Regression models for productivity prediction in cactus pear cv. Gigante

Guimarães, Bruno V. C.; Donato, Sérgio L. R.; Aspiazú, Ignacio; Azevedo, Alcinei M.; Carvalho, Abner J. de

doi:10.1590/1807-1929/agriambi.v24n11p721-727

Acessibilidade / Reportar erro

Brasil

Revista Brasileira de Engenharia Agrícola e Ambiental

Español English

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Articles • Rev. bras. eng. agríc. ambient. 24 (11) • Nov 2020 • https://doi.org/10.1590/1807-1929/agriambi.v24n11p721-727 copy

Regression models for productivity prediction in cactus pear cv. Gigante

Modelos de regressão para predição de produtividade em palma forrageira cv. Gigante

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The understanding of plant behavior and its reflexes on yield is essential for rural planning; thus, the biomathematical models are promising in the yield prediction of cactus pear cv. Gigante. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. Gigante. The study, using homogeneous treatments, was developed at the Instituto Federal Baiano, Campus of Guanambi, Bahia, Brazil. Data were collected in an area consisting of 384 basic units (plants), in which the yield, defined as a dependent variable, and the predictor variables: plant height (PH), cladode length (CL), cladode width (CW), and cladode thickness (CT), number of cladodes (NC), cladode area (CA), and total cladode area (TCA) were evaluated. Simple linear regression models, multiple regression models only with simple effects for the explanatory variables, and the multiple regression models considering the simple and quadratic effects, and all its possible interactions were adjusted. From this last model, a reduced model was obtained by discarding the less relevant effects, using the Stepwise methodology. The use of the vegetative traits, TCA, NC, CA, CL, CT, and CW, through the adoption of multiple linear regression, quadratic interaction or just the variable TCA by the use of simple linear regression, allows the yield prediction of cactus pear, with adjusted R² of 0.82, 0.76, and 0.74, respectively.

Key words:
Opuntia sp.; modeling; estimation; yield

Models	Adjustment quality assessors			R²	R²_aj
Models	AIC	BIC	loglikelihood	R²	R²_aj
$P r o d_{i} 3 = β_{0} + β_{1} P H_{i} + β_{2} T C A_{i} + β_{3} N C_{i} + β_{4} C A_{i} + β_{5} C L_{i} + β_{6} C T_{i} + β_{7} C W_{i} + e_{i}$	4420.73	4456.28	-2201.36	0.7644	0.7600
$P r o d_{i} 4 = β_{0} + β_{1} P H_{i} + β_{2} T C A_{i} + β_{3} C A_{i} + β_{4} C L_{i} + β_{5} C T_{i} + β_{6} C W_{i} + e_{i}$	4418.79	4450.39	-2201.39	0.7644	0.7606
$P r o d_{i} 5 = β_{0} + β_{1} P H_{i} + β_{2} T C A_{i} + β_{3} C A_{i} + β_{4} C L_{i} + β_{5} C T_{i} + e_{i}$	4417.10	4444.76	-2201.55	0.7642	0.7611
$P r o d_{i} 6 = β_{0} + β_{1} P H_{i} + β_{2} T C A_{i} + β_{3} C A_{i} + C T_{i} + e_{i}$	4415.23	4438.93	-2201.61	0.7641	0.7616
$P r o d_{i} 7 = β_{0} + β_{1} P H_{i} + β_{2} T C A_{i} + β_{3} C T_{i} + e_{i}$	4414.31	4434.07	-2202.16	0.7634	0.7616
$P r o d_{i} 8 = β_{0} + β_{1} T C A_{i} + β_{2} C T_{i} + e_{i}$	4413.71	4429.51	-2202.86	0.7626	0.7613

