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Climate change affects not only livelihood but also the motivation of farmers. Perceptions of farmers could be influenced by a risky environment to become either risk averse or risk neutral. Indeed, less attention has been paid by researchers to empirically examine the motivation of farmers to manage their agricultural risks exclusively from the lens of Malaysia. Thus, the research was formulated to assess the motivation of farming community towards agricultural risk management in Malaysia. The research data were gathered through pre designed interview schedule from 360 randomly selected farmers through multistage cluster sampling technique. In order to measure motivation of respondents, likert scale items comprising 1 as strongly disagree to 5 as strongly agree were used. The research findings reveal that farmers were motivated to manage their agricultural risks and overall their motivation level was high. Nevertheless, still efforts are required by all stakeholders to keep the motivation level high of farming community at local, regional and international level. Agricultural extension workers as change agents are required to keep the motivation level high otherwise, food security and bread and butter of farmers would become jeopardized by climate changes at the national and global level.

Nitrogen (N) management is important in sustaining oil palm production. Remote sensing-based approaches via spectral index have promise in assessing the N nutrition content. The objectives of this study are; (i) to examine the N classification capability of three spectral indices (SI) such as visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis + NIR) from the SPOT-6 satellite, and (ii) to compare the performance of linear discriminant analysis (LDA) and support vector machine (SVM) in discriminating foliar N content of mature oil palms. Nitrogen treatments varied from 0 to 2 kg per palm. The N-sensitive SIs tested in this study were age-dependent. The Vis index (BGRI1) (CVA = 79.55%) and Vis + NIR index (NDVI, NG, IPVI and GNDVI) (CVA = 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier.

Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population’s fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson’s correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana’s outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.5

Satellite observations of CO₂ offer a unique portunities to improve our understanding of the carbon sources and sinks. Due to the lack of studies of carbon dioxide (CO₂) concentration in this region, we first confirmed the reliability of the mid-tropospheric Atmospheric Infrared Sounder (AIRS) CO₂ data using atmospheric CO₂ concentration data from the only available Global Atmospheric Watch (GAW) ground-based station observation in Malaysia. In this study, the spatial and temporal distribution of mid-troposphere CO₂ in Malaysia from January 2009 to December 2012 was analyzed based on AIRS satellite product. The results show that the average CO₂ concentrations were high in the eastern part of the study area and lower in the west. From January 2009 to December 2012, the mid-tropospheric CO₂ concentrations increased gradually with annual growth rate about 1.293 ppmv/a. There was a significant seasonal CO2 variation with peak concentration was observed during the North-East monsoon (NEM) and the lowest was during South-West monsoon (SWM). The temporal distribution of CO₂ concentrations was mainly affected by the amount of sunlight and precipitation received during both monsoons. The study suggested that mid-tropospheric AIRS CO₂ data product was able to help in understanding the variations of atmospheric CO₂ concentrations comprehensively.

A field study was carried out to determine the impact of mulching and planting distance on the growth of Orthosiphon stamineus, soil properties and also to observe the changes in pH and EC of soil in response to mulching and planting distance. The experiment was carried out at Ladang 16, Faculty of Agriculture UPM. Factorial Randomized Complete Block Design (RCBD) was incorporated with four replicates for each of the four treatments. The four treatments consisted of mulching, non-mulching, planting distance of 30 cm × 30 cm and planting distance of 45 cm × 45 cm. After eight weeks of planting, the plants were harvested while soil pH and EC were measured on a weekly basis throughout the planting period. Results showed that application of biochar and usage of mulching materials and suitable planting distance does helps to maintain the soil pH and electrical conductivity (EC) at the suitable range for crop growth. The level of the acidity of the soil is in the range of 5.3 to 6.61 which is considered appropriate for O. stamineus. As for influence of planting distance, it is best to give longer time for O. stamineus grows. It is recommended that more planting distance and types of mulching materials to be used to grow O. stamineus.

Rice (Oryza sativa) is a staple food for more than half of the world’s population, including about 30 million Malaysians. Rice self-sufficiency level (SSL) in Malaysia is currently at 70% and the level does not satisfy the local demand. Therefore, rice production needs to be increased to 80% by the year 2020 (MOA, 2015). The Malaysian Government aims to increase average rice grain production from 4.5 mt/ha to 6.0 mt/ha (Ismail, 2017). Among the ways to increase the production of rice is by improving crop management, such as finding best sowing dates, and best management practices such as optimum usage of fertilizer rates and rice varieties that suit weather conditions. Simulations of crop models are alternatives to costly trial experiments in exploring opportunities for increasing agricultural system productivity. Existing rice crop growth models have not been rigorously explored to assess productivity of Malaysian rice systems. Prior to scenario studies using a rice crop growth model, performance of the model must first be evaluated. Therefore, this paper reports preliminary evaluation on the performance of a rice crop growth model called ORYZA (v3). The model was evaluated against rice crop physiological properties collected at two farmers’ plots at IADA KETARA, Terengganu. The preliminary evaluation indicates that ORYZA (v3) has a potential in simulating the physiological properties of MR269, but the model must be calibrated. Calibration of the model is currently on-going.

