Genetic potential of black bean genotypes with predictable behaviors in multienvironment trials
Abstract
The aim of this study was to evaluate the phenotypic stability and specific and broad adaptability of common black bean genotypes for the Central and Center-South regions of Brazil by using the Annicchiarico and AMMI (weighted average of absolute scores: WAAS, and weighted average of absolute scores and productivity: WAASP) methodologies. We carried out 69 trials, with 43 and 26 trials in the Central and Center-South regions, respectively. Thirteen genotypes were evaluated in a randomized block design with three replications, during the rainy, dry, and winter seasons in 2 years. To obtain estimates of specific adaptation, we analyzed the parameters for each method obtained in the two geographic regions separately. To estimate broad adaptation, we used the average of the parameters obtained from each region. The lines identified with high specific adaptation in each region were not the same based on the Annicchiarico and AMMI (WAAS) methodologies. It was not possible to identify the same genotypes with specific or broad stability by using these methods. By contrast, the Annicchiarico and AMMI (WAASP) methods presented very similar estimates of broad and specific adaptation. Based on these methods, the lines with more specific adaptation were CNFP 8000 and CNFP 7994, in the Central and Center-South regions, respectively, of which the CNFP 8000 line was more widely adapted.
INTRODUCTION
In recent years, Brazil has been ranked first in the production and consumption of common bean (Phaseolus vulgaris L.) (FAO, 2015). Black beans are the second most consumed type of bean in Brazil, representing 17% of the consumer market and corresponding to approximately 490,000 tons/year (Del Peloso and Melo, 2005). Together, the Central and Center-South regions of Brazil are responsible for 84% of Brazil’s bean production (Pontes Junior et al., 2014).
The common bean is grown in the majority of the Brazilian states during more than one sowing period per year. This is done using different cultivation systems and under different environmental conditions, which leads to the occurrence of genotype x environment (GxE) interactions (Bertoldo et al., 2009; Pereira et al., 2009a, 2011; Domingues et al., 2013; Corrêa et al., 2016). Because of such interactions, the genotypes should be evaluated in a large number of environments so that the environmental effects can be measured and an improved certainty regarding the usage of new cultivars can be provided.
Adaptability and stability analyses can be used to minimize undesired effects of GxE interactions because they allow for the identification of cultivars that have predictable behaviors in response to variations in environmental conditions (Cruz and Regazzi, 2001). It is important to perform these analyses in different regions, to identify lineages that have both broad and specific adaptabilities and stabilities for each region. The obtained results can provide a level of certainty in decision-making processes. However, because of the low usage rate of common bean seeds by farmers (approximately 19%) (Peske, 2016) and, consequently, the small market potential for new cultivars, common bean cultivars in Brazil are currently only indicated based on the average productivity of the different regions and planting seasons. In addition, cultivars are rarely recommended for specific environments.
Several methods are commonly used to evaluate the stability and phenotypic adaptability of cultivars, and several studies have compared different approaches. Silva and Duarte (2006) and Pereira et al. (2009b) found a low association between the Annicchiarico (Annicchiarico, 1992) and AMMI (additive main effects and multiplicative interaction model; Gauch and Zobel, 1996) methodologies, thereby demonstrating that these methods can be used to complement one another.
