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

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

Agricultural Water Management, 2022, vol. 269, issue C

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.

Keywords: Stress detection; Soil water content; UAV; Drone; Humid regions; Irrigation; Hybrid CWSI (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:269:y:2022:i:c:s0378377422002116

DOI: 10.1016/j.agwat.2022.107664

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