Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Calibration of crop growth model CERES-MAIZE with yield trial data

지역적응 시험 자료를 활용한 옥수수 작물모형 CERES-MAIZE의 품종모수 추정시의 문제점

  • Kim, Junhwan (Division of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration) ;
  • Sang, Wangyu (Division of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration) ;
  • Shin, Pyeong (Division of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration) ;
  • Cho, Hyeounsuk (Division of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration) ;
  • Seo, Myungchul (Division of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration)
  • 김준환 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 상완규 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 신평 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 조현숙 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 서명철 (농촌진흥청 국립식량과학원 작물재배생리과)
  • Received : 2018.11.20
  • Accepted : 2018.12.05
  • Published : 2018.12.30
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Abstract

The crop growth model has been widely used for climate change impact assessment. Crop growth model require genetic coefficients for simulating growth and yield. In order to determine the genetic coefficients, regional growth monitoring data or yield trial data of crops has been used to calibrate crop growth model. The aim of this study is to verify that yield trial data of corn is appropriate to calibrate genetic coefficients of CERES-MAIZE. Field experiment sites were Suwon, Jinju, Daegu and Changwon. The distance from the weather station to the experimental field were from 1.3km to 27km. Genetic coefficients calibrated by yield trial data showed good performance in silking day. The genetic coefficients associated with silking are determined only by temperature. In CERES-MAIZE model, precipitation or irrigation does not have a significant effect on phenology related genetic coefficients. Although the effective distance of the temperature could vary depending on the terrain, reliable genetic coefficients were obtained in this study even when a weather observation site was within a maximum of 27 km. Therefore, it is possible to estimate the genetic coefficients by yield trial data in study area. However, the yield-related genetic coefficients did not show good results. These results were caused by simulating the water stress without accurate information on irrigation or rainfall. The yield trial reports have not had accurate information on irrigation timing and volume. In order to obtain significant precipitation data, the distance between experimental field and weather station should be closer to that of the temperature measurement. However, the experimental fields in this study was not close enough to the weather station. Therefore, When determining the genetic coefficients of regional corn yield trial data, it may be appropriate to calibrate only genetic coefficients related to phenology.

기후변화 영향평가를 위해 작물생육모형을 폭넓게 사용하고 있지만 모형을 구동하기 위해서는 품종모수를 결정하는 것이 필수적이다. 그러나 품종모수 결정을 위한 실험은 장시간의 노력이 필요하여 대부분 작황자료 또는 지역적응 시험 자료를 많이 사용하고 있다. 그러나 밭작물의 경우 작황자료 또는 지역적응 시험을 사용하는 경우에는 포장의 관개량과 시기에 대한 자료가 없고 또한 별도의 기상관측 없이 최인접지역의 기상자료를 사용하기 때문에 문제가 발생할 수 있다. CERES-MAIZE를 이용하여 밭작물인 옥수수에 대해서 이 문제점들을 검토하였다. 출사기와 관련된 품종모수는 최대 27km 내에 기상관측 지점이 있는 경우에도 신뢰성있는 품종모수가 얻어졌다. 온도의 경우에는 지형에 따라 유효한 거리가 달라질 수 있지만 본 연구의 대상 지역에서는 품종 모수 추정에 문제가 크지는 않을 것으로 보인다. 또한 온도 이외의 요소인 강수 또는 관개량은 생물계절관련 품종모수와는 큰 영향이 없기 때문에 비교적 정확도가 높은 결과가 나온 것으로 보인다. 그러나 수량과 관련된 품종 모수 요소에서는 그렇지 못하였다. 이는 밭작물의 경우 강수량에 따라 스트레스 정도가 결정되기 때문에 관개 및 강수량 정보가 중요한데, 관개량에 대한 정보를 작황 또는 지적시험 보고서에서는 얻을 수 없기 때문이다. 더구나 강수량의 경우에 온도보다 더 가까운 위치에 기상관측소가 존재해야만 유의미한 정보를 제공할 수 있다. 그러나 시험포장에 따라 기상관측소와의 거리가 충분히 가깝지 않은 경우가 대부분이었다. 따라서, 작황 또는 지역적응 시험자료를 이용하여 옥수수의 품종모수를 결정할 때는 기상관측 지점이 최소한 20km 이내의 인접지역에서 생물계절과 관련된 모수에 대해서만 결정하는 것이 타당할 것으로 생각된다. 그 반면에 수량과 관련된 요소의 결정은 적절하지 않을 것으로 생각된다. 수량과 관련된 요소를 결정하기 위해서는 가급적 직접 기상관측망을 설치하여 해당 포장에서 관개시기와 관개량을 모두 확보한 실험한 결과를 바탕으로 얻는 것이 적절할 것으로 생각된다.

Keywords

NRGSBM_2018_v20n4_277_f0001.png 이미지

Fig. 1. Comparison of observed silking day and simulated silking day with calibration data set. Genetic coefficients were calibrated under no-irrigation (a) and auto-irrigation (b) condition.

NRGSBM_2018_v20n4_277_f0002.png 이미지

Fig. 2. Comparison of observed silking day and simulated silking day with validation data set. Genetic coefficients were calibrated under no-irrigation(a) and auto-irrigation(b) condition. Gray circles represent Daegu experimental site. Daegu site was excluded for calculating RRMSE and EF. RRMSE and EF in parenthesis were calculate with all site.

NRGSBM_2018_v20n4_277_f0003.png 이미지

Fig. 3. Comparison of observed yield and simulated yield with calibration data set. Genetic coefficients were calibrated under no-irrigation (a) and auto-irrigation (b) condition.

Table 1. Calibrated genetic coefficient of Ilmi-chal with not-irrigation option and auto-irrigation option

NRGSBM_2018_v20n4_277_t0001.png 이미지

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