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:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
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].sup.2.sub.ng]) 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].sup.2.sub.ng] = 98,1; [[sigma].sup.2.sub.ng] = 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].sub.gi] (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].sub.gi] 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].sup.2.sub.ng]) 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.
Arias ERA, Ramalho MAP (1998) Progresso genetico em milho no estado do Mato Grosso do Sul, no periodo de 1986/87 a 1993/94. Pesq. Agropec. Bras. 33: 1549-1554.
Atroch AL, Nunes GHS (2000) Progresso genetico em arroz de varzea umida no estado do Amapa. Pesq. Agropec. Bras. 35: 767-777.
Becker RA, Cleveland WS, Wilks AR (1987) Dynamic graphics for data analysis. Stat. Sci. 2: 355-382.
Bertoldo JG, Coimbra JLM, Gui dolin AF, Nodari RO, Elias HT, Barili LD, Vale NM, Ro zzetto, DS (2009) Rendimen to de graos em feijao preto: o componente que mais interfere no valor fenotipico e o ambiente. Cieno. Rural 39: 1974-1982.
Breseghello F, Rangel PHN, Morais OP (1999) Ganho de produtivi dade pelo melhoramento genetico do arroz irrigado no nordeste do Brasil. Pesq. Agropec. Bras. 34: 399-407.
Carvalho LP, Barbosa MHP, Costa JN, Farias FJC, Santana JCF, Andrade FP (1997) Progresso genetico do algodeiro herbaceo no nordeste. Pesq. Agropec. Bras. 32: 283-291.
Carvalho SIC, Bianchetti LBB, Reifschneider FJB (2009) Registro e protecao de cultivares pelo setor publico: a experiencia do programa de melhoramento de Capsicum da Embrapa Hortalicas. Hort. Bras. 27: 135-138.
Chiorato AF, Carbonell, SAM, Dias LAS, Resende, MDV (2008) Prediction of genotypic values and estimation of genetic parameters in common bean. BABT 51: 465-472.
Coimbra JLM, Guidolin AF, Carvalho FIF, Azevedo R (2000) Correlacoes canonicas: ii--analise do rendimento de graos de feijao e seus componentes. Cieno. Rural 30: 31-35.
Coimbra JLM, Bertoldo AF, Elias HT, Hemp S, Vale NM, Toaldo D, Rocha F, Barili LD, Garcia SH, Guidolin AF, Kopp MM (2009) Mineracao da interacao genotipo x ambiente em Phaseolus vulgaris L. para o Estado de Santa Catarina. Cienc. Rural 39: 355-363.
Dalla Corte A, Moda-Cirino V, Scholz MBS, Destro D (2003) Environment effect on grain quality in early common bean cultivars and lines. CBAB 3: 193-202.
De La Vega A, Delacy IH, Chapman SC (2007) Progress over 20 years of sunflower breeding in central Argentina. Field Crops Res. 100: 61-72.
Laidig F, Drobek T, Meyer U (2008) Genotypic and environmental variability of yield for cultivars from 30 different cropsin German official variety trials. Plant Breed. 127: 541-547.
Littell RC, MillikeN GA, Stroup WW, Wolfinger RD (2006) SAS System for Mixed Models. SAS Institute, Inc. Cary, NC, EEUU.
Machado CF, Teixeira NFP, Filho FRF, Rocha MM, Gomes RLF (2008) Identificacao de genotipos de feijao-caupi quanto a precocidade, arquitetura da planta e produtividade de graos. Rev. Cieno. Agron. 39: 114-123.
Matos JW, Ramalho MPA, Abreu AFB (2007) Trinta e dois anos do programa de melhoramento genetico de feijoeiro comum em Minas Gerais. Cienc. Agrotec. 6: 1749-1754.
Moore DS, Mccabe GP (1989) Introduction to the Practice of Statistics. Freeman. Nova Iorque, EEUU. pp. 179-199.
Piepho HP, Mohring J (2006) Selection in cultivar trials -it is ignorable? Crop Soi. 46: 192-201.
Rane J, Pannu RK, Sohu VS, Saini RS, Shoran BMG, Cros sa J, Vargas M, Joshi AK (2007) Performance of yield and stability of advanced wheat genotypes under heat stress environments of the Indo-Gangetic plains. Crop Soi. 47: 1561-1573.
