5 documents found in 188ms
# 1
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
# 2
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1:improved calculation and mapping, for details see README.PDF
# 3
Mikolaj, Michal
Abstract: This software publication describes the data acquisition, processing and modelling of hydrological, meteorological and gravity time series prepared for the Argentine-German Geodetic Observatory (AGGO) in La Plata, Argentina. The corresponding output data set is available at http://doi.org/10.5880/GFZ.5.4.2018.001 (Mikolaj et al., 2018). Processed hydrological series include soil moisture, temperature, electric conductivity, and groundwater variation. The processed meteorological time series comprise air temperature, humidity, pressure, wind speed, solar short- and long-waver radiation, and precipitation. Modelling scripts include evapotranspiration, combined precipitation, and water content variation in the zone between deepest soil moisture sensor and groundwater. In addition, large-scale hydrological, oceanic as well as atmospheric effect are modelled along with the local hydrological effects. To allow for a comparison of the model outputs to observations, processing script of gravity residuals is provided as well.
# 4
von Bloh, Werner • Schaphoff, Sibyll • Müller, Christoph • Rolinski, Susanne • Waha, Katharina • (et. al.)
Abstract: LPJmL5 is a dynamical global vegetation model that simulates climate and land-use change impacts on the terrestrial biosphere, the water, carbon and nitrogen cycle and on agricultural production. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included into the model. A comprehensive description of the model is given by von Bloh et al. (2017,http://doi.org/10.5194/gmd-2017-228). We here present the LPJmL5 model code described and used by the publications in GMD: Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5) (von Bloh et al., 2017) The model code of LPJmL5 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL5 is given in the INSTALL file. Additionally to the publication a html documentation and manual pages are provided. The LPJmL5 version is based on LPJmL3.5 that is not publicly available. The LPJmL4 version without nitrogen cycle but with an updated phenology scheme can be found on github (https://github.com/PIK-LPJmL/LPJmL).
# 5
Schaphoff (Ed.), Sibyll • von Bloh, Werner • Thonicke, Kirsten • Biemans, Hester • Forkel, Matthias • (et. al.)
Abstract: LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.
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