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  • Anemometer; Bad Lauchstädt; BARO; Barometer; BL_ASC; BL_DAT; BL_DWD; BL_fiktiv; BL_SYNMET; DATE/TIME; Field observation; Germany, Bad Lauchstaedt; Hair hygrometer; historical weather data; Humidity, relative, daily mean; meteorological data; Precipitation, daily total; Pressure, atmospheric, daily mean; Short-wave downward (GLOBAL) radiation, daily sum; Station label; Temperature, air, daily maximum; Temperature, air, daily mean; Temperature, air, daily minimum; Temperature, soil, daily mean; weather data; weather station; Weather station/meteorological observation; Weather Station ID 1; Weather Station ID 113; Weather Station ID 136; Weather Station ID 55; Weather Station ID 56; Wind direction, daily mean; Wind speed, daily mean; Wind vane; WST  (1)
  • ddc:631.4  (1)
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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
    Publication Date: 2023-07-14
    Description: The considered data set contains measurements of 4 automatic weather stations from 1997 until 2018 installed by Helmholtz Centre for Environmental Research - UFZ GmbH. Here, the main station corresponded to typical sensor technology and equipment of the German Meteorological Service [Deutscher Wetterdienst, DWD]. These data were quality-checked and processed as daily values. As far as possible, erroneous values were replaced by means of other stations on site. Very short failures could be supplemented by interpolation or averaging. Hence, values of measured variable can originate from different stations. However, only one value for each measured variable is provided for every day within the table. A unique label for individual weather stations is used to identify the exact origin of data.
    Keywords: Anemometer; Bad Lauchstädt; BARO; Barometer; BL_ASC; BL_DAT; BL_DWD; BL_fiktiv; BL_SYNMET; DATE/TIME; Field observation; Germany, Bad Lauchstaedt; Hair hygrometer; historical weather data; Humidity, relative, daily mean; meteorological data; Precipitation, daily total; Pressure, atmospheric, daily mean; Short-wave downward (GLOBAL) radiation, daily sum; Station label; Temperature, air, daily maximum; Temperature, air, daily mean; Temperature, air, daily minimum; Temperature, soil, daily mean; weather data; weather station; Weather station/meteorological observation; Weather Station ID 1; Weather Station ID 113; Weather Station ID 136; Weather Station ID 55; Weather Station ID 56; Wind direction, daily mean; Wind speed, daily mean; Wind vane; WST
    Type: Dataset
    Format: text/tab-separated-values, 218322 data points
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