Climate change has significantly impact the economic development and trade of developing countries particularly those largely rely on agriculture. According to the World Bank, oil palm was the main contributor of GDP for Malaysia agriculture. Climate change affects oil palm growth and productivity in a number of ways, including reduction in sex-ration, disrupts the pollination process, abortion of newly produced inflorescence, drops in productivity, and increase in ranges and distribution of pests and diseases. Much of the economic researches devoted to employ production function and Ricardian approach. This study, therefore, focuses to model the effect of climate change on oil palm production by using supply response approach. An annual time series data used for the period of 37 years starting from 1980 until 2016, and Autoregressive Distributed Lags (ARDL) co-integration_-employed in achieving the objective of the study. Six econometric models consisting of linear and non-linear equations were constructed by incorporating temperature and rainfall as proxies for climate variable to estimate the yield response model. The results revealed that oil palm production was very negatively affected by changes in temperature compared to changes in rainfall. Meanwhile, the planted area and own price upsurge the supply of palm oil. The results also indicated that Model 3 and Model 6 were the best model to represent the linear and non-linear effect of climate_change on oil palm production, respectively. Quantifying the impact of climate change on palm oil production can help policy makers and relevant stakeholders to determine the best adaptation and mitigation measures.

Pineapple is a tropical plant and a warm seasonal fruit that has been cultivated for many years and has started to grow commercially. Pineapple was rapidly growing in southern Johor as the first place in Malaysia. However, there are risks during operation that can affect the quality and quantity of fruit. Uncertainties happen during operations, starting from the planting process until harvesting. Therefore, this study was conducted to identify the factors that influence operation risk in pineapple production, determine the most crucial factors and identify the relationship between these factors affecting pineapple production, which have been analysed using descriptive analysis, multiple regression analysis and correlation analysis in SPSS version 21. A convenience sample of 132 pineapple smallholder farmers in Muar, Johor, was used to answer the questionnaire. The questionnaire was distributed through face-to-face interviews and phone calls to all respondents. The results revealed that manpower issues were the most important factor influencing operation risk on pineapple production, with a beta value of 0.347, rather than the other three factors. All factors have a linear positive relationship with pineapple production since the p-value is 0.000, which is less than 0.05. Manpower issues have a positive relationship with pineapple production among other factors with the value of pearson correlation is 0.651.