The methodology proposed by Annicchiarico (1992) has been widely used because of its ease of interpretation and because it classifies the most adaptable and stable genotypes with the greatest average productivity (Silva Filho et al., 2008; Pereira et al., 2009b,c, 2012). The AMMI method is also commonly used. It combines analysis of variance for the main additive effects of genotypes and environments with principal component analysis for the multiplicative effect of the GxE interaction (Gauch and Zobel, 1996). However, this methodology does not classify the most productive among the most stable genotypes (Melo et al., 2007; Silva Filho et al., 2008; Pereira et al., 2009b). This shortcoming becomes a problem when recommending new cultivars for the genetic improvement of plants, given that average productivity is one of the most relevant factors for the acceptance of new cultivars. With the goal of addressing this obstacle, it has been proposed that the AMMI methodology can make use of a supplementary genotype (Pacheco et al., 2005) that represents an additional reference genotype with which the evaluated genotypes can be compared. The reference genotype is defined based on the criteria of interest to the researcher. Another difficulty that generally occurs in the majority of studies that use the AMMI method is that the number of significant components defined by the decomposition of the GxE interaction is greater than the number of components used to study the stability (Melo et al., 2007; Rocha et al., 2007; Silva Filho et al., 2008; Pereira et al., 2009c; Gonçalves et al., 2010). To circumvent this problem, the use of a predictive evaluation via a cross-validation process has been proposed (Gauch, 1988). In this approach, the selected model is the one that displays the lowest average predictive difference among a large number of analyses (Oliveira et al., 2003). However, this method is difficult to use and, thus, impractical for implementation in routine plant breeding programs. In this regard, Pereira et al. (2009c) proposed calculation of the weighted average of the absolute scores (WAAS) of the first two principal components for each genotype, which are weighted by the percentage of variation explained by each component such that the genotypes with the lowest WAAS values are the most stable. Unfortunately, this method does not address all components identified as significant, nor does it include information about genotype productivity. Additionally, some of the aforementioned difficulties persist.
In the present study, a new approach to the AMMI methodology is proposed in which information on adaptability is included. Thus, this proposed method can be used in genetic improvement programs by including information from all significant components identified in the GxE interactions. The aim was to evaluate the phenotypic stability and specific and broad adaptability of common black bean genotypes in the Central and Center-South regions of Brazil by using the Annicchiarico and AMMI methods.
MATERIAL AND METHODS
Trials used to recommend black bean cultivars were conducted during the rainy (October/November sowing), dry (January/February sowing), and winter (May/June sowing) seasons in 43 environments of the Central region of Brazil (in the States of Goiás, the Federal District, Mato Grosso, Mato Grosso do Sul, and Tocantins) and 26 environments in the Center-South region (in the States of Paraná, Santa Catarina, São Paulo, and Rio Grande do Sul), totaling 69 environments in 2 years. The experimental design was a completely randomized block design with three repetitions and plots with four 4.0-m long rows. The data on bean productivity were collected from the two center rows. Each trial consisted of 13 genotypes of the common black bean as follows: eight elite lineages (TB 9409, TB 9713, CNFP 10138, CNFP 7966, CNFP 7972, CNFP 7994, CNFP 8000, and CNFP 9328) and five cultivars (BRS Valente, FT Nobre, Diamante Negro, IPR Uirapuru, and FT Soberano).
The data on bean yield (kg/ha) were subjected to an individual analysis of variance in which the effect of the genotypes was fixed and the other effects were random. Selective accuracy (SA) was estimated using the equation,
(Equation 1)
where F is the value of the F-test for the source of variation of the lineages (Resende and Duarte, 2007). Subsequently, a joint analysis was performed on the stability and adaptability of the trials according to the region. For the trials in which the residual variances were not homogenous, the degrees of freedom of the mean error and of the GxE interaction were adjusted according to the Cochran method. The phenotypic means obtained in the joint analysis were subjected to a Scott and Knott means test at 10% probability. The Annicchiarico and AMMI methodologies were used to estimate the adaptability and stability parameters associated with the bean yield of eight elite lineages.
In the Annicchiarico methodology, stability is measured by the superiority of the genotype relative to the mean for each environment. This method is based on the estimation of a recommendation index for a particular genotype that shows a relatively superior behavior. In this method, the data are transformed beforehand into percentages using the mean of the genotypes of each location as a reference. For example, if a cultivar obtains an index equal to 103.0%, this cultivar will perform 3% better than the environmental average with a 75% probability.
