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Differential contribution of breeding institutions to bean genetic gain/Contribucion diferencial de instituciones de mejoramiento de frijol en la ganancia genetica/Contribuicao diferencial de instituicoes de melhoramento de feijao para o ganho genetico.


Bean is an important species for a large part of the world population. In Brazil this crop is cultivated in great part of the national territory, in almost all the seasons of the year. Several breeding institutions struggle to maintain or improve the current productivity and characteristics such as resistance to diseases and pests, higher content of oils, fiber and nutrients, etc. The promising lineages must be assessed in the final stages of the breeding program, before being recommended as a new cultivar.

In this sense, assays for cultivar evaluation are necessary, particularly the Value for Cultivation and Use (VCU) Test for the assessment of promising lineages and registration of new cultivars in the National Registry of Cultivars (Registro Nacional de Cultivares; RNC), of the Ministry of Agriculture and Livestock (Ministerio da Agricultura e Pecuaria; MAPA). Before they can be registered in the RNC, cultivars must be submitted to VCU assays, which express the intrinsic value of the combination of the agronomic characteristics of cultivars with their properties for agricultural, industrial and commercial use and in natura consumption (Carvalho et al., 2009). This procedure for the evaluation of lineages before registration and recommendation is also used in other countries (Piepho and Mohring, 2006) besides Brazil.

Therefore, the evaluation of the set of genotypes used in the annual assays is fundamental to calculate the value of the genetic gain achieved in the breeding institutions that participate in the VCU assays. For suchevaluation, the estimates of the components of phenotypic variance is one of the adequate procedures. The estimate of the variance components provides knowledge about the causes of variation, which leads to the prediction of gains with the selection of genetically superior plants (Coimbra et al., 2009). Thus, estimates of the genetic progress based on values achieved in the VCU assays or other assays have been evaluated in different species: bean (Matos et al., 2007), sunflower (de La Vega et al., 2007), rice (Breseghello et al., 1999; Atroch and Nunes, 2000), cotton (Carv alho et al., 1997) and maize (Arias and Ramalho, 1998), among others. However, the estimates of the phenotypic variance components were not used in any of these studies.

Thus, the present work aimed at estimating the variance components and the genetic gain among and within the bean breeding institutions participating in the VCU assays for seven years, using the method for estimating the phenotypic variance components.

Material and Methods

The present work used data from the Value for Cultivation and Use (VCU) Test, carried out annually in network by the Research Center for Family Agriculture of Chapeco and coordinated by the Empresa de Pesquisa Agropecuaria e Extensao Rural de Santa Catarina (EPAGRI), with participation of other research institutions. A randomized block design was used, with four replications. The experimental unit comprised four lines with 4m of length, 0,45m spacing and useful area of 2,7[m.sup.2]. The seeding rate consisted of 15 seeds per linear meter. Chemical control of pests and weeds and manual weeding were carried out, as needed, as soon as weeds appeared, to allow equal conditions.

The VCU assays were carried out from 2001 to 2007 in ten locations of the State of Santa Catarina (Aguas de Chapeco, Campos Novos, Canoinhas, Chapeco, Ituporanga, Lages, Ponte Serrada, Sao Carlos, Urussanga and Xanxere). For the purposes of this study, 49 genotypes of the black commercial group bean were used, including commercial varieties and lineages of different bean research institutions. The institutions names were coded in the genotype identification to avoid possible conflicts of interest. The first institution (A) is EPAGRI, a state research company, (CHP970409, CHP9858, CHP9701, CHP971308, CHP970821, CHP9704, CHP9965, CHP9859, CHP9713, CHP9702, CHP9727, CHP9954, CHP9706, CHP9708, CHP9726, CHP9714, CHP970809, CHP9712, CHP9720, CHP01178, CHP9736, CHP970617, CHP9718 and CHP9955). The second institution (B) is the Brazilian Enterprise for Agricultural Research (EMBRAPA), a federal breeding company (AN9021332, BRS Campeiro, TB0202, TB9820, J56, Xamego, TB9713, Diamante Negro and SELCP9310635). The third institution (C) is the Instituto Agronomico do Parana (IAPAR), a state research company (LP02130, IPR Grauna, IPR Uirapuru, LP9805, LP0151, LP98123 and Iapar44). The fourth institution (D) is FT Sementes, a private enterprise (FT Soberano, FT Bionobre, FT Nobre, FT 91370 e FT 84113); and the fifth institution (E) is the Technological University (UTF2810433, UTF4, Silvestre, UTF7, UTF53611313 and UTF5).