Regression models	AIC
$P r o d i 9 = β_{0} + β_{1} C L_{i} + β_{2} C W_{i} + β_{3} C T_{i} + β_{4} P H i_{+} β_{5} N C_{i} + β_{6} T C A_{i} + β_{7} C L_{2}^{i} + β_{8} C W_{i}^{2} + β_{9} C T_{i}^{2} + β_{10} C A_{i}^{2} + β_{11} N C_{i}^{2} + β_{12} T C A_{i}^{2} + β_{13} C L_{i} C W_{i} + β_{14} C L_{i} C T_{i} + β_{15} C L_{i} N C_{i} + β_{16} C L_{i} T C A_{i} + β_{17} C L_{i} C L_{i} + β_{18} C L_{i} C T_{i}^{2} + β_{19} C L_{i} N C_{i}^{2} + β_{20} C W_{i} C L_{i}^{2} + β_{21} C W_{i} C A_{i}^{2} + β_{22} C W_{i} N C_{i}^{2} + β_{23} L C_{i} T C A_{i}^{2} + β_{24} C T_{i} N C_{i} + β_{25} C T_{i} C L_{i}^{2} + β_{26} C T_{i} C W_{i}^{2} + β_{27} C T_{i} C T_{i}^{2} + β_{28} C T_{i} C A_{i}^{2} + β_{29} C T_{i} N C_{i}^{2} + β_{30} P H_{i} N C_{i} + β_{31} P H_{i} C W_{i}^{2} + β_{32} P H_{i} C A_{i}^{2} + β_{33} N C_{i} T C A_{i} + β_{34} N C_{i} C W_{i}^{2} + β_{35} N C_{i} C T_{i}^{2} + β_{36} N C_{i} C A_{i}^{2} + β_{37} N C_{i} N C_{i}^{2} + β_{38} N C_{i} T C A_{i}^{2} + β_{39} T C A_{i} C L_{i}^{2} + β_{40} T C A_{i} C W_{i}^{2} + β_{41} T C A_{i} N C_{i}^{2} + β_{42} T C A_{i} T C A_{i}^{2} + β_{43} C L_{i}^{2} C W_{i} + β_{44} C L_{i}^{2} C T_{i}^{2} + β_{45} C L_{i}^{2} C A_{i}^{2} + β_{46} C L_{i}^{2} N C_{i}^{2} + β_{47} C L_{i}^{2} T C A_{i}^{2} + β_{48} C W_{i}^{2} C T_{i}^{2} + β_{49} C W_{i}^{2} C A_{i}^{2} + β_{50} C W_{i}^{2} N C_{i}^{2} + β_{51} C W_{i}^{2} T C A_{i}^{2} + β_{52} C T_{i}^{2} C A_{i}^{2} + β_{53} C A_{i}^{2} N C_{i}^{2} + β_{54} C A_{i}^{2} T C A_{i}^{2} + β_{55} N C_{i}^{2} T C A_{i}^{2} + e_{i}$	3312.43
$P r o d i 10 = β_{0} + β_{1} C L_{i} + β_{2} C W_{i} + β_{3} C T_{i} + β_{4} P H_{i} + β_{5} N C_{i} + β_{6} T C A_{i} + β_{7} C L_{2}^{i} + β_{8} C W_{i}^{2} + β_{9} C T_{i}^{2} + β_{10} C A_{i}^{2} + β_{11} N C_{i}^{2} + β_{12} T C A_{i}^{2} + β_{13} C L_{i} C W_{i} + β_{14} C L_{i} C T_{i} + β_{15} C L_{i} N C_{i} + β_{16} C L_{i} T C A_{i} + β_{17} C L_{i} C L_{i}^{2} + β_{18} C L_{i} N C_{i}^{2} + β_{19} C W_{i} C L_{i}^{2} + β_{20} C W_{i} C A_{i}^{2} + β_{21} C W_{i} T C A_{i}^{2} + β_{22} C T_{i} N C_{i} + β_{23} C