Precision agriculture can increase efficiency and sustainability of grain crops in Brazil, especially in support of soil and crop management. In this study the importance of spatially-variable soil and crop attributes on the variability of grain crop yield and the use of this information to improve management decisions was examined. The study used data from the 2005/6 corn growing season of a 18 ha commercial field on a clayey Latossolo Bruno (FAO: Ferralsol). Soil (0-0.10 m layer) and crop properties were sampled at a density of two per ha. Spatially distributed crop yield was determined with a grain sensor on the harvester. Yield variability (average of 12.4 Mg ha-1, amplitude between 11.1 and 14.0 Mg ha-1) was related to the soil properties - P Mehlich, Mg2+, sum of bases and Ca:Mg, Mg:K and Mg:CEC ratios - but not with crop attributes, according to the results of a Spearman correlation matrix. Regression analysis identified a critical value of Magnesium saturation (Mg:CEC) of 0.10, and a critical Mg:K ratio of 2.3, as the most significant soil properties. Below the critical values, yields were reduced. Cluster analysis confirmed the results obtained by the two previous statistical techniques, and their combined interpretation supported the conclusion that P and Mg were the two nutrients for which spatial management is most needed. Compared to the traditional fixed-rate, whole-field application, using variable rate application would require, for the whole field, 6.6 Mg of additional lime, being 3.4 Mg calcitic lime and 3.2 Mg dolomitic lime, less P2O5 (-235 kg), and more K2O (+ 135 kg) than in the normal uniform management. However, with variable rates such inputs would be applied at adequate rates to specific parts of the field, positively affecting the yields in these parts. When management decisions are supported by appropriate statistical tools, precision agriculture is technically viable for the management of soil chemical properties, allowing for optimized dosages, with a view to an ideal economical yield.
grid soil sampling; Spearman correlation; magnesium; phosphorus; micromanagement
