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  • 1
    Language: English
    In: Ecological Modelling, 2011, Vol.222(12), pp.1998-2010
    Description: ► The individual-based INDISIM-SOM model is far more sensitive to some parameters than to others. ► Key parameters for the evolution of C and N are microbial maintenance, energy, and death probability. ► The nitrification rate, in particular, appears highly affected by the death probability. ► The sensitivity analysis indicates what simplification of the model is possible. ► It also shows which parameters need to be evaluated with more accuracy than is currently achievable. The fate of soil carbon and nitrogen compounds in soils in response to climate change is currently the object of significant research. In particular, there is much interest in the development of a new generation of micro-scale models of soil ecosystems processes. Crucial to the elaboration of such models is the ability to describe the growth and metabolism of small numbers of individual microorganisms, distributed in a highly heterogeneous environment. In this context, the key objective of the research described in this article was to further develop an individual-based soil organic matter model, INDISIM-SOM, first proposed a few years ago, and to assess its performance with a broader experimental data set than previously considered. INDISIM-SOM models the dynamics and evolution of carbon and nitrogen associated with organic matter in soils. The model involves a number of state variables and parameters related to soil organic matter and microbial activity, including growth and decay of microbial biomass, temporal evolutions of easily hydrolysable N, mineral N in ammonium and nitrate, CO and O . The present article concentrates on the biotic components of the model. Simulation results demonstrate that the model can be calibrated to provide good fit to experimental data from laboratory incubation experiments performed on three different types of Mediterranean soils. In addition, analysis of the sensitivity toward its biotic parameters shows that the model is far more sensitive to some parameters, i.e., the microbial maintenance energy and the probability of random microbial death, than to others. These results suggest that, in the future, research should focus on securing better measurements of these parameters, on environmental determinants of the switch from active to dormant states, and on the causes of random cell death in soil ecosystems.
    Keywords: Individual-Based Model ; Soil Microbial Activity ; Soil Organic Matter ; C and N Mineralization ; Microbial Parameters ; Environmental Sciences ; Ecology
    ISSN: 0304-3800
    E-ISSN: 1872-7026
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  • 2
    Language: English
    In: Ecological Modelling, 24 February 2015, Vol.298, pp.24-38
    Description: Since the late 1970s, thousands of scholarly articles, books and reports have dealt with the application of the mathematical theory of geostatistics to characterize the spatial “variability” of soils, and to produce soil property maps. Insensibly, this application of geostatistics appears to have become an end in itself, and the reasons why one should be concerned about the spatial heterogeneity of soil properties are rarely if ever made clear any more. In this context, the purpose of the present critical review article is to return to some of the primal questions that motivated this interest in the topic several decades ago. After a brief review of the background behind the application of geostatistics to soils, a number of situations and modeling efforts are described where, even though soils undoubtedly vary spatially, nothing seems to be gained practically by explicitly accounting for their spatial heterogeneity in order to reach a number of management or research objectives. Contrastedly, whenever the spatial heterogeneity of soil properties in the field might be relevant, it is shown that very different perceptions about it emerge, depending on the type of measurement that is performed. This suggests that the approach one adopts to characterize spatially-varying soil properties should be dictated by whatever goal one pursues. For example, if the objective is to evaluate the “ecosystem services” of soils in a given region and to reach decisions about them, one should probably first consider the (typically large) spatial scale that is most relevant to the decision-making process, then proceed via a top-down approach to characterize the spatial heterogeneity of soil services, if and when appropriate. In other contexts, it is argued that measurements should be patterned after the behavior of plants or microbes present in soils, relative to which, unfortunately, the macroscopic measurements that are now routinely carried out appear largely irrelevant or misleading. The article concludes with a number of potential lessons learned from the analysis of the research on the spatial heterogeneity of soils, which bear relevance to the broader practice of soil science.
    Keywords: Soil Heterogeneity ; Mathematical Modeling ; Microheterogeneity ; Microenvironment ; Plant Roots ; Measuring Devices ; Environmental Sciences ; Ecology
    ISSN: 0304-3800
    E-ISSN: 1872-7026
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  • 3
    Language: English
    In: Ecological Modelling, Feb 24, Vol.298, p.24(15)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ecolmodel.2014.03.018 Byline: Philippe C. Baveye, Magdeline Laba Abstract: * Reviews background of the application of geostatistics to soils. * Soil spatial heterogeneity does not always have to be accounted for explicitly. * Perception of heterogeneity depends closely on measurement carried out. * For cases related to plants and microbes, different measurements are needed. * Lessons from the geostatistical story are applicable to soil science in general. Author Affiliation: (a) Soil and Water Laboratory, Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA (b) SIMBIOS Centre, Abertay University, Kydd Building, 40 Bell Street, Dundee DD1 1HG, Scotland, UK (c) Department of Natural Resources, Cornell University, Ithaca, NY 14850, USA
    Keywords: Soils ; Geostatistics
    ISSN: 0304-3800
    Source: Cengage Learning, Inc.
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