Adaptability and Stability Comparisons of Inbred and Hybrid Cotton in Yield and Fiber Quality Traits

Cotton (Gossypium hirsutum L.) is the most important fiber crop worldwide. Characterizing genotype by environment interaction (GEI) is helpful to identify stable genotypes across diverse environments. This study was conducted in six environments to compare the performance and stability of 11 inbred lines and 30 intraspecific hybrids of cotton. Analysis of variance using the additive main effects and multiplicative interaction model revealed that genotype (G), environment (E), and GEI had highly significant effects on yield and fiber quality traits. Mean comparisons among genotypes showed that most hybrids had higher means for yield and fiber quality traits than inbred genotypes. Additionally, a larger portion of the total variability in yield traits was explained by E than G and GEI. However, G and GEI combined contributed more to the total variance in fiber traits than E. The first three interaction principal components explained the majority of GEI in all traits under study. For most traits, the environments were not clustered together, implying contrasting interaction with genotypes. Stability measurements indicated that most hybrids showed more stable performance than inbred lines for all traits. The hybrids SJ48-1 × Z98-15 and L28-2 × A2-10 displayed both better performance and stability in yield and fiber quality traits. Our results show the importance of hybridization for improving cotton yield and fiber quality in a wide range of environments.


Introduction
Cotton is a prime fiber crop and plays an important role in improving the economy of a country through industrial drive. Globally, cotton crops are cultivated in more than 80 countries and China is one of the leading cotton producing and consuming countries [1]. Thus, cotton is a substantial source of Chinese income. Breeders are interested in developing genotypes that not only have superior yield but also show better adaptation to multiple environments. Hybrid breeding can be used to obtain more stable and better yields in agronomic crops. Much success has already been achieved through hybrid breeding in rice [2] and maize [3]. The commercialization of hybrid cotton in China started around 1980 [4]. The planting area increased in subsequent years with the development of hybrid Bt cotton [5,6]. Enhanced adaptability of a genotype to different environments promotes wide cultivation by farmers. However, different genotypes respond differently to environmental variations. Altered performance of a genotype for a given trait across different environments is defined as genotype by

Plant Materials and Field Experiments
To determine whether hybrids perform better than inbred cotton in different environments, the current study considered 30 intraspecific F 1 hybrids and 11 inbred lines. The inbred lines had diverse genetic backgrounds and performed well in the three main cotton belts of China [1]. Table 1 provides a list of all genotypes. The field experiments were conducted at three different research centers of the Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Alar, and Wuwei, in 2016-2017. Anyang (Yellow River cotton belt) is in Henan Province and has a temperate continental climate. Alar (Northwest inland cotton belt) is in Xinjiang Province and has a temperate continental arid climate. Wuwei (Yangtze River cotton belt) is in Anhui Province, with a subtropical climate. The environmental variables and soil type in these locations all differed. The soil texture in Anyang is loam, whereas Alar and Wuwei have sandy and clay soil, respectively. The seasonal average, maximum, and minimum temperatures, total precipitation, and total sunshine hours of each trial can be seen in Table 2. Seeds were sown in late April at each location. All plant material was organized in a randomized complete block design (RCBD) with three replications. The planting densities in Wuwei, Anyang, and Alar were 1.80, 3.75, and 12 plants per m 2 , respectively. A higher planting density is recommended in Alar because of the arid climate and short frost-free period. The crop was drip-irrigated 8-10 times in Alar throughout the entire growing season. The irrigation application was 6-8 times in Anyang and 4-6 times in Wuwei through the furrow irrigation system. Agronomic practices recommended by the regional authority were applied at each location during the whole growing season [36,37]. Here in Table 2, MT: mean of daily temperature; MMT: mean of daily maximum temperature; MmT: mean of daily minimum temperature; TP: total precipitation; TSS: total sunshine.

