농업기상 센서 데이터를 활용한 인삼재배 광환경 조절 연구
Controlling Photo-Environment of Ginseng Cultivation Using Agricultural Weather Sensor Data
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Photosynthetically active radiation flux density (PPFD) and daily light integral (DLI) values related to plant photosynthesiswere obtained using the sunlight time and insolation data points in the agricultural weather sensor data for Jinan-gun, Jeollabukdo,Korea from 2016 to 2020. The objective was to optimize the photo-environmental conditions for cultivating ginseng. Therange of average monthly sunshine duration was 395.5–664.1 min, with the longest duration observed in June. The range of averageannual accumulated daily insolation was 11.98–17.65 MJ·m-2. The range of average daily external DLI calculated from the insolation and solar time data was 22.3–36.1 mol·m-2·d-1, and the annual cumulative DLI was 8,156–13,175 mol·m-2·d-1. Both the insolation and DLI values were the highest in 2016 and lowest in 2020. Based on the PPFD required for ginseng growth (111– 185 µmol·m-2·s-1), the monthly average daily DLI and monthly cumulative DLI were 3.51–5.87 and 82–228 mol·m- 2·d-1, respectively. The range of five-year average value for the external monthly cumulative DLI was 298–1,459 mol·m-2 ·d-1, and the monthly cumulative DLI values when a black double shading film and blue-white shading film were applied were 101–496 and 36–175 mol·m-2·d-1, respectively. A comparative analysis of DLI values indicated that shading was required to ginseng growth throughout the year under natural light. When the black double shading film was used, shading was required from March to October. When the blue-white shading film was applied from April to August, (i.e., the period with active ginseng growth) the appropriate DLI for ginseng growth could be continuously maintained. Regional weather differences due to climate change are gradually increasing, and even in one region, monthly and cumulative DLI values are different every year. Therefore, in order to implement a precise agricultural environment for ginseng cultivation, precise analysis and continuous research using agricultural weather sensor big data is required.
Abstract
Photosynthetically active radiation flux density (PPFD) and daily light integral (DLI) values related to plant photosynthesiswere obtained using the sunlight time and insolation data points in the agricultural weather sensor data for Jinan-gun, Jeollabukdo,Korea from 2016 to 2020. The objective was to optimize the photo-environmental conditions for cultivating ginseng. Therange of average monthly sunshine duration was 395.5–664.1 min, with the longest duration observed in June. The range of averageannual accumulated daily insolation was 11.98–17.65 MJ·m-2. The range of average daily external DLI calculated from the insolation and solar time data was 22.3–36.1 mol·m-2·d-1, and the annual cumulative DLI was 8,156–13,175 mol·m-2·d-1. Both the insolation and DLI values were the highest in 2016 and lowest in 2020. Based on the PPFD required for ginseng growth (111– 185 µmol·m-2·s-1), the monthly average daily DLI and monthly cumulative DLI were 3.51–5.87 and 82–228 mol·m- 2·d-1, respectively. The range of five-year average value for the external monthly cumulative DLI was 298–1,459 mol·m-2 ·d-1, and the monthly cumulative DLI values when a black double shading film and blue-white shading film were applied were 101–496 and 36–175 mol·m-2·d-1, respectively. A comparative analysis of DLI values indicated that shading was required to ginseng growth throughout the year under natural light. When the black double shading film was used, shading was required from March to October. When the blue-white shading film was applied from April to August, (i.e., the period with active ginseng growth) the appropriate DLI for ginseng growth could be continuously maintained. Regional weather differences due to climate change are gradually increasing, and even in one region, monthly and cumulative DLI values are different every year. Therefore, in order to implement a precise agricultural environment for ginseng cultivation, precise analysis and continuous research using agricultural weather sensor big data is required.
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