The following statistical model was used:


where [mu]: phenotypic effect, [g.sub.i]: genotype random effect, [l.sub.j]: local random effect, [a.sub.k]: year random effect, [gl.sub.ij]: random effect of the genotypexlocation interaction, [ga.sub.ik]: random effect of the genotypexyear interaction, [la.sub.jk]: random effect of the location x year interaction, [gla.sub.ijk]: random effect of the genotype x location x year interaction, [b.sub.l]: block effect, and [e.sub.ijkl]: error.

To achieve the phenotypic or total variance components ([[sigma].sup.2.sub.t]), genotypic variance ([[sigma].sup.2.sub.g]) and the non-genetic variance, in this case, the local effect and all the possible interactions among genotype, location and year ([[sigma].sup.2.sub.location] + [[sigma].sup.2.sub.yearxgenotype] + [[sigma].sup.2.sub.locationxyear] + [[sigma].sup.2.sub.locationxgenotype] + [[sigma].sup.2.sub.locationxyearxgenotype] [[sigma].sup.2.sub.p]), together with the best linear unbiased predictor (BLUP), the restricted maximum likelihood procedure (REML) was used, performed with the PROC MIXED command for experiments arranged in randomized block design (Littell et al, 2006).

To estimate the institutional genetic progress in kg x [ha.sup.-1] and days promoted over the seven years of the study, the following relation was used:

[DELTA]gi = ([[sigma].sup.2.sub.g]/[[sigma].sub.t]) x ([[mu].sub.g]) (2)

where [DELTA][g.sub.i]: institutional genetic progress, [[sigma].sup.2.sub.g]: genotypic variance, at total variance, and [[mu].sub.g]: general phenotypic effect.

Results and Discussion

In the estimate of the variance components per institution, the results demonstrated higher contribution of non-genetic effects ([[sigma]]) for the phenotypic variance ([[sigma].sup.2.sub.f]), compared to the merely genetic effects ([[sigma].sup.2.sub.g]), both for the character grain yield and plant cycle (Table I). It can also be verified that, in general, the genotypic value was close among the institutions evaluated, low for the characters maximum grain yield %[[sigma].sup.2.sub.g] = 7 and maximum plant cycle %[[sigma].sup.2.sub.g] = 1,4. The general sum of the percentages of the genotypic variance components was [[sigma].sup.2.sub.g] = 1,9[degrees]% and [[sigma].sup.2.sub.g] = 0,92% and of the non-genetic, [[sigma]] = 98,1; [[sigma]] = 99,08, for the character grain yield and plant cycle, respectively. Such values revealed the magnitude of the effect of the environment and the genotype x environment interactions on the characters studied. However, the genotypic contributions were similar, but not the same, among the research companies or institutions, so that the institutions D ([[sigma].sup.2.sub.g] = 0%) and E ([[sigma].sup.2.sub.g] = 0,6%) developed genotypes with the lowest estimates of genotypic variance. On the other hand, the institutions A ([[sigma].sup.2.sub.g] = 0,8%), B ([[sigma].sup.2.sub.g] = 5%) and C ([[sigma].sup.2.sub.g] = 3%) achieved higher value in the estimate of genotypic variance (Table I).

The results observed in the estimate of the variance components for the grain yield character and plant cycle within each institution (Table I) are significant for the breeding programs, since with the knowledge about the magnitude of the genotypic or environmental contributions, breeders can direct their program, intensifying or not the selection, predicting the genetic gains achieved with the selection and recommending genotypes for the regions of interest. In the present work, besides the relevant aspects mentioned, it was evident that the highest contribution was the disclosure of the genetic gains achieved in the bean breeding program between and within the breeding institutions studied, and knowledge about the main difficulties, such as the magnitude of the environmental effect.

To estimate the gain in yield unit (kg x [ha.sup.-1]) the relation [[DELTA]] (Eq. 2) was used. For example, in the institution B, for the grain yield character, the genetic value ([[sigma].sup.2.sub.g]) estimated was 58,241 (Table I), the value of total effects ([[sigma].sup.2.sub.t]) was 1,055,939 and the general phenotypic effect ([[mu].sub.g]) was 2,423 kg x [ha.sup.-1]. Based on the [[DELTA]] relation the total genetic progress, in other words, the progress over the seven years (2001-2007), was estimated in kg x [ha.sup.-1].


