AGROFLUX: An innovative sensor platform to study high-frequency responses in water, carbon and greenhouse gas fluxes in a complex arable landscape
Authors: Dubbert Maren , Vaidya Shrijana , Dahlmann Adrian , Schmidt Marten , Verch Gernot , Sommer Michael , Augustin Jürgen , Hoffmann Mathias 
Published: February 2023

Improved agricultural practices increasing the water use efficiency (WUE), reducing greenhouse gas emissions (GHG) and/or improving atmospheric C sequestration rates within the soil are crucial for an adaptation and/or mitigation to the global climate crisis. However, processes driving water (H2O), carbon (C) and GHG fluxes within the soil-plant-atmosphere continuum of agricultural used landscapes are complex and flux dynamics differ substantially in time and space. Hence, to upscale and evaluate the effects/benefits of any new agricultural practice aiming towards improving WUE, soil C sequestration and/or GHG emissions, accurate and precise information on the complex spatio-temporal H2O, C and GHG flux pattern, their drivers and underlying processes are required.
Current approaches to investigate H2O, C and GHG (CH4, N2O and CO2) dynamics as well as underlying processes driving them, are usually laborious and have to choose between high spatial or temporal resolution due to methodological constraints. On the one hand, often used eddy covariance systems are not suitable to account for small scale spatial heterogeneities, despite growing evidence of their importance. On the other hand, chamber systems either lack temporal resolution (manual chambers) or strongly interfere with the measured system (static automatic chambers). Hence, none of these systems enable a proper upscaling and evaluation of effects/benefits of new farming practices for WUE, C sequestration and GHG emissions at especially heterogeneous agricultural landscapes, such as present within inter-alia the also globally widespread hummocky ground moraine landscape of NE-Germany.
In an effort to overcome this, a novel, fully automated robotic field sensor platform was established and combined with an IoT network and remote sensing approaches. Here, an innovative, continuously operating automated robotic field sensor platform is presented. The platform was mounted on fixed tracks, stretching over an experimental field (size) which covers three different, distinct soil types. It carries multiple sensors to measure GHG and water vapor concentrations and isotope signatures of d18O and dD of water vapor. Combined with two chambers which can be accurately positioned in three dimensions at the experimental field below, this system facilitates to detect small-scale spatial heterogeneity and short-term temporal variability of H2O, C and GHG flux dynamics as well as crop and soil conditions over a range of possible experimental setups. The automated, continuous estimation of d18O and dD of evapotranspiration further provides the basis to partition water fluxes alongside the flux based partitioning of C and GHG fluxes. This particularly promotes to assess not only ecosystem but component specific water use efficiencies. Hence, this platform produces a detailed picture of H2O, C and GHG dynamics across different treatments and crop cycles, with a high-degree of accuracy and reproducibility.