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Prediction of Radish Growth as Affected by Nitrogen Fertilization for Spring Production

무의 질소 시비량에 따른 생육량 추정 모델식 개발

  • Lee, Sang Gyu (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration) ;
  • Yeo, Kyung-Hwan (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration) ;
  • Jang, Yoon Ah (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration) ;
  • Lee, Jun Gu (Department of Horticulture, Chonbuk National University) ;
  • Nam, Chun Woo (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration) ;
  • Lee, Hee Ju (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration) ;
  • Choi, Chang Sun (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration) ;
  • Um, Young Chul (Vegetable Research Division, National Institute of Horticultural & Herbal Sciences, Rural Developement Administration)
  • 이상규 (농촌진흥청 국립원예특작과학원 채소과) ;
  • 여경환 (농촌진흥청 국립원예특작과학원 채소과) ;
  • 장윤아 (농촌진흥청 국립원예특작과학원 채소과) ;
  • 이준구 (전북대학교 원예학과) ;
  • 남춘우 (농촌진흥청 국립원예특작과학원 채소과) ;
  • 이희주 (농촌진흥청 국립원예특작과학원 채소과) ;
  • 최장선 (농촌진흥청 국립원예특작과학원 채소과) ;
  • 엄영철 (농촌진흥청 국립원예특작과학원 채소과)
  • Received : 2013.03.18
  • Accepted : 2013.05.06
  • Published : 2013.09.30

Abstract

The average annual and winter ambient air temperatures in Korea have risen by 0.7 and $1.4^{\circ}C$, respectively, during the last 30 years. Radish (Raphanus sativus), one of the most important cool season crops, may well be used as a model to study the influence of climatic change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level, and climate parameters, including air temperature and growing degree days (GDD), on the performance of a radish cultivar 'Mansahyungtong' to estimate crop growth during the spring growing season. The radish seeds were sown from April 24 to May 22, 2012, at internals of 14 days and cultivated with 3 levels of nitrogen fertilization. The data from plants sown on April 24 and May 8, 2012 were used for the prediction of plant growth as affected by planting date and nitrogen fertilization for spring production. In our study, plant fresh weight was higher when the radish seeds were sown on $24^{th}$ of April than on $8^{th}$ and $22^{nd}$ of May. The growth model was described as a logarithmic function using GDD according to the nitrogen fertilization levels: for 0.5N, root dry matter = 84.66/(1+exp (-(GDD - 790.7)/122.3)) ($r^2$ = 0.92), for 1.0N, root dry matter = 100.6/(1 + exp (-(GDD - 824.8)/112.8)) ($r^2$ = 0.92), and for 2.0N, root dry matter = 117.7/(1+exp (-(GDD - 877.7)/148.5)) ($r^2$ = 0.94). Although the model slightly tended to overestimate the dry mass per plant, the estimated and observed root dry matter and top dry matter data showed a reasonable good fit with 1.12 ($R^2$ = 0.979) and 1.05 ($R^2$ = 0.991), respectively. Results of this study suggest that the GDD values can be used as a good indicator in predicting the root growth of radish.

