Quinoa (Chenopodium quinoa Willd) is known and used on large scale by farmers in Colombia, Ecuador, Peru, Bolivia, Chile and Argentina. Quinoa is increasingly cultivated in the world by being present, cultivated or under experimentation, in more than 95 countries around the world (Bazile et al., 2016). In 2017, the global quinoa production volume amounted to about 146.74 thousand metric tons. The amount produced in Brazil is considered insignificant for the world statistics, being only related to the researches of Embrapa, University of Brasília (UnB) or Universidade Estadual do Oeste do Paraná (UNIOESTE) (Spehar et al., 2011; Vasconcelos et al., 2013).
Abstract – The objective of this work was to evaluate corn cultivars grown in the state of Amazonas, Brazil, which simultaneously show high grain yield, adaptability, andstability. The trials were carried out in seven environments in the state of Amazonas, in a randomized complete block design, with two replicates. The grain yieldof 30 corn cultivars was evaluated in four growing seasons, from 2011/2012 to 2014/2015. The genetic parameters were estimated by the REML/Blup methodology. Theselection for adaptabilityandstability was basedonthe predicted genetic value andonthe harmonic mean ofthe relative performance ofthe genetic values. Despite the existence of genotype x environment interaction, cultivars with high adaptabilityandstability were identified. Iranduba – lowland, in 2011/2012 and 2014/2015 – and Rio Preto da Eva – upland, in 2012/2013 – stood out as favorable environments, while Iranduba – upland, in 2011/2012 and 2012/2013 – and Manaus – upland, in 2012/2013 and 2013/2014 – were classified as unfavorable environments. The single-cross hybrid BRS 1055 showed productive superiority and high stability in this region. The Sint 10771, Sint 10781, and Sint 10699 synthetic varieties showed high adaptability. BRS Caimbé shows specific adaptability to cropping in upland environments ofthe state of Amazonas, Brazil.
Abstract – The objective of this work was to determine the efficiency of a simultaneous selection for yield, stability, andadaptabilityof bean genotypesofthe carioca and black groups. In the 2016 harvest, two experiments were carried out in the state of Pernambuco, Brazil: one for the carioca group, with 20 genotypes, in the municipalities of Caruaru, Arcoverde, and Belém de São Francisco; andthe other for the black group, with 12 genotypes, in the municipalities of Caruaru and Arcoverde. The parameters were estimated by mixed models, andselection was performed by the harmonic mean ofthe relative performance of genetic values, using three strategies: selectionbasedonthe predicted genetic value, without interaction; selectionbasedonthe predicted genetic value, considering each location; and simultaneous selection for grain yield, stability, andadaptability. The environments affected the phenotypic expression ofthe carioca bean genotypes, indicating specific adaptation. The average heritability for grain yield showed high values for black bean genotypes, which is a favorable condition for selection, and low values for carioca bean genotypes. The black bean genotypes CNFP 15684, 'BRS Esteio', CNFP 15678, CNFP 15697, CNFP 15695, and 'IPR Uirapuru' show the best performances in the studied environments, simultaneously considering grain yield, adaptability, andstability.
A strategy that provides maximal genetic improvement in maize yield must include simultaneous breeding for yieldandstability, starting from initial segregating generations. F a s o u l a s and F a s o u l a s (1997a, 1997b) set the rules which should be obeyed in order to fulfill the above-mentioned requirements. Breeding of individual plants should be conducted in similar environments, because the phenotype is maximally expressed and there is an increase in heritability of target traits. High selection pressure may increase gene frequency through selfing or crossing. New genotypes should be tested at a number of locations with replications, according to a standardized experimental design (F a s o u l a s and F a s o u l a s , 1995). Thus, each hybrid will express its traits and its identity in a given environment and specific technology.