In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures.

Occurrence of chalkiness in rice is attributed to genetic and environmental factors, especially high temperature (HT). Te HT induces heat stress, which in turn compromises many grain qualities, especially transparency. Chalkiness in rice is commonly studied together with other quality traits such as amylose content, gel consistency, and protein storage. In addition to the fundamental QTLs, some other QTLs have been identifed which accelerate chalkiness occurrence under HT condition. In this review, some of the relatively stable chalkiness, amylose content, and gel consistency related QTLs have been presented well. Genetically, HT efect on chalkiness is explained by the location of certain chalkiness gene in the vicinity of high-temperature responsive genes. With regard to stable QTL distribution and availability of potential material resources, there is still feasibility to find out novel stable QTLs related to chalkiness under HT condition. A better understanding of those achievements is essential to develop new rice varieties with a reduced chalky grain percentage. Therefore, we propose the pyramiding of relatively stable and nonallelic QTLs controlling low chalkiness endosperm into adaptable rice varieties as pragmatic approach to mitigate HT effect

The study was aimed to determine the existing status of extension services provided for agricultural risk management in making use of Information and Communication Technologies (ICTs). There were 360 farmers selected through multistage cluster sampling technique and data were collected from four areas of Malaysia. The results reveal that farmers could get limited information from extension staff on the use of ICTs for disaster information. Moreover, most of the farmers still could not get support from extension staff to integrate ICTs for agricultural risk management. Farmers highlighted that extension staff need training to improve their capacity. Therefore, public and private sector should initiate various training programs firstly for the extension field staff and then diffuse various management skills and techniques into farmer fields for better agricultural risk management using ICTs.

Basal stem rot disease (BSR) in oil palm plantations is caused by Ganoderma boninense fungus. BSR is a major disease attacking oil palm plantations in Malaysia and Indonesia. But for now, the only available treatment is to prolong the life of oil palm trees as there is no effective treatment for BSR. To control this disease, early detection of G. Boninense infection is a decent strategy. Many researchers have used remote sensing techniques for early detection and mapping of BSR disease in oil palm plantations based on G. boninense infection symptoms. The main objective of this project is to study the potential of radar backscattering for classifying severity level of G. boninense disease in oil palm plantation. The processing stage involved the usage of two different machine learning algorithms to classify severity level of G. boninense in oil palm plantation. This study uses Alos Palsar 2 image with dual polarization; HH (Horizontal - transmit and Horizontal - receive) and HV (Horizontal - transmit and Vertical - receive) archived on March 20, 2017. Two classifier models: Multilayer Perceptron (MP) and Kstar are tested by using Weka open source software. The MP classifier model for HV polarization is the best for predicting and classifying severity level of G. boninense in oil palm plantation in terms of correctly classified. Model MP classifier and HV polarization reach 77.17% correctly classified. In addition, this study can separate oil palm by severity of each T0 (92.73%), T1 (0%), T2 (93.33%) and T3 (54.55%).

Metisa plana (Walker) is leaves defoliating insect that is able to cause a staggering loss of USD 2.32 billion within two years to Malaysian oil palm industry. Therefore, an early warning system to predict the outbreak of Metisa plana that is cost, time, and energy effective is crucial. In order to do this, the role of environmental factors such as relative humidity (RH) on the pests’ population’s fluctuations should be well understood. Hence, this study utilized the geospatial technologies to i) to construct the relationship between the geospatially derived relative humidity and Metisa plana outbreak, and ii) to predict the outbreak of Metisa plana in oil palm plantation. Metisa plana census data of larvae instar 1, 2, 3, and 4 were collected approximately biweekly over the period of 2014 and 2015. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images providing values of RH were extracted and apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) prior to census date. Pearson’s correlation, multiple linear regression (MLR) and multiple polynomial regression analysis (MPR) were carried out to analyse the linear relationship between Metisa plana and RH. Artificial neural network (ANN) was then used to develop the best prediction model of Metisa plana’s outbreak. Results show that there are correlations between the presence of Metisa plana with RH, however, the time lag effect was not prominent. MPR was able to produce model with higher R2 in comparison to MLR with the highest R2 for both analysis were 0.48 and 0.15 respectively at T4 to T6. Model with the highest accuracy was achieved by ANN that utilized the RH at T1 to T3 at 95.29%. Based on the result of this study, the prediction of Metisa plana’s landscape ecology was possible with the utilization of geospatial technology and RH as the predictor parameter.

