Alternaria spp. (Pleosporaceae) constitute a cosmopolitan and taxonomically diverse group encompassing saprophytic, endophytic, parasitic, and environmental fungal species. These fungi are among the most widespread and challenging phytopathogens, affecting agronomic, and horticultural crops worldwide, including the Mediterranean region. They cause substantial pre- and post-harvest yield and quality losses and produce over 70 mycotoxins, posing serious risks to food safety and human and animal health. Management strategies, primarily reliant on chemical fungicides, face limitations due to pathogen resistance, public health risks and environmental concerns. Plant Growth-Promoting Rhizobacteria (PGPR) have emerged as effective biocontrol agents capable of suppressing Alternaria spp. through direct mechanisms, including the production of antifungal metabolites and lytic enzymes, as well as indirect mechanisms via the induction of systemic resistance (ISR) and improved plant nutrition and stress tolerance. Bacillus and Pseudomonas species are the most studied PGPR taxa, with many strains showing significant in vitro and in vivo efficacy against key pathogens such as A. alternata, A. solani, and emerging species including A. terricola, A. tenuissima, A. brassicae, A. ricini, and A. porri. Formulation innovations include the application of single strains, multi-strain consortia, and their integration with plant-derived compounds and soil amendments to enhance disease control and productivity. Advances in biotechnological approaches, including omics, transgenesis, genome editing, and predictive modelling, have improved PGPR-based strategies. These ecofriendly approaches mitigate fungal threats, promote plant growth, and support sustainable and resilient agricultural systems aligned with circular economy and global health principles. Critical insights into current limitations and future research challenges were discussed. Alternaria spp. are widespread, diverse pathogens affecting crops and global health. PGPR-based biocontrol offers a sustainable alternative to fungicides. Bacillus and Pseudomonas spp. dominate effective Alternaria spp. biocontrol. Antifungal metabolites, enzymes, and ISR drive PGPR pathogen suppression. Biotechnological advances enhance PGPR biocontrol efficiency and reliability.
PurposeThis study aims to identify the context-dependent optimal machine learning (ML) model for forecasting Tunisia's bank stock price index during the Revolution and COVID-19 crises, and uses SHapley Additive exPlanations (SHAP) analysis to uncover distinct, crisis-specific price drivers.Design/methodology/approachThe author evaluates eight ML models, including regularization techniques (Lasso, Ridge, Elastic Net), tree-based methods (Random Forest, eXtreme Gradient Boosting), Support Vector Regression and deep learning architectures (Long Short-Term Memory, Gated Recurrent Unit [GRU]), using five standard metrics. Model robustness is validated through statistical, economic and temporal stability tests. Finally, the author apply mean SHAP analysis to top performers to identify key predictors and supplemented by a feature stability analysis.FindingsThe analysis reveals a clear performance hierarchy contingent on crisis typology. Specifically, the GRU model achieved superior predictive accuracy during the high-volatility COVID-19 period, whereas Ridge regression demonstrated greater robustness during the prolonged political instability of the Revolution. SHAP interpretability confirmed that short-term moving averages dominated predictions during the pandemic, reflecting a market driven by momentum, while the Revolution period involved a more balanced reassessment of persistent risks, which Ridge's regularization effectively revealed.Practical implicationsThe findings provide market participants with a decisive, crisis-contingent forecasting rule. Specifically, a GRU model driven by technical indicators is recommended for fast-moving global crises like a pandemic, while a shift to a Ridge regression model is warranted during periods of protracted political instability to leverage its stability.Originality/valueTo the best of the authors' knowledge, this study presents the first comparative ML analysis of the Tunisian banking sector across two fundamentally different crises, the Revolution that sparked the Arab Spring and the COVID-19 pandemic, using SHAP analysis to uncover crisis-specific financial drivers complemented by several robustness checks.
Purpose This study aims to analyze the behavior of volatility connectedness among green exchange-traded funds (ETFs) and cryptocurrency markets. The study also examines the hedging effectiveness between green ETFs and cryptocurrencies. Design/methodology/approach This study uses a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to highlight the salient facts of volatility connectedness between green ETFs and cryptocurrencies during the period 22 / 10 / 2021–05 / 01 / 2024. Findings The empirical findings reveal that cryptocurrencies (particularly, Bitcoin and Ethereum) consistently act as net transmitters of shocks, amplifying systemic risk during crisis periods. However, green ETFs primarily behave as net receivers, providing a conditional and modest hedging or safe-haven role. Portfolio-level analyses indicate that green ETFs (especially ICLN) help reduce overall portfolio risk within Minimum Variance Portfolios (MVP) under turbulent market conditions, enhancing risk-adjusted returns when included alongside cryptocurrencies. Conversely, cryptocurrencies contribute to substantial diversification benefits in terms of Minimum Correlation (MCP) and Minimum Connectedness Portfolios (MCoP) but exhibit higher volatility and tail-risk exposure. Dynamic Sharpe ratio analysis further demonstrates that no single allocation dominates across market regimes; instead, adaptive strategies – switching between MVP during calm periods and risk-parity portfolios (RPP) during crises – yield superior performance. These findings underscore the conditional stabilizing role of green ETFs and the dominant diversification and risk-transmission role of cryptocurrencies, highlighting important implications for portfolio management, sustainable investment and regulatory oversight in integrated digital and green finance markets. Practical implications The empirical findings offer insightful implications for policymakers, regulatory authorities and investors to promote sustainable and strategic allocation, as well as support and expand sustainable investment markets. Originality/value This study explores if and to what extent cryptocurrencies could be linked to green ETFs. It also examines the diversification and hedging features of green ETFs for portfolio including Bitcoin and Ethereum given the ongoing debate regarding the effective hedging instruments for cryptocurrency portfolios.
Purpose: This study explores the moderating role of inflation targeting (IT) regime in shaping stock market volatility drivers, leveraging a machine learning (ML) approach to elucidate the complex interplay between monetary policy regimes and macro-financial channels. By analyzing key macro-financial interactions, we aim to provide actionable insights for policymakers and investors in emerging markets.Methodology: This study analyzes 16 IT and 16 non-IT emerging markets (1995:Q1-2024:Q1) using macroeconomic data, policy signals, country-specific factors, and 11 ML techniques (regularization, trees, neural networks) alongside various GARCH-type models. Furthermore, both mean SHAP importance and SHAP interaction techniques are used to rank features and uncover macro-financial channel interactions.Findings: Findings reveal distinct volatility drivers across regimes. Non-IT markets react sharply to immediate market signals and external vulnerabilities, amplified by macro-financial channels, notably institutional quality interacting with global risk. Conversely, IT regimes exhibit greater stability through structured macroeconomic fundamentals and disciplined policy frameworks that buffer short-term shocks. The IT regime's predictability transforms volatility dynamics, replacing sentiment-driven fluctuations with lagged fundamentals and external risk factors, fostering more resilient financial environments compared to non-IT contexts.Practical implications: Non-IT policymakers should strengthen institutions and monitor external balances to mitigate volatility, while considering IT adoption. IT countries must maintain credible regimes with flexible exchange rates to stabilize markets and boost confidence.Originality/Value: This study pioneers optimal volatility measures and ML methods for emerging markets, using SHAP to analyze macro-financial channels and IT's moderating role, advancing theory and guiding policy and investment strategies.