The genotypic recommendation index
(Equation 2)
where all environments are considered and
(Equation 3)
where
(Equation 4)
where
The method produces scores for the interaction principal component analysis (IPCA) for each genotype, which reflect their contribution to the GxE interaction. To identify the most stable genotypes, the means of the absolute scores were obtained following the method of Pereira et al. (2009b), obtained for each genotype for the principal components that were significant at 1% probability by the F-test weighted by the explanatory power of each component. Thus, the genotype with the lowest WAAS is the most stable. The expression used was
(Equation 5)
where
With the goal of adding a new parameter to the AMMI methodology, by associating the stability with the adaptability, the use of the weighted mean of absolute scores and productivity (WAASP) was proposed. This value is the weighted mean of the average productivity of the genotypes, with a weight equal to 3, and that of the WAAS, with a weight equal to 2, that allows for the simultaneous evaluation of adaptability and stability. To obtain the WAASP, the productivity and WAAS data were transformed to the same scale to be directly comparable. The greatest productivity value was considered to be 100%, and all other values were compared relative to this value. In the case of WAAS, all values were subtracted from 100 to invert the scale, and subsequently, the percentage relative to the greatest value for each genotype was obtained.
The expression used was
(Equation 6)
where
To obtain the estimates of the stability and adaptability specific to the regions, the parameters obtained with each method were analyzed separately for the two geographic regions. To estimate the broad stability and adaptability, the mean of the parameters obtained in each region was used, and a broad adaptation of the genotypes was considered for those with the highest estimates for this mean. Furthermore, estimates were obtained separately for the Spearman’s correlation between the AMMI (WAAS) x Annicchiarico and AMMI (WAASP) x Annicchiarico methodologies for the two regions to determine the agreement of the genotype classifications. The estimation of the parameters and the significance tests were performed using Gene (Cruz, 2007) and SAS version 9.1 (SAS Institute, 2008).
RESULTS AND DISCUSSION
Significant genotype effects were found in 74% of the trials, and the values for the coefficient of variation were all less than 25%, indicating good experimental precision (Tables 1 and 2Tables 1 and 2 * and **Significant at 5 and 1% probability, respectively, according to the F-test. CVe: experimental coefficient of variation; SA: selective accuracy. * and **Significant at 5 and 1% probability, respectively, according to the F-test. CVe: experimental coefficient of variation; SA: selective accuracy. †Proportion of explanation of the principal components. ‡Values adjusted by the Cochran method (1954) based on the heterogeneity of the residual MS values. * and **Significant at 5 and 1% probability, respectively, according to the F-test. Means (kg/ha) followed by the same superscript letters do not differ significantly according to the Scott-Knott test at 10% probability. C = classification of the genotypes with regard to the stability and adaptability; wi = recommendation index, classified into favorable environments (wif) and unfavorable environments (wid).Locations for the assessment trials in the Center-South region and a summary of the individual analyses of variance.
