Norida Mazlan

Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers

ABSTRACT Nitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objectives of this study were to evaluate the potential of spectral measurements obtained from leaf scale and machine learning approaches as a rapid tool for quantifying oil palm N status. This study involved assessing the performance of discriminant analysis (DA) and Support Vector Machine (SVM) classifiers for discriminating spectral bands sensitive to N sufficiency levels and comparing the predictive accuracy of DA and SVM for classifying N status of immature and mature oil palms. The experiment was conducted on immature Tenera seedlings (13 months old) and mature Tenera palm stands (9 and 12 years old) that were arranged in Randomized Complete Block Design with treatments varied from 0 to 2 kg N. Generally, the discriminant function of both classifiers was age-dependent. A clear trade-off between the classifiers’ number of spectral bands and their accuracies was observed; the DA with a larger number of optimal spectral bands could discriminate N sufficiency levels of all maturity classes with higher accuracies compared to the SVM, yet the latter could produce reasonable accuracies with a lesser number of spectral bands. N status of all maturity classes could be classified satisfactorily with SVM (71–88%) via the satellite-simulated blue and green bands, signifying the possibility to develop spectral index or an N-sensitive sensor for oil palm.

Genetic analysis and identification of SSR markers associated with rice blast disease in a BC 2 F 1 backcross population

Rice (Oryza sativa L.) blast disease is one of the most destructive rice diseases in the world. The fungal pathogen, Magnaporthe oryzae, is the causal agent of rice blast disease. Development of resistant cultivars is the most preferred method to achieve sustainable rice production. However, the effectiveness of resistant cultivars is hindered by the genetic plasticity of the pathogen genome. Therefore, information on genetic resistance and virulence stability are vital to increase our understanding of the molecular basis of blast disease resistance. The present study set out to elucidate the resistance pattern and identify potential simple sequence repeat markers linked with rice blast disease. A backcross population (BC2F1), derived from crossing MR264 and Pongsu Seribu 2 (PS2), was developed using marker-assisted backcross breeding. Twelve microsatellite markers carrying the blast resistance gene clearly demonstrated a polymorphic pattern between both parental lines. Among these, two markers, RM206 and RM5961, located on chromosome 11 exhibited the expected 1:1 testcross ratio in the BC2F1 population. The 195 BC2F1 plants inoculated against M. oryzae pathotype P7.2 showed a significantly different distribution in the backcrossed generation and followed Mendelian segregation based on a single-gene model. This indicates that blast resistance in PS2 is governed by a single dominant gene, which is linked to RM206 and RM5961 on chromosome 11. The findings presented in this study could be useful for future blast resistance studies in rice breeding programs.

Morphological and molecular characterization of fungal pathogen, Magnaphorthe oryzae

Rice is arguably the most crucial food crops supplying quarter of calories intake. Fungal pathogen, Magnaphorthe oryzae promotes blast disease unconditionally to gramineous host including rice species. This disease spurred an outbreaks and constant threat to cereal production. Global rice yield declining almost 10-30% including Malaysia. As Magnaphorthe oryzae and its host is model in disease plant study, the rice blast pathosystem has been the subject of intense interest to overcome the importance of the disease to world agriculture. Therefore, in this study, our prime objective was to isolate samples of Magnaphorthe oryzae from diseased leaf obtained from MARDI Seberang Perai, Penang, Malaysia. Molecular identification was performed by sequences analysis from internal transcribed spacer (ITS) region of nuclear ribosomal RNA genes. Phylogenetic affiliation of the isolated samples were analyzed by comparing the ITS sequences with those deposited in the GenBank database. The sequence of the isolate demonstrated at least 99% nucleotide identity with the corresponding sequence in GenBank for Magnaphorthe oryzae. Morphological observed under microscope demonstrated that the structure of conidia followed similar characteristic as M. oryzae. Finding in this study provide useful information for breeding programs, epidemiology studies and improved disease management.

Role of biopesticides in developing a sustainable vegetable industry in Malaysia

Agrochemical firms have been actively influencing farmers to continue using chemical pesticides and fertilisers. However, there are two major problems. Since fertilisers rely heavily on fossil fuels, which are non-renewable resources, their ability to produce food and fibre for the world populations in the long run is questionable. The use of chemical pesticides tends to cause serious environmental degradation and poses a threat to human health; hence it may not be sustainable. The objectives of this study are to investigate the level of awareness among vegetable farmers on the application of biopesticides and to examine factors that influence farmers to use biopesticides in vegetable production. The results revealed that a majority of the farmers were aware of biopesticides and have applied biopesticides on their farms. The factors that influenced farmers to apply biopesticides were their awareness as well as effectiveness, availability and also prices of the biopesticides.