1. Facilitating protocols while maintaining accuracy in grapevine pressure chamber measurements- comments on Levin 2019

    http://dx.doi.org/10.1016/j.agwat.2019.105836
  2. Selection of flow rate and irrigation duration for high performance bay irrigation

    http://dx.doi.org/10.1016/j.agwat.2019.105850
  3. Corrigendum to “Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes” [Agric. Water Manage. 189 (2017) 98–110]

    http://dx.doi.org/10.1016/j.agwat.2019.105873
  4. Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data

    http://dx.doi.org/10.1016/j.agwat.2019.105875
  5. Unified framework for model-based optimal allocation of crop areas and water

    http://dx.doi.org/10.1016/j.agwat.2019.105859
  6. Evaluation of within-season grapevine evapotranspiration patterns and drivers using generalized additive models

    http://dx.doi.org/10.1016/j.agwat.2019.105808
  7. Comparisons of spray characteristics between vertical impact and turbine drive sprinklers—A case study of the 50PYC and HY50 big gun-type sprinklers

    http://dx.doi.org/10.1016/j.agwat.2019.105847
  8. Spring safflower water use patterns in response to preseason and in-season irrigation applications

    http://dx.doi.org/10.1016/j.agwat.2019.105876
  9. Water pricing and the value-add of irrigation water in Vietnam: Insights from a crop choice model fitted to a national household survey

    http://dx.doi.org/10.1016/j.agwat.2019.105881
  10. Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors

    http://dx.doi.org/10.1016/j.agwat.2019.105880
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