由水稻矮缩病毒(Rice dwarf viru S,RDV)引起的水稻矮缩病害,最早由日本报道,随后在东南亚等国以及我国的福建、云南等南方稻区普遍发生,云南主要发生于中部及南部地区[1].水稻在苗期至分蘖期感病后,植株矮缩,分蘖增多,叶片浓绿,僵直,出现白斑,生长后期病稻不能抽穗结实,在暴发流行年份可以引起水稻的严重减产. 相似文献
BACKGROUND AND AIMS: Physiological and architectural plant models have originally been developed for different purposes and therefore have little in common, thus making combined applications difficult. There is, however, an increasing demand for crop models that simulate the genetic and resource-dependent variability of plant geometry and architecture, because man is increasingly able to transform plant production systems through combined genetic and environmental engineering. MODEL: GREENLAB is presented, a mathematical plant model that simulates interactions between plant structure and function. Dual-scale automaton is used to simulate plant organogenesis from germination to maturity on the basis of organogenetic growth cycles that have constant thermal time. Plant fresh biomass production is computed from transpiration, assuming transpiration efficiency to be constant and atmospheric demand to be the driving force, under non-limiting water supply. The fresh biomass is then distributed among expanding organs according to their relative demand. Demand for organ growth is estimated from allometric relationships (e.g. leaf surface to weight ratios) and kinetics of potential growth rate for each organ type. These are obtained through parameter optimization against empirical, morphological data sets by running the model in inverted mode. Potential growth rates are then used as estimates of relative sink strength in the model. These and other 'hidden' plant parameters are calibrated using the non-linear, least-square method. KEY RESULTS AND CONCLUSIONS: The model reproduced accurately the dynamics of plant growth, architecture and geometry of various annual and woody plants, enabling 3D visualization. It was also able to simulate the variability of leaf size on the plant and compensatory growth following pruning, as a result of internal competition for resources. The potential of the model's underlying concepts to predict the plant's phenotypic plasticity is discussed. 相似文献
Allometric growth reflects different allocation patterns and relationships of different components or traits of a plant and is closely related to ecosystem carbon storage. As an introduced species, the growth and carbon storage of Sonneratia apetala are still unclear. To derive allometric relationships of the mangrove S. apetala and to estimate carbon storage in mangrove ecosystems, we harvested 12 individual Sonneratia apetala trees from four different diameter classes in the Futian National Nature Reserve, Guangdong, China. Allometric growth models were fitted. The results showed that diameter at breast height (DBH) and wood density were better variables for predicting plant biomass (including above- and below-ground biomass) than plant height. There were significant power function relationships between biomass and DBH, with a mean allometric exponent of 2.22, and stem biomass accounted for 97% of the variation in S. apetala total biomass. Nearly isometric scaling relationships were developed between stem biomass and other biomass components. To better understand the carbon stocks of the S. apetala ecosystem, we categorized all trees into five age classes and quantified vegetation carbon storage. The S. apetala vegetation carbon storage ranged from 96.48 to 215.35 Mg C ha?1, and the carbon storage significantly increased with stand age. The allometric equations developed in this study are useful to estimate biomass and carbon storage of S. apetala ecosystems.