Previsão de safra de arroz no estado do Rio Grande do Sul através de modelagem numérica

Abstract

The objective of this study was to define a methodology for monitoring a flooded rice crop forecast for Rio Grande do Sul, and to evaluate the effect of the flood time on growth, development and rice productivity. Two experiments were conducted during the 2013/14 growing season, using a randomized blocks design with four replications. The treatments in Experiment 1 were flooding in V3, V5, V8 and V9, and in Experiment 2 the treatments were flooding in V5, V8, V9 and V10. The onset of flooding did not influence the emission of leaves, the final leaf number, the final number of tillers and crop development. Leaf growth rate is affected by the onset of flooding when rainfall was less than the crop evapotranspiration. It is not clear if kernel yield is or not affected by the time that flooding starts. To define a methodology for monitoring a flooded rice crop forecast for Rio Grande do Sul, the SimulArroz rice model were coupled to regional climate model RegCM4 for generation the daily seasonal forecast. Nine members of RegCM4 model were used, with different parameterization (01, 07, 10, 13, 19, 22, 31, 34 and 37) and four boots (01, 02, 3:04) per month, with daily data of minimum temperature, maximum temperature and solar radiation. Three points with 45 km resolution grid were used for generating data of the minimum temperature (°C) maximum temperature (°C) and solar radiation (MJ m-2 day-1), covering the municipalities of Restinga Seca, Itaqui and Uruguaiana. The predictions were compared with SimulArroz crop monitoring with INMET automatic weather stations data and data collected in three cropping areas in Restinga Seca and 2 in Itaqui. The compared variables were leaf emission (Haun Stage - HS), final leaf number, development stage (COUNCE et al., 2000) and productivity (Mg ha-1). The best predicting irrigated rice crop forecast in Rio Grande do Sul were: member 31 minimum temperature, member 34 maximum temperature and a member 01 solar radiation (M31M34M01); minimum and maximum temperature and solar radiation boot 01 member 19 (M19S01) and; minimum and maximum temperature and solar radiation boot 03 member 01 (M01S03). The seasonal forecast generated by RegCM4 model coupled to SimulArroz rice model made possible the numerical prediction of rice crop in Rio Grande do Sul.