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Using Sentinel 2 Data to Guide Nitrogen Fertilization in Central Italy: Comparison Between Flat, Low VRT and High VRT Rates Application in Wheat

Authors :
Paolo Benincasa
Marco Vizzari
Francesco Saverio Santaga
Source :
Computational Science and Its Applications – ICCSA 2020, Computational Science and Its Applications – ICCSA 2020 ISBN: 9783030588137, ICCSA (5)
Publication Year :
2020

Abstract

The goal of this research was to compare traditional and variable rate technology (VRT) nitrogen (N) fertilization in winter wheat (Triticum aestivum L.). The study was developed over two years in two different fields (one field per year). Three different N fertilization approaches applied to the second fertilization were compared by integrating NDVI (Normalized Difference Vegetation Index) data from Sentinel 2 satellites (S2), grain yield, and protein content. In both fields used for the experimentation, the three treatments were defined as follows: 1) a standard rate (Flat-N) derived by an N balance approach; 2) a variable rate based on S2 NDVI, where the maximum rate was equal to the standard rate (Var-N-low); 3) a variable rate based on S2 NDVI, where the average rate was equal to the standard rate (Var-N-high). An inverse linear relationship between NDVI and N-rates was applied to calculate VRT doses on the assumption that NDVI and other correlated VIs, before the second N fertilization, are directly related to crop N nutritional status. Results show that differences between treatments in terms of NDVI, grain yield, and protein content were very low and generally not significant, suggesting that a low-N management approach, even using simple linear models based on NDVI and VRT, may considerably improve the economic and environmental sustainability of N fertilization in winter wheat. Further experiments are necessary to better explore the proposed approaches and compare them, by example, with the NDVI proportional methods that could be more suitable when the crop growth is mainly influenced by limiting factors other than N nutrition status.

Details

Language :
English
ISBN :
978-3-030-58813-7
ISBNs :
9783030588137
Volume :
12253
Database :
OpenAIRE
Journal :
Computational Science and Its Applications – ICCSA 2020
Accession number :
edsair.doi.dedup.....bb1c71dfb10dc984b051580290220377