Purpose This study aims to examine the impact of India’s Unified Payments Interface (UPI) on rural financial inclusion and economic growth (2019–2023), focusing on entrepreneurship, household financial behaviour and business formation. It identifies the mechanisms through which UPI adoption drives rural economic transformation. Design/methodology/approach Using a difference-in-differences approach, the study analyses UPI transaction data and household surveys from 200 villages across four Indian states to assess causal effects on financial and economic outcomes. Findings Villages with higher UPI adoption saw a 27% rise in business registrations, a 34% increase in savings accounts and a 42% growth in female-owned enterprises. In comparison, informal borrowing declined by 53% and digital credit access improved by 29%. These effects stemmed from lower transaction costs, better financial information access, network effects driving adoption and increased trust in digital finance. Research limitations/implications The findings are confined to four Indian states – Maharashtra, Karnataka, Uttar Pradesh and Gujarat – and the extrapolation to other regions or emerging economies needs to be taken with caution, observing local economic and institutional conditions. Long-term consequences need to be investigated further. Regional differences, wealth generation and policy reforms applicable to other economies need to be considered in future research. Practical implications Strategic deployment of digital finance, literacy programmes and regulatory safeguards can maximise inclusion and economic impact. UPI adoption also empowers women entrepreneurs, formalises businesses and reduces informal lending dependence, but it requires mitigation of digital exclusion and cybersecurity risks. Originality/value To the best of the authors’ knowledge, this study provides the first causal evidence of UPI’s role in rural economic transformation, introducing a theoretical framework linking digital finance to market integration and financial inclusion.
Background: Self-directed learning (SDL) has a critical role in medical education. It is important to measure SDL readiness (SDLR) of students for selecting the right teaching learning methodology and for curriculum planning. With the advent of increasing use of technology, it is important to assess its association with SDLR in medical students. Thus, a descriptive, cross-sectional study was undertaken to measure SDLR of medical students in two phases and to assess its potential influencers. Methods: SDLR of phase 2 and phase 3 MBBS students was assessed using an abridged 29-item version of Fischer’s SDLR scale (SDLRS). In addition, sociodemographic information of the participants was collected. IBM SPSS, version 27, was used for data analyses. Mann–Whitney U , Kruskal–Wallis, and Chi-square tests were used to analyze SDLRS scores and its association with sociodemographic and behavioral variables across different demographic groups. Results: A Total of 297 students, 150 from phase 2 and 147 from phase 3, participated in the study. Median total SDLRS score was 115 for phase 2 and 116 for phase 3. SDLRS scores across all three domains of self-management, self-control, and desire for learning were comparable across all groups and with most of the sociodemographic variables. A statistically significant association of high SDLRS scores with usage frequency of library ( P = 0.039) and artificial intelligence tools ( P = 0.025) was found. Conclusion: SDLR is consistent across gender, phase, and demographic variables. Institutional educational culture and curricular environment may have a greater influence in shaping SDLR than sociodemographic variables.
Agriculture is a critical sector, especially in developing nations like India, where a majority of the population relies on farming for their livelihood. However, many small and marginal farmers still lack access to advanced tools, expert advice, or timely information to make informed decisions about crop cultivation or disease management. This often results in crop failure, economic loss, and increased dependence on harmful pesticides. The Smart Agro System is designed to address these challenges by providing a multilingual, voice-interactive, AI-powered assistant capable of both plant disease detection and crop recommendation. The proposed system accepts both speech and text input from the user, enabling ease of access for non-technical and illiterate users. It starts by detecting the user’s language automatically, then provides options to either perform crop recommendation, plant disease detection, or both. For disease detection, the user uploads or captures a plant leaf image, which is analyzed using a MobileNet Convolutional Neural Network (CNN) to classify the disease. The detected result is further processed by an offline AI chatbot powered by LLaMA, which generates relevant organic and chemical treatment suggestions. For crop recommendation, the system uses a custom-created dataset with parameters like soil type, water availability, and climate to predict the best crop for that region and season. The entire system is built using Python 3.10, without any web dependency, and includes offline functionalities such as speech-to-text using the speech_recognition library and speech output using Google’s gTTS. Language translation is handled via googletrans, enabling real-time interaction in over 80 languages. Results from experiments show the CNN model achieves high performance with an accuracy of 94.2%, and the response time remains under a few seconds even on low-resource machines. This system provides a low-cost, efficient, multilingual, and AI-driven solution that can revolutionize how farmers interact with digital tools for sustainable agriculture
Biochar is a highly stable form of carbon produced by heating organic material (like wood or agricultural waste) in a low-oxygen environment. It improves soil structure by enhancing water retention, aeration and nutrient availability. This is especially helpful in degraded or sandy soils. Biochar is a long-term carbon sink, meaning it can lock carbon in the soil for hundreds or even thousands of years, helping mitigate climate change by reducing the amount of CO₂ in the atmosphere. It can act like a sponge for nutrients, holding them in the soil and releasing them slowly, which can reduce the need for synthetic fertilizers and lower the risk of nutrient leaching into water systems. Biochar supports beneficial soil microbes, improving soil health and potentially leading to better crop yields. It provides a sustainable use for agricultural and forestry waste, reducing landfill waste and improving the overall sustainability of farming practices. Biochar offers a way to enhance soil productivity while addressing environmental concerns like climate change and waste management. Biochar is a highly stable form of carbon produced by heating organic material (like wood or agricultural waste) in a low-oxygen environment. Biochar has the potential to increase conventional agricultural productivity and enhance the ability of farmers to participate in carbon markets beyond the traditional approach by directly applying carbon into the soil. In view of this context, the biochar plays a significant role in crop productivity and improving soil health.
