Remote spectral analysis of varieties and lines of winter wheat during the flowering period

Authors

DOI:

https://doi.org/10.21498/2518-1017.18.2.2022.265183

Keywords:

winter wheat, variety, breeding lines, flowering, NDVI index, spectral evaluation

Abstract

Purpose. Conduct a spectral assessment of winter wheat varieties (‘MIP Assol’, ‘Balada Myronivska’, ‘Hratsiia Myronivska’, ‘MIP Yuvileina’, ‘MIP Lada’, ‘MIP Dniprianka’, and standard ‘Podolianka’) and perspective breeding lines (‘Erythrospermum 55023’, ‘Lutescens 22198’, ‘Lutescens 37519’, ‘Lutescens 60049’, ‘Lutescens 60107’) of Myronivka Institute breeding during the flowering period and to evaluate the dependence of the obtained NDVI indicator on their productivity.

Methods. The research was conducted during the 2018/19–2020/21 growing seasons in the breeding crop rotation of the winter wheat breeding laboratory of the V. M. Remeslo Myronivka Wheat Institute of the National Academy of Sciences of Ukraine. The main method of research is field, supplemented by analytical studies, measurements, calculations and observations. Obtaining values of vegetation indices of varieties and breeding lines of winter wheat was carried out using the Mavic zoom 2 UAV (unmanned aerial vehicle) using the Parrot Sequoia multispectral camera. Pix4Dcapture and Pix4Dmapper programs were used to create an orthophoto map. Photographing was carried out with a multispectral camera at a height of 30 m above the level of the object under study in order to improve the quality of the orthophoto map with an overlap of 80% of the images and a time interval of 2 seconds. The NDVI index (normalized difference vegetation index) was calculated according to the appropriate formula.

Results. According to the research results, regardless of the conditions of the year, in the first, optimal sowing period (25.09–05.10), the NDVI indicator in the flowering-ripening phase of wheat had higher values than in the second, late period (05–15.10) (average value over three years for the first semester was 0.69, the second – 0.62). In the course of the research, we established the dependence of the vegetation index NDVI on the level of productivity of wheat genotypes. The best varieties and promising lines among those studied were ‘MIP Lada’, ‘Lutescens 55198’ and ‘Lutescens 60049’, as well as ‘MIP Assol’ and ‘Hratsiia Myronivska’, which were less sensitive to sowing dates and had a higher index and control of yield indicators even with late sowing dates.

Conclusions. Although existing today phenotyping methods need to be improved and localized, in the near future they will become an indispensable tool for the breeder, which will increase the volume of studied varieties and improve the quality of the results of morpho-biological analysis

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References

Tester, M., & Langridge, P. (2010). Breeding Technologies to Increase Crop Production in a Changing World. Science, 327(5967), 818–822. doi: 10.1126/science.1183700

Long, S. P., Marshall-Colon, A., & Zhu, X.-G. (2015). Meeting the Global Food Demand of the Future by Engineering Crop Photosynthesis and Yield Potential. Cell, 161, 56–66. doi: 10.1016/j.cell.2015.03.019

Andrew, S., & Bryn, S. (October 14, 2021). Wheat Outlook: Economic Research Service, USDA. WHS-21j (pp. 2–9). Retrieved from https://www.ers.usda.gov/webdocs/outlooks/102370/whs-21j.pdf?v=941.5

Demotes-Mainard, S., & Jeuffroy, M. H. (2004). Effects of nitrogen and radiation on dry matter and nitrogen accumulation in the spike of winter wheat. Field Crops Research, 87(2–3), 221–233. doi: 10.1016/j.fcr.2003.11.014

Meier, U. (2018). Growth stages of mono- and dicotyledonous plants: BBCH monograph. Quedlinburg: Julius Kühn-Institut. doi: 10.5073/20180906-074619

