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Performance assessment of factory and field calibrations for electromagnetic sensors in a loam soil

Author

Listed:
  • Singh, J.
  • Lo, T.
  • Rudnick, D.R.
  • Dorr, T.J.
  • Burr, C.A.
  • Werle, R.
  • Shaver, T.M.
  • Muñoz-Arriola, F.

Abstract

Accurate continuous measurements of temperature (T), apparent electrical conductivity (ECa), apparent dielectric permittivity (εra), and volumetric water content (θv) are extremely valuable to irrigation management and other agronomic decisions. The performance of eight electromagnetic (EM) sensors (TDR315, CS655, HydraProbe2, 5TE, EC5, CS616, Field Connect, AquaCheck), were analyzed through a field study in a loam soil. T, ECa, and εra were compared in reference to overall average among all sensors, and θv in reference to a neutron moisture meter (NMM). The reported T and ECa difference among the sensors were within 1°C and 1dSm−1, respectively, at 0.15 and 0.76m depths. Among the single-sensor probes, the range of depth-combined (0.15 and 0.76m) RMSD for factory calibration varied from 0.039m3m−3 (5TE) to 0.157m3m−3 (CS616). In comparison to single-sensor probes, RMSD of Field Connect at combined depths (0.30 and 0.51m) was moderate (0.083m3m−3), and RMSD of AquaCheck at combined depths (0.30 and 0.61m) was high (0.163m3m−3). Regression calibrations improved θv accuracy substantially beyond factory calibrations, as RMSD of the evaluated sensors except Field Connect was below 0.025m3m−3 using regression calibrations. The betterment in θv accuracy gained by using offset calibrations was smaller and less consistent than the improvements gained by using regression calibrations. The lower and upper bounds of the 95% confidence interval for mean RMSD of most sensors were below 0.02 and 0.04m3m−3, respectively, when using depth-specific offset calibrations. The relative success of offset calibrations for certain sensors in this study is encouraging and may signal new opportunities. Because much of the uncertainty in sensor-reported θv for the sensors under evaluation was systematic, future work should aim to develop universal calibrations or facilitate site-specific calibrations.

Suggested Citation

  • Singh, J. & Lo, T. & Rudnick, D.R. & Dorr, T.J. & Burr, C.A. & Werle, R. & Shaver, T.M. & Muñoz-Arriola, F., 2018. "Performance assessment of factory and field calibrations for electromagnetic sensors in a loam soil," Agricultural Water Management, Elsevier, vol. 196(C), pages 87-98.
    • Handle: RePEc:eee:agiwat:v:196:y:2018:i:c:p:87-98
    DOI: 10.1016/j.agwat.2017.10.020
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    References listed on IDEAS

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    1. Geerts, Sam & Raes, Dirk, 2009. "Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas," Agricultural Water Management, Elsevier, vol. 96(9), pages 1275-1284, September.
    2. Varble, J.L. & Chávez, J.L., 2011. "Performance evaluation and calibration of soil water content and potential sensors for agricultural soils in eastern Colorado," Agricultural Water Management, Elsevier, vol. 101(1), pages 93-106.
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    Cited by:

    1. Stepanovic, Strahinja & Rudnick, Daran & Kruger, Greg, 2021. "Impact of maize hybrid selection on water productivity under deficit irrigation in semiarid western Nebraska," Agricultural Water Management, Elsevier, vol. 244(C).
    2. Meetpal S. Kukal & Suat Irmak & Kiran Sharma, 2019. "Development and Application of a Performance and Operational Feasibility Guide to Facilitate Adoption of Soil Moisture Sensors," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    3. Singh, J. & Lo, T. & Rudnick, D.R. & Irmak, S. & Blanco-Canqui, H., 2019. "Quantifying and correcting for clay content effects on soil water measurement by reflectometers," Agricultural Water Management, Elsevier, vol. 216(C), pages 390-399.
    4. Domínguez-Niño, Jesús María & Oliver-Manera, Jordi & Girona, Joan & Casadesús, Jaume, 2020. "Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors," Agricultural Water Management, Elsevier, vol. 228(C).
    5. Lo, Tsz Him & Rudnick, Daran R. & Singh, Jasreman & Nakabuye, Hope Njuki & Katimbo, Abia & Heeren, Derek M. & Ge, Yufeng, 2020. "Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors," Agricultural Water Management, Elsevier, vol. 231(C).
    6. Singh, Jasreman & Ge, Yufeng & Heeren, Derek M. & Walter-Shea, Elizabeth & Neale, Christopher M.U. & Irmak, Suat & Woldt, Wayne E. & Bai, Geng & Bhatti, Sandeep & Maguire, Mitchell S., 2021. "Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate," Agricultural Water Management, Elsevier, vol. 256(C).
    7. Lo, Tsz Him & Rudnick, Daran R. & Burr, Charles A. & Stockton, Matthew C. & Werle, Rodrigo, 2019. "Approaches to evaluating grower irrigation and fertilizer nitrogen amount and timing," Agricultural Water Management, Elsevier, vol. 213(C), pages 693-706.
    8. Hajdu, Istvan & Yule, Ian & Bretherton, Mike & Singh, Ranvir & Hedley, Carolyn, 2019. "Field performance assessment and calibration of multi-depth AquaCheck capacitance-based soil moisture probes under permanent pasture for hill country soils," Agricultural Water Management, Elsevier, vol. 217(C), pages 332-345.
    9. Kargas, George & Soulis, Konstantinos X., 2019. "Performance evaluation of a recently developed soil water content, dielectric permittivity, and bulk electrical conductivity electromagnetic sensor," Agricultural Water Management, Elsevier, vol. 213(C), pages 568-579.

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