[DELTA][g.sub.B] = (58241/1055939) x (2423) = 133 kg x [ha.sup.-1]

Using the same relation, the values of genetic progress were estimated for all the other institutions. Thus, the phenotypic effect ([[mu].sub.i]), genotypic variance ([[sigma].sup.2.sub.g]), total variance ([[sigma].sup.2.sub.t]) and the genetic gain ([DELTA]g) per institution is shown the in Table II. For the character plant cycle, the genetic evolution over the seven years was similar, excepting the genotypes developed by institution E (Figure 1), whose values of genetic and non-genetic effects for the general phenotypic effect of the institutional genotypes are in Table II.

Based on the results obtained, it is possible to affirm that all the institutions presented genetic gains for the grain yield character and plant cycle over the seven years, but in different magnitudes. Thus, in the estimate of the genetic gains promoted by the selection in the institutions evaluated, it can be observed that the highest gains were achieved in institutions A, B and C for the grain yield character. On the other hand, the gain was lower in institutions D (whose value was close to zero) and E. For the plant cycle character, in this case for precocity, all the institutions presented higher genetic gains when compared to institution E, which achieved small gains (close to zero). It can also be verified that, in all the institutions, the highest contribution for the phenotypic effect was related to the environmental effect, while the genotypic effect had a lower value.

These results are in accordance with Laidig et al. (2008), who evaluated the variance components in 30 different crops in a VCU experiment in West Germany during 16 years, and concluded that the highest contribution for the phenotype was related to the environment. Besides, Rocha et al. (2009) verified, in the estimate of the phenotypes effects square of five locations of bean cultivation (Lages, Campos Novos, Ponte Serrada, Canoinhas and Xanxere, all in Santa Catarina State), that differences among environments were higher, compared to genotype experimental factors and the genotype x environment interaction. Similarly, Rane et al. (2007), evaluating 25 wheat genotypes in 18 locations of India, observed strong influence of the environment on the phenotype value.

Thus, based on the results found, it can be inferred that breeding aiming at increasing the grain yield character or precocity or plant cycle extension requires a great effort from breeders, since there is a higher influence of the environment on both characters. Besides, the institutions evaluated achieved, in a certain way, moderate genetic gains, related to the seven years evaluated.

Although all the institutions achieved genetic gains, the particular genotypes of each one of them were different for the characters grain yield and plant cycle (Table III). The results revealed significant differences by the t test (P<0.005) among the genotypes developed by the five bean breeding institutions evaluated, for the characters grain yield and plant cycle (Table I). However, the differences observed among the phenotype effects of the genotypes of institutions A, B and C for the character grain yield were not statistically significant. Between C and A, [[mu].sub.C]-[[mu].sub.A] = 14kg x [ha.sup.-1]; for C and B, [[mu].sub.C]-[[mu].sub.B] = 37,68kg x [ha.sup.-1]; and between A and B, [[mu].sub.A]-[[mu].sub.B] = 23,68kg x [ha.sup.-1]. On the other hand, the mentioned institutions differed significantly in relation to institutions D and E (Table I), so that the minimum differences among the phenotype effects varied from 235.07 kg x [ha.sup.-1] ([[mu].sub.B]-[[mu].sub.D]) to 353.52 kg x [ha.sup.-1] ([[mu].sub.B]-[[mu].sub.E]). For the plant cycle character, the results differed from those found for the grain yield character (Table III), so that institution C differed significantly from all the institutions evaluated ([[mu].sub.C]-[[mu].sub.A] = 1 day; [[mu].sub.C]-[[mu].sub.B] = 1 day; [[mu].sub.C]-[[mu].sub.D] = 1 day; and [[mu].sub.C]-[[mu].sub.E] = 3 days); institution A did not differ from B and D, but differed from E ([[mu].sub.A]-[[mu].sub.E] = 2 days); institution B only differed from C, as already mentioned; institution D differed from C, but not from the others; and institution E differed from all other institutions, except B. Thus, statistically significant differences were evident among the institutions for the target characters of the study. In general, two main groups were formed related to the contributions for genetic gain: the first was formed by institutions A, B and C; and the second was formed by institutions D and E.

