Both altitude and vegetation are known to affect the amount and quality of soil organic matter (SOM) and the size and activity of soil microbial biomass. However, when altitude and vegetation changes are combined, it is still unclear which one has a greater effect on soil chemical and biochemical properties. With the aim of clarifying this, we tested the effect of altitude (and hence temperature) and vegetation (broadleaf vs pine forests) on soil organic carbon (SOC) and soil microbial biomass and its activity. Soil sampling was carried out in two adjacent toposequences ranging from 500 to 1000 m a.s.l. on a calcareous massif in central Italy: one covered only by Pinus nigra J.F. Arnold forests, while the other covered by Quercus pubescens Willd., Ostrya carpinifolia Scop. and Fagus sylvatica L. forests, at 500, 700 and 1000 m a.s.l., respectively. The content of SOC and water-extractable organic carbon (WEOC) increased with altitude for the pine forests, while for the broadleaf forests no trend along the slope occurred, and the highest SOC and WEOC contents were observed in the soil at 700 m under the Ostrya carpinifolia forest. With regard to the soil microbial community, although the size of the soil microbial biomass (Cmic) generally followed the SOC contents along the slope, both broadleaf and pine forest soils showed similar diminishing trends with altitude of soil respiration (ΣCO2-C), and ΣCO2-C:WEOC and ΣCO2-C:Cmic ratios. The results pointed out that, although under the pine forests’ altitude was effective in affecting WEOC and SOC contents, in the soils along the broadleaf forest toposequence this effect was absent, indicating a greater impact of vegetation than temperature on SOC amount and pool distribution. Conversely, the similar trend with altitude of the microbial activity indexes would indicate temperature to be crucial for the activity of the soil microbial community.

Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of Sentinel-2 (S2) data supporting the definition of reduced soil-sampling schemes. The study occurred in two agricultural fields of 20 hectares each near Deruta, Umbria, Italy. S2 images were acquired for the two bare fields. After a band selection based on bibliography, PCA (Principal Component Analysis) and cluster analysis were used to identify points of two reduced-sample schemes. The data obtained by these samplings were used in linear regressions with principal components of the selected S2 bands to produce maps for clay and organic matter (OM). Resultant maps were assessed by analyzing residuals with a conventional soil sampling of 30 soil samples for each field to quantify their accuracy level. Although of limited extent and with a specific focus, the low average errors (Clay ± 2.71%, OM ± 0.16%) we obtained using only three soil samples suggest a wider potential for this methodology. The proposed approach, integrating S2 data and traditional soil-sampling methods could considerably reduce soil-sampling time and costs in ordinary and precision agriculture applications.

The impact of soil fertilization with animal manure on the spread and persistence of antibiotic resistance in the environment is far from being fully understood. To add knowledge about persistence and correlations between antibiotic residues and antibiotic resistance genes (ARGs) in fertilized soil, a longitudinal soil mesocosm study was conducted. Soil samples were collected from the mesocosms immediately before spreading and then afterward at fifteen time points during a 320-day observation period. Eight ARGs (ermB, sul1, tetA, tetG, tetM, cfr, fexA, and optrA) and the class 1 integron-integrase gene, intI1, were determined in both pig slurry and soil, as well as residues of 36 antibiotics. Soil chemical and biochemical parameters were also measured. Twelve antibiotics were detected in the slurry in the range of 3 µg kg⁻¹–3605 µg kg⁻¹, with doxycycline, lincomycin, and tiamulin being the most abundant, whereas ermB, sul1, and tetM were the predominant ARGs. Before spreading, neither antibiotic residues nor ARGs were detectable in the soil; afterwards, their concentrations mirrored those in the slurry, with a gradual decline over the duration of the experiment. After about three months, the effect of the amendment was almost over, and no further evolution was observed.