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• Machine learning and geostatistical models for soil mapping. • Relationship between soil fertility and environmental/management covariates. • A trade off between spatial cover grids and sample planning optimization. • The benefits of using covariates in sample optimization depend on field management. • As higher the knowledge about field variability, the more accurate is the optimization. The study aimed to evaluate whether the use of covariates in sample optimization helps characterize the spatial variability of soil chemical properties. To this end, data from two agricultural areas located in southeastern Brazil were used. The covariates were used at two stages: sample planning optimization and building predictive methods. The covariates were divided into five categories: relief, vegetation, soil, management, and spatial coordinates. We compared two criteria in sample planning optimization: one aims to ensure spatial distribution by minimizing the mean squared shortest distance (MSSD); the other integrated four criteria through a multipurpose function (SPAN), aiming to represent the distribution and correlation between the covariates, the minimization of the mean squared shortest distance and the distribution of pairs of points for each lag size of the semivariogram. We evaluated three predictive methods (Robust Ordinary Kriging (ROK), Random Forest (RF), and Random Forest-Robust Kriging (RF-RK)) and a null model (mean-based) to predict four soil properties (Cation exchange capacity (CEC), Base saturation (V), Potassium (K) and Phosphorus (P)). We observed that not all areas would benefit from the inclusion of covariates in the sample planning. When our previous knowledge of variability is limited or unknown, we need to evaluate the management in the areas. When the cropping system is more complex and diversified, spatial coverage sampling is more feasible, using the covariates only on the prediction models. [ABSTRACT FROM AUTHOR]

The ability to monitor sugarcane expansion in Brazil, the world's largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the sugarcane area almost doubled over the last decade in the South-Central region of Brazil. Using remote sensing images, the sugarcane cultivation area was annually monitored and mapped between 2003 and 2012, a period of major sugarcane expansion. The objective of this work was to assess the thematic mapping accuracy of sugarcane, in the crop year 2010/2011, with the novel approach of developing a web platform that integrates different spatial and temporal image resolutions to assist interpreters in classifying a large number of points selected by stratified random sampling. A field campaign confirmed the suitability of the web platform to generate the reference data set. An overall accuracy of 98% with an area estimation error of -0.5% was achieved for the sugarcane map of 2010/11. The accuracy assessment indicated that the map is of excellent quality, offering very accurate sugarcane area estimation for the purpose of agricultural statistics. Moreover, the web platform showed to be very effective in the construction of the reference dataset. [ABSTRACT FROM AUTHOR]