Abstract
Abstract: The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y = β0 + β1X1 + β2X2. The Y, X1, and X2 traits were measured in 361, 373, and 416 plants, respectively, of single-, three-way, and double-cross hybrids in the 2008/2009 crop year; and in 1,777, 1,693, and 1,720 plants, respectively, of single-, three-way, and double-cross hybrids in the 2009/2010 crop year, totaling 6,340 plants. Descriptive statistics were calculated, and frequency histograms and scatterplots were created. The sample size (number of plants) for the estimate of the β0, β1, and β2 parameters, of the residual standard error, the coefficient of determination, the variance inflation factor, and the condition number between the explanatory traits of the model (X1 and X2) were determined by resampling with replacement. Measuring 260 plants is sufficient to adjust precise multiple regression models of corn grain yield as a function of ear length and ear diameter. The Y = -229.76 + 0.54X1 + 6.16X2 model is a reference for estimating corn grain yield.
Highlights
Corn (Zea mays L.) is the cereal with the highest production volume worldwide according to the United States Department of Agriculture (Usda, 2019), with an estimated production of 1,099.61 million tons for the 2018/2019 crop in an area of 189.31 million hectares
The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y = β0 + β1X1 + β2X2
The minimum and maximum values of X1 were similar between the six experimental cases (28 ≤ minimum ≤ 56; 211 ≤ maximum ≤ 281) (Table 1), and a similar pattern was observed for X2 and Y
Summary
Multiple linear regression has been used to predict the behavior of one principal variable as a function of two or more explanatory variables in corn. Laurie et al (2004), for example, found, via simulations, that multiple linear regression was the most effective method to detect quantitative trait loci in a cross between high- and low-selection lines for oil concentration in corn. Ge & Wu (2019) used multiple linear regression to predict corn price fluctuation, considering production-consumption and import and export volume as independent variables. Mohammadi (2007) verified, via multiple linear regression, that relative growth rate and specific leaf area were the best predictors of the competitiveness of corn cultivars against weeds Multiple linear regression has been used to predict the behavior of one principal variable as a function of two or more explanatory variables in corn. Laurie et al (2004), for example, found, via simulations, that multiple linear regression was the most effective method to detect quantitative trait loci in a cross between high- and low-selection lines for oil concentration in corn. Ge & Wu (2019) used multiple linear regression to predict corn price fluctuation, considering production-consumption and import and export volume as independent variables. Mohammadi (2007) verified, via multiple linear regression, that relative growth rate and specific leaf area were the best predictors of the competitiveness of corn cultivars against weeds
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