Soil microbial community structure is determined by environmental conditions and influenced by other factors, such as the intensity of the land use management. Studies addressing the effect of environmental factors and management on grassland soil microbial communities at the continental scale are missing, and the wide range of ecosystem services provided by these ecosystems are thus also wanting. To address this knowledge gap, this study presents data on grassland soil microbial communities along a pan-European agro-ecological gradient. The transect included five geographical locations (Sweden, Germany, Switzerland, Portugal mainland, Portugal Azores). At each location, soils were collected in two regions characterized by favourable and less favourable conditions for plant growth. In each of these ten regions, grasslands along a gradient of management intensity were selected, i.e. grassland under intensive, less intensive and extensive management. Phospholipid fatty acid analysis (PLFA) was used to characterize the microbial community structure (PLFA pattern) in relation to climatic and soil properties. Over the whole geographical range, the environmental properties determined the soil microbial community structure. In Sweden and Switzerland, the regional growth conditions had the strongest influence on the soil microbial communities, while in Germany, Portugal mainland and Azores the management intensity was more important. Splitting up this whole community response into individual groups reveals that, in general, saprotrophic fungal biomarkers were highest in extensively managed grasslands while bacterial biomarkers differed mainly between the regions. We conclude that at the transect level, climate and soil properties were the most important factors influencing soil bacterial community structure, while soil fungal groups were more responsive to grassland management intensity. Overall agricultural sustainability could benefit from informed soil health promoting management practices, and this study contributes to such knowledge, showing the importance of management for the soil microbial biomass and community structure.
Arbuscular mycorrhizal fungi (AMF) may affect competitive plant interactions, which are considered a prevalent force in shaping plant communities. Aiming at understanding the role of AMF in the competition between two pasture species and its dependence on soil nutritional status, a pot experiment with mycorrhizal and non-mycorrhizal Lolium multiflorum and Trifolium subterraneum was conducted, with manipulation of species composition (five levels), and nitrogen (N)- and phosphorus (P)- fertilization (three levels). In the non-mycorrhizal state, interspecific competition did not play a major role. However, in the presence of AMF, Lolium was the strongest competitor, with this species being facilitated by Trifolium. While N-fertilization did not change the competitive balance, P-fertilization gave Lolium, a competitive advantage over Trifolium. The effect of AMF on the competitive outcome may be driven by differential C-P trade benefits, with Lolium modulating carbon investment in the mycorrhizal network and the arbuscule/vesicle ratio at the cost of Trifolium.
ABSTRACT Grasslands play a critical role in providing diverse ecosystem services. Sown biodiverse pastures (SBP) rich in legumes are an important agricultural innovation that increases grassland productivity and reduces the need for fertilisers. This study developed a machine learning model to obtain spatially explicit estimations of the productivity of SBP, based on field sampling data from five Portuguese farms during four production years (2018–2021) and under two fertilisation regimes (conventional and variable rate). Weather data (such as temperature, precipitation and radiation), soil properties (including sand, silt, clay and pH), terrain characteristics (including elevation, slope, aspect, hillshade and topographic position index), and management data (including fertiliser application) were used as predictors. A variance inflation factor (VIF) approach was used to measure multicollinearity between input variables, leading to only 11 of the 53 input variables being used. Artificial neural network (ANN) methods were used to estimate pasture productivity, and hyper‐parameterization optimization was performed to fine‐tune the model. Plots under variable rate fertilisation were significantly improved by up to 20 kg P ha −1 applied in the same year. Plots under conventional fertilisation benefitted the most from fertilisation in past years. The model demonstrated good generalisation, with similar estimation errors for both the training and test sets: for an average yield of 6096 kg ha −1 in the sample, the root mean squared errors (RMSE) for the training and test sets were respectively 882 and 1125 kg ha −1 . These results indicate that the model did not overfit the training data and can be used to estimate SBP productivity maps in the sampled farms. However, further studies are required to asses if the obtained model can be applied to new unseen data.
Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45°N). The sensitivity of NEE to mean annual temperature breaks down at ~ 16 °C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.
<p>The use of biochar has increased worldwide in the last years due to its good results for several soil quality indicators. However, restoration potential depends on the type and amount of biochar for each specific soil and land use. In order to investigate this restoration potential differential, we conducted an experiment where we amended two contrasting degraded soils with the same biochar. We installed a controlled and fully randomized percolation lysimeter experiment (3 replicates) with 15 lysimeters on a moderately steep slope angle, monitored for one year. Two types of soil were collected, a low organic matter, hydrophilic vineyard soil and a high organic matter, hydrophobic forest soil. Biochar was applied at 4% for both soils, and an additional treatment at 2% for the forest soil only. Selected soil quality indicators are: soil organic matter, medium weight diameter, aggregate stability, bulk density, pH, electric conductivity, potassium (K), phosphorus (P), soil water repellency, biomass quality. The present study comprises four data collections in different seasons along the year, enabling to compare the development of the biochar effects on different types of soil and its short- and medium-term behaviour.</p>
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