Mapeamento do índice de vegetação da diferença normalizada em lavoura de algodão

Os dados de sensoriamento remoto em campo podem fornecer informações detalhadas sobre a variabilidade de parâmetros biofísicos ligados à produtividade em grandes áreas e apresentam potencial para o monitoramento destes parâmetros, ao longo de todo o ciclo de desenvolvimento da cultura. Este trabalho objetivou mapear a variabilidade espacial do índice de vegetação da diferença normalizada (NDVI) e seus componentes, em duas lavouras comerciais de algodão (Gossipium hirsutum L.), utilizando sensor óptico ativo, em nível terrestre. Os dados foram coletados utilizando-se sensor instalado em um pulverizador autopropelido agrícola. Um receptor GPS foi acoplado ao sensor, para a obtenção das coordenadas dos pontos de amostragem. As leituras foram realizadas em faixas espaçadas em 21,0 m, aproveitando-se as passadas do veículo no momento da pulverização de agroquímicos, e os dados submetidos à análise estatística clássica e geoestatística. Mapas de distribuição espacial das variáveis foram elaborados pela interpolação por krigagem. Observou-se maior variabilidade espacial do NDVI e da reflectância espectral da vegetação na região do infravermelho próximo (IVP) (880 nm) e do visível (590 nm) na lavoura com maior estresse fisiológico, devido ao ataque do percevejo castanho [Scaptocoris castanea (Hem.: Cydnidae)], em relação à lavoura sadia.

Maize and soybeans production in integrated system under no-tillage with different pasture combinations and animal categories

The adoption of no-till system (NTS) combined with crop-livestock integration (CLI) has been a strategy promoted in Brazil, aiming to maximize areas yield and increase agribusiness profitability. This study aimed to evaluate grains yield and phytotechnical attributes from maize and soybean culture by CLI system under NTS after winter annual pure and diversified pastures with the absence or presence of grazing animals. The experiment was installed in Castro (Parana State, Brazil) on in a dystrophic Humic Rhodic Hapludox with a clay texture, using experimental design of randomized complete blocks in 4 x 2 factorial scheme with three replications. Treatments included four pasture combinations (diversified or pure) and animal categories (light and heavy) subjected or not to grazing animals during the winter. During 2008/09 and 2009/10 summers, the area was cultivated with soybeans and maize, respectively, with yield assessment of grains and phytotechnical attributes. Treatments did not alter the yield and weight of a thousand seeds (WTS) of soybeans. In maize culture, the grazing animal during the winter increased the plant population and grains yield, but gave slight decrease in WTS. Pasture combinations (diversified or pure) and animal categories (light and heavy) did not interfere in soybean culture, but benefited the maize crop.

Estimativa de densidade amostral para elaboração de mapas de produtividade

Yield mapping represents the spatial variability concerning the features of a productive area and allows intervening on the next year production, for example, on a site-specific input application. The trial aimed at verifying the influence of a sampling density and the type of interpolator on yield mapping precision to be produced by a manual sampling of grains. This solution is usually adopted when a combine with yield monitor can not be used. An yield map was developed using data obtained from a combine equipped with yield monitor during corn harvesting. From this map, 84 sample grids were established and through three interpolators: inverse of square distance, inverse of distance and ordinary kriging, 252 yield maps were created. Then they were compared with the original one using the coefficient of relative deviation (CRD) and the kappa index. The loss regarding yield mapping information increased as the sampling density decreased. Besides, it was also dependent on the interpolation method used. A multiple regression model was adjusted to the variable CRD, according to the following variables: spatial variability index and sampling density. This model aimed at aiding the farmer to define the sampling density, thus, allowing to obtain the manual yield mapping, during eventual problems in the yield monitor.