Location
Altitude (m)
Season
Mean squared
Productivity
CVe (%)
SA
Treatments
Error
Ponta Grossa-PR
969
Dry/2003
389,129ns
187,132
1949
22.2
0.72
Ponta Grossa-PR
969
Wet/2003
439,048*
175,261
3569
11.7
0.78
Taquarituba-SP
618
Wet/2004
751,390**
117,376
2390
14.3
0.92
Itaberá-SP
651
Wet/2004
299,100*
112,587
2920
11.5
0.79
Paranapanema-SP
610
Wet/2004
467,368**
96,048
2611
11.9
0.00
Abelardo Luz-SC
760
Wet/2003
387,347ns
262,498
3916
13.1
0.57
C. Novos-SC
934
Wet/2003
64,346ns
37,031
1056
18.2
0.65
Abelardo Luz-SC
760
Dry/2003
147,838*
62,043
2191
11.4
0.76
Major Vieira-SC
786
Wet/2003
293,351**
68,292
1831
14.3
0.88
Concórdia-SC
569
Wet/2003
354,428**
113,912
2255
14.9
0.82
Roncador-PR
762
Dry/2003
183,289**
50,485
1383
16.3
0.85
Taquarituba-SP
618
Wet/2003
369,722**
56,549
2217
10.7
0.92
Capão Bonito-SP
705
Wet/2003
366,642ns
284,165
4110
12.9
0.47
Londrina-PR
585
Wet/2003
640,498*
219,548
1938
24.2
0.81
Prudentópolis-PR
840
Dry/2004
220,195*
93,244
2554
11.9
0.76
Ponta Grossa-PR
969
Dry/2004
511,985**
83,494
3163
9.1
0.00
Major Vieira-SC
786
Dry/2004
178,248**
30,298
1663
10.5
0.91
Roncador-PR
762
Wet/2004
101,612**
23,747
871
17.7
0.00
Prudentópolis-PR
840
Wet/2004
470,860**
40,774
2246
8.9
0.96
Laranjeiras-PR
840
Wet/2004
129,640*
43,358
2538
8.2
0.82
C. Novos-SC
934
Wet/2004
80,000*
33,801
1367
13.5
0.76
Abelardo Luz-SC
760
Wet/2004
465,473*
158,017
3958
10.0
0.81
Ponta Grossa-PR
969
Wet/2004
630,750*
213,519
3320
13.9
0.81
Passo Fundo-RS
687
Wet/2003
250,151**
48,213
1858
11.8
0.90
Passo Fundo-RS
687
Wet/2002
148,605*
52,866
3065
7.5
0.00
Abelardo Luz-SC
760
Dry/2004
115,335ns
106,091
2184
14.9
0.28
Mean
-
-
-
-
2428
13.3
0.65
Locations for the assessment trials in the Central region and a summary of the individual analyses of variance.
Location
Altitude (m)
Season
Mean squared
Productivity
CVe
SA
Treatment
Error
Dueré-TO
235
Winter/2004
103,964ns
114,872
1817
18.7
0.00
S. A. Goiás-GO
770
Dry/2003
169,991*
62,414
1277
19.6
0.80
Goiatuba-GO
807
Winter/2003
153,593ns
113,986
1877
17.9
0.51
S. A. Goiás-GO
770
Winter/2003
387,691*
142,640
2019
18.7
0.00
Rio Verde-GO
715
Winter/2003
246,537ns
167,317
2400
17.0
0.57
Urutaí-GO
807
Winter/2003
385,795ns
269,732
2677
19.4
0.55
Cristalina-GO
1189
Winter/2003
1416,966**
252,109
2525
19. 9
0.91
Rio Verde-GO
807
Wet/2003
890,179**
82,075
2288
12.5
0.95
Ipameri-GO
764
Wet/2003
330,685**
79,490
3044
9.3
0.87
Anápolis-GO
1017
Wet/2003
208,086ns
123,104
1870
18.8
0.64
Formosa-GO
916
Wet/2003
518,038**
92,866
2299
13.3
0.91
Panamá-GO
733
Dry/2004
148,973*
58,817
1245
19.5
0.78
Anápolis-GO
1017
Dry/2004
236,833*
94,942
1560
19.8
0.77
S. A. Goiás-GO
770
Dry/2004
433,116**
60,694
1969
12.5
0.93
Dueré-TO
235
Winter/2003
313,776**
74,503
1755
15.6
0.87
Planaltina-DF
944
Winter/2003
389,305*
146,094
2482
15.4
0.79
Sinop-MT
345
Wet/2003
243,799**
61,795
1321
18.8
0.86
Anápolis-GO
1017
Winter/2004
598,836ns
301,948
3082
17.8
0.70
S. A. Goiás-GO
770
