Soil Moisture as Predictor of Plant Water Status

LITERATURE CITED

1. ↵
   1. Amelung W,
   2. Blume HP,
   3. Fleige H,
   4. Korn R,
   5. Kandeler E,
   6. Kögel-Knabner I,
   7. Kretzschmar R,
   8. Stahr K,
   9. Wilke BM

   . 2018. Scheffer/Schachtschabel Lehrbuch der Bodenkunde. 17th Ed. Berlin (Germany): Springer-Verlag. 583 p. https://doi.org/10.1007/978-3-662-55871-3
2. ↵
   1. Balder H,
   2. Strauch KH

   . 2000. Wenn Bäume trotz Wasser verdursten. Deutsche Baumschule. 52:37–40.

   OpenUrl
3. ↵
   1. Centeno A,
   2. Baeza P,
   3. Lissarrague JR

   . 2010. Relationship between soil and plant water status in wine grapes under various water deficit regimes. HortTechnology. 20(3):585–593. https://doi.org/10.21273/HORTTECH.20.3.585

   OpenUrl
4. ↵
   1. Cochard H,
   2. Forestier S,
   3. Améglio T

   . 2001. A new validation of the Scholander pressure chamber technique based on stem diameter variations. Journal of Experimental Botany. 52(359): 1361–1365. https://doi.org/10.1093/jexbot/52.359.1361

   OpenUrlCrossRefPubMedWeb of Science
5. ↵
   1. Fereres E,
   2. Goldhamer DA,
   3. Parsons LR

   . 2003. Irrigation water management of horticultural crops. HortScience. 38(5):1036–1042. https://doi.org/10.21273/HORTSCI.38.5.1036

   OpenUrlFREE Full Text
6. ↵
   1. FLL

   . 2010. Empfehlungen für Baumpflanzungen—Teil 2: Standortvorbereitungen für Neupflanzungen; Pflanzgruben und Wurzelraumerweiterung, Bauweisen und Substrate. Bonn (Germany): Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau (FLL) e. V. 64 p.
7. ↵
   1. Goldhamer DA,
   2. Fereres E,
   3. Mata M,
   4. Girona J,
   5. Cohen M

   . 1999. Sensitivity of continuous and discrete plant and soil water status monitoring in peach trees subjected to deficit irrigation. Journal of the American Society for Horticultural Science. 124(4):437–444. https://doi.org/10.21273/JASHS.124.4.437

   OpenUrlAbstract/FREE Full Text
8. ↵
   1. Intrigliolo DS,
   2. Castel JR

   . 2004. Continuous measurement of plant and soil water status for irrigation scheduling in plum. Irrigation Science. 23:93–102. https://doi.org/10.1007/s00271-004-0097-7

   OpenUrlCrossRefWeb of Science
9. ↵
   1. Intrigliolo DS,
   2. Castel JR

   . 2006. Performance of various water stress indicators for prediction of fruit size response to deficit irrigation in plum. Agricultural Water Management. 83(1-2): 173–180. https://doi.org/10.1016/j.agwat.2005.12.005

   OpenUrl
10. ↵
    1. Irmak S,
    2. Payero JO,
    3. VanDeWalle B,
    4. Rees J,
    5. Zoubek G,
    6. Martin DL,
    7. Kranz WL,
    8. Eisenhauer DE,
    9. Leininger D

    . 2016. Principles and operational characteristics of watermark granular matrix sensor to measure soil water status and its practical applications for irrigation management in various soil textures. Lincoln (NE, USA): University of Nebraska-Lincoln. Extension Report EC783. 14 p. https://extensionpublications.unl.edu/assets/pdf/ec783.pdf
11. ↵
    1. Jones HG

    . 2004. Irrigation scheduling: Advantages and pitfalls of plant-based methods. Journal of Experimental Botany. 55(407):2427–2436. https://doi.org/10.1093/jxb/erh213

    OpenUrlCrossRefPubMedWeb of Science
12. ↵
    1. Larcher W

    . 2001. Ökophysiologie der Pflanzen. 6th Ed. Stuttgart (Germany): Ulmer Verlag. 408 p.
13. ↵
    1. Litvak E,
    2. McCarthy HR,
    3. Pataki DE

    . 2017. A method for estimating transpiration of irrigated urban trees in California. Landscape and Urban Planning. 158:48–61. https://doi.org/10.1016/j.landurbplan.2016.09.021

    OpenUrl
14. ↵
    1. Matyssek R,
    2. Fromm J,
    3. Rennenberg H,
    4. Roloff A

    . 2010. Biologie der Bäume: Von der Zelle zur globalen Ebene. Stuttgart (Germany): Ulmer Verlag. 344 p.
15. ↵
    1. R Core Team

    . 2018. R: A language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. https://www.R-project.org
16. ↵
    1. Richter H

    . 1973. Frictional potential losses and total water potential in plants: A re-evaluation. Journal of Experimental Botany. 24(6):983–994. https://doi.org/10.1093/jxb/24.6.983

    OpenUrlCrossRefWeb of Science
17. ↵
    1. Shock CC,
    2. Barnum JM,
    3. Seddigh M

    . 1998. Calibration of watermark soil moisture sensors for irrigation management. In: Proceedings of the international irrigation show; San Diego (CA, USA). Fairfax (VA, USA): Irrigation Association. p. 139–146. https://www.researchgate.net/publication/228762944_Calibration_of_W_ermark_Soil_Moisture_Sensors_for_Irrigation_Management
18. ↵
    1. van den Honert TH

    . 1948. Water transport in plants as a catenary process. Discussions of the Faraday Society. 3:146–153. https://doi.org/10.1039/DF9480300146

    OpenUrlCrossRefWeb of Science
19. ↵
    1. Williams LE,
    2. Araujo FJ

    . 2002. Correlations among predawn leaf, midday leaf, and midday stem water potential and their correlations with other measures of soil and plant water status in Vitis vinifera. Journal of the American Society for Horticultural Science. 127(3):448–454. https://doi.org/10.21273/JASHS.127.3.448

    OpenUrlAbstract/FREE Full Text
20. ↵
    1. Zuur A,
    2. Ieno E,
    3. Walker N,
    4. Saveliev A,
    5. Smith G

    . 2009. Mixed effects models and extensions in ecology with R. New York (NY, USA): Springer. 596 p. https://doi.org/10.1007/978-0-387-87458-6