Abstract
This study was carried out for the following two purposes through analysis of satellite data and meteorological data. One is to assess the feasibility of developing an efficient cultivation support system for effective reduction of costs relating to sugar beet cultivation. The other is to predict the root yield before the harvesting season. The cumulative temperature, cumulative precipitation and cumulative solar radiation from the end of April to the middle of July were selected as the predictors of the root yield prediction formula. The predictive error was 3.8t/ha, that was a result calculated by the farmers group, after weighted to the predicted root yield using NDVI. The results suggest that it is possible to predict the root yield before three months of the harvesting season. For providing such results to sugar companies in much earlier before the harvesting, the efficiency of sugar beet collection and sugar production will increase substantially.
Highlights
In Japan, the sugar beet is cultivated only in Hokkaido, the northern island
The results show that sugar beet with a larger amount of leaf and stem in July has higher root yield in the harvesting season, and it is suggested that the root yield in the harvesting season is determined by the growth status of beet tops in July
Analysis of satellite data and meteorological data was carried out to assess the feasibility of development of an efficient sugar beet cultivation support system to effectively reduce the costs of beet cultivation, beet collection and sugar production using agricultural spatial information, and to predict the root yield of sugar beet three months before the harvesting season
Summary
An area of the sugar beet cultivation in Tokachi District of Hokkaido is 30,000ha, which is equal to about 45% of the total national production area (MAFF 2006). The production of beet sugar in Hokkaido is about 640,000 tons, which is 3/4 of the total national production (MAFF 2006). Through appropriate scheduling of the collection of sugar materials using the data to predict the root yield before the harvesting, the costs of fuels to be used for transportation is expected to be reduced. These are other very useful effects utilizing the prediction method of the root yield
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