Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments
Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation betweenNconcentration and yield in rice cultivation because an excess ofNcan unbalance the distribution of photo-assimilates in the plant and consequently produce a lower yield. We intended to study...
| Main Authors: | , , , , , |
|---|---|
| Format: | article |
| Language: | Inglés |
| Published: |
MDPI
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.11939/9011 https://www.mdpi.com/2077-0472/14/10/1753 |
| _version_ | 1855032895320621056 |
|---|---|
| author | Fita, David San Bautista, Alberto Castiñeira-Ibañez, Sergio Franch, Belén Domingo, Concha Rubio, Constanza |
| author_browse | Castiñeira-Ibañez, Sergio Domingo, Concha Fita, David Franch, Belén Rubio, Constanza San Bautista, Alberto |
| author_facet | Fita, David San Bautista, Alberto Castiñeira-Ibañez, Sergio Franch, Belén Domingo, Concha Rubio, Constanza |
| author_sort | Fita, David |
| collection | ReDivia |
| description | Rice production remains highly dependent on nitrogen (N). There is no positive linear
correlation betweenNconcentration and yield in rice cultivation because an excess ofNcan unbalance
the distribution of photo-assimilates in the plant and consequently produce a lower yield. We
intended to study these imbalances. Remote sensing is a useful tool for monitoring rice crops. The
purpose of this study was to evaluate the effectiveness of using remote sensing to assess the impact
of N applications on rice crop behavior. An experiment with three different doses (120, 170 and
220 kg N·ha−1) was carried out over two years (2021 and 2022) in Valencia, Spain. Biomass, Leaf
Area Index (LAI), plants per m2, yield, N concentration and N uptake were determined. Moreover,
reflectance values in the green, red, and NIR bands of the Sentinel-2 satellite were acquired. The
two data matrices were merged in a correlation study and the resulting interpretation ended in
a protocol for the evaluation of the N effect during the main phenological stages. The positive
effect of N on the measured parameters was observed in both years; however, in the second year,
the correlations with the yield were low, being attributed to a complex interaction with climatic
conditions. Yield dependence on N was optimally evaluated and monitored with Sentinel-2 data.
Two separate relationships between NIR–red and NDVI–NIR were identified, suggesting that using
remote sensing data can help enhance rice crop management by adjusting nitrogen input based on
plant nitrogen concentration and yield estimates. This method has the potential to decrease nitrogen
use and environmental pollution, promoting more sustainable rice cultivation practices. |
| format | article |
| id | ReDivia9011 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia90112025-04-25T14:49:45Z Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments Fita, David San Bautista, Alberto Castiñeira-Ibañez, Sergio Franch, Belén Domingo, Concha Rubio, Constanza Modelling Sentinel-2 F Plant production Oryza sativa Nitrogen Remote sensing Yields Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation betweenNconcentration and yield in rice cultivation because an excess ofNcan unbalance the distribution of photo-assimilates in the plant and consequently produce a lower yield. We intended to study these imbalances. Remote sensing is a useful tool for monitoring rice crops. The purpose of this study was to evaluate the effectiveness of using remote sensing to assess the impact of N applications on rice crop behavior. An experiment with three different doses (120, 170 and 220 kg N·ha−1) was carried out over two years (2021 and 2022) in Valencia, Spain. Biomass, Leaf Area Index (LAI), plants per m2, yield, N concentration and N uptake were determined. Moreover, reflectance values in the green, red, and NIR bands of the Sentinel-2 satellite were acquired. The two data matrices were merged in a correlation study and the resulting interpretation ended in a protocol for the evaluation of the N effect during the main phenological stages. The positive effect of N on the measured parameters was observed in both years; however, in the second year, the correlations with the yield were low, being attributed to a complex interaction with climatic conditions. Yield dependence on N was optimally evaluated and monitored with Sentinel-2 data. Two separate relationships between NIR–red and NDVI–NIR were identified, suggesting that using remote sensing data can help enhance rice crop management by adjusting nitrogen input based on plant nitrogen concentration and yield estimates. This method has the potential to decrease nitrogen use and environmental pollution, promoting more sustainable rice cultivation practices. 2024-12-04T14:02:36Z 2024-12-04T14:02:36Z 2024 article publishedVersion Fita, D., Bautista, A. S., Castiñeira-Ibáñez, S., Franch, B., Domingo, C., & Rubio, C. (2024). Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments. Agriculture, 14(10), 1753. 2077-0472 https://hdl.handle.net/20.500.11939/9011 10.3390/agriculture14101753 https://www.mdpi.com/2077-0472/14/10/1753 en This research has been funded by the PREDIC-PRO project SCPP2100C008733XVD, of the State Research Agency of theMinistry of Science, Innovation and Universities, and ACIF Generalitat Valenciana, European Union (European Social Fund. Investing in Your Future) (CIACIF/2021/143) and DETECTORYZA project INNEST/2022/227, INNEST/2022/319 and INNEST/2022/361 Regional Operational Programme, FEDER Comunitat Valenciana de la Innovació, Generalitat Valenciana. Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess MDPI electronico |
| spellingShingle | Modelling Sentinel-2 F Plant production Oryza sativa Nitrogen Remote sensing Yields Fita, David San Bautista, Alberto Castiñeira-Ibañez, Sergio Franch, Belén Domingo, Concha Rubio, Constanza Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments |
| title | Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments |
| title_full | Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments |
| title_fullStr | Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments |
| title_full_unstemmed | Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments |
| title_short | Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments |
| title_sort | remote sensing dynamics for analyzing nitrogen impact on rice yield in limited environments |
| topic | Modelling Sentinel-2 F Plant production Oryza sativa Nitrogen Remote sensing Yields |
| url | https://hdl.handle.net/20.500.11939/9011 https://www.mdpi.com/2077-0472/14/10/1753 |
| work_keys_str_mv | AT fitadavid remotesensingdynamicsforanalyzingnitrogenimpactonriceyieldinlimitedenvironments AT sanbautistaalberto remotesensingdynamicsforanalyzingnitrogenimpactonriceyieldinlimitedenvironments AT castineiraibanezsergio remotesensingdynamicsforanalyzingnitrogenimpactonriceyieldinlimitedenvironments AT franchbelen remotesensingdynamicsforanalyzingnitrogenimpactonriceyieldinlimitedenvironments AT domingoconcha remotesensingdynamicsforanalyzingnitrogenimpactonriceyieldinlimitedenvironments AT rubioconstanza remotesensingdynamicsforanalyzingnitrogenimpactonriceyieldinlimitedenvironments |