IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v182y2017icp1-12.html
   My bibliography  Save this article

Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation

Author

Listed:
  • El-Hendawy, Salah E.
  • Hassan, Wael M.
  • Al-Suhaibani, Nasser A.
  • Schmidhalter, Urs

Abstract

Because wheat varieties exhibit a high genotype×environment interaction, several drought tolerance indices (DTIs) have been developed to assist breeders in selecting genotypes with good performance under contrasting water conditions. We compared the relative yield of advanced breeding wheat lines under both well-watered and limited water irrigation conditions using different DTIs and evaluated how spectral reflectance indices (SRIs), as rapid and non-destructive tools, can effectively monitor DTIs and grain yield. Sixty-five recombinant inbred lines (RILs) developed from a cross between drought-tolerant (Sakha 93) and drought-sensitive (Sakha 61) genotypes were subjected to full irrigation (FI) and limited water irrigation (LWI) in the 2014 (F6), 2015 (F7), and 2016 (F8) growing seasons. Eight vegetation- and water-SRIs calculated from canopy reflectance under FI and LWI, and taken at the heading and middle grain filling stages, were related to the DTIs and grain yield. We found that the yield performance of the RILs was not consistent across the two water regimes. Selection based on the DTIs, the stress susceptibility index and the tolerance index failed to identify RILs that had very low yields under both treatments. However, the mean productivity index (MPI) and the geometric mean productivity index (GMP) enabled us to identify RILs that produced desirable yields under both full and limited irrigation, and these drought tolerance indices further exhibited a high heritability. Across the three years of investigation and at the heading and middle grain filling stages, these DTIs were best described either by the vegetation-based dry matter content index (DMCI) or the water-based normalized multi-band drought index (NDMI), or a combination of both. In conclusion, our results demonstrate that a combination of near infrared (NIR) and shortwave infrared (SWIR)-based SRIs can be used as a fast and low-cost predictor for selecting wheat genotypes with superior yield under different water regimes.

