A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data | Un modèle hiérarchique de prévision interannuelle du rendement du blé et des protéines du grain utilisant des indices végétatifs spectraux et des données météorologiques
2020
Li, Z. | Taylor, James | Yan, Hao | Casa, Raffaele | Jin, X.L. | Song, X. | Yang, G | National Engineering Research Center for Information Technology in Agriculture [Beijing] (NERCITA) | Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Università degli studi della Tuscia = Tuscia University [Viterbo] (UNITUS) | Chinese Academy of Agricultural Sciences (CAAS) | BEIJING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE BEIJING CHN ; Partenaires IRSTEA ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE
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显示更多 [+] 显示较少 [-]英语. 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.
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