Abstract
Soil acidification is a major and growing concern in many cropping regions globally. Whilst spatial variability in acidification is a common consideration in the management of soil health and fertility at sub-paddock scale, insufficient focus has been directed toward the identification of this variability. Suitability of portable visible near infrared reflectance (vis–NIR) spectroscopy was assessed in this study as a potential technique to achieve rapid, precise, inexpensive and spatially specific quantification of key soil parameters to inform lime requirements. Spectral fingerprints were taken using a 1 ha grid sampling approach, with four sampling protocols investigated as follows, scans: (i) directly on cleared soil surfaces; (ii) on 0–100 mm undisturbed cores; (iii) on dried 0–100 mm cores; and finally (iv) on dried, ground, sieved and mixed cores. Data was analysed using a partial least squares regression (PLSR) model to identify the strength of linear relationship between reference chemistry data and predictions derived from spectral readings. Lime requirement maps using vis–NIR predictions were then theoretically compared against traditional aggregated sampling patterns while considering the trade-offs between accuracy, economics and agronomy associated with the identification of spatial variability. The vis–NIR measurements demonstrated moderate predictive capabilities in field for determining pH (R2 = 0.3–0.5) and liming requirements (R2 = 0.5–0.6) rapidly at high spatial resolution. Vis–NIR in field mapping techniques, which enable the use of site specific management of soil resources, were found to positively redirect lime resources from alkaline areas toward acidic areas of the paddock, resulting in minimal difference to overall expenditure on lime purchase and potential for increased agronomic benefits over the long-term. Further spectral library development, calibration, and research on in-field sampling methods is recommended.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adamchuk, V., Hummel, J., Morgan, M., & Upadhyaya, S. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71–91. https://doi.org/10.1016/j.compag.2004.03.002
Alewell, C., Baligar, V., Bolan, N., Bowden, B., Braschkat, J., Carver, B., et al. (2003). In Z. Rengel (Ed.), Handbook of soil acidity, soil acidification: The World Story. Marcel Dekker.
Bendor, E., & Banin, A. (1995). Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal, 59(2), 364–372. https://doi.org/10.2136/sssaj1995.03615995005900020014x
Bramley, R. G. V., & Janik, L. J. (2005). Precision agriculture demands a new approach to soil and plant sampling and analysis—examples from Australia. Communications in Soil Science and Plant Analysis, 36(1–3), 9–22. https://doi.org/10.1081/CSS-200042958
Bureau of Meteorology. (2019). Climate data online; Spalding (Bundaleer Reservoir), Bureau of Meteorology. Retrieved March 2, 2020, from http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=136&p_display_type=dailyDataFile&p_startYear=&p_c=&p_stn_num=021009.
Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy- principal components regression analysis of soil properties. Alliance of Crop, Soil, and Environmental Science Societies, 65(2), 480–490. https://doi.org/10.2136/sssaj2001.652480x
Condon, J. (2019). Effective soil sampling—high and low-cost options to gain soil fertility information for management, Grains Research Development Corporation Research Update 2019, Wagga, NSW, Australia.
Conyers, M., & Davey, B. (1990). The variability of pH in acid soils of the southern highlands of New-South-Wales. Soil Science, 150(4), 695–704. https://doi.org/10.1097/00010694-199010000-00004
Curtin, D., & Syers, J. K. (2001). Lime-induced changes in indices of soil phosphate availability. Soil Science Society of America, 65, 147–152. https://doi.org/10.2136/sssaj2001.651147x
Forrester, S. T., Janik, L. J., Soriano-Disla, J. M., Mason, S., Burkitt, L., Moody, P., Gourley, C. J., & McLaughlin, M. J. (2015). Use of handheld mid-infrared spectroscopy and partial least-squares regression for the prediction of the phosphorus buffering index in Australian soils. Soil Research, 53, 67–80. https://doi.org/10.1071/SR14126
Gebbers, R., & Adamchuk, V. (2010). Precision agriculture and food security. Science, 327(5967), 828–831. https://doi.org/10.1126/science.1183899
Gourley, C. J. P. & Weaver, D. M. (2019). A guide for fit for purpose soil sampling, Fertilizer Australia, Canberra, Australia. Retrieved July 23, 2021, from http://www.fertilizer.org.au/Portals/0/Documents/Fertcare/Fertcare%20Soil%20Sampling%20Guide.pdf?ver=2019-06-17-095413-863.
Hall, J., Maschmedt, D., & Billing, B. (2009). The soils of southern South Australia (1st ed.). Government of South Australia Department of Water, Land and Biodiversity Conservation.
Harding, A., & Hughes, B. (2018). Current and potential lime sources in South Australia. Rural Solutions South Australia and Primary Industries and Regions of South Australia.
Haynes, R. J. (1983). Soil acidification induced by leguminous crops. Grass and Forage Science, 38(1), 1–11. https://doi.org/10.1111/j.1365-2494.1983.tb01614.x
Hughes, B., & Merry, R. (2000). Have you tested your soil pH: thinking about lime? Commonwealth Scientific and Industrial Research Organisation.
