Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (SPOT VGT) data (1998-2008) were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index (NDVI) classes generated from the iterative self-organizing data analysis technique algorithm (ISODATA) classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 lasses with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers' fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns (94% overall accuracy, 0.93 Kappa coefficient). We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water controlmeasures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers' choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions
Along with the development of the Internet and technology, food retailers have increasingly adopted online channels that enable consumers to buy food products online. This research aims to investigate the factors that influence consumer attitude and intention towards online food purchasing. A research framework was developed by combining the technology acceptance model with website trust, which is an important facilitator of online shopping. Using an online survey, data were obtained from 319 online food shoppers in an Asian emerging economy, i.e., Vietnam. Results from structural equation modeling show that perceived usefulness, perceived ease of use, and website trust are important drivers of attitude towards online food purchasing. Among these drivers, perceived ease of use has the greatest impact on attitude. Additionally, attitude and website trust exert a direct and positive effect on intention towards online food purchasing. Taken together, these findings have important managerial implications for key stakeholders, such as online food retailers, associations, and policy makers. One key implication is that online food sellers must endeavor to make their websites simple to use, easy to navigate, reliable, and secure. Several potential caveats for future research studies are also presented in this paper.
A new quantitative method extracts a landscape heterogeneity map (LaHMa) from hyper-temporal remote-sensing data. The feature extraction method is data-driven, unbiased, and builds on the commonly used data reduction technique of Iterative Self-Organizing Data Analysis (ISODATA) clustering with the support of divergence separability indices. First, the relevant spatial-temporal variation in normalized difference vegetation index (NDVI) is classified through ISODATA clustering. Second, a series of prepared cluster maps are overlaid to examine and detect the frequency with which boundaries between clusters occur at the same location. This step identifies the boundary strength between clusters and detects spatial heterogeneity within them. Results of the method are explored for the typical agriculture-defined landscape of the Mekong delta, Vietnam, using NDVI-imagery time-series from SPOT-Vegetation and MODIS-Terra. The method extracts useful landscape heterogeneity features and can support land-cover mapping requiring information on fragmentation and land-cover gradients.
This study posits that the declining industry is a good institutional environment to examine the relationship between ownership structure and firm performance of Vietnamese securities firms. This downturn decreases the return on investment of the industry and creates incentives for managers to expropriate shareholders more severely. In addition, different groups of shareholders recognizing the status of the industry may have their own reactions which are likely to affect firm performance. Using pooled OLS regression with a sample of 240 observations from 56 Vietnamese securities firms over the period from 2009 to 2016, we find supporting evidence of convergence-of-interest with a significantly negative relationship between insider ownership and profitability. In addition, foreign ownership is also positively related to firm performance. Firm size affects positively firm performance while number of employees has a negative impact on profitability.
JEL Classifications: M1, G34
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