- Volume 32 Issue 4
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Suitability Classes for Italian Ryegrass (Lolium multiflorum Lam.) Using Soil and Climate Digital Database in Gangwon Province
강원도에서 토양과 기후 데이터베이스를 이용한 이탈리안 라이그라스의 재배 적지 구분
- Kim, Kyung-Dae (Gangwondo Agricultural Research and Extension Services) ;
- Sung, Kyung-Il (Dept. of animal Life System, Kangwon National University) ;
- Jung, Yeong-Sang (Dept. of biological Environment, Kangwon National University) ;
- Lee, Hyun-Il (Dept. of biological Environment, Kangwon National University) ;
- Kim, Eun-Jeong (Dept. of animal Life System, Kangwon National University) ;
- Nejad, Jalil Ghassemi (Dept. of animal Life System, Kangwon National University) ;
- Jo, Mu-Hwan (Foundation for the Rural Youth) ;
- Lim, Young-Chul (National Institute of Animal Science)
- 김경대 (강원도농업기술원) ;
- 성경일 (강원대학교 동물생명시스템학과) ;
- 정영상 (강원대학교 바이오자원환경학과) ;
- 이현일 (강원대학교 바이오자원환경학과) ;
- 김은정 (강원대학교 동물생명시스템학과) ;
- 조무환 (농어촌청소년육성재단) ;
- 임영철 (국립축산과학원)
- Received : 2012.11.07
- Accepted : 2012.12.03
- Published : 2012.12.31
As a part of establishing suitability classification for forage production, use of the national soil and climate database was attempted for Italian ryegrass (Lolium multiflorum Lam., IRG) in Gangwon Province. The soil data base were from Heugtoram of the National Academy of Agricultural Science, and the climate data base were from the National Center for Agro-Meteorology, respectively. Soil physical properties including soil texture, drainage, slope available depth and surface rock contents, and soil chemical properties including soil acidity and salinity, organic matter content were selected as soil factors. The crieria and weighting factors of these elements were scored. Climate factors including average daily minimum temperature, average temperature from March to May, the number of days of which average temperature was higher than
Grant : 양질의 조사료 생산을 위한 맵핑시스템 구축
Supported by : 농촌진흥청
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- A Study on the Prediction of Suitability Change of Forage Crop Italian Ryegrass (Lolium multiflorum L.) using Spatial Distribution Model vol.16, pp.2, 2014, https://doi.org/10.5532/KJAFM.2014.16.2.103
- Detecting the Climate Factors related to Dry Matter Yield of Whole Crop Maize vol.17, pp.3, 2015, https://doi.org/10.5532/KJAFM.2015.17.3.261
- Land Suitability Assessment by Combining Classification Results by Climate and Soil Information Using the Most Limiting Characteristic Method in the Republic of Korea vol.18, pp.3, 2016, https://doi.org/10.5532/KJAFM.2016.18.3.127
- Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea vol.63, pp.3, 2017, https://doi.org/10.1111/grs.12163