Abstract
Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha−1. The mid-portion leaves on the year’s shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets—both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration (n = 1000) and validation (n = 420) of this model resulted in high R2 values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R2 = 0.77) and 2014 (R2 = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24–27 g/kg.
Highlights
Fertilization is considered one of the most effective ways to increase fruit yield and improve its quality
Using the spectral data and N concentration, we investigated the predictive power of principal principal component component regression regression (PCR), (PCR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), back propagation neural network (BPNN), and vegetation indices
Compared to the other two spectral measurements tested in this study, the leaf N concentration was more closely related to the leaf spectral reflectance measured by the black background based on the correlation coefficient and modelling accuracy
Summary
Fertilization is considered one of the most effective ways to increase fruit yield and improve its quality. Among the 17 essential nutrients required by plants, nitrogen (N) is one of the key elements for pear tree growth, fruit yield, and quality. The positive response of tree vegetative and reproductive organs to added N triggered a higher application of N fertilizer without paying more attention to actual N requirement of trees, which might lead to an excessive N status in pear trees. The over-application of N results in vigorous vegetative growth and decreased sugar-to-acid ratio [1], and increases. An excessive soil N availability in summer may delay fruit maturation, negatively impact the total soluble solids (major component is soluble carbohydrate) in fruit, and decrease plant tolerance to pests and diseases, such as psylla (Cacopsylla pyri L.) on “Bartlett”. White Background increase with the increasing Black of principal components and number of iteration in the validation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
