Regression models for productivity prediction in cactus pear cv. Gigante

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

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

Abstract

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.

Highlights

In the search for understanding about which vegetative descriptors are most associated with the production, as well as the possibility of using these to predict yield (Guimarães et al, 2014), aiming at defining the number of animals to be fed or biomass volume to be commercialized, the use of simple linear regression (SLR) (Bertolin et al, 2017), multiple linear regression (MLR) (Soares et al, 2014; Mantai et al, 2015) and the polynomial and quadratic regression models (Amaral et al, 2017) have been used as a reliable tool
When the range of variation is included in the classes of 30%, the variability is considered low, medium, high, and very high, respectively
The coefficient of variation (CV) of the evaluated descriptors ranged between 6.91 and 60.19%, with the lowest values in the traits associated with the cladode, such as the area, length, and width of the cladode, except for the cladode thickness which showed very high variability (Gomes, 2000)

Summary

Introduction

In the search for understanding about which vegetative descriptors are most associated with the production, as well as the possibility of using these to predict yield (Guimarães et al, 2014), aiming at defining the number of animals to be fed or biomass volume to be commercialized, the use of simple linear regression (SLR) (Bertolin et al, 2017), multiple linear regression (MLR) (Soares et al, 2014; Mantai et al, 2015) and the polynomial and quadratic regression models (Amaral et al, 2017) have been used as a reliable tool. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. + β20CTi2TCAi2 + β21PHi2 NCi2 + β22PHi2TCAi2 + ei where: Prodi - Yield of green mass of cladodes associated with ith observation, t ha-1; PH - plant height, cm; TCA - total cladode area, m2; NC - number of cladodes, no; CA - cladode area, cm; CL - cladode length, cm; CT - cladode thickness, cm;. The data were analyzed using the R software (R Development Core Team, 2016)

Results and Discussion

Conclusion

Literature Cited