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A method for spatial prediction of daily soil water status for precise irrigation scheduling


  • Hedley, C.B.
  • Yule, I.J.


Available water holding capacity (AWC) and field capacity (FC) maps have been produced using regression models of high resolution apparent electrical conductivity (ECa) data against AWC (adj. R2=0.76) and FC (adj. R2=0.77). A daily time step has been added to field capacity maps to spatially predict soil water status on any day using data obtained from a wireless soil moisture sensing network which transmitted hourly logged data from embedded time domain transmission (TDT) sensors in ECa-defined management zones. In addition, regular time domain reflectometry (TDR) monitoring of 50 positions in the study area was used to assess spatial variability within each zone and overall temporal stability of soil moisture patterns. Spatial variability of soil moisture within each zone at any one time was significant (coefficient of variation [% CV] of volumetric soil moisture content ([theta])=3-16%), while temporal stability of this pattern was moderate to strong (bivariate correlation, R=0.52-0.95), suggesting an intrinsic soil and topographic control. Therefore, predictive ability of this method for spatial characterisation of soil water status, at this site, was limited by the ability of the sensor network to account for the spatial variability of the soil moisture pattern within each zone. Significant variability of soil moisture within each ECa-defined zone is thought to be due to the variable nature of the young alluvial soils at this site, as well as micro-topographic effects on water movement, such as low-lying ponding areas. In summary, this paper develops a method for predicting daily soil water status in ECa-defined zones; digital information available for uploading to a software-controlled automated variable rate irrigation system with the aim of improved water use efficiency. Accuracy of prediction is determined by the extent to which spatial variability is predicted within as well as between ECa-defined zones.

Suggested Citation

  • Hedley, C.B. & Yule, I.J., 2009. "A method for spatial prediction of daily soil water status for precise irrigation scheduling," Agricultural Water Management, Elsevier, vol. 96(12), pages 1737-1745, December.
  • Handle: RePEc:eee:agiwat:v:96:y:2009:i:12:p:1737-1745

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    References listed on IDEAS

    1. Thompson, R.B. & Gallardo, M. & Valdez, L.C. & Fernandez, M.D., 2007. "Determination of lower limits for irrigation management using in situ assessments of apparent crop water uptake made with volumetric soil water content sensors," Agricultural Water Management, Elsevier, vol. 92(1-2), pages 13-28, August.
    2. Blonquist, J.M. Jr. & Jones, S.B. & Robinson, D.A., 2006. "Precise irrigation scheduling for turfgrass using a subsurface electromagnetic soil moisture sensor," Agricultural Water Management, Elsevier, vol. 84(1-2), pages 153-165, July.
    3. Starr, G.C., 2005. "Assessing temporal stability and spatial variability of soil water patterns with implications for precision water management," Agricultural Water Management, Elsevier, vol. 72(3), pages 223-243, April.
    4. Green, Steve R. & Kirkham, M.B. & Clothier, Brent E., 2006. "Root uptake and transpiration: From measurements and models to sustainable irrigation," Agricultural Water Management, Elsevier, vol. 86(1-2), pages 165-176, November.
    5. DeJonge, Kendall C. & Kaleita, Amy L. & Thorp, Kelly R., 2007. "Simulating the effects of spatially variable irrigation on corn yields, costs, and revenue in Iowa," Agricultural Water Management, Elsevier, vol. 92(1-2), pages 99-109, August.
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    Cited by:

    1. Olutobi Adeyemi & Ivan Grove & Sven Peets & Tomas Norton, 2017. "Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation," Sustainability, MDPI, Open Access Journal, vol. 9(3), pages 1-29, February.
    2. Hunsaker, D.J. & French, A.N. & Waller, P.M. & Bautista, E. & Thorp, K.R. & Bronson, K.F. & Andrade-Sanchez, P., 2015. "Comparison of traditional and ET-based irrigation scheduling of surface-irrigated cotton in the arid southwestern USA," Agricultural Water Management, Elsevier, vol. 159(C), pages 209-224.
    3. López, Juan A. & Navarro, H. & Soto, F. & Pavón, N. & Suardíaz, J. & Torres, R., 2015. "GAIA2: A multifunctional wireless device for enhancing crop management," Agricultural Water Management, Elsevier, vol. 151(C), pages 75-86.
    4. Beeson Jr., R.C., 2011. "Weighing lysimeter systems for quantifying water use and studies of controlled water stress for crops grown in low bulk density substrates," Agricultural Water Management, Elsevier, vol. 98(6), pages 967-976, April.


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