• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2003.07a
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• pp.111-116
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• 2003

토양 경도는 작물의 성장 특히, 뿌리의 성장에 큰 영향을 미친다. 토양의 경도가 증가함에 따라 작물의 수량이 감소되는 현상은 많은 연구에서 보고된 바 있다. 따라서 작물에 따라 적절한 토양 경도를 유지하는 일은 대단히 중요하다. 최근, 정밀 농업에 대한 관심이 증가됨에 따라 토양과 작물의 상태를 정확히 나타낼 수 있는 다양한 변수가 연구되고 있으나, 아직까지 단 하나의 변수로써 생육 상태를 정확히 나타낼 수는 없다. 토양 경도도 작물의 생육 상태에 영향을 미치는 변수로서, 그 영향을 구명할 필요가 있다. (중략)

• Korean Journal of Environment and Ecology
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• v.25 no.4
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• pp.590-600
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• 2011

The research was carried out for prediction of the potential habitats of warm-temperate evergreen broad-leaved trees under the current climate(1961~1990) and three climate change scenario(2081~2100) (CCCMA-A2, CSIRO-A2 and HADCM3-A2) using classification tree(CT) model. Presence/absence records of warm-temperate evergreen broad-leaved trees were extracted from actual distribution data as response variables, and four climatic variables (warmth index, WI; minimum temperature of the coldest month, TMC; summer precipitation, PRS; and winter precipitation, PRW) were used as predictor variables. Potential habitats(PH) was predicted 28,230$km^2$ under the current climate and 77,140~89,285$km^2$ under the three climate change scenarios. The PH masked by land use(PHLU) was predicted 8,274$km^2$ and the proportion of PHLU within PH was 29.3% under the current climate. The PH masked by land use(PHLU) was predicted 35,177~45,170$km^2$ and increased 26.9~36.9% under the three climate change scenarios. The expansion of warm-temperate evergreen broad-leaved trees by climate change progressed habitat fragmentation by restriction of land use. The habitats increase of warm-temperate evergreen broad-leaved trees had been expected competitive with warm-temperate deciduous broadleaf forest and suggested the expand and northward shift of warm-temperate evergreen broad-leaved forest zone.

• The Korean Journal of Mycology
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• v.26 no.2 s.85
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• pp.265-274
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• 1998

This study was carried out to obtain the basic data for an artificial cultivation of Phellinus sp.. The optimum conditions for the mycelial growth on the different sawdusts (Quercus aliena, Morns alba and Alnus japonica) substrate of an isolated Phellinus sp. were optimized by response surface methodology. The ratio of rice bran addition to sawdust and the suitable moisture content for the mycelial growth in the all sawdust media were about 30% (w/w) and $65{\sim}70%$ (w/v), respectively. The initial pHs for the mycelial growth of Quercus aliena and Morns alba were in the range of $pH\;5{\sim}6$, whereas Alnus japonica was obtained at pH 6. The optimum temperature for the mycelial growth was about $25{\sim}30^{\circ}C$, depending on the different kinds of wood substrates. From the response surface analysis, the values of independent variables of Quercus aliena at stationary points were determined to be 31.01 % (w/w) of rice bran, pH of 5.31 and 69.03% (w/v) of moisture content, and the expected value of mycelial growth was about 8.32 cm. Both the ratio of rice bran addition to sawdust $(X_1)$ and moisture content $(X_3)$ were effective to the mycelial growth. In the case of Morns alba, the ratio of rice bran addition to sawdust, initial pH and moisture content at the stationary points were 28.77% (w/w), 5.28 and 69.8 (w/v),respectively, and the expected mycelial growth of 7.60 cm was obtained. Stationary points for the mycelial growth in the sawdust media of Alnus japonica were 28.74% (w/w) of rice bran, pH of 6. 04 and 66.96% (w/v) of moisture content, and the expected values of mycelial growth was about 5.38 cm. Based on the above results, there was correlations between the mycelial growth and independent variables, and the effect of rice bran $(X_1)$ and initial pH $(X_2)$ for the mycelial growth were higher than the moisture content $(X_3)$. The optimum species of sawdust media for the my celial growth of Phellinus sp. was in the order of Quercus aliena > Morns alba > Alnus japonica.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 1992.12a
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• pp.15-16
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• 1992

