@article{FournierSteinerBrochetetal.2022,
  author    = {Fournier, Bertrand and Steiner, Magdalena and Brochet, Xavier and Degrune, Florine and Mammeri, Jibril and Carvalho, Diogo Leite and Siliceo, Sara Leal and Bacher, Sven and Pe{\~n}a-Reyes, Carlos Andr{\´e}s and Heger, Thierry Jean},
  title     = {Toward the use of protists as bioindicators of multiple stresses in agricultural soils},
  series = {Ecological indicators : integrating monitoring, assessment and management},
  volume    = {139},
  journal   = {Ecological indicators : integrating monitoring, assessment and management},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1470-160X},
  doi       = {10.1016/j.ecolind.2022.108955},
  pages     = {8},
  year      = {2022},
  abstract  = {Management of agricultural soil quality requires fast and cost-efficient methods to identify multiple stressors that can affect soil organisms and associated ecological processes. Here, we propose to use soil protists which have a great yet poorly explored potential for bioindication. They are ubiquitous, highly diverse, and respond to various stresses to agricultural soils caused by frequent management or environmental changes. We test an approach that combines metabarcoding data and machine learning algorithms to identify potential stressors of soil protist community composition and diversity. We measured 17 key variables that reflect various potential stresses on soil protists across 132 plots in 28 Swiss vineyards over 2 years. We identified the taxa showing strong responses to the selected soil variables (potential bioindicator taxa) and tested for their predictive power. Changes in protist taxa occurrence and, to a lesser extent, diversity metrics exhibited great predictive power for the considered soil variables. Soil copper concentration, moisture, pH, and basal respiration were the best predicted soil variables, suggesting that protists are particularly responsive to stresses caused by these variables. The most responsive taxa were found within the clades Rhizaria and Alveolata. Our results also reveal that a majority of the potential bioindicators identified in this study can be used across years, in different regions and across different grape varieties. Altogether, soil protist metabarcoding data combined with machine learning can help identifying specific abiotic stresses on microbial communities caused by agricultural management. Such an approach provides complementary information to existing soil monitoring tools that can help manage the impact of agricultural practices on soil biodiversity and quality.},
  language  = {en}
}
@article{VaidyaSchmidtRakowskietal.2021,
  author    = {Vaidya, Shrijana and Schmidt, Marten and Rakowski, Peter and Bonk, Norbert and Verch, Gernot and Augustin, J{\"u}rgen and Sommer, Michael and Hoffmann, Mathias},
  title     = {A novel robotic chamber system allowing to accurately and precisely determining spatio-temporal CO2 flux dynamics of heterogeneous croplands},
  series = {Agricultural and forest meteorology},
  volume    = {296},
  journal   = {Agricultural and forest meteorology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0168-1923},
  doi       = {10.1016/j.agrformet.2020.108206},
  pages     = {9},
  year      = {2021},
  abstract  = {The precise and accurate assessment of carbon dioxide (CO2) exchange is crucial to identify terrestrial carbon (C) sources and sinks and for evaluating their role within the global C budget. The substantial uncertainty in disentangling the management and soil impact on measured CO2 fluxes are largely ignored especially in cropland. The reasons for this lies in the limitation of the widely used eddy covariance as well as manual and automatic chamber systems, which either account for short-term temporal variability or small-scale spatial heterogeneity, but barely both. To address this issue, we developed a novel robotic chamber system allowing for dozens of spatial measurement repetitions, thus enabling CO2 exchange measurements in a sufficient temporal and high small-scale spatial resolution. The system was tested from 08th July to 09th September 2019 at a heterogeneous field (100 m x 16 m), located within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The field is foreseen for a longer-term block trial manipulation experiment extending over three erosion induced soil types and was covered with spring barley. Measured fluxes of nighttime ecosystem respiration (R-eco) and daytime net ecosystem exchange (NEE) showed distinct temporal patterns influenced by crop phenology, weather conditions and management practices. Similarly, we found clear small-scale spatial differences in cumulated (gap-filled) R-eco, gross primary productivity (GPP) and NEE fluxes affected by the three distinct soil types. Additionally, spatial patterns induced by former management practices and characterized by differences in soil pH and nutrition status (P and K) were also revealed between plots within each of the three soil types, which allowed compensating for prior to the foreseen block trial manipulation experiment. The results underline the great potential of the novel robotic chamber system, which not only detects short-term temporal CO2 flux dynamics but also reflects the impact of small-scale spatial heterogeneity.},
  language  = {en}
}