Yield and Fiber Trait Evaluation
In mid-September, the number of bolls was counted for all plants in each line. The number of bolls per plant was obtained by dividing the total number of bolls by the total number of plants. In the last week of October, 50 fully opened bolls were picked randomly from each plot and ginned using a 10-saw laboratory gin. The seed cotton and lint were weighed to calculate the boll weight (g per boll). The seed cotton was harvested by hand, picking twice to avoid environmental damage, and weighed after drying; this weight was converted to seed cotton yield (SCY, kg ha −1 ). Lint yield was calculated by multiplying the seed cotton yield by the lint percentage. Lint percentage was determined as lint weight/seed cotton weight × 100. Subsamples of lint collected from each plot were used to measure fiber quality traits using a High Volume Instrument (HVI) 900 (Test Center of Cotton Fiber Quality affiliated with the Agriculture Ministry of China, Institute of Cotton Research of Chinese Academy of Agricultural Science, Anyang, Henan, China). Data for the fiber quality traits fiber length (FL, mm; upper half mean length), fiber elongation (FE, %), fiber uniformity (FU, %), fiber strength (FS, cN/tex), and micronaire (MIC, unit) were collected from the above standard lab. For fiber elongation (FE, %), no separate/specific HVI calibration was performed except for calibrating the first test sample with the international standard provided by USTER.

Statistical Analysis
Simple analysis of variance with the Statistix 8.1 software showed that the trial year had a significant effect on all traits under study (Supplementary Materials, Table S1). Therefore, the combinations of the three locations and two years were considered six distinct environments. All phenotypic data from the six environments and 41 genotypes were subjected to analysis of variance and principal component analysis (PCA) based on singular value decomposition [38] using the AMMI model [32]. The number of significant interaction principal components (IPCs) was determined by considering their contribution to the total GEI and p-value. The mean performance of different genotypes was compared using Tukey's honest significant difference (HSD) test at p < 0.05 with the Statistix 8.1 software. Stability analysis was conducted with the Eberhart and Russell model [11] and coefficient of variability [35]. The javascript tool Genotype × Environment Analysis with R (GEA-R) [39] was used to perform AMMI model analysis and a visual assessment of all results as described in [34]. The freely available 64-bit version of the R Studio statistical software R Version 3.2.2 [40] was used to perform all statistical analysis.

Genotype by Environment Interaction and Stability Analysis of Yield Parameters
Analysis of variance using the AMMI model for yield parameters is presented in Table 3. The results show that E, G, and G × E had highly significant effects (p < 0.001) on all yield traits. Furthermore, the first three IPCs were highly significant for all yield and contributing traits. E as a main effect explained 97.5% of the total sum of squares of BN, whereas G and G × E explained only 0.9% and 1.5% of the variation, respectively. A mean comparison test of genotypes based on the six environments showed that hybrids K8-1 × RP24-10 and L28-2 × 851-2 had the highest mean ( Table S2). Furthermore, the first three principal components (IPC1 to IPC3) together described 98.8% of GEI. GEI scores for all traits are represented through biplots. The perpendicular line inside this biplot shows the grand mean. All genotypes clustered together had similar performance and the environments clustered together had similar effects. All genotypes and environments on the left and right sides of this perpendicular line showed lower and higher performances than the grand mean, respectively.
E and G described 37.3% and 33.2% of the total sum of squares for FE, respectively. However, G × E had a non-significant effect on FE (Table 4). Mean comparisons among genotypes showed that the inbred line SJ48-1 and hybrid SJ48-1 × DT-8 had the highest means (6.85%) and the inbred line RP24-10 had the lowest mean (6.62%) for FE (Supplementary Materials, Table S3). Furthermore, the first two principal components explained a big portion of GEI. The Anyang environments had negative interaction scores compared with those at Alar and Wuwei (Supplementary Materials, Figure S9a). However, the performances of all environments were very similar. Moreover, the range of genotypic interaction with the environments was very narrow. These findings indicate that FE is mostly dependent on the genetic potential of genotypes and not their interaction effects with different environments. The results of stability measurement showed that the hybrid L28-2 × GC-8 was the most stable. The hybrid SJ48-1 × GC-8 and inbred lines SJ48-1 and GC-8 were adaptable to all environments under study (Supplementary Materials, Figure S9a). The hybrids Zhong901-19 × GC-8, L28-2 × GC-8, L28-2 × 851-2, SJ48-1 × 851-2, ZB-1 × A2-10, ZB-1 × DT-8, SJ48-1 × Z98-15, and SJ48-1 × RP24-10, and inbred line ZB-1 showed low CV% values and stable performance for FE (Supplementary Materials, Figure S10). The results showed that E as a main effect accounted for 45.7% of the variability of FU, whereas G and G × E described 28.2% and 26.1% of the total variance of this trait, respectively (Table 4). Among all genotypes, the hybrid ZB-1 × GC-8 had the highest mean (85.9%) and the inbred line RP24-10 had the lowest mean for FU (82.5%) (Supplementary Materials, Table S3). The first three principal components explained a large portion of GEI. The Anyang environment had negative interaction scores for both years (Supplementary Materials, Figure S11a) and showed better performance than the Alar and Wuwei environments. However, it is difficult to determine which environmental factor led to the difference. Stability analysis for FU revealed that the hybrids Zhong901-19 × RP24-10 and L28-2 × RP24-10 and inbred lines Zhong901-19, L28-2, and DT-8 were the most adaptable (Supplementary Materials, Figure S11b Figure S12).

