Use of multispectral airborne images to improve in-season nitrogen management, predict grain yield and estimate economic return of maize in irrigated high yielding environments
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Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12-VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0-30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYONr) (216.8 kg N ha−1),), but underestimated the grain yield optimum N available (GYONa) (248.6 kg N ha−1). Red-based VIs slightly overestimated the GYONr and GYONa, while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EONr) and economic optimum nitrogen available (EONa) were determined below the GYONr and the GYONa, demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.
Is part ofRemote Sensing, 2018, vol. 10, núm. 4, p. 1-19
European research projects
- Articles publicats (Producció Vegetal i Ciència Forestal) 
- Publicacions de projectes de recerca del Plan Nacional 
- Articles publicats (Grup de Recerca en AgròTICa i Agricultura de Precisió) 
- Articles publicats (Agrotecnio Center) 
- Articles publicats (Medi Ambient i Ciències del Sòl) 
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Erratum: Maresma, A., et al. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sens. 2017, 9, 648 Maresma Galindo, Ángel; Ariza, Mar; Martínez, Elías; Lloveras Vilamanyà, Jaume; Martínez Casasnovas, José Antonio (MDPI, 2018)