Skip to main content

Advertisement

SpringerLink
Log in

Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2 m2 in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3 cm height. At 500 °C day, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r 2 = 0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15 % for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Agnusdei, M. G., Colabelli, M. E., & Fernández Grecco, R. C. (2001). Crecimiento estacional de forraje de pasturas y pastizales naturales Para el sudeste bonaerense, Boletín Técnico 152. INTA EEA Balcarce: CERBAS.

    Google Scholar 

  • Agnusdei, M. G., Assuero, S. G., Lattanzi, F. A., & Marino, M. A. (2010). Critical N concentration can vary with growth conditions in forage grasses: implications for plant N status assessment and N deficiency diagnosis. Nutrient Cycling in Agroecosystems, 88, 215–230.

    Article  CAS  Google Scholar 

  • Andrade, F. H., Echeverría, H. E., González, N. S., & Uhart, S. A. (2002). Requerimientos de nutrientes minerales. In F. H. Andrade & V. O. Sadras (Eds.), Bases Para el manejo del maíz, el girasol y la soja (pp. 207–234). Buenos Aires: Editorial Médica Panamericana.

    Google Scholar 

  • Asay, K.H., Jensen, K.B. (1996). Wheatgrasses. In Moser, L.E., Buxton, D.R., & Casler, M.D. (Eds.), Cool–Season Forage Grasses (pp. 691–724). Agron. Monogr. No. 34, ASA-CSSA-SSSA, Madison, Wisconsin.

  • Borrajo, C.I., & Alonso, S.I. (2014). Tasa de elongación foliar en materiales de agropiro alargado: efecto de la fenología y el agregado de nitrógeno. In 37° Congreso Argentino de Producción Animal. Buenos Aires, Argentina.

  • Cicore, P. L., Sánchez, H. R., Peralta, N. R., Castro Franco, M., Aparicio, V., & Costa, J. L. (2015). Determinación de ambientes edáficos en suelos de la pampa deprimida mediante la conductividad eléctrica aparente y la elevación. Ciencia del Suelo, 33, 229–237.

    Google Scholar 

  • Cosby, B. J., Hornberger, G. M., Clapp, R. B., & Ginn, T. R. (1984). A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research, 20, 682–690.

    Article  Google Scholar 

  • Dusseux, P., Hubert–Moy, L., Corpetti, T., & Vertès, F. (2015). Evaluation of SPOT imagery for the estimation of grassland biomass. International Journal of Applied Earth Observation and Geoinformation, 38, 72–77.

    Article  Google Scholar 

  • Echeverría, H. E., & Bergonzi, R. (1995). Estimación de la mineralización de nitrógeno en suelos del sudeste bonaerense. In Boletín Tecnico 135, CERBAS. INTA EEA: Balcarce.

    Google Scholar 

  • Edirisinghe, A., Hill, M. J., Donald, G. E., & Hyder, M. (2011). Quantitative mapping of pasture biomass using satellite imagery. International Journal of Remote Sensing, 32, 2699–2724.

    Article  Google Scholar 

  • Environmental System Research Institute (ESRI) (2014). ArcGis 10.2-Arc Map vers.10.2. http://www.esri.com/. Accessed 1 May 2014.

  • Errecart, P. M., Agnusdei, M. G., Lattanzi, F. A., Marino, M. A., & Berone, G. D. (2014). Critical nitrogen concentration declines with soil water availability in tall fescue. Crop Science, 54, 318–330.

    Article  Google Scholar 

  • Flynn, E., Dougherty, C., & Wendroth, O. (2008). Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors. Agronomy Journal, 100, 114–121.

    Article  Google Scholar 

  • Frame, J. (1993). Herbage mass. In A. Davies, R. D. Baker, S. A. Grant, & A. S. Laidlaw (Eds.), Sward measurement handbook (pp. 39–67). Reading, UK: BGS Publishing.

    Google Scholar 

  • Friedl, M. A., Michaelsen, J., Davis, F. W., Walker, H., & Schimel, D. S. (1994). Estimating grassland biomass and leaf area index using ground and satellite data. International Journal of Remote Sensing, 15, 1401–1420.

    Article  Google Scholar 

  • Gu, Y., Brown, J., Verdin, J., & Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34, L06407.

    Google Scholar 

  • He, Y., Guo, X., & Wilmshurst, J. F. (2009). Reflectance measures of grassland biophysical structure. International Journal of Remote Sensing, 30, 2509–2521.

    Article  Google Scholar 

  • Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.

    Article  Google Scholar 

  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.

    Article  Google Scholar 

  • Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. New York, NY: Oxford University Press.

    Google Scholar 

  • Jamieson, P. D., Porter, J. R., & Wilson, D. R. (1991). A test of computer simulation model ARC-WHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27, 337–350.

    Article  Google Scholar 

  • Knight, E. J., & Kvaran, G. (2014). LANDSAT–8 operational land imager design, characterization and performance. Remote Sensing, 6, 10286–10305.

    Article  Google Scholar 

  • Koch, B., Khosla, R., Frasier, W. M., Westfall, D. G., & Inman, D. (2004). Economic feasibility of variable–rate nitrogen application utilizing site–specific management zones. Agronomy Journal, 96, 1572–1580.

    Article  Google Scholar 

  • Lobell, D. B., Casman, K. G., & Field, C. B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources, 34, 179–204.

    Article  Google Scholar 

  • Mazzanti, A. E., Wade, M. H., & García, S. C. (1997). Efecto de la fertilización nitrogenada en invierno sobre el crecimiento y la composición química del forraje de raigras anual. Revista Argentina de Producción Animal, 17, 25–32.

