Advertisement

Precision Agriculture

, Volume 20, Issue 1, pp 78–100 | Cite as

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

  • Catherine E. FinkenbinerEmail author
  • Trenton E. Franz
  • Justin Gibson
  • Derek M. Heeren
  • Joe Luck
Article
  • 92 Downloads

Abstract

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.

Keywords

Water use efficiency Soil hydraulic parameters Irrigation management Soil spatial variability 

Notes

Acknowledgements

This research was supported by the University of Nebraska Extension. The authors would also like to thank Paulman Farms for access to the field site and historical datasets and Matthew Russell for assistance collecting soil samples. TEF, DMH, and JL would also like to acknowledge the financial support of the United States Department of Agriculture National Institute of Food and Agriculture, Hatch Project #1009760. Trade names or commercial products are given solely for the purpose of providing information on the exact equipment used in this study and do not imply recommendation or endorsement by the University of Nebraska.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Binley, A., Hubbard, S. S., Hulsman, J. A., Revil, A., Robinson, D. A., Singha, K., et al. (2015). The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales. Water Resources Research, 51(6), 3837–3866.  https://doi.org/10.1002/2015wr017016.CrossRefGoogle Scholar
  2. Bobryk, C. W., Myers, D. B., Kitchen, N. R., Shanahan, J. F., Sudduth, K. A., Drummond, S. T., et al. (2016). Validating a digital soil map with corn yield data for precision agriculture decision support. Agronomy Journal, 108(3), 957–965.  https://doi.org/10.2134/agronj2015.0381.CrossRefGoogle Scholar
  3. Bogena, H. R., Huisman, J. A., Baatz, R., Franssen, H. J. H., & Vereecken, H. (2013). Accuracy of the cosmic-ray soil water content probe in humid forest ecosystems: The worst case scenario. Water Resources Research, 49(9), 5778–5791.  https://doi.org/10.1002/wrcr.20463.CrossRefGoogle Scholar
  4. Brevik, E. C., Fenton, T. E., & Lazari, A. (2006). Soil electrical conductivity as a function of soil water content and implications for soil mapping. Precision Agriculture, 7(6), 393–404.  https://doi.org/10.1007/s11119-006-9021-x.CrossRefGoogle Scholar
  5. Campbell, R. B., Bower, C. A., & Richards, L. A. (1948). Change of electrical conductivity with temperature and the relation of osmotic pressure to electrical conductivity and ion concentration for soil extracts. Soil Science Society of America Proceedings, 13, 66–69.CrossRefGoogle Scholar
  6. Chan, S., Njoku, E. G. & Colliander A. (2014). Soil Moisture Active Passive (SMAP), Algorithm Theoretical Basis Document, Level 1C Radiometer Data Product, Revision A. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA. Retrieved 15 February, 2017 from http://smap.jpl.nasa.gov/.
  7. Coopersmith, E. J., Cosh, M. H., & Daughtry, C. S. T. (2014). Field-scale moisture estimates using COSMOS sensors: A validation study with temporary network and Leaf-Area-Indices. Journal of Hydrology, 519, 637–643.  https://doi.org/10.1016/j.hydrol.2014.07.060.CrossRefGoogle Scholar
  8. Decagon Devices, Inc. (2015). WP4C dewpoint potentiameter operator’s manual. Pullman, WA.Google Scholar
  9. Evans, R.G., Han, S., Kroeger, M.W., & Schneider, S. M. (1996). Precision center pivot irrigation for efficient use of water and nitrogen. In: P. C. Robert, R. H. Rust, & W.E. Larson (Eds.), Precision agriculture (pp. 75–84). ASA, CSSA, SSSA, Madison, WI.  https://doi.org/10.2134/1996.precisionagproc3.c8
  10. Franz, T. E., King, E. G., Caylor, K. K., & Robinson, D. A. (2011). Coupling vegetation organization patterns to soil resource heterogeneity in a central Kenyan dryland using geophysical imagery. Water Resources Research.  https://doi.org/10.1029/2010wr010127.Google Scholar
  11. Franz, T. E., Wahbi, A., Vreugdenhil, M., Weltin, G., Heng, L., Oismueller, M., et al. (2016). Using cosmic-ray neutron probes to monitor landscape scale soil water content in mixed land use agricultural systems. Applied and Environmental Soil Science.  https://doi.org/10.1155/2016/4323742.Google Scholar
  12. Franz, T. E., Wang, T., Avery, W., Finkenbiner, C., & Brocca, L. (2015). Combined analysis of soil moisture measurements from roving and fixed cosmic-ray neutron probes for multiscale real-time monitoring. Geophysical Research Letters, 42(9), 3389–3396.  https://doi.org/10.1002/2015gl063963.CrossRefGoogle Scholar
