• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.6
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• pp.17-25
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• 2018

This study was conducted to calculate the optimum height of trees, estimating a model for the prediction of tree growth for the landscape improvement of the Gyeonghoeru area. For the verification of measures for management, this study conducted a photographic survey of the Gyeonghoeru area and used the Pressler's formula to examine the growth rate of the pine forest of Mansesan. The results of the study are as follows. First, as a result of a field survey and landscape analysis, trees in the Gyeonghoeru area are large ones with more than a diameter at breast height of 30cm, except for weeping cherry trees and persimmon trees, and especially, it is necessary to manage them or replace with small trees through the landscape of Mansesan, which screens the landscape and pruning the trees in the terraced flower garden in the north. Second, as a result of a measurement of the growth rate of trees, for 10 years on average, they grew up by 14% in source diameter and 5% in tree height 5% in south of Mansesan and by 7% in source diameter and 2.4% in tree height in the north of Mansesan. Furthermore, when a simulation was prepared based on the measured growth rate of trees, it was found out that 20 years later, on the landscape on the second floor of Gyeonghoeru, the pine forest of Mansesan would cut off the skyline of Mt. Inwang-san. Third, this study analyzed a landscape improvement simulation and proposed a plan for tree management to take a view of the landscape of the Gyeonghoeru area. This study has a significance that it drew an efficient planting maintenance policy, considering the landscape characteristics of the Gyeonghoeru area.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• 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).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.241-247
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• 2001

Boundary line method was adopted to analyze the relationships between rice yield and meteorological conditions during rice growing period. Boundary lines of yield responses to mean temperature($T_a$) and sunshine hour( $S_{h}$) and diurnal temperature range($T_r$) were well-fitted to hyperbolic functions of f($T_a$) =$$\beta$_{0t}$(1-EXP(-$$\beta$_{1t}$ $\times$ ($T_a$) ) and f( $S_{h}$)=$$\beta$_{0t}$((1-EXP($$\beta$_{1t}$$\times$ $S_{h}$)), to quadratic function of f($T_r$) =$\beta$$_{0r}$(1-($T_r$ 1r)$^2$), respectively. to take into account to, the sterility caused by low temperature during reproductive stage, cooling degree days [$T_c$ =$\Sigma$(20-$T_a$] for 30 days before heading were calculated. Boundary lines of yield responses to $T_c$ were fitted well to exponential function of f($T_c$) )=$\beta$$_{0c}$exp(-$$\beta$_{1c}$$\times$$T_c$ ). Excluding the constants of $\beta$$_{0s}$ from the boundary line functions, formed are the relative function values in the range of 0 to 1. And these were used as yield indices of the meteorological elements which indicate the degree of influence on rice yield. Assuming that the meteorological elements act multiplicatively and independently from each other, meteorological yield index (MIY) was calculated by the geometric mean of indices for each meteorological elements. MIY in each growth period showed good linear relationship with rice yield. The MIY's during 31 to 45 days after transplanting(DAT) in vegetative stage, during 30 to 16 days before heading (DBH) in reproductive stage and during 20 days after heading (DAH) in ripening stage showed greater explainablity for yield variation in each growth stage. MIY for the whole growth period was calculated by the following three methods of geometric mean of the indices for vegetative stage (MIVG), reproductive stage (HIRG) and ripening stage (HIRS). MI $Y_{I}$ was calculated by the geometric mean of meteorological indices showing the highest determination coefficient n each growth stage of rice. That is, (equation omitted) was calculated by the geometric mean of all the MIY's for all the growth periods devided into 15 to 20 days intervals from transplanting to 40 DAH. MI $Y_{III}$ was calculated by the geometric mean of MIY's for 45 days of vegetative stage (MIV $G_{0-45}$ ), 30 days of reproductive stage (MIR $G_{30-0}$) and 40 days of ripening stage (MIR $S_{0-40}$). MI $Y_{I}$, MI $Y_{II}$ and MI $Y_{III}$ showed good linear relationships with grain yield, the coefficients of determination being 0.651, 0.670 and 0.613, respectively.and 0.613, respectively.

• Journal of Korean Society of Forest Science
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• v.99 no.1
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• pp.9-15
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• 2010

This study was conducted to develop a stand growth model and a stand yield table for Eucalyptus pellita and Acacia mangium plantations in Kalimantan, Indonesia. To develop a stand growth model, Weibull robability density function, a diameter class model, was applied in this study. In the development of stand growth model by site index and stand age, a hierarchy is generally required - estimation, recovery and prediction of the diameter class model. A number of grow equations were also involved in each process to estimate diameter, height, basal area, minimum or maximum diameter. To examine whether the grow equations are adequate for Eucalyptus pellita or Acacia mangium plantations, a fitness index was analyzed for each equation. The results showed that fitness indices were ranged from 65 to 89% for Eucalyptus pellita plantations and from 72 to 95% for Acacia mangium plantations. As being highly adequate for the plantations, a stand yield table was developed based on the resulted growth model, and applied to estimate the stand growth with midium site index for 10-year period. The highest annual stand growth of Eucalyptus pellita plantations was estimated to be 21.25 $m^3$/ha, while that of Acacia mangium plantations was 27.5 $m^3$/ha. In terms of annual stand growth, Acacia mangium plantations appeared to be more beneficial than Eucalyptus pellita plantations. Also, to estimate commercial timber volume available from the plantations, an assumption that a log would be cut by 2.7 m in length and the rest of the log would be cut by 1.5m was involved. The commercial timber volume available from Eucalyptus pellita plantations was 68.0 $m^3$/ha, 33% from the total stand volume, 203.2 $m^3$/ha. Also 96.7 $m^3$/ha of commercial timbers were available from Acacia mangium plantations, which was 42% from the 232.9 $m^3$/ha in total. Presenting a good information about the stand growth in Eucalyptus pellita and Acacia mangium plantations, this study might be useful for whom proceeds or considers an abroad plantation for merchantable timber production or carbon credit in tropical regions.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.592-598
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• 2012

