Weeds are a major problem in cropping systems and in urban areas. The aim of this study was to assess the effectiveness of organic mulching with olive leaves and almond hulls to control weeds in fennel (Foeniculum vulgare Mill.) and in flower beds (urban areas). A 3-cm thick layer of olive leaves or almond hulls was applied as mulching material in fennel. Control consisted of both an unmulched treatment and a weed free control. Moreover, in a flower bed of a railway station, plots mulched with 3 cm layer of olive leaves and almond hulls were compared with an unmulched treatment. Weed infestation was evaluated and the weights of the whole plant and of the marketable part of fennel (grumolo) measured. Mulching with olive leaves and almond hulls reduced weed infestation in both vegetable crop and flower beds. However, olive leaves reduced the weights of the whole plant and of the grumolo. The adoption of almond hulls and olive leaves as organic mulches could be an effective strategy for weed control. Further investigations should be carried out to assess whether the effectiveness of these mulching materials is mainly due to a mechanical activity or allelopathic compounds also play a significant role in weed suppression

Defining the optimal sampling time across the growing season is crucial to standardize sampling protocols for soil physical status monitoring and to achieve comparable results under different experimental conditions and on different sites. In this study, the seasonal variability of soil physical and hydraulic properties under two conservative soil management strategies, minimum tillage and no-tillage, was evaluated in a long-term field experiment. On two sampling dates, autumn 2021 and summer 2022, soil bulk density (BD) and volumetric soil water content at the time of the experiments (θi) were measured in each experimental unit and Beerkan infiltration experiments were performed. The soil water retention curve and the hydraulic conductivity function were then estimated using the Beerkan estimation of soil transfer parameters (BEST) methodology. In this way, the saturated hydraulic conductivity (Ks) and a set of capacitive indicators—plant available water capacity (PAWC), soil macroporosity (PMAC), air capacity (AC) and relative field capacity (RFC)—were obtained. Results underlined the role of soil moisture conditions as a main factor affecting variability in soil physical properties. Different soil moisture under autumn and summer samplings significantly affected BD (1.0093 and 1.1905 g cm−3, respectively, in autumn and summer) and Ks (0.0431 and 0.0492 mm s−1). Relationships observed between BEST-derived variables, such as PMAC (or AC) and RFC, and measured variables, such as BD, showed consistent results, with increases in PMAC to BD decreases. However, a comparison of capacity-based indicators obtained by BEST with those obtained from measured soil water retention curves, in a previous year but under comparable soil conditions, highlighted some discrepancies. This finding drives the focus towards the need to use more robust datasets deriving from experimental measurements or from coupling information obtained from measured and estimated data. Finally, this study provided further evidence that, in the long-term field experiment investigated, the two soil management systems allowed keeping the values of key soil physical quality indicators, such as bulk density and saturated hydraulic conductivity, within the optimal or near- optimal reference ranges.

Knowledge of the spatial distribution of soil organic carbon (SOC) is of crucial importance for improving crop productivity and assessing the effect of agronomic management strategies on crop response and soil quality. Incorporating secondary variables correlated to SOC allows using information often available at finer spatial resolution, such as proximal and remote sensing data, and improving prediction accuracy. In this study, two nonstationary interpolation methods were used to predict SOC, namely, regression kriging (RK) and multivariate adaptive regression splines (MARS), using as secondary variables electromagnetic induction (EMI) and ground-penetrating radar (GPR) data. Two GPR covariates, representing two soil layers at different depths, and X geographical coordinates were selected by both methods with similar variable importance. Unlike the linear model of RK, the MARS model also selected one EMI covariate. This result can be attributed to the intrinsic capability of MARS to intercept the interactions among variables and highlight nonlinear features underlying the data. The results indicated a larger contribution of GPR than of EMI data due to the different resolution of EMI from that of GPR. Thus, MARS coupled with geophysical data is recommended for prediction of SOC, pointing out the need to improve soil management to guarantee agricultural land sustainability.