  13. Franz, T. E., Zreda, M., Rosolem, R., & Ferre, P. A. (2012). Field validation of cosmic-ray soil moisture sensor using a distributed sensor network. Vadose Zone Journal.  https://doi.org/10.2136/vzj2012.0046.Google Scholar
  14. Gibson, J., & Franz, T. E. (2018). Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions. Journal of Hydrology.  https://doi.org/10.1016/j.jhydrol.2018.03.046.Google Scholar
  15. Glasstone, S., & Edlund, M. C. (1952). Elements of nuclear reactor theory. New York: Van Nostrand.Google Scholar
  16. Haghverdi, A., Leib, B. G., Washington-Allen, R. A., Ayers, P. D., & Buschermohle, M. J. (2015a). High-resolution prediction of soil available water content within the crop root zone. Journal of Hydrology, 520, 167–179.  https://doi.org/10.1016/j.jhydrol.2015.09.061.CrossRefGoogle Scholar
  17. Haghverdi, A., Leib, B. G., Washington-Allen, R. A., Ayers, P. D., & Buschermohle, M. J. (2015b). Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture, 117, 154–167.  https://doi.org/10.1016/j.compag.2015.06.019.CrossRefGoogle Scholar
  18. Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures for cosmic-ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029–5043.  https://doi.org/10.1002/2013wr015138.CrossRefGoogle Scholar
  19. Hedley, C. (2015). The role of precision agriculture for improved nutrient management on farms. Journal of the Science of Food and Agriculture, 95(1), 12–19.  https://doi.org/10.1002/jsfa.6734.CrossRefGoogle Scholar
  20. Hedley, C. B., Roudier, P., Yule, I. J., Ekanayake, J., & Bradbury, S. (2013). Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling. Geoderma, 199, 22–29.  https://doi.org/10.1016/j.geoderma.2012.07.018.CrossRefGoogle Scholar
  21. Hezarjaribi, A., & Sourell, H. (2007). Feasibility study of monitoring the total available water content using non-invasive electromagnetic induction-based and electrode-based soil electrical conductivity measurements. Irrigation and Drainage, 56(1), 53–65.  https://doi.org/10.1002/ird.289.CrossRefGoogle Scholar
  22. Howell, T. A., Evett, S. R., O’Shaughnessy, S. A., Colaizzi, P. D., & Gowda, P. H. (2012). Advanced irrigation engineering: Precision and precise. Journal of Agricultural Science and Technology, A(2), 1–9.Google Scholar
  23. Inc, Dualem. (2013). DUALEM-21S user’s manual. Milton: Dualem Inc.Google Scholar
  24. Iwema, J., Rosolem, R., Rahman, M., Blyth, E., & Wagener, T. (2017). Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration. Hydrology and Earth System Sciences, 21, 2843–2861.  https://doi.org/10.5194/hess-21-2843-2017.CrossRefGoogle Scholar
  25. Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., & Zacharias, S. (2015). Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resources Research, 51(7), 5772–5790.  https://doi.org/10.1002/2015wr017169.CrossRefGoogle Scholar
  26. Korres, W., Koyama, C. N., Fiener, P., & Schneider, K. (2010). Analysis of surface soil moisture patterns in agricultural landscapes using empirical orthogonal functions. Hydrology and Earth System Sciences, 14(5), 751–764.  https://doi.org/10.5194/hess-14-751-2010.CrossRefGoogle Scholar
  27. Martini, E., Werban, U., Zacharias, S., Pohle, M., Dietrich, P., & Wollschläger, U. (2016). Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: Validation with data from a wireless soil moisture monitoring network. Hydrology and Earth System Sciences, 21, 495–513.  https://doi.org/10.5194/hess-21-495-2017.CrossRefGoogle Scholar
  28. McCarthy, A. C., Hancock, N. H., & Raine, S. R. (2014). Devlopment and simulationof sensor-based irrigation control strategies for cotton using the VARIwise simulation framework. Computers and Electronics in Agriculture, 101, 148–162.  https://doi.org/10.1016/j.compag.2013.12.014.CrossRefGoogle Scholar
  29. 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(1), 19–32.  https://doi.org/10.1016/j.biosystemseng.2006.01.002.CrossRefGoogle Scholar
  30. McJannet, D., Franz, T., Hawdon, A., Boadle, D., Baker, B., Almeida, A., et al. (2014). Field testing of the universal calibration function for determination of soil moisture with cosmic-ray neutrons. Water Resources Research, 50(6), 5235–5248.  https://doi.org/10.1002/2014wr015513.CrossRefGoogle Scholar
  31. Molden, D. (2007). Water responses to urbanization. Paddy and Water Environment, 5(4), 207–209.  https://doi.org/10.1007/s10333-007-0084-8.CrossRefGoogle Scholar
