• Title/Summary/Keyword: fertilizer composition

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Chemical Characteristics of Shallow Groundwater in an Agricultural District of Hyogyo-ri Area, Chungnam Province (충남 효교리 농업지역 천부지하수의 화학적 특성)

  • Jeon, Hang-Tak;Hamm, Se-Yeong;Choi, Eun-Gyeong;Kim, HyunKoo;Kim, MoonSu;Park, Ki-Hoon;Lim, Woo-Ri
    • Journal of the Korean earth science society
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    • v.41 no.6
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    • pp.630-646
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    • 2020
  • In rural areas, nitrate-nitrogen (NO3-N) pollution caused by agricultural activities is a major obstacle to the use of shallow groundwater as domestic water or drinking water. In this study, the water quality characteristics of shallow groundwater in Hyogyo-ri agricultural area of Yesan-gun, Chungcheongnam-do province was studied in connection with land use and chemical composition of soil layer. The average NO3-N concentration in groundwater exceeds the domestic and agricultural standard water qualities of Korea and is caused by anthropogenic sources such as fertilizer, livestock wastewater, and domestic sewage. The groundwater type mainly belongs to Ca(Na)-Cl type, unlike Ca-HCO3 type, a general type of shallow groundwater. The average NO3-N concentration (7.7 mg L-1) in groundwater in rice paddy/other (upstream, ranch, and residential) area is lower than the average concentration (22.8 mg L-1) in farm field area, due to a lower permeability in paddy area than that in farm field area. According to the trend analysis by the Mann-Kendall and Sen tests, the NO3-N concentration in the shallow groundwater shows a very weak decreasing trend with ~0.011 mg L-1yr-1 with indicating almost equilibrium state. Meanwhile, SO42- and HCO3- concentrations display annual decreasing trend by 15.48 and 13.15%, respectively. At a zone of 0 to 5 m below the surface, the average hydraulic conductivity is 1.86×10-5 cm s-1, with a greater value (1.03×10-4cm s-1) in sand layer and a smaller value (2.50×10-8 cm s-1) in silt layer.

Analysis of Literatures Related to Crop Growth and Yield of Onion and Garlic Using Text-mining Approaches for Develop Productivity Prediction Models (양파·마늘 생산성 예측 모델 개발을 위한 텍스트마이닝 기법 활용 생육 및 수량 관련 문헌 분석)

  • Kim, Jin-Hee;Kim, Dae-Jun;Seo, Bo-Hun;Kim, Kwang Soo
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.23 no.4
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    • pp.374-390
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    • 2021
  • Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.