The differences observed among the institutions for the characters under study may result from the duration of the bean breeding program, as institutions A, B and C (group 1) are older than D and E (group 2). Since the former had had more time in bean breeding, the number of genotypes achieved can be higher, and thus, the contribution for the genetic gain would also be higher. It can be highlighted that the institutions of group 2 cannot be considered non-effective in terms of genetic gain, but it is of lower magnitude. Besides, since the genotypes of institutions A, B and C present higher genetic component in relation to the other institutions, these institutions may be using better adapted parents, and the genotypes selected in the segregating populations may be more adapted to the environmental effects. Besides, the genetic variability present in the genotypes of the mentioned institutions may be broader, with a higher possibility of choosing potential parents for the crossings, which would result in more promising lineages and, thus, a more efficient selection. The results corroborate this premise, since in the present work it was observed that the environmental effect is the main responsible for the change in the phenotypic value. Therefore, the genotypes that potentiated the effects of their interaction with the cultivation environment were the most promising. Consequently, the institutions presenting such genotypes achieved higher genetic gains. It should be pointed out that in the estimates of the genetic progress the pool of genotypes of each institution was considered; in other words, some genotypes are lineages in the first evaluation, intermediate, under final evaluation, or in pre-release. This difference among the levels of the genotypes may explain in part the discrepancy of the values achieved in the genetic gain (Ag) per institution. Institution A is an example of this, in which most lineages evaluated came from preliminary assays; in other words, some participated in the VCU for the first time. Besides, the objective of this work was not comparative, but elucidative.

In order to demonstrate the differences among the genotypes within institutions, the genotypes were grouped (Table IV) as follows: superior group (genotypes above the general phenotypic effect for the characters grain yield and plant cycle) and inferior group (genotypes below the general phenotypic effect for the characters grain yield and plant cycle, simultaneously). The results demonstrated that the groups were different by the t test (P>0.005) for both characters in all the institutions, except for the plant cycle character in institution D, due to the differences between more productive and less productive genotypes in the same institution. It is important for breeders to identify the most responsive genotypes to selection or environmental demands, so that superior genetic gain can be achieved with their recommendation. In this sense, the selection of superior genotypes should be based on the variance components in relation to the phenotypic effect components (Simeao et al., 2002). For Bertoldo et al. (2009), knowledge about the variance components may contribute to breeding programs, identifying promising genotypes, among other aspects.

Therefore, genotypic values were predicted within each institution so as to reveal which potential genotypes contributed to the characters being evaluated. Out of the 49 genotypes evaluated, 18 presented predicted genotypic values above the general phenotypic effect (2430kg x [ha.sup.-1]) for the character grain yield; 10 from institution A; 5 from B; 4 from C and 3 from E (Table V). For the plant cycle character, out of the 49 genotypes evaluated, 11 were from institution A; 4 from B; 2 from C and 4 from D (Table VI). In other words, 20 genotypes presented predicted genotypic values above the general phenotypic effect (89,06 days). The genotypic value was not predicted for the institutions D (grain yield) and E (plant cycle) due to the lack of effective genotypic contributions for the characters analyzed.

The results obtained corroborate the existence of variability among the genotypes evaluated, since differences were revealed by the t test and by the prediction of the genotypic values. Besides, higher genetic gains can be achieved for grain yield, since the genotypic contribution was higher for this character, in comparison to the plant cycle character. Thus, the genotypes CHP970409 from institution A (6,71% of genetic gain), AN9021332 from institution B (5,21%), LP02130 from institution C (with 6,59%) and UTF4 (3,89%) stood out for grain yield (Table V). For the character plant cycle, aiming at precocity, which is targeted in most breeding programs (Dalla Corte et al, 2003; Machado et al., 2008), the genotypes CHP970809 from institution A (1,63%), BRS Campeiro from institution B (1,45%), Iapar44 from institution C (0,84%) and FT 84113 from institution D (0,09%) stand out for genetic gain. However, if the objective is to achieve a late cycle, the genotypes CHP970409 from institution A (1,34%), SELCP9310635 from B (3,91%), LP02130 and IPR Grauna from C (5,91% and 4,91%, respectively) and FT Soberano from institution D (1,91) are the most promising. According to Chiorato et al. (2008), the genotypes with high phenotype effects and wide variability should be selected, in grain yield, for considerable genetic gain.

