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Assessing the nitrogen status of almond trees by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates
Year:
2020
Source of publication :
Authors :
ירמיהו, אורי
;
.
פז-כגן, טרין
;
.
רפפורט, טל
;
.
שמילוביץ', זאב
;
.
שפרלינג, אור
;
.
Volume :
Co-Authors:
Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

Precise nitrogen (N) fertilization requires new indices of plants’ nutritional status. Non-structural carbohydrates (NSC) are the energetic currency of plants and can, thus, serve as a physiological indicator for their condition. Nevertheless, only a few records exist about NSC composition and allocation in crops, and their relationship with N uptake and the current methods to detect NSC compositions in plants are cumbersome and expensive, which limits their use. The current work aimed to associate the nutritional status of almond trees with the carbohydrate compositions in their roots, branches, and leaves by a high-throughput technique. We found that low N availability forces trees to allocate carbohydrates to their roots. High N availability, on the other hand, promoted above-ground vegetative growth, and minimized carbohydrate storage in the leaves. These observations implied that carbohydrate distribution could, indeed, serve as an indication of the nutritional status of the trees. To measure NSC content efficiently, we attempted to quantify soluble carbohydrates and starch in dried and powdered tissues by visible-to-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) reflectance spectroscopy, which is an inexpensive, safe, and non-destructive technique. We applied several multivariate statistical models based on the spectral datasets, including partial least squares-regression (PLS-R) and discriminant analysis (PLS-DA) as supervised registration and classification models. PLS-DA results of the N gradient in the roots and leaves showed an overall accuracy of 94% and 98%, respectively. PLS-R model performances of soluble carbohydrates and starch improved, in terms of the coefficient of determination (R2), if the leaf and root samples were integrated. Moreover, we found that the SWIR spectral region (1100–2500 nm) had unique reflectance features that revealed the carbohydrate composition and starch concentrations in the different plant tissues. The analyses also clustered the reflectance by tree part (root, branch, or leaf tissues) and N availability, forming a holistic model that can identify the nutritional status of trees. Conclusively, it is suggested that reflectance spectroscopy at the SWIR spectral region could guide precise fertilization by high-throughput identification of plants’ seasonal metabolism. 

Note:
Related Files :
Multivariate statistics
Non-structural carbohydrates
nutritional status
Partial least squares
precision agriculture
Soluble carbohydrates
Starch
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.compag.2020.105755
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
50308
Last updated date:
02/03/2022 17:27
Creation date:
21/09/2020 12:31
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Scientific Publication
Assessing the nitrogen status of almond trees by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates
Assessing the nitrogen status of almond trees by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates

Precise nitrogen (N) fertilization requires new indices of plants’ nutritional status. Non-structural carbohydrates (NSC) are the energetic currency of plants and can, thus, serve as a physiological indicator for their condition. Nevertheless, only a few records exist about NSC composition and allocation in crops, and their relationship with N uptake and the current methods to detect NSC compositions in plants are cumbersome and expensive, which limits their use. The current work aimed to associate the nutritional status of almond trees with the carbohydrate compositions in their roots, branches, and leaves by a high-throughput technique. We found that low N availability forces trees to allocate carbohydrates to their roots. High N availability, on the other hand, promoted above-ground vegetative growth, and minimized carbohydrate storage in the leaves. These observations implied that carbohydrate distribution could, indeed, serve as an indication of the nutritional status of the trees. To measure NSC content efficiently, we attempted to quantify soluble carbohydrates and starch in dried and powdered tissues by visible-to-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) reflectance spectroscopy, which is an inexpensive, safe, and non-destructive technique. We applied several multivariate statistical models based on the spectral datasets, including partial least squares-regression (PLS-R) and discriminant analysis (PLS-DA) as supervised registration and classification models. PLS-DA results of the N gradient in the roots and leaves showed an overall accuracy of 94% and 98%, respectively. PLS-R model performances of soluble carbohydrates and starch improved, in terms of the coefficient of determination (R2), if the leaf and root samples were integrated. Moreover, we found that the SWIR spectral region (1100–2500 nm) had unique reflectance features that revealed the carbohydrate composition and starch concentrations in the different plant tissues. The analyses also clustered the reflectance by tree part (root, branch, or leaf tissues) and N availability, forming a holistic model that can identify the nutritional status of trees. Conclusively, it is suggested that reflectance spectroscopy at the SWIR spectral region could guide precise fertilization by high-throughput identification of plants’ seasonal metabolism. 

Scientific Publication
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