Search

Agricultural production in a highly managed watershed is challenging to capture in models due to its complexity and large hydrological, management, and crop data requirements. We evaluated hydro-crop simulation of Malaysia’s largest rice granary using the Soil Water Assessment Tool (SWAT). The 4515 km2 watershed is characterized by dams in the upper sub-watersheds that drain towards 1007 km2 of rice paddies. Monthly averaged streamflow simulation produced Nash-Sutcliffe efficiencies and coefficients of determination between 0.84 and 0.90 in both calibration and validation periods. Meanwhile, the normalized root mean square error and the percent bias for rice yield simulation in off and main seasons of rice planting were within ± 10%. Investigation of rice yield loss processes revealed a stronger control by solar radiation on crop growth processes compared to other hydroclimatic and environmental stresses. Our findings show that SWAT can reliably simulate streamflow and rice growth; however, it may not capture all yield loss processes.

Vernonia amygdalina Del. is a botanical plant used for self-medication purposes. It is important to understand its acclimatization process, which is influenced by abiotic factors and agronomic practices on growth performance. The present study was conducted to determine the optimum light intensity, growing media, and harvest time required to maximize the growth performance of V. amygdalina. The treatments consisted of three light intensities (30, 50 and 100%), four growing media (soil, cocopeat, empty fruit bunch and burnt paddy husk) and six harvest times. The highest biomass yield of V. amygdalina was achieved with exposure to 50% light intensity. Highest plant height and specific leaf area were found on V. amgdalina grown under 30% light intensity, while 50% light intensity attained the highest photosynthetic rate on empty fruit bunch growing media. Both total phenolic and flavonoid contents of V. amygdalina increased when harvested at 9 and 18 weeks after transplanting. Therefore, the practice of using oil palm empty fruit bunch with 50% of light intensity exposure and harvest at 18 weeks after transplanting is recommended to optimize the growth and phytochemicals yield of V. amygdalina.

Basal Stem Rot (BSR), a disease caused by Ganoderma boninense (G. boninense), has posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has the potential to cause substantial economic losses. The breeding programme is currently searching for G. boninense-resistant planting materials, which has necessitated intense manual screening in the nursery to track the progression of disease development in response to different treatments. The combination of hyperspectral image and machine learning approaches has a high detection potential for BSR. However, manual feature selection is still required to construct a detection model. Therefore, the objective of this study is to establish an automatic BSR detection at the seedling stage using a pre-trained deep learning model and hyperspectral images. The aerial view image of an oil palm seedling is divided into three regions in order to determine if there is any substantial spectral change across leaf positions. To investigate if the background images affect the performance of the detection, segmented images of the plant seedling have been automatically generated using a Mask Region-based Convolutional Neural Network (RCNN). Consequently, three models are utilised to detect BSR: a convolutional neural network that is 16 layers deep (VGG16) model trained on a segmented image; and VGG16 and Mask RCNN models both trained on the original images. The results indicate that the VGG16 model trained with the original images at 938 nm wavelength performed the best in terms of accuracy (91.93%), precision (94.32%), recall (89.26%), and F1 score (91.72%). This method revealed that users may detect BSR automatically without having to manually extract image attributes before detection.

Ganoderma boninense (G.boninense) is the causal agent of basal stem rot (BSR) which significantly reduced the productivity of oil palm plantations in Southeast Asia. At early stage, the disease did not show any physical symptoms that could be seen with naked eyes resulted in detection difficulties. To date, there was no effective detection for this disease, and conventional methods such as manual and laboratory-based required trained specialists as well as time-consuming. Therefore, this study was conducted using hyperspectral remote sensing to investigate the differences in spectral reflectance of young leaf (frond one (F1) of healthy and G. boninense infected oil palm seedlings. The seedlings were inoculated with G. boninense pathogen at five months old. At five months after inoculation, 558 spectral signatures of F1 were extracted from acquired hyperspectral images. Noise removal was done to the extracted spectral signatures to remove outliers in the data. Then, the spectral signatures were averaged and plotted to observe the differences. Differences in reflectance of healthy and G. boninense infected seedlings were seen evidently in the near-infrared (NIR) region. Thus, this study showed evidence that F1 spectral reflectance has the ability to detect early stage of G. boninense infection at oil palm seedlings.

In rice-producing countries, the abundance of rice residue must be sustainably managed to avoid environmental problems. Moreover, repeated usage of inorganic fertilizers has caused rapid soil deterioration in these arable lands. Converting rice husk residue into rice husk biochar (RHB) and reapplying it back to paddy fields as a soil amendment can be a sustainable approach in rice production. Due to its adsorptive capability, nutrient retention capacity and high silica content, RHB can improve soil fertility and improving the effectiveness of fertilizer when mixed together due to its nutrient retention capacity and high silica content. High silica content in rice husk biochar provides better nutrient retention, turgidity, and structure for plant. The objectives of this review article are to compile studies related to rice husk biochar properties and factors affecting it and application of rice husk biochar in Asian agriculture.

