Spatial variability of soil properties and its influence on maize yield formation in precision farming systems of the Ukrainian Polissia
Article Sidebar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Main Article Content
Abstract
Spatial heterogeneity of soil properties is one of the key factors determining crop productivity and the efficiency of resource use in modern agricultural systems. This issue is particularly relevant for the Polissia region, where soils are characterized by significant variability in agrophysical and physico-mechanical fertility indicators (structure, soil hardness & compaction), organic matter content, acidity (pH levels), and nutrient availability. Conventional uniform management practices often fail to account for this variability, resulting in inefficient fertilizer use and reduced economic returns. The aim of this study was to determine the patterns of grain maize (Zea mays L.) productivity formation under conditions of spatial soil heterogeneity within a precision farming system. The research was conducted under the implementation of precision farming technologies at a high-tech agricultural enterprise located within the Vysokivska rural territorial hromada of the Zhytomyr district, Zhytomyr region. The spatial variability of soil cover properties and maize yields was assessed by integrating yield map data obtained from combine harvester monitoring systems, soil agrochemical analysis results, and satellite-based vegetation indices characterizing crop development dynamics throughout the growing season. Spatial data processing and visualization were performed using digital farm management platforms, specifically Cropwise Operations Center and John Deere Operations Center, which facilitate the accumulation, analysis, and integration of agronomic and geospatial data within a precision agriculture framework. Geostatistical and GIS-based methods were employed to delineate management zones and evaluate the interrelationships between soil parameters and maize productivity. All target plots are situated within the Haplic Albeluvisols (Light Grey Forest Soils) soil association, with minor inclusions of Haplic Phaeozems (Dark grey forest soils) and Haplic Chernozems, which is characteristic of this particular sub-region of the Ukrainian Polissia. Geostatistical and GIS-based methods were used to identify management zones and assess relationships between soil parameters and maize productivity. The results demonstrated significant spatial variability of yield within the field, which was strongly associated with differences in soil fertility indicators, particularly humus content, soil acidity, and nutrient availability. The findings confirm the importance of site-specific management practices in conditions of heterogeneous soil cover. Implementation of precision agriculture tools enables optimization of fertilizer application and improved resource use efficiency, thereby enhancing the sustainability and economic performance of maize production systems in the Polissia region.
Article Details
References
Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability. 3(4). 254–256. https://doi.org/10.1038/s41893-020-0510-0. DOI: https://doi.org/10.1038/s41893-020-0510-0
Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science. 327(5967), 828–831. https://doi.org/10.1126/science.1183899. DOI: https://doi.org/10.1126/science.1183899
Lykhovyd, P. (2022). Theoretical bases of crop production on the reclaimed lands in the conditions of climate change. RS Global Sp. z O.O. https://doi.org/10.31435/rsglobal/050. DOI: https://doi.org/10.31435/rsglobal/050
McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future Directions of Precision Agriculture. Precision Agriculture. 6(1). 7–23. https://doi.org/10.1007/s11119-005-0681-8. DOI: https://doi.org/10.1007/s11119-005-0681-8
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering. 114(4). 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009. DOI: https://doi.org/10.1016/j.biosystemseng.2012.08.009
Nyéki, A., Daróczy, B., Kerepesi, C., Neményi, M., & Kovács, A. (2022). Spatial Variability of Soil Properties and Its Effect on Maize Yields within Field – A Case Study in Hungary. Agronomy. https://doi.org/10.3390/agronomy12020395. DOI: https://doi.org/10.3390/agronomy12020395
Nykytiuk, Y., & Kravchenko, O. (2025). Agroecological assessment of maize suitability in the Polissia and Forest-Steppe of Ukraine under future climate projections. Agrology. https://doi.org/10.32819/202515. DOI: https://doi.org/10.32819/202515
Norberto, M., Sillero, N., Coimbra, J., & Cunha, M. (2023). Filling the maize yield gap based on precision agriculture - A MaxEnt approach. Comput. Electron. Agric., 211, 107970. https://doi.org/10.2139/ssrn.4374834. DOI: https://doi.org/10.1016/j.compag.2023.107970
Bhagwan, P., Anjaiah, T., Ravali, C., Devi, M., Neelima, T.,
Chary, D., & Chatterjee, S. (2025). Delineating soil fertility management zones using geostatistics and fuzzy clustering in semi-arid maize systems in India. Environmental Monitoring and Assessment, 197. https://doi.org/10.1007/s10661-025-14608-z. DOI: https://doi.org/10.1007/s10661-025-14608-z
Derevenets-Shevchenko, K., Shevchenko, S., Shevchenko, О.,
Tkalich, Y., & Hulenko, O. (2025). Modern trends in maize cultivation in Ukraine: National practices and international experience. Agrology. https://doi.org/10.32819/202520. DOI: https://doi.org/10.32819/202520
Moemeni, F., & Amirinejad, A. (2025). Enhancing Soil Health and Crop Productivity through Spatial Variability Analysis in Precision Agriculture. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.100972. DOI: https://doi.org/10.2139/ssrn.5126028
Abdel‐Rahman, M., Zakarya, Y., Metwaly, M., & Koubouris, G. (2020). Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques. Sustainability. https://doi.org/10.3390/su13010194. DOI: https://doi.org/10.3390/su13010194
Niu, J., Tang, H., Zhang, Y., Gao, B., Ji, W., Yu, Q., & Huang, Y. (2025). Optimization of Crop Yield Prediction Models Based on Stratified Soil Properties. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.101466. DOI: https://doi.org/10.1016/j.atech.2025.101466
Feng, P., Wang, B., Harrison, M., Wang, J., Liu, K., Huang, M.,
Liu, D., Yu, Q., & Hu, K. (2022). Soil properties resulting in superior maize yields upon climate warming. Agronomy for Sustainable Development. 42. https://doi.org/10.1007/s13593-022-00818-z. DOI: https://doi.org/10.1007/s13593-022-00818-z
Alasinrin, S., Sainju, U., Busari, M., Salako, F., Isimikalu, T., Ologunde, O., Kareem, I., Affinnih, O., & Bello, S. (2025). Selected soil physical properties and maize yield influenced by tillage systems and fertilizer types in southwest Nigeria. Plant Science Today. https://doi.org/10.14719/pst.7109. DOI: https://doi.org/10.14719/pst.7109
Chen, F., Xu, X., Chen, S., Wang, Z., Wang, B., Zhang, Y.,
Zhang, C., Feng, P., & Hu, K. (2024). Soil buffering capacity enhances maize yield resilience amidst climate perturbations. Agricultural Systems. https://doi.org/10.1016/j.agsy.2024.103870. DOI: https://doi.org/10.1016/j.agsy.2024.103870