Biochar is applied to soils to enhance fertility and sequester carbon, but its impacts on soil fauna remain debated. This work assessed the behavioural responses of collembolans (Folsomia candida) and woodlice (Porcellionides pruinosus) to soils amended with different biochar derived from Ricinus communis and Brassica juncea feedstocks, both pyrolyzed at 400 °C and 600 °C, and tested at 1% and 10% (w/w). Results revealed that F. candida avoided R. communis biochar at high concentrations, suggesting a stronger role of dosage than pyrolysis temperature. In contrast, P. pruinosus was attracted to B. juncea biochar at 400 °C but avoided the sample pyrolyzed at 600 °C, while gregariousness was affected at the highest concentration, highlighting the influence of dose and pyrolysis. Furthermore, we examined whether woodlice presence alters collembolans responses through ecological facilitation–non-trophic interactions that benefit at least one species. Remarkably, collembolans no longer avoided soils previously inhabited by woodlice. Multivariate analysis revealed that responses of soil arthropods to biochar were primarily determined by the type of feedstock, while ecological facilitation was mainly driven by soil conductivity, suggesting that woodlice actively modulate soil properties likely by bioturbation, or faecal deposition. In an ecological perspective, facilitation by woodlice may buffer stress and enhance habitat suitability for other soil fauna, while their high sensitivity positions them as key early indicators of biochar-induced changes in soil ecosystem functioning. It is recommended that ecological sustainable management be adopted, as high soil biodiversity can prevent soil degradation and facilitate natural processes that can mitigate the effects of biochar.
Early detection of fruit fly (Diptera: Tephritidae) infestation in traded fruit is important for maintaining phytosanitary security, particularly for quarantine pests. This study evaluated the integration of micro-focus X-ray computed tomography (& micro;CT) and deep learning as a non-destructive, automated, and scalable solution for postharvest pest detection. The Mediterranean fruit fly (medfly), Ceratitis capitata Wiedemann, 1824, was used as a model species and Navel oranges, Citrus sinensis (Osbeck), served as the host commodity. Controlled infestation experiments were conducted using both artificially punctured fruit to promote oviposition and intact fruit to avoid confounding effects associated with mechanical damage. Oranges were exposed to varying adult medfly densities to generate different infestation levels. Following exposure, the infested oranges were subjected to varying incubation periods to simulate different infestation stages. High-resolution & micro;CT scans were first assessed manually and subsequently analyzed using a staged deep learning pipeline incorporating convolutional neural networks (CNNs), 3D CNNs, CNN-Long Short-Term Memory (LSTM) hybrids and Transformer-based models. Manual mu CT inspection achieved mean correct classification rates of 0.84 in punctured fruit and 0.69 in intact fruit, with detection performance increasing with infestation stage. Deep learning analyses were conducted on a curated punctured-fruit dataset using a slice-level classification and voting-based aggregation strategy. Among the evaluated transfer-learning architectures, DenseNet121 achieved the best overall performance, reaching an accuracy of 0.88, a precision of 0.86, a sensitivity of 1.00, and an AUC of 0.80; because the manual and automated analyses were performed on different sets of fruit, these accuracies are complementary rather than directly comparable. Grad-CAM and attention visualizations confirmed that model predictions were driven by biologically meaningful tissue regions associated with larval damage. Although the deep learning workflow was developed and validated only on punctured fruit, the results demonstrate that mu CT images contain sufficient information for automated detection of medfly infestation when infestation-associated damage is visible. These findings support the potential of combining mu CT imaging and artificial intelligence (AI) for automated, reproducible and nondestructive postharvest pest detection and enhanced phytosanitary compliance in postharvest supply chains.
Co-creation has increasingly gained traction as a means to search for innovative solutions to wicked problems, including in the pursuit of the green transition. Whereas several contributions on the forms, determinants and successful outcomes of co-creation for the green transition focus on the Global North, experiences from the Global South – where non-state actors often drive local green co-creation – are increasingly drawing attention. The extant literature seems to suggest that state actors play different roles in different countries and regions, with a directive leadership role being more pronounced in the Global North than in the Global South. To explore the role(s) government actors play in the orchestration of local green co-creation in the Global South, we rely on a multiple case study drawing on the GOGREEN project database, examining the governance factors that shape the co-creation of green transitions through the study of 36 case studies across 28 countries. Relevant case studies from the Global South suggest two possible roles that government actors may play: (i) governments welcome local co-creation processes ‘out of opportunity’, exploiting opportunities for co-creation where non-state actors mobilize local resources and government has limited capacity; and (ii) governments support local green co-creation ‘out of necessity’ in contexts where non-state actors initiate such processes in response to pressing needs despite initial reluctance by government actors due to various reasons.
In this paper, we propose and analyze a Continuous Interior Penalty (CIP) stabilized Virtual Element Method (VEM) for three-dimensional advection-diffusion-reaction equations on general polyhedral meshes. While CIP-VEM schemes have been recently explored in a two-dimensional setting, their analysis heavily relies on global mesh quasi-uniformity and constant physical parameters. We overcome these limitations by introducing a novel three-dimensional variant of the Oswald-type quasi-interpolant. This allows us to establish robust, uniform error estimates in the hyperbolic limit under a realistic local quasi-uniformity assumption and variable coefficients. Finally, we provide a comprehensive set of three-dimensional numerical experiments to validate the theoretical convergence rates and demonstrate the absence of non-physical oscillations.
Geogenic arsenic (As) contamination poses a major environmental concern, yet mapping its Natural Background Levels (NBLs) remains challenging in data-scarce, human-impacted regions within heterogeneous sedimentary aquifers. NBLs are essential for distinguishing natural from anthropogenic contributions and for establishing threshold values in environmental policy and groundwater management. This study applies a Random Forest (RF) algorithm to predict arsenic NBL exceedance of the WHO guideline (10 μg/L) in shallow groundwater of the Ferrara province (Po Plain, Italy). Three model configurations were tested using: (i) in-situ hydrochemical variables (hydrochemical model), (ii) spatially continuous variables (geospatial model), and (iii) a combination of both (mixed model). The mixed model achieved the highest performance (AUC = 0.88), followed by the hydrochemical model (AUC = 0.86). The geospatial model performed slightly lower (AUC = 0.83) but enabled the generation of a continuous 50 m-resolution probability map, identifying 17% of the area as potentially exceeding 10 μg/L. Iron, redox potential and dissolved oxygen were the dominant hydrochemical predictors, while organic carbon, clay content, and surficial geology were the dominant spatial variables, reflecting geochemical and sedimentary controls on As mobilization through reductive dissolution processes. Elevated exceedance probabilities are identified in the northeastern sector, proximal to the Po River Delta, and in peat-rich central areas. This study demonstrates that spatially continuous variables can effectively complement hydrochemical data for continuous NBL mapping, capturing meso-scale heterogeneity that single, groundwater body-scale values conventionally adopted in European NBL frameworks are unable to resolve, thus offering a useful tool for contamination assessment and groundwater management.
