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Revisiting crop water stress index based on potato field experiments in Northern Germany

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  • Ekinzog, Elmer Kanjo
  • Schlerf, Martin
  • Kraft, Martin
  • Werner, Florian
  • Riedel, Angela
  • Rock, Gilles
  • Mallick, Kaniska

Abstract

Different types of the Crop Water Stress Index (CWSI) have been useful for water stress monitoring and irrigation management in semi-arid regions, however little research exists on its effective application in humid regions. This study aims to assess the effectiveness of three CWSI models (CWSIe - empirical, CWSIt - theoretical, CWSIh - hybrid) for crop water stress monitoring in an experimental field for potato crops in Northern Germany. Irrigation experiments with three treatments (optimum-OP, reduced-RD and no) were conducted in the summer of 2018 and 2019. Continuous canopy temperatures (Tc) for OP and RD irrigation treatments together with meteorological measurements were used to derive CWSI from the different models. Additionally, Visible/near infrared (VNIR) and Thermal Infrared (TIR) drone images were collected on several days during the growing season to create CWSI maps. The different CWSI models were correlated with volumetric soil water content (θ) measurements for comparison and relationships were established between CWSI and θ for prediction. Results showed that CWSI accurately estimates soil water content under atmospheric conditions similar to those in semi-arid regions. The predictive performance of different CWSI models were fairly good (R2 =0.57–0.63) (situation in 2019). CWSIe and CWSIh performed better than CWSIt. CWSI-θ relations calibrated in one year effectively predicted θ in another year with errors of 1.2–2.2% absolute soil water content. CWSIh could be a promising alternative to the traditional CWSI as it combines aspects of CWSIe (empirical upper limit) and of CWSIt (theoretical lower limit) which has advantages for operational use. Finally, the drone-based CWSI and θ maps (derived from the developed CWSI- θ relations) captured well the applied irrigation patterns and could help to decide when to irrigate and how much water to apply.

Suggested Citation

  • Ekinzog, Elmer Kanjo & Schlerf, Martin & Kraft, Martin & Werner, Florian & Riedel, Angela & Rock, Gilles & Mallick, Kaniska, 2022. "Revisiting crop water stress index based on potato field experiments in Northern Germany," Agricultural Water Management, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:agiwat:v:269:y:2022:i:c:s0378377422002116
    DOI: 10.1016/j.agwat.2022.107664
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    1. Agam, N. & Cohen, Y. & Berni, J.A.J. & Alchanatis, V. & Kool, D. & Dag, A. & Yermiyahu, U. & Ben-Gal, A., 2013. "An insight to the performance of crop water stress index for olive trees," Agricultural Water Management, Elsevier, vol. 118(C), pages 79-86.
    2. Meinardi, Dominic & Schröder, Johanna & Riedel, Angela & Röttcher, Klaus & Kraft, Martin & Grocholl, Jürgen & Dittert, Klaus, 2021. "Sensorgestützte Beregnung von Kartoffeln: Entwicklung des Crop Water Stress Index für Nordostniedersachsen," Thünen Working Papers 179, Johann Heinrich von Thünen Institute, Federal Research Institute for Rural Areas, Forestry and Fisheries.
    3. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Gleason, Sean, 2018. "Comparison of three crop water stress index models with sap flow measurements in maize," Agricultural Water Management, Elsevier, vol. 203(C), pages 366-375.
    4. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
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    1. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).

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