Microsoft Word - 15-Agra_48148 1223 Bioscience Journal Original Article Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 PHENOTYPIC ADAPTABILITY OF COTTON GENOTYPES TO THE BRAZILIAN CERRADO FOR YIELD AND FIBER QUALITY ADAPTABILIDADE FENOTÍPICA DE GENÓTIPOS DE ALGODÃO AO CERRADO BRASILEIRO PARA PRODUTIVIDADE E QUALIDADE DE FIBRAS. Mayara Fávero COTRIM¹; Francisco José Correa FARIAS²; Luiz Paulo de CARVALHO²; Larissa Pereira Ribeiro TEODORO¹; Carlos Antonio da SILVA JUNIOR³; Paulo Eduardo TEODORO¹ 1. Universidade Federal de Mato Grosso do Sul - UFMS, Chapadão do Sul, MS, Brasil; 2. Embrapa Algodão, Campina Grande, PB, Brasil; 3. Universidade do Estado do Mato Grosso - UNEMAT, Sinop, MT, Brasil. eduteodoro@hotmail.com ABSTRACT: Studies on the adaptability and stability are fundamental for plant breeding as they are an alternative to reduce the effects of genotypes x environments interaction (GxE). Moreover, they help identify cultivars with predictable behavior, which are responsive to environmental improvements, subsidizing cultivar recommendation. This study aimed to evaluate the genotypes x environments interaction in cotton genotypes grown in the Brazilian Cerrado and identify genotypes for favorable and unfavorable environments. During the 2013/2014 and 2014/2015 seasons, 19 competition trials were carried out with cotton in a randomized block design, with 12 treatments, and four replications. The traits cotton seed yield, fiber percentage, fiber length, and fiber strength were evaluated. Results revealed significant GxE interaction for all the fiber traits evaluated. Genotype BRS 369 RF revealed general adaptability and high predictability for the fiber traits evaluated. KEYWORDS: Gossypium hirsutum. Adaptability and stability. Plant breeding. INTRODUCTION The production and release of cotton cultivars (Gossypium hirsutum) in Brazil rely on the producers’ demands, who aim to meet the requirements of the textile industry (MORELLO et al. 2010, MORELLO et al. 2012, MORELLO et al. 2015). Before the cultivar recommendation, multiple trials are carried out to evaluate genotypes in different environments. The differential behavior of a genotype for a given trait in function of the environmental variation is defined as genotypes x environments interaction (GxE). Therefore, investigating this interaction is crucial for precisely recommending the best genotypes for a given region. Breeders carry out several trials at the final stages of breeding programs. Embrapa Algodão has two experimental networks in two large regions: Cerrado and the semi-arid. The Cerrado biome occupies about 22% of the national territory and has peculiar climatic characteristics regarding rainfall regime, temperature, relative humidity of the air, and different types of soil. For these reasons, the GxE interaction in Cerrado environments should be investigated for the recommendation of cotton genotypes. Several methodologies and methods have been made available for the investigation of the GxE interaction. The method proposed by Eberhart and Russel (1966) is based on the linear regression between the phenotypic value of a given trait in function of the environmental index. This index measures the quality of the environment, in which favorable environments have positive values, and unfavorable environments have negative values. The method also investigates the predictability of the genotype’s behavior and thus enables the recommendation of genotypes for favorable and unfavorable environments and for environments with wide adaptability. The GxE interaction in agronomic traits such as cotton seed yield, fiber percentage, and fiber yield has been investigated in Brazil (SOUZA et al. 2006; SUINAGA; BASTOS; PACIFICI, 2006; FARIAS et al., 2016; TEODORO et al., 2018). These studies have identified genotypes that respond to environmental improvement, genotypes with higher rusticity, and genotypes with wide adaptability. The adequate recommendation for each environment increases the national production. However, studies investigating the GxE interaction in technological traits of cotton cultivars are still scarce in Brazil, and the only one available is that of Carvalho, Farias and Rodrigues (2015). The authors used the Eberhart and Russel (1966) method to identify genotypes with high predictability for all traits. Received: 08/04/19 Accepted: 20/12/19 1224 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 Carvalho, Farias and Rodrigues (2015) also demonstrated that the GxE interaction in technological traits is complex, with a wide variation in genotype order throughout environments. Thus, this factor must be considered in genotypes recommendation to meet the demands of the textile industry. This study aimed to evaluate the GxE interaction in cotton genotypes grown in the Brazilian Cerrado and identify genotypes for favorable and unfavorable environments. MATERIAL AND METHODS Nineteen trials of cotton cultivars were carried out during the 2013/2014 and 2014/2015 seasons. The environments consisted of combining municipalities and seasons, according to the edaphoclimatic characteristics (Table 1) and graphical representation of the locations (Figure 1). The 12 standard cultivars were TMG 41 WS, TMG 43 WS, IMA CV 690, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DP 555 BGRR, DELTA OPAL, BRS 286, BRS 335, BRS 368 RF, and BRS 369 RF. Cultivars were not the same in all years and locations, and thus the selection was based on the rainfall in the largest number of experiments, aiming to decrease the imbalance of the analyses of variance. Table 1. Location and altitude (ALT), latitude (LAT), longitude (LONG), annual rainfall (RAIN), and average annual temperature (TEMP) in the 2013/2014 and 2014/2015 seasons. Location/State Sigla¹ Season Alt. (m) Lat. (S) Long. (W) Rain (mm) Temp. (ºC) Climate2 Trindade/MG TRI 2013/2014 927 21º06' 44º10' 1467 23.2 Aw Santa Helena do Goias/GO SHE1 2013/2014 562 17º48' 50º35' 1539 24.3 Aw SHE2 2014/2015 Pedra Preta/MT PPA1 2013/2014 248 16º37' 54º28' 489 25.1 Bsh PPA2 2014/2015 Primavera do leste/MT PVA1 2013/2014 465 15º33' 54º17' 1784 22.0 Aw PVA2 2013/2014 PVA3 2014/2015 PVA4 2014/2015 Campo Verde/MT CV1 2013/2014 736 15º32' 55º10' 1902 26.3 Af CV2 2014/2015 Sinop/MT SIN 2013/2014 345 11º51' 55º30' 1818 25.0 Aw Luiz Eduardo Magalhães/BA LEM 2013/2014 769 12°5' 45°47' 1511 24.2 Aw São Desidério/BA SDES 2013/2014 497 12º21' 44º58' 1289 24.7 Aw Montividiu/GO MON 2013/2014 821 17º26' 51º10' 1512 23.0 Aw Magalhães de Almeida/MA MAG 2013/2014 36 03º23' 42º12' 1430 27.2 Aw Teresina/PI TER 2013/2014 72 05º05' 42º48' 1349 27.6 Aw Chapadão do Sul/MS CHA 2014/2015 810 18º47' 52º37' 1600 22.7 Aw Sorriso/MT SOR 2014/2015 365 12º32' 55º42' 1883 25.0 Aw 1: locations abbreviations 2: Köppen classification. The experiment was carried out in a randomized block design with 12 treatments and four replications. The experimental unit consisted of four 5-m rows spaced at 0.90m between rows, with a density of 9 m-1 plants. In each experimental unit, cotton seed yield (Y) was evaluated in the two central rows, corrected to 13% moisture, and extrapolated to kg ha-1 by the covariance method (VENCOVSKY; CRUZ, 1991). Data were subject to joint analysis of variance, according to the model described in Equation 1: Yijk = µ + B/Ejk + Gi + Ej + GEij + eijk (1) where: Yijk is the observation in the k-th block, evaluated in the i-th genotype and j-th environment; μ is the overall mean of the experiments; B/Ejk is the effect of block k within the environment j; Gi is the effect of the i-th genotype considered as fixed; Ej is the effect of the j-th environment considered as random; GEij is the random effect of the genotype i 1225 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 x environment j interaction; eijk is the random error associated with the observation Yijk. The means of the genotypes were compared by the Scott-Knott test at the 5% probability. After verifying the significance of the GxE interaction, data were subject to the analyses of adaptability and stability, using the methodologies of Eberhart and Russell (1966), according to the following statistical model: ijj1i0iij ψIββY  (2) where: ijY is the mean of genotype i in environment j; 0iβ is the linear coefficient of the i-th genotype; 1iβ is the regression coefficient that measures the response of the i-th genotype to the variation of the environment j; jI is