주산지 기상정보를 활용한 주요 채소작물의 단수 예측 모형 개발Development on Crop Yield Forecasting Model for Major Vegetable Cropsusing Meteorological Information of Main Production Area
- Other Titles
- Development on Crop Yield Forecasting Model for Major Vegetable Cropsusing Meteorological Information of Main Production Area
- Authors
- 임철희; 김강선; 이은정; 허성봉; 김태연; 김용석; 이우균
- Issue Date
- 2016
- Publisher
- 한국기후변화학회
- Keywords
- Crop Yield Forecasting Model; Meteorological Information; Major Vegetable Crop; Multiple Linear Regression
- Citation
- 한국기후변화학회지, v.7, no.2, pp.193 - 203
- Indexed
- KCI
OTHER
- Journal Title
- 한국기후변화학회지
- Volume
- 7
- Number
- 2
- Start Page
- 193
- End Page
- 203
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/91304
- DOI
- 10.15531/KSCCR.2016.7.2.193
- ISSN
- 2093-5919
- Abstract
- The importance of forecasting agricultural production is receiving attention while climate change is accelerating. This study suggested three types of crop yield forecasting model for major vegetable crops by using downscaled meteorological information of main production area on farmland level, which identified as limitation from previous studies. First, this study conducted correlation analysis with seven types of farm level downscaled meteorological informations and reported crop yield of main production area. After, we selected three types of meteorological factors which showed the highest relation with each crop species and regions. Parameters were deducted from meterological factor with high correlation but crop species number was neglected. After, crop yield of each crops was estimated by using the three suggested types of models. Chinese cabbage showed high accuracy in overall, while the accuracy of daikon and onion was quiet revised by neglecting the outlier. Chili and garlic showed differences by region, but Kyungbuk chili and Chungnam, Kyungsang garlic appeared significant accuracy. We also selected key meteorological factor of each crops which has the highest relation with crop yield. If the factor had significant relation with the quantity, it explains better about the variations of key meteorological factor. This study will contribute to establishing the methodology of future studies by estimating the crop yield of different species by using farmland meterological information and relatively simplify multiple linear regression models.
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Collections - College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles
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