A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data
By Zhenhai Li, James A. Taylor, Hao Yang, Raffaele Casa, Xiuliang Jin, Zhenhong Li, Xiao-yu Song, Guijun Yang. Field crops research (Print), 2020
59 citations recorded by the source corpus.
Abstract 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.
Subfields: Analytical Chemistry, Ecology, Plant Science
View Paper: https://www.semanticscholar.org/paper/b46eeae7f3005cd2474faa4d9f691064057af371
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In Analytical Chemistry
In Ecology
- Zhenhai Li
- Tao Yang
- Pablo J. Zarco-Tejada
- Bo Ming
- Rajeev K. Varshney
- Chenwei Nie
- Urs Schmidhalter
- Shaokun Li
In Plant Science
- Pablo J. Zarco-Tejada
- Rajeev K. Varshney
- Chenwei Nie
- Urs Schmidhalter
- Matthew P Reynolds
- Zhenhai Li
- Yonggui Xiao
- Malcolm J. Hawkesford
Citations in the corpus
- High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, methodology): “[240] combined field spectral VIs and meteorological data through hierarchical linear modeling (HLM) to estimate wheat yield, and their results showed that the approximated power of yield estimation derived through HLM was higher than that obtained using ordinary least-squares regression, with R(2) ”