Dynamics of biometric indicators of maize plants under the influence of sowing rates and field productivity zones

Lesia Harbar, Bohdan Vaskivskyi
Abstract

Biometric indicators of maize are an important indicator of the effectiveness of agricultural technologies and reflect the adaptive response of crops to spatial heterogeneity of growing conditions. The aim of the study was to determine the effect of zonal productivity and sowing rates on the height and dry matter formation of maize in the early stages of organogenesis. Field studies were conducted in 2023-2024 within three field productivity zones: high, medium and low. Five sowing rates were varied, ranging from 65 to 85 thousand seeds/ha. The results of the studies show that the productivity zone had the main influence on the biometric parameters of plants, while the sowing rate had an additional but less significant effect. At the V2-V3 stage of corn development, the maximum dry matter content (up to 23.5%) was observed in the high-yield zone at a sowing rate of 70 thousand seeds/ha. In low-productivity zones, the indicators decreased to 15.5%. Plant height in this phase ranged from 22.5 cm in the high zone to 16.6 cm in the low productivity zone. In the early flowering phase (R1), a decrease in dry matter content was observed towards less productive zones: from 31.7% (high zone, 70 thousand/ha) to 25.3% (low zone, 85 thousand/ha) in 2023. Plant height varied from 252 cm in high-yielding areas to 143 cm in low-yielding areas. The biometric parameters of maize can be used as a reliable criterion for assessing the response of crops to differentiated technological techniques, which is a promising direction in precision farming systems. The use of zonally differentiated sowing rates allows for more efficient use of resources and optimisation of agricultural technologies for growing maize in fields with varying productivity