Discussion
Investigating GEI is important for identifying high-yielding and stable genotypes for specific or diverse environments. The current study aimed to compare the performance, adaptability, and stability of inbred and hybrid cotton lines in yield and fiber quality traits. Phenotypic data for the 41 genotypes tested in six environments were analyzed with the AMMI model. The analysis of variance showed that the effects of E, G, and GEI were highly significant for all studied traits. The results further suggested that most of the phenotypic variation of yield traits was explained by E rather than G or GEI. It was observed that more than 80% of the total variance of BN, BW, SCY, LY, and LP was explained by E. In contrast, fiber quality traits had less variation due to E compared with yield traits. These findings were similar to many previously published reports on cotton [20][21][22][23][41][42][43][44], in which the researchers concluded that the majority of the total variance of yield traits was caused by E rather than G or GEI. However, a major portion of the total variance of fiber traits was attributable to G and GEI. Research results in wheat [45], rice [29], maize [46,47], and other crops [34,48] also highlight the importance of E in determining phenotypic variation in yield-contributing traits. In this study, the significant contribution of E to the total variance of all traits reflects the diverse environmental conditions across the testing locations. Moreover, yield traits are quantitative, as they are controlled by multiple genes. Therefore, the larger variation in yield traits is probably due to the various genes interacting with the environment. The major contribution of E to the total variability of cotton yield traits demands more studies to explore the actual causes of GEI.
The results of the mean comparison tests for genotypes reveal that many hybrids had better performance in yield and fiber quality than the inbred lines. Analysis using the AMMI model showed that a large part of GEI was explained by the first three principal components. Further results indicate that the environments at the study locations were not clustered together due to their unpredictable GEI effects. Therefore, they could not be grouped into so-called mega-environments. Mega-environments are homogeneous groups of locations that enable fewer representative environments to be selected for the assessment of genetic potential of genotypes to reduce research costs [49]. However, the identification of mega-environments is not easy. Baxevanos et al. [50] used four different data sets of different years and locations to study GEI in cotton. Their results revealed crossover G × E interactions, but were not appropriate for the identification of mega-environments. Another study of agronomic traits in cotton by Campbell and Jones [22] also reported similar results. Research results in many other crops have also shown similar inconsistency in location grouping, showing the complexity of identifying mega-environments [34,[51][52][53]. Failure to classify environments into mega-environments can be resolved by considering several weather, soil, and management statistics at the testing locations [34]. Regardless, for the crossover GEI effects of studied locations, the environment of Anyang was discriminating for most of yield and fiber traits during both years.
Highly unpredictable environment variables require stability testing of the mean performance of genotypes in different environments. Thus, stability measurement is vital to identify high-yielding and stable genotypes. The results of stability analysis in the current study revealed that hybrid genotypes showed better and more stable performance than inbred cotton lines for yield and fiber quality traits. Furthermore, the hybrids were more adaptable to different environments. We found many outstanding hybrids that showed adaptable and stable performances for specific traits or sets of traits. For example, the hybrid SJ48-1 × Z98-15 produced the highest SCY and LY values and showed adaptability in these traits as well as BN and BW. Another hybrid, L28-2 × A2-10, also showed better performance and adaptability than the other lines in traits, like BN, SCY, FUHML, and FS. Two stability measurements were used in this study that were not consistent regarding the identification of stable genotypes. For instance, six hybrids showed higher SCY values and adaptability to the tested environments on the basis of the Eberhart and Russell model. However, based on CV%, nine hybrids had good and stable performance compared with the other genotypes. The reason for this difference may be the calculation methods that stability analysis depends on. Our results agree with previous research findings [54][55][56]. Conceptually, a successful genotype not only produces high yield but also shows stable performance in diverse environments. The hybrids identified in the current study with good and stable performance can be called successful genotypes for the tested locations or regions with similar agro-ecologies.
Recently, cotton and other crops have been facing increased challenges from biotic and abiotic stressors. The consequences of these factors should be kept in mind before selection and release of approved genotypes. In this regard, we believe that hybridization in cotton can be a useful tool to cope with these challenges. Superior offspring in a desired character can be produced by crossing genetically superior and physiologically effective inbred parents. Previously, studies comparing transgenic cultivars and non-transgenic recurrent parents have shown a significant difference between these cultivars in yield and quality traits [24,41,57]. Studies of hybrids of rice [16,58] and maize [59,60] have concluded that hybrid genotypes are not only high-yielding but also show stable performance in different environments. The ultimate objective of the current study was to compare the performance and stability of hybrids and inbred lines of cotton in different environments. The results enable us to understand the actual behavior of genotypes. High-yielding and stable genotypes were identified that can be used to exploit the genetic mechanism of yield vigor of cotton with sophisticated genetic tools. Furthermore, this study also provides valuable GEI statistics for a range of yield and fiber traits. However, further research is required to uncover the elusive aspects of GEI for yield traits of cotton.

