Characterization of Streptomyces spp. isolated from the rhizosphere of oil palm and evaluation of their ability to suppress basal stem rot disease in oil palm seedlings when applied as powder formulations in a glasshouse trial

2017 ◽  
Vol 34 (1) ◽  
Author(s):  
S. A. Shariffah-Muzaimah ◽  
A. S. Idris ◽  
A. Z. Madihah ◽  
O. Dzolkhifli ◽  
S. Kamaruzzaman ◽  
...  
Keyword(s):  
Oil Palm ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Streptomyces Spp ◽  
Rot Disease

scholarly journals Karakterisasi Bakteri Endofit Kitinolitik sebagai Agens Biokontrol Patogen Ganoderma boninense pada Kelapa Sawit [Characterization of Chitinolytic Endophyte Bacteria as Biocontrol Agents of Ganoderma boninense Pathogen on Oil Palm]

Buletin Palma ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 35
Author(s):  
Rahma Rahma ◽  
Tutik Kuswinanti ◽  
Ade Rosmana
Keyword(s):  
Oil Palm ◽  
Biochemical Characterization ◽  
Stem Rot ◽  
Ganoderma Boninense ◽  
Chitinolytic Activity ◽  
Basal Stem Rot ◽  
Chitinolytic Bacteria ◽  
Biochemical Identification ◽  
Rot Disease

<p>Basal stem rot caused by <em>Ganoderma boninense</em> is one crucial disease in oil palm. The used of chitinolytic bacteria is one part of biological control to suppress <em>G. boninense</em>. This study aimed to isolate and characterize chitinolytic bacteria associated with roots of oil palm which have the potential as agents biocontrol against the pathogens of <em>G. boninense</em>, the caused of basal stem rot disease of oil palm. The study was conducted in North Luwu, East Luwu, and Makassar Districts from January to September 2017. The research was done in  four stages that consist of isolation of endophytic bacterial, testing chitinolytic activity, testing antagonistic and biochemical identification. The results showed that from 14 bacterial isolates obtained, two of them had chitinolytic activity with index of 2,35 and 3,37 respectively. Both of these bacteria can inhibit <em>G. boninense</em> on solid medium by 24,9% and 69,4% respectively and on the liquid medium by 47,5% and 68,5% respectively five days after inoculation. Based on biochemical characterization, these bacteria have similarities with <em>Bacillus</em> sp, and <em>Serratia</em> sp. To be concluded, <em>Serratia</em>sp. is a potential medium to be for controling basal stem rot disease on oil palm.</p><p align="center"><strong>ABSTRAK</strong></p>Busuk pangkal batang yang disebabkan oleh cendawan <em>Ganoderma</em> <em>boninense</em> merupakan salah satu penyakit penting pada kelapa sawit. Pemanfaatan bakteri kitinolitik merupakan salah satu bagian pengendalian hayati untuk mengontrol <em>G. boninense. </em>Penelitian ini bertujuan untuk mengisolasi dan mengkarakterisasi bakteri kitinolitik yang berasosiasi dengan akar tanaman kelapa sawit yang berpotensi sebagai agens biokontrol terhadap <em>G. boninense, </em>penyebab penyakit busuk pangkal batang kelapa sawit. Penelitian dilaksanakan di Kabupaten Luwu Utara, Luwu Timur, dan Makassar sejak bulan Januari sampai September 2017. Penelitian ini terdiri atas empat tahap, yaitu isolasi bakteri endofit, uji aktivitas kitinolitik, uji antagonis dan identifikasi secara biokimia. Hasil penelitian diperoleh 14 isolat bakteri dimana dua bakteri diantaranya memiliki aktivitas kitinolitik dengan indeks masing masing 2,35 dan 3,37. Kedua bakteri tersebut dapat menghambat <em>G. boninense</em> pada medium padat masing masing 24,9% dan 69,4% serta pada medium cair masing masing 47,5% dan 68,5% lima hari setelah inokulasi. Berdasarkan karakterisasi secara biokimia, bakteri tersebut memiliki kemiripan dengan <em>Bacillus </em>sp, dan <em>Serratia </em>sp. Hasil penelitian menunjukkan bahwa <em>Serratia </em>sp. berpotensi dalam mengendalikan penyakit busuk pangkal batang pada kelapa sawit.<em></em><p> </p><p> </p>

scholarly journals EFFECT OF MICRONUTRIENTS-ENRICHED FERTILIZERS ON BASAL STEM ROT DISEASE INCIDENCE AND SEVERITY ON OIL PALM (ELAEIS GUINEENSIS JACQ.) SEEDLINGS

