Archives of Current Research International

Aim of the Study: The quick surging up of social physics offers a huge scope for social science research to deal with an array of researches with non-linear nature and mobility. While quantum social science has stepped into higher echelons of entanglement and duality, Heisenberg’s uncertainty has rightly been extrapolated into farmers’ uncertainty with a probability of existential survival.  Farmers in low and middle income economies face a persistent uncertainty arising from climate variability, market instability, technological change and policy shifts. These overlapping pressures make farming an increasingly precarious livelihood. Using a Heisenberg inspired perspective, this study views agricultural uncertainty as a structural and multidimensional condition shaped by unavoidable trade offs rather than a problem that can be fully resolved.

Study Design: An analytical and explanatory research design was adopted, integrating concepts from quantum social science with multivariate statistical analysis to capture the nonlinear and interdependent nature of agricultural uncertainty.

Place and Duration of the Study: The study draws on primary data from 150 small and marginal farmers in Nadia district, West Bengal and examines perceived uncertainty related to weather, markets, technology, economy and livelihood.

Methodology: Primary data on 21 socio-economic, agronomic and psycho-social variables were collected using structured instruments. Principal Component Analysis (PCA) was employed to reduce these variables into latent dimensions. Canonical Correlation Analysis (CCA) was used to examine linkages among perceived uncertainties related to weather, market, technology, economy and livelihood.

Results: Principal Component Analysis reduced 21 socio-economic, agronomic and psycho-social variables into five latent dimensions: Economic & Farm Resources, Human Capital, Psycho-Health Status, Cropping Intensity and Household Demography. Together, these factors explained 82.92% of the total variance. Canonical correlation analysis reveals that farmers’ uncertainty is predominantly driven by market and weather factors, while income stability, experience, training and information access play critical roles in mitigating uncertainty.

Conclusion: The findings underline that increasing control or intensification in one aspect of farming often generates new uncertainties elsewhere. Addressing farmers’ vulnerability therefore requires integrated approaches that combine economic support, knowledge access and psycho-social resilience rather than isolated risk reduction measures.

Policy Implications: Enhancing income stability, market transparency, climate resilient infrastructure can reduce major economic and weather uncertainty. Farmer training, information access and extension services are essential for building human capital to cope up with uncertainty.  Mental health and social support measures can enhance farmers’ resilience.