T_{i} C L_{i}^{2} + β_{24} C T_{i} C W_{i}^{2} + β_{25} C T_{i} C T_{i}^{2} + β_{26} C T_{i} C A_{i}^{2} + β_{27} C T_{i} N C_{i}^{2} + β_{28} P H_{i} N C_{i} + β_{29} P H_{i} C W_{i}^{2} + β_{30} P H_{i} C A_{i}^{2} + β_{31} N C_{i} T C A_{i} + β_{32} N C_{i} C W_{i}^{2} + β_{33} N C_{i} C T_{i}^{2} + β_{34} N C_{i} C A_{i}^{2} + β_{35} N C_{i} N C_{i}^{2} + β_{36} N C_{i} T C A_{i}^{2} + β_{37} T C A_{i} C W_{i}^{2} + β_{38} T C A_{i} N C_{i}^{2} + β_{39} T C A_{i} T C A_{i} + β_{40} C L_{i}^{2} C W_{i}^{2} + β_{41} C L_{i}^{2} N C_{i}^{2} + β_{42} C L_{i}^{2} T C A_{i}^{2} + β_{43} C W_{i}^{2} C T_{i}^{2} + β_{44} C W_{i}^{2} C A_{i}^{2} + β_{45} C W_{i}^{2} T C A_{i}^{2} + β_{46} C T_{i}^{2} C A_{i}^{2} + β_{47} C A_{i}^{2} N C_{i}^{2} + β_{48} C A_{i}^{2} T C A_{i}^{2} + β_{50} N C_{i}^{2} T C A_{i}^{2} + e_{i}$	3303.71
$P r o d_{i} 11 = β_{0} + β_{1} C L_{i} + β_{2} C W_{i} + β_{3} C T_{i} + β_{4} P H_{i} + β_{5} N C_{i} + β_{6} T C A_{i} + β_{7} C L_{i}^{2} + β_{8} C W_{i}^{2} + β_{9} C T_{i}^{2} + β_{10} C A_{i}^{2} + β_{11} N C_{i}^{2} + β_{12} T C A_{i}^{2} + β_{13} C L_{i} C W_{i} + β_{14} C L_{i} C T_{i} + β_{15} C L_{i} C L_{i}^{2} + β_{16} C L_{i} N C_{i}^{2} + β_{17} C W_{i} C L_{i}^{2} + β_{18} C W_{i} C A_{i}^{2} + β_{19} C W_{i} T C A_{i}^{2} + β_{20} C T_{i} N C_{i} + β_{21} C T_{i} C L_{i}^{2} + β_{22} C T_{i} C W_{i}^{2} + β_{23} C T_{i} C T_{i}^{2} + β_{24} C T_{i} C A_{i}^{2} + β_{25} C T_{i} C L_{i}^{2} + β_{26} C T_{i} C W_{i}^{2} + β_{27} C T_{i} C T_{i}^{2} + β_{28} C T_{i} C A_{i}^{2} + β_{29} C T_{i} N C_{i}^{2} + β_{30} P H_{i} C W_{i}^{2} + β_{31} P H_{i} C A_{i}^{2} + β_{32} N C_{i} T C A_{i} + β_{33} N C_{i} C W_{i}^{2} + β_{34} N C_{i} C T_{i}^{2} + β_{35} N C_{i} C A_{i}^{2} + β_{36} N C_{i} T C A_{i}^{2} + β_{37} T C A_{i} C W_{i}^{2} + β_{38} C L_{i}^{2} C W_{i}^{2} + β_{39} C L_{i}^{2} N C_{i}^{2} + β_{40} C L_{i}^{2} T C A_{i}^{2} + β_{41} C W_{i}^{2} C A_{i}^{2} + β_{42} C W_{i}^{2} T C A_{i}^{2} + β_{43} C A_{i}^{2} N C_{i}^{2} + β_{44} C A_{i}^{2} T C A_{i}^{2} + β_{45} N C_{i}^{2} T C A_{i}^{2} + e_{i}$	3292.14