Recently, the developments in sensor technologies, such as wireless communication, micro electro mechanical systems (MEMS) and embedded systems have contributed significantly to wireless sensor network (WSN). The agriculture industry is applying WSN in their systems for surveillance and monitoring the environment. For plantation applications, a WSN is used for control and protection and for providing a real-time system. These applications include, for instance, to detect the presence of disease, temperature and moisture of the soil. The quality and coverage of a wireless link in a plantation area needs to be understood due to the effect of vegetation such as propagation loss resulting from the ground and canopy reflection. In this paper, the development of wireless sensor networks in canopy areas is reviewed. The problem of propagation link in canopy areas is identified. The structure of WSN is discussed with suggestions to create a reliable communication link. From the studies reviewed, the information to design a wireless sensor network in canopy areas for application in plantations is provided.

Monitoring nitrogen (N) in oil palm is crucial for the production sustainability. The objective of this study is to examine the capability of visible (Vis), near infrared (NIR) and a combination of Vis and NIR (Vis + NIR) spectral indices acquired from different sensors for predicting foliar N content of different palm age groups. The N treatments varied from 0 to 2 kg per palm, subjected according to immature, young mature and prime mature classes. The Vis + NIR indices from the ground level-sensor that is green + red + NIR (G + R + NIR) was the best index for predicting N for immature palms (R2 = 0.91), while Vis indices blue + red (B + R) and Green Red Index from the space-borne sensor were significantly useful for N assessment of young and prime mature palms (R2 = 0.70 and 0.50), respectively. The application of vegetation indices for monitoring N status of oil palm is beneficial to examine extensive plantation areas.

Basal Stem Rot (BSR) disease is one of the most destructive diseases affecting oil palm plantation in Malaysia. The first critical step for a successful control of BSR is its detection and diagnosis. This study presents a new approach of high spatial resolution aerial Red, Green, Blue (RGB) image for BSR detection using a low-altitude remote sensing Unmanned Aerial Vehicle (UAV) platform. The co-occurrence measures for textural analysis were performed to the RGB band to determine the best parameter for BSR detection. Descriptive statistical as well as One way ANOVA were executed to indicate which bands are giving significant level (p<0.05). Apparently, the test gave three significant properties which are Correlation taken from R, G and B band. The developed conditional statement of detection was tested for distinguishing between healthy and BSR-infected trees. Total accuracy acquired for healthy trees was 86.21% whereas for BSR-infected trees was 75.00%. Hence, average accuracy assessment was 80.61%.

Agricultural crop abandonment negatively impacts local economy and environment since land, as a resource for agriculture, is not optimally utilized. To take necessary actions to rehabilitate abandoned agricultural lands, the identification of the spatial distribution of these lands must be acknowledged. While optical images had previously illustrated potentials in the identification of agricultural land abandonment, tropical areas often suffer cloud coverage problem that limits the availability of the imageries. Therefore, this study was conducted to investigate the potential of ALOS-1 and 2 (Advanced Land Observing Satellite-1 and 2) PALSAR (Phased Array L-band Synthetic Aperture Radar) images for the identification and classification of abandoned agricultural crop areas, namely paddy, rubber and oil palm fields. Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR; nonetheless, oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes. The accuracy obtained for these abandoned lands of paddy, rubber and oil palm was 93.33% ± 0.06%, 78% ± 2.32% and 63.33% ± 1.88%, respectively. This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops. The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.

Arsenic is a heavy metal that can exhibit both metallic and nonmetallic properties; concentrations in uncontaminated soil are generally in the ranges of 0.2 to 40 ppm while contaminated soils have been recorded to reach concentrations of up to 2500 ppm. Although arsenic can exist naturally, arsenic contamination occurs due to anthropogenic activities like the smelting of metals, vehicular emissions and the application of pesticides. Inorganic compounds of As can be very harmful to animals and human beings, effecting the nervous and cardiovascular system which eventually leads to death. As Malaysia faces increasing contamination problems, phytoremediation could prove to be savior as it is not only cheap, but also efficient. Cyperus rotundus, Imperata cylindrica, Ludwiga octovalvis, Lycpodium cernuum, Melastoma malabathricum, Mimosa pudica and Nelumbo nucifera are few plant species planted on soil contaminated with As.