In plant breeding, besides distinguishing the variances, the differences among the phenotypes effects of the genotypes and knowledge about the relation among the breeding target characters are very important. Since the selection of superior genotypes aim at identifying several characters simultaneously, knowledge about the phenotypic correlations can help in the selection of a plant ideotype best suited to the demands of a modern and competitive agriculture (Coimbra et al., 2000).

Therefore, the phenotypic correlations were estimated within each institution (Figure 1). Thus, it can be said that the characters grain yield and plant cycle presented significant and positive correlations in all the evaluated institutions, with correlations of 0,41 (institution A), 0,37 (B), 0,37 (C), 0,39 (D) and 0,38 (E).

The dispersion of the phenotypic correlation (Figure 1) between the grain yield and plant cycle characters evidenced significance in the correlation between them. The dispersion matrix (Becker et al., 1987) presents the relations among several variables, taken two at a time. The confidence ellipses (Moore and McCabe, 1989), on the other hand, are used as a graphic indicator of correlation. Consequently, when two variables are correlated, the confidence ellipse is circular and, as the correlation among the variables becomes stronger, the ellipse becomes more elongated (SAS, 1997). Therefore, based on the correlation matrix (Figure 1), it can be verified that the existence of positive and significant correlation between the two variables is not similar for all the genotypes evaluated. However, there is a general trend for most genotypes to be included in the correlation. Besides, it can be observed that the presence of ellipse corroborates the presence of positive and significant correlation between the two variables. In other words, there is evidence that genotypes with longer plant cycle may allow higher increases in grain yield. Coimbra et al. (2000) verified that the primary and secondary components of the grain yield of black bean are not independent, concluding that taller plants and those with higher reproductive cycle influence negatively the number of grains per legume and positively the number of legumes per plant and the grain mass. One of the plausible explanations for the low genetic gain in the institutions is the fact that the ideotyped genotypes have a precocious cycle, since those with higher grain yield, in general, are those with a long cycle, due to the significant and positive relation between the characters, as verified in the phenotypic correlation.


1--In the estimate of the variance components, the results evidenced higher contribution of non-genetic effects ([[sigma]]) for the phenotypic variance ([[sigma].sup.2.sub.f]), compared to the solely genetic effects ([[sigma].sup.2.sub.g]), both for the character grain yield, and the character plant cycle.

2--All the evaluated institutions presented genetic gains for the grain yield and plant cycle characters over the seven years, but with different magnitudes.

3--The highest genetic gains were achieved in institutions A ([DELTA]G = 19kg x [ha.sup.-1]), B ([DELTA]G = 133kg x [ha.sup.-1]), and C ([DELTA]G = 71kg x [ha.sup.-1]) for the character grain yield.

4--For the character plant cycle, all the institutions presented higher genetic gains, excepting the institution E, which achieved small gains (close to zero).

5--The characters grain yield and plant cycle presented significant and positive correlation in all the institutions evaluated.


The authors thank UDESC, CNPq, CAPES and FAPESC for the scholarship granting and the financial support for the development of this work, and the Epagri for the supply of the data of the VCU assays.


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Received: 10/08/2011. Modified: 09/14/2012. Accepted: 07/27/2013.

Juliano Garcia Bertoldo. Doctor in Plant Genetic Resources, Universidade Federal de Santa Catarina (UFSC), Brazil. Researcher, Fundado de Pesquisa Agropecuaria do Rio Grande do Sul (FEPAGRO), Brazil. e-mail:

Pedro Patric Pinho Morais. Master in Science, UDESC, Brazil. e-mail: patricpinho

Leandro Hellebrandt Kruger. Master in Science, UDESC, Brazil. e-mail:

Nicole Trevisani. Master in Science, UDESC, Brazil. e-mail:

Jefferson Luis Meirelles Coimbra. Doctor in Agronomy, Universidade Federal de Pelotas (UFPEL), Brazil. Professor, UDESC, Brazil. Address: Centro de Ciencias Agroveterinarias, Instituto de Melhoramento e Genetica Molecular (IMEGEM), UDESC. Av. Luis de Camoes 2090, CEP 88520-000, Lages-SC, Brazil. e-mail:

Altamir Frederico Guidolin. Doctor in Science, Universidade de Sao Paulo (USP), Brazil. Professor, UDESC, Brazil. e-mail:

Haroldo Tavares Elias. Doctor in Agronomy, Universidade Estadual de Maringua, Brazil. Reseacher, Empresa de Pesquisa Agropecuaria e Extensao Rural de Santa Catarina (EPAGRI), Brazil. e-mail: htelias@



Institutions    [[sigma].sup.2.    [[sigma].sup.2.   [[sigma].sup.2.
               sub.g] ([dagger])] (cents)     sub.t] (?)

