Skip to main content

Soil Sensing

  • Chapter
  • First Online:
Sensing Approaches for Precision Agriculture

Abstract

In addition to the overview of diversity in soil sensing technologies, this chapter presents four case studies to illustrate the practical use of these technologies to enhance precision agriculture in Canada, the United Kingdom, Sweden and Papua New Guinea. These studies represent investigations of different instruments, field conditions and targeted soil properties. However, in all four cases, proximal soil sensing was used to predict selected soil properties to generate relatively accurate maps that could help to implement site-specific crop management successfully.

Asim Biswas: Introduction

Viacheslav I. Adamchuk and Hsin-Hui Huang: Introduction and Case Study 4.1

Jonathan E. Holland and James A. Taylor: Case Study 4.2

Bo Stenberg and Johanna Wetterlind: Case Study 4.3

Kanika Singh, Budiman Minasny, Chris Fidelis, David Yinil, Todd Sanderson, Didier Snoeck and Damien J. Field: Case Study 4.4

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adamchuk VI, Hummel JW, Morgan MT et al (2004) On-the-go soil sensors for precision agriculture. Comput Electron Agric 44(1):71–91

    Article  Google Scholar 

  • Adamchuk VI, Ferguson RB, Hergert GW (2010) Soil heterogeneity and crop growth. In: Oerke EC, Gerhards R, Menz G, Sikora RA (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, pp 3–16

    Chapter  Google Scholar 

  • Adamchuk VI, Jonjak AK, Wortmann CS et al (2011a) Case studies on the accuracy of soil pH and lime requirement maps. In: Stafford J (ed) Precision Agriculture: Papers from the 8th European Conference on Precision Agriculture, Prague, Czech Republic, 11–14 July 2011, pp 289–301

    Google Scholar 

  • Adamchuk VI, Viscarra Rossel RA, Marx DB et al (2011b) Using targeted sampling to process multivariate soil sensing data. Geoderma 163(1):63–73

    Article  Google Scholar 

  • Adamchuk VI, Viscarra Rossel RA, Sudduth KA et al (2011c) Sensor fusion for precision agriculture. In: Thomas C (ed) Sensor fusion – foundation and applications. InTech, Rijeka, pp 27–40

    Google Scholar 

  • Adamchuk VI, Allred B, Doolittle J et al (2017) Tools for proximal soil sensing. In: Ditzler C, Scheffe K, Monger HC (eds) Soil survey manual, USDA handbook 18. Government Printing Office, Washington, DC, pp 355–394

    Google Scholar 

  • Adamchuk V, Ji W, Viscarra Rossel R et al (2018) Proximal soil and plant sensing. In: Shannon DK, Clay DE, Kitchen NR (eds) Precision agriculture basics. ASA-CSSA-SSSA, Madison, Wisconsin, pp 119–140

    Chapter  Google Scholar 

  • Akaike H (1973) Information theory and an extension of the maxi-mum likelihood principle. In: Petrov BN, Caski F (eds) Proceedings of the second international symposium on information theory. Akademiai Kiado, Budapest, pp 267–281

    Google Scholar 

  • Bellon-Maurel V, McBratney A (2011) Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils – critical review and research perspectives. Soil Biol Biochem 43(7):1398–1410

    Article  CAS  Google Scholar 

  • Blakemore LC, Searle PL, Daly BK (1987) Methods for chemical analysis of soils. NZ Soil Bur Sci Rep 80. Lower Hutt New Zealand

    Google Scholar 

  • Bleeker P (1983) Soils of Papua New Guinea. Soils Papua New Guinea, Canberra

    Google Scholar 

  • Brungard CW, Boettinger JL, Duniway MC et al (2015) Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma 239:68–83

    Article  Google Scholar 

  • Brus DJ, de Gruijter JJ (1997) Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion). Geoderma 80(1–2):1–44

    Article  Google Scholar 

  • Brus DJ, de Gruijter JJ, Van Groenigen JW (2006) Designing spatial coverage samples using the k-means clustering algorithm. Dev Soil Sci 31:183–192

    Google Scholar 

  • Buytaert W, Deckers J, Wyseure G (2007) Regional variability of volcanic ash soils in South Ecuador: the relation with parent material, climate and land use. Catena 70(2):143–154

    Article  Google Scholar 

  • Castanedo F (2013) A review of data fusion techniques. Scientific World Journal 2013:19 p

