Italian ryegrass (IRG) has become a vital forage crop due to its increasing cultivation area and its role in enhancing forage self-sufficiency. However, its production is susceptible to environmental factors such as climate change and drought, necessitating precise yield prediction technologies. This study aimed to assess the growth characteristics of IRG and predict dry matter yield (DMY) using vegetation indices derived from unmanned aerial vehicle (UAV)-based remote sensing. The Green Leaf Index (GLI), normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), and optimized soil-adjusted vegetation index (OSAVI) were employed to develop DMY estimation models. Among the indices, GLI demonstrated the highest correlation with DMY (R² = 0.971). The results revealed that GLI-based UAV observations can serve as reliable tools for estimating forage yield under varying environmental conditions. Additionally, post-winter vegetation coverage in the study area was assessed using GLI, and 54% coverage was observed in March 2023. This study assesses that UAV-based remote sensing can provide high-precision predictions of crop yield, thus contributing to the stabilization of forage production under climate variability.

Sorghum-Sudangrass hybrids (SSH) is a grass cereal hybrid crop with a high yield potential under different climatic conditions. The aim of this study was to evaluate the growth characteristics, dry matter yield and nutrient content of the SSH cultivars such as Dairy Mens Dream, Superdan and Supergreen from 2021 to 2023 in Jeju Island, Korea. Among the three cultivars, Superdan grew significantly taller with a larger stem diameter at both harvest times. In contrast, Dairy Mens Dream had least height and less stem diameter, but the dry matter yield was greater for Dairy Mens Dream than other cultivars. Furthermore, the crude protein content at the first cut was higher than 8.5% compared to the second cut (6.5 – 7.0%). The neutral detergent fiber (NDF) and acid detergent fiber (ADF) content was higher in the second harvest of all cultivars compared to the first harvest of SSH in 2021. The SSH cultivation in 2021 and 2023 had higher ADF and NDF content than in 2022, possibly due to the average precipitation and optimum temperature suitable for SSH production in 2021 and 2023. The selected three SSH cultivars showed significant growth characteristics, dry matter content, and nutritional value. The overall data suggested that all three SSH showed significant productivity and nutritional content in the Jeju region of sub-tropical climatic condition.

최근 하천 환경 개선을 위한 노력과 연속성확보의 일환으로 인공구조물에 대한 철거 정책이 시행되고 있으나, 인공구조물 철거가 하천 환경에 미치는 장기적인 영향에 대해서는 명확하게 알려지지 않았다. 이에 본 연구는 하천 내 인공구조물 철거의 장기적인 영향을 분석하고, 지속 가능한 하천 관리 방안을 모색하기 위해 수행되었다. 설악산에 위치한 가는고래골의 인공구조물을 대상으로 2021년 8월부터 2023년 10월까지 8회에 걸쳐 조사한 결과, 조사기간동안 총 3문 3강 8목 11과 85종이 출현하였다. 총 출현종수는 보의 철거 전(총 44종)과 비교하여 철거직후(43종) 다소 감소하였으나, 이후 점진적으로 증가(2022년 56종, 2023년 64종)하였으며, 특히 절지동물 문에 해당하는 분류군의 증가가 확인되었다. 인공구조물과의 거리에 따른 출현종수는 구조물과 가까울수록 종수가 현저히 감소하였으며, 이격될수록 종수가 증가하였다. 장기적인 조사결과 인공구조물과 이격되어 있는 정점부터 점진적인 종수의 안정화가 확인되었으며, 인공구조물 철거지점과 가까운 지점에서는 지속적인 하상 변화가 관찰되고 회복이 느렸다. 이에 인공구조물 철거 후, 빠른 회복을 위해서는 초기 주변환경을 고려한 서식환 경의 조성 및 관리에 관한 추가적인 연구가 필요할 것으로 보인다.

Since rice is the main food in Korea, there are no regulations on corn milling yet. Corn is known as one of the world's top three food crops along with wheat and rice, and it is known that 3.5 billion people worldwide use corn for food. In addition, corn mills are not developed or sold in Korea, but the use of corn mills is increasing significantly in many countries in Southeast Asia. In the Philippines, as Korea's rice mill import increases, Korea's KAMICO (Korea Agricultural Machinery Industry Cooperative) and domestic company A agreed to develop a corn mill jointly with PHilMech, an organization affiliated with the Philippine Ministry of Agriculture. However, research on corn milling was very insignificant, so the development was carried out based on the technology of Korea's rice mill. Rice milling is performed by peeling off the skin of rice and producing brown or white rice, so it is carried out by removing the skin and cutting the skin. On the other hand, in the corn mill, the skin of the corn is peeled, pulverized and selected to produce main products suitable for edible use. Therefore, in order to develop a corn mill, processes such as peeling, transfer, grinding, sorting, and by-product separation are required, and suitable parts must be developed. In addition, the performance must be gradually improved through experiments in which corn is repeatedly milled. The Philippines produces 7.98 million tons/year of corn, which is about 100 times that of Korea, and is mostly consumed as a staple food. This is about 10% of the total crop production in the Philippines. In addition, the main cultivation complexes of corn are the mountainous regions of Tarlac or Pangasinan, and the produced corn is 72.4% of the so-called yellow corn called Arabel and Sarangani, and the remaining 27.6% are known as white corn. In this study, it was intended to produce grains of 2.5 mm or less suitable for food for yellow corn and to develop a corn mill for 200 kg per hour. Detailed conditions for development are stipulated as more than 55% of the main product recovery rate, more than 31% of the by-product recovery rate, less than 5% of the raw material loss rate, and more than 80% of the embryo dislocation rate. In this study, to achieve this, the overall process of the corn mill was developed, and the optimal conditions for the corn mill were obtained through the development of parts and empirical tests to improve performance. In addition, it was intended to achieve the development goal by evaluating and analyzing the performance of each part so that it did not conflict.

This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.