2014 ◽  
Vol 11 (10) ◽  
pp. 1841-1859 ◽  
Author(s):  
Fabien Fonguimgo Tengoua ◽  
Mohamed M. Hanafi ◽  
A. S. Idris ◽  
Kadir Jugah ◽  
Jamaludin Nurul Mayziatul Azwa ◽  
...  
Keyword(s):  
Oil Palm ◽  
Elaeis Guineensis ◽  
Disease Incidence ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Rot Disease

Identification of Ganoderma, the causal agent of basal stem rot disease in oil palm using a molecular method

2005 ◽  
Vol 159 (1) ◽  
pp. 159-170 ◽  
Author(s):  
C. Utomo ◽  
S. Werner ◽  
F. Niepold ◽  
H.B. Deising
Keyword(s):  
Oil Palm ◽  
Causal Agent ◽  
Stem Rot ◽  
Molecular Method ◽  
Basal Stem Rot ◽  
Rot Disease

scholarly journals GIS Mapping of Basal Stem Rot Disease in Relation to Soil Series Among Oil Palm Smallholders

2016 ◽  
Vol 11 (1) ◽  
pp. 2-12 ◽  
Author(s):  
K. Parthiban ◽  
R. Vanitah ◽  
K. Jusoff ◽  
A.A. Nordiana ◽  
A.R. Anuar ◽  
...  
Keyword(s):  
Oil Palm ◽  
Stem Rot ◽  
Gis Mapping ◽  
Soil Series ◽  
Basal Stem Rot ◽  
Rot Disease

scholarly journals Classification of Non-Infected and Infected with Basal Stem Rot Disease Using Thermal Images and Imbalanced Data Approach

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2373
Author(s):  
Izrahayu Che Hashim ◽  
Abdul Rashid Mohamed Shariff ◽  
Siti Khairunniza Bejo ◽  
Farrah Melissa Muharam ◽  
Khairulmazmi Ahmad
Keyword(s):  
Oil Palm ◽  
Potential Temperature ◽  
Model Performance ◽  
Imbalanced Data ◽  
Temperature Characteristic ◽  
Stem Rot ◽  
Operating Characteristics ◽  
Basal Stem Rot ◽  
Palm Trees ◽  
Rot Disease

Basal stem rot (BSR) disease occurs due to the most aggressive and threatening fungal attack of the oil palm plant known as Ganoderma boninense (G. boninense). BSR is a disease that has a significant impact on oil palm crops in Malaysia and Indonesia. Currently, the only sustainable strategy available is to extend the life of oil palm trees, as there is no effective treatment for BSR disease. This study used thermal imagery to identify the thermal features to classify non-infected and BSR-infected trees. The aims of this study were to (1) identify the potential temperature features and (2) examine the performance of machine learning (ML) classifiers (naïve Bayes (NB), multilayer perceptron (MLP), and random forest (RF) to classify oil palm trees that are non-infected and BSR-infected. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approaches such as random undersampling (RUS), random oversampling (ROS) and synthetic minority oversampling (SMOTE) in these classifications due to the different sample sizes. The study found that the Tmax feature is the most beneficial temperature characteristic for classifying non-infected or infected BSR trees. Meanwhile, the ROS approach improves the curve region (AUC) and PRC results compared to a single approach. The result showed that the temperature feature Tmax and combination feature TmaxTmin had a higher correct classification for the G. boninense non-infected and infected oil palm trees for the ROS-RF and had a robust success rate, classifying correctly 87.10% for non-infected and 100% for infected by G. boninense. In terms of model performance using the most significant variables, Tmax, the ROS-RF model had an excellent receiver operating characteristics (ROC) curve region (AUC) of 0.921, and the precision–recall curve (PRC) region gave a value of 0.902. Therefore, it can be concluded that the ROS-RF, using the Tmax, can be used to predict BSR disease with relatively high accuracy.

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