완전제어형 식물공장에서 퀴노아 (Chenopodium quinoa Willd.)의 생장을 예측하기 위한 모델 개발 Development of Models for Estimating Growth of Quinoa (Chenopodium quinoa Willd.) in a Closed-Type Plant Factory System원문보기
작물 생육 모델은 작물의 생육을 이해하고 통합하기 위해 유용한 도구이다. 완전제어형 식물공장에서 엽채류로 활용하기 위한 퀴노아(Chenopodium quinoa Willd.)의 초장, 광합성률, 생장 모델을 예측하기 위한 모델을 1차식, 2차식 및 비선형 및 선형지수 등식을 사용하여 개발하였다. 식물 생육과 수량은 정식 후 5일간격으로 측정하였다. 광합성과 생장 곡선 모델을 계산하였다. 초장과 정식 후 일수(DAT)간의 선형 및 곡선 관계를 얻었으나, 초장을 정확하게 예측하기 위한 모델은 선형 등식이었다. 광합성률 모델을 비선형 등식을 선택하였다. 광보상점, 광포화점, 및 호흡률은 각각 29, 813 and $3.4{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$였다. 지상부 생체중과 건물중은 선형관계를 보였다. 지상부 건물중의 회귀계수는 0.75 ($R^2=0.921^{***}$)였다. 선형지수 수식을 사용하여 시간 함수에 따른 퀴노아의 지상부 건물중 증가를 비선형 회귀식으로 수행하였다. 작물생장률과 상대생장률은 각각 $22.9g{\cdot}m^{-2}{\cdot}d^{-1}$ and $0.28g{\cdot}g^{-1}{\cdot}d^{-1}$였다. 이러한 모델들은 정확하게 퀴노아의 초장, 광합성률, 지상부 생체중과 건물중을 예측할 수 있다.
작물 생육 모델은 작물의 생육을 이해하고 통합하기 위해 유용한 도구이다. 완전제어형 식물공장에서 엽채류로 활용하기 위한 퀴노아(Chenopodium quinoa Willd.)의 초장, 광합성률, 생장 모델을 예측하기 위한 모델을 1차식, 2차식 및 비선형 및 선형지수 등식을 사용하여 개발하였다. 식물 생육과 수량은 정식 후 5일간격으로 측정하였다. 광합성과 생장 곡선 모델을 계산하였다. 초장과 정식 후 일수(DAT)간의 선형 및 곡선 관계를 얻었으나, 초장을 정확하게 예측하기 위한 모델은 선형 등식이었다. 광합성률 모델을 비선형 등식을 선택하였다. 광보상점, 광포화점, 및 호흡률은 각각 29, 813 and $3.4{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$였다. 지상부 생체중과 건물중은 선형관계를 보였다. 지상부 건물중의 회귀계수는 0.75 ($R^2=0.921^{***}$)였다. 선형지수 수식을 사용하여 시간 함수에 따른 퀴노아의 지상부 건물중 증가를 비선형 회귀식으로 수행하였다. 작물생장률과 상대생장률은 각각 $22.9g{\cdot}m^{-2}{\cdot}d^{-1}$ and $0.28g{\cdot}g^{-1}{\cdot}d^{-1}$였다. 이러한 모델들은 정확하게 퀴노아의 초장, 광합성률, 지상부 생체중과 건물중을 예측할 수 있다.
Crop growth models are useful tools for understanding and integrating knowledge about crop growth. Models for predicting plant height, net photosynthesis rate, and plant growth of quinoa (Chenopodium quinoa Willd.) as a leafy vegetable in a closed-type plant factory system were developed using empir...