Suggested Citation

  • El-Hendawy, Salah E. & Hassan, Wael M. & Al-Suhaibani, Nasser A. & Schmidhalter, Urs, 2017. "Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation," Agricultural Water Management, Elsevier, vol. 182(C), pages 1-12.
  • Handle: RePEc:eee:agiwat:v:182:y:2017:i:c:p:1-12
    DOI: 10.1016/j.agwat.2016.12.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377416304929
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2016.12.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. El-Hendawy, Salah E. & Schmidhalter, Urs, 2010. "Optimal coupling combinations between irrigation frequency and rate for drip-irrigated maize grown on sandy soil," Agricultural Water Management, Elsevier, vol. 97(3), pages 439-448, March.
    2. El-Hendawy, Salah E. & Kotab, Maher A. & Al-Suhaibani, Nasser A. & Schmidhalter, Urs, 2014. "Optimal coupling combinations between the irrigation rate and glycinebetaine levels for improving yield and water use efficiency of drip-irrigated maize grown under arid conditions," Agricultural Water Management, Elsevier, vol. 140(C), pages 69-78.
    3. Elazab, Abdelhalim & Bort, Jordi & Zhou, Bangwei & Serret, María Dolors & Nieto-Taladriz, María Teresa & Araus, José Luis, 2015. "The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions," Agricultural Water Management, Elsevier, vol. 158(C), pages 196-208.
    4. Payero, José O. & Tarkalson, David D. & Irmak, Suat & Davison, Don & Petersen, James L., 2008. "Effect of irrigation amounts applied with subsurface drip irrigation on corn evapotranspiration, yield, water use efficiency, and dry matter production in a semiarid climate," Agricultural Water Management, Elsevier, vol. 95(8), pages 895-908, August.
    5. Perry, Chris & Steduto, Pasquale & Allen, Richard. G. & Burt, Charles M., 2009. "Increasing productivity in irrigated agriculture: Agronomic constraints and hydrological realities," Agricultural Water Management, Elsevier, vol. 96(11), pages 1517-1524, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammed Mohi-Ud-Din & Md. Alamgir Hossain & Md. Motiar Rohman & Md. Nesar Uddin & Md. Sabibul Haque & Eldessoky S. Dessoky & Mohammed Alqurashi & Salman Aloufi, 2022. "Assessment of Genetic Diversity of Bread Wheat Genotypes for Drought Tolerance Using Canopy Reflectance-Based Phenotyping and SSR Marker-Based Genotyping," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    2. El-Hendawy, Salah E. & Al-Suhaibani, Nasser A. & Elsayed, Salah & Hassan, Wael M. & Dewir, Yaser Hassan & Refay, Yahya & Abdella, Kamel A., 2019. "Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates," Agricultural Water Management, Elsevier, vol. 217(C), pages 356-373.
    3. Peng, Zhigong & Lin, Shaozhe & Zhang, Baozhong & Wei, Zheng & Liu, Lu & Han, Nana & Cai, Jiabing & Chen, He, 2020. "Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters," Agricultural Water Management, Elsevier, vol. 240(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Attia, Ahmed & El-Hendawy, Salah & Al-Suhaibani, Nasser & Alotaibi, Majed & Tahir, Muhammad Usman & Kamal, Khaled Y., 2021. "Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation," Agricultural Water Management, Elsevier, vol. 249(C).
    2. Zou, Haiyang & Fan, Junliang & Zhang, Fucang & Xiang, Youzhen & Wu, Lifeng & Yan, Shicheng, 2020. "Optimization of drip irrigation and fertilization regimes for high grain yield, crop water productivity and economic benefits of spring maize in Northwest China," Agricultural Water Management, Elsevier, vol. 230(C).
    3. Stepanovic, Strahinja & Rudnick, Daran & Kruger, Greg, 2021. "Impact of maize hybrid selection on water productivity under deficit irrigation in semiarid western Nebraska," Agricultural Water Management, Elsevier, vol. 244(C).
    4. Gao, Yang & Yang, Linlin & Shen, Xiaojun & Li, Xinqiang & Sun, Jingsheng & Duan, Aiwang & Wu, Laosheng, 2014. "Winter wheat with subsurface drip irrigation (SDI): Crop coefficients, water-use estimates, and effects of SDI on grain yield and water use efficiency," Agricultural Water Management, Elsevier, vol. 146(C), pages 1-10.
    5. Phogat, V. & Skewes, M.A. & McCarthy, M.G. & Cox, J.W. & Šimůnek, J. & Petrie, P.R., 2017. "Evaluation of crop coefficients, water productivity, and water balance components for wine grapes irrigated at different deficit levels by a sub-surface drip," Agricultural Water Management, Elsevier, vol. 180(PA), pages 22-34.
    6. Yan, Nana & Wu, Bingfang & Perry, Chris & Zeng, Hongwei, 2015. "Assessing potential water savings in agriculture on the Hai Basin plain, China," Agricultural Water Management, Elsevier, vol. 154(C), pages 11-19.
    7. Lv, Zhaoyan & Diao, Ming & Li, Weihua & Cai, Jian & Zhou, Qin & Wang, Xiao & Dai, Tingbo & Cao, Weixing & Jiang, Dong, 2019. "Impacts of lateral spacing on the spatial variations in water use and grain yield of spring wheat plants within different rows in the drip irrigation system," Agricultural Water Management, Elsevier, vol. 212(C), pages 252-261.
    8. Kundu, M. & Sarkar, S., 2009. "Growth and evapotranspiration pattern of rajmash (Phaseolus vulgaris L.) under varying irrigation schedules and phosphate levels in a hot sub-humid climate," Agricultural Water Management, Elsevier, vol. 96(8), pages 1268-1274, August.
    9. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    10. Hu, Yajin & Ma, Penghui & Zhang, Binbin & Hill, Robert L. & Wu, Shufang & Dong, Qin’ge & Chen, Guangjie, 2019. "Exploring optimal soil mulching for the wheat-maize cropping system in sub-humid drought-prone regions in China," Agricultural Water Management, Elsevier, vol. 219(C), pages 59-71.
    11. Ghahroodi, E. Mokari & Noory, H. & Liaghat, A.M., 2015. "Performance evaluation study and hydrologic and productive analysis of irrigation systems at the Qazvin irrigation network (Iran)," Agricultural Water Management, Elsevier, vol. 148(C), pages 189-195.
    12. Dai, Zhiguang & Fei, Liangjun & Huang, Deliang & Zeng, Jian & Chen, Lin & Cai, Yaohui, 2019. "Coupling effects of irrigation and nitrogen levels on yield, water and nitrogen use efficiency of surge-root irrigated jujube in a semiarid region," Agricultural Water Management, Elsevier, vol. 213(C), pages 146-154.
    13. Ćosić, Marija & Djurović, Nevenka & Todorović, Mladen & Maletić, Radojka & Zečević, Bogoljub & Stričević, Ružica, 2015. "Effect of irrigation regime and application of kaolin on yield, quality and water use efficiency of sweet pepper," Agricultural Water Management, Elsevier, vol. 159(C), pages 139-147.
    14. Dorijan Radočaj & Ante Šiljeg & Rajko Marinović & Mladen Jurišić, 2023. "State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review," Agriculture, MDPI, vol. 13(3), pages 1-16, March.
    15. Chauhdary, Junaid Nawaz & Bakhsh, Allah & Engel, Bernard A. & Ragab, Ragab, 2019. "Improving corn production by adopting efficient fertigation practices: Experimental and modeling approach," Agricultural Water Management, Elsevier, vol. 221(C), pages 449-461.
    16. Zhou, Xinyao & Zhang, Yongqiang & Sheng, Zhuping & Manevski, Kiril & Andersen, Mathias N. & Han, Shumin & Li, Huilong & Yang, Yonghui, 2021. "Did water-saving irrigation protect water resources over the past 40 years? A global analysis based on water accounting framework," Agricultural Water Management, Elsevier, vol. 249(C).
    17. Tarkalson, David D. & King, Bradley A. & Bjorneberg, Dave L., 2022. "Maize grain yield and crop water productivity functions in the arid Northwest U.S," Agricultural Water Management, Elsevier, vol. 264(C).
    18. Kukal, M.S. & Irmak, S., 2020. "Impact of irrigation on interannual variability in United States agricultural productivity," Agricultural Water Management, Elsevier, vol. 234(C).
    19. Robel Admasu & Abraham W Michael & Tilahun Hordofa, 2019. "Senior Irrigation Researcher, Melkassa Agricultural Research Center, Ethiopia," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 16(4), pages 83-87, January.
    20. Jackson, Tamara M. & Khan, Shahbaz & Hafeez, Mohsin, 2010. "A comparative analysis of water application and energy consumption at the irrigated field level," Agricultural Water Management, Elsevier, vol. 97(10), pages 1477-1485, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:182:y:2017:i:c:p:1-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.