Nitrogen resorption from senescing leaves in 28 plant species in a semi-arid region of northern China

2005 ◽  
Vol 63 (1) ◽  
pp. 191-202 ◽  
Author(s):  
Z.-Y. Yuan ◽  
L.-H. Li ◽  
X.-G. Han ◽  
J.-H. Huang ◽  
G.-M. Jiang ◽  
...  
2020 ◽  
Vol 284 ◽  
pp. 107904
Author(s):  
Zuosinan Chen ◽  
Zhiqiang Zhang ◽  
Ge Sun ◽  
Lixin Chen ◽  
Hang Xu ◽  
...  

2017 ◽  
Vol 11 (2) ◽  
pp. 455-467
Author(s):  
Yusufjon Gafforov ◽  
Davron Rakhimov

The first and really only significant data on Botryosphaeriaceae mycobiota from the arid and semi-arid region of Uzbekistan are presented. This study reports 27 species of Diplodia-like fungi (Botryosphaeriaceae) from the study area; nine species are newly reported for Uzbekistan. Most species of Diplodia and Dothiorella were found on host plants of the families Amaranthaceae, Asteraceae, Fabaceae, Lamiaceae, Rosaceae, and Salicaceae. An annotated list of Diplodia-like species is given, including their host plant species, notes on taxonomy, ecology, and geographical distributions. A geo-referenced distribution map is included.


2019 ◽  
Vol 18 (3-4) ◽  
pp. 204-219
Author(s):  
A. Kefifa ◽  
A. Saidi ◽  
K. Hachem ◽  
O. Mehalhal

This paper presents the first quantitative ethnobotanical study of the flora in the semi-arid region in the southwest part of Algeria. The aim of this ethnobotanical survey in the region of El Bayadh situated in the semi-arid part of Algeria was to identify the main medicinal plants used by the local inhabitants to treat different diseases and to collect all the data on their therapeutic characteristics. One hundred informants of different ages were interviewed for this study (69 women and 31 men). Both quantitative and qualitative information were collected through open semi-structured face-to-face interviews with the local people. Data were organized and analyzed by descriptive statistics. The ethnobotanical data were analyzed using various important quantitative indices calculated for each of the recorded medicinal plant species like use value (UV), relative frequency of citation (RFC), relative importance index (RII), informants’ agreement ratio (IAR), informant consensus factor (ICF), fidelity level (FL), and family importance value index (FIV). In addition, a correlation analysis was performed to check the level of association between RFC and both UV and RII. It was reported that 44 useful plant species, belonging to 26 botanical families were used in the treatment of various diseases. The Asteraceae family was the most common family (6 species, 13.64%, FIV = 0.94) of all the medicinal plants recorded in this study. Leaves were the most commonly used plant part, accounting for 50.77% of the plants reported. Eighty-three diseases were identified and grouped into eleven categories, dominated by diseases of the nervous system and sensory organs (ICF = 0.94), which were treated with local medicinal plants. There is a clear dominance of Artemisia herba alba Asso. (Chih) in the three important ethnobotanical indices (UV, RFC, and RII).We found in this study five plant species having maximum fidelity level (100%) where they were used to treat only one disease. The Pearson correlation coefficient between RFC and UV (0.986**), and between RFC and RII (0.713**) showed highly positive significant association between RFC and both UV and RI of plant use in the study area. We deduce that herbal medicine is used in selfmedication of the local population; however, we also draw attention to the fact that the incoherent and limitless use of the medicinal flora constitutes a potential risk contributing to the degradation of the plant biodiversity of the area of study. These results may complement the database of the national medicinal flora and support research in phytochemistry and pharmacology to discover new drugs and approve ethnomedicinal knowledge.


2012 ◽  
Vol 5 (1) ◽  
pp. 80-88 ◽  
Author(s):  
DeMing Jiang ◽  
ChunPing Miao ◽  
XueHua Li ◽  
XiaoLan Li ◽  
Alamusa ◽  
...  

2003 ◽  
Vol 29 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Yoshiaki ISHII ◽  
Yu Ling LI ◽  
Qing Tu SI ◽  
Keiji SAKAMOTO ◽  
Lin He WANG ◽  
...  

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