Abstract. Arsa IGBA, Lalel HJD, Pollo R. 2019. Proximate composition and aroma quality of five aromatic upland-rice accessions from Sumba Barat Daya District, East Nusa Tenggara Province, Indonesia. Trop Drylands 3: 35-40. This research aimed to know the proximate composition and aroma quality of five aromatic upland-rice accessions from South West Sumba, East Nusa Tenggara Province and their correlation with the physical and chemical properties of the soil taken from the planting area of each accession. The five accessions are ACC-04, ACC-05, ACC-06, ACC-08, and ACC-09. Proximate composition analysis of each aromatic upland-rice accession was carried out by method recommended by AOAC (1970) to determine the water content, ash, fat, protein, carbohydrate, starch, amylose, and amylopectin contents of the rice. The rice aroma score was determined by a sensory test. Analysis of physical and chemical characters of soil was done on a composited soil taken from planting area of each aromatic upland-rice accession at the time of harvest. The results showed that ACC-04 had the highest ash content, AC-09 had the highest fat content and protein contents, and ACC-06 showed the highest carbohydrate content of rice. Furthermore, the best aroma quality of rice based on aroma score and 2-AP content was shown by, respectively, ACC-06 and ACC-05. There was no significant correlation between proximate composition and aroma quality with any component of environmental factors, thus, they were more likely to be determined by genetic factors.
Radar is able to provide information about extreme weather observations in the form of heavy rain, so it is important to find the level of accuracy of the radar in providing extreme weather information. So that with accurate data disaster mitigation can be done by creating an early warning system using radar data in order to minimize the impact that will occur. Comparative analysis of the estimated rainfall events on the radar with surface observation data shows a good level of accuracy, but the blankness of the data on the radar due to damage thus influences the decision making of the forecasters when providing extreme weather information quickly to the public. By knowing the radar accuracy level is quite good in estimating rain events, BMKG can provide weather information in the form of appropriate early warning so that people can anticipate extreme weather events
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