인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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
Ghaf, a resilient tree in arid environments, plays a critical role in ecological restoration, desertification mitigation, and cultural heritage preservation. However, the seeds' inherent challenges, notably their hard outer coating restricting germination, emphasize the pressing need for innovative strategies. This work aimed to investigate the optimization of Ghaf seed germination process through seed priming with ZnO nanoparticles treatment (duration (t), concentration (c), temperature (T), and agitation (a), employing the Taguchi method for efficient experimental design. Furthermore, the study includes Analysis of Variance (ANOVA), analysis for the regression model to assess the significance of predictor variables and their interactions, thereby strengthening the statistical validity of our optimization approach. Notably, it revealed that concentration is a pivotal influencer in optimization of Ghaf seed germination. The results showed that the concentration of ZnO nanoparticles has no linear relation with any other parameters. To verify the outcomes, validation tests were performed utilizing the predicted optimal parameters. The observed low error ratio, falling within the range of 1 to 6%, confirmed the success of the Taguchi methodology in identifying optimal levels of the factors chosen. Significantly, ZnO-primed seeds showcased a remarkable enhancement in Ghaf seed germination, increasing from 15 to 88%. This study introduces a novel approach utilizing ZnO nanoparticle treatment optimized through the Taguchi method, significantly enhancing seed germination rates of Ghaf seeds and offering a promising avenue for sustainable agricultural practices in arid environments.
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.
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