More than 50% of the world’s population consumes rice. Accurate and up-to-date information on rice field extent is important to help manage food and water security. Currently, field surveys or MODIS satellite data are used to estimate rice growing areas. This study presents a cost-effective methodology for near-real-time mapping and monitoring of rice growth extent and cropping patterns over a large area. This novel method produces high-resolution monthly maps (10 m resolution) of rice growing areas, as well as rice growth stages. The method integrates temporal Sentinel-1 data and rice phenological parameters with the Google Earth Engine (GEE) cloud-based platform. It uses monthly median time series of Sentinel-1 at VH polarization from September 2016 to October 2018. The two study areas are the northern region of West Java, Indonesia (0.75 million ha), and the Kedah and Perlis states in Malaysia (over 1 million ha). K-means clustering, hierarchical cluster analysis (HCA), and a visual interpretation of VH polarization time series profiles are used to generate rice extent, cropping patterns, and spatiotemporal distribution of growth stages. To automate the process, four supervised classification methods (support vector machine (SVM), artificial neural networks (ANN), random forests, and C5.0 classification models) were independently trialled to identify cluster labels. The results from each classification method were compared. The method can also forecast rice extent for up to two months. The VH polarization data can identify four growth stages of rice—T&P: tillage and planting (30 days); V: vegetative-1 and 2 (60 days); R: reproductive (30 days); M: maturity (30 days). Compared to field survey data, this method measures overall rice extent with an accuracy of 96.5% and a kappa coefficient of 0.92. SVM and ANN show better performance than random forest and C5.0 models. This simple and robust method could be rolled out across Southeast Asia, and could be used as an alternative to time-consuming, expensive field surveys.

The commercial stingless beekeeping industry has good economic growth potential in Malaysia, which is a tropical country rich in flora that serves as food sources for stingless bees. Local consumption of honey is increasing and its production locally can be raised to meet the demand by sustainable stingless beekeeping. An important indicator that measures the success of the industry is the economic efficiency of the farm. This study hence examined the cost efficiency (CE), technical efficiency (TE), and allocative efficiency (AE) of commercial stingless beekeeping farms in the East Coast and Northern regions of Peninsular Malaysia using Cost Data Envelopment Analysis (DEA) under the assumption of Constant Return to Scale (CRS). Interviews with 47 respondents in the East Coast region and 28 respondents in the Northern region showed that the average CE scores for the two regions were 0.291 and 0.172, respectively. TE for the East Coast region and Northern region were 0.656 and 0.385 while mean AE scores of 0.445 and 0.404 were obtained. Factors that influenced the efficiency of the farms included farmers’ years of schooling and extension officer visits. In conclusion, CE, TE, and AE of commercial stingless bee honey production in Peninsular Malaysia were fairly low, and the determinants responsible for this low efficiency is of concern. Economic efficiency in stingless beekeeping needs to be further elevated to optimise productivity.

Rice is the staple crop for more than half the world’s population, but there is a lack of high-resolution maps outlining rice areas and their growth stages. Most remote sensing studies map the rice extent; however, in tropical regions, rice is grown throughout the year with variable planting dates and cropping frequency. Thus, mapping rice growth stages is more useful than mapping only the extent. This study addressed this challenge by developing a phenology-based method. The hypothesis was that the unsupervised classification (k-means clustering) of Sentinel-1 and 2 time-series data could identify rice fields and growth stages, because (1) the presence of flooding during transplanting can be identified by Sentinel-1 VH backscatter; and (2) changes in the canopy of rice fields during growth stages (vegetative, generative, and ripening phases) up to the point of harvesting can be identified by Normalized Difference Vegetation Index (NDVI) time series. Using the proposed method, this study mapped rice field extent and cropping calendars across Peninsular Malaysia (131,598 km2) on the Google Earth Engine (GEE) platform. The Sentinel-1 and 2 monthly time series data from January 2019 to December 2020 were classified using k-means clustering to identify areas with similar phenological patterns. This approach resulted in 10-meter resolution maps of rice field extent, intensity, and cropping calendars. Validation using very high-resolution street view images from Google Earth showed that the predicted map had an overall accuracy of 95.95%, with a kappa coefficient of 0.92. In addition, the predicted crop calendars agreed well with the local government’s granary data. The results show that the proposed phenology-based method is cost-effective and can accurately map rice fields and growth stages over large areas. The information will be helpful in measuring the achievement of self-sufficiency in rice production and estimates of methane emissions from rice cultivation.

Strawberry (Fragaria x ananassa) is a highly perishable product and rarely to be kept for more than 7 days. Cold chain system is the only methods apply in Malaysia strawberry industry to prolong the shelf life and preserve the quality of strawberry. UV light treatment proved to be effective in inhibit microbial loads and delay ripening process of fruit products where short wavelength (254 nm) ultraviolet-C was proved in keeping the freshness and quality of fruits or vegetables. This experiment was conducted to determine the effect of UV-C irradiation on extending shelf life and to determine the optimum radiation intensity of UV-C treatment on postharvest quality of treated strawberry. A total of 399 of strawberries cv. Festival were harvested in red colour with maturity stage of >80 – 85% from Cameron Highlands’s strawberry farm. The strawberries were exposed to different doses (0, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0 kJ/m2) of ultraviolet-C (254nm, UV-C) radiation. The treated strawberries were randomly placed in the polystyrene trays and stored in the dark chilling room at -5Cº for 9 days. The significant differences were found when the highest level of dosage was applied. UV-C treated strawberries with the highest doses (1.0 kJ/mº) are significantly firmer (0.557N), higher total soluble solids content (7.5 ºBrix) and ascorbic acid contain (0.518mg/ml) on day 9 (p

Purpose: Chilli production in Malaysia is declining due to production problems such as pest and diseases which cause plants to become stunted, unhealthy, and damaged. To overcome this problem, most farmers prefer using chemical fertilizers and pesticides due to their fast reaction and effectiveness. In long term, application of these chemicals input may lead to an environmental damage and human health problems. To conserve our nature, the use of natural input in agriculture is highly recommended.Research Method: This study was conducted to identify the potential of plant extract, namely kaffir lime in increasing the plant productivity and reducing the severity of pests for chilli trees. The study was performed by spraying the chilli trees with 25, 50, and 75% (v/v) kaffir lime extracts. Controls consisted of plants without any treatment as negative control and plants applied with a commercial chemical pesticide as positive control.Findings: Results indicated that there were significant differences (p<0.05) between treatments with respect to all parameters including pre-harvest (plant height and pest severity) and postharvest (fresh weight and number of chilli pods) parameters except for the stem and crown diameter, soluble solid concentration, fruit firmness and colour measurement of chilli. The results found that chilli plants sprayed with 25% kaffir lime extract had the lowest pest infestation. Moreover, application of the 25% kaffir lime extract had produced the heaviest weight of chilli fruits and the highest number of chilli pods compared to other treatments including the positive control (chemical pesticide). Therefore, it was concluded that 25% kaffir lime extract produced healthier chilli plants, higher yield and better quality of chilli.Originality/ Value: Limited number of studies are available on effectiveness of kaffir lime-based insecticides. Therefore, this study was conducted as an effort towards understanding the versatility of kaffir lime leaves extraction as an alternative pesticide on chilli plants' pre-and postharvest performance.