中国农业科学 ›› 2023, Vol. 56 ›› Issue (6): 1061-1073.doi: 10.3864/j.issn.0578-1752.2023.06.004

• 耕作栽培·生理生化·农业信息技术 • 上一篇    下一篇

白粉病对小麦光合特性的影响及病害严重度的定量模拟

常春义(), 曹元, Ghulam Mustafa, 刘红艳, 张羽, 汤亮, 刘兵, 朱艳, 姚霞, 曹卫星, 刘蕾蕾()   

  1. 南京农业大学农学院/国家信息农业工程技术中心/智慧农业教育部工程研究中心/农业农村部农作物系统分析与决策重点实验室/江苏省信息农业重点实验室/江苏省现代作物生产协同创新中心,南京 210095
  • 收稿日期:2022-06-28 接受日期:2022-08-02 出版日期:2023-03-16 发布日期:2023-03-23
  • 联系方式: 常春义,E-mail:2018101017@njau.edu.cn。
  • 基金资助:
    国家自然科学基金创新研究群体项目(32021004); 江苏省重点研发计划(BE2019383); 江苏省农业科技自主创新资金项目(CX(21)1006); 江苏省农业科技自主创新资金项目(CX(22)3201); 南京农业大学学科建设专项(ZJ22195018)

Effects of Powdery Mildew on Photosynthetic Characteristics and Quantitative Simulation of Disease Severity in Winter Wheat

CHANG ChunYi(), CAO Yuan, GHULAM Mustafa, LIU HongYan, ZHANG Yu, TANG Liang, LIU Bing, ZHU Yan, YAO Xia, CAO WeiXing, LIU LeiLei()   

  1. College of Agriculture, Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/ Engineering Research Center of Smart Agriculture, Ministry of Education/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095
  • Received:2022-06-28 Accepted:2022-08-02 Published:2023-03-16 Online:2023-03-23

摘要:

【目的】明确白粉病胁迫对小麦光合特性的影响规律,构建白粉病胁迫下小麦光合生产的模拟模型。【方法】以小麦为试验材料,分别于拔节期和孕穗期进行不同接种程度的小麦白粉病试验,明确白粉病对小麦光合特性的影响规律;在此基础上构建小麦白粉病严重度预测模型,量化白粉病对小麦的生理影响;基于单叶净光合速率(Pn)和叶面积指数(LAI),实现小麦白粉病严重度预测模型与作物生长模型(WheatGrow)的耦合。【结果】白粉病胁迫下,小麦单叶Pn和LAI均呈现下降趋势,与对照(CK)相比分别平均下降18.81%和23.41%,且与初始接种程度相比,发病时期对小麦Pn和LAI的影响更为严重;小麦白粉病田间病情发展具有明显的平缓期、指数爆发期和稳定期,总的来说各处理下小麦白粉病流行的时间动态变化特征符合Logistic函数,基于白粉病胁迫对小麦影响的拟合结果,构建小麦白粉病病害胁迫因子,用以反映白粉病对小麦生理指标影响的胁迫效应;基于WheatGrow模型的光合生产子模型,结合小麦白粉病病害胁迫因子,提出模拟白粉病对小麦叶片Pn和LAI效应的算法,并利用独立年份的数据资料对改进后的WheatGrow模型进行检验。【结论】耦合白粉病胁迫因子的WheatGrow模型对白粉病胁迫下小麦叶片Pn、LAI、地上部生物量和产量的预测精度均好于原模型,模拟精度较原模型分别提高了53.29%、43.61%、60.09%和67.57%,改进后的模型可为小麦白粉病严重度的预测与小麦产量损失的定量评估等提供数字化工具和技术支撑。

关键词: 冬小麦, 白粉病, 光合特性, 病害严重度预测模型, WheatGrow模型, 耦合

Abstract:

【Objective】 The objective of this paper was to clearly demonstrate the effects of powdery mildew on photosynthetic characteristics of winter wheat and to establish a model for simulating effects of powdery mildew stress on wheat photosynthetic productivity. 【Method】 To clarify the effects of powdery mildew on wheat photosynthetic characteristics, the powdery mildew experiments of wheat were conducted under two initial inoculation degrees of wheat powdery mildew at jointing and booting stages. On this basis, a prediction model of wheat powdery mildew severity was established to quantify the physiological effects of powdery mildew on wheat. And then, based on the single leaf net photosynthetic rate (Pn) and leaf area index (LAI), the wheat powdery mildew severity prediction model was coupled with the crop growth model (WheatGrow). 【Result】 Under the stress of powdery mildew, Pn and LAI showed a decreasing trend. Compared with the control (CK), the averaged Pn and LAI decreased by 18.81% and 23.41%, respectively. Moreover, the effects of stages of powdery mildew on Pn and LAI were more serious than the initial inoculation degrees. In general, the development of wheat powdery mildew in the field had obvious gentle period, exponential outbreak period and stable period, and the temporal dynamic characteristics of wheat powdery mildew epidemic under each treatment accorded with Logistic function. Therefore, based on the Logistic fitting results, the wheat powdery mildew disease stress factor was established to reflect the stress effects of powdery mildew on wheat physiological indexes. In addition, based on the photosynthesis productivity sub-model of WheatGrow and the effect factor of wheat powdery mildew severity, the algorithms to simulate the effects of powdery mildew on Pn and LAI were established, and then the improved WheatGrow model was estimated by using the powdery mildew experimental datasets in independent years. 【Conclusion】 The integrated model with powdery mildew stress algorithms was better than the original WheatGrow model in predicting Pn, LAI, aboveground biomass and yield under powdery mildew stress condition, with the simulation accuracy improved by 53.29%, 43.61%, 60.09% and 67.57%, respectively. The improved model could provide the digital tool and technical support for prediction of wheat powdery mildew severity and the quantitative evaluation of wheat yield loss.

Key words: winter wheat, powdery mildew, photosynthetic characteristics, prediction model of disease severity, WheatGrow model, coupling