Protected cultivation is an innovative way of raising seasonal and off-seasonal crops under a controlled environment. Vegetables and flower crops have tremendous potential to augment productivity, generate employment, utilize land efficiently and enhance export. This study was undertaken to assess the economic feasibility of protected cultivation in the high export potential zones of the Pune and Nasik districts of Maharashtra, India, by employing project analytical tools and the regression model. The results revealed that the cultivation of flowers and vegetables under protected cultivation was highly lucrative with high investment. The protected cultivation of rose and capsicum had higher cultivation cost (300%), gross return (250%) and net return (190%) as compared to open cultivation. Moreover, most of the crops grown in polyhouses are highly profitable at different discount rates (7%, 10% and 12%), whereas a few crops were rewarding under shade net condition with subsidies. Factors such as literacy (p < 0.05), income (p < 0.05), access to subsidy (p < 0.05) and the risk orientation index (p < 0.01) were found statistically significant in technology adoption. In the context of a changing climate and shrinking land resources, water scarcity, incidence of pests and diseases, an ever-increasing population, low productivity under open conditions and changes in consumer’s preference are the drivers for switching over to protected cultivation. In the recent past, protected cultivation has been gaining importance in different parts of the country, including Maharashtra. The policy implications are creating modern infrastructure, enhanced application of ICTs, maximum crop production with minimum utilization of land and institutional support to promote technology on a commercial scale.

Structural equation modeling (SEM) was employed to analyze the influence of exogenous variables (research and extension (RE), marketing aspects (MA), and infrastructure development (ID)) on the endogenous variable chickpea production development (CPD) to restructure policy interventions in India. Results of the measurement model revealed that all the latent variables have construct validity (both convergent validity and discriminant validity) and composite reliability. Confirmatory factor analysis revealed that all indicators of both exogenous and endogenous variables are significant. Yield-increasing production technologies (PT), minimum support prices (MSP), and storage structures (SS) and the three exogenous variables (research and extension, marketing, and infrastructure development) are the strongest indicators. For the endogenous variable CPD, remunerative prices (RP) is the strongest indicator and also serves as a driving force for other indicators. The results of the structural model revealed that RE is the most effective construct followed by ID and MA, and they cumulatively explained 89 percent of the total variation in CPD. Among these three constructs, MSP is the key indicator of MA with the highest loading factor (0.799), and hence it should be given the highest priority for promoting CPD in India.