Logistic model to selection of energy cane clones

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

doi:10.1590/0103-8478cr20190750

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

Logistic model to selection of energy cane clones

Modelo logístico na seleção de clones de cana de energia

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

Logistic regression analysis is a technique that may aid genetic breeding programs in the selection of clones, especially in the early stages where experimental accuracy is low. This research aimed to identify the most important agronomic traits for energy cane clonal selection, and to verify the efficiency of the logistic model in predicting the genotypes to be selected. Evaluations were carried out on 220 clones in the first ratoon. The data were subjected to binary logistic regression analysis. Stalk number per meter was the most important trait in the selection of energy cane clones. In addition, plants with lower grade for smut incidence had a greater chance of being selected. The predictive capacities of the qualitative and quantitative models were 94% and 88%, respectively. The use of a qualitative model proved to be effective at predicting the number of energy cane genotypes to be selected and could be used as a selection strategy.

Key words:
genetic improvement; biomass; Saccharum spp

Authorship

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.

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

Jonathan William Trautenmüller

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

Bruno Portela Brasileiro

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

Ricardo Augusto de Oliveira

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

João Carlos Bespalhok Filho

http://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

Figures | Tables | Formulas

Figures (1)
Tables (6)
Formulas (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