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http://dx.doi.org/10.7780/kjrs.2017.33.5.2.5

Development of Garlic & Onion Yield Prediction Model on Major Cultivation Regions Considering MODIS NDVI and Meteorological Elements  

Na, Sang-il (National Institute of Agricultural Sciences, Rural Development Administration)
Park, Chan-won (National Institute of Agricultural Sciences, Rural Development Administration)
So, Kyu-ho (National Institute of Agricultural Sciences, Rural Development Administration)
Park, Jae-moon (National Institute of Agricultural Sciences, Rural Development Administration)
Lee, Kyung-do (National Institute of Agricultural Sciences, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.33, no.5_2, 2017 , pp. 647-659 More about this Journal
Abstract
Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.
Keywords
Garlic; Onion; MODIS; Meteorological Element; Yield Prediction Model;
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Times Cited By KSCI : 8  (Citation Analysis)
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