the environmental index             ga Y g Y I i j ij j j j ; ijψ are the random errors, in which each component can be decomposed as ijijij εδΨ  , where ijδ is the deviations of regression and ijε is the mean experimental error. The analysis of adaptability and stability proposed by Eberhart and Russell (1966) is based on the linear regression analysis, in which genotypes with general or wide adaptability are those with =1; genotypes with specific adaptability to favorable environments are those with >1; and genotypes with specific adaptability to unfavorable environments are those with <1. The stability was evaluated by the deviations of regression ( 2di) associated with the coefficient of determination (R²); genotypes with 2di= 0 and R² above 70% were considered of to have predictable behavior, and genotypes with 2di≠ 0 and R² below 70% were considered to have unpredictable behavior. The hypotheses of interest are H0: β1i = 1 versus H1: β1i ≠ ; and 0σ:H 2di0  versus 0σ:H 2 di1  . The hypotheses were evaluated by the t and F tests at the 5% probability, respectively. Afterward, genotypes were classified into one of the six classes described in Table 2. All analyses were carried out in the Genes software (CRUZ, 2006). RESULTS AND DISCUSSION Joint analysis of variance and classification of environments The F test (Table 3) indicated the similarity between the effects of genotypes for cotton seed yield (Y). However, the other traits had significance for the effects of genotypes (p≤0.05), indicating genetic variability for traits FP, FL, and FR. The presence of the GxE interaction indicated the existence of variability of genotypes behavior throughout the environments for all the evaluated traits. Similar results were observed by other authors when studying the presence and absence of the GxE interaction in cotton in several locations in Brazil (SOUZA et al., 2006; SUINAGA; BASTOS; PACIFICI, 2006; SILVA FILHO et al., 2008; CARVALHO; FARIAS; RODRIGUES 2015; FARIAS et al. 2016). Table 3. Summary of the analysis of variance for cotton seed yield (Y), fiber percentage (FP), fiber length (FL), and fiber strength (FS) of 12 early cotton genotypes carried out in 19 locations in the Brazilian Cerrado, in the 2013/2014 and 2014/2015 seasons. Sources of Variation DF Y FP FL FR Mean Square Block/Environment 54 582221.44 4.32 0.73 2.91 Genotypes (G) 11 1310128.57ns 136.68* 23.79* 79.78* Environments (E) 18 61511492.56* 104.32* 35.18* 88.02* GxE 198 874829.53* 7.84* 1.06* 3.76* Residue 627 333939.13 2.51 0.58 2.33* CV (%) 13.88 3.77 2.55 5.04 *, ns: significant and non-significant, respectively, by the F test at the 5% of probability. DF: degrees of freedom. CV: coefficient of variation The significance of the GxE interaction may be due to predictable factors (soil management practices, pests and diseases, supplementary irrigation, base fertilization) and/or unpredictable 1226 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 factors (temperature, relative humidity of the air, and solar radiation during the crop cycle). The causes of this interaction are attributed to the adaptive factors and the scale of variables measurement. Therefore, the use of methods such as that of Eberhart and Russel (1966) are necessary to investigate the GxE interaction in these traits and identify genotypes with adaptation and behavior predictability for the environments of the Brazilian Cerrado. Table 4 shows estimates of the environmental indices obtained by the Eberhart and Russel (1966) method for the 19 evaluated environments. Positive values indicate that the environment had mean above the overall mean of all environments, and consequently, negative values indicate that the mean of the environment is below the overall mean of all environments. Environments PVA1 and SDES were the only ones with positive values, which characterizes them as favorable environments. Genotypes recommended for these environments should be able to respond to these favorable conditions, that is, they should be able to respond satisfactorily to high investments in agricultural practices (such as fertilization, irrigation, and pest, diseases, and weeds control). Table 4. Environmental index (Ij) of cotton