Ganoderma boninense is a causal agent of basal stem rot (BSR) and is responsible for a significant portion of oil palm (Elaeis guineensis) losses, which can reach US$500 million a year in Southeast Asia. At the early stage of this disease, infected palms are symptomless, which imposes difficulties in detecting the disease. In spite of the availability of tissue and DNA sampling techniques, there is a particular need for replacing costly field data collection methods for detecting Ganoderma in its early stage with a technique derived from spectroscopic and imagery data. Therefore, this study was carried out to apply the artificial neural network (ANN) analysis technique for discriminating and classifying fungal infections in oil palm trees at an early stage using raw, first, and second derivative spectroradiometer datasets. These were acquired from 1,016 spectral signatures of foliar samples in four disease levels (T1: healthy, T2: mildly-infected, T3: moderately infected, and T4: severely infected). Most of the satisfactory results occurred in the visible range, especially in the green wavelength. The healthy oil palms and those which were infected by Ganoderma at an early stage (T2) were classified satisfactorily with an accuracy of 83.3%, and 100.0% in 540 to 550 nm, respectively, by ANN using first derivative spectral data. The results further indicated that the sensitive frond number modeled by ANN provided the highest accuracy of 100.0% for frond number 9 compared with frond 17. This study showed evidence that employment of ANN can predict the early infection of BSR disease on oil palm with a high degree of accuracy.

Temperature has major influence in insect development and outbreak. At present, the common method of collecting temperature information mainly relies on ground weather stations. However, this method is unfeasible for a large-scale area as weather stations distributions are sparse. This, however, can be compensated by the temperature measured through remote sensing satellites known as Land Surface Temperature (LST). Hence, this paper reviews the advantages and disadvantages of Thermal Infrared (TIR) and Microwave (MW) sensors for the acquisition of LST. This review will focus on the availability, suitability and adaptability of those sensors in providing LST for insect pest monitoring with the comparison being concentrated on their spatial and temporal characteristics, along with their accuracies.

Oil palm is a commercial crop that is important for its food value and as a biofuel, along with its other benefits towards the economy and human health. Currently, Malaysia cultivates approximately 5.64 million ha of oil palm. To date, a study identifying abandoned oil palm areas using satellite images is almost non-existent. Conventionally, the monitoring of abandoned oil palm lands is tedious and time consuming, especially over large areas. Hence, in this article, the capability of high resolution satellite image via Satellite Pour I’Observation de la Terre-6 (SPOT-6) products to extract abandoned oil palm areas was explored, as was the use of multi-temporal Landsat Operational Land Imager (OLI) imagery to develop the phenology of abandoned oil palm sites. Homogeneity measures derived through SPOT images played a more important role to identify abandoned oil palm than crop phenology characteristics extracted from high spectral resolution of Landsat images. With the advancement of object-oriented classification, monitoring of abandoned oil palm areas can be done semi-automatically with an accuracy of 92±1%.

Microbial biomass is the mass of living microorganisms in a particular ecosystem at a given time . Microbial biomass responds rapidly to different land use management techniques in at different location in forest reserves of Perak, Malaysia

Pesticide helps to enhance agricultural production, however, it significantly affect both socio and environmental entities of a country. In Nigeria, pesticide is widely used, thus its traces have been detected in water, soil and air. Several studies have already indicated that most of the environmental ecology (air, water, and soil) in Nigeria has been contaminated by persistent organic pesticides like organochlorine and organophosphate. Other reasons of high pesticide residues present in Nigeria environment is due to inappropriate dosage applied of pesticides that leaves behind excess. However, its residues are found above safety levels in the air, water, and soil across the nation. Based on previous analysis, it is indicated that 125,000-130,000 metric ton pesticides are being applied annually in Nigeria. Despite banned of some pesticides such as dichlorodiphenyltrichloroethane- and -Hexachlorocyclohexane, they are still being used by farmers in their agricultural production. Many of the Class 1 (high extremely toxic) pesticides are still being used in developing countries like Nigeria. Hence, there is need to sensitize and educate the general public especially the end-users (farmers) particularly on management practices of pesticides. Considering these entire hazardous situations, in this article the history of pesticide used in Nigeria has been reviewed in detail. The article also discussed the effects of pesticide use in Nigerian waters, soil and on crops. The risk of residual pesticide on agricultural workers, pesticide residue risk preventive measures by the Federal Government of Nigeria, banned and restricted pesticides in Nigeria, Nigerian Government and its efforts to eliminate persistent organic pesticides in use are also reviewed in details.