Forest stand yield and its changes along with 10 thinning scenarios were estimated using a forest stand yield model for six major tree species in Korea, such as Pinus densiflora in Gangwon province, Pinus densiflora in other regions, Pinus koraiensis, Larix leptolepis, Quercus acutissima Carruth, Quercus mongolica. The 10 thinning scenarios were generated based on a number of constraints and assumptions. For instance, it was assumed that thinning is implemented between 15 years and 40 years with 5 year period and its duration should be at least 10 years. Also, the overall removal rate from the thinning treatments was assumed to be not greater than 60%. Under the 10 scenarios, the overall stand yield volumes from thinning and final harvesting were estimated for each species and site index. The results showed that highest yield volumes were obtained for Pinus densiflora in Gangwon province, Pinus koraiensis and Quercus mongolica when 30% of basal areas were thinned at 20 and 40 years, while highest yield volumes were obtained for Pinus densiflora in other regions and Larix leptolepis when 20% of basal areas were thinned at 20, 30 and 40 years. Those two scenarios gave the same amount of highest yield volume for Quercus acutissima Carruth. Also the results indicated that thinning treatment is effective to increase overall stand yield volume and its effects are larger with a higher site index. The largest thinning effects were found in Pinus densiflora in Gangwon province (28%) and Larix leptolepis (25%), while limited in Pinus koraiensis (12%). The forest stand yield model, used in this research, could be an effective tool for estimating the stand dynamics with various thinning treatments, but it could be improved in a further research that validates its applicability in the field.

• Journal of Korean Society of Forest Science
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• v.104 no.3
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• pp.421-426
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• 2015

The objective of this paper is to look into the growth of Cryptomeria japonica stand in South Korea along with the evaluation on their yields, followed by their carbon stocks and removals. A total of 106 sample plots were selected from Jeonnam, Gyeongnam, and Jeju, where the groups of standard are grown. We only used 92 plots data except outlier. As part of the analysis, the Weibull diameter distribution was applied. In order to estimate the diameter distribution, the growth estimation equation for each of the growth factors including the height, the diameter at breast height, and the basal area was drafted out and the verification for each equation was examined. The site index for figuring out the forest productivity of Cryptomeria japonica stand for each district was also developed as a Schumacher model and 30yr was used as a reference age for the estimation of the site index. It was found that the site index for Cryptomeria japonica stand in South Korea ranges from 10 to 16 and this result was used as a standard for developing the stand yield table. According to the site 14 in the stand yield table, the mean annual increment (MAI) of the Cryptomeria japonica reaches $7.6m^3/ha$ on its 25yr and its growing stock is estimated to be at $190.1m^3/ha$. This volume is about $20m^3$ as high as that of the Chamaesyparis obtusa. Furthermore, the annual carbon absorptions for a Cryptomeria japonica stand reached the peak at 25yr, which is 2.14 tC/ha/yr, $7.83tCO_2/ha/yr$. When compared to the other conifers, this rate is slightly higher than that of a Chamaecyparis obtusa ($7.5tCO_2/ha/yr$) but lower than that of the Pinus koraiensis ($10.4tCO_2/ha/yr$) and Larix kaempferi ($11.2tCO_2/ha/yr$). With such research result as a base, it is necessary to come up with the ways to enhance the utilization of Cryptomeria japonica as timbers, besides making use of their growth data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.78-82
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• 2022

Phenology determines the timing of crop development, and the timing of phenological events is strongly influenced by the temperature during the growing season. In process-based model, leaf area is simulated dynamically by coupling of morphology and phenology module. Therefore, the prediction of leaf appearance rate and final leaf number affects the performance of whole crop model. The dataset for the model equation was collected from SPA R chambers with five different temperature treatments. Beta distribution function (proposed by Yan and Hunt (1999)) was used for describing the leaf appearance rate as a function of temperature. The optimum temperature and the critical value were estimated to be 26.0℃ and 35.3℃, respectively. For evaluation of the model, the accumulated number of onion leaves observed in a temperature gradient chamber was compared with model estimates. The model estimate is the result of accumulating the daily increase in the number of onion leaves obtained by inputting the daily mean temperature during the growing season into the temperature model. In this study, the coefficient of determination (R²) and RMSE value of the model were 0.95 and 0.89, respectively.

• Journal of Bio-Environment Control
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• v.31 no.2
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• pp.98-103
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• 2022

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars ('Arihyang', 'Jukhyang', 'Keumsil', 'Maehyang', and 'Seollhyang'). The coefficient of determination (R²) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R² value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars 'Jukhyang', 'Seolhyang', and 'Maehyang' could be non-destructively predicted using the model developed in this study, as they had R² values over 0.96. The cultivars 'Arihyang' and 'Geumsil' had slightly low R² values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.