Modelo logístico na seleção de clones de cana de energia

Borella, Juliane; Trautenmüller, Jonathan William; Brasileiro, Bruno Portela; Oliveira, Ricardo Augusto de; Bespalhok Filho, João Carlos

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CROP PRODUCTION • Cienc. Rural 50 (9) • 2020 • https://doi.org/10.1590/0103-8478cr20190750 copiar

Modelo logístico na seleção de clones de cana de energia

Autoria SCIMAGO INSTITUTIONS RANKINGS

RESUMO:

A análise de regressão logística é uma técnica que pode auxiliar programas de melhoramento genético na seleção de clones, especialmente nos estágios iniciais, em que a precisão experimental é baixa. Este trabalho teve como objetivo identificar os principais caracteres agronômicos para a seleção clonal de cana energia e verificar a eficiência do modelo logístico na predição dos genótipos a serem selecionados. As avaliações foram realizadas em 220 clones na primeira soca. Os dados foram submetidos à análise de regressão logística binária. O número de colmos por metro foi um caractere de maior importância na seleção de clones de cana energia. Além disso, os clones com nota menor para incidência de carvão tiveram maior chance de serem selecionados. As capacidades preditivas dos modelos qualitativos e quantitativos foram de 94% e 88%, respectivamente. O uso de um modelo qualitativo mostrou-se eficaz na previsão do número de genótipos de cana energia a serem selecionados e pode ser usado como estratégia de seleção.

Palavras-chave:
melhoramento genético; biomassa; Saccharum spp

Autoria

Juliane Borella ^*E-mail:borella.juli@gmail.com. *Corresponding author

Departamento de Fitotecnia e Fitossanidade, Universidade Federal do Paraná (UFPR), 80035-050, Curitiba, PR, Brasil. Universidade Federal do Paraná (UFPR)BrazilCuritiba, PR, BrazilDepartamento de Fitotecnia e Fitossanidade, Universidade Federal do Paraná (UFPR), 80035-050, Curitiba, PR, Brasil.

https://orcid.org/0000-0002-3962-6196

Jonathan William Trautenmüller

https://orcid.org/0000-0003-3507-2146

Bruno Portela Brasileiro

https://orcid.org/0000-0003-2807-7993

Ricardo Augusto de Oliveira

https://orcid.org/0000-0002-7472-9238

João Carlos Bespalhok Filho

https://orcid.org/0000-0003-2705-9750

E-mail:borella.juli@gmail.com. ^*Corresponding author

DECLARATION OF CONFLICT OF INTERESTS

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

AUTHORS’ CONTRIBUTIONS

All authors critically revised the manuscript and approved of the final version.

SCIMAGO INSTITUTIONS RANKINGS

Figuras | Tabelas | Fórmulas

Figuras (1)
Tabelas (6)
Fórmulas (3)

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Figure 1
Number of clones selected by the mass method (A), by the qualitative model (B) and by the quantitative model (C), considering the smut incidence (SI) and stalk number (SN).

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Table 1
Description of the traits evaluated in the second selection phase of energy cane and their respective notes. Source: RIDESA.

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Table 2
Notes on brown rust incidence (BRIn), orange rust incidence (ORIn), and smut incidence (SIn) in energy cane.

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Table 3
Coefficients estimated by the logistic regression model for qualitative and quantitative traits (general qualitative model and general quantitative model), in the mass selection, in energy cane.

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Table 4
Parameters estimated by logistic regression models of qualitative traits (qualitative reduced model) and quantitative (quantitative reduced model) traits used in the logistic models adjusted to the mass selection performed in the second test phase, in energy cane.

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Table 5
Classification of the number of selected and discarded genotypes, using qualitative and quantitative logistic regression models with mass selection applied in the second selection phase as a response.

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Table 6
Means of the genotypes selected (1) and non-selected (0) by the models and the mass selection performed in energy cane phase T2.

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Figure 1 Number of clones selected by the mass method (A), by the qualitative model (B) and by the quantitative model (C), considering the smut incidence (SI) and stalk number (SN).

Table 1 Description of the traits evaluated in the second selection phase of energy cane and their respective notes. Source: RIDESA.

Traits	------------------------------------------------------------------Notes-----------------------------------------------------------------------
	1	2	3	4	5
PV	Excellent	Great	Good	Regular	Terrible
Pn	Absent	¼ do Ø	1/2 do Ø	> 50% Ø
LSn	Absent	Little	Regular	Abundant
FLn	Absent	Induction	Panicle
SRn	Easy	Medium	Difficult
SDn	Thin (<2.0 cm)	Medium (2.0-3 cm)	Thick (3-3.5 cm)
SHn	Low	Medium	High
SNn	Low	Medium	High
BPn	2 mm	3 mm	4 mm
GHn	Erect	Semi-erect	Decumbent

PVn: plant vigor; Pn: pith; LSn: lateral sprouting; FLn: flowering, SRn: straw removal, SDn: stalk diameter, SHn: stalk height, SNn: stalk number, BPn: bud prominence, GHn: growth habit.

Table 2 Notes on brown rust incidence (BRIn), orange rust incidence (ORIn), and smut incidence (SIn) in energy cane.

Notes	----------------------------------------------------------------Traits-------------------------------------------------------------
	BRIn	ORIn (%)	SIn (unit)
1	Highly resistant	0.06	0
2	Resistant	0.14	1-15
3	Moderately resistant	0.36	15-30
4	Moderately susceptible	0.89	30-45
5	Susceptible	2.17	45-60
6	Highly susceptible	5.18	>60
7	-	11.87	-
8	-	24.9	-
9	-	45	-

Table 3 Coefficients estimated by the logistic regression model for qualitative and quantitative traits (general qualitative model and general quantitative model), in the mass selection, in energy cane.

