Kazi Noor-E-Alam Jewel

Agroforestry enhances livelihoods, conserves biodiversity, and mitigates climate change but faces adoption challenges. This study uniquely addresses a gap in existing research by specifically analyzing the socioeconomic determinants of agroforestry adoption among pineapple farmers in Bangladesh's Moulvibazar and Tangail districts—a context where such crop‐focused adoption studies are scarce. A survey of 300 respondents was conducted, and data were collected through structured questionnaires and interviews, capturing demographic, economic, and institutional variables. Principal component analysis (PCA), decision tree analysis, and structural equation modeling (SEM) were employed due to their ability to reduce data dimensionality, identify interaction patterns, and test complex relationships among latent variables, respectively—analytical strengths that conventional regression lacks in capturing multidimensional adoption drivers. The results revealed significant regional differences: Tangail exhibited higher land ownership (9.36 ha on average), annual income (6001.89 USD), and institutional support, while Moulvibazar showed greater awareness (97.22%) and training participation (89.18%). Land size, education, and extension services were identified as significant factors influencing adoption rates, with smallholders in Tangail achieving 90% adoption when supported by training and institutional services. The PCA identified total land and training as primary contributors, while SEM highlighted education (−0.46), total land (0.55), and training (0.25) as key determinants. Despite higher incomes, Tangail's greater income inequality (Gini 0.5974 vs. 0.2926) highlights the need for inclusive benefit‐sharing mechanisms. Barriers such as lack of capital, limited market access, and inadequate institutional support were prevalent in both regions. Policy recommendations should prioritize financial incentives, accessible credit schemes, strengthened market linkages, and farmer‐centered training to enhance equitable and sustainable agroforestry adoption.

This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.

Species diversity and soil quality are deteriorating due to continuous disturbances in ecosystems caused by human interference. However, agroforestry is considered a good approach to minimizing environmental problems. Therefore, the objective of this study was to determine the impacts of participatory agroforestry on restoring soil fertility, farm productivity and biodiversity in the degraded Madhupur Sal forest of Bangladesh. The study purposefully selected 40 common agroforestry programs in Madhupur Sal forest for the collection of soil and plant data from 2020 to 2023. Agroforestry programs have improved soil organic matter, soil carbon, pH, and available N, P and K content to a substantial degree and protected soil degradation, enhancing yield. The soil improvement index represents the potentiality of agroforestry in restoring soil nutrients and carbon in the form of organic matter, which is an important indicator for carbon sequestration and mitigating the impacts of climate change. The resultant cost–benefit and land equivalent ratios were steadily higher, which corroborates the greater productivity and profitability of agroforestry compared to monoculture systems. In contrast, agroforestry restored 31 plant species, opening up opportunities for restoring plant species in the threatened forest ecosystem. Therefore, this study recommended selecting appropriate site-specific species for managing agroforestry and restoring ecosystems.