Vulnerability of paddy farmers to climate change variability in Peninsular Malaysia

Climate changes greatly affect agricultural crop production and the associated farming community. The magnitude of the climatic stressor, the sensitivity and capacity of the affected communities to adapt with such stressors affect farmer vulnerability. This study assessed the vulnerability of paddy farmers to climate change variability in Peninsular Malaysia. The study employed an integrated vulnerability assessment approach using three component of vulnerability i.e. exposure, sensitivity and adaptive capacity. Trend analysis was conducted using Mann – Kendall to detect temperature and rainfall variability from 1981- 2014. DSSAT Ceres- rice model was used to predict rice yield for the study areas from 2016 until 2035 and determine the sensitivity of rice yield to temperature and rainfall changes. Household survey was conducted using multi- stage systematic random sampling on 450 sampled respondents to measure their adaptive capacity. Trend analysis shows that annual maximum temperature warming trend ranges from 0.0080C to 0.014oC per year, while annual minimum temperature warming trend ranges from 0.018oC to 0.063oC per year.Rainfall variability analysis revealed greater variability in terms of annual, monthly as well as seasonal patterns for all the areas under this study. The mean annual rainfall ranges from as low as 2016.7mm to as high as 2576.5mm for all the areas. The DSSAT model rice yield prediction result shows that Muda Agricultural Development Area (MADA) Kedah, will have highest increase in yield of 24.9% in 2029, with few years of decreasing yield by up to - 10.1% in 2035. In the Integrated Agricultural Development Area (IADA) Northwest Selangor, the model predicted decrease in yield of up to -7.1% by the year 2024. In the Kemubu Agricultural Development Area (KADA) Kelantan, the model predicted highest increase in the yield (14.0%) in the year 2028 and highest yield decline of - 13.3% by the year 2030. 22.9% of respondents were found to be less vulnerable, 32% were vulnerable and45.1% were highly vulnerable.Based on granaries, MADA has the highest vulnerability followed by KADA with IADA as the least vulnerable. Ordinal logistic regression revealed that 17 factors have significant influence on the vulnerability outcome of the respondents. Conclusively, the respondents in the study areas are vulnerable to the effects of climate change variability. Therefore, decision makers should tailor policies to address local specific conditions by placing climate change vulnerability issues within the broader developmental context