Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2014
Conference lecture – Biochar and abiotic NO transformations
Simon Weldon, Peter Dörsch, Daniel Rasse
Abstract
Studies examining the effect of biochar on N2O turnover in soil have demonstrated that biochar affects both the rate and product ratio of denitrification. The mechanisms proposed include pH effects on N2O reductase , sorption of N2O and electron shuttling to N2O reductases. Recent studies suggest that pyrolysis alters the redox chemistry of biochar leading to the formation of redox active compounds which are thought to mediate the observed suppression of N2O in biochar amended soil. Redox active components however may not only be of significance to biological processes but also catalyze abiotic reactions of N-species which could confound the estimation of biological effects. Here we report experiments designed to examined abiotic interaction between biochar and NO in anoxic water slurries with biochar of increasing pyrolysis temperature. We determined the fate of NO added to the headspace of closed anoxic bottles using high frequency measurements of NO, N2O and N2. Our initial results show a swift disappearance of added NO which can not entirely be attributed to sorption to the biochar. Small but constant quantities of N2O were generated after NO addition indicating abiotic turnover of NO by biochar. NO is an important signal molecule in the regulation of denitrification and hence it is important to elucidate possible abiotic feedbacks of NO reactions in soil. The results will be discussed relative to the redox properties of the biochars tested.
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In an attempt to discern stochastic and deterministic parts of measured signals, we analyze time series from the viewpoint of ordinal pattern statistics. After choosing a suitable embedding dimension $D$, the occurrencies of all $D!$ patterns form a probability distribution $P$. The latter is input to information and complexity functionals describing, e.g., chaotic regimes or stochastic properties due to long-range correlations. Here, we use an information quantifier which is local in pattern probability space, the Fisher information $F$. This is calculable only after fixing a pattern coding scheme, i.e. numbering each and every pattern. It has been demonstrated that $F$ discerns different dynamic regimes for the logistic map to a certain extent; however, this depends on the details of the coding scheme. Here, we seek to find an optimal coding scheme for long-range correlated stochastic processes, mimicking many records e.g. from the geosciences. To increase the contrast between colored noise and deterministic processes, $F$ should be minimal for the former. Structurally similar ordinal patterns should be located adjacent to each other. Similarity is related to the number of inversions in the respective patterns. In practical terms, it is impossible to try all $D!!$ coding schemes whenever$D > 3$; however, we demonstrate a classification of coding schemes into equivalence classes based on the number of "jumps" in the patterns. These are used to improve the Keller and Lehmer coding schemes. The approach has a potential to provide an analytical understanding of the Fisher information for stochastic processes. Results for these optimizations will be shown for both the logistic map and colored ($k$-) noise. As a byproduct, an innovative method to estimate the scaling exponent $k$ emerges. Finally, we comment shortly on the importance of finite size effects, which is always an issue when dealing with observed data.
Authors
Svein SolbergAbstract
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Isabella Børja Douglas Lawrence Godbold Jan Čermák Kjell Andreassen Lise Dalsgaard Arthur Gessler Rainer Hentschel Zachary E. Kayler Nadezhda Nadezhdina Sabine Rosner Ole Einar Tveito Svein Solberg Jan Svetlik Mari Mette TollefsrudAbstract
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Authors
Holger Lange Lise Dalsgaard Signe Kynding Borgen Silje Skår O. Janne Kjønaas Kjell AndreassenAbstract
Reliable methods are required to predict changes in soil carbon stocks. Process-based models often require many parameters which are largely unconstrained by observations. This induces uncertainties which are best met by using repeated measurements from the same sites. Here, we compare two carbon models, Yasso07 and Romul, in their ability to reproduce a set of field observations in Norway. The models are different in the level of process representation, structure, initialization requirements and calibration- and parameterization strategy. Field sites represent contrasting tree species, mixture and soil types. The number of repetitions of C measurements varies from 2 to 6 over a period of up to 35 years, and for some of the sites, which are part of long-term monitoring programs, plenty of auxiliary information is available. These reduce the danger of overparametrization and provide a stringent testbed for the two models. Focus is on the model intercomparison, using identical site descriptions to the extent possible, but another important aspect is the upscaling of model results to the regional or national scale, utilizing the Norwegian forest inventory system. We suggest that a proper uncertainty assessment of soil C stocks and changes has to include at least two (and preferably more) parametrized models.
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Atle WibeAbstract
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Arnold ArnoldussenAbstract
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