-----------------------------------------------------------------------General qualitative model------------------------------------------------------------
	${\hat{β}}_{i}$	Standard error	Chi square	P value
Intercept	1.577	4.345	0.363	0.716
PVn	-3.798	0.775	-4.902	0.000^**
SIn	-1.378	0.604	-2.281	0.022^*
BRIn	-0.353	0.298	-1.184	0.236
ORIn	-2.235	323,227	-0.007	0.994
Pn	-0.112	0,304	-0.369	0.712
FLn	-0.321	0,301	-1.064	0.287
SRn	0.165	0,631	0.261	0.794
SDn	1.201	0,992	1.209	0.226
SHn	1.103	0,661	1.668	0.095
SNn	1.964	0.868	2.262	0.024^*
LSn	0.954	0.849	1.125	0.261
BPn	0.449	1.029	0.436	0.663
GHn	-0.338	0.810	-0.417	0.676
AIC	-----------------------------------------------------------------------78.445---------------------------------------------------
----------------------------------------------------------------------General quantitative model------------------------------------------------------------
Intercept	-12.573	3.593	-3.499	0.000^**
SC	0.092	0.119	0.772	0.440
FIB	-0.209	0.168	-1.248	0.212
SD	0.159	0.860	0.186	0.853
SH	3.097	0.801	3.867	0.000^**
SN	0.182	0.047	3.816	0.000^**
SI	-0.127	0.042	-3.011	0.002^*
SW	0.227	0.100	2.272	0.023^*
AIC	--------------------------------------------------------------------149.32--------------------------------------------------

PVn: plant vigor; SIn: smut incidence; BRIn: brown rust incidence; ORIn: orange rust incidence; Pn: pith; FLn: flowering; SRn: straw removal; SDn: stalk diameter; SHn: stalk height; SNn: stalk number; LSn: lateral sprouting; BPn: bud prominence; GHn: growth habit; SC: sucrose content; FIB: fiber content; SD: stalk diameter; SH: stalk height; SN: stalk number; SI: smut incidence; SW: stalk weight; AIC: Akaike Information Criteria; ^**; ^*: significant at 1% and 5%, respectively, according to the Wald test.

Table 4 Parameters estimated by logistic regression models of qualitative traits (qualitative reduced model) and quantitative (quantitative reduced model) traits used in the logistic models adjusted to the mass selection performed in the second test phase, in energy cane.

--------------------------------------------------Qualitative reduced model------------------------------------------------------
	${\hat{β}}_{i}$	Standard error	Chi square	P value	OR
Intercept	7.575	2.016	3.757	0.000^**
PVn	- 3.982	0.736	-5.409	0.000^**	0.019
SIn	-1.052	0.462	-2.279	0.022^*	0.349
SNn	1.489	0.675	2.204	0.027^*	4.433
AIC	----------------------------------------------------------68.277-------------------------------------------
------------------------------------------------Quantitative reduced model-----------------------------------------------------
	${\hat{β}}_{i}$	Standard error	Chi square	P value	OR
Intercept	-13.537	2.383	-5.682	0.000^**
SH	2.594	0.722	3.589	0.000^**	13.379
SN	0.170	0.047	3.584	0.000^**	1.185
SI	-0.126	0.040	-3.083	0.002^*	0.881
SW	0.288	0.076	3.785	0.000^**	1.335
AIC	--------------------------------------------------------145.063----------------------------------------

PVn: plant vigor; SIn: smut incidence; SNn: stalk number; SH: stalk height; SN: stalk number; SI: smut incidence and SW: stalk weight; OR: odds ratio; AIC: Akaike Information Criteria and ^**; ^*: significant at 1% and 5%, respectively, according to the Wald test.

Table 5 Classification of the number of selected and discarded genotypes, using qualitative and quantitative logistic regression models with mass selection applied in the second selection phase as a response.

Mass selection	-----------------------------------------------Qualitative reduced model-------------------------------------------
	Select (1)	Rejected (0)	Total	PC
Select (1)	34	4	38	94%
Rejected (0)	8	174	182
Total	42	178	220
Mass selection	--------------------------------------------Quantitative reduced model---------------------------------------------
Mass selection	Select (1)	Rejected (0)	Total	PC
Select (1)	16	22	38	88%
Rejected (0)	4	178	182
Total	20	220	220

PC: Predictive Capacity of the model.

Table 6 Means of the genotypes selected (1) and non-selected (0) by the models and the mass selection performed in energy cane phase T2.

Selection	Answer	SC	FIB
Qualitative model	0	8.946	14.134
	1	9.281	14.255
Quantitative model	0	9.013	14.113
	1	8.975	14.556
Mass selection	0	8.991	14.151
	1	9.101	14.187

SC: sucrose content; FIB: fiber content.

{\hat{β}}_{i}

{\hat{β}}_{i}

{\hat{β}}_{i}

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Universidade Federal de Santa Maria Universidade Federal de Santa Maria, Centro de Ciências Rurais , 97105-900 Santa Maria RS Brazil , Tel.: +55 55 3220-8698 , Fax: +55 55 3220-8695 - Santa Maria - RS - Brazil
E-mail: cienciarural@mail.ufsm.br

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[1] E-mail:borella.juli@gmail.com. ^*Corresponding author