인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Thinning in hinoki cypress plantation generally increases light, water and nutrient availability for remaining trees. Soil nitrification often increases after thinning. Abundance of understory vegetation also increases. These changes may affect nitrogen use strategy of hinoki cypress and understory vegetation. Deciduous understory vegetation generally has higher leaf nitrogen concentration and their ability to absorb soil nitrogen can prevent nitrogen losses from the ecosystem after thinning. If nitrification increases after thinning, the nitrate should have lower δ¹⁵N value while remaining ammonium should have higher δ¹⁵N. The preferential uptake of nitrate and ammonium should affect δ¹⁵N of plants. Therefore changes of nitrogen utilization by trees can be predicted from their δ¹⁵N values. In this study, we investigated changes of δ¹⁵N in hinoki cypress plantations at two different altitudes (Tengu 1150m, Furumiya 710m) in Shikoku Island, southern Japan. Two adjacent plots (20m x 20m) were located. One plot was thinned in 2002 and the other was remained as a control. Organic layer and surface soil at 5cm depth were samples in 2002. Surface soil at 5 cm depth was collected from 2005-2007 and water content and water extractable ammonium and nitrate were measured. Leaves of hinoki cypress and understory vegetation (Lindera triloba in Tengu and Lindera sericea in Furumiya) were collected in July from 2002-2007. Nitrogen concentration of these samples were measured by NC analyzer while their δ¹⁵N were measured by Isotope Ratio Mass Spectrometry. There were significant difference in soil properties between thinned and control plots. Soil in the thinned plots had higher water content at both areas. Effects of thinning on water extractable ammonium and nitrate were different between high and low altitude ... 전체 초록 보기
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.