Abstract

Simulation models have been widely used to generate yield data by forecasting climate variables and changes in growing seasons. The aim of this study was to calibrate genetic coefficients and simulate growth, development and yield in maize and sorghum crops based on historical meteorological data for the municipality of Juranda (2007 to 2013), in the central-west region of Paraná State, Brazil. Treatments were established based on three planting dates in two growing seasons for a group of super early maturity maize hybrids (DKB 330 Pro), and two groups of sorghum hybrids, the first a super early variety (ADV 123) and the second with a normal cycle (1G282). The variables assessed were number of days from planting to flowering, leaf area index (LAI), and 1000 seed weight and yield. Statistical coefficients were used to evaluate calibration accuracy. The results demonstrated that the models were highly efficient at simulating crop cycles, yield and leaf area index, with agreement indices and modeling efficiency values above 0.90. The results indicated that the CERES-Maize and CERES-Sorghum models generated satisfactory and comparative simulations of maize and sorghum yield for the study area on different planting dates.

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