seed yield (Y), fiber percentage (FP), fiber length (FL), and fiber strength (FS) of 12 cotton genotypes in the 19 trials carried out in Cerrado in 2013/14 and 2014/15 seasons. Environment¹ Ij Y FP FL FS TRI 1881.59 0.1951 0.6318 -1.0498 SHE1 946.37 -0.7849 0.6649 0.5927 PVA1 1162.16 0.5265 1.5330 1.0809 CV1 -373.16 -0.5335 -0.2070 -0.7629 SIN -2747.02 2.8638 -1.9776 -2.6516 PVA2 101.84 -0.1414 0.4633 0.5404 PPA1 609.04 -1.3783 0.7585 -1.4252 LEM -257.66 0.5692 0.6347 1.0438 SDES 1345.84 0.9109 0.6262 0.1307 MONT -1348.88 0.3988 -1.4586 0.2125 MAG 264.83 -2.0941 0.0860 1.9065 TER 29.99 1.9051 -1.0628 1.0036 PPA2 375.28 -0.2328 0.1130 -0.8877 CV2 -440.47 1.2611 -0.2088 0.1873 SHE2 -934.86 -2.1762 -0.2080 -0.9346 CHAP 605.47 1.1151 -0.0165 -1.3631 PVA3 629.80 -3.2372 0.4305 3.1186 SOR -1863.59 0.3701 -0.9459 0.3709 PVA4 13.37 0.4626 0.1433 -1.1131 1Abbreviations described in Table 1. Conversely, environments CV1, SIN, and SHE2 were classified as unfavorable, except for the trait fiber percentage (FP) in the SIN environment. In these environments, genotypes with higher rusticity are desirable since a lower level of technology is employed. The classification of the other environments did not agree with each other for all traits, revealing higher complexity in the recommendation of cotton genotypes when considering both agronomic and technological traits. Adaptability and stability of agronomic traits Table 5 shows the mean cotton seed yield ( ) and the parameters of adaptability ( ) and stability ( 2di and R 2), based on the Eberhart and Russell (1966) method. Considering these parameters, genotype IMA CV 690, besides the high yield, is also the most suitable for cultivation in favorable environments ( > 1) and has high behavior predictability (R²>90%). This genotype can respond favorably to the environmental stimulus and should be used by producers who use high technology level. 1227 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 Table 5. Mean cotton seed yield (kg ha-1), coefficients of regression ( ), deviations of regression ( 2di), and coefficient of determination (R2) obtained by the Eberhart and Russel (1966) for12 cotton genotypes in 19 environments in the Brazilian Cerrado. Genotypes Means( ) 2 di(x10 6) R2 TMG 41 WS 4225.87 a 0.99ns 1.22* 86.60 TMG 43 WS 4079.31 b 0.84* 1.22* 82.62 IMA CV 690 4473.31 a 1.17* 1.16* 90.33 IMA 5675 B2RF 3943.15 b 0.74* 2.21* 71.26 IMA 08 WS 4078.12 b 0.76* 1.14* 80.19 NUOPAL 4115.30 b 0.99 ns 0.53 ns 90.74 DP 555 BGRR 4222.43 a 1.10 ns 0.02 ns 95.08 DELTA OPAL 4135.37 b 1.18* 0.68* 92.60 BRS 286 4188.34 a 1.01 ns 1.25* 87.05 BRS 335 4053.95 b 1.06 ns 1.70* 85.88 BRS 368 RF 4191.66 a 0.98 ns 0.61* 90.03 BRS 369 RF 4246.14 a 1.11 ns 0.45ns 92.89 Mean 4162.74 *.ns: significant and non-significant, respectively, by the F test at the 5% of probability. Among the genotypes, the most recommended for unfavorable environments were TMG 43 WS and IMA 08 WS. Although their mean was below the overall mean of all environments, they have behavior predictability (R²> 80%) and may be a good option for producers who grow cotton in the off-season, which is a period of high climatic instability in the Brazilian Cerrado. According to the method applied, the ideal genotype should have high mean for the evaluated trait, = 1 (general adaptability), not-significant 2 di, and high R² (> 70%). Thus, genotypes BRS 369 RF and DP 555 BGRR were considered of general adaptability and are close to the ideal genotype described by Eberhart and Russel (1966). Therefore, they can be grown in any environment of the Brazilian Cerrado. Regarding fiber percentage, only genotype IMA 5675 B2RF showed adaptability to favorable environments ( >1) and predictability ( 2di= 0). However, its mean ( 2di= 0) was below the overall mean of all environments (Table 6). Stability indices equal or close to zero evidence predictability in the phenotypic expression. Similarly, genotype BRS 286 also showed adaptability to favorable environments ( >1) and mean ( ) below the overall mean; however, BRS 286 is associated with a high coefficient of determination (R²>80%). Carvalho, Farias and Rodrigues (2015) found different results when evaluating the fiber percentage