Prospects for basal stem rot disease based on soil apparent electrical conductivity in oil palm plantation

Basal stem rot (BSR) disease is the most common manifestation of Ganoderma infection in the region. Losses begin to have a financial effect once the infection affects more than 10% of the stand. On average there is a decline of the yield of the fresh fruit bunch (FFB) of 0.16 t/ha per year for every palm lost, and when the stand had declined by 50%, the average FFB yield reduction was 35%. The route of BSR colonization is unpredictable and seems that there is no tools or mechanism available in the market to identify the threat at the initial stage. This study was conducted to look at the relationship of BSR disease which may be significant to soil nutrients and soil apparent electrical conductivity (ECa). It was conducted at three different areas namely Jenderata Estate, Seberang Perak, and Kluang. The soil series for both Jenderata and Seberang Perak was in the Jawa series with the age of nine years of the oil palm tree. Meanwhile, Kluang had Melaka soil series with the age of 25 years of the oil palm tree. The soil sample was taken at all study areas with grid sampling method and Veris EC sensor was pulled across the oil palm field with Trimble AG132 DGPS system used for geo-referencing. Besides, the incidence level of BSR infection both in a healthy or infected tree was observed and recorded by the Malaysian Palm Oil Board (MPOB) expert team. Interpolation techniques were done for all data by using ArcMap to identify the soil variability. The relationship of soil parameters, soil ECa, and soil nutrient contents was analysed using the statistical method in the SPSS software package. The result showed that low magnesium (Mg) located in the infected area at Seberang Perak and Kluang although both areas had different soil series. Besides, an independent t-test at both studies showed Mg had significance effect on BSR infection level. However, the independent t-test showed only phosphorus (P) had significant effect on BSR infection level in Seberang Perak. It can be concluded that Mg and P had significantly correlated to the BSR infection. The algorithm was developed based on the Mg and P as it had a correlation with soil ECa and had a significant independent t-test. A predicted model using regression was used for both Mg and P to develop a predicted spatial variability map for both soil nutrients with soil ECa as the independent variable. Furthermore, the software was developed by using MATLAB to produce a predicted BSR map in oil palm plantation based on the developed algorithm. The results obtained from the software shows that the map pattern was slightly different while the data between software and conventional method using ArcMap was slightly different at ± 0.0003 cmol/kg for both Mg and P. Therefore, this software is expected to be a reliable method to predict thus to prevent the BSR infection at the initial stage in oil palm plantation.

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