variation and 12.41 % G × E variation) were also observed. The analysis of variance revealed 43.27% environmental variation, 42.74% genotypic variation, and 13.99 % G × E variation for SCW (Table 1). For pol% (sucrose% in juice), the analysis of variance revealed the presence of a highly significant variation for genotype (82.48%), environment main effect (9.45%), and moderate interaction effects (8.07%).
The difference between the principal components (IPCA) values was also highly significant, and the first two components accounted for 77.03% of the whole effect on the variation of the cane yield, 81.62% for CCS t ha−1, 73.16% for NMC (thousand ha−1), 82.06% for SCW (kg), and 75.04% for Pol% (Table 1).

3.1.1. Biplot Analysis for Determination of Main Effect and Environment Influence

PC1 (principle component 1) Vs CY (Cane yield) environments E5, E6, E7, and E8 [higher ECiw 8 (E5, E6) and ECiw 12 (E7, E8)] demonstrated lower average main effects, whereas environments E1, E2 (Normal ECiw), expressed the highest main effects and were gainful for most of the genotypes. The PC1 scores for environments E3 and E4 (4 ECiw) are near zero. Genotypes G20, G24, G21, G6, G4, G13, G8, G13, and G15, expressed higher main effects; for the cane yield, on the contrary, genotypes G12, G2, G10, G19, G1, and G18 exhibited lower main effects. (Figure 1a). The AMMI biplot using PC1 and PC2 scores for the cane yield is displayed in Figure 1b. Genotypes G23, G24, G6, G14, G3, G21, and G20 are stable near the origin, hence they have better adaptation across the environments.
The AMMI biplot for CCS yield drawn using PC Vs CCSY (Figure 2a) and PC1 Vs PC2 (Figure 2b) indicates E1, E2, E3, and E4 as the favorable environments, whereas E5, E6, E7, and E8 as poor environments. Similarly, G21, G24, G4, G6, G13, and G20 are rated as the better performer genotypes for the trait, while G1, G2, G10, and G12 are rated as the poor performer genotypes. Genotypes G24, G21, and G16 are located near the origin in the PC1 Vs PC2 biplot and are stable performers (Figure 2b).

3.1.2. AMMI Stability Value (ASV)

As per ASV, a stable variety is one with close to zero value. The ASV explained G14 (Co 13033), G23 (Co 15026), G7 (Co 05009), G17 (Co 13036), and G2 (Co 15025) as the top ranked genotypes for the cane yield (Table 2) due to a lower ASV, hence they are classified as the most stable genotypes for the cane yield. For CCS yield, G6 (Co 0238), G16 (Co 13035), G14 (Co 13033), G3(Co 98014) and G22 (Co 15025) found most stable genotypes. G19 (Co 14035), G17 (Co 13036), G15 (Co 13034), G21 (Co 15023) and G10 (Co 11027) for NMC; G17 (Co 13036), G11 (Co 12026), G21 (Co 15023), G9 (Co 06034), G24 (Co 15027) for SCW; whereas, G21 (Co 15023), G12 (Co 12027), G5 (Co 0237), G9 (Co 06034), and G16 (Co 13035) for Pol%, were the most stable genotypes for respective traits. On the contrary, G12 (Co 12027), G2 (CoS 767), G4 (Co 0118) for cane yield; G9(Co 06034), G11 (Co 12026), G8 (Co 05011) for CCS yield; G9 (Co 06034), G11 (Co 12026), G8 (Co 05011) for NMC; G5 (Co 0237), G22 (Co 15025), G1 (Co 1148) for SCW and G6 (Co 0238), G5 (Co 0237), and G4 (Co 0118) for pol% due to highest ASV ranks found most unstable for the respective traits.

3.1.3. Genotype Stability Index (GSI) and Sustainability Index (SI)

For the cane yield the GSI of genotypes G24 (Co 15027), G21 (Co 15023), G23 (Co 15026) and G17 (Co 13036) was lowest (Table 2). Similarly for CCS yield G16 (Co 13035), G6 (Co 0238), G24 (Co 15027), G21 (Co 15023), and G3 (Co 98014) were the best performer genotypes as per GSI values. G21 (Co 15023) and G24 (Co 15027) also had better GSI for rest of the traits viz., NMC, SCW and Pol%.
The SI (Supplementary Table S2), of genotypes G18, G1, G10, G2, G16, G20, and G21, was moderate for the cane yield, whereas rest of the genotypes represents low to very low category. For CCS yield, the SI, was moderate for G18, while rest of the genotypes categorized into low to very low category. G18, G22, G1, G21, G24 and G20 had high, while rest of the genotypes had moderate values of SI for NMC. For SCW, G2, G3, G4, G6, G15, G16, G18, G19, G20, G21, and G24 represents high SI, while rest of the genotypes reflects moderate category. All the genotypes, represents very high category of SI for pol% in juice.