최근 30년 동안 우리나라의 평균온도와 겨울철 온도가 각각 $0.7^{\circ}C$$1.4^{\circ}C$가 상승하였고 지속적으로 상승할 것으로 예측된다. 무는 매우 중요한 작물로 온난화에 따른 생육 모델 연구는 중요하다. 본 실험은 기상 이변에 따른 무의 생육량을 추정하기 위하여 정식시기와 질소 시비량을 다르게 처리하여 시험하였다. 파종시기는 4월 24일부터 5월 22일까지 14일 간격으로 3회에 걸쳐 실시하였고, 질소 시비량은 표준시비량의 0.5, 1.0, 2.0배 수준으로 3처리를 하였다. 그 결과, 무 파종 후 2개월째 생육은 4월 24일 처리구가 5월 8일과 22일 처리구보다 지상부 생체중이 높게 나왔다. 수확량 예측을 위한 생육 모델식은 질소 시비량별 GDD에 따른 지하부 건물중은 0.5N 처리구에서는 Y = 84.66 / (1+exp (-(GDD - 790.7) / 122.3)) ($r^2$ = 0.92), 1.0N 처리는 Y = 100.6 / (1+exp (-(GDD - 824.8) / 112.8))($r^2$ = 0.92), 2.0N 처리는 Y = 117.7 / (1+exp (-(GDD - 877.7) / 148.5)) ($r^2$ = 0.94) 로 나타낼 수 있었다. 구축된 모델식에 생육데이터를 사용하여 검정한 결과를 보면 기울기가 1.05-1.12로 다소 높게 추정하였지만 모델식으로 적용하는 것에는 무리가 없는 것으로 나타났다. 따라서 봄무 생산량 예측 시 GDD를 사용하여 수확량을 예측할 수 있을 것으로 사료되었다.

Keywords

References

  1. Cho, Y.Y. and J.E. Son. 2009. Determination of suitable parameters for developing adequate growth model of pak-choi plants. Hort. Environ. Biotechnol. 50:532-535.
  2. Eitzinger, J., M. Trnka, J. Hosch, Z. Zalud, and M. Dubrovsky. 2004. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecol. Modeling 171:223-246. https://doi.org/10.1016/j.ecolmodel.2003.08.012
  3. Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT cropping system model. Europ. J. Agron. 18:235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
  4. Kim, Y.H., S.Y. Hong, and H.Y. Lee. 2010. Construction of X-band automatic radar scatterometer measurement system and monitoring of rice growth. K. J. Soil Sci. Fert. 43:374-383.
  5. Lee, B,W., Y.H. Lee, J.E. Lee, K.H. Moon, D.J. Kim, K.J. Lee, and D.H. Choi. 2010. Develop crop growth models for soybean, potato, and oilseed rape, evaluate the temperature response of major upland crop under the changed climate condition. Res. Rept. RDA. p. 89.
  6. Lee, S.G., T.C. Seo, Y.A. Jang, J.G. Lee, C.W. Nam, C.S. Choi, K.H. Yeo, and Y.C. Um. 2012. Prediction of Chinese Cabbage Yield as Affected by Planting Date and Nitrogen Fertilization for Spring Production. J. Bio-Environ. Control 21:271-275
  7. Lenz-Wiedemann, V.I.S., C.W. Klar, and K. Schneider. 2010. Development and test of a crop growth model for application within a global change decision support system. Ecol. Modeling 221:314-329. https://doi.org/10.1016/j.ecolmodel.2009.10.014
  8. Liu, H.L., J.Y. Yang, C.F. Drury, W.D. Reynolds, C.S. Tan, Y.L. Bai, P. He, J. Jin, and G. Hoogenboom. 2010. Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuos maize production. Nutr. Cycl. Agroecosyst 89:313-328.
  9. Lobell, D.B. and M.B. Burke. 2010. On the use of statistical models to predict crop yield responses to climate change. Agricultural Forest Meteorol. 150:1443-1452. https://doi.org/10.1016/j.agrformet.2010.07.008
  10. National Institute of Horticultural & Herbal Science (NIHHS). 2009. Annual report in 2009. NIHHS, Suwon, Korea.
  11. Nendel, C., U. Schmutz, A. Venezia, F. Piro, and C.R. Rahn. 2009. Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels. Plant Soil 325:319-334. https://doi.org/10.1007/s11104-009-0015-0
  12. Park, S.Y., J.S. Lee, M.H. Seo, and J.S. Lee. 2002. Technology of radish cultivation. Rural Development Administration, Suwon, Korea. p. 177.
  13. Yeo, K.H., Y.Y. Cho, and Y.B. Lee. 2010. Estimation of growth and yield for single-stemmed rose 'Vital' in a single stem system. Hort. Environ. Biotechnol. 52:455-465.