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In С. Stanley, E. Freden, P. Mercanti, & M. A. Becker (Eds.), Third Earth Resources Technology Satellite-1 Symposium. Volume 1: Technical Presentations. NASA SP-351: proceedings of a symposium held by Goddard Space Flight Center at Washington, D.C. (pp. 309–317). Washington, D.C.: Goddard Space Flight Center. Retrieved from https://ntrs.nasa.gov/api/citations/19740022614/downloads/19740022614.pdf

Duan, T., Chapman, S. C., Guo, Y., & Zhengy, B. (2017). Dynamic mo­nitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research, 210, 71–80. doi: 10.1016/j.fcr.2017.05.025

Hassan, M. A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., Xia, X., Xiao, Y., & He, Z. (2018). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 282, 95–103. doi: 10.1016/j.plantsci.2018.10.022

Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., … Fritschi, F. (2017). Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43–58. doi: 10.1016/j.isprsjprs.2017.10.011

Dospekhov, B. A. (1985). Metodika polevogo opyta (s osnovami statisticheskoy obrabotki rezul’tatov issledovaniy) [Methods of field experience (with the basics of statistical processing of research results)]. (5th ed., rev.). Moscow: Agropromizdat. [In Russian]

Volkodav, V. V. (Ed.). (2003). Metodyka provedennia ekspertyzy ta derzhavnoho sortovyprobuvannia sortiv roslyn zernovykh, krupianykh ta zernobobovykh kultur [Methodology for examination and state variety testing of plant varieties of grain, grain and leguminous crops]. In Plant Varieties Rights Protection (Vol. 2, Part. 3). Kyiv: Alefa. [In Ukrainian]

Weier, J., & Herring, D. (August 30, 2020). Measuring Vegetation (NDVI & EVI). Washington, DC: NASA Earth Observatory. Retrieved from https://earthobservatory.nasa.gov/features/MeasuringVegetation

Rabab, S., Breen, E., Gebremedhin, A., Shi, F., Badenhorst, P., Chen, Y.-P. P., & Daetwyler, H. D. (2021). A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images. Remote Sensing, 13(6), 2–18. doi: 10.3390/rs13061212

Posudin, Yu. I. (2003). Metody vymiriuvannia parametriv nav­ko­lyshnoho seredovyshcha [Methods of measuring environmental parameters]. Kyiv: Svit. [In Ukrainian]

Achasov, A. B., Achasova, A. O., Titenko, A. V., Seliverstov, O. Yu., & Sedov, A. O. (2015). UAV usage for crop estimation. Visnyk of V. N. Karazin Kharkiv National University. Series «Еcоlogy», 13, 14–18. [In Ukrainian]

Eroshenko, F. V. (2016). Active photosynthetic potential. Eurasian Union of Scientists, 2, 117–120. [In Russian]

Zhukov, A. V., Kunakh, O. N., Zadorozhnaya, G. A., & Andrusevich, E. V. (2013). Landscape ecology as a basis of the spatial analysis of the agrocoenosis productivity. Ecology and Noospherology, 24(1–2), 68–80. [In Russian]

Paruelo, J. M., & Lauenroth, W. K. (1998). Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands. Journal of Biogeography, 25, 721–733.

Zholobak, G., Dugin, S., Sybirtseva, O., Kazantsev, T., & Romanciuc, I. (2020). Determination of nitrogen and chlorophyll content in two varieties of winter wheat plants means of ground and airborne spectrometry. Ukrainian Journal of Remote Sen­sing, 26, 4–13. doi: 10.36023/ujrs.2020.26.178 [In Ukrainian]

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Published

2022-08-01

How to Cite

Topko, R. I., & Kovalyshyna, H. M. (2022). Remote spectral analysis of varieties and lines of winter wheat during the flowering period. Plant Varieties Studying and Protection, 18(2), 148–157. https://doi.org/10.21498/2518-1017.18.2.2022.265183

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Section

PLANT PRODUCTION