Marcos Sales Rodrigues

Spatial relationships between soil attributes and corn yield in no-tillage system

Soil properties play an important role in spatial variability of crop yield. However, a low spatial correlation has generally been observed between maps of crop yield and of soil properties. The objectives of the present investigation were to assess the spatial pattern variability of soil properties and of corn yield at the same sampling intensity, and evaluate its cause-and-effect relationships. The experimental site was structured in a grid of 100 referenced points, spaced at 10 m intervals along four parallel 250 m long rows spaced 4.5 m apart. Thus, points formed a rectangle containing four columns and 25 rows. Therefore, each sampling cell encompassed an area of 45 m2 and consisted of five 10 m long crop rows, in which the referenced points represented the center. Samples were taken from the layers 0-0.1 m and 0.1-0.2 m. Soil physical and chemical properties were evaluated. Statistical analyses consisted of data description and geostatistics. The spatial dependence of corn yield and soil properties was confirmed. The hypothesis of this study was confirmed, i.e., when sampling the soil to determine the values of soil characteristics at similar to sampling intensity as for crop yield assessments, correlations between the spatial distribution of soil characteristics and crop yield were observed. The spatial distribution pattern of soil properties explained 65 % of the spatial distribution pattern of corn yield. The spatial distribution pattern of clay content and percentage of soil base saturation explained most of the spatial distribution pattern of corn yield.

ANÁLISE ESPACIAL DA PRODUTIVIDADE DE SERAPILHEIRA EM UMA MATA DE GALERIA

The gallery forests have great genetic diversity and important ecological functions, such as protecting the headwaters, controlling erosion, and functioning as buffer zones and filtering chemicals. Litterfall, which can be used as an indicator of ecological productivity, is widely collected using litter traps that are randomly distributed in a forest plot. However, vegetation distribution may present spatial dependence, 1 Engenheira Florestal, MSc., Doutoranda pelo Programa de Pós-Graduação em Ciências Florestais, Faculdade de Tecnologia, Universidade de Brasília, Campus Universitário Darcy Ribeiro, CEP 70910-900, Brasília (DF), Brasil. priscila.ueg@gmail.com 2 Engenheiro Florestal, Dr., Professor Associado do Departamento de Engenharia Florestal, Faculdade de Tecnologia, Universidade de Brasília, Campus Universitário Darcy Ribeiro, CEP 70910-900, Brasília (DF), Brasil. mauronappo@yahoo.com.br 3 Engenheiro Agrônomo, Dr., Professor Adjunto do Colegiado de Engenharia Agronômica, Universidade Federal do Vale do São Francisco, Campus Ciências Agrárias, Rodovia BR 407, 12 Lote 543, Projeto de Irrigação Nilo Coelho – S/N C1, CEP 56300-990, Petrolina (PE), Brasil. marcos.rodrigues@univasf.edu.br 4 Biólogo, Dr., Professor da Universidade Estadual de Goiás, Rod. GO 330, km 241 s/n, Anel Viário Zona Rural, CEP 75780-000, Ipameri (GO), Brasil. ismaelmpufg@gmail.com 5 Engenheira Agrônoma, Dra., Professora da Universidade Estadual de Goiás, Rod. GO 330, km 241 s/n, Anel Viário Zona Rural, CEP 75780-000, Ipameri (GO), Brasil. glauciapela@gmail.com Recebido para publicação em 25/07/2013 e aceito em 20/08/2014

Terrain Analysis for Locating Erosion Channels: Assessing LiDAR Data and Flow Direction Algorithm

Terrain analysis can be used to locate concentrated flow erosion (e.g., ephemeral gully ero‐ sion) across landscapes. For example, studies have found that ephemeral gullies were likely to occur when field specific thresholds were exceeded for the following terrain attributes: the product of upslope area, slope, and plan curvatures [1]; topographic wetness index, up‐ slope area, and slope [2]; and the topographic wetness index and the product of the upslope area and slope [3]. Another study used a cartographic classification and threshold procedure for erosion channel identification [4].