This study aims to empirically test the relationship between agriculture economic growth and energy consumption in India covering the annual time series data for the period 1985-2017 on four economic indicators namely agricultural value added (constant 2010 US$) as an alternate favoring fiscal development of agriculture, energy spending represented by agricultural electricity consumption (GWh), agricultural gas consumption (mmcft) and agricultural oil consumption (tons) in India. The study variables are assessed for stationary using the ADF tests and after confirming the same order of integration, the Johansen's Co-integration Test is exercised to find the extended association amid agriculture growth and energy consumption. Both the Trace and Lmax tests found that there exists one co-integrating equation in the system. The co-integration test confirms the long run equilibrium relation between energy consumption and agricultural economic growth in India. The short run relationships are tested by using the VECM methodology and finally the impulse responses are studied for the forecast horizon of ten years period to assess the performance of agricultural growth Vis a Vis energy consumption by imposing one standard deviation shock to the independent variables.

This research explores indicators of the attitudes, preferences, and features of customers who buy at farmers’ markets in India, using an intercept survey design. Single-stage purposive sampling was carried out in which consumers were targeted at weekend farmers’ markets at nine different locations within the state of Maharashtra, India. Over a 2-month period of data collection (eight weekend visits) a total of 255 consumers were interviewed on site at the time of purchase, from whom we collected 235 completed questionnaires. Consumers in the sample were divided into three clusters and were rated positively on all seven factors considered. The findings of the study are that in cluster 1, about 80% of consumers were willing to pay more at the farmers’ market rather than to go to a nearby retail outlet or supermarket. Cluster 2 comprised those consumers who prefer value for money while cluster 3 includes those consumers who gave a high rating to the hygiene and service conditions at the market. This research concludes that consumers are positive about the operation of farmers’ markets held near their home.

This article examines the cointegration relationship among the per capita gross domestic product (GDP), per capita energy consumption, and per capita carbon dioxide (CO2) emissions, the effect of energy consumption and per capita GDP on CO2 emissions for India covering the period 1980–2014. All the data variables are non-stationary in level form but are integrated of order one. Johansen’s rank procedure and Engle–Granger vector autoregressive (VAR) is used to determine the cointegration relation and direction of causality among the variables (Murthy, 2011). Vector error correction mechanism is used to study the short-run behavior of the variables; impulse responses to the shocks of variables are also studied in order to identify the variance decomposition of variables energy consumption, CO2 emissions, and economic activity. VAR model is used for short-term forecasting of CO2 emissions. Results have identified that the data variables energy consumption, CO2 emissions, and GDP are cointegrated and VAR is proved to be good tool for short-term forecasting of CO2 emissions.

This research paper examines the causal relationship between India's economic growth and sectoral contribution to Gross Domestic Product (GDP) and vice versa, in the short-run and long-run, over a 10 years time period. Johansen's method of cointegration is used to study the cointegration between the sectoral contributions to Indian GDP vis-à-vis India's economic growth. Further, the route of interconnection between economic growth and sectoral contribution is tested by using Vector Auto Regression (VAR) model. Special attention was given for investigating impulse responses of economic growth depending on the innovations in sectoral contribution using time-series data from 1960 to 2015. This paper highlighted a dynamic co-relationship among industrial sector contribution and agricultural sector contribution and economic development. In the long run, one percent change in industrial sector contribution causes an increase of 3.42 percent in the economic growth and an increase of 1.12 percent in the primary sector contribution, while in the short run industrial and service sector contributions showed significant impact on economic development and agriculture sector. The changing composition of sector contribution is going to be an important activity for the policymakers to monitor and control where the technology and integration of sectors play a significant role in economic development.

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