MODIS land surface temperature data (MODIS Ts) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS Ts products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (Ta) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between Ts and Ta has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of Ta from MODIS Ts products in order to overcome the limitation of Ta that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS Ts products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against Ta and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (Ta_r) and surface temperature (Ts_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the er

Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy.

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

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 ac

The Cameron Highlands has experienced multiple land encroachment activities and repeated deforestation, leading to extensive land-use and land-cover change (LULCC) during the past six decades. This study aims to determine the LULCC against topography in Cameron Highlands between 2009 and 2019 by using geospatial techniques to analyze Landsat 7 (ETM+) and 8 (OLI/TIRS), ASTER GDEM and MODIS imaging sensors. The results showed a decline of 35.98 km2 in primary forests over ten years across the Cameron Highlands, while agricultural lands and urban areas flourished by a rise of 51.61 km2 and 11.00 km2 respectively. It can be noted that the elevation most affected is between 1000 and 1500 m, across all classes. Further results showed the expansion of both agriculture and urban development onto slopes above 35°, leading to an instability of soil structure. In a comparison of the base years of 2009 with 2019, mean LST results have shown temperatures rising by 7.5°C, while an average between 3 and 4°C across the region is recorded. The results obtained provide new information for government bodies and land planners to coordinate their actions without further jeopardizing the environment of the Cameron Highlands.

In the past decade, the inevitable increase in temperature has caused Malaysia to experience more extreme heat events, and yet very little research has been dedicated in exploring the heat-related vulnerability of exposed population. In this study, the extreme heat vulnerability index (EHVI) has been evaluated to identify the most vulnerable districts to extreme heat events. We evaluated exposure, population sensitivity and adaptive capacity from sociodemographic and remote sensing data. We have applied multivariate analysis on 13 indicators for every 87 districts to elucidate the extreme heat vulnerability in Peninsular Malaysia. The EHVI was generated by summing up the normalized extreme heat exposure scores and factor scores from the multivariate analysis. Our findings clarify that the most vulnerable populations are confined in the urban and northern region of Peninsular Malaysia. The source of vulnerability varied between both regions, with urbanization and population density increase the vulnerability in urban areas, high heat exposure and sensitive population are the dominant factors of vulnerability in the northern region. These findings are valuable in identifying districts vulnerable to extreme heat and help regulatory body; in designing effective adaptation and preparedness strategies to increase the population resilience towards extreme heat.

Preserving mental health during the COVID-19 crisis should be a priority for individuals worldwide. In this regard, mental health professionals should advise the general public on the actions/activities that they can take to prevent mental health issues from becoming the next pandemic. However, the general public should also actively take measures to improve their mental wellbeing. Music therapy, aromatherapy or indoor nature therapy may or may not have the potential to preserve mental wellbeing, but individuals should experiment with them to ascertain the effects on themselves. Moreover, the guidelines provided by WHO should also be adhered to, as a healthy mind starts with a healthy body.

Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects both mature trees and seedlings. The current practice of detection still depends on manual inspection by a human expert every two weeks. This study aimed to detect early G. boninense infections using visible-near infrared (VIS-NIR) hyperspectral images where there are no BSR symptoms present. Twenty-eight samples of oil palm seedlings at five months old were used whereby 15 of them were inoculated with the G. boninense pathogen. Five months later, spectral reflectance oil palm leaflets taken from fronds 1 (F1) and 2 (F2) were obtained from the VIS-NIR hyperspectral images. The significant bands were identified based on the high separation between uninoculated (U) and inoculated (I) seedlings. The results indicate that the differences were evidently seen in the NIR spectrum. The bands were later used as input parameters for the development of Support Vector Machine (SVM) classification models, and these bands were optimized according to the classification accuracy achieved by the classifiers. It was observed that the U and I seedlings were excellently classified with 100% accuracy using 35 bands and 18 bands of F1. However, the combination of F1 and F2 (F12) gave better accuracy than F2 and almost similar to F1 for specific classifiers. This finding will provide an advantage when using aerial images where there is no need to separate F1 and F2 during the data pre-processing stage.

