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Lookup NU author(s): Professor Zhenhong Li
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2019 Elsevier B.V. The use of remote sensing data for predicting wheat yield and quality is becoming a more feasible alternative to destructive and post-harvest laboratory-based test methods. However, most prediction models which make use of remote sensing data are statistical rather than mechanistic, therefore difficult to extend at interannual and regional scales. In this work, an interannual expandable wheat yield and quality predicting model using hierarchical linear modeling (HLM) was developed, integrating hyperspectral and meteorological data. The results showed that the ordinary least squares (OLS) regression for predicting wheat yield and grain protein content (GPC), one key indicator of grain quality, had low stability at the interannual extension. The predictive power for yield by HLM method was higher than OLS, with R2, RMSEv and nRMSE values of 0.75, 1.10 t/ha, and 20.70 %, respectively. GPC prediction by the HLM method was enhanced when the gluten type was considered, with R2, RMSEv and nRMSE values of 0.85, 1.02 %, and 6.87 %, respectively. The results of this study confirmed that HLM can be a robust method for improving yield and GPC predicting stability under various growing seasons in winter wheat.
Author(s): Li Z, Taylor J, Yang H, Casa R, Jin X, Li Z, Song X, Yang G
Publication type: Article
Publication status: Published
Journal: Field Crops Research
Year: 2020
Volume: 248
Print publication date: 01/03/2020
Online publication date: 30/12/2019
Acceptance date: 24/12/2019
Date deposited: 04/03/2020
ISSN (print): 0378-4290
ISSN (electronic): 1872-6852
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.fcr.2019.107711
DOI: 10.1016/j.fcr.2019.107711
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