Variances for grain yield (kg x [ha.sup.-1])

A                8173.00           1019917.00        1028090.00
B               58241.00            997698.00        1055939.00
C               28617.00            948369.00         976986.00
D                      1            949340.00         949340.00
E                8946.00            138330.00        1392576.00
Total          103977.00           5298954.00        5402931.00

Variances for plant cycle (days)

A               0.85                78.32             79.17
B               0.75                79.33             80.08
C               0.97                78.72             79.69
D               1.14                80.85             81.99
E              0.001                81.53             81.53
Total           3.72               398.76            402.48

Institutions       e       [[sigma].sup.2.   [[sigma].sup.2.
                   ] (%)] (%)

Variances for grain yield (kg x [ha.sup.-1])

A              174408.00   0.8                99.2
B              171610.00   5.0                95.0
C              200074.00   3.0                97.0
D              196019.00   0.0               100.0
E              149946.00   0.6                99.4
Total             --       1.9                98.1

Variances for plant cycle (days)

A              12.01        1.00              99.00
B               9.52        0.93              99.07
C              11.50        1.21              98.79
D               9.14        1.39              98.61
E               9.23       0.001             100.00
Total             --        0.92              99.08

([dagger]) Sum of the genotypic variances (genotype (G));
(cents) sum of the non-genetic variances (year (A),
location (L), interactions (A x L, A x G, L x G, A x L x G));
(?) sum of the total variances ([[sigma].sup.2.sub.g] +


GENOTYPIC VARIANCE ([[sigma].sup.2.sub.g]),
TOTAL VARIANCE ([[sigma].sup.2.sub.t]) AND
(kg x [ha.sup.-1]) AND PLANT CYCLE (DAYS)

Institutions                     Values

               [[mu].sub.i]         [[sigma].sup.2.
               (kg x [ha.sup.-1])   sub.g]

A              2479                  8173
B              2456                 58241
C              2493                 28617
D              2221                     0
E              2102                  8946

               [M.sub.i]            [[SIGMA].sup.2.
               (days)               sub.g]

A              89.06                0.75
B              89.06                0.85
C              89.06                0.97
D              89.06                1.14
E              89.06                   0

Institutions                  Values

               [[sigma].sup.2.   [[DELTA].sub.g]
               sub.t]            (kg x [ha.sup.-1])

A              1028090            19
B              1055939           133
C               976986            71
D               949340             0
E              1392576            16

               [[SIGMA].sup.2.   [[DELTA].sub.g]
               sub.t]            (days)

A              80.08             0.83
B              79.17             0.95
C              79.69             1.08
D              81.99             1.23
E              81.53                0

A: Epagri, B: Embrapa, C: Iapar; D: Ft Sementes,
and E: Technological University.



Institutions        Differences among
                  the phenotypes effects

                  Grain yield       Plant cycle
               (kg x [ha.sup.-1])     (days)

C x A           14.00                0.77 *
C x B           37.68                0.58 *
C x D          272.75 *              0.62 *
C x E          391.20 *              3.12 *
A x B           23.68               -0.19
A x D          258.75 *             -0.14
A x E          377.20 *              2.35 *
B x D          235.07 *              0.04
B x E          353.52 *              2.54
D x E          118.45 *              2.49 *

* significant at 5% of error probability by the t test.



Institutions   Group of genotypes **       Character

                                           Grain yield    Plant cycle
                                              (kg x         (days)

                 Superior                  2658 *         90.97 *
A                Inferior                  2203 *         87.57 *
               General phenotypic effect   2479           88.97
                 Superior                  2628 *         90.72 *
B                Inferior                  2246 *         86.02 *
               General phenotypic effect   2456           89.16
                 Superior                  2612 *         91.90 *
C                Inferior                  2153 *         87.70 *
               General phenotypic effect   2493           89.74
                 Superior                  2350 *         89.27 ns
D                Inferior                  1891 *         87.00 ns
               General phenotypic effect   2221           89.11
                 Superior                  2443 *         88.72 *
E                Inferior                  1778 *         85.76 *
               General phenotypic effect   2102           86.62