3.1.4. GGE Biplots for Cane Yield and CCS Yield

For the cane yield and CCS yield, environments E1 and E2 have the lengthiest environmental vector with narrow angles to AEC, have better discriminative power, and are deliberated as ideal environments for the expression of the respective traits, i.e., cane yield and CCS yield (Figure 3 and Figure 4). The shortest environmental vector observed for E7 and E8 for these traits, indicating lesser differentiation power of the environments in relation to the genotypes, i.e., all the genotypes had the average or similar performance in those environments.
The mean vs. stability biplots envisage the mean performance of the genotypes through the environments. The line in Figure 5 and Figure 6 passing through the origin represents the “average-environment coordinates”, and the right-hand side of the vertical line symbolizes a higher mean yield for the genotypes. The second axis denotes stability. For the cane yield, G21, G24, G16, G6, and G20 are positioned closer to the AEC, and the right-hand side indicates that these are highly productive and stable genotypes for the cane yield. Though genotypes G4, G18, G13 etc., are positioned towards a higher mean yield, they are also unstable due to their far away position from the AEC line. For the CCS yield, G20, G24, G21, G5, and G16 are located near the AEC towards the arrow, away from the origin, which indicates their higher productiveness and stable performance for the trait.
The ranking of genotypes (Figure 7 and Figure 8) revealed the best productiveness of genotypes G21 and G24 for the cane yield, whereas G20, G24, G6, and G21 for the CCS yield due to occupying the first concentric circles for these traits.
The GGE biplot polygons for the cane yield and CCS yield are displayed in Figure 9 and Figure 10. Genotypes G4, G21, G18, G1, G2, G10, G12, and G5 were the vertex genotypes for cane yield, whereas G13, G4, G20, G18, G2, G12, and G5 were the vertex genotypes for the CCS yield, confirming the biggest distance from the origin. For the cane yield, the eight environments were grouped into two mega-environments, i.e., the mega environment (i) formed by E2 and the mega environment (ii) formed by E1, E3, E4, E5, E6, E7, and E8. Genotypes G4, G21, and G18 were the vertexes of the mega environment (2) indicating that they are better performers at all the studied levels of irrigation water salinity. For CCS t ha−1 as well, the eight environments formed two mega groups (Figure 10) viz., the mega environment (i) represented by E1, E2, E3, E4, E5, and E6 environments, whereas E7 and E8 positioned in Mega environment (ii). G4 and G20 were the vertices genotypes in the mega environment (i) whereas G18 was the vertices genotype in the mega environment (ii), indicating their superior performance in these mega environments. There were also vertexes of genotypes which were located in the regions with no environment at all, viz., G5, G12, G10, G2 for cane yield while G5, G12, G2, for CCS t ha−1, indicates their poor performance in all the environments.

4. Discussions

Identifying the salt-tolerant genotype with stable performance across different salinity levels is a challenging task for any breeder due to very high environmental and GE interactions. AMMI incorporates ANOVA and PCA into a single model and enables a simple visual interpretation of the GE interaction. It enables the clustering of genotypes based on the similarity of response characteristics and identifying potential trends across environments [63]. In our study, a large sum of the square for environments indicated that the environments were diverse, with large differences among environmental means, causing most of the variation in the cane yield, CCS yield, and NMC. The significant G × E interaction indicated the differential response of the genotypes at different salinity levels. A lot of findings are in support of the result that the environment’s main effect used to be higher for the yield and related traits [64,65,66]. In our study, the AMMI model with only two PCA interactions was the best predictive model, which is in agreement with many researchers [65,66,67,68,69,70].

