Estimation models of physiological and ecological parameters of rice damaged by <i>Cnaphalocrocis medinalis</i> under controlled field experiments and natural field experiments

PDF HTML阅读 XML下载 导出引用 引用提醒 控制大田和自然大田稻纵卷叶螟危害水稻生理生态参数估算模型 DOI: 10.5846/stxb202108072162 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41975144）；江苏省重点研发计划（现代农业）（BE2019387） Estimation models of physiological and ecological parameters rice damaged by Cnaphalocrocis medinalis under controlled field experiments natural Author: Affiliation: Fund Project: 摘要 | 图/表 访问统计 参考文献 相似文献 引证文献 资源附件 文章评论 摘要:为了准确监测和客观评估稻纵卷叶螟对水稻生长发育和产量形成的危害，利用ASD Field Spec3地物波谱仪和SPAD-502叶绿素仪分别采集控制大田试验（2015年和2019年）和自然大田试验（2020年）在各生育期（拔节期、孕穗期、灌浆期、成熟期）水稻的冠层高光谱数据和SPAD值，调查采集样点的虫量和水稻卷叶率，对比分析两种试验中稻纵卷叶螟的虫害发生特征、水稻冠层光谱特征和水稻生理生态参数特征，建立基于高光谱参数的水稻受稻纵卷叶螟危害的生理生态参数估算模型。结果表明，（1）两种试验的水稻SPAD值和冠层的红边至近红外波段的反射率均随着稻纵卷叶螟虫害程度的加重而降低，而可见光波段的反射率则相反；（2）自然大田试验的SPAD值和红光至近红外波段的冠层反射率在水稻生长发育前期要显著低于控制大田试验，而到了后期则反而要略高于控制大田试验；（3）综合分析筛选出自然大田试验和控制大田试验中的多个虫害特征参数和植被指数分别构建出了SPAD的单因子和多因子估算模型，各模型均达到了较好的估算效果，在单因子模型中EVI的二项式函数模拟效果最好，而多因子线性回归估测模型的模拟效果优于所有的单因子模型；（4）通过2021年对这些模型的应用检验发现：这些模型中基于虫量、卷叶率、OSAVI、EVI和DVI的单因子估算模型的SPAD估算值与实测值拟合度很高，其Rv2均超过了0.8，达到了比较理想的估算效果，这为稻纵卷叶螟危害下的水稻SPAD值估测提供了一种精度较高且可行的估算方法。 Abstract:In order to accurately monitor objectively evaluate the damage leaf roller, Guenee (C. medinalis), growth, breeding yield formation, in this paper, canopy hyperspectral data soil plant analyzer development (SPAD) values (in 2015 2019) 2020) were measured instruments ASD Spec3 SPAD-502 at different growth stages (jointing stage, booting filling stage maturing stage). The pest number percentage rolled leaves resulted from C. was manual investigations. characteristics canopy, medinalis' occurrence two analyzed. Models for estimating after being based on established. results showed that:(1) experiments, reflectance red edge near-infrared band SPAD value decreased with aggravation degree infestation, while reflectivity visible light opposite. (2) test wavelength range significantly lower than those control early development, slightly higher that later stage. (3) After comprehensively analyzing experimental data, multiple characteristic vegetation indices selected. A set single factor estimation multi-factor constructed, respectively. Each model achieved good effect, among which binomial function simulation effect Enhanced Vegetation Index (EVI) best model. However, linear regression better all single-factor models. (4) Through application these 2021, it found estimated number, leaf, Optimized Soil-Adjusted (OSAVI), EVI Difference (DVI) highly consistent value, their Rv2 more 0.8. It indicated five relatively ideal effect. This study provided a high-precision feasible method infestation medinalis.