• Horticultural Science & Technology
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• v.29 no.3
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• pp.211-216
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• 2011

This study was carried out to clarify precise $CO_2$ demands of paprika plants (Capsicum annumm L.) by measuring photosynthesis rates of the leaves in high, low positions, and the $CO_2$ consumption of a whole plant in a large sealed chamber. A photosynthesis measuring system (LI-6400) was used to measure the photosynthetic rates of the leaves located in different positions. A large sealed chamber that can control inside environmental factors was developed for measuring $CO_2$ consumption by a whole paprika plant. With increase of radiation, photosynthetic rates of the leaves in higher position became larger than those in lower position. The $CO_2$ consumption by the plant was estimated by using decrement of $CO_2$ concentration from initial level of 1500 ${\mu}mol{\cdot}mol^{-1}$ in the chamber with increase of integrated radiation. A regression model for estimating $CO_2$ consumption by the plant (leaf area = 7,533.4 $cm^2$) was expressed with integrated radiation (x) and was suggested as $y=-0.06234+3.671^*x/(2.589+x)$ ($R^2=0.9966^{***}$). The photosynthetic rate of the whole plant measured in the chamber was 3.4 ${\mu}mol\;CO_2{\cdot}m^{-2}{\cdot}s^{-1}$ under 300 ${\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ light intensity, which is in-between photosynthetic rates of the leaves in high and low positions. For this reason, some differences between required and supplied $CO_2$ amounts in greenhouses might occur when depending too much on photosynthetic rates of leaves. Therefore, we can estimate more accurately $CO_2$ amount required in commercial greenhouses by using $CO_2$ consumption model of a whole plant obtained in this study in addition to leaf photosynthetic rate.

• Horticultural Science & Technology
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• v.28 no.5
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• pp.836-844
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• 2010

This study was carried out to investigate plant characteristics and nutritional components of the genetically modified (GM) Chinese cabbage and its control line grown in the central and northern parts of Korea in order to establish the evaluating protocol and standard assessment. The GM and non-GM Chinese cabbage was planted with normal and concentrated density at two locations in spring and fall of 2008 and 2009. From the statistic analysis on plant characteristics and nutritional components, there were not many significant differences between GM and non-GM Chinese cabbage. Only few differences in the plant characteristics were found between the dense and normal planting. In the dense planting, there was no significant difference between GM and non-GM Chinese cabbages except for three out of 18 plant traits, such as leaf shape, hairiness and midrib length. On the other hand, nine plant traits including leaf length, leaf width, leaf color, leaf shape, fresh weigh of ground part, number of leaf, midrib length, midrib width and root diameter were slightly different between GM and non-GM Chinese cabbage in the normal planting. In case of leaf length, midrib length, midrib width and fresh weigh of ground part, there were significantly differences not only between two lines, but also between two locations. From nutritional component analysis, only five fatty acids were identified in the Chinese cabbage: palmitic acid, oleic acid, stearic acid, linoleic acid and linolenic acid. Except linoleic acid, four fatty acids in one gram of dried sample from GM line were little higher than those from non-GM line. However, there were no significant differences in total contents of fatty acids not only between GM and non-GM Chinese cabbage line, but also between northern and central cultivating areas in the normal and dense planting. According to the composition of inorganic elements identified in the samples from both lines, there were six macro-elements, such as N, P, Ca, K, Mg and Na, and four micro-elements, Cu, Fe, Mn and Zn. Based on the result from PCA analysis, specific clusters were not found between GM Chinese cabbage and the control line, but found between two regions.

• Journal of Life Science
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• v.25 no.3
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• pp.349-356
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• 2015

Thanks to recent advances in next-generation sequencing (NGS) technology, diverse livestock species have been dissected at the genome-wide sequence level. As for cattle, there are currently four Korean indigenous breeds registered with the Domestic Animal Diversity Information System of the Food and Agricultural Organization of the United Nations: Hanwoo, Chikso, Heugu, and Jeju Heugu. These native genetic resources were recently whole-genome resequenced using various NGS technologies, providing enormous single nucleotide polymorphism information across the genomes. The NGS application further provided biological such that Korean native cattle are genetically distant from some cattle breeds of European origins. In addition, the NGS technology was successfully applied to detect structural variations, particularly copy number variations that were usually difficult to identify at the genome-wide level with reasonable accuracy. Despite the success, those recent studies also showed an inherent limitation in sequencing only a representative individual of each breed. To elucidate the biological implications of the sequenced data, further confirmatory studies should be followed by sequencing or validating the population of each breed. Because NGS sequencing prices have consistently dropped, various population genomic theories can now be applied to the sequencing data obtained from the population of each breed of interest. There are still few such population studies available for the Korean native cattle breeds, but this situation will soon be improved with the recent initiative for NGS sequencing of diverse native livestock resources, including the Korean native cattle breeds.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.531-537
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• 2013

The average annual and winter ambient air temperatures in Korea have risen by 0.7 and $1.4^{\circ}C$, respectively, during the last 30 years. Radish (Raphanus sativus), one of the most important cool season crops, may well be used as a model to study the influence of climatic change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level, and climate parameters, including air temperature and growing degree days (GDD), on the performance of a radish cultivar 'Mansahyungtong' to estimate crop growth during the spring growing season. The radish seeds were sown from April 24 to May 22, 2012, at internals of 14 days and cultivated with 3 levels of nitrogen fertilization. The data from plants sown on April 24 and May 8, 2012 were used for the prediction of plant growth as affected by planting date and nitrogen fertilization for spring production. In our study, plant fresh weight was higher when the radish seeds were sown on $24^{th}$ of April than on $8^{th}$ and $22^{nd}$ of May. The growth model was described as a logarithmic function using GDD according to the nitrogen fertilization levels: for 0.5N, root dry matter = 84.66/(1+exp (-(GDD - 790.7)/122.3)) ($r^2$ = 0.92), for 1.0N, root dry matter = 100.6/(1 + exp (-(GDD - 824.8)/112.8)) ($r^2$ = 0.92), and for 2.0N, root dry matter = 117.7/(1+exp (-(GDD - 877.7)/148.5)) ($r^2$ = 0.94). Although the model slightly tended to overestimate the dry mass per plant, the estimated and observed root dry matter and top dry matter data showed a reasonable good fit with 1.12 ($R^2$ = 0.979) and 1.05 ($R^2$ = 0.991), respectively. Results of this study suggest that the GDD values can be used as a good indicator in predicting the root growth of radish.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.