Fita-Silvestre, D.; San Bautista Primo, A.; Castiñeira-Ibáñez, S.; Franch, B.; Domingo Carrasco, C.; Rubio Michavila, C. (2024). Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments. Agriculture. 14(10). https://doi.org/10.3390/agriculture14101753
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/210294
Título:
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Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments
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Autor:
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Fita-Silvestre, David
San Bautista Primo, Alberto
Castiñeira-Ibáñez, Sergio
Franch, Belén
Domingo Carrasco, Concha
Rubio Michavila, Constanza
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Entidad UPV:
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Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació
Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural
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Fecha difusión:
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Resumen:
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[EN] Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation between N concentration and yield in rice cultivation because an excess of N can unbalance the distribution of ...[+]
[EN] Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation between N concentration and yield in rice cultivation because an excess of N can 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.
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Palabras clave:
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Rice
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Nitrogen
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Remote sensing
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Yield
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Modelling
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Sentinel-2
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Derechos de uso:
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Reconocimiento (by)
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Fuente:
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Agriculture. (eissn:
2077-0472
)
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DOI:
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10.3390/agriculture14101753
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Editorial:
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MDPI AG
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Versión del editor:
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https://doi.org/10.3390/agriculture14101753
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Código del Proyecto:
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info:eu-repo/grantAgreement/AEI//CPP2021-008733//Sensores remotos para la obtención de información predictiva de la producción de cultivos cereales (PREDIC-PRO)/
...[+]
info:eu-repo/grantAgreement/AEI//CPP2021-008733//Sensores remotos para la obtención de información predictiva de la producción de cultivos cereales (PREDIC-PRO)/
info:eu-repo/grantAgreement/AGENCIA VALENCIANA DE LA INNOVACION//INNEST%2F2022%2F361//AGRICULTURA DE PRECISIÓN EN EL CULTIVO DEL ARROZ: DETECCIÓN PRECOZ DE SÍNTOMAS DE PYRICULARIA ORYZAE Y DETERMINACIÓN DE LA DOSIS (DETECTORYZA)/
info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//CIACIF%2F2021%2F143//APLICACIÓN Y USO DE SENSORES REMOTOS PARA LA MODELIZACIÓN Y ANÁLISIS DE LA RESPUESTA
PRODUCTIVA EN EL CULTIVO DEL ARROZ/
info:eu-repo/grantAgreement/AVI//INNEST%2F2022%2F319/
info:eu-repo/grantAgreement/AVI//INNEST%2F2022%2F227/
info:eu-repo/grantAgreement/AEI//SCPP2100C008733XVD/
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Agradecimientos:
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This research has been funded by the PREDIC-PRO project SCPP2100C008733XVD, of the State Research Agency of the Ministry of Science, Innovation and Universities, and ACIF Generalitat Valenciana, European Union (European ...[+]
This research has been funded by the PREDIC-PRO project SCPP2100C008733XVD, of the State Research Agency of the Ministry 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.
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Tipo:
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Artículo
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