Critical variables for estimating productivity in maize as a function of plant population and spacing

Abstract

The objective of this study was to find a group of independent variables that would influence and estimate maize (Zea mays L.) productivity, modeled by multiple linear regression. For that, an experimental delinquency in random order was used in a 2 × 2 factorial scheme, from two populations (45,000 and 65,000 ha-1 plants) and two spacings (0.45 and 0.90 m), with 20 replicates. Soil attributes and maize production components were evaluated. The soil attributes evaluated were bulk density, macroporosity, microporosity, total porosity, soil moisture and mechanical resistance to penetration, at depths of 0-0.15 and 0.15-0.30 m. The maize production components were plant height (PH), height of the first ear insertion (HEI), stalk diameter (SD), number of rows per ear (NRE) and number of grains per row (NGR). There was a positive correlation between the variables and production per hectare, except for grain moisture, soil moisture, macroporosity (0.15-0.30 m) and microporosity (0.00-0.15 m). The number of ears per hectare, the number of grains per row and the 100-grain weight served to estimate maize productivity. The methodology applied in this study was adequate for estimating production with an accuracy of 98% and can be applied to other experiments.

Yield potential and selection of off-season maize for silage and grain using GT Biplot under limited water and frost

“…The three formed groups aimed to gather the variables with perpendicular lines that characterize the genotypes, forming a polygon (Yan and Tinker 2006;Oliveira et al 2018;Sharifi and Ebadi 2018). Variables such as plant height and green mass yield are extremely important in silage production (Crevelari et al 2018), and characteristics related to the grain, such as ear length, the number of grain rows, the number of grains per row, and grain yield, are essential for selecting genotypes (Pereira et al 2018a). Thus, the 1P2224 and 1Q2383 genotypes encompass these characteristics for cultivation in the region (Figure 2A).…”

“…There is a high diversity of maize cultivars for grain and silage production, but different types of commercial and developing maize must be characterized to expand their crops and select those with a dual-consumption purpose (grain and silage) in a given crop area and subjected to certain soil and climate conditions. In the absence of regional evaluation data on maize cultivars for silage, there is the alternative of cultivars with dented or semi-dented grains with larger biomass and size and higher green and dry mass production; also, grain characteristics such as ear length, the number of grain rows, the number of grains per row, and grain yield are essential for selecting genotypes (Duarte et al 2014;Crevelari et al 2018;Pereira et al 2018a). The genotype by trait (GT) biplot tool is a method to analyze several traits in genotype selection and the relationship between genotypes and traits with graphical visualization (Sharifi and Ebadi 2018;Yan and Frégeau-Reid 2018).…”