4.1. Biplot Analysis for Determination of Main Effect and Environment Influence

For the AMMI1 biplot, exposing main effects means on the abscissa and PC1 values as the ordinates, genotypes, or environments that locate almost on a vertical line have interrelated means and those that locate nearly on a horizontal line have similar interaction patterns [64]. These authors proved that the genotypes/environments with great PC1 scores (positive or negative) have higher interactions, whereas genotypes/environments with PC1 scores near zero have slight interactions [64]. In general, environments with scores near zero have little interaction across environments and provide low discrimination among genotypes [68]. In our study, PC1 Vs CY (Cane yield), environments E5, E6, E7, and E8 [higher ECiw 8 (E5, E6) and ECiw 12 (E7, E8)] demonstrated lower average main effects and, thus, were rated as deprived environments. Environments E1 and E2 (Normal ECiw) expressed the highest main effects and were gainful for most of the genotypes. The PC1 scores for environments E3 and E4 (4 ECiw) are near zero, which indicates that ECiw 4 ds m−1 shows the least interaction among the studied environments in the expression of the cane yield. Genotypes G20, G24, G21, G6, G4, G13, G8, and G15, expressed higher main effects for the cane yield, which indicates their better adaptation under favorable environments. Contrary, genotypes G12, G2, G10, G19, G1, and G18 exhibited lower main effects for cane yield, indicating their specific adaptation under environments of higher salinity [ECiw 8 (E5, E6) and ECiw 12 (E7, E8)]. The genotypes near the origin are indifferent to the environmental interaction and vice versa [69]. The ideal environment and genotype are those which are near the origin [34]. In our study, the cane yield genotypes G23, G24, G6, G14, G3, G21, and G20, due to their position near the origin, are comparatively stable under the various salinity environments (Figure 2a).

4.2. Genotype Stability Index (GSI) and Sustainability Index (SI)

Since ASV takes into account both IPCA1 and IPCA2, most of the variation in the GE is justified. The rank of ASV and trait mean is such that the lowest ASV is rank one, while the highest trait mean is rank one; the ranks are then summed in a single simultaneous selection index of trait, and its stability, called the GSI, incorporates both the mean of the trait and the ASV index in a single criterion [52]. This is also known as the yield stability index (YSI) [70]. A low value for this parameter represents stable genotypes with a high mean yield. In our study, for the cane yield, the GSI of genotypes G24 (Co 15027), G21 (Co 15023), G23 (Co 15026), and G17 (Co 13036) were the lowest (Table 2), which indicates they are high yield performers coupled with stability under salinity conditions. Similarly, for the CCS yield, G16 (Co 13035), G6 (Co 0238), G24 (Co 15027), G21 (Co 15023), and G3 (Co 98014) were the best performer genotypes, as per the GSI values. G21 (Co 15023) and G24 (Co 15027) also had better GSI for the rest of the traits viz., NMC, SCW, and Pol%. In our earlier study, these entries were identified as tolerant or moderately tolerant against salinity [5].
The sustainability index (SI) is the ratio of the average performance of the genotype across the environments (excluding its standard deviation) to its best performance in a particular environment [53]. It categorizes the genotypes into five groups viz., very low (up to 20%), low (21–40%), moderate (41–60%), high (61–80%), and very high (above 80%). This index is being used by various researchers in the selection of stable genotypes [70,71,72,73]. In our study, except for a few (G18, G1, G10, G2, G16, G20 and G21 had moderate SI for CY; G18 had moderate SI for CCS yield), most of the genotypes represent lower groups of SI for the cane yield and CCS yield. This indicates that these traits were greatly affected by the increased level of irrigation-induced salinity. On the contrary, NMC and SCW represent a moderate-to-high category of SI, which indicates a moderate reduction in the expression of these traits, with an increased level of irrigation-induced salinity stress and the least impact on the juice quality as compared with yield and contributing traits.

