Retrieved from Volume 28, No. 4, 2024
Pages 54 -67
Received 31.07.2024
Revised 11.10.2024
Accepted 10.12.2024
Retrieved from Volume 28, No. 4, 2024
Pages 54 -67
Abstract
The territories of Polissia and Forest-Steppe are of great importance to Ukraine, offering significant potential for the advancement of agriculture and forestry. In light of the pressing issue of global climate change, it is imperative that the active economic utilisation of these territories be guided by a commitment to the maintenance of ecological systems and the sustenance of ecosystem functions. The objective of this article was to ascertain the patterns of both spatial and temporal precipitation variability and to establish the impact of anthropogenic land transformation as a consequence of agricultural production. Within the study area, the average precipitation level was 625±68 mm and ranged from 535 to 1,160 mm. During the study period (1960-2023), there was a trend of increasing and decreasing precipitation. The southeast of the region was characterised by a decreasing trend in precipitation, while in the northwest and north, the time trend showed an increase in precipitation. Precipitation in the Carpathian region is much higher than in other parts of the territory. The presence of an oscillatory regularity in precipitation rhythm allows for consideration of two practical aspects: the possibility of forecasting future precipitation dynamics based on available information from previous years, and accounting for the fluctuating precipitation dynamics when recommending optimal crop rotations and selecting the most suitable range of crop varieties for cultivation
Keywords:
climate; spatial pattern; temporal dynamics; landscape diversity; land cover[1] Allen, M.R., & Ingram, W.J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6903), 224-232. doi: 10.1038/nature01092.
[2] Arnell, N.W., Lowe, J.A., Challinor, A.J., & Osborn, T.J. (2019). Global and regional impacts of climate change at different levels of global temperature increase. Climatic Change, 155(3), 377-391. doi: 10.1007/s10584-019-02464-z.
[3] Basche, A.D., Kaspar, T.C., Archontoulis, S.V., Jaynes, D.B., Sauer, T.J., Parkin, T.B., & Miguez, F.E. (2016). Soil water improvements with the long-term use of a winter rye cover crop. Agricultural Water Management, 172, 40-50. doi: 10.1016/j.agwat.2016.04.006.
[4] Benziane, S. (2024). Survey: Rainfall prediction precipitation, review of statistical methods. Wseas Transactions on Systems, 23, 47-59. doi: 10.37394/23202.2024.23.5.
[5] Bodner, G., Nakhforoosh, A., & Kaul, H.-P. (2015). Management of crop water under drought: A review. Agronomy for Sustainable Development, 35(2), 401-442. doi: 10.1007/s13593-015-0283-4.
[6] Dinno, A. (2024). Paran: Horn’s test of principal components/factors. doi: 10.32614/CRAN.package.paran.
[7] Dirmeyer, P.A., Niyogi, D., de Noblet‐Ducoudré, N., Dickinson, R.E., & Snyder, P.K. (2010). Impacts of land use change on climate. International Journal of Climatology, 30(13), 1905-1907. doi: 10.1002/joc.2157.
[8] Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R package for exploring spatial heterogeneity using geographically weighted models. Journal of Statistical Software, 63(17), 1-50. doi: 10.18637/jss.v063.i17.
[9] Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717-1736. doi: 10.1080/13658816.2011.554838.
[10] Hendri, E.P., & Fadhlia, S. (2024). Times series data analysis: The Holt-Winters model for rainfall prediction in West Java. International Journal of Applied Mathematics, Sciences, and Technology for National Defense, 2(1), 1-8. doi: 10.58524/app.sci.def.v2i1.325.
[11] Hernández-Rodríguez, M., Romo-Lozano, J.L., Barrios-Puente, G., & Cuevas-Alvarado, C.M. (2023). Climate change and its effects on agriculture in Mexico. Agrociencia, 57(2). doi: 10.47163/agrociencia.v57i2.2523.
[12] Horn, J.L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185. doi: 10.1007/BF02289447.
[13] Hristov, J., et al. (2020). Analysis of climate change impacts on EU agriculture by 2050. Luxembourg: Publications Office of the European Union. doi: 10.2760/121115.
[14] Kaiser, H.F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. doi: 10.1007/BF02291575.
[15] Kyyak, V., Bilonoha, V., & Kyyak, N. (2023). Spatial and functional structure of the population area in plants – the need for differentiation. Studia Biologica, 17(4), 173-186. doi: 10.30970/sbi.1704.740.
