Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha−1) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro® and RapidEye™, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, 2013). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the N2O emissions, nitrate leaching, and farmer’s profit.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Analytical Spectral Devices, A. (2002). FieldSpec User’s guide, ASD Part#600000. Boulder CO: Rev. C.
Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 12.
Basso, B., Cammarano, D., Fiorentino, C., & Ritchie, J. T. (2013). Wheat yield response to spatially variable nitrogen fertilizer in Mediterranean Environment. European Journal of Agronomy, 51, 5.
Basso, B., Cammarano, D., Troccoli, A., Chen, D., & Ritchie, J. T. (2010). Long-term wheat response to nitrogen in a rainfed Mediterranean environment: Field data and simulation analysis. European Journal of Agronomy, 33(2), 132–138.
Basso, B., Fiorentino, C., Cammarano, D., Cafiero, G., & Dardanelli, J. (2012). Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment. European Journal of Agronomy, 41, 13.
Basso, B., Ritchie, J., Cammarano, D., & Sartori, L. (2011). A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field. European Journal of Agronomy, 35(4), 215–222.
Basso, B., Ritchie, J. T., Grace, P. R., & Sartori, L. (2006). Simulation of tillage systems impact on soil biophysical properties using SALUS model. Italian Journal of Agronomy, 4, 11.
Blackmer, T. M., Schepers, J. S., Varvel, G. E., & Walter-Shea, E. A. (1996). Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agronomy Journal, 88, 4.
Cammarano, D., Fitzgerald, G., Basso, B., & Grace, P. R. (2011a). Remote estimation of chlorophyll on two wheat cultivars in two rainfed environments. Crop and Pasture Science, 62, 6.
Cammarano, D., Fritzgerald, G., Basso, B., Oleary, G., Grace, P. R., & Fiorentino, C. (2011b). Use of the canopy chlorophyl content index (CCCI) for remote estimation of wheat nitrogen content in rainfed environments. Agronomy Journal, 103(6), 1597–1603.
Cammarano, D., Fritzgerald, G., Casa, R., & Basso, B. (2014). Assessing the robustness of vegetation indices to estimate wheat N in Mediterranean environments. Remote Sensing, 6, 2827–2844. doi:10.3390/rs6042827.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 11.
Daughtry, C. S. T., Walthall, C. L., Kim, M. S., de Colstoun, E. B., & McMurtrey, J. E, I. I. I. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 10.
Demetriades-Shah, T. H., Steven, M. D., & Clark, J. C. (1990). High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, 9.
Dzotsi, K. A., Basso, B., & Jones, J. W. (2013). Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecological Modelling, 260, 62–76. doi:10.1016/j.ecolmodel.2013.03.017.
Eitel, J. U. H., Long, D. S., Gessler, P. E., & Smith, A. M. S. (2007). Using in situ measurements to evaluate new RapidEye satellite series for prediction of wheat nitrogen status. Internationl Journal Remote Sensing, 28, 7.
FAO. (2008). Current world fertilizer trends and outlook to 2011/12. Rome: Food and Agriculture Organization.
Fitzgerald, G. J., Rodriguez, D., & O’Leary, G. (2010). Measuring and predicting canopy nitrogen concentration in wheat using a spectral index—the Canopy Chlorophyll Content Index (CCCI). Field Crop Research, 116, 6.
Fitzgerald, G. J., et al. (2006). Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precision Agriculture, 7, 15.
Flowers, M. (2001). Remote sensing of winter wheat tiller density for early nitrogen application decisions. Agronomy Journal, 93, 6.
Gitelson, A., Kaufman, Y., & Merzlyak, M. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 9.
Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher crop leaves. International Journal of Remote Sensing, 18, 6.
IFA. (2008). 76th IFA annual conference. In: I.F.A. Industry (Ed.), Vienna.
Justes, E., Mary, B., Meynard, J. M., & Thelier-Huche, L. (1994). Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany, 74, 10.
Labsphere. (1998). Reflectance characteristics of spectralon panels. Reflectance calibration laboratory. Sutton: Labsphere Inc.
Li, Y., Chen, D., Baker-Reid, F., & Eckard, R. (2008). Simulation of N2O emissions from rain-fed wheat and the impact of climate variation in southeastern Australia. Plant and Soil, 309, 239–251.
Long, D. S., Engel, R. E., & Carlson, G. R. (2000). Method for precision nitrogen management in spring wheat: IIImplementation. Precision Agriculture, 2, 13.
Maas, S. J., & Rajan. N. (2010). Normalizing and converting image DC data using scatter plot matching. Remote Sensing, 2, 1644–1661. doi:10.3390/rs2071644.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(2013), 358–371.
Rodriguez, D., Fitzgerald, G. J., Belford, R., & Christensen, L. K. (2006). Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Australian Journal of Agricultural Research, 57, 8.
Senthilkumar, S., Basso, B., Kravchenko, A. N., & Robertson, G. P. (2009). Contemporary evidence of soil carbon loss in the U.S. Corn Belt. Soil Science Society of American Journal, 73, 8.
Staff, S. S. (1999). Soil taxonomy (2nd ed.). Washington, DC: USDA, National natural resources Conservation Service.
The MathWorks Inc. (2010). MATLAB version 7.10.0. Natick: MathWorks Inc.
Wiegand, C. L., Gerbermann, A. H., Gallo, K. P., Blad, B. L., & Dusek, D. (1990). Multisite analyses of spectral-biophysical data for corn. Remote Sensing of Environment, 33, 15.
The study was funded with the support of the S.I.Cer.Me project, Italian Ministry of Agriculture.
About this article
Cite this article
Basso, B., Fiorentino, C., Cammarano, D. et al. Variable rate nitrogen fertilizer response in wheat using remote sensing. Precision Agric 17, 168–182 (2016). https://doi.org/10.1007/s11119-015-9414-9
- Nitrogen uptake
- Wheat yield
- Spatial and temporal variability
- Mediterranean environment
- Precision agriculture