4.3. GGE Biplot Analysis

The GGE biplot analysis is a very useful statistical tool in examining the aggression of the genotype by environment (GE) interaction as well as identifying mega environments and superior genotypes. It is a scatter plot which graphically shows both the entries (e.g., genotypes) and the tester’s (e.g., environments) of two-way data for the picturing of the mega environments, the ranking of the genotypes, and the identification of stable environments [74]. The GGE Biplot analysis is being extensively used by researchers in the delineation of Genotype and Genotype by Environment interaction in various crops, including sugarcane [75,76]. The discriminativeness vs. representative explanations of GGE Biplots recognizes the best environments with the highest discriminative power to discriminate the genotypes. The average environmental coordinates (AEC) and test environments are proficient in envisaging the three types of environments viz., type-1 (short vectors with ordinary distinguish power showing the average performance of genotypes), type-2 (longest vectors with the utmost discriminative power, proficient to discriminate the performance of the genotypes), and type-3 (longest vector with great angles, suitable to harsh effects of environments) [77]. In our study, for the cane yield and CCS yield environments, E1 and E2 have the lengthiest environmental vector with narrow angles to AEC and better discriminative power, and they are deliberated as ideal environments for the expression of the respective traits, i.e., the cane yield and CCS yield (Figure 3 and Figure 4). The shortest environmental vector was observed for E7 and E8 for these traits, indicating lesser differentiation power of the environments in relation to the genotypes, i.e., all the genotypes had the average or similar performance in those environments.
The mean vs. stability biplots envisage the mean performance of the genotypes through the environments. The line in Figure 4 and Figure 5 passing through the origin represents the “average-environment coordinates” and the right-hand side of the vertical line symbolizes higher mean yield for the genotypes. The second axis denotes stability; genotypes nearer to the origin are more stable [78,79,80] and the genotypes situated closer and in the track of AEC are considered perfect and best performers [77]. In our study, for the cane yield G21, G24, G16, G6, and G20 are positioned closer to the AEC and on the right-hand side, which indicates that these are highly productive and stable genotypes for the cane yield. Though genotypes G4, G18, and G13 are also positioned towards higher mean yield but are unstable due to far away position from the AEC line. For CCS yield G20, G24, G21, G5, and G16 are located near the AEC towards the arrow, away from the origin, indicating their higher productiveness and stable performance for the trait.
The ranking of genotypes on biplots comforts to pinpoint the best genotypes based on their positions in the concentric circle [77]. A line passing through the biplot origin from the lower right to the upper left is the AEC as defined by the first two PCs of the environment scores. In our study, genotypes G21 and G24 were shown for the cane yield, whereas G20, G24, G6, and G21 were shown for the CCS yield due to occupying the first concentric circles for these traits are the best productive genotypes.
The GGE biplot polygon recognized the pictorial grouping of environments based on crossed GEI between the highest yield genotypes [81]. It is constructed by an asymmetrical polygon. The number of lines starting at the bi-plot’s origin and intercepting the polygon vertically is equal to the sides in the polygon. The genotypes that are out of the origin in all directions polygons denote the vertices; hence all genotypes are inside the polygon [78]. An imaginary environment is denoted by a line that vertically crosses a side of the polygon, if both genotypes that formed that side, have better productivity, the genotype’s comparative rank would be reversed in environments at the line’s opposite extreme (crossed GE). Therefore, the lines radiating from the origin split the bi-plot into sections, and each section had a vertex (genotype) that represents the best yield performance in environments contained in that section, which is named a mega-environment. The vertex genotypes are either the best or the poorest performers in some or all environments [60]. Genotypes G4, G21 and G18 were the vertexes of better performing mega environment for cane yield, indicating that they are a better performer at all the studied levels of irrigation water salinity. For CCS t ha−1, the eight environments formed two mega groups viz., mega environment (i) represented by E1, E2, E3, E4, E5, and E6 environments, whereas E7 and E8 positioned in the mega environment (ii). G4 and G20 were the vertices genotypes in the mega environment (i) whereas G18 was the vertices genotype in mega environment (ii), indicating their superior performance in these mega environments. There were also vertexes of genotypes which were located in the regions with no environment at all, viz., G5, G12, G10, G2 for cane yield while G5, G12, G2, for CCS t ha−1, indicates their poor performance in all environments.

5. Conclusions

The genotype and environment main effects as well as G × E interaction effects were significant for all the studied traits. The genotype G21 (Co 15023) was found as the most stable genotype under different saline water environments, as it was among the best-ranked genotypes for AMMI analysis, GSI, SI, and GGE biplot analysis for the CY and CCS yield. The AMMI and GGE biplot analysis reveals the superiority of G6 (Co 0238) for CY and CCS yield. Hence, the cultivation of these genotypes would help farmers to obtain sustainable income in harsh environmental conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15021119/s1, Table S1: List of the studied genotypes along with information on their parentage and key characters, Table S2: Sustainability Index of different sugarcane genotypes against Irrigation water salinity

Author Contributions

R.K.: Conceptualization, investigation, data visualization, original draft preparation; P.D.: investigation, data visualization and editing; N.K.: Conceptualization, investigation and final draft editing; M.R.M.: investigation; C.A.: Data visualization, Draft Editing; M.H.K.: data analysis; M.L.C.: Investigation, S.K.P.: Investigation and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

The research was self funded by the institute.

