The lowland soils are characterized by high susceptibility to water saturation. This anaerobic condition is usually reported in paddy fields and alters the decomposition process of soil organic compounds. The aim of this study was to evaluate the soil microbial and enzymatic activity of a lowland soil at different soil moisture contents. A poorly drained Albaqualf cultivated with irrigated rice was used to evaluate microbial and enzymatic activity in treatments with different levels of soil moisture, being: i) 60% of field capacity (FC) (60%FC); ii) 100% of FC (100%FC); iii) flooded soil with a 2 cm water layer above soil surface, and iv) soil kept at 60%FC with late flood after 29 days the incubation. The greater soil microbial activity was observed in the 100%FC treatment, being 41% greater than 60%FC treatment and only 2% higher than flooded treatment. The enzymatic activity data by fluorescein diacetate (FDA) hydrolysis corroborated the higher CO₂ release in treatments with higher soil moisture content. Differently from the results reported, the main methodologies to evaluate microbial activity still recommend maintenance of the soil with a moisture content close to 60% of the FC. However, in lowland soil with history of frequent paddy fields, the maintenance of moisture close to 60% of the FC can limit the microbial activity. The soil respiration technique can be used to evaluate the microbial activity in flooded soil conditions. However, further studies should be conducted to understand the effect of the cultivation history on the microbial community of these environments.

Springtails (Collembola) are very small terrestrial arthropods commonly found to be associated with edaphic environments

Plant-atmosphere exchange fluxes of CO₂ measured with the Eddy covariance method are used extensively for the assessment of ecosystem carbon budgets worldwide. The present paper describes eddy flux measurements for a managed upland grassland in Central France studied over two decades (2003–2021). We present the site meteorological data for this measurement period, and we describe the pre-processing and post-processing approaches used to overcome issues of data gaps, commonly associated with long-term EC datasets. Recent progress in eddy flux technology and machine learning now paves the way to produce robust long-term datasets, based on normalised data processing techniques, but such reference datasets remain rare for grasslands. Here, we combined two gap-filling techniques, Marginal Distribution Sampling (short gaps) and Random Forest (long gaps), to complete two reference flux datasets at the half-hour and daily-scales respectively. The resulting datasets are valuable for assessing the response of grassland ecosystems to (past) climate change, but also for model evaluation and validation with respect to future global change research with the carbon-cycle community.