    Google Scholar 

  • McCutcheon, M. C., Farahani, H. J., Stednick, J. D., Buchleiter, G. W., & Green, T. R. (2006). Effect of soil water on apparent soil electrical conductivity and texture relationships in a dryland field. Biosystems Engineering, 94, 19–32.

    Article  Google Scholar 

  • Moral, F. J., Terrón, J. M., & Marques da Silva, J. R. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil & Tillage Research, 106, 335–343.

    Article  Google Scholar 

  • Pachepsky, Y. A., Timlin, D. J., & Rawls, W. J. (2001). Soil water retention as related to topographic variables. Soil Science Society of America Journal, 65, 1787–1795.

    Article  CAS  Google Scholar 

  • Paruelo, J. M., Oesterheld, M., Di Bella, C., Arzadum, M., Lafountaine, J., Cahuepé, M., & Rebella, C. M. (2000). Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science, 3, 189–195.

    Article  Google Scholar 

  • Peralta, N., Franco Castro, M., & Costa, J.L. (2011). Relación espacial entre variables de sitio y rendimiento para la delimitación de zonas de manejo mediante el uso de herramientas informáticas. In 3° Congreso Argentino de Agroinformática Cordoba, Argentina.

  • Porter, T. F., Chen, C., Long, J. A., Lawrence, R. L., & Sowell, B. F. (2014). Estimating biomass on CRP pastureland: a comparison of remote sensing techniques. Biomass and Bioenergy, 66, 268–274.

    Article  Google Scholar 

  • Qi, J., Chehbouni, A., Huete, A., Kerr, Y., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126.

    Article  Google Scholar 

  • Rosegrant, M. W., Cai, X., & Cline, S. A. (2002). World water and food to 2025: dealing with scarcity. Washington DC: International Food Policy Research Institute.

    Google Scholar 

  • Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., & Harlan, J.C. (1974). Monitoring the vernal advancement of retrogradation of natural vegetation, NASA/GSFC, Type III, Final Report, Greenbelt, MD.

  • Sanderson, M. A., Rotz, C. A., Fultz, S. W., & Rayburn, E. B. (2001). Estimating forage mass with a commercial capacitance meter, rising plate meter, and pasture ruler. Agronomy Journal, 93, 1281–1286.

    Article  Google Scholar 

  • SAS Institute INC (2007). SAS/STAT-JMP users guide. In Release 7. Cary: NC.USA.

    Google Scholar 

  • Schellberg, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braune, M. (2008). Precision agriculture on grassland: applications, perspectives and constraints. European Journal of Agronomy, 29, 59–71.

    Article  Google Scholar 

  • Serrano, J., Peca, J., Marques da Silva, J., & Shahidian, S. (2011). Calibration of a capacitance probe for measurement and mapping of dry matter yield in Mediterranean pastures. Precision Agriculture, 12, 860–875.

    Article  Google Scholar 

  • Soil Survey Staff (2010). Keys to soil taxonomy. USDA-NRCS: US.

    Google Scholar 

  • Trotter, M. G., Lamb, D. W., Donald, G. E., & Schneider, D. A. (2010). Evaluating an active optical sensor for quantifying and mapping green herbage mass and growth in a perennial grass pasture. Crop and Pasture Science, 61, 389–398.

    Article  Google Scholar 

  • Vásquez, P. M., Costa, J. L., Monterubbianesi, G., & Godz, P. (2001). Predicción de la productividad primaria de pastizales naturales de la pampa deprimida utilizando propiedades del Horizonte a. Ciencia del Suelo, 19, 136–143.

    Google Scholar 

  • Wang, X., Ge, L., & Li, X. (2013). Pasture monitoring using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia. Remote Sensing, 5, 3611–3636.

    Article  Google Scholar 

  • Whitehead, D.C. (1995). Grassland nitrogen. CAB INTERNATIONAL. Wallingford. Oxon OX10 8DE, UK.

  • Zhang, C., & Guo, X. (2008). Monitoring northern mixed prairie health using broadband satellite imagery. International Journal of Remote Sensing, 29, 2257–2271.

    Article  Google Scholar 

Download references

Acknowledgments

This study was made possible thanks to the financial support of the INTA projects PNPA 11260714 and PN SUELOS 1134023. The present work is part of a thesis submitted by Pablo Leandro Cicore to meet the requirement of the Doctoral degree in Agricultural Science from Facultad de Ciencias Agrarias–National University of Mar del Plata (FCA-UNMdP).

Author information

Authors and Affiliations

  1. Estación Experimental Agropecuaria Balcarce, Instituto Nacional de Tecnologia Agropecuaria (INTA), Ruta Nacional 226 km 73.5, C.C.276, CP 7620, Balcarce, Buenos Aires, Argentina

    Pablo Cicore & José Luis Costa

  2. Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Escola de Ciências e Tecnologia, University of Évora, Évora, Portugal

    João Serrano, Shakib Shahidian, Adelia Sousa & José Rafael Marques da Silva

  3. Faculty of Agricultural Sciences, National University of Mar del Plata (FCA-UNMdP), Balcarce, Argentina

    José Luis Costa

  4. Applied Management and Space Centre for Interdisciplinary Development and Research on Environment (DREAMS), Lisbon, Portugal

    José Rafael Marques da Silva

  5. Centro de Inovação em Tecnologias de Informação (CITI), Évora, Portugal

    Adelia Sousa & José Rafael Marques da Silva

Authors
  1. Pablo Cicore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shakib Shahidian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adelia Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Luis Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. José Rafael Marques da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo Cicore.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cicore, P., Serrano, J., Shahidian, S. et al. Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones. Environ Monit Assess 188, 513 (2016). https://doi.org/10.1007/s10661-016-5512-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5512-z

Keywords

Search

Navigation