• Journal of Radiation Protection and Research
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• v.29 no.4
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• pp.213-219
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• 2004

Soybean plants were exposed to HTO vapor in an exposure box for 1 hour at different growth stages. Relative concentrations of TFWT at the end of exposure (percent ratios of TFWT concentrations to mean HTO concentrations in air moisture in the box during exposure) decreased on the whole in the order of leaf > shell > seed > stem with the highest values of 40.2% and 6.4% for leaf and stem, respectively. TFWT concentrations reduced by factors of several thousands to several hundred-thousands from the end of exposure till the harvest. The reduction factor decreased in the order of leaf > shell > seed > stem. Relative OBT concentrations at harvest (ratios of the OBT concentration in the dry plant part at harvest to the initial leaf TFWT concentration, ml $g^{-1}$) were in the range of $2.2{\times}10^{-5}{\sim}9.5{\times}10^{-3}$ for seeds being the highest when the exposure was performed at the actively seed-developing stage. The exposure time-dependent variation in the OBT concentration was much greater in seeds and shells than in leaves and stems. It was indicated that OBT would contribute to almost all the radiation dose due to the consumption of soybean seeds in most cases after an acute exposure of growing plants to HTO vapor. Present results are applicable to establishing and validating soybean $^3H$ models for an acute accidental release of HTO.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.164-173
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• 2017

Alternative cropping systems would be one of climate change adaptation options. Suitable areas for a crop could be identified using a climate suitability model. The EcoCrop model has been used to assess climate suitability of crops using monthly climate surfaces, e.g., the digital climate map at high spatial resolution. Still, a high-performance computing approach would be needed for assessment of climate suitability to take into account a complex terrain in Korea, which requires considerably large climate data sets. The objectives of this study were to implement a script for R, which is an open source statistics analysis platform, in order to use the EcoCrop model under a parallel computing environment and to assess climate suitability of maize using digital climate maps at high spatial resolution, e.g., 1 km. The total running time reduced as the number of CPU (Central Processing Unit) core increased although the speedup with increasing number of CPU cores was not linear. For example, the wall clock time for assessing climate suitability index at 1 km spatial resolution reduced by 90% with 16 CPU cores. However, it took about 1.5 time to compute climate suitability index compared with a theoretical time for the given number of CPU. Implementation of climate suitability assessment system based on the MPI (Message Passing Interface) would allow support for the digital climate map at ultra-high spatial resolution, e.g., 30m, which would help site-specific design of cropping system for climate change adaptation.

• Journal of Korea Water Resources Association
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• v.50 no.6
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• pp.387-395
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• 2017

Sea level rise, accompanied by climate change, is expected to exacerbate seawater intrusion in the coastal groundwater system. As the salinity of saturated groundwater increases, salinity can increase even in the unsaturated soil above the groundwater surface, which may cause crop damage in the agricultural land. The other adverse impact of sea level rise is reduced unsaturated soil thicknesses. In this study, a composite model to assess impacts of sea level rise in coastal agricultural land is proposed. The composite model is based on the combined applications of a three dimensional model for simulating saltwater intrusion into the groundwater and a vertical one dimensional model for simulating unsaturated zone flow and transport. The water level and salinity distribution of groundwater are calculated using the three dimensional seawater intrusion model. At some uppermost nodes, where salinity are higher than the reference value, of the 3D mesh one dimensional unsaturated zone modeling is conducted along the soil layer between the ground water surface and the ground surface. A particular location is judged salinized when the concentration at the root-zone depth exceeds the tolerable salinity for ordinary crops. The developed model is applied to a hypothetical agricultural reclamation land. IPCC RCP 4.5 and 8.5 scenarios were used as sea level rise data. Results are presented for 2050 and 2100. As a result of the study, it is predicted that by 2100 in the climate change scenario RCP 8.5, there will be 7.8% increase in groundwater saltwater-intruded area, 6.0% increase of salinized soil area, and 1.6% in increase in water-logging area.

• Korean Journal Plant Pathology
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• v.3 no.3
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• pp.210-216
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• 1987

A computer program written to predict blast occurrence based on micro climatic events was developed and tested as an on-site microcomputer in field plots in 1984 and 1985. A microcomputer unit operating on alkaline batteries; continuously monitored air temperature, leaf wetness, and relative humidity; interpreted the microclimate information in relation to rice blast development and displayed daily values (0-8) of blast units of severity (BUS). Cumulative daily BUS values (CBUS) were highly correlated with blast development on the two susceptible cultivars, M-201 and Brazos grown in field plots. When CBUS values were used to predict the logit of disease proportions, the average coefficients of determination $(R^2)$ between these two factors were 71 to $91\%$, depending on cultivar and year. This was a significant improvement when compared to 61 to $79\%$ when days were used as a predictor of logit disease severity. The ability of CBUS to predict logit disease severity was slightly less with Brazos than M-201. This is significant inasmuch as Brazos showed field resistance at mid-sea­son. The results in this study indicate that the model has the potential for future use and that the model could be improved by incorporating other variables associated with host plants and pathogen races in addition to the key environmental variables.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.205-213
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• 2018

Development and validation of crop models often require measurements of biomass for the crop of interest. Considerable efforts would be needed to obtain a reasonable amount of biomass data because the destructive sampling of a given crop is usually used. The Kinect sensor, which has a combination of image and depth sensors, can be used for estimating crop biomass without using destructive sampling approach. This approach could provide more data sets for model development and validation. The objective of this study was to examine the applicability of the Kinect sensor for estimation of chinese cabbage fresh weight. The fresh weight of five chinese cabbage was measured and compared with estimates using the Kinect sensor. The estimates were obtained by scanning individual chinese cabbage to create point cloud, removing noise, and building a three dimensional model with a set of free software. It was found that the 3D model created using the Kinect sensor explained about 98.7% of variation in fresh weight of chinese cabbage. Furthermore, the correlation coefficient between estimates and measurements were highly significant, which suggested that the Kinect sensor would be applicable to estimation of fresh weight for chinese cabbage. Our results demonstrated that a depth sensor allows for a non-destructive sampling approach, which enables to collect observation data for crop fresh weight over time. This would help development and validation of a crop model using a large number of reliable data sets, which merits further studies on application of various depth sensors to crop dry weight measurements.