Winter/2004
103,861ns
71,688
2487
10.8
0.56
Rio Verde-GO
807
Winter/2004
218,253ns
115,424
2995
11.3
0.69
Itumbiara-GO
448
Winter/2004
256,492*
107,752
1961
16.7
0.76
Planaltina-DF
944
Wet/2003
152,205ns
190,776
2481
17.6
0.00
Urutaí-GO
807
Winter/2004
77,882**
19,450
1672
8.3
0.87
Morrinhos-GO
771
Winter/2004
210,337**
60,844
1944
12.7
0.00
Sinop-MT
345
Dry/2004
91,467*
35,689
1153
16.4
0.78
Cáceres-MT
118
Winter/2004
280,151ns
151,610
2295
16.9
0.68
Dianópolis-TO
693
Winter/2003
309,476ns
210,484
3331
13.8
0.57
Palmas-TO
230
Winter/2003
166,305*
57,386
2177
11.0
0.00
Cáceres-MT
118
Winter/2003
53,470ns
255,509
2840
17.8
0.00
Morrinhos-GO
771
Wet/2004
244,289**
53,762
2018
11.5
0.88
Urutaí-GO
807
Wet/2004
484,146**
81,348
2040
13.9
0.91
Cristalina-GO
1189
Winter/2004
480,477**
86,650
2951
9.9
0.00
C. Alegre-GO
877
Wet/2003
103,934ns
68,173
1195
21.9
0.59
S. A. Goiás-GO
770
Wet/2004
173,477**
19,065
1519
9.1
0.94
Anápolis-GO
1017
Wet/2004
256,431**
38,172
2578
7.6
0.92
Rio Verde-GO
807
Wet/2004
684,442**
191,497
2564
17.1
0.85
Cristalina-GO
1189
Wet/2004
428,605**
96,722
1644
18.9
0.88
G. Dourados-MS
400
Dry/2003
140,838*
64,131
1695
14.9
0.74
Dourados-MS
430
Dry/2003
197,857**
28,164
1808
9.3
0.93
Aquidauana-MS
147
Dry/2003
374,454**
13,062
1719
6.6
0.98
Aquidauana-MS
147
Dry/2004
181,766**
12,810
1719
6.6
0.96
Planaltina-DF
944
Winter/2004
370,446*
151,787
3422
11.4
0.77
Planaltina-DF
944
Wet/2004
544,875**
130,046
3379
10.7
0.87
Mean
-
-
-
-
2165
14.6
0.66
Summary of the joint analysis of variance with the use of the original GxE interaction via the AMMI model for 13 black common bean genotypes evaluated in the Central and Center-South regions of Brazil.
Source of variation
Central
Center-South
%†
d.f.
SS
MS
%†
d.f.
SS
MS
Genotype (G)
-
12
23,734,170
1,977,848**
-
12
10,568,653
880,721*
Environment (E)
-
42
620,921,839
14,783,853**
-
25
749,435,646
29,977,426**
GxE
-
353‡
141,402,904
400,575**
-
205‡
90,913,404
443,480**
IPCA 1
24
53
33,930,159
640,192**
23
36
21,066,863
585,191**
Residual 1
-
300
107,472,745
358,242**
-
169
69,846,542
413,293**
IPCA 2
18
51
25,874,470
507,343**
18
34
16,557,859
486,996**
Residual 2
-
249
81,598,275
327,704**
-
135
53,288,683
394,731**
IPCA 3
15
49
21,257,749
433,832**
15
32
13,701,942
428,186**
Residual 3
-
200
60,340,526
301,703**
-
103
39,586,741
384,337**
IPCA 4
9
47
12,514,278
266,261**
10
30
8,929,265
297,642**
Residual 4
-
153
47,826,249
312,590**
-
73
30,657,476
419,965**
IPCA 5
8
45
10,713,039
238,068ns
9
28
7,854,755
280,527**
Residual 5
-
108
37,113,210
343,641**
-
45
22,802,721
506,727**
IPCA 6
-
-
-
-
8
26
6,994,674
Residual 6
-
-
-
-
-
19
15,808,047
832,002**
Error
-
717†
-
154,356
-
422†
-
157,555
Estimates of the parameters of adaptability and phenotypic stability of 13 genotypes of the black common bean, which were evaluated in the Central and Center-South regions of Brazil during 2003 and 2004 by the Annicchiarico (1992) method.