and observed low predictability in favorable environments for nine genotypes tested in the Brazilian Northeast region. Table 6. Mean fiber percentage (%), coefficients of regression ( ), deviations of regression ( 2di), and coefficient of determination (R2) obtained by the Eberhart and Russel (1966) method for 12 cotton genotypes in 19 environments in the Brazilian Cerrado. Genotypes Mean ( ) 2 di(x10 6) R2 TMG 41 WS 40.33 b 0.63* 1.05* 35.77 TMG 43 WS 41.91 b 0.89ns 0.54* 61.08 IMA CV 690 44.21 a 1.28* 6.13* 35.88 IMA 5675 B2RF 41.42 b 1.24* 0.29ns 79.52 IMA 08 WS 42.90 a 0.82ns 1.26* 45.31 NUOPAL 39.89 b 0.53* 0.52* 36.09 DP 555 BGRR 44.12 a 1.22ns 0.84* 70.15 DELTA OPAL 40.84 b 0.94ns 1.76ns 46.09 BRS 286 41.21 b 1.33* 0.34* 80.87 BRS 335 41.77 b 0.82ns 0.77* 52.85 BRS 368 RF 42.05 a 1.11ns 0.16ns 78.23 BRS 369 RF 42.30 a 1.13ns -0.14ns 86.01 Mean 41.91 *.ns: significant and non-significant, respectively, by the F test at 5% probability. 1228 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 Genotypes NUOPAL and TMG 41 WS are considered as adapted to unfavorable environments and as having low behavior predictability. However, the reliable recommendation of these genotypes is not possible due to low values of the coefficients of determination of both genotypes (R2<40%), besides the unsatisfactory mean (below the overall mean). These results confirm the complexity of the GxE interaction in relation to fiber traits, which reinforces the need for evaluating genotypes in appropriate environments trials for which the materials are being developed. Genotype BRS 369 RF was the ideal genotype, surpassing the others due to the high mean ( ), adaptability to several environments ( =1), and high predictability ( 2di= 0 and R²>86%). Identifying the genotypes with better representativeness and performance is crucial, especially at the final stages of breeding programs. Adaptability and stability of technological traits Genotype BRS 286 responded to favorable environments regarding fiber length. However, this genotype showed low predictability ( 2di<0), as indicated in Table 7. Since this genotype did not respond to environmental improvement, more evaluations with genotypes at a different location are recommended. Table 7. Mean fiber length (mm), coefficients of regression ( ), deviations of regression ( 2di), and coefficient of determination (R2), obtained by the Eberhart and Russel (1966) method for 12 cotton genotypes in 19 environments of the Brazilian Cerrado. Genotypes Mean( ) 2 di (x10 6) R2 TMG 41 WS 29.13 b 0.69* 0.06ns 63.75 TMG 43 WS 29.29 b 0.92ns 0.00ns 81.88 IMA CV 690 29.36 b 0.90ns 0.09ns 73.07 IMA 5675 B2RF 29.39 b 1.05ns 0.13* 75.66 IMA 08 WS 31.04 a 0.96ns 0.35* 59.19 NUOPAL 30.19 a 0.94ns 0.01ns 80.99 DP 555 BGRR 29.50 b 1.12ns -0.03ns 90.12 DELTA OPAL 29.76 b 0.87ns 0.13* 68.39 BRS 286 29.84 b 1.35* 0.34* 74.54 BRS 335 30.46 a 0.90ns 0.07ns 74.62 BRS 368 RF 29.51 b 1.07ns -0.02ns 88.20 BRS 369 RF 30.09 a 1.16ns -0.02ns 89.51 Mean 29.79 *.ns: significant and non-significant, respectively, by the F test at 5% probability. When evaluating the unfavorable environments, genotype TMG 41WS showed high stability at the locations CV1, SIN, and SHE2, as previously verified in the environmental index. However, since the coefficient of determination is below 70%, this information is not precise. Several studies on different levels of association between adaptability and stability methodologies recommend the use of more than one method for the safe prediction of genotype performance (SILVA; DUARTE, 2006; ROOSTAEI; MOHAMMADI; AMRI, 2014). Means ( ) above the overall mean for fiber length were observed for genotypes BRS 335, NUOPAL, and BRS 369RF. The wide adaptation ( = 1) to the change of environment and the classification as ideal genotype was confirmed by their R2>70%. Among these genotypes, BRS 369 RF stood out for having this same behavior for more than one trait. For the trait fiber strength (Table 8), genotypes IMA 08 WS and BRS 286 showed high predictability ( 2di=0) mainly in favorable environments ( > 1), as observed at the locations SDES and PVA1, both with positive environmental index. Moreover, the results for fiber strength were similar to those obtained for fiber length, in which BRS 286 genotype is considered to be the ideal genotype, confirming high reproducibility in environments with high technology, such as favorable environments. Similar to what occurred to FL and Y, results did not reveal any precise recommendation of genotypes for unfavorable environments ( < 1). Although genotypes NUOPAL and BRS 368 RF had 2di=0, their coefficients of determination were below the ideal (R2<70%), demonstrating inconsistencies. 