Reação de cultivares de milho inoculados com a bactéria Pantoea ananatis, agente causal da Mancha Branca do milho.

Suplemento. Edição dos Resumos do XLI Congresso Brasileiro de Fitopatologia, Belo Horizonte, MG, ago. 2008

Viabilidade de isolados de Pantoea ananatis agente causal da Mancha Branca do milho, em laboratório.

Edição dos Resumos do XLI Congresso Brasileiro de Fitopatologia, Belo Horizonte, MG, ago. 2008

Análise da intensificação da agricultura no Mato Grosso à partir de dados TRMM 3B42 e de series temporais MODIS/EVI.

The Brazilian state of Mato Grosso (southern Amazonia) is one of the main national producer of agricultural products such as soybean, cotton and corn. After having based its development on the expansion of arable lands through deforestation for thrity years, the agricultural sector is now increasing its productive potential through the adoption of new agricultural management practices such as double cropping systems. Remote sensing tehcniques such as classification of MODIS/TERRA EVI times series are efficient tools for monitoring this phenomena. It appears that double cropping systems with wo comercial crops (soybean and corn or soybean and cotton) improved from 6% to 26% of the total cultivated area in Mato Grosso between 2000- 2001 and 2006-2007 harvests. However, when studying at a county level, those rates vary from 1 to more than 50%, attesting that it exists a strong spatial variability concerning the application of this agricultural management practice. It is argued that this rate is in part drove by the importance of total agricultural areas in a place and by pluviometric conditions. This hypothesis is confirmed by crossing MODIS data with rainfall data. These data are issue from the TRMM 3B42 products, which are computed into parameters such as duration, onset, end of the rain season and total annual rainfalls. Those parameters are found to explain 42% of the spatial variability of the application of double cropping systems in Mato Grosso. O objetivo deste artigo é de mapear as áreas cultivadas com duas safras e de estimar se o grau de intensificação encontrado em uma área pode ser relacionado às condições pluviométricas

Detecting outliers and asserting consistency in agriculture ground truth information by using temporal VI data from modis.

Collecting ground truth data is an important step to be accomplished before performing a supervised classification. However, its quality depends on human, financial and time ressources. It is then important to apply a validation process to assess the reliability of the acquired data. In this study, agricultural infomation was collected in the Brazilian Amazonian State of Mato Grosso in order to map crop expansion based on MODIS EVI temporal profiles. The field work was carried out through interviews for the years 2005-2006 and 2006-2007. This work presents a methodology to validate the training data quality and determine the optimal sample to be used according to the classifier employed. The technique is based on the detection of outlier pixels for each class and is carried out by computing Mahalanobis distances for each pixel. The higher the distance, the further the pixel is from the class centre. Preliminary observations through variation coefficent validate the efficiency of the technique to detect outliers. Then, various subsamples are defined by applying different thresholds to exclude outlier pixels from the classification process. The classification results prove the robustness of the Maximum Likelihood and Spectral Angle Mapper classifiers. Indeed, those classifiers were insensitive to outlier exclusion. On the contrary, the decision tree classifier showed better results when deleting 7.5% of pixels in the training data. The technique managed to detect outliers for all classes. In this study, few outliers were present in the training data, so that the classification quality was not deeply affected by the outliers