| 王乃江,刘璐,冯浩,魏永胜.基于结构方程模型的关中地区玉米穗部性状关系研究[J].干旱地区农业研究,2025,(1):151~159 |
| 基于结构方程模型的关中地区玉米穗部性状关系研究 |
| Study on relationships among maize ear traits in Guanzhong region based on structural equation model |
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| DOI：10.7606/j.issn.1000-7601.2025.01.16 |
| 中文关键词: 玉米 农田环境 穗部性状 结构方程模型 |
| 英文关键词:maize field environment ear traits structural equation model |
| 基金项目:中国博士后科学基金面上项目（2022M722611）；国家重点研发计划项目（2021YFD1900700）；陕西省重点研发计划项目（2023-ZDLNY-56） |
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| 中文摘要: |
| 为构建“玉米穗长-穗粗-穗行数-行粒数-百粒重-穗粒重”关系的结构方程模型，揭示农田环境变化下关中地区玉米穗部性状之间的因果效应，依据关中地区田间管理经验，在陕西杨凌设置雨养和灌溉2个主处理，免耕、不同灌水定额、不同施肥量、不同有机物料添加和不同覆盖措施5个副处理，共33个处理，进行了连续3 a（2014—2016年）的田间定位试验。结果表明： 6个穗部性状均易受田间环境变化的影响，变异系数均介于10.87%~22.49%（中等变异），其中穗粒重对田间环境变化最敏感，其次为百粒重。玉米穗部性状关系的初始结构方程模型与田间实测数据适配欠佳，但在修订后，6个适配度评价指标均在合理范围内。穗粒重受百粒重的影响（0.667）>穗行数（0.554）>穗长（0.463）>行粒数（0.283）>穗粗（0.102），百粒重受行粒数的影响（-0.734）>穗长（-0.398）>穗行数（0.318）>穗粗（0.151），行粒数受穗长的影响（0.939）>穗行数（-0.167），穗粗受穗长的影响（0.606）>穗行数（0.272）。研究结果可为关中地区通过改善穗部性状来提高玉米产量提供理论参考。 |
| 英文摘要: |
| In this study, a in\|situ field experiment was conducted from 2014 to 2016 at Yangling District, Shaanxi Province. According to local field management practices, thirty\|three treatments including two main treatments (rain\|fed and irrigation) and five secondary treatments (no\|tillage as well as different irrigation quotas, inorganic fertilization application rates, organic materials, and mulching practices) were designed. The maize ear length (EL), ear diameter (ED), kernel row number (KRN), kernel number per row (KNR), 100-kernel weight (HKW), and kernel weight per ear (KWE) in fields were investigated. Structural equation model was used to quantify the relationship EL-ED-KRN-KNR-HKW-KWE for revealing the causal effect among ear traits. The results showed that six ear traits were vulnerable to changing field environment with coefficient of variation (CV) from 10.87% to 22.49%. The variation magnitude of six ear traits was moderate, KWE was most senstive to field environment, and HKW was second. There was a poor fitting between initial structural equation model and field measured data. However, for modified structural equation model, six fitting indices fell within the reasonable ranges. HKW had the greatest effect on KWE with total effect of 0.677, followed by KRN, EL, KNR, and ED with that of 0.554, 0.463, 0.283, and 0.102, respectively. The effect on HKW was KNR>EL>KRN>ED with total effect of -0.734, -0.398, 0.318 and 0.151, respectively. The total effect on KNR was 0.939 for EL and -0.167 for KRN. The total effect of EL on ED (0.606) was greater than that of KRN on ED (0.272). These findings provide theoretical reference for increasing maize grain yield by improving ear traits in Guanzhong region. |
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