[16] Lahmer, W., Pfützner, B., & Becker, A. (2001). Assessment of land use and climate change impacts on the mesoscale. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8), 565-575. doi: 10.1016/S1464-1909(01)00051-X.
[17] Leng, G., & Hall, J.W. (2020). Predicting spatial and temporal variability in crop yields: An inter-comparison of machine learning, regression and process-based models. Environmental Research Letters, 15(4), article number 044027. doi: 10.1088/1748-9326/ab7b24.
[18] Lobachevska, O., & Karpinets, L. (2024). Water exchange of the forest ecosystems epigeic bryophytes depending on changes of the structural and functional organization of their turfs and the influence of local growth environmental conditions. Studia Biologica, 18(2), 139-156. doi: 10.30970/sbi.1802.766.
[19] Maier, M.J. (2022). REdaS: Companion package to the book ‘R: Einführung durch angewandte Statistik’. doi: 10.32614/CRAN.package.REdaS.
[20] Mitchell, B.G., Brody, E.A., Yeh, E.-N., Mcclain, C., Comiso, J., & Maynard, N.G. (1991). Meridional zonation of the Barents Sea ecosystem inferred from satellite remote sensing and in situ bio-optical observations. Polar Research, 10(1), 147-162. doi: 10.3402/polar.v10i1.6734.
[21] Moteva, M., Gigova, A., Mitova, T., Tanaskovik, V., Kabranova, R., Dimov, Z., & Krużel, J. (2018). Rapeseed (Brassica napus, L.) – biological requirements, growing conditions and need for irrigation. Journal of Agricultural, Food and Environmental Sciences, 72(1), 183-191. doi: 10.55302/JAFES18721183m.
[22] Nguyen, T.T., Ngo, H.H., Guo, W., Wang, X.C., Ren, N., Li, G., Ding, J., & Liang, H. (2019). Implementation of a specific urban water management – Sponge City. Science of the Total Environment, 652, 147-162. doi: 10.1016/j.scitotenv.2018.10.168.
[23] Petrychenko, V., Korniychuk, O., & Voronetska, I. (2018). Biological farming in conditions of transformational changes in the agrarian production of Ukraine. Agricultural Science and Practice, 5(2), 3-12. doi: 10.15407/agrisp5.02.003.
[24] Roy, P.S., Ramachandran, R.M., Paul, O., Thakur, P.K., Ravan, S., Behera, M.D., Sarangi, C., & Kanawade, V.P. (2022). Anthropogenic land use and land cover changes – a review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing, 50(8), 1615-1640. doi: 10.1007/s12524-022-01569-w.
[25] Sang, Y.-F., Fu, Q., Singh, V.P., Sivakumar, B., Zhu, Y., & Li, X. (2020). Does summer precipitation in China exhibit significant periodicities? Journal of Hydrology, 581, article number 124289. doi: 10.1016/j.jhydrol.2019.124289.
[26] Sposaro, M.M., Berry, P.M., Sterling, M., Hall, A.J., & Chimenti, C.A. (2010). Modelling root and stem lodging in sunflower. Field Crops Research, 119(1), 125-134. doi: 10.1016/j.fcr.2010.06.021.
[27] Stephens, C., & Ngari, A. (2024). Regional climate drivers, trends and forecast change. In A. Dansie, H.K. Alleway & B. Böer (Eds.), The water, energy, and food security nexus in Asia and the Pacific (pp. 109-128). Cham: Springer. doi: 10.1007/978-3-031-25463-5_5.
[28] Tacarindua, C.R.P., Shiraiwa, T., Homma, K., Kumagai, E., & Sameshima, R. (2013). The effects of increased temperature on crop growth and yield of soybean grown in a temperature gradient chamber. Field Crops Research, 154, 74-81. doi: 10.1016/j.fcr.2013.07.021.
[29] The R Project for Statistical Computing. (n.d.). Retrieved from https://www.r-project.org/.
[30] Tripathi, R.P., & Mishra, R.K. (1986). Wheat root growth and seasonal water use as affected by irrigation under shallow water table conditions. Plant and Soil, 92(2), 181-188. doi: 10.1007/BF02372632.
[31] WorldClim. (n.d.). Retrieved from https://www.worldclim.org/.