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The current challenge of corn (Zea maysL.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super‐traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha–1and high, 180 kg ha–1). At full bloom, the spectral variables green (550 nm), red (660 nm), red‐edge (735 nm), and near‐infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red‐edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil‐adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red‐edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield.

AbstractThe objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Paraná State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops.

Brazil has the largest commercial beef cattle stock on Earth, and most of the cattle produced in the country is bred and finished on pastures. The cattle ranching sector represents a significant source of the country's greenhouse gas (GHG) emissions. Agricultural intensification has been highlighted as one of the main strategies in reaching global food security and reducing deforestation. The Sudden Death Disease (SDD) of pastures, which affects the most planted cultivar of Urochloa brizantha, is degrading pastures in the Amazon, contributing to low production yields and high emission rates. This paper discusses the intensification of pasture production systems and SDD, to examine the potential for pasture renovation to address livestock productivity and GHG balance, emissions and potential sinks. Does SDD represent a blessing or a curse to climate change mitigation in the Brazilian Amazon? A collection of pasture samples were assessed to measure wet and dry weight in areas with and without SDD, which were related to remote sensing data to provide an overall estimate of the total area affected by the SDD in Alta Floresta, a municipal county of southern Brazilian Amazonia. We found that 77.1% of all pastures had been committed to the syndrome, which has forced farmers to renew their pastures. This also has great potential in increasing soil carbon stocks, effectively reducing the CO2footprint of meat production in those areas. Therefore, we firmly believe that SDD management has provided an opportunity to rebalance the emissions/sequestration equation associated with meat production by the cattle ranching sector in this Amazonin frontier.