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基于干物重的冬小麦越冬期后器官形态参数模拟研究
投稿时间:2018-10-11  修订日期:2019-04-16
中文关键词:  冬小麦  干物重  越冬期后  生长模型  几何参数
基金项目:
作者单位E-mail
李世娟 农业部信息服务技术重点实验室\中国农业科学院农业信息研究所 lishijuan@caas.cn 
诸叶平 农业部信息服务技术重点实验室/中国农业科学院农业信息研究所 zhuyeping@caas.cn 
张红英 中国农业科学院农业信息研究所 lishijuan@caas.cn 
刘升平 农业部信息服务技术重点实验室 liushengping@caas.cn 
刘海龙 农业部信息服务技术重点实验室 liuhailong@caas.cn 
杜鸣竹 农业部信息服务技术重点实验室 dumingzhu@caas.cn 
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中文摘要:计算机图形学、仿真技术、多媒体等技术的发展,加速了作物虚拟研究的发展。随着作物理想株型培育、群体产量预测、栽培管理措施推荐等实际农业生产需求的增加,研究人员越来越重视作物生长机理模型与形态结构模型的集成与融合。作物生长机理模型可以定量描述作物生长发育及其与环境因子的动态关系,具有通用性、动态性和预测性的特点,但基于生长机理模型模拟结果的作物虚拟技术与方法尚缺乏研究。针对基于生理过程的小麦功能模型与三维结构模型之间不能很好衔接的问题,本文开展越冬期后不同小麦品种的主茎干物重在不同器官之间的分配研究,以有效积温和干物重为连接纽带,构建小麦叶片、叶鞘、茎杆、穗各器官的几何特征模拟模型,并用独立数据进行了验证。结果显示,穗干物重分配指数模拟效果最好,n-RMSE值和EF值分别为6.58%和0.98;叶片、叶鞘和茎杆的分配指数模拟效果较好,n-RMSE值分别为13.86%、10.83%和14.87%,EF值分别为0.98、0.98和0.91。麦穗形态参数模型和叶鞘长度模型具有非常好的模拟性能;叶片长度和最大叶宽模型、茎杆长度和直径模型具有较好的模拟性能;叶鞘形态参数模型对于叶鞘展开宽度的模拟效果一般,需要在后续研究中对拟合方程和模型参数进一步修正。该系列模型以小麦生长模型模拟的干物重为参数输入,能够生成小麦主茎三维形态模拟所需的各器官逐日几何特征参数,参数反映了品种特性、生长环境及气象因素对作物生长的影响,是一种实现小麦功能模型与结构模型实际结合的有效方法。
Simulating winter wheat geometrical parameters of each organ after the wintering period using dry matter from growth mechanism model
Author NameAffiliationE-mail
li shijuan Agricultural Information Institute Chinese Academy of Agricultural Sciences lishijuan@caas.cn 
zhu yeping Agricultural Information Institute Chinese Academy of Agricultural Sciences zhuyeping@caas.cn 
zhang hongying Agricultural Information Institute Chinese Academy of Agricultural Sciences lishijuan@caas.cn 
liu shengping Agricultural Information Institute Chinese Academy of Agricultural Sciences liushengping@caas.cn 
liu hailong Agricultural Information Institute Chinese Academy of Agricultural Sciences liuhailong@caas.cn 
du mingzhu Agricultural Information Institute Chinese Academy of Agricultural Sciences dumingzhu@caas.cn 
Key words:winter wheat(Triticum aestivum L.)  dry matter  after the wintering period  growth model  geometrical parameters
Abstract: With the development of computer graphics, simulation technology, multimedia and other technologies, virtual research has expanded rapidly in many disciplines. Virtual crop research began in the 1980s and has undergone more than 40 years. According to the data sources, there are two main research directions in crop virtual research. One is crop geometry simulation and visualization based on external morphological parameters. Without regard for the impact of external morphology and the management measures on the crop, this type of models are focus on the authenticity of visual effects, so they generally have no biological significant. Another is the primary structure-function simulation model of crop morphological structure based on simple statistics. This type of primary functional-structure model considers environmental parameters, crop developing processes and a series of important growth characteristic parameters. But most of them are empirical models, in which consider the effects of certain environmental factors on plant growth, with the assumption of other environmental factors are appropriate. Thus the modeling method is not closely integrated with physiological processes. The model cannot reflect the impact of changes such as instant photosynthesis and water and fertilizer dynamics on the growth of crops, and thus cannot reflect the instantaneous changes of the virtual forms of crops. The crop growth mechanism model uses data related with soil, meteorological and species as parameters to simulate the dry matter, leaf area and water-fertilizer dynamics in soil-crop system day by day, which can quantitatively describe the dynamic relationship between crop growth and environmental factors. It is versatile, dynamic and predictive. With the increase of actual agricultural production demands such as crop ideal plant type cultivation, population yield prediction, and cultivation management measures recommended in recent years, researchers have paid more and more attention to the integration and fusion of crop growth mechanism model with morphological structure model. Aiming at the problem that the winter wheat (Triticum aestivum L.) functional model and the three-dimensional structural model can’t be well connected, this paper studied the distribution of the dry matter in different organs after the wintering period for three wheat varieties. Based on the effective accumulated temperature and dry matter, we constructed the geometrical simulation model of various organs such as wheat leaf, sheath, stem and ear, then verified by independent data. The results showed that the ear dry matter distribution index had the best simulation effect, with the n-RMSE value and EF value of 6.58% and 0.98, respectively. The distribution index of leaf, leaf sheath and stem are well simulated. The n-RMSE values are 13.86%, 10.83% and 14.87%, and the EF values are 0.98, 0.98 and 0.91, respectively. The ear morphological parameter model and the sheath length model performed pretty good. The n-RMSE values of the simulated and measured values of ear length, width and thickness are 7.39%, 9.61% and 6.22%, and the EF values are 0.83, 0.94, 0.92, respectively. The n-RMSE and EF values of leaf sheath length are 8.31% and 0.81. The simulation results for sheath expansion width of leaf sheath morphology parameter model has a general simulation effect, and the fitting equation and model parameters need to be further corrected in the future study. This series of models uses dry matter simulated from the wheat growth model as input, can generate daily geometrical parameters of each organ required for three-dimensional morphological simulation of wheat main stem. The parameters reflect the effects of variety characteristics, environment and meteorological factors on crop growth. It has agronomic significance and is an effective method to realize the actual combination of wheat functional model and structural model.
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