Development and Use of Digital Climate Models in Northern Gyunggi Province - II. Site-specific Performance Evaluation of Soybean Cultivars by DCM-based Growth Simulation

경기북부지역 정밀 수치기후도 제작 및 활용 - II. 콩 생육모형 결합에 의한 재배적지 탐색

  • 김성기 (경기도 농업기술원 북부농업시험장) ;
  • 박중수 (경기도 농업기술원 북부농업시험장) ;
  • 이영수 (경기도 농업기술원 북부농업시험장) ;
  • 서희철 (경희대학교 생태시스템공학과/생명자원과학연구원) ;
  • 김광수 (미국 아이오와주립대 농학과) ;
  • 윤진일 (경희대학교 생태시스템공학과/생명자원과학연구원)
  • Published : 2004.03.01

Abstract

A long-term growth simulation was performed at 99 land units in Yeoncheon county to test the potential adaptability of each land unit for growing soybean cultivars. The land units for soybean cultivation(CZU), each represented by a geographically referenced land patch, were selected based on land use, soil characteristics, and minimum arable land area. Monthly climatic normals for daily maximum and minimum temperature, precipitation, number of rain days and solar radiation were extracted for each CZU from digital climate models(DCM). The DCM grid cells falling within a same CZU were aggregated to make spatially explicit climatic normals relevant to the CZU. A daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CROPGRO-soybean model suitable for 2 domestic soybean cultivars were derived from long-term field observations. Three foreign cultivars with well established parameters were also added to this study, representing maturity groups 3, 4, and 5. Each treatment was simulated with the randomly generated 30 years' daily weather data(from planting to physiological maturity) for 99 land units in Yeoncheon to simulate the growth and yield responses to the inter-annual climate variation. The same model was run with input data from the Crop Experiment Station in Suwon to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for evaluation. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific cultivar. A computer program(MAPSOY) was written to help utilize the results in a decision-making procedure for agrotechnology transfer. transfer.

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References

  1. Boote, K. J., J. W. Jones, and G. Hoogenboom, 1998: Simulation of crop growth: CROPGRO Model. Chapter 18, 651-692pp. In R. M. Peart and R. B. Curry (eds.) Agricultural Systems Modeling and Simulation. Marcel Dekker, Inc., New York
  2. Kim, Y. H., H. D. Kim, S. W. Han, J. Y. Choi, J. M. Koo, U. Chung, J. Y. Kim, and J. I. Yun, 2002: Using spatial data and crop growth modeling to predict performance of South Korean rice varieties in Western Coastal Plains in North Korea. Korean Journal of Agricultural and Forest Meteorology 4, 224-236
  3. Yajima, M., 1996: Monitoring and forecasting of rice growth and development using crop-weather model. In: R. Ishii and T. Horie(eds.), Crop Research in Asia: Achievements and Perspective. Asian Crop Science Association, 280-285
  4. Yoon, S. T., and Y. H. Chu, 2003: Selection of optimum soybean variety for Gyeonggi northern areas. Journal of Korean Society of International Agriculture 15(4), 309-317
  5. Yun, J. I., 2003: Predicting regional rice production in South Korea using spatial data and crop-growth modeling. Agricultural Systems 77, 23-38
  6. Yun, J. I., and K. H. Lee, 2000: Agroclimatology of North Korea for paddy rice cultivation: preliminary results from a simulation experiment. Korean Journal of Agricultural and Forest Meteorology 2, 47-61