  32. Pan, L., Adamchuk, V. I., Martin, D. L., Schroeder, M. A., & Ferguson, R. B. (2013). Analysis of soil water availability by integrating spatial and temporal sensor-based data. Precision Agriculture, 14(4), 414–433.  https://doi.org/10.1007/s11119-013-9305-x.CrossRefGoogle Scholar
  33. Perry, M. A., & Niemann, J. D. (2006). Analysis and estimation of soil moisture at the catchment scale using EOFs. Journal of Hydrology, 334(3–4), 388–404.  https://doi.org/10.1016/j.jhydrol.2006.10.014.Google Scholar
  34. Peters, A., & Durner, W. (2008). Simplified evaporation method for determining soil hydraulic properties. Journal of Hydrology, 356(1–2), 147–162.  https://doi.org/10.1016/j.jhydrol.2008.04.016.CrossRefGoogle Scholar
  35. Ranney, K. J., Niemann, J. D., Lehman, B. M., Green, T. R., & Jones, A. S. (2015). A method to downscale soil moisture to fine resolutions using topographic, vegetation, and soil data. Advances in Water Resources, 76, 81–96.  https://doi.org/10.1016/j.advwatres.2014.12.003.CrossRefGoogle Scholar
  36. Rivera Villarreyes, C. A., Baroni, G., & Oswald, S. E. (2011). Integral quantification of seasonal soil moisture changes in farmland by cosmic-ray neutrons. Hydrology and Earth Systems Sciences, 15, 3843–3859.  https://doi.org/10.5194/hess-15-3843-2011.CrossRefGoogle Scholar
  37. Rodríguez-Pérez, J. R., Plant, R. E., Lambert, J.-J., & Smart, D. R. (2011). Using apparent soil electrical conductivity (ECa) to characterize vineyard soils of high clay content. Precision Agriculture, 12(6), 775–794.  https://doi.org/10.1007/s11119-011-9220-y.CrossRefGoogle Scholar
  38. Scanlon, B. R., Faunt, C. C., Longuevergne, L., Reedy, R. C., Alley, W. M., McGuire, V. L., et al. (2012). Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley. Proceedings of the National academy of Sciences of the United States of America, 109(24), 9320–9325.  https://doi.org/10.1073/pnas.1200311109.CrossRefGoogle Scholar
  39. Schindler, U., Durner, W., von Unold, G., Mueller, L., & Wieland, R. (2010). The evaporation method: Extending the measurement range of soil hydraulic properties using the air-entry pressure of the ceramic cup. Journal of Plant Nutrition and Soil Science, 173(4), 565–572.  https://doi.org/10.1002/jpln.200900201.CrossRefGoogle Scholar
  40. Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017). Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity. Hydrology and Earth System Sciences, 21, 5009–5030.  https://doi.org/10.5194/hess-21-5009-2017.CrossRefGoogle Scholar
  41. Schultz, B., Thatte, C. D., & Labhsetwar, V. K. (2005). Irrigation and drainage: Main contributors to global food production. Irrigation and Drainage, 54, 263–278.CrossRefGoogle Scholar
  42. Shangguan, W., Dai, Y., Duan, Q., Liu, B., & Yuan, H. (2014). A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems, 6, 249–263.  https://doi.org/10.1002/2013MS000293.CrossRefGoogle Scholar
  43. Soil Survey Staff (2016) Natural Resources Conservation Service, United States Department of Agriculture. Soil Survey Geographic (SSURGO) Database. Retrieved 1 February, 2016 from https://sdmdataaccess.sc.egov.usda.gov.
  44. Sorensen, R., Zinko, U., & Selbert, J. (2006). On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10, 101–112.CrossRefGoogle Scholar
  45. UNDP. (2007). Human Development Report 2006-Beyond scarcity: Power, poverty and the global water crisis. New York: United Nations Development Programme.Google Scholar
  46. USDA NASS. (2012). Census of agriculture. Unitied States Department of Agriculture. Retrieved 2 April, 2018 https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_1_US/usv1.pdf.
  47. Werbylo, K. L., & Niemann, J. D. (2014). Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture downscaling. Journal of Hydrology, 516, 304–316.  https://doi.org/10.1016/j.jhydrol.2014.01.030.CrossRefGoogle Scholar
  48. Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., et al. (2012). COSMOS: The COsmic-ray Soil Moisture Observing System. Hydrology and Earth System Sciences, 16(11), 4079–4099.  https://doi.org/10.5194/hess-16-4079-2012.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Catherine E. Finkenbiner
    • 1
    • 3
    Email author
  • Trenton E. Franz
    • 1
  • Justin Gibson
    • 1
  • Derek M. Heeren
    • 2
  • Joe Luck
    • 2
  1. 1.School of Natural ResourcesUniversity of Nebraska-LincolnLincolnUSA
  2. 2.Department of Biological Systems EngineeringUniversity of Nebraska-LincolnLincolnUSA
  3. 3.Department of Biological & Ecological EngineeringOregon State UniversityCorvallisUSA

Personalised recommendations