• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.7
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• pp.599-607
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• 2006

The experiments were carried out to develop simulation model for estimating the yield of soybean in upland and paddy field condition. Field experiments were done at National Institute of Crop Science in 2005. The evaluated soybean cultivars were Taekwangkong, Daewonkong, and Hwangkeumkong. Soybean seeds were planted by hill seeding with 3-4 seeds and row and hill spacing were $60{\times}10cm$ in upland and $60{\times}15cm$ in paddy field. Seeds were sown on row (without making ridge) and on the top of ridge in upland and paddy field, respectively. Field parameters were measured yield components ($plants/m^{2}$, pod no./plant, and 100-seed weight, seed yield and growth characteristics (stem length, leaf area at each stage, and dry weight of shoot) and after measuring they were compared the relationships with seed yield and yield components and seed yield and growth characteristics. Seed yield of soybean was affected by cultivars and planting density. Seed yield was higher in upland than paddy field due to the higher planting density in upland field. The upland soybeans generally had lower 100-seed weight than that of paddy field. Seed yield of soybean in a paddy field was greatest in Taekwangkong and followed by Daewonkong and Hwangkeumkong. The harvest index of taekwangkong and Hwanggumkong was higher in upland than paddy field, however, it was higher in paddy field than upland in Daewonkong. Seed yield was greatest in Daewonkong in both experimental fields. The greatest stem length was observed in taekwangkong and Hwanggumkong (R6) in late growth stage in paddy field. Dry weight of shoot and pod, pod number, stem length, and stem diameter were higher grown in paddy field than grown in upland. Crop growth rate (CGR) of cultivars was higher in paddy field after 8 WAS(weeks after sowing) and it was greatest at 13 WAS in Daewonkong among the cultivars. In upland field, CGR was greatest in Taekwangkong and then followed by Daewonkong and Hwanggumkong during 12 and 15 WAS. There was no significant relationships between 100-seed weight and seed yield in both experimental fields. A significant positive relationship was observed between seed number and seed yield. The correlation coefficients between leaf area and shoot dry weight were about 0.8 during the whole growth stage except 5 WAS and 4-5 WAS in paddy field and upland, respectively. This experiment was done just one year and drained paddy field condition was not satisfied drained condition successfully at 7th leaf age of soybean by the heavy rain, so we suggest that the excessive soil water reduced seed yield in paddy field and the weather condition should be considered for utilizing of these results.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.2
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• pp.112-126
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• 1991

The atmospheric carbon dioxide concentration is ever-increasing and expected to reach about 600 ppmv some time during next century. Such an increase of $CO_2$ may cause a warming of the earth's surface of 1.5 to 4.5$^{\circ}C$, resulting in great changes in natural and agricultural ecosystems. The climatic scenario under doubled $CO_2$ projected by general circulation model of Goddard Institute for Space Studies(GISS) was adopted to evaluate the potential impact of climate change on agroclimatic resources, net primary productivity and rice productivity in Korea. The annual mean temperature was expected to rise by 3.5 to 4.$0^{\circ}C$ and the annual precipitation to vary by -5 to 20% as compared to current normal climate (1951 to 1980), resulting in the increase of possible duration of crop growth(days above 15$^{\circ}C$ in daily mean temperature) by 30 to 50 days and of effective accumulated temperature(EAT=∑Ti, Ti$\geq$1$0^{\circ}C$) by 1200 to 150$0^{\circ}C$. day which roughly corresponds to the shift of its isopleth northward by 300 to 400 km and by 600 to 700 m in altitude. The hydrological condition evaluated by radiative dryness index (RDI =Rn/ $\ell$P) is presumed to change slightly. The net primary productivity under the 2$\times$$CO_2$ climate was estimated to decrease by 3 to 4% when calculated without considering the photosynthesis stimulation due to $CO_2$ enrichment. Empirical crop-weather model was constructed for national rice yield prediction. The rice yields predicted by this model under 2 $\times$ $CO_2$ climatic scenario at the technological level of 1987 were lower by 34-43% than those under current normal climate. The parameters of MACROS, a dynamic simulation model from IRRI, were modified to simulate the growth and development of Korean rice cultivars under current and doubled $CO_2$ climatic condition. When simulated starting seedling emergence of May 10, the rice yield of Hwaseongbyeo(medium maturity) under 2 $\times$ $CO_2$ climate in Suwon showed 37% reduction compared to that under current normal climate. The yield reduction was ascribable mainly to the shortening of vegetative and ripening period due to accelerated development by higher temperature. Any simulated yields when shifted emergence date from April 10 to July 10 with Hwaseongbyeo (medium maturity) and Palgeum (late maturity) under 2 $\times$ $CO_2$ climate did not exceed the yield of Hwaseongbyeo simulated at seedling emergence on May 10 under current climate. The imaginary variety, having the same characteristics as those of Hwaseongbyeo except growth duration of 100 days from seedling emergence to heading, showed 4% increase in yield when simulated at seedling emergence on May 25 producing the highest yield. The simulation revealed that grain yields of rice increase to a greater extent under 2$\times$ $CO_2$-doubled condition than under current atmospheric $CO_2$ concentration as the plant type becomes more erect.