* Significant at 5% of error probability. Superior group:
genotypes with phenotypic effect above the general phenotypic
effect; inferior group: genotypes with phenotypic effect below
the general phenotypic effect, simultaneously.


kg x [ha.sup.-1]) AND CONFIDENCE INTERVALS (95%) FOR
([mu] = 2430kg x [ha.sup.-1])

Institutions   Genotypes        g         [mu]+g   g%

A              CHP970409         162.53    2585     6.71
               CHP9858            79.22    2502     3.27
               CHP9701            78.59    2501     3.24
               CHP971308          65.07    2488     2.69
               CHP970821          60.76    2484     2.51
               CHP9704            40.75    2464     1.68
               CHP9965            36.89    2460     1.52
               CHP9859            35.97    2459     1.48
               CHP9713            18.43    2441     0.76
               CHP9702             9.34    2432     0.39
               CHP9727            -7.38    2415    -0.3
               CHP9954            -7.28    2416    -0.3
               CHP9706            -8.52    2414    -0.35
               CHP9708           -25.25    2398    -1.04
               CHP9726           -25.31    2397    -1.04
               CHP9714           -30.94    2392    -1.28
               CHP970809         -31.79    2391    -1.31
               CHP9712            40.54    2382    -1.67
               CHP9720            49.06    2374    -2.02
               CHP01178          -52.87    2370    -2.18
               CHP9736           -56.60    2366    -2.34
               CHP970617         -71.68    2351    -2.96
               CHP9718           -90.02    2333    -3.72
               CHP9955           -90.31    2332    -3.73
B              AN9021332         126.42    2549     5.21
               BRS Campeiro      334.55    2757    13.8
               TB0202            232.19    2655     9.58
               TB9820             55.64    2478     2.3
               J56                40.08    2463     1.65
               Xamego            -98.56    2324    -4.07
               TB9713           -101.09    2322    -4.17
               Diamante Negro   -192.1     2231    -7.93
               SELCP9310635     -397.14    2026    16.39
C              LP02130           159.69    2582     6.59
               IPR Grauna        142.17    2565     5.87
               IPR Uirapuru      128.12    2551     5.29
               LP9805             21.35    2444     0.88
               LP0151           -115.26    2308    -4.76
               LP98123          -159.24    2264    -6.57
               Iapar44          -176.84    2246    -7.3
D              --                 --        --     --
E              UTF2810433         29.33    2452     1.21
               UTF4               94.33    2517     3.89
               Silvestre         -60.53    2362     1.21
               UTF7              -22.32    2400    -0.92
               UTF53611313       -40.80    2382    -1.68

Institutions   Genotypes        Confidence   intervals

A              CHP970409           2703        3174
               CHP9858             2591        2976
               CHP9701             2460        2802
               CHP971308           2310        2985
               CHP970821           2308        2983
               CHP9704             2423        2765
               CHP9965             2575        3046
               CHP9859             2520        2904
               CHP9713             2032        2463
               CHP9702             2255        2726
               CHP9727             1533        2348
               CHP9954             2475        2859
               CHP9706             1949        2614
               CHP9708             1911        2576
               CHP9726             1937        2371
               CHP9714             1949        2382
               CHP970809           2070        2746
               CHP9712             1877        2552
               CHP9720             1315        2084
               CHP01178            1992        2658
               CHP9736             1975        2641
               CHP970617           2144        2810
               CHP9718             1864        2298
               CHP9955             2095        2760
B              AN9021332           2169        2597
               BRS Campeiro        2637        2924
               TB0202              2365        3040
               TB9820              2107        2772
               J56                 2247        2724
               Xamego              2007        2510
               TB9713              2311        2781
               Diamante Negro      2074        2361
               SELCP9310635        1552        2320
C              LP02130             2341        3007
               IPR Grauna          2474        2760
               IPR Uirapuru        2463        2750
               LP9805              1926        2479
               LP0151              2319        2790
               LP98123             2081        2551
               Iapar44             1664        2218
D              --                   --          --
E              UTF2810433          2152        2817
               UTF4                1743        2302
               Silvestre           1201        2053
               UTF7                2070        2736
               UTF53611313         1277        2092


kg x [ha.sup.-1]) AND CONFIDENCE INTERVALS (95%) FOR THE
([mu] = 89,06 Days)