Crop growth models are useful tools for understanding and integrating knowledge about crop growth. Models for predicting plant height, net photosynthesis rate, and plant growth of quinoa (Chenopodium quinoa Willd.) as a leafy vegetable in a closed-type plant factory system were developed using empirical model equations such as linear, quadratic, non-rectangular hyperbola, and expolinear equations. Plant growth and yield were measured at 5-day intervals after transplanting. Photosynthesis and growth curve models were calculated. Linear and curve relationships were obtained between plant heights and days after transplanting (DAT), however, accuracy of the equation to estimate plant height was linear equation. A non-rectangular hyperbola model was chosen as the response function of net photosynthesis. The light compensation point, light saturation point, and respiration rate were 29, 813 and $3.4{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, respectively. The shoot fresh weight showed a linear relationship with the shoot dry weight. The regression coefficient of the shoot dry weight was 0.75 ($R^2=0.921^{***}$). A non-linear regression was carried out to describe the increase in shoot dry weight of quinoa as a function of time using an expolinear equation. The crop growth rate and relative growth rate were $22.9g{\cdot}m^{-2}{\cdot}d^{-1}$ and $0.28g{\cdot}g^{-1}{\cdot}d^{-1}$, respectively. These models can accurately estimate plant height, net photosynthesis rate, shoot fresh weight, and shoot dry weight of quinoa.
Crop growth models are useful tools for understanding and integrating knowledge about crop growth. Models for predicting plant height, net photosynthesis rate, and plant growth of quinoa (Chenopodium quinoa Willd.) as a leafy vegetable in a closed-type plant factory system were developed using empirical model equations such as linear, quadratic, non-rectangular hyperbola, and expolinear equations. Plant growth and yield were measured at 5-day intervals after transplanting. Photosynthesis and growth curve models were calculated. Linear and curve relationships were obtained between plant heights and days after transplanting (DAT), however, accuracy of the equation to estimate plant height was linear equation. A non-rectangular hyperbola model was chosen as the response function of net photosynthesis. The light compensation point, light saturation point, and respiration rate were 29, 813 and $3.4{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, respectively. The shoot fresh weight showed a linear relationship with the shoot dry weight. The regression coefficient of the shoot dry weight was 0.75 ($R^2=0.921^{***}$). A non-linear regression was carried out to describe the increase in shoot dry weight of quinoa as a function of time using an expolinear equation. The crop growth rate and relative growth rate were $22.9g{\cdot}m^{-2}{\cdot}d^{-1}$ and $0.28g{\cdot}g^{-1}{\cdot}d^{-1}$, respectively. These models can accurately estimate plant height, net photosynthesis rate, shoot fresh weight, and shoot dry weight of quinoa.
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
제안 방법
, Lincoln, NE, USA). Plants were randomly selected with 10 replications, and measurements were made on fully expanded leaves.
The experiment had a completely randomized design. Statistical analyses were carried out using the SAS system (Release 9.
The response of the net photosynthesis rate (Pn) of quinoa to various light levels was measured and modeled (Fig. 3). The non-rectangular hyperbola model (Goudriaan and Van Larr, 1994) was chosen as the response function of net photosynthesis.
, 2009; Schlick and Bubenheim, 1996); hence, there are a number of benefits to using these as vegetables. This experiment was carried out to collect basic data which could be used for the predicting potential effects on the growth rates of quinoa in a closed-type plant factory system.
대상 데이터
The experiment was conducted in a closed-type plant factory (700×500×300cm, L×W×H) at Jeju National University.
San Jose, CA, USA). The number of plants for making the plant height model was 16 plants, and the number of validated plants was 144 plants. Variables were estimated using the Gauss-Newton algorithm, a nonlinear least squares technique.
데이터처리
The slopes, intercepts and regression coefficients of the models were compared using the SAS REG procedure. Correlation coefficients were calculated between the measured and estimated data.
The experiment had a completely randomized design. Statistical analyses were carried out using the SAS system (Release 9.01, SAS institute Inc., Cary, NC, USA) and SigmaPlot (Version 9.01, Sistat Software Inc. San Jose, CA, USA). The number of plants for making the plant height model was 16 plants, and the number of validated plants was 144 plants.
The slopes, intercepts and regression coefficients of the models were compared using the SAS REG procedure.
이론/모형
The number of plants for making the plant height model was 16 plants, and the number of validated plants was 144 plants. Variables were estimated using the Gauss-Newton algorithm, a nonlinear least squares technique. The slopes, intercepts and regression coefficients of the models were compared using the SAS REG procedure.
