• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.62-70
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• 2021

The purpose of this study was to determine the optimal digestible energy levels on pregnancy mares. Physical changes and fecal microorganism in pregnant horse and changes in birth characteristics of neonatal foals were investigated. The experiment was conducted by 18 mares (Jeju corssed bred, older than 36 months) into three treatment groups. The experimental diet consisted of 80%, 100%, 120% digestible energy levels based on NRC. The average daily intake was lower in the 120% (8.75±1.01) than the 100% (9.34±0.92), 80% (9.14±0.88) and there was significant difference (p<0.05). The feed efficiency was lower in the 120% than 80%, 100% (p<0.05). Total cholesterol, HDL-cholesterol, LDL-cholesterol and triglyceride was higher in 120% than others (p<0.05). However there were no health problem and there was no difference between the treatment groups in the birth characteristics of neonatal foals. At the phylum level, Fibrobactres was difference by digestible energy levels, 80% (8.53%) was higher than 100%, 120%. At the genus level, Bacteroides and Kineothrix increased in fecal proportions with increasing digestible energy levels (p<0.05). Fibrobacter showed higher composition at 80% than 100% and 120% (p<0.05).

• Korean Journal of Poultry Science
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• v.48 no.1
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• pp.13-22
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• 2021

The present experiment was conducted to evaluate the effect of stocking density on the growth performance, immune status, and meat quality of broilers. In total, 385 one-day-old Ross 308 broilers were randomly assigned to one of four distinct stocking densities: 26 birds/㎡, 22 birds/㎡, 19 birds/㎡, and 16 birds/㎡. They were fed the diet ad libitum for 5 weeks. Immunoglobulin (Ig) and corticosterone levels were evaluated, and growth performance, blood parameters, and breast meat quality were determined. It was observed that the weight gain and feed intake of growers (21~35 d) and broilers (0~35 d) were significantly reduced as the stocking density increased (P<0.05). However, the feed intake of starters (0~21 d) significantly increased as the stocking density increased (P<0.05). There were no significant differences in the biochemical profiles among the four different stock densities. Furthermore, no significant differences were observed in the stress parameters: (heterophils / lymphocytes ratio and corticosterone), IgA, and IgM; however, IgG significantly increased with stocking density (P<0.05). The pH, water holding capacity, and cooking loss of the muscle were all unaffected by the stocking density, but the shear force (tenderness) increased slightly as the density increased. The findings of this study suggest that a lower stocking density (16 birds/㎡) significantly improved the shear force of breast meat and IgG in broilers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.327-339
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• 2020

Transpiration is the movement of water into the atmosphere through leaf stomata of plant, and it accounts for more than half of evapotranspiration from the land surface. The measurements of transpiration could be conducted in various ways including eddy covariance and water balance method etc. However, the transpiration measurements of individual trees are necessary to quantify and compare the water use of each species and individual component within stands. For the measurement of the transpiration by individual tree, the thermometric methods such as heat dissipation and heat pulse methods are widely used. However, it is difficult and labor consuming to maintain the transpiration measurements of individual trees in a wide range area and especially for long-term experiment. Therefore, the sharing of sapflow data through database should be useful to promote the studies on transpiration and water balance for large spatial scale. In this paper, we present sap flow database, which have Granier type sap flux data from 18 Korean pine (Pinus koraiensis) since 2011 and 16 (Quercus aliena) since 2013 in Mt.Taehwa Seoul National University forest and 18 needle fir (Abies holophylla), seven (Quercus serrata), three (Carpinus laxiflora and C. cordata each since 2013 in Gwangneung. In addition, the database includes the sapling transpiration of nine species (Prunus sargentii, Larix kaempferii, Quercus accutisima, Pinus densiflora, Fraxinus rhynchophylla, Chamecypans obtuse, P. koraiensis, Betulla platyphylla, A. holophylla, Pinus thunbergii), which were measured using heat pulse method since 2018. We believe this is the first database to share the sapflux data in Rep. of Korea, and we wish our database to be used by other researchers and contribute a variety of researches in this field.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Journal of Mushroom
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• v.18 no.4
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• pp.317-322
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• 2020

