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The study unravels the effects of climate-induced variations in staple crop yields on various migratory inflows in India while adjusting for seasonal weather and sociodemographic factors.

The instrumental variable approach is used to assess the potential effects of climate and nonclimate parameters on various migration types, exploiting panel data at the district level from the 2001 and 2011 Census years, with agriculture acting as the mediator.

As weather-driven variations in rice and wheat yield increase by 10%, the share of migration within and between districts to population decreases by 0.017 and 0.002, respectively. However, rice and wheat yields increase by 494.60 and 524.40%, respectively, with a marginal increase in the share of migration within states to population. Also, the elasticities of disadvantaged groups, literate locals and agricultural workers vary for different relocations.

The current study affirms climate migration through the agricultural channel at a finer spatial scale, asserting the sensitivity aspect of disparate movements to periodic weather and heterogeneous clusters. This is critical for effectively implementing targeted public policies in the face of increasing climate risks.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-10-2022-0710

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.