http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Seung-Oh Hur,Jung-Hun Ok,Seon-Ah Hwang,Hee-Rae Cho,Yong-Seon Zhang,Hyup-Sung Lee 한국토양비료학회 2020 한국토양비료학회지 Vol.53 No.4
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.
토양특성 기반 토양수분 함량 예측을 위한 PTF 적용성 검정
허승오(Seung-Oh Hur),손연규(Yeon-Gyu Sonn),현병근(Byung-Kewn Hyun),신국식(Kook-Sik Shin),오택근(Taek-Keun Oh),김정규(Jeong-Gyu Kim) 충남대학교 농업과학연구소 2014 Korean Journal of Agricultural Science Vol.41 No.4
Identifying soil water content as a major factor for evaluating irrigation and water resource is a primary module to develop a prediction model. A variety of PTFs (Pedo-Transfer Functions) are applied in the models to estimate soil water content, the analysis techniques, however, which compare the estimated from models and the measured by instruments, are not reached at the level to demonstrate the effectiveness of the PTFs in Korea. Many soil physicians such as Eom, Peterson, Rawls, Saxton, Bruand, Baties, Tomasella & Hodnett (T&H), and Minasny, have developed analytic models using PTFs. Soil data for the analysis used soil water contents on 347 soil series (10 kPa), 358 soil series (33 kPa), 356 soil series (1,500 kPa) established by NAAS (National Academy of Agricultural Science). A coefficient of determination on soil water content at 10, 33 and 1,500 kPa was the highest as 0.5932 in EM (Eom model), 0.6744 in REM (Rawls model) and 0.6108 in REM, respectively. In conclusion, it is strongly suggested that the use of EM or REM is suitable for estimating soil water content in Korea although SM (Saxton model) has been widely used.