Regression models for productivity prediction in cactus pear cv. Gigante

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.

摘要 了解植物生长特性及其对产量的影响，对农业生产规划至关重要；因此，生物数学模型在梨果仙人掌（cactus pear）品种Gigante的产量预测中具有良好应用前景。本研究旨在通过简单回归与多元回归分析，拟合适用于梨果仙人掌Gigante品种的产量预测模型。本研究采用均质化试验处理方案，在巴西巴伊亚州瓜纳比校区的巴伊亚联邦理工学院开展。研究在包含384个基本观测单元（单株植株）的试验地块中采集数据，以产量作为因变量，对以下预测变量进行测定：株高（Plant Height, PH）、茎节长度（Cladode Length, CL）、茎节宽度（Cladode Width, CW）、茎节厚度（Cladode Thickness, CT）、茎节总数（Number of Cladodes, NC）、单茎节面积（Cladode Area, CA）及总茎节面积（Total Cladode Area, TCA）。本研究共拟合三类模型：简单线性回归模型、仅包含解释变量主效应的多元回归模型，以及同时考虑解释变量主效应、二次效应及其所有可能交互效应的多元回归模型。基于最后一类模型，本研究采用逐步回归（Stepwise）法剔除相关性较低的效应项，得到简化模型。通过采用多元线性回归、含二次效应与交互效应的多元回归，或仅以总茎节面积为变量的简单线性回归，利用总茎节面积、茎节总数、单茎节面积、茎节长度、茎节厚度及茎节宽度等营养生长性状，可实现梨果仙人掌的产量预测，三者对应的调整决定系数（adjusted R²）分别为0.82、0.76与0.74。