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当前位置: 首页 > 文章 > Should phenological information be applied to predict agronomic traits across growth stages of winter wheat? 作物学报(英文版) 2022,10 (5)
Position: Home > Articles > Should phenological information be applied to predict agronomic traits across growth stages of winter wheat? The Crop Journal 2022,10 (5)

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

作  者：
Yu Zhao;Meng Yang;Shengbo Han;Haikuan Feng;Guijun Yang;Zhenhai L
单  位：
Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China; Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, Chin;College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong, China; Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
关键词：
Agronomic traits;Phenological effect;Vegetation index;Hierarchical linear model;Winter whea
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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