Rocha F, Toaldo D, Barili LD, Vale NM, Garcia S, Coimbra JLM, Vogt GA, Guidolin AF (2009) Efeito de ambiente sobre a produtividade de feijao carioca para o estado de Santa Catarina. Bragantia 68: 621-627.
SAS (2007) SAS 9.1.3 (TS1M3) for Windows Microsoft. SAS Institute, Inc. Cary, NC, EEUU.
Simeao RM, Sturion JA, Resende MDV (2002) Avaliacao Genetica em erva-mate pelo procedimento BLUP individual multivariado sob interacao genotipo x ambiente. Pesq. Agropec. Bras. 37: 1589-1596.
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: email@example.com.
Pedro Patric Pinho Morais. Master in Science, UDESC, Brazil. e-mail: patricpinho @hotmail.com.
Leandro Hellebrandt Kruger. Master in Science, UDESC, Brazil. e-mail: firstname.lastname@example.org.
Nicole Trevisani. Master in Science, UDESC, Brazil. e-mail: email@example.com.
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: firstname.lastname@example.org.
Altamir Frederico Guidolin. Doctor in Science, Universidade de Sao Paulo (USP), Brazil. Professor, UDESC, Brazil. e-mail: email@example.com.
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@ epagri.sc.gov.br
TABLE I ESTIMATES OF THE VALUE OF THE GENOTYPIC ([[sigma].sup.2.sub.g]), NON-GENOTYPIC ([[sigma].sup.2.sub.ng]) AND TOTAL ([[sigma].sup.2.sub.t) VARIANCES OBTAINED FROM THE EVALUATION OF 49 GENOTYPES OF BLACK BEAN USED IN THE VCU TEST FOR THE STATE OF SANTA CATARINA (2001-2007), COMPARING FIVE BEAN BREEDING INSTITUTIONS FOR GRAIN YIELD (kg x [ha.sup.-1]) AND PLANT CYCLE (DAYS) Institutions [[sigma].sup.2. [[sigma].sup.2. [[sigma].sup.2. sub.g] ([dagger]) sub.ng] (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. sub.ng] (%) sub.ng] (%) 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] + [[sigma].sup.2.sub.ng]). TABLE II VALUES OF PHENOTYPIC EFFECT ([[mu].sub.i]), GENOTYPIC VARIANCE ([[sigma].sup.2.sub.g]), TOTAL VARIANCE ([[sigma].sup.2.sub.t]) AND THE GENETIC GAIN ([[DELTA].subn.g]) OBTAINED IN THE FIVE INSTITUTIONS FOR GRAIN YIELD (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. TABLE III COMPARISON AMONG THE FIVE INSTITUTIONS THAT PARTICIPATED IN THE VCU TEST OF BLACK BEAN IN THE STATE OF SANTA CATARINA (2001-2007) FOR GRAIN YIELD AND PLANT CYCLE Institutions Differences among the phenotypes effects ([[mu].sub.i]-[[mu].sub.j] 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. TABLE IV COMPARISON OF THE GROUPS OF GENOTYPES WITHIN INSTITUTIONS PARTICIPATING IN THE VCU TEST OF BLACK BEAN IN THE STATE OF SANTA CATARINA (2001-2007) FOR GRAIN YIELD AND PLANT CYCLE Institutions Group of genotypes ** Character Grain yield Plant cycle (kg x (days) [ha.sup.-1]) 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. TABLE V GENOTYPE PREDICTED VALUE THROUGH BLUP (g), PREDICTION OF THE GENOTYPIC VALUES ([mu]+g), ACHIEVED GAIN (g%; kg x [ha.sup.-1]) AND CONFIDENCE INTERVALS (95%) FOR THE GRAIN YIELD CHARACTER OF 49 BLACK BEAN GENOTYPES, SUBDIVIDED INTO FIVE BEAN BREEDING INSTITUTIONS ([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 TABLE VI GENOTYPE PREDICTED VALUE THROUGH BLUP (g), PREDICTION OF THE GENOTYPIC VALUES ([mu] + g), ACHIEVED GAIN (g%; kg x [ha.sup.-1]) AND CONFIDENCE INTERVALS (95%) FOR THE PLANT CYCLE CHARACTER OF 49 BLACK BEAN GENOTYPES, SUBDIVIDED INTO FIVE BEAN BREEDING INSTITUTIONS ([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 intervals 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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