$P r o d_{i} 12 = β_{0} + β_{1} C L_{i} + β_{2} C W_{i} + β_{3} C T_{i} + β_{4} P H_{i} + β_{5} N C_{i} + β_{6} T C A_{i} + β_{7} C L_{i}^{2} + β_{8} C W_{i}^{2} + β_{9} C T_{i}^{2} + β_{10} C A_{i}^{2} + β_{11} N C_{i}^{2} + β_{12} T C A_{i}^{2} + β_{13} C L_{i} C W_{i} + β_{14} C L_{i} C T_{i} + β_{15} C L_{i} C L_{i}^{2} + β_{16} C L_{i} N C_{i}^{2} + β_{17} C W_{i} C L_{i}^{2} + β_{18} C W_{i} C A_{i}^{2} + β_{19} C W_{i} T C A_{i}^{2} + β_{20} C T_{i} N C_{i} + β_{21} C T_{i} C L_{i}^{2} + β_{22} C T_{i} C W_{i}^{2} + β_{23} C T_{i} C T_{i}^{2} + β_{24} C T_{i} C A_{i}^{2} + β_{25} P H_{i} C W_{i}^{2} + β_{26} P H_{i} C A_{i}^{2} + β_{27} N C_{i} T C A_{i} + β_{28} N C_{i} C W_{i}^{2} + β_{29} N C_{i} C T_{i}^{2} + β_{30} N C_{i} C A_{i}^{2} + β_{31} N C_{i} T C A_{i}^{2} + β_{32} T C A_{i} C W_{i}^{2} + β_{33} C L_{i}^{2} C W_{i}^{2} + β_{34} C L_{i}^{2} N C_{i}^{2} + β_{35} C L_{i}^{2} T C A_{i}^{2} + β_{36} C W_{i}^{2} C A_{i}^{2} + β_{37} C W_{i}^{2} T C A_{i}^{2} + β_{38} C A_{i}^{2} N C_{i}^{2} + β_{39} C A_{i}^{2} T C A_{i}^{2} + e_{i}$	3291.18
$P r o d_{i} 27 = β_{0} + β_{1} T C A_{i} + β_{2} C W_{i} + β_{3} C A_{i}^{2} + β_{4} N C_{i}^{2} + β_{5} T C A_{i}^{2} + β_{6} C L_{i} C W_{i}^{2} + β_{7} C L_{i} N C_{i}^{2} + β_{8} C W_{i} C L_{i}^{2} + β_{9} C W_{i} C A_{i}^{2} + β_{10} C W_{i} T C A_{i}^{2} + β_{11} C T_{i} C T_{i}^{2} + β_{12} P H_{i} C W_{i}^{2} + β_{13} P H_{i} C A_{i}^{2} + β_{14} N C_{i} C W_{i}^{2} + β_{15} N C_{i} C T_{i}^{2} + β_{16} N C_{i} C A_{i}^{2} + β_{17} C L_{i} C W_{i}^{2} + β_{18} C W_{i}^{2} T C A_{i}^{2} + β_{19} C L_{i}^{2} T C A_{i}^{2} + β_{20} C W_{i}^{2} C A_{i}^{2} + β_{21} C W_{i}^{2} T C A_{i}^{2} + β_{22} C A_{i}^{2} T C A_{i}^{2} + e_{i}$	3290.37

Unidade Acadêmica de Engenharia Agrícola Unidade Acadêmica de Engenharia Agrícola, UFCG, Av. Aprígio Veloso 882, Bodocongó, Bloco CM, 1º andar, CEP 58429-140, Campina Grande, PB, Brasil, Tel. +55 83 2101 1056 - Campina Grande - PB - Brazil
E-mail: revistagriambi@gmail.com

Acompanhe os números deste periódico no seu leitor de RSS

[1] Bruno V. C. Guimarães - E-mail: bvinicius20@yahoo.com.br