성능/효과
It is concluded from this modeling study that growth and yield of quinoa in a closed-type plant factory system respond to photosynthetic active radiation. The non-rectangular hyperbola model was chosen as the response function for net photosynthesis.
참고문헌 (17)
Arnold, J.G., M.A. Weltz, E.E. Alberts, and D.C. Flanagan. 1995. Plant growth component. Chapter 8 in USDA-water erosion prediction project: hillslope profile and watershed model documentation, 8.1-8.41. NSERL report No. 10. D.C. Flanagan, Nearing, M.A., and J.M. Laflen (eds.). West Lafayette, Ind: USDA-ARS National Soil Erosion Research Laboratory.
Calama, R., J. Puertolas, G. Madrigala, and M. Pardosa. 2013. Modeling the environmental response of leaf net photosynthesis in Pinus pinea L. natural regeneration. Ecol. Model. 251:9-21.
Cannell, M.G.R. and J.H.M. Thornley. 1988. Temperature and $CO_2$ responses of leaf and canopy photosynthesis: a clarification using the non-rectangular hyperbola model of photosynthesis. Ann. Bot. 82:883-892.
Cho, Y.Y. and J.E. Son. 2009. Determination of suitable parameters for developing adequate growth model of pakchoi plants. Hortic. Environ. Biotechnol. 50:532-535.
Gawlik-Dziki, U., M. Swiecaa, M., Sulkowski, D.Dziki, B. Baraniaka, and J. Czyz. 2013. Antioxidant and anticancer activities of Chenopodium quinoa leaves extracts-In vitro study. Food Chem.Toxicol. 57:154-160.
Goudriaan, J. and J.L. Monteith. 1990. A mathematical function for crop growth based on light interception and leaf area expansion. Ann. Bot. 66:695-701.
Goudriaan, J. and H.H. Van Laar. 1994. Modelling potential crop growth processes: Textbook with exercises. Current issues in production ecology 2. Dordrecht: Kluwer Academic Publishers.
Ioslovich, I. and P.O. Gutman. 2000. Optimal control of crop spacing in a plant factory. Automatica 36:1665-1668.
Kim, S.H., K.A. Shackel, and J.H. Lieth. 2004. Bending alters water balance and reduces photosynthesis of rose shoot. J. Amer. Soc. Hortic. Sci. 129:896-901.
Lieth, J.H. and C.C. Pasian. 1990. A model for net photosynthesis of rose leaves as a function of photosynthetically active radiation, leaf temperature, and leaf age. J. Amer. Soc. Hortic. Sci. 115:486-491.
Miglietta, F. and M. Bindi. 1993. Crop growth simulation models for research, farm management and agrometeorology. EARSel, Advances in Remote Sensing 2(2-VI):148-157.
Mo, X., S. Liua, Z. Lina, Y. Xub, Y. Xianga, and T.R. McVicarc. 2005. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecol. Model. 183:301-322.
Morimoto, T., T. Torii, and Y. Hashimoto. 1995. Optimal control of physiological processes of plants in a green plant factory. Control Eng. Pract. 3:505-511.
Pasko, P., H. Barton, P. Zagrodzki, S. Gorinstein, M. Folta, and Z. Zachwieja. 2009. Anthocyanins, total polyphenols and antioxidant activity in amaranth and quinoa seeds and sprouts during their growth. Food Chem. 115:994-998.
Salas Fernandez, M.G., P.W. Becraft, Y. Yin, and T. Lubberstedt. 2009. From dwarves to giants? Plant height manipulation for biomass yield. Trends Plant Sci. 14:454-461.
Schlick, G. and D.L. Bubenheim. 1996. Quinoa: candidate crop for NASA's controlled ecological life support systems. p. 632-640. In: J. Janick (ed.), Progress in new crops. ASHS Press, Arlington, VA. https://www.hort.purdue.edu/newcrop/proceedings1996/V3-632.html. Accessed 10 Sep. 2018.
Thornley, J.H.M. 2002. Instantaneous canopy photosynthesis: analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis. Ann. Bot. 89:451-458.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.