Institutions   Genotypes          g     [mu] + g     g%

A              CHP970409        1.20    90.26      1.34
               CHP9858          0.99    90.05      1.11
               CHP9701          0.82    89.88      0.92
               CHP971308        0.81    89.87      0.91
               CHP970821        0.72    89.78      0.81
               CHP9704          0.66    89.72      0.75
               CHP9965          0.45    89.51      0.51
               CHP9859          0.41    89.47      0.46
               CHP9713          0.38    89.44      0.43
               CHP9702          0.28    89.34      0.32
               CHP9727          0.18    89.24      0.20
               CHP9954          -0.01   89.05      -0.01
               CHP9706          -0.11   88.95      -0.13
               CHP9708          -0.17   88.89      -0.20
               CHP9726          -0.20   88.86      -0.22
               CHP9714          -0.23   88.83      -0.26
               CHP970809        -0.35   88.71      -0.39
               CHP9712          -0.40   88.66      -0.44
               CHP9720          -0.42   88.64      -0.47
               CHP01178         -0.49   88.57      -0.55
               CHP9736          -0.91   88.15      -1.02
               CHP970617        -1.08   87.98      -1.21
               CHP9718          -1.10   87.96      -1.23
               CHP9955          -1.45   87.61      -1.63

B              AN9021332        0.33    89.39      3.91
               BRS Campeiro     1.08    90.14      1.21
               TB0202           0.76    89.82      0.85
               TB9820           0.49    89.55      0.55
               J56              0.00    89.06      0.00
               Xamego           -0.28   88.78      -0.31
               TB9713           -0.40   88.66      -0.44
               Diamante Negro   -0.69   88.37      -0.78
               SELCP9310635     -1.29   87.77      -1.45

C              LP02130          1.58    104.84     5.91
               IPR Grauna       -0.02   89.03      4.91
               IPR Uirapuru     -1.18   77.30      2.91
               LP9805           0.57    89.63      0.64
               LP0151           -0.08   88.99      -0.09
               LP98123          -0.12   88.94      -0.13
               Iapar44          -0.75   88.31      -0.84

D              FT Soberano      1.16    100.65     1.91
               FT Bionobre      -1.38   75.30      0.91
               FT Nobre         0.16    89.22      0.18
               FT 91370         0.15    89.21      0.16
               FT 84113         -0.09   88.97      -0.09

E              --               --      --         --

Institutions   Genotypes        Confidence

A              CHP970409        88.48   95.02
               CHP9858          83.26   89.02
               CHP9701          87.94   94.48
               CHP971308        89.90   94.23
               CHP970821        82.84   88.61
               CHP9704          89.87   95.63
               CHP9965          87.43   90.89
               CHP9859          89.48   95.24
               CHP9713          89.39   95.16
               CHP9702          82.20   87.97
               CHP9727          82.01   87.77
               CHP9954          86.09   90.16
               CHP9706          86.77   90.23
               CHP9708          88.53   91.58
               CHP9726          86.73   90.19
               CHP9714          87.56   91.25
               CHP970809        88.26   91.32
               CHP9712          81.14   86.91
               CHP9720          87.24   90.92
               CHP01178         87.99   92.32
               CHP9736          84.64   91.70
               CHP970617        86.33   90.01
               CHP9718          84.09   90.63
               CHP9955          85.80   89.49

B              AN9021332        89.01   93.33
               BRS Campeiro     90.88   95.21
               TB0202           89.03   91.55
               TB9820           87.67   91.99
               J56              87.44   91.13
               Xamego           82.56   88.33
               TB9713           83.05   89.59
               Diamante Negro   81.89   87.66
               SELCP9310635     86.46   88.99

C              LP02130          91.10   95.43
               IPR Grauna       82.98   88.74
               IPR Uirapuru     87.39   89.92
               LP9805           89.24   91.77
               LP0151           86.16   90.96
               LP98123          89.79   94.12
               Iapar44          85.33   90.13

D              FT Soberano      89.06   91.59
               FT Bionobre      83.68   88.48
               FT Nobre         88.08   90.61
               FT 91370         85.75   90.55
               FT 84113         85.52   90.32

E              --               --
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Title Annotation:articulo en ingles
Author:Bertoldo, Juliano Garcia; Morais, Pedro Patrie Pinho; Kruger, Leandro Hellebrandt; Trevisani, Nicole
Date:Jul 1, 2013
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