Choice of climate risk adaptive measures in shrimp farming—A case study from the Mekong, Vietnam

AbstractExtreme climate events challenge the livelihoods of shrimp farmers worldwide. A comprehensive analysis of farmers’ choices of adaptive measures is essential for developing approaches that can lessen the effects of these climate risks. This study presents the determinants that influence the choice of adaptive measures in response to two climate risks, drought, and irregular weather, using a survey of 437 shrimp farmers in the Vietnamese Mekong region and applying a multinomial logit model. Five adaptation choices identified include changing feeding schedules/stocking densities, changing water exchange schedules, water conservation, water treatments, and early harvesting. The results revealed that education, training, extension services, credit access, farm size, pond numbers, and the farmers’ perception of drought and irregular weather are the main factors influencing farmers’ choices of adaptive measures. Intensive and extensive farmers chose different adaptations to climate risks, with the former applying a variety of measures while the latter chose to change water exchange schedules. The conclusions bring policy implications concerning how to cope with climate risks.Keywords: Adaptationclimate risksmultinomial logit modelshrimp aquacultureVietnam AcknowledgmentsThe authors are very grateful to local officials, shrimp technicians, and farmers who participated in the research interviews. In addition, the authors acknowledge the interviewer team’s assistance in the data collection in the Mekong region, Vietnam. Finally, the authors would like to thank the associate editor and two reviewers for their invaluable comments that significantly improved our manuscript.Authors’ contributionsThe first author develops the survey and data collection, organizes, analyzes, and interprets the data, and develops the paper. The second author contributes to data analysis and interpretation and development of the paper. Both authors have reviewed the final document and agree with its contents.Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 Drought results in high temperatures and insufficient precipitation over extended periods, seriously affecting shrimp aquaculture (NACA, Citation2012; Quach et al., Citation2015).2 Irregular weather such as sudden changes in temperature and heavy rainfall, occurs unpredictably, leading to variations in water temperature and quality, which can induce stress and increase chance of shrimp disease (NACA, Citation2012; Quach et al., Citation2015).3 It is noted that climate change is just one aspect of the multifaceted risks that aquaculture producers encounter. Khan et al. (Citation2018) examined production risk in pangas fish farming, Theodorou et al. (Citation2020) studied the risk level of harmful algal blooms related to mussel site closures, and Moor et al. (Citation2022) discussed the environmental risk of Mexico clam aquaculture. We recognize the complexity of the risk landscape faced by producers, but here specifically focus on the adaptation choices of Vietnamese shrimp farmers in response to climate risks.4 The survey consists of (1) the information of climate factors that shrimp farmers perceived in their most recent crop, (2) farmer’s adaptive measures in response to these climate risks, (3) biosecurity applications, (4) information on farming characteristics (e.g., land uses, culture period), and (5) disease issues in shrimp farming.5 The first author can be contacted for a full tabular presentation of the review.6 We define a seven-point Likert scale consisting of −3: Extremely positively impacted (cost reduction of more than 50%), −2: Major positively impacted (cost decline between 10%-50%), −1: Minor positive impact (costs decline by less than 10%), 0: No consequence, 1: Minor negative impact (costs rise by less than 10%), 2: Major negative impact (costs rise between 10% and 50%), 3: Catastrophic/ extremely negative impact (costs rise by more than 50%).7 Bivariate Probit models were also applied for robustness checks, and the results do not differ to any significant degree. The choice of change in water exchange schedules is used as the base in this modeling. No multicollinearity among the explanatory variables was found in the estimation.8 Super intensive includes a bio-floc waste-water treatment, as a closed system for assuring biosecurity and water quality, resulting in less pollution and lower impact of climate risks (i.e., irregular weather) (Nguyen et al., Citation2019). This system allows increased stocking density and more crops per year. In our survey, there are no super-intensive farms.Additional informationFundingThe NORHED NORAD Climate change project SRV-13/0010 is gratefully acknowledged for financial support.