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.

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.

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.

Andrographis paniculata Burm f. is herbal plant that has shown potential for commercialisation. To obtain optimal production, proper plant management techniques must be identified. Therefore, this study aims to determine the effects of shading and mulching materials on the growth of A. paniculata. The study employed both shading and non-shading treatments to examine the effects, and five different types of mulching treatment were utilised: Non-mulched, coconut husk, rice husk, gravel, and sand. The results revealed that there were no significant differences between plants grown with or without shading, as well as between plants with or without mulching. Therefore, it was concluded that A. paniculata can survive and grow under both shaded and direct sunlight conditions, without the necessity of mulching. However, if mulching is deemed necessary, it is recommended to utilise finer materials that can easily decompose in the soil. This study represents an original idea that was developed and carried out to answer questions related to the cultivation of A. paniculata.