• Journal of Korea Foresty Energy
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• v.22 no.3
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• pp.1-10
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• 2003

The successional status and potential natural vegetation were examined in the natural deciduous forest in Mt. Chombong area. The examination was based on the subsequent process of generation replacement by understory saplings for the dominant canopy trees within 106 20mx20m square sample plots. The transition matrix model, which was modified from mathematical theory of Markov chain, was employed to analyze the successional status of the study forest. The model suggests that study forest is still seral, and it is considered to be more than 500 years away from the steady state or climax in terms of species composition. The simulations predict a remarkable decrease in the proportion of species composition of the present dominant Quercus mongolica and Kalopanax pictus from current 42.6% and 8.1% to less than 13.3% and 0.5%, respectively, at the steady state. On the contrary, the proportions of Abies holophylla, Acer mono, Fraxinus mandshurica, Tilia amurensis, and Acer pseudo-sieboldianum will increase at the steady state. The change of predicted composition ratio was generally coincide with the result of tolerance index to be compared with the study model. The hypothesis and sensitivity of the model were also discussed to evaluate the applicability to the real situation. The overall results indicated that the present dynamics of the forest must reflect the seral state due to previous disturbance mainly by human related interference.

• Journal of Distribution Research
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• v.5 no.2
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• pp.49-68
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• 2001

In this chapter, we summarize the results on the optimal location selection and present limitation and direction of research. In order to reach the objective, this study selected and tested the interaction model which obtains the value of co-ordinates on location selection through the optimization technique. This study used the original variables in the model, but the results indicated that there is difference in reality. In order to overcome this difference, this study peformed market survey and found the new variables (first data such as price, quality and assortment of goods, and the second data such as aggregate area, and area of shop, and the number of cars in the parking lot). Then this study determined an optimal variable by empirical analysis which compares an actual value of market share in 1988 with the market share yielded in the model. However, this study found the market share in each variables does not reflect a reality due to an assumption of λ-value in the model. In order to improve this, this study performed a sensitivity analysis which adds the λ value from 1.0 to 2.9 marginally. The analyzed result indicated the highest significance with the market share ratio in 1998 at λ of 1.0. Applying the weighted value to a variable from each of the first data and second data yielded the results that more variables from the first data coincided with the realistic rank on sales. Although this study have some limits and improvements, if a marketer uses this extended model, more significant results will be produced.

• Journal of Bio-Environment Control
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• v.29 no.1
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• pp.43-51
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• 2020

For developing a canopy photosynthesis model, an efficient method to measure the photosynthetic rate in a growth chamber is required. The objective of this study was to develop a method for establishing canopy photosynthetic rate curves of romaine lettuce (Lactuca sativa L.) with light intensity and CO₂ concentration variables using controlled growth chamber. The plants were grown in plant factory modules, and the canopy photosynthesis rates were measured in sealed growth chambers made of acrylic (1.0 × 0.8 × 0.5 m). First, the canopy photosynthetic rates of the plants were measured, and then the time constants were compared between two application methods: 1) changing light intensity (340, 270, 200, and 130 μmol·m^-2·s^-1) at a fixed CO₂ concentration (1,000 μmol·mol^-1) and 2) changing CO₂ concentration (600, 1,000, 1,400, and 1,800 μmol·mol^-1) at a fixed light intensity (200 μmol·m^-2·s^-1). Second, the canopy photosynthetic rates were measured by changing the light intensity at a CO₂ concentration of 1,000 μmol·mol^-1 and compared with those measured by changing the CO₂ concentration at a light intensity of 200 μmol·m^-2·s^-1. The time constant when changing the CO₂ concentration at the fixed light intensity was 3.2 times longer, and the deviation in photosynthetic rate was larger than when changing the light intensity. The canopy photosynthetic rate was obtained stably with a time lag of one min when changing the light intensity, while a time lag of six min or longer was required when changing the CO₂ concentration. Therefore, changing the light intensity at a fixed CO₂ concentration is more appropriate for short-term measurement of canopy photosynthesis using a growth chamber.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.5
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• pp.405-412
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• 1993

This experiment was conducted to investigate the effects of water depths on seedling stand and early growth of califonia rice varieties, S201, M202, A301, Italico livorno and Korean variety, Hwaseongbyeo, and barnyardgrass (Echinochloa crus-galli) The coleoptile length of rice was longer with deep water depth while for the radicle length shorten. As water depth was increased, the percentage of seedling stand were decreased slightly in rice, while sharply increased in barnyardgrass. Plant height of rice with increasing water depth were longer, whereas that of barnyardgrass reduced significantly with weaker. Tiller number of rice and barnyardgrass were significantly reduced as water depth increased. Dry matter weight and healthy score of rice seedling at 35DAS were highest in 7.5cm water depth followed saturated moisture, 15, and 22.5cm water depth, while for barnyardgrass those were especially negatively affected by deep water depth. These results showed that the seedling stand and early growth of barnyardgrass was highly suppressed by deeper water levels compared with rice. Rice cultivars which are showes growth characteristics in deeper water levels at early growth stage were Italico livorno and S201 in Japonica / Indica.