Growth, Production and Corn Yield Estimates Under Different Levels of Water Replacement and Doses of Nitrogen

Abstract

The aim of this study was to evaluate growth, production and water balance in maize, and to adjust multiple regression models for estimating production components, total biomass accumulation and grain yield under different levels of water replacement and doses of nitrogen. The experiment was conducted at the Centre for Agricultural Sciences of the Federal University of Alagoas (CECA/UFAL). The experimental design was of randomised blocks in split plots. The AG 7088 VT PRO3 maize genotype was used. The treatments consisted of irrigation depths (40, 80, 120,160 and 180% of the crop evapotranspiration - ETC) and doses of nitrogen (N) (0, 75, 150 and 225 ha-1). The following growth variables were evaluated: canopy height (CH), root collar height (RH), leaf length (LL), leaf width (LW), number of green leaves (NGR), number of dry leaves (NDL), leaf area (LA), green leaf weight (GLW) and dry leaf weight (DLW), together with the following production components: Productivity (PROD), 1000-grain weight (TGW), ear length (EL), number of rows per ear (NRE), number of grains per row (NGR) and weight of grain per ear (GWE). There was a significant effect (p<0.005) from irrigation depth (D) on the PC1 variables as a function of the ETc, with no significant effect on the other PCs. There was a significant effect (p<0.001) from the nitrogen dose (N) on PC1, with no significant effect on PC2 or PC3. There was no significant effect from the D x N interaction. The greatest leaf area (9,355 cm²), canopy height (221 cm), leaf width (10.33 cm) and weight of grain per ear (209.74 g) were obtained with the dose of 225 kg N, using complementary irrigation at 200% of the ETC. However, the highest productivity (8.57 t) is achieved with 150 kg N, with complementary irrigation at 200% of the ETC, a value 41.70% higher than the dose of 0 kg N with irrigation at 40% of the ETC. The multiple regression model showed a significant fit to the variables under analysis and is an important tool to aid in decisions on maize crop management.