The detailed study ofthe GxE interacion allows selecing the best genotypes for the various environmental condiions, and it results from the esimated phenotypic adaptabilityandstability (Silva et al. 2014), which enable idenifying genotypes with behaviors that can be predicted according to environmental variaions. For selecion ofthe most stable and adaptable hybrid, it was the simultaneous selecion method basedon performance of geneic values (HMRPGV). Depending onthe evaluated trait, thegenotypes were classiied diferently through HMRPGV values (Table 4). However, in general considering all the studied traits, hybrids BRS Gigante Amarelo, HFOP-09, H09-09, GP09-02 and GP09-03, and BRS Sol do Cerrado stood out (Table 4). The most relevant traits for recommending the release of a hybrid are producivity, fruit mass, fruit length and diameter, and percentage of pulp. For the producivity trait, the most stable and adaptable in various environments were the hybrids BRS Gigante Amarelo, GP09-03, H09-09, BRS Sol do Cerrado, and H09-14. For fruit mass, the following genotypes were highlighted: BRS Gigante Amarelo, H09-30, HFOP-09, GP09-02, and H09-09. Fruits with larger lengths and/or diameters tend to have larger peel mass and thickness, which are not appreciated especially by the juice industry, because they negaively inluence pulp mass and percentage of pulp (Negreiros et al. 2007, Freitas et al. 2011). However, hybrids BRS Gigante Amarelo, HFOP-09, H09-09, GP09-02, and BRS Sol do Cerrado were also found to have higher pulp mass, which indicates that this is not always a valid associaion (Table 4).
The results ofstability (HMGV), adaptability (RPGV), and simultaneous stabilityandadaptability (HMRPGV) ofthegenotypes evaluated are presented in Table 6. The five best genotypes, basedonthe criteria HMGV, RPGV, and HMRPGV were the best basedonthe criterion ofthe mean genotypic value (Table 5). The coincidence was 100%, with inversion of order among the coincident ones between thegenotypic value andthe other parameters. According to Resende (2007a), the use of these attributes or selection criteria can provide further refinement in selection. Torres et al. (2016) state that this is an indication that these genotypes have high adaptive synergism in the 36 environments tested and exhibit good predictability, i.e., maintenance of grains yield in the different environments.
The lines considered promising were the same in the methodology of Lin & Binns (1988) modified by Oliveira et al. (2006) and in the WAASP, both in the overall analysis and in the favorable and unfavorable environments. The only difference was their ranking in relation to the studied environments (Table 4). This shows the potential ofthe common bean lines from the breeding program of Embrapa Arroz e Feijão for the conditions ofthe family farming system, in which the species is usually grown in marginal areas; identifying these genotypes may make the crop more competitive in these sites (Sena et al., 2008). In both methods, thegenotypes with the lowest average yield values also presented low stabilityand, therefore, a larger contribution to the interaction. The similarities between the two methodologies in identifying the best andthe worst genotypes suggest that only one is enough to aid the breeder in theselection process.
The REML/BLUP procedure has been one ofthe most used techniques in studies onadaptabilityandgenotypicstability; it is basedon mixed models. This procedure estimates the components of variance by the Restricted Maximum Likelihood (REML) andthe prediction of genetic values by the Best Linear Unbiased Prediction (BLUP) (CARIAS et al., 2014). It was originally recommended for studies on quantitative genetics andselectionof perennial plants (RESENDE, 2007a,b); however, it has also been used in annual species such as rice (BORGES et al., 2010), common bean (CHIORATO
ABSTRACT: High yieldstabilityandadaptabilityof yellow passion fruit varieties (Passifl ora edulis Sims. f. fl avicarpa Deg.) are highly desirable attributes when exploring different environments. This study aimed to evaluate theadaptabilityandyieldstabilityof yellow passion fruit varieties using AMMI (additive main effects and multiplicative interaction) and other ancillary statistics. Twelve varieties were evaluated in eight environments. Analysis of variance showed effects at- tributable to the varieties (G), environment (E) and their interaction (G×E). The ﬁ rst two multipli- cative components ofthe interaction accounted for 69% ofthe sum of squares. The scores ofthe principal interaction components showed high variability for the environments relative to the variety effects. High varietal phenotypic stability was observed in three environments; which can be used in yellow passion fruit breeding programs for initial selection trials. A biplot-AMMI analy- sis andyieldstability index incorporating the AMMI stability value andyield capacity in a single non-parametric index were useful for discriminating genotypes with superior and stable fruit yield. AMMI analysis also allowed for the identiﬁ cation of more productive varieties in speciﬁ c environments, leading to signiﬁ cant increase in passion fruit productivity.