Genotype
Central
Center-South
Mean
wi
C
wid
C
wif
C
Mean
wi
C
wid
C
wif
C
CNFP 8000
2352a
105.2
1
105.9
2
104.3
1
2592a
102.0
2
102.9
3
101.3
4
FT Nobre
2286a
103.1
3
106.0
1
100.2
4
2441c
97.8
5
101.3
4
93.2
12
IPR Uirapuru
2278a
104.6
2
103.7
3
99.3
6
2444c
96.0
6
94.0
6
100.7
2
CNFP 7994
2261a
100.1
4
97.6
7
103.0
2
2627a
105.8
1
109.0
1
101.8
1
BRS Valente
2232a
99.6
5
99.3
5
99.8
5
2354d
92.7
10
89.9
12
96.8
9
CNFP 10138
2227a
98.4
7
96.2
8
100.9
3
2425c
94.4
8
90.1
11
101.4
3
CNFP 7966
2226a
99.3
6
101.2
4
97.1
7
2389c
95.7
7
93.9
7
98.4
8
CNFP 9328
2112b
94.2
8
98.3
6
89.6
12
2495b
99.3
4
99.0
5
99.6
6
D. Negro
2109b
92.9
9
90.1
10
96.4
8
2324d
92.3
11
91.5
10
93.4
11
TB 9713
2082b
91.9
10
90.7
9
93.3
9
2526b
101.2
3
105.3
2
96.2
7
FT Soberano
2047b
91.3
11
90.0
11
92.8
10
2288d
91.1
12
93.7
8
87.9
13
CNFP 7972
1983c
88.0
12
88.8
12
87.0
13
2319d
90.2
13
84.3
13
99.6
5
TB 9409
1959c
84.8
13
80.4
13
90.3
11
2338d
93.4
9
92.9
9
94.4
10
As previously mentioned, the most stable and adapted genotypes were also the most productive. This is expected given that the model used in the Annicchiarico method to measure genotype superiority uses the mean for each environment as a reference. Therefore, the risk of adopting a particular cultivar is estimated, and this estimate is obtained relative to the mean (Silva Filho et al., 2008; Pereira et al., 2009c, 2012).
According to the AMMI model, the original GxE interaction may be decomposed into 12 components (i.e., ranks within the GE matrix) for the Central and Center-South regions. In this type of analysis, the appropriate model associates significance with the axes and non-significance with the residuals. However, in this study, the analysis was problematic in that non-significance was found for the component (axis), and the residuals continued to be significant (Table 3). Thus, the selected model was the last one to display significance for the component. Oliveira et al. (2003) suggested that with regard to the level of significance, the use of 1% instead of 5% reduces the likelihood of a type I error; however, this approach increases the likelihood of the occurrence of a type II error. According to Gauch and Zobel (1996), the first AMMI components capture a greater percentage of the real performance “pattern”, and with the subsequent accumulation of components, there is a decrease in the percentage of the “pattern” and an increase in imprecise information (i.e., “noise”), thus reducing the predictive power of the AMMI analysis. Therefore, the significance level adopted in the present study to classify the principal components as significant was 1%, thereby selecting the AMMI 4 and AMMI 5 models for the Central and Center-South regions, respectively (Table 3).