1. 연구의 필요성 및 목적 필요성 - 기상변화에 따른 수도생육의 예측을 통한 적절한 Crop management - 수도수확량 예측을 통한 계획생산의 가능 - 최적 물관리를 위한 기초자료제공 목적 수도의 생육 및 수확량을 예측 할 수 있는 생리학적(physiological ) 모형인 SIMRIW을 우리의 기후조건과 수도품종에 적용하여 모형의 매개변수를 보정하고, 모형의 적용성을 검사하는데 있다. (중략)

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.1
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• pp.9-15
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• 2000

The objective of this study is to evaluate the use of RADARSAT and Landsat TM data for the monitoring of rice growth. The relationships between backscatter coefficients($\sigma$$^{0}$ ) of RADARSAT data and digital numbers (DN) of Landsat TM and rice growth parameters were investigated. Radar backscatter coefficients were calculated by calibration process and then compared with rice growth parameters; plant height, leaf area index (LAI), and fresh and dry biomass. When radar backscatter coefficient ($\sigma$$^{0}$ ) of rice was expressed as a function of time, it is shown that the increasing trend ranged from -22--20dB to -9--8dB as growth advances. The temporal variation of backscatter coefficient was significant to interpret rice growth. According to the relationship between leaf area index and backscatter coefficient, backscatter coefficient underestimated leaf area index at the beginning of life history and overestimated, at the reproductive stage. The same increasing trend between biomass and backscatter coefficient was shown. From these results, RADARSAT data appear positive to the monitoring of rice growth. Each band of time-series Landsat TM data had a significant trend as a rice crop grows during its life cycle. Spectral indices, NDVI[(TM4-TM3)/(TM4+TM3)] and RVI(TM4/TM2), derived from Landsat TM equivalent bands had the same trend as leaf area index.

• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.34-39
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• 2023

Growth modeling in plant factories can not only control stable production and yield, but also control environmental conditions by considering the relationship between environmental factors and plant growth rate. In this study, using the expolinear function, we modeled perilla [Perilla frutescens (L.) Britt.] cultivated in a plant factory. Perilla growth was investigated 12 times until flower bud differentiation occurred after planting under light intensity, photoperiod, and the ratio of mixed light conditions of 130 μmol·m^-2·s^-1, 12/12 h, red:green:blue (7:1:2), respectively. Additionally, modeling was performed to predict dry and fresh weights using the expolinear function. Fresh and dry weights were strongly positively correlated (r = 0.996). Except for dry weight, fresh weight showed a high positive correlation with leaf area, followed by plant height, number of leaves, number of nodes, leaf length, and leaf width. When the number of days after transplanting, leaf area, and plant height were used as independent variables for growth prediction, leaf area was found to be an appropriate independent variable for growth prediction. However, additional destructive or non-destructive methods for predicting growth should be considered. In this study, we created a growth model formula to predict perilla growth in plant factories.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.47-55
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• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• Horticultural Science & Technology
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• v.33 no.6
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• pp.911-922
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• 2015

This study was carried out to evaluate growth characteristics of Kimchi cabbage cultivated in various highland areas, and to create a predicting model for the production of highland Kimchi cabbage based on the growth parameters and climatic elements. Regression model for the estimation of head weight was designed with non-destructive measured growth variables (NDGV) such as leaf length (LL), leaf width (LW), head height (HH), head width (HW), and growing degree days (GDD), which was $y=6897.5-3.57{\times}GDD-136{\times}LW+116{\times}PH+155{\times}HH-423{\times}HW+0.28{\times}HH{\times}HW{\times}HW$, ($r^2=0.989$), and was improved by using compensation terms such as the ratio (LW estimated with GDD/measured LW ), leaf growth rate by soil moisture, and relative growth rate of leaf during drought period. In addition, we proposed Excel spreadsheet model for simulation of yield prediction of highland Kimchi cabbage. This Excel spreadsheet was composed four different sheets; growth data sheet measured at famer's field, daily average temperature data sheet for calculating GDD, soil moisture content data sheet for evaluating the soil water effect on leaf growth, and equation sheet for simulating the estimation of production. This Excel spreadsheet model can be practically used for predicting the production of highland Kimchi cabbage, which was calculated by (acreage of cultivation) ${\times}$ (number of plants) ${\times}$ (head weight estimated with growth variables and GDD) ${\times}$ (compensation terms derived relationship of GDD and growth by soil moisture) ${\times}$ (marketable head rate).