Data Availability Statement

The data presented in the study are available on request from the corresponding author. The data are not publicly available due to restrictions.

Acknowledgments

The authors are grateful to ICAR-SBI, Coimbatore, ICAR-SBI RC, Karnal and ICAR-CSSRI, Karnal for providing necessary facilities to conduct this experiment. Thanks are also due to B.N. Manjhi and Vijay Kumar in managing the field experiments.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a). AMMI biplot PC1 Vs Cane Yield (CY); (b). AMMI Biplot PC1 Vs PC2 for Cane yield (t ha−1); Where G = Genotypes (1–24) and E = Environments (1–8) of different ECiw levels viz., Normal (E1, E2), 4EC (E3, E4), 8EC (E5, E6) and 12 EC (E7, E8).
Figure 1. (a). AMMI biplot PC1 Vs Cane Yield (CY); (b). AMMI Biplot PC1 Vs PC2 for Cane yield (t ha−1); Where G = Genotypes (1–24) and E = Environments (1–8) of different ECiw levels viz., Normal (E1, E2), 4EC (E3, E4), 8EC (E5, E6) and 12 EC (E7, E8).
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Figure 2. (a). AMMI biplot PC1 Vs CCS Yield (CCSY); (b). AMMI Biplot PC1 Vs PC2 for CCS yield (t ha−1); Where G = Genotypes (1–24) and E = Environments (1–8) of different ECiw levels viz., Normal (E1, E2), 4EC (E3, E4), 8EC (E5, E6) and 12 EC (E7, E8).
Figure 2. (a). AMMI biplot PC1 Vs CCS Yield (CCSY); (b). AMMI Biplot PC1 Vs PC2 for CCS yield (t ha−1); Where G = Genotypes (1–24) and E = Environments (1–8) of different ECiw levels viz., Normal (E1, E2), 4EC (E3, E4), 8EC (E5, E6) and 12 EC (E7, E8).
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Figure 3. Discriminativeness vs. representativeness for Cane yield (t ha−1).
Figure 3. Discriminativeness vs. representativeness for Cane yield (t ha−1).
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Figure 4. Discriminativeness vs. representativeness for CCS yield (t ha−1).
Figure 4. Discriminativeness vs. representativeness for CCS yield (t ha−1).
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Figure 5. Mean Vs. Stability biplot for Cane yield (tha−1).
Figure 5. Mean Vs. Stability biplot for Cane yield (tha−1).
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Figure 6. Mean Vs. Stability biplot for CCS yield (t ha−1).
Figure 6. Mean Vs. Stability biplot for CCS yield (t ha−1).
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Figure 7. GGE biplot ranking of genotypes for cane yield (t ha−1).
Figure 7. GGE biplot ranking of genotypes for cane yield (t ha−1).
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Figure 8. GGE biplot ranking of genotypes for CCS yield (t ha−1).
Figure 8. GGE biplot ranking of genotypes for CCS yield (t ha−1).
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Figure 9. ‘Which won where’ polygon for Cane yield (t ha−1).
Figure 9. ‘Which won where’ polygon for Cane yield (t ha−1).
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Figure 10. ‘Which won where’ polygon for CCS yield (t ha−1).
Figure 10. ‘Which won where’ polygon for CCS yield (t ha−1).
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Table
Table 1. Analysis of variance of main effects and interactions for Cane yield, CCS t ha−1, NMC, SCW and Pol % and variability explained in percent.
Table 1. Analysis of variance of main effects and interactions for Cane yield, CCS t ha−1, NMC, SCW and Pol % and variability explained in percent.
SourcedfCane Yield ha−1CCS t ha−1NMC ‘000 no. ha−1SCW (kg)Pol%
M.S.VE (%)M.S.VE (%)M.S.VE (%)M.S.VE (%)M.S.VE (%)
Genotypes234453.61 **25.4168.57 **24.881413.3 **21.810.75 **42.7445.60 **82.48
Environments738,571.59 **66.98627.28 **67.6014,003.6 **65.782.48 **43.2717.17 **9.45
Interactions161190.48 **7.618.12 **8.12114.9 **12.410.03 **13.990.64 *8.07
IPCA129605.88 **57.2911.84 **65.10320.5 **50.250.11 **54.511.88 **53.02
