| 摘要: |
| 为满足冬小麦调控管理决策技术方法的需要,采用均匀试验设计、视觉技术和逐步回归分析方法,研究了冬小麦生产动态试验优化设计过程和冬小麦调控管理模型,结果如下:经分析讨论得到了将小麦叶色、株高、叶面积指数等群体状态量综合在一起的“群体综合指数”计算公式,并用于动态试验优化设计过程和调控模型的构建;结合北京地区冬小麦8901品种的生产实际,安排了17个因素的分阶段田间动态试验,部分试验处理实测产量超过6 500 kg/hm2;初步建立了优质面包麦8901的生产调控模型,各阶段模型的复相关系数为0.956 6~0.997 7,生殖生长阶段之后模型的预测误差绝对值<6.92%,之前的模型除了2个处理外也均在此范围。研究结果为进行多因素作物生产试验研究和进一步研究田间数字监测技术提供了新的方法。 |
| 关键词: 冬小麦,动态试验优化设计,群体综合指数,调控决策,模型 |
| DOI：10.11841/j.issn.1007-4333.2006.06.148 |
| 投稿时间：2006-03-21 |
| 基金项目:国家科技攻关计划 |
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| Research on optimization design of dynamic experiments and regulation decision-making model for winter wheat production |
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| Abstract: |
| Based on the uniform design and the computer vision technology we studied the technology and method of regulation decision-making for winter wheat production in this paper.The research includes the trial design method and the related population growth status index of winter wheat.A dynamic trial design method was set up.The trial includes total 17 factors in different growth phrases.Some treatments yielded 6?570?kg/hm~2.The computing formulation of the population synthesized index was set up.The formulation includes status variables of winter wheat population such as the plant height,the leaf color and the leaf area index.The population synthesized index was involved as a factor in the trial design.The regulation decision-making models were built up by the stepwise regressive method in different growth phrases.The regressions are remarkable(R>0.95) and the predicted errors are from 6.92% to 6.92% with two exceptions. |
| Key words: winter wheat,dynamic optimization experiment design,population synthesized index,regulation decision-making,model |