Soursop (Annona muricata L.) has been increasingly cultivated in Malaysia. In view of the importance of the crop, there is a need to understand the effects of agronomic management such as NPK fertiliser application and the inoculation of arbuscular mycorrhizal fungi (AMF) on soursop growth and nutrient uptake. Therefore, this study aimed to determine the effects of AMF and fertiliser on the growth and nutrient uptake of soursop seedlings. The experiment was conducted under glasshouse condition in UPM, Serdang, Selangor, Malaysia using completely randomised design (CRD) with five treatments which comprised AMF inoculations with full and half dose of NPK 15:15:15 fertilisation. The treatments were: T1- Control (without AMF and NPK fertiliser); T2- AMF only; T3- AMF with 50% NPK fertiliser; T4- AMF with full amount (100%) NPK fertiliser; and T5- full amount (100%) NPK fertiliser only (without AMF). Plant growth, soil microbial population AMF development, ‘nutrient’ status of the plants and soils were determined after the 8th week of planting. Soursop seedlings grown in soils treated with 100% NPK 15:15:15 fertiliser (T5) had the highest chlorophyll content, root volume, N uptake and soil N and K. Surprisingly, inoculation of AMF (T2) had similar effects to that of NPK 15:15:15 fertiliser (T5) on plant P uptake. Mycorrhizal spore production even at low numbers (66 spores/10 g soil) indicated probable symbiotic interaction with soursop seedling roots at the nursery stage.

Climate changes are changing intentions of farmers to tackle climate variations in various ways. Information and Communication Technologies are proving to assist farmers to manage agricultural risk timely and with fewer efforts. Intention of farmers to use ICTs in the context of agricultural risk management is important to understand. Therefore, the present study was designed to examine intention of the farmers from the context of Malaysia. The field survey of three selected states was conducted in which 350 farmers were chosen through multi stage cluster sampling technique. The Likert scale items measuring 1 as strongly disagree to 5 as strongly agree were used in the research instrument to assess intention of the respondents. The findings revealed that the farmers showed positive intention to use ICTs for agricultural risk management from the future lens. The overall level of intention was also high. However, internet speed, small screen display and battery issues could halt intention of the farmers to harness potential of digital technologies as reported by the farmers. Thus, the study recommends that agricultural extension service providers are required to introduce various digital skill development programs for the farmers exclusively resource poor and less digital familiar farmers to reduce the risk in the agricultural sector stem from climate changes.

Mountainous regions are more sensitive to climatic condition changes and are susceptible to recent increases in temperature. Due to urbanization and land use/land cover (LULC) issues, Cameron Highlands has been impacted by rising land surface temperature (LST) variation. Thus, this study was carried out to explore the impact of the LULC change on LST in the Cameron Highlands from 2009 to 2019 using remote sensing images acquired from Landsat 7 ETM+, Landsat 8 Operational Land Imager (OLI/TIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A split-window algorithm was applied to Landsat 8 images (2013–2019) to derive the LST. Air temperature data of the study area were also obtained to cross-validate data sources. Based on the validation results, the accuracy of LULC and LST outputs were more than 94.6% and 80.0%, respectively. The results show that the current trend of urban growth continues at a rate of 0.16% per year, and the area experienced an LST increase of 2 °C between 2009 and 2019. This study is crucial for land planners and environmentalists to understand the impacts of LULC change on LST and to propose appropriate policy measures to control development in Cameron Highlands.

The primary aim of this research was to examine the factors influencing behavioral intention of farmers to use ICTs for agricultural risk management. The past research reveals that many researchers had tried to determine factors affecting behavioural intentions of the respondents and TPB has been applied as technology acceptance model in various contexts. However, predicting behavioral intentions to use ICTs for agricultural risk management has not been evaluated from the actual field. Therefore, the data were collected from 360 farmers through multistage cluster sampling technique. Multiple linear regression through SPSS was administered for statistical procedure. The findings confirmed that the theory (TPB) was statistically feasible to predict behavioral intention of farmers to use ICTs for agricultural risk management. The results further reveal that attitude was one of the most influenced of intention, which was followed by perceived behavioral control and lastly the subjective norms. Thus, using TPB in this context would be helpful for other researchers and academia to understand the influence of each construct in the model.

The growth of oil palm is highly dependent on the availability of nutrients, supplied through fertilizers. This is especially true for peat soils where there is a lack of Zn and Cu due to the soil being deficient of mineral and clay content. The deficiency of Zn and Cu has been found to inhibit oil palm plant and root growth. The objective of this study is to determine the effect of different concentration of Zn and Cu chelates on the root development of oil palm roots cell walls. The oil palm seedlings were harvested after two different growing periods; the first harvest was conducted after three months of planting while the second harvest was after 6 months of planting. The harvested oil palm seedlings were measured for root length, root thickness and root surface area. The best root growth for the copper treatments was with concentrations of 8.00 mg Cu/palm. The cell epidermis size and cortex cells of the oil palm seedlings in this treatment was the smallest, meaning that it did not experience any Cu toxicity, while it had the longest root length, root surface area, root fresh weight and root dry weight. For the zinc treatments, the best root growth was at 5.00 mg/palm. This is because in this treatment, the oil palm seedlings achieved the highest root length, root surface area, root fresh weight and root dry weight. This means that the best concentration of Cu to promote root growth of oil palm seedlings is 8.00 mg Cu/palm while the best concentration of Zn was 5.00 mg Zn/palm. Further research needs to also be conducted using a combination of both Zn and Cu applications at the same time, to determine if the combined micronutrients had different effects on the oil palm seedlings roots growth.