以淮山（Dioscorea fordii Prain et Burkill）茎段为材料，以MS为基本培养基，在（25±1）℃、光照强度24～30umol/m^2·s^-1、光照时间10h／d条件下对淮山的组织培养与快速繁殖技术进行了研究．结果表明，组合MS＋NAA0.03mg/L...以淮山（Dioscorea fordii Prain et Burkill）茎段为材料，以MS为基本培养基，在（25±1）℃、光照强度24～30umol/m^2·s^-1、光照时间10h／d条件下对淮山的组织培养与快速繁殖技术进行了研究．结果表明，组合MS＋NAA0.03mg/L＋KT3．00mg／L＋Ad5.00mg/L＋PVP100mg/L对芽的诱导最好，平均芽数达3.5445个，出芽率为94.45％；组合MS＋NAA0.01mg/L＋KT3.00mg/L＋Ad7.50mg/L＋PVP300mg/L对多芽体的增殖最好，平均增重2.9686倍；组合MS＋IBA0.20mg/L＋PVP300mg/L对生根最好，平均根数达15．5452条．展开更多
Background： Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors （BEFs）. Yet, national and regional biomass estimates are generally calculated based o...Background： Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors （BEFs）. Yet, national and regional biomass estimates are generally calculated based on BEFs, especially when using national forest inventory data. Comparison of regression equations based and BEF-based biomass estimates are scarce. Thus, this study was intended to compare these two commonly used methods for estimating tree and forest biomass with regard to errors and biases. Methods： The data were collected in 2012 and 2014. In 2012, a two-phase sampling design was used to fit tree component biomass regression models and determine tree BEFs. In 2014, additional trees were felled outside sampling plots to estimate the biases associated with regression equation based and BEF-based biomass estimates; those estimates were then compared in terms of the following sources of error： plot selection and variability, biomass model, model parameter estimates, and residual variability around model prediction. Results： The regression equation based below-, aboveground and whole tree biomass stocks were, approximately, 7.7, 8.5 and 8.3 % larger than the BEF-based ones. For the whole tree biomass stock, the percentage of the total error attributed to first phase （random plot selection and variability） was 90 and 88 % for regression- and BEF-based estimates, respectively, being the remaining attributed to biomass models （regression and BEF models, respectively）. The percent bias of regression equation based and BEF-based biomass estimates for the whole tree biomass stock were -2.7 and 5.4 %, respectively. The errors due to model parameter estimates, those due to residual variability around model prediction, and the percentage of the total error attributed to biomass model were larger for BEF models （than for regression models）, except for stem and stem wood components. Conclusions＂ The regression equation based biomass stocks were found to be slightly larger, associated with relatively smaller errors and least biased than the BEF-based ones. For stem and stem wood, the percentages of their total errors （as total variance） attributed to BEF model were considerably smaller than those attributed to biomass regression equations.展开更多
On the basis of spectroscopic evidence (MS, ~1HNMR, ^(13)CNMR, CD, ~1H-~1H and ~1H-^(13)C cosy NMR) and chemical synthesis, the structure of isodopharicin E (1), isolated from the dry leaves and tender branches of Iso...On the basis of spectroscopic evidence (MS, ~1HNMR, ^(13)CNMR, CD, ~1H-~1H and ~1H-^(13)C cosy NMR) and chemical synthesis, the structure of isodopharicin E (1), isolated from the dry leaves and tender branches of Isodon pharicus (Prain) Murata was elucidated as 3R, 3'R, 13S, 13'S-tetrahydroxy-11S, 11'S-diacetoxy(16S-O-15')-bisentkaur-15'-en-15-one.展开更多
We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliar...We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliary variables of living Androstachys johnsonii trees;in the second phase, we performed destructive biomass measurements on a randomly selected subset of trees from the first phase. The second-phase data were used to fit regression models to estimate below and aboveground biomass. These models were then applied to the first-phase data to estimate biomass stock. The estimated forest biomass and carbon stocks were 167.05 and 82.73 Mg·ha-1, respectively. The percent error resulting from plot selection and allometric equations for whole tree biomass stock was 4.55% and 1.53%, respectively, yielding a total error of 4.80%. Among individual variables in the first sampling phase, diameter at breast height (DBH) measurement was the largest source of error, and tree-height estimates contributed substantially to the error. Almost none of the error was attributable to plot variability. For the second sampling phase, DBH measurements were the largest source of error, followed by height measurements and stem-wood density estimates. Of the total error (as total variance) of the sampling process, 90% was attributed to plot selection and 10% to the allometric biomass model. The total error of our measurements was very low, which indicated that the two-phase sampling approach and sample size were effective for capturing and predicting biomass of this forest type.展开更多