For the common bean, methodologies that identify materials with broad adaptations are advantageous. This is mainly because of the seed production market, given that the rate of seed usage by farmers is low (close to 15%), which makes it difficult to have a specific indication for each crop region. The identification of the most stable lineages using the AMMI method was performed using information from the significant principal components to obtain the mean of the absolute scores for each genotype weighted by the percent of explanation of each component (WAAS) (Tables 3 and 5 Means followed by the same superscript letters do not differ significantly according to a Scott-Knott test at 10% probability. WAAS = weighted mean of the absolute scores. C = classification of the genotypes with regard to the stability. WAASP = weighted mean of absolute scores and productivity.Values of the significant principal components (IPCAs) for each genotype of the Central and Center-South regions of Brazil that were used to calculate the WAAS and for classification of the genotypes with regard to the stability using the AMMI (WAAS) and AMMI (WAASP) methods.
Genotypes
Central
Mean
IPCA1
IPCA2
IPCA3
IPCA4
IPCA5
WAAS
C
WAASP
C
CNFP 8000
2352a
-6.25
24.06
-6.65
-0.57
-
10.5
6
98.7
1
FT Nobre
2286a
11.88
-12.47
8.52
3.67
-
10.2
4
97.2
2
IPR Uirapuru
2269a
-4.20
-3.17
32.92
3.11
-
10.3
5
96.7
3
CNFP 7994
2261a
-12.78
24.90
-0.95
15.50
-
13.8
10
95.0
6
BRS Valente
2232a
1.62
-9.14
8.67
28.97
-
9.0
3
96.4
4
CNFP 10138
2227a
-12.83
16.20
5.86
-22.86
-
13.5
9
94.3
7
CNFP 7966
2226a
20.03
12.96
1.59
-0.65
-
11.3
7
95.2
5
CNFP 9328
2112b
36.66
-0.85
-15.80
-3.10
-
17.5
13
89.6
11
D. Negro
2109b
-0.36
-18.48
17.45
-18.00
-
11.6
8
92.1
8
TB 9713
2083b
-14.43
-23.72
-19.94
-3.04
-
16.7
12
89.2
12
FT Soberano
2047b
-3.67
-9.79
-15.77
4.37
-
8.2
2
92.0
9
CNFP 7972
1983c
11.45
5.14
-4.65
-7.39
-
7.6
1
90.6
10
TB 9409
1959c
-27.15
-5.65
-11.26
-0.02
-
14.0
11
87.2
13
Center-South
CNFP 7994
2627a
-9.38
-11.82
18.66
-0.04
-12.41
11.0
8
97.5
1
CNFP 8000
2592a
-17.09
-17.34
12.12
-11.85
-10.22
14.7
11
95.1
4
TB 9713
2516b
16.27
7.09
-21.60
-14.55
-12.68
14.5
10
93.4
6
CNFP 9328
2496b
2.01
15.10
-11.78
-3.23
-14.43
8.7
5
95.4
2
IPR Uirapuru
2444c
9.80
2.04
5.07
8.80
7.10
6.5
2
95.2
3
FT Nobre
2441c
-12.11
33.34
12.95
-1.31
3.87
15.1
12
91.5
10
CNFP 10138
2425c
-19.07
1.14
-8.66
4.77
1.67
8.7
4
93.8
5
CNFP 7966
2389c
-7.17
-13.83
-11.89
10.85
1.89
9.6
6
92.6
9
BRS Valente
2364d
5.99
5.43
6.82
4.04
20.59
7.5
3
92.9
8
TB 9409
2338d
0.77
3.27
8.81
-13.41
3.05
4.9
1
94.3
7
D. Negro
2324d
9.24
-15.50
-7.49
-18.55
19.67
12.8
9
89.8
12
CNFP 7972
2319d
-12.05
-3.44
-16.37
19.32
1.67
10.6
7
90.6
11
FT Soberano
2288d
32.80
-5.48
13.36
15.16
-9.77
17.3
13
87.0
13
It can be observed that the genotypes classified as being among the most stable for the two regions, with the exception of BRS Valente in the Central region, were not the most productive (Table 5). For the Center-South region, the TB 9409 and BRS Valente genotypes, which ranked first and third in stability, respectively, were the least productive when classified according to the test of means (Table 5). The same result occurred for the Central region with the CNFP 7972 and FT Soberano genotypes, which were classified as the most stable genotypes but not the most productive. This may be considered a negative aspect of this methodology, given that to make recommendations for cultivars the factor of greatest relevance is the average productivity. This cannot be ignored in the final decision of selecting favorable lineages in a cultivar development program.
