Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Abstract

Climate change can affect the development of soybean cultivation, impacting your productivity. Thus, agrometeorological information is essential in order to improve productivity strategies. The objective of the paper was to analyze the influence and occurrence of seasonality of the following agrometeorological variables on soybean productivity: mean air temperature [TMean] (ºC), accumulated rainfall value [Rain] (mm), global solar radiation [Sr] (MJ m-2 day-1), and potential evapotranspiration [ETp] (mm), in ten-day variations of the the maximum vegetative development date (MVDD), in the 2011/2012 and 2013/2014 harvest years in the state of Paraná. The study was based on spatial distribution of variables, using univariate and bivariate Global Moran’s Indexes, and multivariate clustering analysis. To verify seasonality in the time distribution of the agrometeorological variables in the ten-day variations close to soybean MVDD, we used the circular statistics, through the mean vector length (R). Result it was identified regions of the state that have higher and lower rainfall and seasonality, also have higher and lowest productivity, respectively. That the variation in soybean productivity between harvest years was correlated with the agrometeorological variables, and rainfall volume is an important factor in productivity. The other agrometeorological variables occurred uniformly, especially in 2011/2012 harvest year, in the Northwest, Central-northern and West mesoregions. Furthermore, there was clustering of regions with similar spatial distribution of the evapotranspiration and rainfall variables in 2aDMDV2d in the 2011/2012 and 2013/2014 harvest year, showed the same spatial distribution of the agrometeorological variables and the productivity variable.