Adaptation, resilience and sustainable livelihoods in the communities of the Lower Mekong Basin, Cambodia

Abstract

This paper analyses key contributors to sustainable livelihoods in the Lower Mekong Basin (LMB), Cambodia, by focusing upon villagers' access to assets, adaptation to shock and stress, and their degree of resilience to declines in natural resources. The study reveals that their access to the five assets for sustainable livelihoods is limited; that their capacity to adapt to shock and stress is low due to floods, drought and high food prices; and that their resilience to declines in natural resources is weak. Improvement in their capacity to adapt and in their resilience will be influenced by the degree to which they can access human, physical and social assets.

Keywords::

• adaptation
• resilience
• sustainable livelihoods
• Lower Mekong Basin
• Cambodia

Acknowledgements

The authors greatly appreciate the editorial board of the journal and the anonymous referees for their valuable comments on this paper; and Dr Estelle Dryland at Macquarie University, Sydney, for her language editing. Also, the authors gratefully acknowledge Sela Samath, Mouy Oum, Ratha Phol, Kearn Kim and Sokly Eam for assisting in the fieldwork and data collection.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed at http://dx.doi.org//10.1080/07900627.2015.1012659

Notes

1. Simultaneous Multiple Regression is used to predict an interval (rate of food shortage seriousness) dependent variable from a combination of eight interval predictors (perceptions of uncertainty and change influencing the villagers' livelihoods). With regards to the dependent variable, the villagers were asked to rate the degree of food shortage seriousness they had experienced during the past 10 years. Also, the villagers rated the eight proposed predictor predictors. Weight Average Index (WIA) was applied for all variables across the following scale of five: (1) Considerably Less [0.20]; (2) Less; Moderate [40]; (3) Moderate [0.60]; (4) High [0.80]; and (5) Very High [1.00].

2. Logistic regression is used to predict a categorical variable (rural poverty line) from a set of predictors (the five assets, size of agricultural land, levels of water, fishery and forest availability). The variable of rural poverty line, which is dichotomous (above or below the poverty line), is transformed from a scale variable of daily incomes. Certain predictors (the five assets) are dichotomous, while others are interval. The villagers were asked: if they could access the five assets that would ensure their sustainable livelihoods; to rate the degree of uncertainty and change influencing their livelihoods; and about the levels of water, fishery and forest availability.

3. ANOVA is applied to test whether there is significant difference between mean of daily incomes of the villagers in the three parts. A One-Sample t-test was used to compare the mean score of the sample with a known value (national poverty line; rural poverty line).