To circumvent the problem that the most stable genotypes were not among the most productive according to the AMMI methodology, the WAASP method was proposed to add information regarding the average productivity and stability. WAASP allowed for the identification of the CNFP 8000, FT Nobre, and IPR Uirapuru genotypes as the most adapted and stable for the Central region with values of 98.7, 97.2, and 96.7, respectively. In addition, the TB 9404, TB 9713, and CNFP 9328 genotypes were identified as being the least adapted and stable (Table 5). In the Center-South region, the WAASP method identified the CNFP 7994 (97.5), CNFP 9328 (95.4), and IPR Uirapuru (95.2) genotypes as the most stable, whereas the FT Soberano, Diamante Negro, and CNFP 7972 genotypes were among the least stable and adapted (Table 5). Based on the average WAASP values of the two regions, the genotypes with the broadest adaptation were CNFP 8000, CNFP 7994, and IPR Uirapuru. These genotypes may be recommended for the two regions without causing losses for the producers. CNFP 8000 was commercially launched as a cultivar with the name of BRS Esplendor in 13 Brazilian states (Costa et al., 2011).
In the Central region, the Spearman’s correlation estimates for AMMI (WAAS) x Annicchiarico and AMMI (WAASP) x Annicchiarico were 0.13 and 0.92, respectively. In the Center-South region, the corresponding estimates were 0.35 and 0.80, respectively. It can be observed that with the use of the WAASP methodology, a greater similarity was obtained with the Annicchiarico results, indicating that it is possible to add information about average productivity of the beans to the AMMI methodology. Integration of bean productivity information into the AMMI method allows for identification of stable lineages with the best agronomic performance. This is a positive result because one of the cited advantages of the Annicchiarico method is the identification of the most stable genotypes among the most productive ones, which was also achieved by the WAASP methodology.
Because the stability and adaptability parameters of each methodology are different, the genotypes identified as being the most stable differed in some cases. Therefore, in the selection of the methods to be used, factors such as ease of analysis and interpretation of the results should be considered. In a study comparing the different methods for the analysis of stability and adaptability, Pereira et al. (2009b, 2012) recommended the use of the Annicchiarico methodology due its ease of use and because it identifies the most stable and adapted genotypes from among the most productive ones. Oliveira et al. (2003) suggested that, for the purposes of recommending cultivars, the most stable genotypes should also display a desirable performance, which is evaluated by the means. By taking these criteria into consideration when evaluating the stability of the suite of final tests with the goal of indicating which cultivars to use, the Annicchiarico and AMMI (WAASP) methodologies would be recommended for programs that develop cultivars. This is because these methods classify the most stable and adapted genotypes from among the most productive ones, whereas this is not the case with the AMMI (WAAS) analysis.
In conclusion, the lineage with the greatest specific adaptation for the Central region was CNFP 8000, whereas CNFP 7994 was identified for the Center-South region. In addition, the genotype with the broadest adaptation was CNFP 8000. The Annicchiarico and AMMI (WAAS) methodologies did not identify the same genotypes from among the most stable and adapted ones. The Annicchiarico and AMMI (WAASP) methodologies identified similar genotypes as being the most stable and adapted ones. The lineages with the greatest specific adaptation for the Central and Center-South regions of Brazil did not coincide when using the Annicchiarico and AMMI methodologies. The AMMI-WAASP methodology is efficient and easy to implement in plant-breeding programs.