Marcelo Marques Costa

Cokrigagem na estimativa dos teores de Ca e Mg em um Latossolo Vermelho distroférrico

Sought, with this study, estimating, through the use of cokriging, values of calcium and magnesium, using as covariate pH. In a Rhodic Hapludox, sampled the layer of 0-0.2 m, using the grid of 25 x 25 m, totaling 60 points from 5.02 ha. Was held, initially, descriptive analysis and correlation, and subsequently, geostatistical analysis to identify the spatial dependence of variables. Using the technique of cokriging pH values were efficient in the estimation of Ca and Mg, due to the high correlation between this covariate and the variables to be estimated.

Quantitative losses in mechanized harvesting corn crop in small footprint and conventional

The objective of this study was to evaluate the quantitative losses occurred in the mechanical harvesting of the corn grown in conventional and reduced gaps. The experimental design was randomized blocks with three replications and treatments distributed in a factorial (2 x 3 x 3), and two spacings (0.45 m and 0.90 m), three speeds of the combine (1.8 km. h-1 3.5 km. h-1 and 4.1 km. h-1 ) and three openings of the concave (25 mm, 30 mm and 35 mm). For treatments where the spacing was 0.45 m, the harvester worked with a power of two lines dividing line between the platform crop while in treatments where the spacing was 0.90 m, the harvester worked with a power dividing line between a line of platform. The plant population was the same for the two spacings used in the experiment. The natural losses were higher in the conventional spacing, but the header losses of harvest had the lowest use this space 1

Weeds Variability Evaluation on No-Tillage System Using Digital Images

The aim of this work was to describe the weed spatial variability in a no-tillage system area in Jatai, GO, Brazil. A regular grid was used on a 25 hectares field accomplishing 36 sample points. The total shoot dry matter of weeds was determined on an area of 0.5 m2 and also separated on broadleaf and grassy types. Images of the sample area were sorted by a supervised classifier into three classes: straw, leaves and uncovered. The soybean leaves were manually segmented from the leave class. The images were also processed using an automatic threshold method, separating the leaves from the background. On the processed images were calculated the covered areas by each class. All the variables were submitted to correlation and geostatistical analysis. A significant correlation was verified between covered area by plants and the shoot dry matter. The supervised classification and the automatic threshold method achieved similar results. When the soybean leaves was segmented, the calculation of the broadleaf weed infestation was improved. Spatial dependence was only verified when the two types of weeds were studied separately.