• 한국약용작물학회:학술대회논문집
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• 2010.10a
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• pp.330-331
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• 2010

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.187-187
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• 2019

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.1-14
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• 2015

Climate change caused by elevated greenhouse gases would affect crop production through different pathways in agricultural ecosystems. Because an agricultural ecosystem has complex interactions between societal and economical environment as well as organisms, climate, and soil, adaptation measures in response to climate change on a specific sector could cause undesirable impacts on other sectors inadvertently. An integrated system, which links individual models for components of agricultural ecosystems, would allow to take into account complex interactions existing in a given agricultural ecosystem under climate change and to derive proper adaptation measures in order to improve crop productivity. Most of models for agricultural ecosystems have been used in a separate sector, e.g., prediction of water resources or crop growth. Few of those models have been desiged to be connected to other models as a module of an integrated system. Threfore, it would be crucial to redesign and to refine individual models that have been used for simulation of individual sectors. To improve models for each sector in terms of accuracy and algorithm, it would also be needed to obtain crop growth data through construction of super-sites and satellite sites for long-term monitoring of agricultural ecosystems. It would be advantageous to design a model in a sector from abstraction and inheritance of a simple model, which would facilitate development of modules compatible to the integrated prediction system. Because agricultural production is influenced by social and economical sectors considerably, construction of an integreated system that simulates agricultural production as well as economical activities including trade and demand is merited for prediction of crop production under climate change.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.17-26
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• 2024

This study aimed to design and build a web-based decision support system for wheat cultivation management. The system is designed to collect and measure the weather environment at the growth stage on a daily basis and predict the soil moisture content. Based on this, APSIM, one of the process-based crop models, was used to predict the potential yield of wheat cultivation in real time by making decisions at each stage. The decision-making system for wheat crop management was designed to provide information through a web-based dashboard in consideration of user convenience and to comprehensively evaluate wheat yield potential according to past, present, and future weather conditions. Based on the APSIM model, the system estimates the current yield using past and present weather data and predicts future weather using the past 40 years of weather data to estimate the potential yield at harvest. This system is expected to be developed into a decision support system for farmers to prescribe irrigation and fertilizer in order to increase domestic wheat production and quality by enhancing the yield estimation model by adding influence factors that can contribute to improving wheat yield.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.221-237
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• 2005

For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. $6,600m^2$ in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of $10\times10m$. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay $(7.6\%)$ and the largest in Si $(21.4\%)$. The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to $38\%$, and that of rice yield ranged from 11 to $21\%$. CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as $76\%$ of yield variability, of which $21.6\%$ is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about $50\%$ of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.

• Korean Journal of Medicinal Crop Science
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• v.27 no.2
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• pp.75-85
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• 2019

Background: Medicinal plants are widely used in Asia. They have proven to be an invaluable asset in modern drug discovery and their demand has been steadily increasing across various industries. Methods and Results: Using 4,867 valid patents related to 12 oriental medicinal plants of 10 country groups, the growth and development potential of patents was evaluated. The cites per patent (CPP) and patent family size (PFS) indices were used to evaluate the market capability and technological level of the collected patents. Meanwhile, the patent impact index (PII) and technology strength (TS) were used to compare the technological competitiveness of patents among various technology types and markets. Both CPP and PFS indices showed that magnolia-vine and balloon flower have numerous core or original patents. Furthermore, an increase in both PII and TS indices was observed. A newly designed intellectual property multi-layer (IPM) model predicted that the medicine, genome and cosmetic categories have a high possibility of patent application growth. Conclusions: The IPM model can be used to provide the scope of particular technology fields for patent development. In addition, this study can assist patents to advance in the international market and guide the development of a national industrial strategy.