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

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  • Hedley, C.B.
  • Yule, I.J.

Abstract

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.

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  • 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

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    1. Bhatti, Sandeep & Heeren, Derek M. & Barker, J. Burdette & Neale, Christopher M.U. & Woldt, Wayne E. & Maguire, Mitchell S. & Rudnick, Daran R., 2020. "Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery," Agricultural Water Management, Elsevier, vol. 230(C).
    2. Fernández-Pacheco, D.G. & Ferrández-Villena, M. & Molina-Martínez, J.M. & Ruiz-Canales, A., 2015. "Performance indicators to assess the implementation of automation in water user associations: A case study in southeast Spain," Agricultural Water Management, Elsevier, vol. 151(C), pages 87-92.
    3. El-Naggar, A.G. & Hedley, C.B. & Horne, D. & Roudier, P. & Clothier, B.E., 2020. "Soil sensing technology improves application of irrigation water," Agricultural Water Management, Elsevier, vol. 228(C).
    4. 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.
    5. 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.
    6. 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.
    7. Olutobi Adeyemi & Ivan Grove & Sven Peets & Tomas Norton, 2017. "Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation," Sustainability, MDPI, vol. 9(3), pages 1-29, February.

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