The phenology of figs （Ficus spp. Moraceae） is being monitored in the dry forests of Mudumalai, southern India and individuals belonging to the genus are marked with unique tag numbers and their vegetative and repro...The phenology of figs （Ficus spp. Moraceae） is being monitored in the dry forests of Mudumalai, southern India and individuals belonging to the genus are marked with unique tag numbers and their vegetative and reproductive phenologies have been monitored since August 2000 on a monthly basis. The influence of abiotic factors on fig phenology and the differences between fig and non- fig phenologies are being examined. The seasonality of different phenophases of fig phenology is also being examined. The maximum intensity of leaf flush occurred in drier months. Fig and non-fig species showed significant differences with expansion and senescence phenophases of leafing. Flowering also occurred in drier months. There were significant differences between fig and non-fig species with reproductive phenophases. As with non-fig species, leafing was influenced by maximum temperatures. Both vegetative and reproductive phenophases were significantly seasonal.展开更多
Accurate assessment of water deficit and related uncertainties in water-scarce areas is strategically important in various fields of water resources management. This study developed a hybrid approach integrating conce...Accurate assessment of water deficit and related uncertainties in water-scarce areas is strategically important in various fields of water resources management. This study developed a hybrid approach integrating conceptual water balance model and econometric regression to estimate water shortage and its related uncertainties in water-scarce areas. This hybrid approach was used to assess the agricultural water deficit of Beijing, an extremely water-scarce area in China. A predictive model of agricultural water demand was developed using the stepwise multiple regression method, and was validated by comparing the predicted values with observed data. Scenario analysis was employed to investigate the uncertainties of agricultural water shortage and agricultural water demand. This modeling approach can assist water administration in creating sustainable water allocation strategies in water-scarce areas.展开更多
基金funded by the Ministry of Environment,Forests and Climate Change(MOEFCC)Government of India under an umbrella grant to Centre for Ecological Sciences,Indian Institute of Science
文摘The phenology of figs （Ficus spp. Moraceae） is being monitored in the dry forests of Mudumalai, southern India and individuals belonging to the genus are marked with unique tag numbers and their vegetative and reproductive phenologies have been monitored since August 2000 on a monthly basis. The influence of abiotic factors on fig phenology and the differences between fig and non- fig phenologies are being examined. The seasonality of different phenophases of fig phenology is also being examined. The maximum intensity of leaf flush occurred in drier months. Fig and non-fig species showed significant differences with expansion and senescence phenophases of leafing. Flowering also occurred in drier months. There were significant differences between fig and non-fig species with reproductive phenophases. As with non-fig species, leafing was influenced by maximum temperatures. Both vegetative and reproductive phenophases were significantly seasonal.
基金supported by the Sino-Swiss Science and Technology Cooperation Program of Switzerlandthe Ministry of Science and Technology of China(Grant No.2009DFA22980)
文摘Accurate assessment of water deficit and related uncertainties in water-scarce areas is strategically important in various fields of water resources management. This study developed a hybrid approach integrating conceptual water balance model and econometric regression to estimate water shortage and its related uncertainties in water-scarce areas. This hybrid approach was used to assess the agricultural water deficit of Beijing, an extremely water-scarce area in China. A predictive model of agricultural water demand was developed using the stepwise multiple regression method, and was validated by comparing the predicted values with observed data. Scenario analysis was employed to investigate the uncertainties of agricultural water shortage and agricultural water demand. This modeling approach can assist water administration in creating sustainable water allocation strategies in water-scarce areas.
