Soil tillage affects soybean growth and promotes heavy metal accumulation in seeds.

When soybean is grown in soils with high heavy metal concentrations, it may introduce those contaminants into the human food chain, posing risks to human health. This study evaluated the effect of tilling the soil with high Cu, Zn, and Mn levels on soybean physiology and metal accumulation in seeds. Disturbed and undisturbed soil samples were collected in two different sites: a vineyard with high heavy metal concentration and a grassland area, containing natural vegetation.

Effect of hairy vetch cover crop on maize nitrogen supply and productivity at varying yield environments in Southern Brazil.

Cover crops play a critical role on conservation and sustainable agriculture due to their well-documented benefits on both soil and crop productivity. Inclusion of legumes (e.g.

Physiological responses of soybean (Glycine max (L.) Merrill) cultivars to copper excess.

Successive applications of copper fungicides on vines have resulted in increased copper content in vineyard soils over the years. This high copper content has affected the growth of young vines in eradicated vineyards. Thus, the cultivation of annual species for a few years is an alternative to copper phytostabilization, because it would be a good way to decrease copper availability to plants.

Pre-planting weed detection based on ground field spectral data.

Background: Site-specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non-targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non-targets for each VI using training and validation data sets.

Results: The main outcomes of this study for effectively distinguishing weeds from other non-targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near-infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.