中国农业科学 ›› 2012, Vol. 45 ›› Issue (12): 2364-2374.doi: 10.3864/j.issn.0578-1752.2012.12.004

• 耕作栽培·生理生化·农业信息技术 • 上一篇    下一篇

基于过程的小麦株型指标动态模拟

 张文宇, 汤亮, 姚鑫锋, 杨月, 曹卫星, 朱艳   

  1. 南京农业大学农学院/国家信息农业工程技术中心/江苏省信息农业高技术研究重点实验室,南京 210095
  • 收稿日期:2011-11-23 出版日期:2012-06-15 发布日期:2012-05-08
  • 通讯作者: 通信作者朱 艳,E-mail:yanzhu@njau.edu.cn
  • 作者简介:张文宇,E-mail:research@wwery.cn
  • 基金资助:

    教育部新世纪优秀人才支持计划项目(NCET-08-0797)、国家自然科学基金项目(30800136)、江苏省自然科学基金项目(BK2009307)、江苏高校优势学科建设工程资助项目(PAPD)

Process-Based Simulation Model for Growth Dynamics of Plant Type Index in Wheat

 ZHANG  Wen-Yu, TANG  Liang, YAO  Xin-Feng, YANG  Yue, CAO  Wei-Xing, ZHU  Yan   

  1. 南京农业大学农学院/国家信息农业工程技术中心/江苏省信息农业高技术研究重点实验室,南京 210095
  • Received:2011-11-23 Online:2012-06-15 Published:2012-05-08

摘要: 【目的】揭示小麦株型指标变化规律及播种密度对株型指标的影响。【方法】基于不同播种密度和不同株型品种的小麦田间试验,通过连续观测主要生育时期小麦主茎叶型和茎型指标,分析并模拟分层叶面积、叶向值、株高构成指数等株型指标的动态变化规律及播种密度对其的影响。【结果】不同株型品种分层叶面积指数(LAI)均表现为中部>上部>下部的分布特征,并随生育期的推进逐渐向中上部集中,且冠层中上部总是高密度群体LAI较大。所有品种高低密度间株高构成指数(穗下节与倒二节间长度之和与株高的比值,IL)均表现出显著差异,不同株型品种株高构成指数(n节间长与n节间加n-1节间长度之和的比值,In)表现为从下至上先减小后增大的趋势,且上部节间受密度影响较大。较为紧凑的矮抗58叶向值在不同密度下表现平稳,随生育进程略微表现为增大—减小—趋缓的趋势;较为披散的扬麦12号中高密度下随生育进程均表现为平缓—减小—平缓的趋势;扬麦16号则表现为平缓—减小—略微增大的趋势。在分析株型指标变化趋势和课题组已有形态模型的基础上,通过冠层切割和叶面积积分的方法模拟了叶面积指数的分层动态变化,利用组合的形态参数模拟了株高构成指数和叶向值的动态变化,并通过对形态指标的归类分析,构建了综合性株型构成指数,综合体现了叶型和茎型的动态变化。利用独立试验资料对分层叶面积指数、株高构成指数和叶向值的动态变化模型进行了检验,其平均RRMSE分别为17.44%、7.64%和10.66%。【结论】经检验,该模型对上述小麦株型指标具有较好的预测性。

关键词: 小麦, 株型, 模拟, 分层叶面积, 叶向值, 株高构成指数, 综合性株型构成指数

Abstract: 【Objective】This study was designed to explore the dynamic changing characteristics of wheat (Triticum aestivum L.) plant type index and the effects of planting density on plant type index.【Method】Based on the field experiments with three cultivars (Aikang 58 with erect leaf, Yangmai 12 and Yangmai 16 with flat leaf) and three planting densities in wheat, the time-course measurements were carried out on the indices of leaf type and stem type at main growing stages of wheat, and then the dynamic changing patterns of plant type indices and the effects of planting density on the dynamics of plant type indices in wheat were analyzed and simulated.【Result】The results indicated that the layer LAI of different plant-type cultivars had a trend of “middle layer> upper layer> lower layer”, and the layer LAI was gradually concentrated in the middle or upper layer with the growing progress. In addition, the larger LAI of the middle or upper layer was observed on high population density. The significant differences of plant height component index (the ratio of the length of rachis and penultimate internodes to plant height, IL) between high and low population densities were observed. The plant height component index (the ratio of the length of n internode to the length of n internode adding (n-1) internode, In) of different plant-type cultivars were all decreased first and increased later from lower to higher leaf positions. In addition, the remarkable effect of planting density was observed on In of upper internode. As for Aikang 58, a relatively constant LOV with a slight “up-down-easing down” trend was observed. As for Yangmai 12, the change pattern of LOV was in “gentle-decrease-gentle” trend. As for Yangmai 16, a trend of “gentle-decrease-increased slightly” was showed. Based on the analysis of plant type index dynamics and the existing morphological model, the dynamics of layer LAI was simulated by using the methods of canopy cutting and leaf area integration, and the dynamics of the plant height component index and LOV were simulated by using combined morphological parameters. In addition, the comprehensive plant type component index was constructed by integrating different morphological indices, and reflected the dynamic changes of the leaf type and stem type synthetically. Testing of the dynamic models of layer LAI, plant height component index and LOV with independent dataset showed that the average RRMSE values of layer LAI and LOV were 17.44%, 7.64%, and 10.66%, respectively.【Conclusion】The results indicated a good performance and reliability of models for predicting plant type index in wheat. The model can not only provide technical supports for the simulation of canopy structure in wheat, but also lay a foundation for the simulation of light distribution and photosynthesis within wheat canopy.

Key words: wheat, plant type, simulation, layer LAI, plant height component index, leaf orientation value, comprehensive plant type component index