This review considers the role of Big Data (BD), the digital revolution, the application of Internet of Things (loTs) and sensor technologies in the agriculture sector. The introduction is focussed on the ongoing research efforts on BD within agriculture sector, basic features of BD and latest development in BD analytics tools. In subsequent sections, the importance of BD applications in the agriculture sector and examples of their success stories in increasing farm productivity, current scenario on BD and digital agriculture, the future prospects of BD and bottlenecks in its implementation in agriculture sector are discussed. Agriculture sector is undergoing a new revolution and transformation, driven by IoT, sensor technologies, BD and cloud computing. This digital revolution in agriculture is very promising and will enable the agriculture sector to move to the next level of farm productivity and profitability. This transformation process looks irreversible and poised to revolutionize not only agriculture but the entire farm-to-food sector.

Purpose -The purpose of this paper is to explore the emissions of SO 2 , NO 2 and SPM in India during 1991-2003. The Environmental Kuznets' Curve (EKC) is applied to explore the relationship between economic development measured in terms of State Domestic Product (SDP) per capita and different air quality parameters for industrial and residential locations respectively. Several developmental factors contribute to change in emissions of these air quality parameters. These factors generally include the scale effect, composition effect and the pollution abatement effect. Design/methodology/approach -The methodology has focused on testing the EKC hypothesis at state level in India, using cross-section and time series data for 15 major states. The study has made use of fixed effect version of pooled data estimation technique. Findings -The findings in the paper indicate only a directional inverted U-shaped EKC relationship for both industrial and residential locations, without being significant statistically. Basically, some developmental factors such as population density, urbanization and policy variables are significant with expected signs in explaining the relationship for most of the cases. The calculated turning point of SDP per capita for different air quality parameters ranges between $163.46 and $408.66 Research limitations/implications -The present study has been restricted to a shorter time period (i.e. 1991-2003) because of the unavailability of continuous time series data. The study only includes 15 major Indian states and excludes other states due to lack of proper data sources. Practical implications -The inclusion of several developmental variables (such as urbanization, infrastructure development, population density, policy) helps to detect whether the emissions of different air quality is mainly due to economic growth or other reasons. Originality/value -The investigation in the paper allows determination of the level of SDP per capita, the emissions of different types of air quality will start to decrease in different Indian states.

Purpose The purpose of this paper is to assess the sustainability of current accounts for five major South Asian economies, namely, India, Pakistan, Bangladesh, Sri Lanka and Nepal, for the period 1985–2016. Design/methodology/approach The study employs the intertemporal solvency model of Hakkio and Rush (1991) and Husted (1992). Autoregressive Distributed Lag bounds test, Gregory and Hansen’s test and Carrion-i-Silvestre and Sanso’s test are used to assess the cointegration between current account inflows and outflows. The coefficients of long-run relationship are obtained using dynamic ordinary least squares. Besides the econometric investigation, the study also examines some other indicators such as the composition of current account, size of external debt, etc., to shed further light on the sustainability of current accounts. Findings The study finds support for the long-run relationship between the current account outflows and inflows for all the countries. The estimates of slope coefficient indicate strong sustainability in case of India, Bangladesh and Nepal, whereas weak sustainability holds for Sri Lanka and Pakistan underscoring the need for policy interventions. In a comparative perspective, the current accounts in India, Nepal and Bangladesh conform more to a sustainable behavior in terms of the size of deficits, external debt stock and compliance to the intertemporal budget constraint. Originality/value The study employs econometric techniques allowing for structural breaks in the assessment of current account sustainability. Besides using the intertemporal model, the study also examines factors such as composition of current accounts, size of external debts, etc., to evaluate sustainability.

PurposeThe purpose of this paper is to examine the relationship between tourism sector development and poverty reduction in India using annual data from 1970 to 2018. The paper attempts to answer the critical question: Is tourism pro-poor in India?Design/methodology/approachStationarity properties of the series are checked by using the ADF unit root test. The paper uses the Auto Regressive Distributed Lag (ARDL) bound testing approach to cointegration to examine the existence of long-run relationships; error-correction mechanism for the short-run dynamics, and Granger non-causality test to test the direction of causality.FindingsThe cointegration test confirms a long-run relationship between tourism development and poverty reduction for India. The ARDL test results suggest that tourism development and economic growth reduces poverty in both the long run and the short run. Furthermore, inflation had a negative and significant short-run impact on the poverty reduction variable. The causality test confirms that there is a positive and unidirectional causality running from tourism development to poverty reduction confirming that tourism development is pro-poor in India.Research limitations/implicationsThis study implies that poverty in India can be reduced by tourism sector growth and price stability. For a fast-growing economy with respect to economic growth and tourism sector growth, this may have far-reaching implications toward inclusive growth in India.Originality/valueThis paper is the first of its kind to empirically examine the causal relationship between tourism sector development and poverty reduction in India using modern econometric techniques.

Purpose The purpose of this paper is to examine the nexus among economic growth, nonrenewable energy consumption and renewable energy consumption in India over the period 1971-2017. Design/methodology/approach This study uses nonlinear autoregressive distributed lags model and asymmetric causality test to explore nonlinearities in the dynamic interaction among the variables. Findings The findings indicate that the impact of nonrenewable energy consumption and renewable energy consumption on the economic growth is asymmetric in both long run and short run. In long run, a positive shock in nonrenewable energy consumption and renewable energy consumption exerts a positive impact on growth. However, the negative shocks in nonrenewable energy consumption produce larger negative effects on the growth. The results of nonlinear causality test indicate a unidirectional causality from nonrenewable energy consumption and renewable energy consumption to economic growth and thus support “growth hypothesis” in context of India. Practical implications The findings imply that policy measures to discourage nonrenewable energy consumption may produce deflationary effects on economic growth in India. Further, the findings demonstrate the potential role of renewable energy consumption in promoting economic growth. Originality/value To the best of the authors’ knowledge, this study is the first attempt to explore nonlinearities in the relationship between economic growth and the components of energy consumption in terms of renewable and nonrenewable energy consumption.