    Google Scholar 

  • Dhawale N, Adamchuk VI, Huang HH et al (2016) Integrated analysis of multilayer proximal soil sensing data. In: Proceedings of the 13th International Conference on Precision Agriculture, St. Louis, Missouri, USA. July 31 – August 4, 2016

    Google Scholar 

  • Dong J, Zhuang D, Huang Y et al (2009) Advances in multi-sensor data fusion: algorithms and applications. Sensors 9(10):7771–7784

    Article  PubMed  PubMed Central  Google Scholar 

  • Fridgen JJ, Kitchen NR, Sudduth KA et al (2004) Management Zone Analyst (MZA): software for subfield management zone delineation. Agron J 96(1):100–108

    Google Scholar 

  • Gee GW, Bauder JW (1986) Particle-size analysis. In: Klute A (ed) Physical and mineralogical methods, 2nd edn. Soil Science Society of America, Madison, pp 383–411

    Google Scholar 

  • Grunwald S, Thompson JA, Minasny B et al (2012) Digital soil mapping in a changing world. In: Digital soil assessments and beyond. CRC Press, pp 301–305

    Chapter  Google Scholar 

  • Guerrero C, Wetterlind J, Stenberg B et al (2016) Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy? Soil Tillage Res 155:501–509

    Article  Google Scholar 

  • Hawes C, Alexander CJ, Begg GS et al (2018) Plant responses to an integrated cropping system designed to maintain yield whilst enhancing soil properties and biodiversity. Agronomy 8:229

    Article  CAS  Google Scholar 

  • Henaka Arachchi MPNK, Field DJ, McBratney AB (2016) Quantification of soil carbon from bulk soil samples to predict the aggregate-carbon fractions within using near- and mid-infrared spectroscopic techniques. Geoderma 267(1):207–214

    Article  CAS  Google Scholar 

  • Hengl T, Rossiter DG, Stein A (2003) Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Aust J Soil Res 41:1403–1422

    Google Scholar 

  • Hengl T, de Jesus JM, MacMillan RA et al (2014) SoilGrids1km? Global soil information based on automated mapping. PLoS One 9:e105992

    Article  PubMed  PubMed Central  Google Scholar 

  • Holland JE, Biswas A, Huang J et al (2017) Scoping for scale-dependent relationships between proximal gamma radiometrics and soil properties. Catena 154:40–49

    Article  CAS  Google Scholar 

  • Hummel JW, Gaultney LD, Sudduth KA (1996) Soil property sensing for site-specific crop management. Comput Electron Agric 14:121–136

    Article  Google Scholar 

  • Ji W, Adamchuk V, Chen S et al (2019) Simultaneous measurement of multiple soil properties through proximal sensor fusion: a case study. Geoderma 341:111–128

    Article  CAS  Google Scholar 

  • Jiang Q, Peng J, Biswas A et al (2019) Characterising dryland salinity in three dimensions. Sci Total Environ 682:190–199

    Article  CAS  PubMed  Google Scholar 

  • Kaufmann MS, von Hebel C, Weihermüller L et al (2020) Effect of fertilizers and irrigation on multi-configuration electromagnetic induction measurements. Soil Use Manage 36:104–116

    Article  Google Scholar 

  • Kennard RW, Stone LA (1969) Computer aided design of experiments. Dent Tech 11(1):137–148

    Google Scholar 

  • Kim J, Grunwald S, Rivero RG et al (2012) Multi-scale modeling of soil series using remote sensing in a wetland ecosystem. Soil Sci Soc Am J 76(6):2327–2341

    Article  CAS  Google Scholar 

  • Konam J, Namaliu Y, Daniel R et al (2011) Integrated pest and disease management for sustainable cocoa production: a training manual for farmers and extension workers, 2nd edn. ACIAR Monograph No. 131. Aust Cent Int Agric Res, Canberra, p 36

    Google Scholar 

  • Kuang B, Mouazen AM (2012) Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale. Eur J Soil Sci

    Google Scholar 

  • Kuang B, Mahmood HS, Quraishi MZ et al (2012) Sensing soil properties in the laboratory, in situ, and on-line. Adv Agron 114:155–223

    Article  CAS  Google Scholar 

  • Lin LI-K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics

    Google Scholar 

  • MAFF (1986) The analysis of agricultural materials, Reference Book 427. HMSO, London

    Google Scholar 

  • Mahmood HS, Hoogmoed WB, Henten EJ (2012) Sensor data fusion to predict multiple soil properties. Precis Agric 13:628–645

