Abstract
A four-year (1999-2002) multivariable data set for one specific agricultural used field located in North Rhine-Westphalia is documented in detailed. The data set focusses on the small-scale heterogeneity of soil properties varying in the spatial and temporal dimension. Initial soil sampling was conducted at altogether 80 sampling points arranged in a regular and a nested grid along the 20 ha large field. Information about the soil inventory (soil texture, soil organic carbon) exists for three subsequent soil layers to a total depth of 90 cm and for every sampling point. Subsequently, the same points and layers were examined for the soil variables soil moisture and soil nitrogen biannually. Additional information about crop rotation, tillage, site-specific fertilization, yield performance and weather complete the data set was that used for model inter-comparison within the crop modelling part (CropM) of the international FACCE JPI MACSUR2 project.
Highlights
Soils play a relevant role for crop production and determine significantly the impact of climate change on crop growth (Kersebaum and Nendel, 2014)
Additional information about crop rotation, tillage, site-specific fertilization, yield performance and weather variables complete the dataset that was used for model inter-comparison within the crop modelling part (CropM) of the international FACCE JPI MACSUR2 project
1 INTRODUCTION: Soils play a relevant role for crop production and determine significantly the impact of climate change on crop growth (Kersebaum and Nendel, 2014)
Summary
Soils play a relevant role for crop production and determine significantly the impact of climate change on crop growth (Kersebaum and Nendel, 2014). As soon as knowledge about the soil inventory of any agricultural field is available, the application of process-based crop models enables for site-specific management recommendations considering for example nitrogen fertilization (Kersebaum et al, 2005). The sensitivity of models to site-specific soil characteristics is an essential precondition to successfully simulate the interaction of processes in the soil-plant-atmosphere system for an integrated impact assessment, e.g. for climate change impacts. This is not always assured and requires additional adaptation and validation (Martre et al, 2015). With respect to the large within-field heterogeneity of soil properties and the high temporal and spatial density of the sampling design the presented dataset is unique and provides the link between spatial yield variability and soil states
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