A PROTOCOL FOR INSTRUMENTAL MONITORING OF THE SOYBEAN CANOPY LIGHT ENVIRONMENT AND SELECTED PLANT PHYSIOLOGICAL CHARACTERISTICS UNDER FIELD CONDITIONS
DOI:
https://doi.org/10.31861/biosystems2026.02.298Keywords:
Glycine max, soybean canopy, illuminance, atLEAF CHL PLUS, leaf optical index, leaf temperature, field monitoring, repeated measurementsAbstract
Assessing the spatial heterogeneity of the soybean canopy requires the coordinated use of indicators characterizing the light environment, the optical properties of leaves, and leaf surface temperature. With limited instrument availability, these characteristics can be measured using portable devices. However, the different physical nature of the measurements complicates their direct comparison and physiological interpretation.
The aim of this study was to develop and describe a reproducible field protocol for monitoring soybean (Glycine max (L.) Merr.) crops that integrates illuminance measurements using a lux meter, leaf optical index measurements using the atLEAF CHL PLUS chlorophyll meter, and leaf surface temperature measurements using an infrared pyrometer. The methodological basis was a field experiment conducted in 2026 at the Zhuchka Biological Research Farm of Yuriy Fedkovych Chernivtsi National University.
The protocol covers the identification of experimental plots, the establishment of a permanent panel of tagged plants and an additional sample, the recording of phenological stages, the allocation of measurements across canopy layers, quality control of primary records, and the calculation of derived indicators. The principles governing dataset organization and statistical analysis are also specified, taking into account the block structure of the experiment, the hierarchical nesting of observations, and repeated measurements over time. The limitations of using lux-meter illuminance to characterize the light environment, the leaf optical index to assess leaf status, and leaf surface temperature to diagnose physiological stress are discussed.
The proposed protocol represents a methodological integration of accessible instrumental procedures rather than a new physical measurement method or a validated system for physiological diagnosis.
References
1. DSTU 4828:2007. Oilseeds. Methods for determination of moisture content. [Effective from 2009-01-01]. Kyiv: Derzhspozhyvstandart of Ukraine, 2008. 16 p.
2. DSTU 4582:2006. Soybean. Specifications. [Effective from 2007-07-01]. Kyiv: Derzhspozhyvstandart of Ukraine, 2007. 12 p.
3. atLEAF CHL PLUS: handheld chlorophyll meter description. Офіційний сайт виробника. URL: https://www.atleaf.com/atLEAF_CHL_PLUS (дата звернення: 09.07.2026).
4. Fehr, W., & Caviness, C. (1977). Stages of soybean development.
5. Hoshmand, R. (2018). Design of experiments for agriculture and the natural sciences. CRC Press. https://surl.li/kaddfj (дата звернення: 03.05.2026).
6. Jones, H. G. (1999). Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agricultural and forest meteorology, 95(3), 139-149. https://doi.org/10.1016/S0168-1923(99)00030-1.
7. Mohamed, N. E., Mustafa, A. R. A., Bedawy, I. M., Ahmed, A. S., Abdelsamie, E. A., Mohamed, E. S., ... & Shokr, M. S. (2024). Utilizing infrared thermometry to assess the crop water stress index of wheat genotypes in arid regions under varying irrigation regimes. Agronomy, 14(8), 1814. https://doi.org/10.3390/agronomy14081814
8. Naeve, S. L. (2018). Soybean growth stages. University of Minnesota Extension. https://extension.umn.edu/agriculture/crop-production/soybean/soybean-growth-stages (дата звернення: 03.09.2026).
9. Novichonok, E. V., Novichonok, A. O., Kurbatova, J. A., & Markovskaya, E. F. (2016). Use of the atLEAF+ chlorophyll meter for a nondestructive estimate of chlorophyll content. Photosynthetica, 54(1), 130-137. https://doi.org/10.1007/s11099-015-0172-8
10. Ohno, Y., Goodman, T., Blattner, P., Schanda, J., Shitomi, H., Sperling, A., & Zwinkels, J. (2020). Principles governing photometry. Metrologia, 57(2), 020401 https://www.bipm.org/documents/20126/28126817/bipm%20publication-ID-3320/6fd541a7-7d45-b7a4-fda6-0c8c879d4526 (дата звернення: 02.09.2026).
11. Pagliari, P., Galindo, F. S., Strock, J., & Rosen, C. (2022). Use of repeated measures data analysis for field trials with annual and perennial crops. Plants, 11(13), 1783. https://doi.org/10.3390/plants11131783.
12. Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., ... & Araya‐Ajoy, Y. G. (2020). Robustness of linear mixed‐effects models to violations of distributional assumptions. Methods in ecology and evolution, 11(9), 1141-1152. https://doi.org/10.1111/2041-210X.13434
13. Sreekanta, S., Haaning, A., Dobbels, A., O’Neill, R., Hofstad, A., Virdi, K., ... & Lorenz, A. J. (2024). Variation in shoot architecture traits and their relationship to canopy coverage and light interception in soybean (Glycine max). BMC Plant Biology, 24(1), 194. https://doi.org/10.1186/s12870-024-04859-2.
14. Uddling, J., Gelang-Alfredsson, J., Piikki, K., & Pleijel, H. (2007). Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynthesis research, 91(1), 37-46. https://doi.org/10.1007/s11120-006-9077-5.
15. Walter, A., & Schöbel, H. (2023). Shed light on photosynthetic organisms: a physical perspective to correct light measurements. Photosynthesis Research, 156(3), 325-336. https://doi.org/10.1007/s11120-023-01001-5.
16. Yang, S., Sun, J., Wang, Z., Li, J., Xu, M., & Fan, G. (2026). Research on marginal effects of dry matter accumulation and extinction coefficient in maize–soybean intercropping composite population. Agriculture, 16(3), 371. https://doi.org/10.3390/agriculture16030371
17. Zhu, B., Liu, F., Xie, Z., Guo, Y., Li, B., & Ma, Y. (2020). Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season. Annals of botany, 126(4), 701-712. https://doi.org/10.1093/aob/mcaa046
