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  • 1
    Publication Date: 2024-01-26
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The increasing demand for biomass for food, animal feed, fibre and bioenergy requires optimization of soil productivity, while at the same time, protecting other soil functions such as nutrient cycling and buffering, carbon storage, habitat for biological activity and water filter and storage. Therefore, one of the main challenges for sustainable agriculture is to produce high yields while maintaining all the other soil functions. Mechanistic simulation models are an essential tool to fully understand and predict the complex interactions between physical, biological and chemical processes of soils that generate those functions. We developed a soil model to simulate the impact of various agricultural management options and climate change on soil functions by integrating the relevant processes mechanistically and in a systemic way. As a special feature, we include the dynamics of soil structure induced by tillage and biological activity, which is especially relevant in arable soils. The model operates on a 1D soil profile consisting of a number of discrete layers with dynamic thickness. We demonstrate the model performance by simulating crop growth, root growth, nutrient and water uptake, nitrogen cycling, soil organic matter turnover, microbial activity, water distribution and soil structure dynamics in a long‐term field experiment including different crops and different types and levels of fertilization. The model is able to capture essential features that are measured regularly including crop yield, soil organic carbon, and soil nitrogen. In this way, the plausibility of the implemented processes and their interactions is confirmed. Furthermore, we present the results of explorative simulations comparing scenarios with and without tillage events to analyse the effect of soil structure on soil functions. Since the model is process‐based, we are confident that the model can also be used to predict quantities that have not been measured or to estimate the effect of management measures and climate states not yet been observed. The model thus has the potential to predict the site‐specific impact of management decisions on soil functions, which is of great importance for the development of a sustainable agriculture that is currently also on the agenda of the ‘Green Deal’ at the European level.〈/p〉
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Description: https://git.ufz.de/bodium/bodium_v1.0
    Keywords: ddc:631.4 ; agriculture ; computational model ; simulation ; soil microbiology ; soil structure ; sustainable soil
    Language: English
    Type: doc-type:article
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  • 2
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    PANGAEA
    In:  Supplement to: Westermann, Sebastian; Wollschläger, Ute; Boike, Julia (2010): Monitoring of active layer dynamics at a permafrost site on Svalbard using multi-channel ground-penetrating radar. The Cryosphere, 4, 475-487, https://doi.org/10.5194/tc-4-475-2010
    Publication Date: 2023-03-07
    Description: Multi-channel ground-penetrating radar is used to investigate the late-summer evolution of the thaw depth and the average soil water content of the thawed active layer at a high-arctic continuous permafrost site on Svalbard, Norway. Between mid of August and mid of September 2008, five surveys have been conducted over transect lengths of 130 and 175 m each. The maximum thaw depths range from 1.6 m to 2.0 m, so that they are among the deepest thaw depths recorded for Svalbard so far. The thaw depths increase by approximately 0.2 m between mid of August and beginning of September and subsequently remain constant until mid of September. The thaw rates are approximately constant over the entire length of the transects within the measurement accuracy of about 5 to 10 cm. The average volumetric soil water content of the thawed soil varies between 0.18 and 0.27 along the investigated transects. While the measurements do not show significant changes in soil water content over the first four weeks of the study, strong precipitation causes an increase in average soil water content of up to 0.04 during the last week. These values are in good agreement with evapotranspiration and precipitation rates measured in the vicinity of the the study site. While we cannot provide conclusive reasons for the detected spatial variability of the thaw depth at the study site, our measurements show that thaw depth and average soil water content are not directly correlated. The study demonstrates the potential of multi-channel ground-penetrating radar for mapping thaw depth in permafrost areas. The novel non-invasive technique is particularly useful when the thaw depth exceeds 1.5 m, so that it is hardly accessible by manual probing. In addition, multi-channel ground-penetrating radar holds potential for mapping the latent heat content of the active layer and for estimating weekly to monthly averages of the ground heat flux during the thaw period.
    Keywords: AWI_PerDyn; AWIPEV; GPR; Ground-penetrating radar; LH_Transect14; LH_Transect15; NYA_Meteorological_Obs; Ny-Ålesund, Spitsbergen; Permafrost Research (Periglacial Dynamics) @ AWI; T14; T15
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 3
    Publication Date: 2023-03-07
    Keywords: AWI_PerDyn; AWIPEV; DATE/TIME; GPR; Ground-penetrating radar; Ground-penetrating radar (GPR); LH_Transect14; NYA_Meteorological_Obs; Ny-Ålesund, Spitsbergen; Permafrost Research (Periglacial Dynamics) @ AWI; T14; Uniform resource locator/link to graphic; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
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  • 4
    Publication Date: 2023-03-07
    Keywords: AWI_PerDyn; AWIPEV; DATE/TIME; GPR; Ground-penetrating radar; Ground-penetrating radar (GPR); LH_Transect15; NYA_Meteorological_Obs; Ny-Ålesund, Spitsbergen; Permafrost Research (Periglacial Dynamics) @ AWI; T15; Uniform resource locator/link to graphic; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
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