This title appears in the Scientific Report :
2019
Geophysics-based soil mapping for improved simulation of crop productivity beyond the field scale
Geophysics-based soil mapping for improved simulation of crop productivity beyond the field scale
In precision agriculture and agroecosystem modelling, spatially distributed information on soil characteristics is vital since soil is a key control for water and nutrient availability. Non-invasive geophysical methods such as electromagnetic induction (EMI) measurements in combination with soil sam...
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Personal Name(s): | Brogi, Cosimo (Corresponding author) |
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Huisman, Johan Alexander / Herbst, Michael / Klosterhalfen, Anne / Weihermüller, Lutz / von Hebel, Christian / van der Kruk, Jan / Vereecken, Harry | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Imprint: |
2019
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Conference: | AGU Fall Meeting 2019, Washington DC (USA), 2018-12-10 - 2018-12-14 |
Document Type: |
Conference Presentation |
Research Program: |
Terrestrial Systems: From Observation to Prediction |
Publikationsportal JuSER |
In precision agriculture and agroecosystem modelling, spatially distributed information on soil characteristics is vital since soil is a key control for water and nutrient availability. Non-invasive geophysical methods such as electromagnetic induction (EMI) measurements in combination with soil sampling can provide subsurface information with a high spatial resolution. However, at scales larger than the field-scale, it is still challenging to derive relevant subsurface information from such geophysical data sets, and the added value of such high-resolution top and subsoil information for the analysis of patterns in plant productivity has not been investigated yet. In this study, we measured high-resolution multi-configuration EMI data on 51 adjacent agricultural fields (102 ha) near Selhausen, Germany. Measurements were collected in 2016 within a few days after harvest of each field. In each field, we obtained six apparent electrical conductivity (ECa) maps with increasing depth of investigation (DOI). Since a direct correlation between EMI measurement and a single soil property is often difficult to obtain, we used a supervised image classification method to classify each field and obtained a 1 m resolution map of the study area that identifies a total of 18 zones with similar EMI response. One hundred ground truth locations were randomly selected and information on horizon type, depth and texture until a maximum depth of 2 m were collected to provide each zone with a typical soil profile. In a second step, we used this geophysics-based high resolution soil map to run an ensemble of agroecosystem models in order to investigate the effect of subsurface heterogeneity on crop productivity in the presence of water scarcity for the 2016 growing season. For this, we used the AgroC model, which is a one-dimensional model that couples SoilCO2, RothC, and SUCROS subroutines to predict plant productivity. The simulations were subsequently compared to patterns in leaf area index (LAI) derived from satellite images. The results showed a clear correlation between LAI estimates from satellite images and predicted patterns in plant productivity, which illustrates the added value of the geophysics-based soil map for agroecosystem modelling and precision agriculture. |