Understanding the spatial structure of soil properties at field scale and introducing this information into appropriate data analysis methods can help in detecting the effects of different soil management practices and in supporting precision agriculture applications. The objectives of this study were: (i) assessing the spatial structure of soil physical and hydraulic properties in a long-term field experiment; (ii) defining a set of spatial indicators for gaining an integrated view of the studied system. In seventy-two georeferenced locations, soil bulk density (BD), initial volumetric soil water content (θi) and cumulative infiltration curve as function of the time (I(t)) were measured. The soil water retention curve (θ(h)) and the hydraulic conductivity function (K(h)) were then estimated using the Beerkan Estimation of Soil Transfer parameters (BEST) methodology. The volumetric soil water contents at soil matrix (h = −10 cm), field capacity (h = −100 cm) and wilting point (h = −15,300 cm) were considered. In addition, a set of capacitive indicators—plant available water capacity (PAWCe), soil macroporosity (PMACe), air capacity (ACe) and relative field capacity (RFCe)—were computed. The data were first analyzed for overall spatial dependence and then processed through variography for structural analysis and subsequent spatial interpolation. Cross-correlation analysis allowed for assessing the spatial relationships between selected physical and hydraulic properties. On average, optimal soil physical quality conditions were recorded; only PMACe values were indicative of non-optimal conditions, whereas mean values of all the other indicators (BD, Ks, PAWCe, ACe, RFCe) fell within optimal ranges. The exponential model was found to be the best function to describe the spatial variability of all the considered variables, except ACe. A good spatial dependence was found for most of the investigated variables and only BD, ACe and Ks showed a moderate autocorrelation. Ks was confirmed to be characterized by a relatively high spatial variability, and thus, to require a more intensive spatial sampling. An inverse spatial cross-correlation was observed between BD and Ks up to a distance of 10 m; significant cross-correlations were also recorded between Ks and PMACe and ACe. This result seems to suggest the possibility to use these soil physical quality indicators as covariates in predictive multivariate approaches.

The aim of this study was to screen the phytotoxicity of different retentates concentrated in polyphenols and extracted from olive mill wastewater (OMW), namely, nano filtration retentate (RNF) and inverse osmosis retentate (ROI). The activity of both retentates was evaluated using bioassays on dry seeds (with concentrations of 0.0, 0.1, 0.5, 1.0, 5.0, and 10.0% and compared with CaCl2 solutions to evaluate the salinity effects), on germinated seeds (with concentrations of 0.0, 5.0, and 10.0%), and on the emergence of seedlings from the soil (with concentrations of 0.0, 5.0, and 10.0%). Three indicator plant species were used: Lepidium sativum L. (cress), Solanum lycopersicum L. (tomato), and Triticum turgidum subsp. durum Desf. (durum wheat). The results were expressed as the germination rate or emergence rate (GR or ER, respectively) and as the average germination time or average emergence time (AGT or AET, respectively) depending on the bioassays. Salinity showed a certain effect on the GR. Total or near-total inhibition of germination was obtained with the highest concentrations (5.0–10.0%). The dose of 1.0% of RNF and that of 0.5% of ROI caused delays in the germination of cress. The germination of tomato was delayed by RNF and ROI at concentrations of 0.5% and 1.0%. The AGT of durum wheat was not affected by RNF, but was slightly affected by ROI. The development of the seedlings was inhibited by both retentates. The results in the Petri dishes were also confirmed in pots. Retentates could be evaluated as a basis for the development of bioherbicides.

Weed management is not yet environmentally, agronomically, economically and socially sustainable in olive orchards. It is necessary to study appropriate integrated weed management systems (IWMSs) based on the knowledge of weed population and effects of weeding practices over time. The aim of this study was to evaluate the effects of different weed managements on seasonal floristic composition of a super high-density olive orchard, also exploiting the essential principles of an IWMS. Five weeding techniques were compared: chemical control (CHI), mowing (MEC), plastic (nonwoven tissue, TNT and polyethylene, PEN) and organic (with de-oiled olive pomace, DOP) mulching. Weed monitoring was carried out on six dates in a three-year period. The infestation of each of the main 18 weed species recorded (%) and the total infestation (%) on each monitoring date were determined. Results underlined that all weeding practices investigated in this multi-year study affected the floristic composition, weed characteristics(hemicryptophytes, cryptophytes and therophytes) and seed bank. TNT and PEN were the most effective methods for weed management. Particularly, total infestation coefficient was significantly lowest when plots were managed with TNT (13.91%) and PEN (14.38%) and highest for MEC (141.29%). However, DOP also significantly reduced infestation compared to CHI and MEC. Therefore, DOP could constitute an excellent strategy for weed management in super high-density olive groves, since it also has the possibility of distributing mulching materials in a mechanized way in field and can result in improvement of soil fertility and the possibility of valorizing waste. Further studies should be carried out to investigate the mechanism of action (physical and allelochemical) of de-oiled pomace or other organic agro-industrial materials and the recovery time of these mulching materials in super high-density olive orchards.