IPCA227224.25 **19.743.23 **16.52157.0 **22.910.06 **27.550.87 **22.02
IPCA325148.54 **12.102.00 **9.48125.1 **16.910.03 **12.150.46 ns11.25
IPCA423113.50 **8.511.46 **6.3465.2 **8.100.01 *3.770.22 ns5.97
Residuals38422.16 ns000.46 ns0015.18 ns000.002 ns000.49 ns00
Note: df = degree of freedom, M.S = Mean sum of square, VE% = variability explained %, * and ** indicates 5% and 1% level of significance, ns = non significance.
Table
Table 2. Average values of observed traits for genotypes, AMMI stability value (ASV), rank of the ASV (RA), rank of trait mean (RM) and genotype selection index (GSI).
Table 2. Average values of observed traits for genotypes, AMMI stability value (ASV), rank of the ASV (RA), rank of trait mean (RM) and genotype selection index (GSI).
ClonesCane Yield ha−1CCS t ha−1NMC Thousands ha−1SCW (kg)Pol%
MeanASVRARMGSIMeanASVRARMGSIMeanASVRARMGSIMeanASVRARMGSIMeanASVRARMGSI
G157.677.781810287.584.0821123371.572.93131140.800.6622163815.410.69152136
G253.558.482320435.734.3122224454.742.15916250.750.4918183615.880.2241923
G371.232.21714217.970.594111565.741.9785130.790.237172417.160.3371219
G437.338.242232510.805.332432759.683.501612281.160.2363917.301.2022931
G570.604.881423376.343.7219183746.404.552123440.730.8824194318.852.2923326
G644.694.45135189.830.2016760.093.491510251.110.321241617.002.40241337
G761.761.18316196.370.706172359.943.851811290.720.3111203116.101.18211738
G840.337.91199288.643.261692569.805.49222240.830.4516143016.070.88201838
G942.273.211021316.321.4311193054.098.342417410.680.214232718.130.5610414
G1039.587.471718355.324.8323234650.911.18520250.800.4415153014.850.84172239
G1131.542.17622285.951.189213052.466.632318410.690.152222417.190.58111122
G1263.988.752424485.273.7218244246.512.311022320.660.6321244519.040.29628
G1343.288.01218299.553.782082860.233.62178251.010.441482217.320.15189
G1456.600.72117186.400.503161945.403.161424380.890.5819133316.930.2021416
G1566.495.741611278.593.5217102755.900.80314170.950.4213122517.290.2251015
G1655.153.281171810.380.3324667.251.47114150.970.602092918.000.8819524
G1767.081.38412167.440.888142255.690.78215170.950.141111215.630.2132023
G1842.097.932062610.203.091552065.164.31206261.020.461762317.780.6714620
G1942.032.87919286.091.6113203356.120.77113140.710.288212916.660.60121628
G2078.934.96151169.621.541271963.132.41117181.220.301021214.360.84182442
G2170.872.66841212.062.641411568.440.874371.010.1637919.740.8016117
G2251.231.97515207.470.665131851.533.901919380.970.6723103316.820.5391524
G2354.610.82213156.850.787152248.452.881221331.080.30951414.560.67132336
G2476.223.441221411.421.261021260.211.4669151.230.2351617.600.438715
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Kumar, R.; Dhansu, P.; Kulshreshtha, N.; Meena, M.R.; Kumaraswamy, M.H.; Appunu, C.; Chhabra, M.L.; Pandey, S.K. Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress. Sustainability 2023, 15, 1119. https://doi.org/10.3390/su15021119

AMA Style

Kumar R, Dhansu P, Kulshreshtha N, Meena MR, Kumaraswamy MH, Appunu C, Chhabra ML, Pandey SK. Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress. Sustainability. 2023; 15(2):1119. https://doi.org/10.3390/su15021119

Chicago/Turabian Style

Kumar, Ravinder, Pooja Dhansu, Neeraj Kulshreshtha, Mintu Ram Meena, Mahadevaswamy Huskur Kumaraswamy, Chinnaswamy Appunu, Manohar Lal Chhabra, and Sstish Kumar Pandey. 2023. "Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress" Sustainability 15, no. 2: 1119. https://doi.org/10.3390/su15021119

APA Style

Kumar, R., Dhansu, P., Kulshreshtha, N., Meena, M. R., Kumaraswamy, M. H., Appunu, C., Chhabra, M. L., & Pandey, S. K. (2023). Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress. Sustainability, 15(2), 1119. https://doi.org/10.3390/su15021119

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