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.
Key words: Production components, sowing, multivariat.
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