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Friedrich Rinne Albrecht Hase A. Loewy Werner Gottstein 《Die Naturwissenschaften》1926,14(18):389-391
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Albrecht Hase R. Hesse Adolf Mayer Paul Weiss Karl Bělař 《Die Naturwissenschaften》1929,17(44):863-866
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Frederike Albrecht 《Disasters》2018,42(2):336-360
This study investigates if and to what extent natural disasters affect social capital. Twelve different events in Europe are examined in a quantitative analysis, using data derived from the European Social Survey and the EM‐DAT International Disaster Database. The study uses social trust as an indicator of social capital and offers evidence that a change in social trust is a possible occurrence during or after a disaster, but that it is not an inevitable consequence of it. The results reveal that social trust decreases after a disaster with a death toll of at least nine. Changes in social capital, therefore, are found to be more probable as the severity of the event increases. National, rather than regional, disasters lead more frequently to significant shifts in social trust. This evaluation of 12 separate cases pinpoints several disasters that have had an effect on social trust, but it does not identify any general patterns, underlining the significance of contextual dependency. 相似文献
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Y. Liang H.T. Gollany R.W. Rickman S.L. Albrecht R.F. Follett W.W. Wilhelm J.M. Novak C.L. Douglas Jr. 《Ecological modelling》2009
Soil carbon (C) models are important tools for examining complex interactions between climate, crop and soil management practices, and to evaluate the long-term effects of management practices on C-storage potential in soils. CQESTR is a process-based carbon balance model that relates crop residue additions and crop and soil management to soil organic matter (SOM) accretion or loss. This model was developed for national use in U.S and calibrated initially in the Pacific Northwest. Our objectives were: (i) to revise the model, making it more applicable for wider geographic areas including potential international application, by modifying the thermal effect and incorporating soil texture and drainage effects, and (ii) to recalibrate and validate it for an extended range of soil properties and climate conditions. The current version of CQESTR (v. 2.0) is presented with the algorithms necessary to simulate SOM at field scale. Input data for SOM calculation include crop rotation, aboveground and belowground biomass additions, tillage, weather, and the nitrogen content of crop residues and any organic amendments. The model was validated with long-term data from across North America. Regression analysis of 306 pairs of predicted and measured SOM data under diverse climate, soil texture and drainage classes, and agronomic practices at 13 agricultural sites having a range of SOM (7.3–57.9 g SOM kg−1), resulted in a linear relationship with an r2 of 0.95 (P < 0.0001) and a 95% confidence interval of 4.3 g SOM kg−1. Using the same data the version 1.0 of CQESTR had an r2 of 0.71 with a 95% confidence interval of 5.5 g SOM kg−1. The model can be used as a tool to predict and evaluate SOM changes from various management practices and offers the potential to estimate C accretion required for C credits. 相似文献