Conclusions
Using the AMMI model, the current study revealed that the majority of the total variance of yield component traits was explained by E. In contrast, the variability in fiber quality traits was mostly attributable to the effects of G and GEI. Comparison of mean performance among genotypes revealed that hybrids had superior yield and fiber quality to inbred lines. The GEI results showed that the environments at the study locations had contrasting effects on genotype performance. The environment at the Anyang location was favorable for the majority of traits under study. Stability measurements using different approaches revealed that the hybrids had better and more stable performance in diverse environments than the inbred lines. The hybrid SJ48-1 × Z98-15, followed by L28-2 × A2-10, showed the best performance and stability in yield and fiber quality traits. Therefore, these genotypes can be recommended for wider cultivation or for regions with similar agro-climates. Collectively, our results suggest that hybridization in cotton can be useful to obtain high yield and fiber quality in diverse environments. However, further research to identify and quantify the relationship between environmental variables and genotype will help to achieve this goal.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4395/9/9/516/s1, Table S1: Analysis of variance with p value for yield and fiber quality traits tested in three locations and two years.  Figures S1, S6, S9, S11, and S14 shows mean performance and stability of 41 genotypes at six environments for number of bolls per plant, lent percentage, fiber elongation, fiber uniformity and fiber micronaire, respectively. Figures S2-S5, S7, S8, S10, S12, S13, and S15 represents CV% of 41 genotypes at six environments for number of bolls per plant, boll weight, seed cotton yield, lint yield, lent percentage, fiber upper half mean length, fiber elongation, fiber uniformity, fiber strength, and fiber micronaire, respectively.
Author Contributions: C.X. and J.W. designed the experiments. L.G., T.Q., H.T., and H.W. constructed hybrid plant population and field investigations. K.S., X.Z., X.Q., M.Z., J.F. and B.Z. performed data curation. K.S. and M.S.I. performed data analysis. K.S. wrote the manuscript. C.X. and J.W. contributed to the preparation of the final manuscript. All authors read and approved the final manuscript.