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Mapping Optimum Nitrogen Crop Uptake

Published online by Cambridge University Press:  01 June 2017

J. Villodre ,
I. Campos ,
H. Lopez-Corcoles ,
J. Gonzalez-Piqueras ,
L. González ,
V. Bodas ,
S. Sanchez-Prieto ,
A. Osann  and
A. Calera
J. Villodre*
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
I. Campos
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
H. Lopez-Corcoles
Affiliation:
Instituto Técnico Agronómico de Albacete (ITAP) Avda. Gregorio Arcos 19, 02005 Albacete (Spain)
J. Gonzalez-Piqueras
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
L. González
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
V. Bodas
Affiliation:
Aliara Agrícola S.L. Calle Matadero 11. 45600. Talavera de La Reina (Toledo). Spain
S. Sanchez-Prieto
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
A. Osann
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
A. Calera
Affiliation:
GIS and Remote Sensing Group. Instituto de Desarrollo Regional. Universidad de Castilla-La Mancha. Campus Universitario SN. Albacete (Spain)
*
†E-mail: [email protected]
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Abstract

This work proposes a methodology that uses remote sensing (RS) images to obtain optimum nitrogen crop uptake (Nuptake) maps, for the all pixels in the image included in the field during the entire growing season. The Nuptake was determined from relationship between critical nitrogen concentration (Nc) and biomass where biomass was estimated by a crop growth model based on the water use efficiency. The paper proposes the use of this methodology in commercial wheat farm. The results are discussed with respect to field measurements of crop biomass and N concentration on different dates and in zones with different nitrogen treatments from 8 commercial wheat farms in Albacete, Spain during 2015 and 2016.

Keywords

Remote sensingBiomassCritical nitrogen concentration (Nc)Variable Rate TechnologyVariable fertilization
Type
Precision Nitrogen
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 322 - 327
Copyright
© The Animal Consortium 2017 

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References

Anffe ANF de, F 2015. Continúa la recuperación del consumo de fertilizantes en España (Fertilizer consumption in Spain is still recovery). Vida rural 11, 26–29.Google Scholar
Calera, A, González-Piqueras, J and Melia, J 2004. Monitoring barley and corn growth from remote sensing data at field scale. International Journal of Remote Sensing 25, 97–109.CrossRefGoogle Scholar
Campos, I, Gonzalez, L, Villodre, J, Calera, M, Campoy, J, Jiménez, N, et al. 2017. Mapping within-field biomass variability: a remote sensing-based approach, In Proceedings of 11th European Conference on Precision Agriculture, UK (these proceedings).Google Scholar
Campos, I, Neale, CMU, Calera, A, Balbontin, C and González-Piqueras, J 2010. Assesing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agricultural Water Management 98, 45–54.CrossRefGoogle Scholar
Chen, X, Vierling, L and Deering, D 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment 98, 63–79.Google Scholar
Glenn, EP, Neale, CMU, Hunsaker, DJ and Nagler, PL 2011. Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrological Processes 25, 4050–4062.CrossRefGoogle Scholar
González-Dugo, MP, Escuin, S, Cano, F, Cifuentes, V, Padilla, FLM, Tirado, JL, et al. 2013. Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. II. Application on basin scale. Agricultural Water Management 125, 92–104.CrossRefGoogle Scholar
González-Piqueras, J, López, H, Sánchez, S, Villodre, J, Bodas, V, Campos, I, et al. 2017. Monitoring crop N status by using red edge-based indices. In Proceedings of 11th European Conference on Precision Agriculture, UK (these proceedings).Google Scholar
Gonzalez, L, Calera, M, Villodre, J, Bodas, V, Campos, I and Calera, A 2015. Secuencias temporales de imágenes y datos meteorológicos para caracterizar variabilidad en parcelas de trigo y maíz (Image time series and meteorological data to characterize variability in wheat and maize fields). In Proceedings of XVI Congreso de La Asociación Española de Teledetección. Sevilla, Spain.Google Scholar
Justes, E, Jeuffroy, MH and Mary, B 1997. Wheat, barley, and durum wheat. In Diagnosis of the Nitrogen Status in Crops. Springer, pp. 73–91.CrossRefGoogle Scholar
Justes, E, Mary, B, Meynard, J-M, Machet, J-M and Thelier-Huches, L 1994. Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany 74, 397–407.Google Scholar
Lemaire, G 2012. Diagnosis of the Nitrogen Status in Crops. Springer Science & Business Media, Berlin, Germany.Google Scholar
MartínezBeltrán, C, Jochum, MAO, Calera, A and Meliá, J 2009. Multisensor comparison of NDVI for a semi-arid environment in Spain. International Journal of Remote Sensing 30, 1355–1384.CrossRefGoogle Scholar
Steduto, P, Hsiao, TC and Fereres, E 2007. On the conservative behavior of biomass water productivity. Irrigation Science 25, 189–207.Google Scholar