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Use of fluorescence indices as predictors of crop N status and yield for greenhouse sweet pepper crops.

Authors :
de Souza, Romina
Peña-Fleitas, M. Teresa
Thompson, Rodney B.
Gallardo, Marisa
Grasso, Rafael
Padilla, Francisco M.
Source :
Precision Agriculture. Feb2022, Vol. 23 Issue 1, p278-299. 22p.
Publication Year :
2022

Abstract

To increase nitrogen (N) use efficiency and reduce water pollution from vegetable production, it is necessary to optimize N management. Fluorescence-based optical sensors are devices that can improve N fertilization through non-destructive field monitoring of crop variables. The aim of this work was to compare the performance of five fluorescence indices (SFR-R, SFR-G, FLAV, NBI-R, and NBI-G) to predict crop variables, as dry matter production, crop N content, crop N uptake, Nitrogen Nutrition Index (NNI), absolute and relative yield, in sweet pepper (Capsicum annuum) crops grown in greenhouse. Fluorescence measurements were periodically made with the Multiplex® 3.6 sensor throughout three cropping cycles subjected to five N application treatments. The performance of fluorescence indices to predict crop variables considered calibration and validation analyses. In general, the five fluorescence indices were strongly related with NNI, crop N content and relative yield. The best performing indices to predict crop N content and NNI at the early stages of the crops (i.e., vegetative and flowering phenological stages) were the SFR indices, both under red (SFR-R) and green (SFR-G) excitation. However, in the final stage of the crop (i.e., harvest stage), the best performing indices were NBI, both under red (NBI-R) and green (NBI-G) excitation, and FLAV. The two SFR indices best predicted relative yield of sweet pepper at early growth stages. Overall, the fluorescence sensor and the fluorescence indices evaluated were able to predict crop variables related to N status in sweet pepper. They have the capacity to be incorporated into best N management practices. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13852256
Volume :
23
Issue :
1
Database :
Academic Search Index
Journal :
Precision Agriculture
Publication Type :
Academic Journal
Accession number :
154611655
Full Text :
https://doi.org/10.1007/s11119-021-09837-4