The present study describes Philodromus paiki sp. nov., which was previously misidentified as P. fuscomarginatus (De Geer, 1778), P. poecilus (Thorell, 1872), and P. spinitarsis Simon, 1895 in Korea, as a new species with diagnosis, detailed descriptions, and taxonomic photographs. Additionally, P. spinitarsis is also described to correct previous misidentifications of Korean records of the species.

A male Spheropistha melanosoma Yaginuma, 1957 from Korea in the family Theridiidae Sundevall, 1833 is described with measurements and morphological photos of the diagnostic characteristics. This species was collected with a sweep net around arable lands in a mountainous mixed forest during the investigation of spider fauna on Ulleungdo Island in 2019.

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMYnormal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 ㎧). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

Two linyphiid spiders, Saitonia kawaguchikonis Saito & Ono, 2001 and Asthenargus niphonius Saito & Ono, 2001 were confirmed from Korea for the first time. Males of S. kawaguchikonis and a female of A. niphonius were collected with pitfall traps in a leaf litter of mixed forests in three National Parks (Hallyeohaesang National Park, Mt. Naejangsan, and Mt. Sobaeksan) during the seasonal surveys for the spider fauna in mountainous terrain from 2018 to 2020. These two species were formerly known from China and Japan, or only from Japan, respectively. The present study describes these two species with measurements, morphological illustrations, and a distribution map. This report adds the genus Asthenargus Simon & Fage, 1922 from Korea to the Korean spider fauna for the first time.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

This study aimed to discuss the optimal seeding and harvesting dates with growing degree days(GDD) via meta-data of whole crop maize(WCM). The raw data (n=3,152) contains cultivation year, cultivars, location, seeding and harvesting dates collected from various reports such as thesis, science journals and research reports (1982-2012). The processing was: recording, screening and modification of errors; Then, the final dataset (n=121) consists of seeding cases (n=29), and harvesting cases (n=92) which were used to detect the optimum. In addition, the optimal periods considering tolerance range and GDD also were estimated. As a result, the optimum seeding and harvesting periods were 14th April ~ 3rd May and 15th August ~ 4th September, respectively; where, their GDDs were 23.7~99.6℃ and 1,328.7~ 1,602.1℃, respectively. These GDDs could be used as a judge standard for selecting the seeding and harvesting dates.

We designed a system that can automatically collect, convey, and control cool air of 15oC-20oC containing carbon dioxide from a mushroom cultivation house to a strawberry plastic house. We recorded the temperature at various positions from July to August 2017. The average temperature of the green house during day and at night was maintained at 33oC and 26oC, respectively. In the moveable three-tier cylindrical bed, the average temperature around root was maintained at 26oC and 21oC during day and at night, respectively. On the high-bench in the green house, the temperature was maintained at 32oC and 30oC during day and at night, respectively. The carbon dioxide concentration was maintained around 800-1,600 ppm in the mushroom cultivation system and 400-800 ppm in the strawberry plastic house. The growth characteristics of the strawberry treated with moveable three-tier cylindrical bed were significantly different from those of the untreated high-bench bed. In addition, during the summer season, moveable three-tier cylindrical bed showed more tendency to increase in normal fruit number (NFN) and to decrease in defective fruit number (DFN) compare to the high-bench bed. Therefore, the moveable three-tier cylindrical bed showed a tendency to be more than 2 times higher yields than that of the high-bench bed. It was confirmed that everbearing strawberry cultivars could be cultivated in green house due to the cool air supply from the mushroom cultivation system in the summer season.

This study was conducted to perform the suitability analysis of whole-crop rye (Secale cereale L.) based on the climatic information in the Republic of Korea to present useful information for producers and policy makers to determine the site-selection for the cultivation of the whole-crop rye. The criteria to analyze the climatic suitability of whole-crop rye was developed firstly. Then, the climatic suitability map for spatial analysis was developed through weighted overlaying the raster layers of climatic items in the evaluation criteria. Meanwhile, 16 geographically representative weather stations were selected to show examples of the calculation process of the climatic suitability score of a specific cultivation area. The results of the climatic suitability mapping indicated that the climatic conditions in most arable lands of the Republic of Korea such as the coastal, southern, western areas in the southern region of the Korean Peninsula and central areas in Jeju Island are suitable for the cultivation of whole-crop rye. The climatic suitability scores of the 16 weather stations were all in line with the results of the climatic suitability map.

The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

The objective of this study was to construct a forage rye (FR) dry matter yield (DMY) estimation model based on climate data by locations in South Korea. The data set (n = 549) during 29 years were used. Six optimal climatic variables were selected through stepwise multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the six climatic variables and cultivated locations as dummy variables was constructed as follows: DMY = 104.166SGD + 1.454AAT + 147.863MTJ + 59.183PAT150 4.693SRF + 45.106SRD 5230.001 + Location, where SGD was spring growing days, AAT was autumnal accumulated temperature, MTJ was mean temperature in January, PAT150 was period to accumulated temperature 150, SRF was spring rainfall, and SRD was spring rainfall days. The model constructed in this research could explain 24.4 % of the variations in DMY of FR. The homoscedasticity and the assumption that the mean of the residuals were equal to zero was satisfied. The goodness-of-fit of the model was proper based on most scatters of the predicted DMY values fell within the 95% confidence interval.