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The adoption of climate-smart agricultural practices (CSAPs) is important for sustaining Indian agriculture in the face of climate change. Despite considerable effort by both national and international agricultural organizations to promote CSAPs in India, adoption of these practices is low. This study aims to examine the elements that affect the likelihood and intensity of adoption of multiple CSAPs in Bihar, India.

The probability and intensity of adoption of CSAPs are analyzed using multivariate and ordered probit models, respectively.

The results show significant correlations between multiple CSAPs, indicating that their adoptions are interrelated, providing opportunities to exploit the complementarities. The results confirm that both the probability and intensity of adoption of CSAPs are affected by numerous factors, such as demographic characteristics, farm plot features, access to market, socio-economics, climate risks, access to extension services and training. Farmers who perceive high temperature as the major climate risk factor are more likely to adopt crop diversification and minimum tillage. Farmers are less likely to adopt site-specific nutrient management if faced with short winters; however, they are more likely to adopt minimum tillage in this case. Training on agricultural issues is found to have a positive impact on the likelihood and the intensity of CSAPs adoption.

The major policy recommendations coming from of our results are to strengthen local institutions (public extension services, etc.) and to provide more training on CSAPs.

By applying multivariate and ordered probit models, this paper provides some insights on the long-standing discussions on whether farmers adopt CSAPs in a piecemeal or in a composite way.

Conservation agriculture-based wheat production system (CAW) can serve as an ex ante measure to minimize loss due to climate risks, especially the extreme rainfall during the wheat production season in India. This study aims to examine whether farmers learn from their past experiences of exposure to climate extremes and use the knowledge to better adapt to future climate extremes.

The authors used data collected from 184 farmers from Haryana over three consecutive wheat seasons from 2013-2014 to 2015-2016 and multivariate logit model to analyse the driver of the adoption of CAW as an ex ante climate risk mitigating strategies based on their learning and censored Tobit model to analyse the intensity of adoption of CAW as an ex ante climate risk mitigation strategy. Farmer’s knowledge and key barriers to the adoption of CAW were determined through focus group discussions.

The analysis shows that the majority of farmers who had applied CAW in the year 2014-2015 (a year with untimely excess rainfall during the wheat season) have continued to practice CAW and have increased the proportion of land area allocated to it. Many farmers shifted from CTW to CAW in 2015-2016.

While farmers now consider CAW as an ex ante measure to climate risks, a technology knowledge gap exists, which limits its adoption. Therefore, designing appropriate methods to communicate scientific evidence is crucial.

This paper uses three years panel data from 184 farm households in Haryana, India, together with focus groups discussions with farmers and interviews with key informants to assess if farmers learn adaptation to climate change from past climate extremes.

The authors present a dynamic macroeconomic model for assessment of disaster risk reduction (DRR) policies under multiple hazards. The model can be used to analyze and compare various potential policies in terms of their economic consequences. The decomposition of these effects into multiple benefits helps policy makers and other stakeholders better understand the ex ante and ex-post advantages of DRR investments. The purpose of this paper is to address these issues.

A dynamic real business cycle model is at the core of this research. In the model multiple natural hazards modeled stochastically cause shocks to the economy. Economic outcomes, most importantly, output can be assessed before and after disasters and under various DRR policies. The decomposition of benefits aims to quantify the concept of triple dividends.

In case study applications in Tanzania and Zambia, the authors find that investments into physical infrastructure and risk transfer instruments generate a variety of benefits even in the absence of disaster. A land use restriction with planned relocation for example reduces output in the short run but in the long run increases it. Overall, policy effects of various DRR interventions evolve in a nonmonotonic manner and should be evaluated over a long period of time using dynamic simulation.

The novelty of this study lies in the economic quantification of multiple benefits described in the triple dividends literature. This helps comparing ex ante, ex-post and volatility-related economic effects of multiple disasters and related physical and financial DRR investment options. As observed in the case studies, the model can also identify overlooked temporal heterogeneity of co-benefits of DRR investments.