Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91∼0.92, GNDVI were 0.92∼0.94, NGRDI were 0.71∼0.85 and NDREI were 0.84∼0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible–NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The R2 value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

The objective of study was to find the relationship in hilly pasture landscape expectancy and tourist’s satisfaction on grassland facility. It was followed by literature reviews and visitors’ survey in Daegwallyeong Yangtte Farm on 31, July, 2014. 367 respondents were analyzed by F-test, t-test, Chi-square and Fisher’s Exact Test at 0.05 level after data screening process. Computing factors were sex, marital status, age, academic career and occupation. The results was as followed; First, major respondents group of survey were in oder of woman, forty years old group, married office worker, and university graduate. Second, the expectancy for grassland was significant difference in age, but pasture color expectancy was not significant difference in gender, age, educational background, marital status, and there was significant difference in favorite grassland type with age, marital status. Third, favorite fence type was not significant difference along with all group of participants. However, color and material of fence was significant difference in marital status. Fourth, preferred ranch road was significant difference with occupation and marital status. There was significant difference in favorite grassland type near ranch road along with age and occupation type. Fifth, the mean satisfaction was 3.6 point in 5.0. Therefore, all respondents were generally satisfaction in visited. Tourists were more interested in ranch landscape than experience or contacts to animal.

Red sorrel (Rumex acetosella L.), as one of exotic weeds in Korea, was dominated in grassland and reduced the quality of forage. Improving current pasture productivity by precision management requires practical tools to collect site-specific pasture weed data. Recent development in unmanned aerial vehicle (UAV) technology has offered cost effective and real time applications for site-specific data collection. To map red sorrel on a hill pasture, we tested the potential use of an UAV system with digital cameras (visible and near-infrared (NIR) camera). Field measurements were conducted on grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17, 2014. Plant samples were obtained at 20 sites. An UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number values of Red, Green, Blue, and NIR channels were extracted from aerial photos. Multiple linear regression analysis results showed that the correlation coefficient between Rumex content and 4 bands of UAV image was 0.96 with root mean square error of 9.3. Therefore, UAV monitoring system can be a quick and cost effective tool to obtain spatial distribution of red sorrel data for precision management of hilly grazing pasture

In order to study the effect of barley, Italian ryegrass (IRG), and legume mixture on nitrogen fixation and transfer to grasses on spring paddy field, an experiment was carried out from Oct. 2006 to June 2007 in Naju, Korea. A split plot design with three replications was used for the experiment. One reference plot was assigned for each treatment to determine nitrogen fixation. Main plots consisted of Chinese milk vetch, crimson clover, forage pea, and hairy vetch with barley, respectively. Subplot treatment were barley or IRG with four seeding ratio of legumes (50:50, 60:40, 70:30, and 80:20). To estimate N fixation by legumes, 15N isotope dilution technique was used. 15N fertilizer [(15NH4)2SO4 solution at 99.8 atom N] was uniformly applied to 600 cm2 in the middle of each plot on April 15, 2007. Plots were harvest by hand on June 8, 2007. Dried sample were ground to a fine power and analyzed for total N isotope N. 15N was determined using elemental analyzer-isotope ratio mass spectrometry. The calculation of N transfer was determined with the isotope dilution method. The content of N was higher in legumes than that in barley or Italian ryegrass. Nitrogen level in forage pea was significantly higher than that of other legumes. There were significantly differences in N content between legumes in IRG mixture. Atom % 15N excess was significantly different in legumes with barley. The 60:40 sub plot had higher (p<0.05) atom % 15N than other seeding ratio treatments. The enrichment ranged from 0 to 0.58. Compared to barley, the enrichment of IRG with its accompanied legumes was higher, ranging from 0.38 to 1.0. The N derived from the atmosphere (Ndfa) ranged from 0% to 49.5% with barley-legume mixture. It ranged from 0 to 60.5% in IRG-legume plots. N transfer from legumes to neighboring grasses was 12.3 to 90.9 kg/ha for barley-legume mixture and 31.7 to 107.8 kg/ha for IRG plots. IRG plots showed higher N transfer for IRG-legume mixture in general based on difference method. Based on 15N dilution method, the N transfer was 0 to 36.1 kg/ha for barley-legume mixture and 0 to 50.6 kg/ha for IRG plots. There was a tendency toward higher N transfer on the difference method than that of the 15N dilution method.

data. Recent developments in unmanned aerial vehicle (UAV) technology provide cost effective and real time applications for site specific data collection. For the mapping of herbage biomass (BM) on a hill pasture, we tested a UAV system with digital cameras (visible and near-infrared (NIR) camera). The field measurements were conducted on the grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17 and June 27, 2014. Plant samples were obtained from 28 sites. A UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number (DN) values of Red and NIR channels were extracted from the aerial photos and a normalized differential vegetation index using DN (NDVIdn) was calculated. The results show that the correlation coefficient between BM and NDVIdn was 0.88. For the precision management of hilly grazing pastures, UAV monitoring systems can be a quick and cost effective tool to obtain site-specific herbage BM data.

Total organic carbon(TOC), Total nitrogen(TN), and carbon and nitrogen stable isotopes were measured in the sediment and suspended parties in fresh lake water and saline estuarine water to determine the sources of Particulate organic matter(POM) in the sediments of the Youngsan river estuary. POM in the freshwater discharge water was mostly phytoplankton origin with little trace of terrestrial plants. POM from phytoplankton blooms formed in estuarine water in response to the nutrient enriched freshwater discharges was the most important sources of POM in the sediment near the dike, comprising more than 40% of the total organic matter. POM from freshwater phytoplankton and oceanic phytoplankton were also important sources of the sediment POM, and their contributions varied with the distances from the dike. Contribution of freshwater phytoplankton to sediment POM decreased from the dike to the outside of the estuary.