Gastrointestinal nematode infection in beef cattle raised in silvopastoral and conventional systems in São Paulo state, Brazil.

The use of silvopastoral systems (SPS) can be a good alternative to reduce the environmental impacts of livestock breeding in Brazil. Despite the advantages offered by public policies, many producers hesitate to use this system. One of the reasons is the lack of information on health and productivity of cattle raised under these conditions. The experiment reported here was designed to compare the behavior of infection by gastrointestinal nematodes and weight gain of beef cattle raised in a SPS and a conventional pasture system. We monitored the number of eggs per gram of feces, the prevalent nematode genus, data on climate, forage availability, weight gain and packed cell volume (PCV) of the animals bred in the two systems. The infection by nematodes was significantly higher in the cattle raised in the SPS (p\0.05). The coprocultures revealed the presence of nematodes of the genera Haemonchus, Cooperia, Oesophagostomum and Trichostrongylus, in both systems, but the mean infestation rates of Haemonchus and Cooperia were higher in the SPS (p\0.05). The average of PCV values did not differ between the cattle in the two systems. The individual weight gain and stocking rate in the period did not vary between the systems (p[0.05). Despite the higher prevalence of nematodes in the SPS, no negative impact was detected on the animals? weight gain and health. The results of this experiment indicate that under the conditions studied, there is no need to alter the parasite management to assure good productive performance of cattle

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests.

The seasonal climate drivers of the carbon cy- cle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combina- tion of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measure- ments and 35 litter productivity measurements), their asso- ciated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonal- ity in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rain- fall is < 2000 mm yr-1 (water-limited forests) and to radia- tion otherwise (light-limited forests). On the other hand, in- dependent of climate limitations, wood productivity and lit- terfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosyn- thetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest pro- ductivity in a drier climate in water-limited forest, and in cur- rent light-limited forest with future rainfall < 2000 mm yr-1.

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.