et al. 2010). The graphical analysis mean versus stability using the GGE biplot (Figure 3b) allows identifying the magnitude ofthestabilityof each genotype by the projection vector length (Yan and Tinker 2006), where a longer projection, regardless ofthe direction, represents a greater tendency of GEI. The smaller this vector, the more stable the genotype. In the AMMI1 analysis, the abscissa represents the average effect of genotype and environment while the ordinate infers thestability (IPCA1 scores) (Figure 3c). The results show that the axis ofthe first principal component ofthe interaction (IPCA1) explained more than 90% ofthe data variability, justifying the choice for the AMMI1 model. When the statistical methods used are not associated with GY, theselectionofgenotypes must come from the joint evaluation ofthestability parameters and productive performance of each genotype. Therefore, Figure 4 shows the Spearman correlation coefficients between the 7 methodologies to study GEI; however, in this case, only 1 ranking was obtained between each method by integrating the GY effect. All methods were associated with each other, andthe correlation magnitudes ranged from r s = 0.50 * to r
The GGE Biplot analysis was proposed as a graph able to interpret the GE interaction in SREG model (Yan et al. 2000). This method considers that the primary environmental effect is not relevant to the genotype selection (G), and, therefore, the effect of G is presented as a multiplicative effect of GE. The axes ofthe analysis graphs are the first two major components ofthe multivariate analysis, assuming the effects of environments as fixed andthe others as random (Miranda et al. 2009). Thus, in theselectionof cultivars and formation of mega- environments, the adaptive capacity ofgenotypes is mostly important in relation to environmental conditions, and changes in this trait are due only to the G and GE effects (Yan et al. 2000).
Abstract – The objective of this work was to evaluate theadaptabilityand multi-trait stabilityof wheat (Triticum aestivum) genotypes according to the phenotypic index of seed vigor (PIV). Thirty wheat genotypes were grown in seven environments in the state of Rio Grande do Sul, Brazil, during one crop season. In each environment, a randomized complete block design with three replicates was used. The PIV was elaborated from the following traits: first germination count, germination percentage, accelerated aging, and electrical conductivity. The evaluated phenotypic index makes it possible to define macroenvironments for the production of wheat seeds with high physiological potential and to understand the implications ofthe genotype x environment interaction. The phenotypic index of seed vigor is effective to rank genotypes considering multi-trait selection related to the vigor of wheat seeds produced in Southern Brazil.
The significant effect ofthe G × E interaction revealed that thegenotypes had variable performance in the tested environments, i.e., a change in the av- erage rank ofthegenotypes was verified among the environments, justifying the conduction of a more refined analysis so that to increase the efficiency oftheselectionand indication of cultivars. In this sense, AMMI analysis represents a potential tool that can be used to deepen the understanding of factors involved in the manifestation ofthe G × E interaction. Through this, it was estimated that the effect ofthe G × E interaction through multivariate analysis (principal components analysis, PCA and singular-value decom- position, SVD) could describe the pattern adjacent to the data from an interaction matrix (G × E), making the decomposition ofthe sum of squares ofthe G × E interaction (SS G×E ) in axis or interaction principal components analysis (IPCA).
In Brazil, the sunflower genotypes experiment andselection have been carried out by Network of Trial for the Evaluation of Sunflower Genotypesand coordinated by Embrapa. In these field trials, thegenotypesselection is made basedonthe mean performance of grain and oil yield. However, this selection might be difficult when there are different responses because ofthe environmental variations. The influence ofgenotypes x environments interaction might be reduced through studies regarding theadaptabilityandstability. Studies evaluating the grain and oil yieldof conventional sunflower cultivars have been performed by Grunvald et al. (2008, 2009) and Porto et al. (2008, 2009). Evaluations should be performed continuously in order to obtain information from new and more productive genotypes to be provided to farmers. Recently, high oleic cultivars have been analyzed at field trial, however there is no information about the behavior of these genotypes grown in the country yet.
Using genotypes with wide adaptabilityandstability can mitigate the effect of G×E interactions; however, it is necessary to adopt a methodology that allows the simultaneous selection for stabilityandadaptability, such as the harmonic mean ofthe relative performance of genetic values – HMRPGV (Resende 2002a, 2002b). The HMRPGV is a useful tool to ensure that the highest percentage of genetic gain is achieved among the forest production environments, prioritizing more productive, stable, and adapted genetic materials (Santos et al., 2013; Pagliarini et al., 2016).
There are no reports of researches about thestabilityandadaptabilityof arracacha in Brazil, aiming at theselectionof more adapted clones. The few publications are from trials of evaluation of cultivars, but generally refer to one environment in specific regions. An important example of these studies was published by Madeira et al. (2002) in the evaluation of arracacha clones in Lavras-MG. The traditional cultivar ASA, at that time still incipient in relation to the planted area, already stood out for its precocity and capacity of response to the improvement ofthe environment, since at that time the predominant cultivar, Amarela Comum, was cultivated with low technological level. In Nova Friburgo-RJ, Portz et al. (2003) reported the productive potential of clone 92739 (ASA) compared to nine other clones from Embrapa Hortaliças.
