Interactions of bacteria and fungi on decomposing litter: differential extracellular enzyme activities

Fungi and bacteria are key agents in plant litter decomposition in freshwater ecosystems. However, the specific roles of these two groups and their interactions during the decomposition process are unclear. We compared the growth and patterns of degradative enzymes expressed by communities of bacteria and fungi grown separately and in coexistence on Phragmites leaves. The two groups displayed both synergistic and antagonistic interactions. Bacteria grew better together with fungi than alone. In addition, there was a negative effect of bacteria on fungi, which appeared to be caused by suppression of fungal growth and biomass accrual rather than specifically affecting enzyme activity. Fungi growing alone had a high capacity for the decomposition of plant polymers such as lignin, cellulose, and hemicellulose. In contrast, enzyme activities were in general low when bacteria grew alone, and the activity of key enzymes in the degradation of lignin and cellulose (phenol oxidase and cellobiohydrolase) was undetectable in the bacteria-only treatment. Still, biomass-specific activities of most enzymes were higher in bacteria than in fungi. The low total activity and growth of bacteria in the absence of fungi in spite of apparent high enzymatic efficiency during the degradation of many substrates suggest that fungi provide the bacteria with resources that the bacteria were not able to acquire on their own, most probably intermediate decomposition products released by fungi that could be used by bacteria.     

Automated Classification of an Environmental Sensitivity Index

Environmental Sensitivity Indices (ESI) composed of many field-data are essential for monitoring and control systems. At the beginning of the last decade an ESI of the German Wadden Sea was developed for use by the relevant authorities. This ESI was derived by experts semi-manually analysing the extensive field data-set. An algorithm is presented here which emulates human expert-decisions on the classification of sensitivity classes. This will permit the necessary regular updates of ESI-determination when new field data become available using automated classifications procedures. After tuning the algorithm parameters it generates decisions identical to those of human experts in about 97% of all locations tested. In addition, the algorithm presented also enables erroneous or extremely seldom field data to be identified.     

A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).     

Comparative analysis of socio-economic and environmental performances for Chinese EIPs: case studies in Baotou, Suzhou, and Shanghai

To decouple economic growth from environmental degradation, the Chinese government proposed the circular economy (CE) strategy as part of its 11th 5-Year Plan. This strategy expands the application of CE from individual enterprises to eco-industrial parks (EIPs) and to the cities, provinces, and regions. We carried out field studies in three EIPs in Baotou, Suzhou, and Shanghai. In this paper, we discuss the current state of CE and the sustainable development of EIPs in China. We first provide detailed information on the three EIPs' infrastructures, preferential policies, CE frameworks, and eco-chains. We then examine the status of sustainable development in the three EIPs from the perspectives of socio-economic, resource and material efficiency, and environmental performance. The results indicate that the overall performance of the three EIPs is reasonably good with respect to socio-economics, resources and materials, and efficiency and environmental protection, whereas green management is rather weak and thus requires further improvement. We found that the CE frameworks along with eco-chains within the EIPs are effectively improving resource and material efficiency. Moreover, we demonstrate that there are positive associations among socio-economic, resource and material, and environmental indicators. Given the large presence of EIPs in the local economies, these results suggest that EIPs play a key role in promoting sustainable development in China.