Motivation: Tea, derived from the Camellia sinensis plant, holds the position of being the most widely consumed manufactured beverage globally. Its cultivation necessitates specific agro-climatic conditions, leading to its production being confined to select regions, notably including India. India contributes about 20.81% to the world’s tea output. The production and processing of tea leaves to final product consume energy in terms of machinery, fertilizer, irrigation etc. The energy consumption involved in tea production is a pressing concern, given the associated high costs and CO₂ emissions resulting from fossil fuel usage. To achieve a net-zero carbon balance, there is need to pay attention towards promoting renewable energy technologies as a means to mitigate the CO₂ emissions stemming from fossil fuels in India’s tea sector.Objectives: Aligned with the objective of sustainability through the integration of renewable energy sources, a pilot study was conducted in the primary tea-growing regions of northeastern India during 2021–22. The primary aims of this study were twofold: to gauge the quantity of CO₂ emissions originating from conventional energy sources and to explore the feasibility of incorporating renewable energy sources as viable substitutes.Data and methods: Data on various inputs used in tea production were collected from Assam and West Bengal states of India by using a stratified random sampling method with equal probability and without replacement.Results: The findings of this investigation underscore a noteworthy potential for the adoption of renewable energy, particularly solar energy, within the tea estates situated in the north eastern region of India. Such a transition would yield benefits for both the tea estates themselves and the overall environment.

Digital Agro-Advisory Platforms (DAAPs) are emerging as vital tools to address gaps in agricultural extension services, particularly in developing economies like India. Kisan Sarathi, implemented by the Indian Council of Agricultural Research (ICAR), represents a pioneering initiative to deliver real-time, multilingual, and location-specific advisories to farmers. Despite its rapid expansion, limited empirical research has examined the behavioural factors driving farmers’ sustained use of such platforms. This study extends the Technology Acceptance Model (TAM) by integrating self-efficacy and system quality to investigate determinants of farmers’ usage behaviour of Kisan Sarathi across the Indo-Gangetic Plains. Using stratified two-stage sampling, data were collected from 350 registered farmers in Haryana, Uttar Pradesh, and West Bengal. Structural Equation Modelling (SEM) and Random Forest techniques were employed. SEM revealed that perceived usefulness, perceived ease of use, self-efficacy, and system quality significantly influenced farmers’ attitudes, which strongly predicted behavioural intention and subsequent usage behaviour. Random Forest analysis further identified perceived usefulness, attitude, and behavioural intention as the strongest predictors across states, highlighting regional variations in system quality. The findings underscore the importance of user confidence, system performance, and farmer-centric design. The study contributes to the ICT4Ag literature by advancing TAM with psychological and technical dimensions, while offering insights for policymakers, developers, and extension agencies seeking to design inclusive digital advisory systems and reduce the digital divide in agriculture.

Research under classical statistics often relies on precise, determinate data to estimate population parameters. However, in certain situations, data may be indeterminate or imprecise. Neutrosophic statistics, a generalization of classical statistics, has been introduced to address these challenges by handling vague, indeterminate, and uncertain information effectively. Several estimators, including ratio estimators, have been proposed in neutrosophic statistics. These ratio estimators perform well when the correlation between the auxiliary and study variables is strong. However, in this study, regression-type estimators were developed, demonstrating superior performance in cases where the correlation between the study and auxiliary variables is high, weak, or moderate. The performance of the proposed estimator was evaluated using simulated data as well as four real-world datasets with indeterminate data, including blood pressure, temperature, natural growth rate, and solar energy data. The proposed neutrosophic regression estimator consistently outperformed the existing neutrosophic ratio estimator, modified neutrosophic ratio estimators, and the neutrosophic exponential ratio estimator, as indicated by performance measures such as mean squared error (MSE) and percent relative efficiency (PRE). This paper highlights the advantages of the neutrosophic regression estimator in improving estimation accuracy when dealing with uncertain and ambiguous data, with any range of correlation between the study and the auxiliary variables considered under the study.

Provides neutrosophic extensions of Lahiri's method to select a random sample of size n using probability proportional to size without replacement (PPSWOR) sampling. It computes the corresponding neutrosophic inclusion probabilities and provides estimates of the population total and mean using both the neutrosophic Horvitz Thompson (NHT) estimator and the neutrosophic generalized regression (NGREG) estimator and its percent relative efficiency.