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An insight to the performance of crop water stress index for olive trees

Author

Listed:
  • Agam, N.
  • Cohen, Y.
  • Berni, J.A.J.
  • Alchanatis, V.
  • Kool, D.
  • Dag, A.
  • Yermiyahu, U.
  • Ben-Gal, A.

Abstract

Optimization of olive oil quantity and quality requires finely tuned water management, as increased irrigation, up to a certain level, results in increasing yield, but a certain degree of stress improves oil quality. Monitoring tools that provide accurate information regarding orchard water status would therefore be beneficial. Amongst the various existing methods, those having high resolution, either temporally (i.e., continuous) or spatially, have the maximum adoption potential. One of the commonly used spatial methods is the Crop Water Stress Index (CWSI). The objective of this research was to test the ability of the CWSI to characterize water status dynamics of olive trees as they enter into and recover from stress, and on a diurnal scale. CWSI was tested in an empirical form and in two analytical configurations. In an experiment conducted in a lysimeter facility in the northwestern Negev, Israel, irrigation was withheld for 6 days for 5 of 15 trees, while daily irrigation continued for the rest of the trees. After resuming irrigation, the trees were monitored for 5 additional days. Water status measurements and thermal imaging were conducted daily between 12:00 and 14:00. Diurnal monitoring (predawn to after dusk) of the same indicators was conducted on the day of maximum stress. Continuous meteorological data were acquired throughout the experimental period. Empirical and analytical CWSI were calculated based on canopy temperature extracted from thermal images. The empirical CWSI differentiated between well watered and stressed trees, and depicted the water status dynamics during the drought and recovery periods as well as on a diurnal scale. Analytical approaches did not perform as well at either time scale. In conclusion, the empirical CWSI seems to be promising even given its limitations, while analytical forms of CWSI still require improvement before they can be used as a water status monitoring tool for olive orchards. Practically, it is proposed to compute the wet temperature analytically and set the dry temperature to 5°C higher than air temperature.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:118:y:2013:i:c:p:79-86
    DOI: 10.1016/j.agwat.2012.12.004
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    1. Ben-Gal, Alon & Kool, Dilia & Agam, Nurit & van Halsema, Gerardo E. & Yermiyahu, Uri & Yafe, Ariel & Presnov, Eugene & Erel, Ran & Majdop, Ahmed & Zipori, Isaac & Segal, Eran & Rüger, Simon & Zimmerma, 2010. "Whole-tree water balance and indicators for short-term drought stress in non-bearing 'Barnea' olives," Agricultural Water Management, Elsevier, vol. 98(1), pages 124-133, December.
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    2. Luan, Yajun & Xu, Junzeng & Lv, Yuping & Liu, Xiaoyin & Wang, Haiyu & Liu, Shimeng, 2021. "Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging," Agricultural Water Management, Elsevier, vol. 246(C).
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    5. 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.
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    7. Chaturvedi, Ashish K. & Surendran, U & Gopinath, Girish & Chandran, K Madhava & NK, Anjali & CT, Mohamed Fasil, 2019. "Elucidation of stage specific physiological sensitivity of okra to drought stress through leaf gas exchange, spectral indices, growth and yield parameters," Agricultural Water Management, Elsevier, vol. 222(C), pages 92-104.
    8. Ben-Gal, Alon & Ron, Yonatan & Yermiyahu, Uri & Zipori, Isaac & Naoum, Sireen & Dag, Arnon, 2021. "Evaluation of regulated deficit irrigation strategies for oil olives: A case study for two modern Israeli cultivars," Agricultural Water Management, Elsevier, vol. 245(C).
    9. Puppo, Lucía & García, Claudio & Bautista, Eduardo & Hunsaker, Douglas J. & Beretta, Andrés & Girona, Joan, 2019. "Seasonal basal crop coefficient pattern of young non-bearing olive trees grown in drainage lysimeters in a temperate sub-humid climate," Agricultural Water Management, Elsevier, vol. 226(C).
    10. Apolo-Apolo, O.E. & Martínez-Guanter, J. & Pérez-Ruiz, M. & Egea, G., 2020. "Design and assessment of new artificial reference surfaces for real time monitoring of crop water stress index in maize," Agricultural Water Management, Elsevier, vol. 240(C).
    11. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    12. Ramírez-Cuesta, J.M. & Ortuño, M.F. & Gonzalez-Dugo, V. & Zarco-Tejada, P.J. & Parra, M. & Rubio-Asensio, J.S. & Intrigliolo, D.S., 2022. "Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery," Agricultural Water Management, Elsevier, vol. 267(C).
    13. Egea, Gregorio & Padilla-Díaz, Carmen M. & Martinez-Guanter, Jorge & Fernández, José E. & Pérez-Ruiz, Manuel, 2017. "Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards," Agricultural Water Management, Elsevier, vol. 187(C), pages 210-221.
    14. 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.
    15. 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).
    16. Osama Elsherbiny & Yangyang Fan & Lei Zhou & Zhengjun Qiu, 2021. "Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data," Agriculture, MDPI, vol. 11(1), pages 1-21, January.

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