Pir Mehr Ali Shah Arid Agriculture University

As global efforts to address climate change continue, digital assets such as regenerative finance (ReFi) and renewable energy tokens are gaining attention in the context of financing decarbonization and clean energy investment. This study examines the connectedness between ReFi tokens, renewable energy tokens, energy, and carbon credit markets using the Quantile VAR (QVAR) model with data from October 20, 2021, to February 28, 2025. Connectedness is higher during periods of heightened market instability, such as wartime, than during stable conditions. At the median quantile, ReFi tokens and most clean energy assets act as shock transmitters, while renewable energy tokens, dirty energy assets and carbon credit assets are net recipients. In extreme quantiles, the pattern shifts with ReFi tokens acting as receivers. Portfolio analysis reveals that including ReFi and renewable energy tokens improves the diversification in energy-carbon portfolios. These results guide portfolio managers to adopt risk-adjusted strategies and policymakers to consider these tokens in regulated sustainability markets.

Organic, inorganic, and microbial contaminants in soil and wastewater pose critical risks to human health, agricultural productivity, and environmental sustainability. Heavy metals (HMs) such as Cd, Pb, Ni, and Cr are of particular concern due to their non-degradable nature, long-term mobility, and ability to bioaccumulate in food chains. Conventional remediation techniques, including membrane filtration, chemical precipitation, and electrochemical treatments, are often costly, energy-intensive, and less effective for large-scale or mixed-pollutant systems. In this context, biochar has emerged as a multifunctional, carbon-rich material with physicochemical properties that support the immobilization, adsorption, and degradation of a wide range of contaminants. This review provides an integrative, mechanism-driven assessment of biochar-based remediation that goes beyond descriptive summaries. Specifically, it (1) links biochar production methods and physicochemical properties to contaminant-specific removal efficiencies across soil and wastewater systems; (2) critically analyzes direct and indirect HM adsorption mechanisms, emphasizing their interactions with soil biogeochemical processes and long-term stability; (3) synthesizes emerging evidence on biochar-plant defense crosstalk, demonstrating how reduced metal bioavailability modulates reactive oxygen species (ROS), membrane integrity, and antioxidant responses under stress conditions; and (4) evaluates biochar within a circular economy framework by integrating waste valorization, energy recovery, environmental trade-offs, and techno-economic feasibility. By coupling mechanistic insights with system-level analysis, this review identifies the key knowledge gaps related to biochar aging, contaminant remobilization, and field-scale validation, and guides developing resilient soil-plant-water remediation strategies.

The 2022present study was conducted to evaluate the effectiveness of the combined application of Azadirachta indica (neem) and biocontrol agents against Meloidogyne javanica on peach. The results demonstrated that the combination of A. indica with Pochonia chlamydosporia remained superior by recording the lowest number of root galls, followed closely by Purpureocillium lilacinum, which was statistically at par with the chemical control (Rugby), showing gall reductions by 72.54

Pollination optimization in apple orchards faces increasing challenges from climate variability and declining pollinator populations, necessitating precision timing strategies. This study introduces a novel Pollination Importance Index (PII) integrated with a hybrid multi-task deep learning framework (PII-CNN-LSTM) to identify critical pollination windows. The PII dynamically quantifies pollination potential by incorporating flower receptivity, resource availability, biotic stress, and pollinator activity across five apple flower growth stages. The PII-CNN-LSTM architecture simultaneously performs growth stage classification and importance prediction through CNN spatial feature extraction and LSTM temporal modeling, enhanced by attention mechanisms and residual connections. Comparative evaluation against PII-CNN-BiLSTM, PII-CNN-GRU, and PII-CNN-TCN architectures demonstrated superior performance with 97% classification accuracy and minimal prediction error (validation loss: 0.0065, MAE: 0.0505). The model achieved exceptional full-bloom stage identification (99% F1-score), corresponding to its dominant 61.5% contribution to overall pollination importance. Cross-validation using 2024–2025 ground truth data and real-time drone deployment confirmed robust generalizability with temporal correlations exceeding 0.94. The framework successfully identified the critical pollination window from 3rd to 9th days, with optimal intervention timing at 5th to 7th days when importance scores exceeded 0.40. This biologically-grounded temporal precision enables targeted deployment of pollination resources during peak receptivity periods, reducing the need for continuous monitoring and intervention throughout the entire flowering season. The biologically-grounded approach provides scalable, data-driven decision support for precision agriculture, representing a significant advancement in agricultural automation and orchard productivity optimization.

Morels (Morchella spp.) are highly valued edible and medicinal ascomycetes in temperate ecosystems, yet their taxonomy remains difficult because of marked morphological plasticity and the limited resolving power of single-locus markers. This study documents wild Morchella diversity from the Murree Hills, Pakistan, using an integrated morpho-cultural and molecular approach. Fifteen ascocarps collected from six localities during July-August 2024 were used for tissue isolation, yielding thirty-nine cultures, with multiple cultures recovered from individual ascocarps as independent isolation replicates. From these, nine representative isolates were selected for ITS-based sequencing and phylogenetic assessment. Culture recovery averaged 2.60 isolates per ascocarp, indicating successful establishment of cultures across sampling sites. Morphological and cultural observations supported the recognition of two principal forms: a pale morphotype assignable to the Esculenta clade (cf. Morchella deliciosa) and a darker morphotype affiliated with the Elata clade (within the Morchella elata complex). Bayesian ITS phylogeny and sequence-similarity comparisons supported this two-lineage pattern at the clade level. Because ITS alone is insufficient for confident delimitation of closely related Morchella species, these results should be regarded as preliminary lineage-level documentation rather than definitive species resolution. Nevertheless, this study provides baseline evidence for the occurrence of Esculenta- and Elata-affiliated morels in an understudied Himalayan region and establishes a foundation for future multilocus, population-level, and ecological investigations.