文摘以淮山（Dioscorea fordii Prain et Burkill）茎段为材料，以MS为基本培养基，在（25±1）℃、光照强度24～30umol/m^2·s^-1、光照时间10h／d条件下对淮山的组织培养与快速繁殖技术进行了研究．结果表明，组合MS＋NAA0.03mg/L＋KT3．00mg／L＋Ad5.00mg/L＋PVP100mg/L对芽的诱导最好，平均芽数达3.5445个，出芽率为94.45％；组合MS＋NAA0.01mg/L＋KT3.00mg/L＋Ad7.50mg/L＋PVP300mg/L对多芽体的增殖最好，平均增重2.9686倍；组合MS＋IBA0.20mg/L＋PVP300mg/L对生根最好，平均根数达15．5452条．
文摘马肠薯蓣（Dioscorea simulans Prain et Burkill）隶属于薯蓣科（Dioscoreaceae）薯蓣属（Dioscorea Linn.）根状茎组（Sect.Stenophora Uline）^（[1]）278。Prain等^（[2]）根据1928年秦仁昌采自广西罗城县大林山的标本（秦仁昌5319,5335）命名马肠薯蓣,并建立Sect.Illigerestrum Prain et Burkill^（[3]）,将马肠薯蓣归入该组。该组地上茎几乎不分枝,左旋;叶3裂,无毛;雄花簇生成小伞状或总状,花被片呈轮状排列;花药6枚,3大、3小;雌花花被片呈轮状排列,总状花序。
基金funded by the Swedish International Development Cooperation Agency(SIDA)Professor Agnelo Fernandes and Madeirarte Lda for financial and logistical support
文摘Background： Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors （BEFs）. Yet, national and regional biomass estimates are generally calculated based on BEFs, especially when using national forest inventory data. Comparison of regression equations based and BEF-based biomass estimates are scarce. Thus, this study was intended to compare these two commonly used methods for estimating tree and forest biomass with regard to errors and biases. Methods： The data were collected in 2012 and 2014. In 2012, a two-phase sampling design was used to fit tree component biomass regression models and determine tree BEFs. In 2014, additional trees were felled outside sampling plots to estimate the biases associated with regression equation based and BEF-based biomass estimates; those estimates were then compared in terms of the following sources of error： plot selection and variability, biomass model, model parameter estimates, and residual variability around model prediction. Results： The regression equation based below-, aboveground and whole tree biomass stocks were, approximately, 7.7, 8.5 and 8.3 % larger than the BEF-based ones. For the whole tree biomass stock, the percentage of the total error attributed to first phase （random plot selection and variability） was 90 and 88 % for regression- and BEF-based estimates, respectively, being the remaining attributed to biomass models （regression and BEF models, respectively）. The percent bias of regression equation based and BEF-based biomass estimates for the whole tree biomass stock were -2.7 and 5.4 %, respectively. The errors due to model parameter estimates, those due to residual variability around model prediction, and the percentage of the total error attributed to biomass model were larger for BEF models （than for regression models）, except for stem and stem wood components. Conclusions＂ The regression equation based biomass stocks were found to be slightly larger, associated with relatively smaller errors and least biased than the BEF-based ones. For stem and stem wood, the percentages of their total errors （as total variance） attributed to BEF model were considerably smaller than those attributed to biomass regression equations.
文摘On the basis of spectroscopic evidence (MS, ~1HNMR, ^(13)CNMR, CD, ~1H-~1H and ~1H-^(13)C cosy NMR) and chemical synthesis, the structure of isodopharicin E (1), isolated from the dry leaves and tender branches of Isodon pharicus (Prain) Murata was elucidated as 3R, 3'R, 13S, 13'S-tetrahydroxy-11S, 11'S-diacetoxy(16S-O-15')-bisentkaur-15'-en-15-one.
文摘We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliary variables of living Androstachys johnsonii trees;in the second phase, we performed destructive biomass measurements on a randomly selected subset of trees from the first phase. The second-phase data were used to fit regression models to estimate below and aboveground biomass. These models were then applied to the first-phase data to estimate biomass stock. The estimated forest biomass and carbon stocks were 167.05 and 82.73 Mg·ha-1, respectively. The percent error resulting from plot selection and allometric equations for whole tree biomass stock was 4.55% and 1.53%, respectively, yielding a total error of 4.80%. Among individual variables in the first sampling phase, diameter at breast height (DBH) measurement was the largest source of error, and tree-height estimates contributed substantially to the error. Almost none of the error was attributable to plot variability. For the second sampling phase, DBH measurements were the largest source of error, followed by height measurements and stem-wood density estimates. Of the total error (as total variance) of the sampling process, 90% was attributed to plot selection and 10% to the allometric biomass model. The total error of our measurements was very low, which indicated that the two-phase sampling approach and sample size were effective for capturing and predicting biomass of this forest type.
