On the information content of incubation studies

The measurement of the production of CO2 from soils in incubation studies has been used for many years to gain information about the influence of different soils types, changing temperatures and water contents, as well as the addition of amendments on the soil respiration. While in the early years the kinetic modelling (or fitting) was restricted to the single or one pool model due to the possibility of solving the problem by log-transforming the observed data an using a linear regression for the estimation of the rate constant (by doing so an analytical solution can be applied), more recent publications chose multi-pool models (2, 3, and even 4-pools), which can will be fitted iteratively using appropriate computer software. In general, there are different methods used in literature to estimate the kinetic parameters resulting in different kinetic parameter values even for the same data set. Additionally, screening of existing literature revealed that the 2-pool model (or even higher pool models) were sometimes obviously wrong fitted or over fitted. In our presentation, we will show how different constrains in the fitting process will influence the results of the kinetic parameter values, how obviously wrong fitting and overfitting can be easily detected, and how the information content of the incubation data can be easily judged prior any fitting. Finally, we will provide recommendations how to extract information from incubation experiments

Deriving soil hydraulic parameters in a high spatial resolution for a heterogeneous agricultural field-site

Providing information about the structure of the subsurface plays an important role for setting up soil hydraulic models, which are in turn an important prerequisite for ecosystem modelling approaches. Because soils often show a high within-field heterogeneity in terms of texture, stone content, and bulk density, they may also exhibit a wide range of hydraulic properties in the field. During water stress periods and especially on agricultural fields, which are characterized by uniform vegetation, the occurrence of a within-field heterogeneity in terms of soil hydraulic properties can be observed as it affects the different water status of the plants. The patterns of visible plant water stress and areas of low apparent electrical conductivities measured by electromagnetic induction measurements (EMI) often coincide, for e.g. within sugar beet cropped fields. Such observations have also been made beforehand at the current study site; an agricultural field (2.7 ha) that is situated in an area developed by fluvial processes. To account for this, the current approach included a sampling campaign on a field with 70 drilling locations for texture and organic carbon analyses. Furthermore, soil water retention functions and saturated hydraulic conductivity were determined at 20 sampling locations. Our approach for ecosystem modelling is based on 4 m² grid cells over the whole study site. To consider within-field heterogeneity in the ecosystem model, soil hydraulic parameters were predicted for each grid cell, whereby different approaches such as spatial interpolation, Miller-Miller scaling, and the use of pedotransfer functions were taken into account to identify the most appropriate approach

Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics-based quantitative soil information

Agroecosystem models that simulate crop growth as a function of weather conditionsand soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field-scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics-based soil map was intersected with land use information. Soilhydraulic parameters were calculated using pedotransfer functions. Simulations of soilwater content dynamics performed with the agroecosystem model AgroC were com-pared with soil water content measured at two locations, resulting in RMSE of 0.032and of 0.056 cm3cm−3, respectively. The AgroC model was then used to simulate thegrowth of sugar beet (Beta vulgaris L.), silage maize (Zea maysL.), potato (SolanumtuberosumL.), winter wheat (Triticum aestivumL.), winter barley (Hordeum vulgareL.), and winter rapeseed (Brassica napusL.) in the 1- by 1-km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAINDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics-based soilmapping was valuable for practical agricultural applications

Development and analysis of the Soil Water Infiltration Global database.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type (~40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it