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Spikelet Number Estimation Model Using Nitrogen Nutrition Status and Biomass at Panicle Initiation and Heading Stage of Rice
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  • Spikelet Number Estimation Model Using Nitrogen Nutrition Status and Biomass at Panicle Initiation and Heading Stage of Rice
  • Spikelet Number Estimation Model Using Nitrogen Nutrition Status and Biomass at Panicle Initiation and Heading Stage of Rice
저자명
Cui. Ri-Xian,Lee. -Byun-Woo
간행물명
Korean journal of crop science
권/호정보
2002년|47권 5호|pp.390-394 (5 pages)
발행정보
한국작물학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

Spikelet number per unit area(SPN) is a major determinant of rice yield. Nitrogen nutrition status and biomass during reproductive stage determine the SPN. To formulate a model for estimating SPN, the 93 field experiment data collected from widely different regions with different japonica varieties in Korea and Japan were analyzed for the upper boundary lines of SPN responses to nitrogen nutrition index(NNI), shoot dry weight and shoot nitrogen content at panicle initiation and heading stage. The boundary lines of SPN showed asymptotic responses to all the above parameters(X) and were well fitted to the exponential function of $f(X)=alphacdot{1-etacdotexp(gamma;cdot;X)}$. Excluding the constant, from the boundary line equation, the values of the equation range from 0 to 1 and represent the indices of parameters expressing the degree of influence on SPN. In addition to those indices, the index of shoot dry weight increase during reproductive stage was calculated by directly dividing the shoot dry weight increase by the maximum value ($800 extrm{g/m}^{-2}$) of dry weight increase as it showed linear relationship with SPN. Four indices selected by forward stepwise regression at the stay level of 0.05 were those for NNI ($I_{NNI}_P$) at panicle initiation, NNI($I_{NNI}_h$) and shoot dry weight($I_{DW}_h$) at heading stage, and dry weight increase($I_{DW}$) between those two stages. The following model was obtained: SPN=48683ㆍ $I_{DWH}$$^{0.482}$ㆍ $I_{NNIp}$$^{0.387}$ㆍ $I_{NNIH}$$^{0.318}$ㆍ $I_{DW}$ $^{0.35}$). This model accounted for about 89% of the variation of spikelet number. In conclusion this model could be used for estimating the spikelet number of japonica rice with some confidence in widely different regions and thus, integrated into a rice growth model as a component model for spikelet number estimation.n.n.