    Article  Google Scholar 

  • Mahmood H, Hoogmoed W, van Henten E (2013) Proximal gamma-ray spectroscopy to predict soil properties using windows and full-spectrum analysis methods. Sensors 13(12):16263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McBratney AB, Santos MLM, Minasny B (2003) On digital soil mapping. Geoderma 117(1–2):3–52

    Article  Google Scholar 

  • Minasny B, McBratney AB (2006) Latin hypercube sampling as a tool for digital soil mapping. Dev Soil Sci 31:153–606

    Google Scholar 

  • Nelson PN, Webb MJ, Berthelsen S et al (2011) Nutritional status of cocoa in Papua New Guinea. ACIAR Technical Reports No. 76. Aust Cent Int Agric Res, Canberra, p 67

    Google Scholar 

  • Ng W, Minasny B, Montazerolghaem M et al (2019) Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma

    Google Scholar 

  • Peters J (ed) (2006) Standards for electromagnetic induction mapping in the grains industry. Grains Research and Development Corporation, Australian Government

    Google Scholar 

  • Saifuzzaman M, Adamchuk V, Buelvas R et al (2019) Clustering tools for integration of satellite remote sensing imagery and proximal soil sensing data. Remote Sens (Basel) 11(9):1036

    Article  Google Scholar 

  • Shenk JS, Westerhaus MO (1991) Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci

    Google Scholar 

  • Shibusawa S (2006) Soil sensors for precision agriculture. In: Srinivasan A (ed) Handbook of precision agriculture: principles and applications. CRC Press, New York

    Google Scholar 

  • Shoji S, Takahashi T (2002) Environmental and agricultural significance of volcanic ash soils. Glob J Environ Res 6:113–135

    Google Scholar 

  • Singh K, Majeed I, Panigrahi N et al (2019) Near infrared diffuse reflectance spectroscopy for rapid and comprehensive soil condition assessment in smallholder cacao farming systems of Papua New Guinea. Catena 183:1–14

    Article  Google Scholar 

  • Smith CAS, Daneshfar B, Frank G (2012) Use of weights of evidence statistics to define inference rules to disaggregate soil survey maps. In: Minasny B, Malone BP, McBratney A (eds) Digital soil assessments and beyond. CRC Press, Sydney, pp 215–220

    Google Scholar 

  • Snoeck D, Koko L, Joffre J et al (2016) In: Lichtfouse E (ed) Sustainable agriculture reviews. Springer, pp 155–202

    Chapter  Google Scholar 

  • Soriano-Disla JM, Janik LJ, Viscarra Rossel RA et al (2014) The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl Spectrosc 49(2):139–186

    Article  CAS  Google Scholar 

  • Stenberg B, Viscarra Rossel RA (2010) Diffuse reflectance spectroscopy for high-resolution soil sensing. In: Proximal Soil Sensing. Springer, pp 29–47

    Chapter  Google Scholar 

  • Stenberg B, Viscarra Rossel RA, Mouazen AM et al (2010) Visible and near infrared spectroscopy in soil science. Adv Agron 107:163–215

    Article  CAS  Google Scholar 

  • Sudduth KA, Hummel JW, Birrell SJ (1997) Sensors for site-specific management. In: Pierce FJ, Sadler EJ (eds) The state of site-specific management for agriculture. ASA-CSSA-SSSA, Madson, pp 183–210

    Google Scholar 

  • Taghizadeh-Mehrjardi R, Nabiollahi K, Kerry R (2016) Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma 266:98–110

    Article  CAS  Google Scholar 

  • Taylor JA, Whelan BM, McBratney AB (2007) Establishing broadacre management classes. Agron J 99:1366–1376

    Article  Google Scholar 

  • Taylor JA, Short M, McBratney AB et al (2010) Comparison of the ability of multiple soil sensors to predict soil properties in a Scottish potato production system. In: Viscarra Rossel RA, McBratney AB, Minasny B (eds) Proximal soil sensing. Progress in Soil Science series. Springer. ISBN: 978-90-481-8858-1

    Google Scholar 

  • Thomas GW (1983) Exchangeable cations. Methods of soil analysis: part 2 chemical and microbiological properties 9:159–165

    Google Scholar 

  • Triantafilis J, Lesch SM, La Lau K et al (2009) Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model. Aust J Soil Res 47:651–663