Abstract: The aim of this study was to ascertain the association between the REML/BLUP and GGE Biplot methodologies for selectionof superior genotypes in regard to adaptabilityandyieldstability for various regions ofthe Middle North region of Brazil. Sixteen soybean genotypes were evaluated in eight environments during the 2015/2016 and 2016/2017 crop seasons, analyzing the following traits: number of days to maturity, plant height, one hundred seed weight, and grain yield. In this study, the REML/BLUP andthe GGE Biplot methods are highly correlated in terms of genotype ranking for selectionand recommendation purposes. Thegenotypes BRASBT13-0528, M8372 IPRO, and BRASBT13-0621 most approximate a hypothetical ideal genotype.
Abstract: The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yieldof ten landrace varieties of lima bean and evaluate adaptabilityandstability through the Lin and Binns method for identification ofthe best performing variety. Trials were conducted in the municipalities of Teresina, PI, and São Domingos do Maranhão, MA, through measurement of 12 traits, except for grain yield in São Domingos do Maranhão. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptabilityandstability analyses indicated that the environments are discriminant for selectionof promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.
frequently results in significant genotype (G) x environment (E) interaction (GEI). We compared statistical methods to analyze adaptabilityandstabilityof wheat genotypes in value for cultivation and use (VCU) trials. We used yield performance data of 22 wheat genotypes evaluated in three locations (Guarapuava, Cascavel, and Abelardo Luz) in 2012 and 2013. Each trial consisted of a complete randomized block design with three replications. The GEI was evaluated using methodologies basedon mixed models, analysis of variance, linear regression, multivariate, and nonparametric analysis. The Spearman’s rank correlation coefficient was used to verify similarities in the genotype selection process by different methodologies. The Annicchiarico, Lin and Binns modified methodologies, as well as the Harmonic Mean ofthe Genetic Values (HMGV) allowed to identify simultaneously highly stable and productive genotypes. The grain yield is not associated with Wricke, Eberhart and Russell stability parameters, scores ofthe first principal component ofthe AMMI1 method, and GGE biplot stability, indicating that stable genotypes are not always more productive. The data analyzed in this study showed that the AMMI1 and GGE biplot methods are equivalent to rank genotypes for stabilityandadaptability.
Individual selection was applied in July 2005, in the ratoon cane stage. In 2005, the first clonal multiplication (phase T2) was carried out, with planting in the same municipalities. Each genotype of phase T2 was planted in two 5 m long furrows, spaced 1.5 m apart, in an experiment arranged in an augmented block design. The clone then named “PRP036066” was selected in 2008 due to its excellent performance throughout three growing seasons. In the next stage (phase T3) in 2010, evaluations and clonal selection were carried out basedon data of two growing seasons, at eight locations in the state of Paraná [Mandaguaçú (lat 23º 21’ S, long 52º 05’ W and alt 580 m asl), Bandeirantes (lat 23º 06’ S, long 50º 22’ W and alt 492 m asl), Paranavaí (lat 23º 05’ S, long 52º 27’ W and alt 503 m asl), Colorado (lat 22° 50’ S, long 51° 54’ W and alt 400 m asl), Goioerê (lat 24° 10’ S, long 53° 01’ W and alt 550 m asl), Perobal (lat 23° 54’ S, long 53° 24’ W and alt 410 m asl), Astorga (lat 23° 11’ S, long 51° 09’ W and alt 634 m asl), and São Pedro do Ivaí (lat 23° 52’ S, long 51°41’ W and alt 400 m asl)]. In 2010, the clonal multiplication phase (MPh) was planted and in the following year, the clone now called RB036066 was included in the final test phase of PMGCA, called experimental phase (EPh), conducted at 10 locations in the state of Paraná. In the EPh, yield traits such as ton of sugarcane per hectare and sucrose content, as well as their adaptabilityandyieldstability were evaluated in the different soil and climate conditions ofthe North and Northwest regions ofthe State of Paraná (Figure 2). The EPh phase lasted three growing seasons. During this period, the reaction to the main diseases ofthe South-central sugarcane region was also evaluated. Between 2011 and 2012, experiments were conducted to evaluate the maturation period of RB036066 at nine locations in the state of Paraná.