This article attempts to examine the negative impact of climate change on agricultural livelihood and human social life. Natural climatic variations have always been a challenge for human sustenance as they are predicated on a host of factors that include natural, human-made and unbalanced environmental conditions. India too, with its geographic zones such as mountains, small islands, wetlands, coastal areas, deserts, semi-arid lands and plains, is exposed to challenges of climatic change. The impact of climate is particularly severe on the livelihoods of the rural poor. For instance, people living near coastal regions are constantly prone to severe floods. This study specifically focusses on coastal Odisha and the impact of floods which have been triggered by climate change. The study, looking at the effect on crop production and socio-economic conditions, has followed a two-pronged approach, conducting a field survey and collecting data from secondary sources.

The paper attempts to examine the factors that influence climate-smart adaptation (CSA) strategies. The study used binary logit and multivariate probit models to understand the dynamics and factors of agricultural households’ behavioural decisions on CSA strategies. Based on the results of the binary logit model, the study indicated that factors such as access to extension services and training, gender, educational level, land ownership, access to irrigation, access to credit and crop damage level positively and significantly influenced farmers’ decisions to use CSA strategies. Similarly, the results of the multivariate probit model reveal that factors such as educational level, access to extension services and training, and land ownership had significant impacts on the adoption of the majority of CSA strategies. To improve the intensity of CSA strategies, the study recommends expanding training and extension services to farming masses, the expansion of irrigation facilities and weather information at the farm level.

This article attempts to examine the changing distributional structure of land among various social groups at the micro-level and its implications on inequality among various social group households. Our research focusses on a case study—Gudivada village located in Nalgonda district in Telangana to understand the changes that have taken place in the landholding pattern and ensuing inequality among social groups in the studied time. The social groups have been categorised as Scheduled Castes (SCs), Scheduled Tribes (STs), Other Backward Classes (OBCs) and Other Castes (OCs), respectively. The study found that there is a prevalence of inequality in the distribution of land among social groups which later has decreased. The landholding patten in the village has changed from being dominated by OCs to now OBCs, increasing their landholding. However, no significant growth was seen in the area owned by SCs and STs over the same period. Similarly, the decomposition of asset inequality in the studied village revealed a clear distinction between within and between social groups, that is inequality based on the assets possession is higher of within social groups (assessing inequality among the population within a particular social groups such as SCs, STs, OBCs or OCs) compared to between social groups (assessing inequality among the population between social groups such as between SCs and STs or between OBCs and OCs and so on).

The relationship between farm size and productivity has been a topic of interest in agricultural research for decades due to the significance of agriculture in rural economies and its potential to reduce poverty and promote inclusive growth. The relationship between farm size and productivity is influenced by factors such as the type of crop being produced, costs of cultivation, farm management practices, access to inputs and markets and socio-economic conditions. This paper aims to investigate the relationship between farm size and productivity in the context of farming households, their cost of cultivation and the types of crops they produce. Using the Cobb–Douglas production function, the present study estimates the regression function for principal crops such as cotton and paddy in the study area. The findings reveal strong evidence of an inverse relationship between farm size and productivity, indicating that small and marginal farmers are more productive in wetland cultivation (paddy). In contrast, medium and large farmers are more productive in dry land cultivation (cotton). The paper also investigates the availability and accessibility of credit facilities for different farm sizes. It concludes that small and marginal farmers depend mainly on non-institutional credit agencies compared to medium and large farmers.

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