    Article  CAS  Google Scholar 

  • Triantifilis J, Earl NY, Gibbs ID (2012) Digital soil-class mapping across the Edgeroi district usings numerical clustering and gamma-ray spectrometry data. In: Minasny B, Malone BP, McBratney A (eds) Digital soil assessments and beyond: proceedings of the 5th global workshop on digital soil mapping. CRC Press, Sydney, pp 187–191

    Google Scholar 

  • Vašát R, Heuvelink GBM, Borůvka L (2010) Sampling design optimization for multivariate soil mapping. Geoderma 155(3–4):147–153

    Article  Google Scholar 

  • Viscarra Rossel RA, Adamchuk VI, Sudduth KA et al (2011) Proximal soil sensing: an effective approach for soil measurements in space and time. Adv Agron 113:237–283

    Google Scholar 

  • Webster R, Oliver MA (2007) Geostatistics for environmental scientists. Wiley, Chichester

    Book  Google Scholar 

  • Weindorf DC, Bakr N, Zhu Y (2014) Advances in portable X-ray fluorescence (PXRF) for environmental, pedological, and agronomic applications. In: Sparks DL (ed) Advances in agronomy. Academic Press, San Diego, pp 1–45

    Google Scholar 

  • Wetterlind J (2012) Project final report (in Swedish) to Stiftelsen Svensk Växtnäringsforskning, KSLA, H09-0011-SVX

    Google Scholar 

  • Wetterlind J, Stenberg B (2010) Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples. Eur J Soil Sci 61:823–843

    Article  CAS  Google Scholar 

  • Witten IH, Frank E, Hall MA et al (2017) Credibility: evaluating what’s been learned. In: Data mining, 4th edn. Morgan Kaufmann, pp 161–203

    Chapter  Google Scholar 

  • WRB (2006) World Reference Base for Soil Resources. World Soil Resources Reports No. 103. FAO, Rome

    Google Scholar 

  • Xu D, Chen S, Viscarra Rossel AR et al (2019) Vis NIR and XRF sensor fusion for predicting paddy soil chromium content. Geoderma 352:61–69

    Article  CAS  Google Scholar 

  • Zalik KR (2008) An efficient k-means clustering algorithm. Pattern Recog. Lett 29(9):1385–1391

    Google Scholar 

  • Zani C, Gowing J, Abbott GD et al (2020) Grazed temporary grass-clover leys in crop rotations can have a positive impact on soil quality under both conventional and organic agricultural system. Euro. J, Soil Sci

    Google Scholar 

  • Zhang Y, Biswas A, Ji W et al (2017) Depth-specific prediction of soil properties in situ using vis-NIR spectroscopy. Soil Sci Soc Am J 81(5):993–1004

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Case study 4.1 was supported by funds from the Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant and through Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) New Directions Research Program (NDRP). Special thanks to Paul Hermans of DuPont Pioneer (Richmond, Ontario, Canada) for providing Veris MSP data and to Jeremy Nixon of J & H Nixon Farms and Michael Schouten of Schouten Dairy Farms. The authors acknowledge the contributions of the past and current PA and Sensor Systems (PASS) research team members Dr. Wenjun Ji, Dr. Long Qi, Maxime Leclerc, Dr. Yuanyuan Fu, and Matthieu Claustre for performing soil sampling.

The authors of case study 4.2 are very grateful for the assistance of Mr. M. Botha, Dr. D. Ronga and Dr. D. Cammarano with the data collection for this study. Dr. Philippe Lagacherie provided assistance with the RFA modelling and Mr. Rick Taylor (DUALEM Inc.) calculated the integrated ECa variable. Financial support was provided from the Scottish Society of Crop Research for soil sample analysis costs.

For Case study 4.3, Stiftelsen Lantbruksforskning is acknowledged for funding project H1033307 and Eurofins Agro, Kristianstad, Sweden, for providing data and selection of farms.

Case study 4.4 is supported by the Australian Centre for International Agricultural Research project ‘Optimising soil management and health in PNG integrated Cocoa farming systems’; Field D; Australian Centre for International Agricultural Research (ACIAR) Research and Development Programs (R&D Programs).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Viacheslav I. Adamchuk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Adamchuk, V.I. et al. (2021). Soil Sensing. In: Kerry, R., Escolà, A. (eds) Sensing Approaches for Precision Agriculture. Progress in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-030-78431-7_4

Download citation

Publish with us

Policies and ethics