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We assessed the impact of increasing olive mill waste (OMW) concentrations (10%, 35%, 70%, and 100% v/v) on soil free-living nematode communities and Lactuca sativa (lettuce) growth, 10 and 45 days after application (DAA). L. sativa plants showed a survival threshold at OMW10%, with higher concentrations proving fatal. Contrary to expectations, nematode abundance increased with OMW concentration. OMW10% induced a rapid surge in nematode abundance, stabilizing at 45 DAA, resembling control values. OMW35%, OMW70%, and OMW100% plots exhibited persistent, gradual increases, surpassing control values at 45 DAA. All treatments favored fungal feeders, resulting in the overdominance of the genus Aphelenchus both at 10 and 45 DAA. Even though OMW did not exert a toxic effect on nematode populations, this shift in the community structure towards the dominance of a single genus could suggest an imbalance in the soil community, which could have negative implications for soil health and ecosystem functioning. Overall, our study provides insights into the complex interactions between OMW, soil nematode communities, and plant growth, emphasizing the importance of understanding soil ecology for sustainable agricultural management. [ABSTRACT FROM AUTHOR]

In a field experiment, composted coffee waste (CW) was used as soil amendment at three different rates (2%, 4%, and 8%) in plots cultivated with wheat; the effects on the soil nematode community and plant growth were studied. By sampling twice, i.e., three and six months after the application of treatments (3MAA and 6MAA), the duration of these effects was also evaluated. Treatment using composted coffee waste (CW) led to an increased abundance of all soil nematodes, especially of bacterivores and fungivores, probably via a bottom-up effect of CW on the soil community. The most-affected genera were the enrichment opportunists Panagrolaimus and Rhabditis, which increased after CW addition in a dosage-dependent way at 3MAA, while at 6MAA they were replaced by general opportunists, mainly bacterivorous and fungivorous genera; a nematotoxic effect of CW was also observed in the 6MAA condition. The nematode indices and the metabolic footprint indicated an enriched and vigorous soil three months after CW addition and a lower enrichment status of the soil together with a higher fungal participation in the decomposition pathway six months after treatments. However, in the 6 MAA condition, the soil nutrient values were higher in the CW-treated plots. None of our treatments inhibited or enhanced plant growth. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are extensively used with outstanding prediction accuracy. However, in some cases, their overfitting capabilities, along with inadvertent biases, might produce overly optimistic results. Spatial data are a special kind of data that could introduce biases to ML due to their intrinsic spatial autocorrelation. To address this issue, a special resampling method has emerged called spatial cross-validation (SCV). The purpose of this study was to evaluate the performance of SCV compared with conventional random cross-validation (CCV) used in most ML studies. Multiple ML models were created with CCV and SCV to predict groundwater electrical conductivity (EC) with data (A) from Rhodope, Greece, in the summer of 2020; (B) from the same area but at a different time (summer 2019); and (C) from a new area (the Salento peninsula, Italy). The results showed that the SCV provides ML models with superior generalization capabilities and, hence, better prediction results in new unknown data. The SCV seems to be able to capture the spatial patterns in the data while also reducing the over-optimism bias that is often associated with CCV methods. Based on the results, SCV could be applied with ML in studies that use spatial data. [ABSTRACT FROM AUTHOR]

Graphene waste has had enormous growth due to many industrial applications. Agriculture exploits waste through the circular economy, and graphene waste is thereby investigated in this study as a soil conditioner for improving the physical–hydraulic properties of soil. Experiments were performed on three differently textured soils amended with traditional soil conditioners (compost, biochar, and zeolites) and graphene. The conditioners were applied at two different doses of 10% and 5% dry weight (d.w.) for compost, biochar, and zeolites, and 1.0% and 0.5% d.w. for graphene. We compared (i) the major porosity classes related to water-retention characteristics (drainage, storage, and residual porosity), (ii) bulk density, and (iii) van Genuchten water-retention curve (WRC) characteristics. Graphene application caused the largest decrease in dry bulk density (ρb), lowering the soil bulk density by about 25%. In fact, graphene had ρb of 0.01 g/cm3. The effects of graphene were more intense in the finer soil. Compost and biochar showed similar effects, but of lower magnitude compared to those of graphene, with ρb of 0.7 and 0.28 g/cm3, respectively. Although zeolites had ρb of 0.62 g/cm3, they showed quite different behavior in increasing the mixtures' ρb. Graphene and biochar showed the most pronounced effects in the clayey soil, where storage porosity showed a reduction of >30% compared to the control. For storage porosity, the graphene treatments did not show statistically significant differences compared to the control. The results show that, when the conditioner increased drainage porosity, there was a high probability of a concomitant reduction in storage porosity. This finding indicates that graphene use for improving soil aeration and drainage conditions is viable, especially in fine soils. [ABSTRACT FROM AUTHOR]

Thornthwaite's formula is globally an optimum candidate for large scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has the lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (former FAO-56), which is the most data demanding method and is commonly used as benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature and Er data of the period 1950-2000 at 30 arc-sec resolution (~1 km at equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er / Ep ratios by setting the ratios' weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep < 45 mm month-1 because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe, California-USA and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula Eps using local coefficients led to a reduction of RMSE from 37.2 to 30.0 mm m-1 for monthly and from 388.8 to 174.8 mm y-1 for annual step estimations compared to Ep using as benchmark the values of Er method. The corrected Eps and the original Ep Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as benchmark the respective indices estimated by Er. The analysis was made using the validation data of the stations and the results showed that the correction of Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with Er from 63 % to 76 % for the case of Thornthwaite classification, and from 76 % to 93 % for the case of UNEP classification. The performance of both aridity indices using the corrected formula was extremely improved in the case of non-humid classes. The global database of local correction factors can support applications of reference evapotranspiration and aridity indices assessment with the minimum data requirements (i.e. temperature) for locations where climatic data are limited. The global grids of local correction coefficients for Thornthwaite formula produced in this study are archived in PANGAEA database and can be assessed using the following link: https://doi.pangaea.de/10.1594 /PANGAEA.932638; (Aschonitis et al., 2021). [ABSTRACT FROM AUTHOR]