Literature DB >> 28795772

The potential of the spectral 'water balance index' (WABI) for crop irrigation scheduling.

Tal Rapaport1,2, Uri Hochberg3, Amnon Cochavi2, Arnon Karnieli1, Shimon Rachmilevitch2.   

Abstract

Hyperspectral sensing can detect slight changes in plant physiology, and may offer a faster and nondestructive alternative for water status monitoring. This premise was tested in the current study using a narrow-band 'water balance index' (WABI), which is based on independent changes in leaf water content (1500 nm) and the efficiency of the nonphotochemical quenching (NPQ) photo-protective mechanism (531 nm). The hydraulic, photo-protective and spectral behaviors of five important crops - grapevine, corn, tomato, pea and sunflower - were evaluated under water deficit conditions in order to associate the differences in stress physiology with WABI suitability. Rapid alterations in both leaf water content and NPQ were observed in grapevine, pea and sunflower, and were effectively captured by WABI. Apart from water status monitoring, the index was also successful in scheduling the irrigation of a vineyard, despite phenological and environmental variability. Conversely, corn and tomato displayed a relatively strict stomatal regime and/or mild NPQ responses and were, thus, unsuitable for WABI-based monitoring. WABI shows great potential for irrigation scheduling of various crops, and has a clear advantage over spectral models that focus on either of the abovementioned physiological mechanisms.
© 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Entities:  

Keywords:  crop physiology; hyperspectral spectroscopy; irrigation; precision agriculture; water balance index (WABI)

Mesh:

Substances:

Year:  2017        PMID: 28795772     DOI: 10.1111/nph.14718

Source DB:  PubMed          Journal:  New Phytol        ISSN: 0028-646X            Impact factor:   10.151


  5 in total

Review 1.  Soybean cyst nematode detection and management: a review.

Authors:  Youness Arjoune; Niroop Sugunaraj; Sai Peri; Sreejith V Nair; Anton Skurdal; Prakash Ranganathan; Burton Johnson
Journal:  Plant Methods       Date:  2022-09-07       Impact factor: 5.827

2.  Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes.

Authors:  Salah E El-Hendawy; Majed Alotaibi; Nasser Al-Suhaibani; Khalid Al-Gaadi; Wael Hassan; Yaser Hassan Dewir; Mohammed Abd El-Gawad Emam; Salah Elsayed; Urs Schmidhalter
Journal:  Front Plant Sci       Date:  2019-11-28       Impact factor: 5.753

3.  Prediction of leaf water potential and relative water content using terahertz radiation spectroscopy.

Authors:  Marvin Browne; Nezih Tolga Yardimci; Christine Scoffoni; Mona Jarrahi; Lawren Sack
Journal:  Plant Direct       Date:  2020-04-17

4.  Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.

Authors:  Juanjuan Zhang; Wen Zhang; Shuping Xiong; Zhaoxiang Song; Wenzhong Tian; Lei Shi; Xinming Ma
Journal:  Plant Methods       Date:  2021-03-31       Impact factor: 4.993

5.  Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions.

Authors:  Adel H Elmetwalli; Salah El-Hendawy; Nasser Al-Suhaibani; Majed Alotaibi; Muhammad Usman Tahir; Muhammad Mubushar; Wael M Hassan; Salah Elsayed
Journal:  Sensors (Basel)       Date:  2020-11-17       Impact factor: 3.576

  5 in total

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