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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.11
- 수록면
- 458 - 470 (13page)
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이 논문의 연구 히스토리 (2)
2020
Prediction Model for Hourly Soil Temperature by Soil Depth to Irrigated Sandy Loam Soil in Greenhouse
Seung-Oh Hu
,
Jung-Hun Ok
,
Seon-Ah Hwang
외 3명
한국토양비료학회지
2020.11
학술저널
Prediction Model for Hourly Soil Temperature by Soil Depth to Irrigated Sandy Loam Soil in Greenhouse
Seung-Oh Hu
,
Jung-Hun Ok
,
Seon-Ah Hwang
외 3명
한국토양비료학회 학술발표회 초록집
2020.10
학술대회자료
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초록· 키워드
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The soil temperature in the greenhouse reacts differently with changes of soil temperature affected by the outside temperature because it is heated in winter or cooled in summer to maintain the temperature. This study was conducted to analyze the changes of soil temperature by soil depth in the greenhouse and to create a model to predict soil temperature. As a result of measuring and analyzing from December 13, 2019, to May 28, 2020, the average soil temperature was lowest in January, and then continuously increased from February to May. The amplitude, which is the difference between the highest and lowest soil temperature, tends to decrease by the depth increases from 0-10㎝ to 50-60㎝. This tendency to decrease could be expressed as a function of exponential decrease by soil depth. As a result of comparison with the Fourier series and sinusoidal function models, the sinusoidal model shows statistically the same value with the Fourier series model and is more useful. However, since the sinusoidal function model is less accurate in predicting temperature change with a slope, a corrected model that can reflect the temperature change slope was required. As a result of the analysis, the following model could be used.
[수식은 본문 참조]
where, f(χ) is soil temperature, A₀ is initial temperature, An is amplitude, T is period, ø is phase, χ is time. This is a model in which a sine wave function representing periodicity is combined with a quadratic function that can take into account the slope of temperature change. If the quadratic function coefficient is positive, it can simulate the tendency to increase and decrease when the coefficient is negative. This model generally well-simulate soil temperature by soil depth during the measured period. The significance of this study is to analyze and predict the soil temperature in the greenhouse. Besides, the advantage of being able to take into account the gradient of temperature change can be used to predict soil temperature under outdoor conditions.
[수식은 본문 참조]
where, f(χ) is soil temperature, A₀ is initial temperature, An is amplitude, T is period, ø is phase, χ is time. This is a model in which a sine wave function representing periodicity is combined with a quadratic function that can take into account the slope of temperature change. If the quadratic function coefficient is positive, it can simulate the tendency to increase and decrease when the coefficient is negative. This model generally well-simulate soil temperature by soil depth during the measured period. The significance of this study is to analyze and predict the soil temperature in the greenhouse. Besides, the advantage of being able to take into account the gradient of temperature change can be used to predict soil temperature under outdoor conditions.
목차
ABSTRACT
Introduction
Materials and Methods
Results and Discussion
Conclusions
References
참고문헌 (22)
참고문헌 신청
Barman, D., D.K. Kundu, S. Pal, S. Pal, A.K. Chakraborty, A.K. Jha, S.P. Mazumdar, R. Saha, and P. Bhattacharyya. 2017. Soil temperature prediction from air temperature for alluvial soils in lower Indo-Gangetic plain. Int. Agrophys. 31:9-22.
Bilgili, M., B. Sahinand, and L. Sangun. 2013. Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models. Environ. Monit. Assess. 185:347-358.
Dolschak, K., K. Gartner, and T.W. Berger. 2015. A new approach to predict soil temperature under vegetated surfaces. Model. Earth Syst. Environm:1-32.
Ha, S.K., Y.S. Jung, L.Y. Kim, and B.C. Kim. 1996. Characteristics of soil temperature changes in selected regions of Korea. RDA. J. Agri. Sci. 38:427-434.
Halama, J.J., B.L. Barnhart, R.E. Kennedy, R.B. McKane, J.J. Graham, P.P. Pettus, A.F. Brookes, K.S. Djang, and R.S. Waschmann. 2018. Improved soil temperature modeling using spatially explicit solar energy drivers. Water 10:1398-1412.
Bilgili, M., B. Sahinand, and L. Sangun. 2013. Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models. Environ. Monit. Assess. 185:347-358.
Dolschak, K., K. Gartner, and T.W. Berger. 2015. A new approach to predict soil temperature under vegetated surfaces. Model. Earth Syst. Environm:1-32.
Ha, S.K., Y.S. Jung, L.Y. Kim, and B.C. Kim. 1996. Characteristics of soil temperature changes in selected regions of Korea. RDA. J. Agri. Sci. 38:427-434.
Halama, J.J., B.L. Barnhart, R.E. Kennedy, R.B. McKane, J.J. Graham, P.P. Pettus, A.F. Brookes, K.S. Djang, and R.S. Waschmann. 2018. Improved soil temperature modeling using spatially explicit solar energy drivers. Water 10:1398-1412.
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