Keywords

Zea mays L., plant height, differentiated sowing, plant density, dry matter

Suggested citation
Harbar, L., & Vaskivskyi, B. (2025). Dynamics of biometric indicators of maize plants under the influence of sowing rates and field productivity zones. Scientific Reports of the National University of Life and Environmental Sciences of Ukraine, 21(4),81-91. https://doi.org/10.31548/dopovidi/4.2025.81
References
  1. Anselmi, A.A., Molin, J.P., Bazame, H.C., & Corrêdo, L.P. (2021). Definition of optimal maize seeding rates based on the potential yield of management zones. Agriculture, 11(10), article number 911. doi: 10.3390/agriculture11100911.
  2. Bakó, K., Rácz, C., Dövényi-Nagy, T., Molnár, K., & Dobos, A. (2025). Advancements in leaf area index estimation for maize using modeling and remote sensing techniques: A review. Agronomy15(3), article number 519. doi: 10.3390/agronomy15030519.
  3. Bantchina, B.B., Qaswar, M., Arslan, S., Ulusoy, Y., Gündoğdu, K.S., Tekin, Y., & Mouazen, A.M. (2024). Corn yield prediction in site-specific management zones using proximal soil sensing, remote sensing, and machine learning approach. Computers and Electronics in Agriculture, 225, article number 109329. doi: 10.1016/j.compag.2024.109329.
  4. Bűdi, K., Bűdi, A., Tarcsi, Á., & Milics, G. (2025). Variable rate seeding and accuracy of within‑field hybrid switching in maize (Zea mays L.). Agronomy, 15(3), article number 718. doi: 10.3390/agronomy15030718.
  5. Convention on Biological Diversity. (1992, June). Retrieved from https://www.cbd.int/doc/legal/cbd-en.pdf.
  6. Cui, J., Cui, Z., Lu, Y., Lv, X., Cao, Q., Hou, Y., Yang, X., & Gu, Y. (2022). Maize grain yield enhancement in modern hybrids associated with greater stalk lodging resistance at a high planting density: A case study in northeast China. Scientific Reports, 12, article number 14647. doi: 10.1038/s41598-022-18908-z.
  7. Djaman, K., Allen, S., Djaman, D.S., Koudahe, K., Irmak, S., Puppala, N., Darapuneni, M.K., & Angadi, S.V. (2022). Planting date and plant density effects on maize growth, yield and water use efficiency. Environmental Challenges, 6, article number 100417. doi: 10.1016/j.envc.2021.100417.
  8. DSTU ISO 11260:2001. (2001). Soil quality – determination of cation exchange capacity and base saturation using barium chloride solution (ISO 11260:1994, IDT). Retrieved from https://online.budstandart.com/ua/catalog/doc-page.html?id_doc=57138.
  9. Du, Z., Yang, L., Zhang, D., Cui, T., He, X., Xiao, T., Xie, C., & Li, H. (2022). Corn variable-rate seeding decision based on gradient boosting decision tree model. Computers and Electronics in Agriculture, 198, article number 107025. doi: 10.1016/j.compag.2022.107025.
  10. Gallardo‑Romero, D.J., Apolo‑Apolo, O.E., Martínez‑Guanter, J., & Pérez‑Ruiz, M. (2023). Multilayer data and artificial intelligence for the delineation of homogeneous management zones in maize cultivation. Remote Sensing, 15(12), article number 3131. doi: 10.3390/rs15123131.
  11. Havlin, J.L., Tisdale, S.L., Nelson, W.L., & Beaton, J.D. (2013). Soil fertility and fertilizers (8th ed.). Upper Saddle River, NJ: Pearson Education.
  12. Lacolla, G., Caranfa, D., De Corato, U., Cucci, G., Mastro, M.A., & Stellacci, A.M. (2023). Maize yield response, root distribution and soil desiccation crack features as affected by row spacing. Plants, 12(6), article number 1380. doi: 10.3390/plants12061380.
  13. Li, D., et al. (2022). Corn nitrogen nutrition index prediction improved by integrating genetic, environmental, and management factors with active canopy sensing using machine learning. Remote Sensing14(2), article number 394. doi: 10.3390/rs14020394.
  14. Liu, W., et al. (2020). Contribution of total dry matter and harvest index to maize grain yield – a multisource data analysis. Food and Energy Security, 9(4), article number e256. doi: 10.1002/fes3.256.
  15. Munnaf, M.A., Haesaert, G., & Mouazen, A.M. (2022). Site‑specific seeding for maize production using management zone maps delineated with multi‑sensors data fusion scheme. Soil and Tillage Research, 220, article number 105377. doi: 10.1016/j.still.2022.105377.
  16. Saleem, N., Jubery, Z.T., Balu, A., Zhou, Y., Li, Y., Schnable, P.S., Krishnamurthy, A., & Ganapathysubramanian B. (2025). Accessing the effect of phyllotaxy and planting density on light use efficiency in field‑grown maize using 3D reconstruction. ARXIVdoi: 10.48550/arXiv.2503.06887.
  17. Șarauskis, E., Kazlauskas, M., Naujokienė, V., Bručienė, I., Steponavičius, D., Romaneckas, K., & Jasinskas, A. (2022). Variable rate seeding in precision agriculture: Recent advances and future perspectives. Agriculture, 12(2), article number 305. doi: 10.3390/agriculture12020305.
  18. Shatkovskyi, A., Zhuravlov, O., Melnychuk, F., Ovchatov, I., & Yarosh, A. (2020). Influence of irrigation methods on corn’s productivity. Plant and Soil Science, 11(4), 34-42. doi: 10.31548/agr2020.04.034.
  19. Silva, E.E., Baio, F.H.R., Kolling, D.F., Schneider Júnior, R., Zanin, A.R.A., Neves, D.C., Fontoura, J.V.P.F., & Teodoro, P.E. (2021). Variable-rate in corn sowing for maximizing grain yield. Scientific Reports, 11, article number 12711. doi: 10.1038/s41598-021-92238-4.
  20. Tian, P., Liu, J., Zhao, Y., Huang, Y., Lian, Y., Wang, Y., & Ye, Y. (2022). Nitrogen rates and plant density interactions enhance radiation interception, yield, and nitrogen use efficiencies of maize. Frontiers in Plant Science, 13, article number 974714. doi: 10.3389/fpls.2022.974714.
  21. Videgain, M., Martínez-Casasnovas, J.A., Vigo, A., Vidal, M., & García Ramos, F.J. (2024). On-farm experimentation of precision agriculture for differential seed and fertilizer management in semi-arid rainfed zones. Precision Agriculture, 25(6), 3048-3069. doi: 10.1007/s11119-024-10189-y.
  22. Zhang, M., Zhao, X., Han, X., Chen, Y., Dang, P., Xue, J., Qin, X., & Siddique, K.H.M. (2025). Optimizing planting density for enhanced maize yield and resource use efficiency in China. A meta-analysis. Agronomy for Sustainable Development, 45(3), article number 29. doi: 10.1007/s13593-025-01027-0.
  23. Zhu, Z., Friedman, S.P., Chen, Z., Zheng, J., & Sun, S. (2022). Dry matter accumulation in maize in response to film mulching and plant density in northeast China. Plants11(11), article number 1411. doi: 10.3390/plants11111411.