This experiment was conducted to solve the failure of fruiting body production in the bottle cultivation of the oyster mushroom cultivar 'Heuktari'. The effects of incubation temperature on primordium formation and fruiting body yield of the oyster mushroom cultivar 'Heuktari' were investigated. The proper temperature for mycelium growth of 'Heuktari' on potato dextrose agar (PDA) medium is 23-26℃. The mycelial growth of 'Heuktari' was faster than that of Chunchu 2ho. During mycelial culture in sawdust medium, the temperature of the medium in the bottle initially increased, reached the highest point in the middle of the culture, and then decreased. The higher the set temperature, the shorter the incubation period. When the incubation temperatures were 20℃ and 24℃, respectively, the undeveloped primordium formation rates were low (1.8% and 4.2%, respectively). However, the rate of undeveloped primordium formation increased, and the yield decreased at incubation temperatures of 16℃ and 28℃. Mushroom farms that set incubation temperatures to 18℃ and maintained the medium temperature at less than 28℃ showed undeveloped primordium formation rates ranging between 0.3-0.8%. The rate of undeveloped primordium formation increased and the yield decreased in the farms with high incubation temperatures (above 28℃). We found that in order to reduce undeveloped primordium formation, the air inside the incubation room should be circulated continuously so that the temperature of the medium does not rise above 28℃, and dense incubation conditions should be avoided.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.16-22
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• 2021

This experiment was conducted to study on the growth characteristics and seed productivity of Italian ryegrass (Lolium multiflorum Lam., IRG) planted in the Spring in Gangwon Highland according to the seeding distance (20, 30 and 40 cm). The field was located in highland around 600 m above sea level. The experimental design was randomized block design with three replication and the tested IRG variety was 'Greencall' developed by National Institute of Animal Science (NIAS). IRG was sown on March 26, 2020, and harvested on July 2. The plant height was the shortest at 80.5 cm in the 40 cm seeding distance plot (P<0.05), and there was no significant difference between the 20 and 30 cm seeding distance. The number of spike per square meter (㎡) was significantly higher in the 20 cm seeding distance plot than that of 40 cm (937 vs. 571). The dry matter (DM) content of seed and straw after harvesting was 49.70 and 33.36 % on average, and there was no significant difference between treatments (P>005). However, there was a significant difference in the fresh and DM yield of seeds and straw (P<0.05). DM yield of seeds was significantly higher in 20 cm distance than that of 40 cm, and the yield of straw was the same trend. On the other hand, there was no significant difference in DM yield between 20 cm and 30 cm and also in the feed value of straw after seed harvesting among seeding distance. The average CP, ADF, NDF, and TDN contents were 6.91, 36.76, 61.75 and 59.86%, respectively, and the RFV value was 91. Considering the above results, the production of Italian ryegrass seeds sown in the spring in the highlands of the Gangwon is lower than that of autumn sowing, but it is judged that it needs to be reviewed in case it is unavoidable. In the future, there should be an economic analysis and the development of technology that can increase production.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1465-1483
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• 2020

Evapotranspiration is a concept that includes the evaporation from soil and the transpiration from the plant leaf. It is an essential factor for monitoring water balance, drought, crop growth, and climate change. Actual evapotranspiration (AET) corresponds to the consumption of water from the land surface and the necessary amount of water for the land surface. Because the AET is derived from multiplying the crop coefficient by the reference evapotranspiration (ET0), an accurate calculation of the ET0 is required for the AET. To date, many efforts have been made for gridded ET0 to provide multiple products now. This study presents a comparison between the ET0 products such as FAO56-PM, LDAPS, PKNU-NMSC, and MODIS to find out which one is more suitable for the local-scale hydrological and agricultural applications in Korea, where the heterogeneity of the land surface is critical. In the experiment for the period between 2016 and 2019, the daily and 8-day products were compared with the in-situ observations by KMA. The analyses according to the station, year, month, and time-series showed that the PKNU-NMSC product with a successful optimization for Korea was superior to the others, yielding stable accuracy irrespective of space and time. Also, this paper showed the intrinsic characteristics of the FAO56-PM, LDAPS, and MODIS ET0 products that could be informative for other researchers.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.