1229 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 Table 8. Mean of fiber strength (UHM), coefficients of regression ( ), deviations of regression ( 2di), and coefficient of determination (R2) obtained by the Eberhart and Russel (1966) method for 12 cotton genotypes in 19 Brazilian Cerrado environments. Genotypes Mean ( ) 2 di (x10 6) R2 TMG 41 WS 32.07 a 1.14ns 0.51* 69.80 TMG 43 WS 30.93 a 1.24ns -0.15ns 87.68 IMA CV 690 30.97 a 0.57* 0.90* 29.95 IMA 5675 B2RF 28.69 b 1.26* 0.69* 70.71 IMA 08 WS 29.99 a 1.30* 0.02ns 84.45 NUOPAL 30.84 a 0.72* -0.05ns 65.82 DP 555 BGRR 28.91 b 0.81ns 0.35ns 57.87 DELTA OPAL 31.61 a 0.98ns -0.22ns 83.94 BRS 286 30.45 a 1.26* 0.12ns 81.33 BRS 335 29.87 b 1.02ns -0.11ns 81.23 BRS 368 RF 29.50 b 0.62* -0.08ns 60.34 BRS 369 RF 30.01 b 1.02ns 0.49* 65.44 Mean 30.32 *.ns: significant and non-significant, respectively, by the F test at the 5% of probability. Only genotype TMG 43 WS showed general adaptability. Thus, this genotype is the only one with wide adaptability for fiber strength, that is, it can maintain similar behavior if grown in environments similar to those reported in this study. Results revealed significant changes in genotypes behavior at different locations, reinforcing the need for developing more genotypes adapted to different environments. However, these results indicate that, since several cultivars are released continuously, they need to be evaluated in more locations and years. CONCLUSIONS Genotype BRS 369 RF is considered as an ideal genotype regarding agronomic and technological fiber traits (except for fiber strength), revealing the best performance in multiple Brazilian Cerrado environments. Genotype BRS 286 is predictable and responsive to environmental improvements for fiber technological traits and fiber percentage, being the most suitable for cultivation in favorable environments. In unfavorable environment conditions, no genotype showed satisfactory performance considering more than one trait. However, genotypes TMG 43 WS and IMA 08 WS revealed stability for cotton seed yield and could be a good option for cotton producers who use low technology in the Brazilian Cerrado. RESUMO: Estudos sobre a adaptabilidade e estabilidade são fundamentais para o melhoramento de plantas, pois são considerados uma alternativa para reduzir os efeitos da interação genótipos x ambientes (GxE) e identificar cultivares com comportamento previsível e responsivo a melhorias ambientais, subsidiando a recomendação de cultivares. Este trabalho teve como objetivo investigar a interação genótipos x ambientes em genótipos de algodoeiro cultivados no Cerrado brasileiro e identificar genótipos para ambientes favoráveis e desfavoráveis. Durante as safras 2013/2014 e 2014/2015, foram realizados 19 ensaios de competição com algodão em delineamento de blocos ao acaso, com 12 tratamentos e quatro repetições. As características produtividade de sementes de algodão, porcentagem de fibras, comprimento de fibra e resistência das fibras foram avaliadas. Os resultados revelaram interação significativa da GxE para todas as características da fibra avaliada. O genótipo BRS 369 RF revelou adaptabilidade geral e alta previsibilidade para as características da fibra avaliada. PALAVRAS-CHAVE: Gossypium hirsutum. Adaptabilidade e estabilidade. Melhoramento de plantas. 1230 Phenotypic adaptability of cotton… COTRIM, M. F. et al. Biosci. J., Uberlândia, v. 36, n. 4, p. 1223-1230, July/Aug. 2020 http://dx.doi.org/10.14393/BJ-v36n4a2020-48148 REFERENCES CARVALHO, L. P.; FARIAS, F. J. C.; RODRIGUES, J. I. S. Selection for increased fiber length in cotton progenies from Acala and non-Acala types. 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