Learning adaptation to climate change from past climate extremes: Evidence from recent climate extremes in Haryana, India
Year: 2019
Type: article
Abstract: Purpose Conservation agriculture-based wheat production system (CAW) can serve as anex antemeasure 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. Design/methodology/approach 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 anex anteclimate risk mitigating strategies based on their learning and censored Tobit model to analyse the intensity of adoption of CAW as anex anteclimate risk mitigation strategy. Farmer’s knowledge and key barriers to the adoption of CAW were determined through focus group discussions. Findings 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. Practical implications While farmers now consider CAW as anex antemeasure to climate risks, a technology knowledge gap exists, which limits its adoption. Therefore, designing appropriate methods to communicate scientific evidence is crucial. Originality/value 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.
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDGs: Climate action, Zero hunger
Open Access status: gold
Estimated APC paid: $1,839
OpenAlex ID: W2990375428
