Effects of three esca-associated fungi on Vitis vinifera L.: III. Enzymes produced by the pathogens and their role in fungus-to-plant or in fungus-to-fungus interactions

When the esca-associated fungi Phaeomoniella chlamydospora (Pch), Togninia minima (Tmi) and Fomitiporia mediterranea (Fme) were grown in liquid stationary cultures, it was seen that they were able to live in media containing resveratrol (RES) or tannic acid (TA) as the sole carbon source and that the fungi were able to convert both compounds. Particular attention is paid here to detecting RES and TA conversion. Pch, Tmi and Fme were partially inhibited by RES or TA. Pch, Tmi and Fme produced extracellular tannase, laccase and peroxidase enzymes in liquid or agarized cultures, whether glucose was present or not. When colonies of Pch, Tmi and Fme were confronted, they showed spatially and temporally heterogeneous patterns of laccase and peroxidase activity. The results indicate the non-synergistic, competitive association of Pch and Tmi and the inhibition of Fme growth. Muconic acid, a well-known intermediate in a large number of lignin and phenol oxidative processes, can partly or completely inhibit the lignolytic agent Fme, but is tolerated by Pch and Tmi. An explanation for wood pigmentation patterns by Pch, Tmi and Fme is given.     

Application of Reclaimed Wastewater in the Irrigation of Rosebushes

The use of reclaimed wastewater in agriculture can be a solution for regions with water shortages or low rainfall periods; besides fulfilling the crop's water needs, it would also promote the recycle of nutrients. However, care should be taken regarding soil salinization, especially in closed environments such as greenhouses for the cultivation of ornamental plants. The domestic effluents are rich in sodium which can accumulate on soil and cause soil sealing. This study evaluated the use of effluents from anaerobic filters and intermittent sand filters in the production of rosebushes (Rosa hybrida “Ambiance”). The crop yield of the rosebushes irrigated with reclaimed wastewater exceeded the one obtained with traditional cultivation, reaching a value 31.8 % higher when employing nitrified effluent originated from intermittent sand filters, with no difference in the product quality. The salinity levels are below the critical limits found in the literature; however, there was a significant increase compared to the irrigation with drinking water.     

Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

Within the southern Ecuadorian Andes, landslides have an impact on landscape development. Landslide risk estimation as well as hydrological modelling requires physical soil data. Statistical models were adapted to predict the spatial distribution of soil texture from terrain parameters. For this purpose, 56 soil profiles were analysed horizon-wise by pipette and laser method. Results by pipette compared to laser method showed the expected shift to higher silt and lower clay contents. Linear regression equations were adapted. The performance of regression tree (RT) and Random Forest (RF) models was compared by hundredfold model runs on random Jackknife partitions. Digital soil maps of sand, silt and clay percentage mean and standard deviation indicate model variability and prediction uncertainty.RF models performed better than RT models. All terrain factors considered in the analysis influenced soil texture of the surface horizon, but altitude a.s.l. was assigned the highest variable importance during model construction. Shallow subsurface flow is considered responsible for increasing sand/clay ratios with increasing altitude, on steep slopes and with overland flow distance to the channel network by removing clay particles downslope. Deeper soil layers are not influenced by this process and therefore, did not show the same texture properties. However, the influence of parent material and landslides on the spatial distribution of soil texture cannot be neglected. Model performance, most probably, could be improved by a bigger dataset.