Background and Objectives: Nitrogen (N) fertilizer management with other soil amendments is crucial for optimal growth and productivity of grain crops. There has been increasing interest on converting rice straw to biochar and examining its use as a soil amendment. This study was conducted to evaluate the effects of different nitrogen fertilizer managements combined with rice straw derived biochar on rice growth and yield attributes. Materials and Methods: This greenhouse pot study was performed in a randomized complete block design with 4 replications. Treatments comprised of 150 kg ha–1 N fertilizer as a control (T1), 9 t ha–1 rice straw biochar added to 30, 60, 90, 120 and 150 kg N ha –1 (T2, T3,T4, T5 and T6, respectively). Growth measurement was carried out at week 2, 6 and 10 after transplanting, while yield attributes were obtained upon harvesting at 110 days. The effects of N and biochar treatments data were analysed using two-way analysis of variance (ANOVA) in PROC ANOVA in SAS Ver. 9.4 while treatments means differences were compared using least significant difference (LSD), both at p<0.05. Results: Treatment 5 (120 kg N ha–1 with biochar) was able to promote more plant growth and yield attributes than the other treatments, especially in plant height, leaf area, relative chlorophyll content and tiller number. Increased yield over the control treatment was found similar to 90 and 120 kg N ha–1 with biochar, reducing fertilization up to 40% and resulted in 20% grain yield improvement compared to the control. Therefore, rice straw biochar addition at 9 t ha–1 with 90-120 kg N ha–1 can be recommended for sustainable rice productivity and improvement of rice yield and farmers’ profits. Conclusion: The use of biochar in addition to the chemical fertilizers in rice production systems is an economically feasible and practical nutrient management practice.

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.

Nitrogen (N) fertilizer is an important plant nutrient for plant growth and yield. This study was conducted with four levels of N i.e., 0, 85, 170 and 250 kg ha-1using two rice cultivars i.e., 'MR219' and 'MR220'. The experiment was carried out in a factorial design with four replications and grown under flooded condition. The study focused on analysing the effect of N fertilizer level and cultivar to growth, biomass and grain yield. Statistical results showed that N significantly affected the plant height, SPAD reading, biomass and yield but did not affect the varieties. The interaction effect between the N level and cultivars also does not show significant difference. With the increment in N application, all parameters were also increased significantly. Maximum plant height (70.46 cm), SPAD reading (39.13), biomass (927.29 g m-2) and yield (830.99 g m-2) were obtained from 250 kg ha-1 nitrogen application. Correlation and regression analysis between plant heights, SPAD reading, biomass and grain yield exhibited a strong and positive relationship. The result also indicate that plant height is a better indicator to estimate biomass (R=0.794, R2=0.630) and grain yield (R=0.634, R2=0.397) compared to SPAD reading.

Presently non-invasive light-based chlorophyll determination has become the standard for a quick overview of general plant health and yield estimation. For oil palm which is a perennial crop, however, the determination should be done by considering effects of oil palm age and frond number. In this study, spectral reflectance obtained from (a) spectroradiometer and (b) SPAD were used to investigate variations in chlorophyll a, b, and total. It was found that SPAD values were inadequate to predict leaf chlorophyll content in young and mature oil palm for both frond 9 and 17 (r2 = 0.0001 – 0.40 and r2 = 0.019 – 0.16, respectively). Correlation analysis showed that spectroradiometer reflectance at 700 - 1350nm wavelength has relationship with chlorophyll content as high as r = 0.4. There was also other peaks at 400nm, 1600nm, and 1900nm which could be of interest in future studies.

Nitrogen (N) management is important in sustaining oil palm production. Remote sensing based approaches such as spectral index has promise in assessing the N nutrition content of many crops. The objectives of this study are to examine the N classification capability of three spectral indexes (SI): visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis+NIR) using data from the SPOT-6 satellite. N treatments varied from 0 to 2 kg per palm and were applied to both mature palms. The N-sensitive SIs tested in this study were age-dependent. The Vis index such as BGRI1 (CVA= 79.55%) and the Vis+NIR indices such as NDVI, NG, IPVI and GNDVI (CVA= 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier. Nonetheless, the SVM classifier showed promising potential in classifying foliar N content of mature palms that can possibly be used further for developing a new index in assessing N content of oil palms.