House Anti-Virus Haemagglutinin Heterohybridoma [BGSD 69E7]

There is an increased interest in the use of soil-biodegradable plastic mulch due to limited disposal options for conventional polyethylene mulch. However, information about the impact of continuous use of soil-biodegradable plastic mulch on the environment is limited. Here, we show the effects on soil and groundwater quality from the use of soil-biodegradable plastic mulches for crop production for four consecutive seasons. Two soil-biodegradable plastic mulch products were assessed at two locations (Knoxville, TN and Mount Vernon, WA) having different climates (humid subtropical and cool Mediterranean), with cellulosic-paper mulch, poly-ethylene mulch, and no-mulch included as control treatments. Soil physical, chemical, and biological properties were first assessed in the spring of 2015 (prior to any field operations), and then a few days after harvest in the fall of 2015, 2016, 2017, and 2018. Water samples were collected in the fall of 2018 from lysimeters installed at 55-cm depth and analyzed for nutrient composition. Compared to the no-mulch treatment, the soil-biodegradable plastic mulches and polyethylene mulch increased the soil aggregate stability (by 6-16%) and water infiltration rate (by 10-12%) by protecting the soil surface from disturbance. Residual nitrate and nitrite under the plastic mulch after harvest were lower than under no-mulch (by 4.1 kg ha(-1) to 7.3 kg ha(-1)) due to increased yield and associated enhanced nutrient uptake. However, plastic mulching, especially the polyethylene mulch, reduced soil microbial activity, measured as burst CO2-C by 6 g kg(-1 )day(-1) to 54 kg(-1) day(-1), but had no effect on extractable organic carbon concentrations nor specific extracellular enzyme activity rates. Within the fouryear period, the soil-biodegradable plastic mulches had overall positive effects on soil and groundwater quality, except for reduced burst microbial respiration, which was more pronounced in Mount Vernon.

Information regarding the spatial distribution of palm trees in tropical forests is crucial for commercial exploitation and management. However, spatially continuous knowledge of palms occurrence is scarce and difficult to obtain with conventional approaches such as field inventories. Here, we developed a new method to map Amazonian palm species at the individual tree crown (ITC) level using RGB images acquired by a low-cost unmanned aerial vehicle (UAV). Our approach is based on morphological operations performed in the score maps of palm species derived from a fully convolutional neural network model. We first constructed a labeled dataset by dividing the study area (135 ha within an old-growth Amazon forest) into 28 plots of 250 m x 150 m. Then, we manually outlined all palm trees seen in RGB images with 4 cm pixels. We identified three palm species: Attalea butyracea, Euterpe precatoria and Iriartea deltoidea. We randomly selected 22 plots (80%) for training and six plots (20%) for testing. We changed the plots for training and testing to evaluate the variability in the classification accuracy and assess model generalization. Our method outperformed the average producer's accuracy of conventional patch-wise semantic segmentation (CSS) in 4.7%. Moreover, our method correctly identified, on average, 34.7 percentage points more ITCs than CSS, which tended to merge trees that are close to each other. The producer's accuracy of A. butyracea, E. precatoria and I. deltoidea was 78.6 +/- 5.5%, 98.6 +/- 1.4% and 96.6 +/- 3.4%, respectively. Fortunately, one of the most exploited and commercialized palm species in the Amazon (E. precatoria, a.k.a, Acai) was mapped with the highest classification accuracy. Maps of E. precatoria derived from low-cost UAV systems can support management projects and community-based forest monitoring programs in the Amazon.