Hummel, J., Sudduth, K., & Hollinger, S. (2001). Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and Electronics in Agriculture, 32(2), 149–165. https://doi.org/10.1016/S0168-1699(01)00163-6
Iticha, B., & Takele, C. (2019). Digital soil mapping for site-specific management of soils. Geoderma, 351, 85–91. https://doi.org/10.1016/j.geoderma.2019.05.026
Kennard, R. W., & Stone, L. A. (1969). Computer aided design of experiments. Technometrics, 11, 137–148. https://doi.org/10.1080/00401706.1969.10490666
Kodaira, M., & Shibusawa, S. (2013). Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping. Geoderma, 199, 64–79. https://doi.org/10.1016/j.geoderma.2012.09.007
Lu, G., & Wong, D. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers and Geoscience, 34(9), 1044–1055. https://doi.org/10.1016/J.CAGEO.2007.07.010
McCord, A. K., & Payne, R. A. (2004). Report on the condition of agricultural land in South Australia. Government of South Australia, Department of Water Land and Biodiversity Conservation.
McKenzie, N. J., Hairsine, P. B., Gregory, L. J., Austin, J., Baldock, J., Webb, M., Mewett, J., Cresswell, H. P., Welti, N., & Thomas, M. (2017). Priorities for improving soil condition across Australia’s agricultural landscapes. Australian Government, Department of Agriculture and Water Resources, Commonwealth Scientific and Industrial Research Organization.
Merry, R. H. & Janik, L. J. (1999). New methodology for lime requirements, Final Report RIRDC project CSO-9a. Rural Industries Research and Development Corporation.
Montanarella, L., Pennock, D., McKenzie, N., Alavipanah, S., Alegre, J., Alshankiti, A., Arrouays, D., Aulakh, M. S., Badraoui, M., Costa, I. D., & Black, H. (2015). Status of the world’s soil resources—technical summary. Food and Agriculture Organisation of the United Nations and Intergovernmental Technical Panel on Soils.
Saarela, I., & Vuorinen, M. (2009). Stratification of soil phosphorous, pH, and macro-cations under intensively cropped grass ley. Nutrient Cycling in Agroecosystems, 86, 367–381. https://doi.org/10.1007/s10705-009-9298-z
Soriano-Disla, J. M., Janik, L. J., Viscarra-Rossel, R. A., Macdonald, L. M., & McLaughlin, M. J. (2016). The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical and biological properties. Applied Spectroscopy Reviews, 49(2), 139–186. https://doi.org/10.1080/05704928.2013.811081
Srodon, J., & McCarty, D. (2008). Surface area and layer charge of smectite from CEC and EGME/H2O-retention measurements. Clays and Clay Minerals, 56(2), 155–174. https://doi.org/10.1346/CCMN.2008.0560203
Stenberg, B., Viscarra-Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010). Chapter five: Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107, 163–215. https://doi.org/10.1016/S0065-2113(10)07005-7
Tian, D., & Niu, S. (2015). A global analysis of soil acidification caused by nitrogen addition. Environmental Research Letters, 10(2), 024019. https://doi.org/10.1088/1748-9326/10/2/024019
Varmuza, K., & Filzmoser, P. (2009). Introduction to multivariate statistical analysis in chemometrics (1st ed.). CRC Press.
Viscarra-Rossel, R., & McBratney, A. (1998). Soil chemical analytical accuracy and costs: Implications from precision agriculture. Australian Journal of Experimental Agriculture, 38(7), 765–775. https://doi.org/10.1071/EA97158
Viscarra-Rossel, R., Walvoort, D., McBratney, A., Janik, L., & Skjemstad, J. (2006). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131, 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007
Vohland, M., & Emmerling, C. (2011). Determination of total soil organic C and hot water-extractable C from vis-NIR soil reflectance with partial least squares regression and spectral features selection technique. European Journal of Soil Science, 62(4), 598–606. https://doi.org/10.1111/j.1365-2389.2011.01369.x
Webster, R., & Oliver, M. (1992). Sample adequately to estimate variograms of soil properties. European Journal of Soil Science, 43(1), 177–192. https://doi.org/10.1111/j.1365-2389.1992.tb00128.x
Wetterlind, J., Stenberg, B., & Soderstorm, M. (2008). The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture, 9(1–2), 57–69. https://doi.org/10.1007/s11119-007-9051-z
Wetterlind, J., Stenberg, B., & Soderstrom, M. (2010). Increased sample point density in farm soil mapping by local calibration of visible and near infrared prediction models. Geoderma, 156(3–4), 152–160. https://doi.org/10.1016/j.geoderma.2010.02.012
Willmott, J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079
Workman, J., Mobley, P., Kowalski, B., & Bro, R. (1996). Review of chemometrics applied to spectroscopy 1985–95. Applied Spectroscopy Reviews, 31(1–2), 73–124. https://doi.org/10.1080/05704929608000565
Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture - a worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132. https://doi.org/10.1016/S0168-1699(02)00096-0
Zhao, D., Zhao, X., Khongnawang, T., Arshad, N., & Triantafilis, J. (2018). A vis-NIR spectral library to predict clay in Australian Cotton Growing Soil. Soil Science Society of America Journal, 82(6), 1347–1357.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Sleep, B., Mason, S., Janik, L. et al. Application of visible near-infrared absorbance spectroscopy for the determination of Soil pH and liming requirements for broad-acre agriculture. Precision Agric 23, 194–218 (2022). https://doi.org/10.1007/s11119-021-09834-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-021-09834-7