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    Canopy reflectance in two castor bean varieties (Ricinus communis L.) for growth assessment and yield prediction on coastal saline land of Yancheng District, China

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    Abstract
    A field experiment was conducted in 2007–2009 in coastal saline regions of Yancheng city in Jiangsu province of China (120°13′E, 33°38′N). The experiment was to investigate relationships among canopy spectral reflectance, canopy chlorophyll density (CCD), leaf area index (LAI), and yield of two Chinese castor varieties (Zi Bi var. and Yun Bi var.) across four N fertilizer rates of 0, 90, 180, and 360 kg N ha?1. These N rates were used to generate a wide range of difference in canopy structure and seed yield. Measurements of canopy reflectance were made throughout the growing season using a hand-held spectroradiometer. Samples for CCD and LAI were obtained on days that reflectance measurements were made. Fifteen hyperspectral reflectance indices were calculated. Canopy spectral characteristics were heavily influenced by saline soil background in the rapid growing period (RGP), thus hyperspectral data obtained in this period were not suited for reflecting castor growth condition or predicting final yield. CCD increased linearly with most reflectance indices in the full coverage period (FCP) and senescent period (SP) for the two castor varieties, whereas LAI did not. Most of reflectance indices were significantly correlated with yield of two varieties in different growing periods. The OSAVI model provided the best yield prediction for Zi Bi var. with predicted values very close to observed ones (R2 = 0.799), and the mSRVI705 model was well used for Yun Bi var. yield estimation (R2 = 0.759). These results indicate that the hyperspectral data measured at appropriate time could be well used for castor yield estimation.
    Article Outline
    1. Introduction
    2. Materials and methods
    2.1. Experimental design and treatments
    2.2. Canopy reflectance measurements
    2.3. Biomass and partition
    2.4. Data analysis
    3. Results
    3.1. CCD, LAI and yield
    3.2. Canopy level reflectance measurements
    3.3. Relationships between CCD, LAI and yield
    3.4. Relationships between CCD and LAI and VIs
    3.5. The model of yield prediction
    4. Discussion
    4.1. Several important variables for castor growth assessment and yield prediction
    4.2. About the model of yield prediction
    5. Conclusion
    Acknowledgements
    References
     

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