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Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

文献类型： 外文期刊

作者： Zhao, Yu 1 ; Meng, Yang 1 ; Han, Shaoyu 1 ; Feng, Haikuan 1 ; Yang, Guijun 1 ; Li, Zhenhai 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Nanjing Agr Univ, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing 210095, Jiangsu, Peoples R China

3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Shandong, Peoples R China

关键词： Agronomic traits; Phenological effect; Vegetation index; Hierarchical linear model; Winter wheat

期刊名称：CROP JOURNAL （ 影响因子：4.647； 五年影响因子：5.781 ）

ISSN： 2095-5421

年卷期： 2022 年 10 卷 5 期

页码：

收录情况： SCI

摘要： Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index (VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model (HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model's performance by sensitivity analysis. Results demonstrated that (1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD. (2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81-0.86 and 0.66-0.73, whereas LAI, LGB, LNC, and SPAD prediction mod -els produced sensitivity indexes of 0.01-0.02, 0.01-0.02, 0.01-0.02, and 0.02-0.08, respectively. (3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB, LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons. (c) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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