등숙기 온도 및 일사량과 생육형질을 이용한 벼 종실중 및 종실질소함량 추정

Estimating Grain Weight and Grain Nitrogen Content with Temperature, Solar Radiation and Growth Traits During Grain-Filling Period in Rice

  • 투고 : 2010.01.12
  • 발행 : 2010.12.31

초록

이 등(2009b)의 보고에서는 등숙기 기온과 일사량이 종실중 및 종실질소함량에 미치는 영향과 이들 관계를 분석하였고, 이 등(2009a)의 보고에서는 등숙기 생육형질이 종실중 및 종실질소함량에 미치는 영향과 이들 관계를 분석하였다. 본 연구는 종실중 및 종실질소함량과 등숙기 기상(온도, 일사량) 및 등숙기 생육형질과의 관계를 이용하여 등숙기간 중 종실중 및 종실질소함량의 형성과정을 추정하는 모형을 구축하고자 하였다. 1. 출수후 등숙 진전에 따른 유효적산온도(AET, 임계온도 $7^{\circ}C$)와 적산일사량(AR)의 상승적에 따른 종실중과 종실질소함량의 변화를 나타내었을 때 통일한 AET ${\times}$ AR에서도 종실 종 및 종실질소함량의 상당한 변이가 존재하였다. 2. Logistic 함수를 이용하여 AET ${\times}$ AR에 따른 종실중과 종실질소함량의 최대경계선을 추정하였으며, 이를 각각 최대 종실중(GWmax)과 최대 종실질소함량(GNmax) 추정식으로 이용하였다. 3. 등숙기 생육형질 종 엽면적지수, 지상부 총건물중, 영화수 및 지상부 총질소함량이 등숙기간 중 종실중과 종실질소함량의 변이에 관여하였으며, 이들 등숙기 생육형질과 GWmax 및 GNmax를 이용하여 다음과 같은 종실중과 종실질소함량 추정식을 설정하였다. $$GW_E\;=\;6.557{\cdot}GWmax{\cdot}TDW^{0.5223}{\cdot}GNO^{-0.5582}$$ $$GN_E\;=\;150.20{\cdot}GNmax{\cdot}TNU^{0.5203}{\cdot}GNO^{-0.6205}$$ 4. 설정된 종실중 및 종실질소함량 추정 모델식을 이용하여 실제 종실중 및 종실질소함량을 추정하는 모형을 구축하였는데, 종실중 및 종실질소함량을 일부 과대 또는 과소 추정하였으나 대체적으로 실측값과 일치하는 경향이었으며, 등숙기 생육형질에 의하여 발생한 다양한 종실중 및 종실질소함량 변이를 비교적 잘 추정하였다.

This experiment was conducted to construct process models to estimate grain weight (GW) and grain nitrogen content (GN) in rice. A model was developed to describe the dynamic pattern of GW and GN during grain-filling period considering their relationships with temperature, solar radiation and growth traits such as LAI, shoot dry-weight, shoot nitrogen content, grain number during grain filling. Firstly, maximum grain weight (GWmax) and maximum grain nitrogen content (GNmax) equation was formulated in relation to Accumulated effective temperature (AET) ${\times}$ Accumulated radiation (AR) using boundary line analysis. Secondly, GW and GN equation were created by relating the difference between GW and GWmax and the difference between GN and GNmax, respectively, with growth traits. Considering the statistics such as coefficient of determination and relative root mean square of error and number of predictor variables, appropriate models for GW and GN were selected. Model for GW includes GWmax determined by AET ${\times}$ AR, shoot dry weight and grain number per unit land area as predictor variables while model for GN includes GNmax determined by AET ${\times}$ AR, shoot N content and grain number per unit land area. These models could explain the variations of GW and GN caused not only by variations of temperature and solar radiation but also by variations of growth traits due to different sowing date, nitrogen fertilization amount and row spacing with relatively high accuracy.

키워드

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