Modelling bioenvironmental factors for oil palm plantations in Riau Indonesia towards fresh fruit bunch productivity

• 第一著者: Salmiyati, Salmiyati
• フォーマット: 学位論文
• 言語: 英語
• 出版事項: 2017
• 主題: QH Natural history
• オンライン･アクセス: http://eprints.utm.my/79504/1/SalmiyatiPFBME2017.pdf

Productivity of Fresh Fruit Bunch (FFB) varies in each country. Indonesia is the largest oil palm plantation country, but it has the lowest FFB productivity due to bioenvironmental factors. This study identified bioenvironmental factors that affect FFB productivity, and explored a new model for prediction of FFB productivity. To identify the factors, data were collected from literature, survey, focus group discussion, and field observation of 200 blocks of oil palm plantation in an area of 2,300 hectares in the Riau province. Correlation among bioenvironmental factors and FFB productivity was statistically analysed using linear regression. From the findings, a model was developed based on multiple regression of the Cobb Douglas function. The new model was simulated using Dynamic System software (Stella 9.0.2) to define the relationship between factors and estimate the FFB productivity of oil palm until it reaches its productive age. Simulations were conducted and verified by comparing the results with previous related studies. Besides, results were also validated using a Chi Square test from field survey in oil palm plantations. The statistical results confirmed that FFB productivity was affected by ten groups of bioenvironmental factors. These factors that can increase FFB productivity by nearly 7.58% include good management of oil palm biophysics, bioagent, water conservation, soil conservation, fertilizer, topography, and climate. On the contrary, management of pest, diseases, and herbicide can decrease FFB productivity by nearly 0.31%. Furthermore, simulation results indicated that the optimum productive oil palm was 30 years old, and that there were no significant differences in the validation results between the field survey and simulation, showing that the model is sustainable. These results have shown that the model can be used as support for FFB productivity management based on bioenvironmental factors.