Regulatory mechanisms in agroecosystems: A retrospective and forecast of spatial and temporal dynamics of precipitation as a factor of crop yield
Abstract
The research tested the hypothesis that the climate of the studied area has the property of spatial and temporal regularity, and that this regularity is hierarchically organized, which makes it possible to predict the state of the climate in the coming decades. The practical aspect of the information obtained is the assessment of possible prospects for changes in the yields of the most common crops in the region. The spatial variability of precipitation between the years 1960 and 2023, soil properties and landscape cover structure were investigated within 10 administrative regions of northern and northwestern Ukraine. This region covers the Polissia and Forest-Steppe geographical zones. The MEM spatial variables were able to explain 95.1% of the variability in precipitation. ANOVA revealed that 8 canonical axes were statistically significant. The contribution of the spatial MEM variables to the explanation of the canonical axes is different, which allows us to identify the hierarchical structure of variability of the main spatial precipitation patterns in the region. The RDA1 and RDA2 axes represent the large-scale component of precipitation variability. RDA1 indicates the differentiation of precipitation patterns in the meridional direction with the allocation of the eastern and western sectors of the region. The canonical axes denoting the main spatial patterns of precipitation variability correlated with soil properties and land cover types. RDA1 did not correlate with soil properties, but had a positive correlation with the proportion of broadleaf forests and mosaic of herbaceous cover and shrubs in the landscape cover. This axis had a negative correlation with the proportion of agricultural land. RDA2 was positively correlated with soil organic matter and sand content, but negatively correlated with clay and silt content. This axis increased with an increase in the proportion of broadleaf, coniferous or mixed forests or a mosaic of herbaceous vegetation and shrubs in the landscape cover structure. RDA2 decreased with an increase in the proportion of agricultural crops or sparse vegetation cover. RDA3 was independent of soil organic matter content, but positively correlated with clay and silt content and negatively correlated with sand content. This axis was positively correlated with the proportion of agricultural area, the mosaic of herbaceous vegetation and shrubs, and negatively correlated with the proportion of coniferous or mixed forests. RDA4 was positively correlated with soil organic matter content and negatively correlated with soil silt content. This axis increased with increasing proportions of rainfed crops and sparse vegetation cover, but decreased with increasing proportions of herbaceous cover, coniferous and mixed forests. RDA5 was positively correlated with organic matter and silt content, but negatively correlated with sand content. This axis increased with increasing proportions of mosaic with crops, but decreased with increasing proportions of coniferous and mixed forests. RDA6 was positively correlated with silt content but negatively correlated with sand content. This axis increased with increasing proportions of agricultural crops, but decreased with increasing proportions of broadleaf or mixed forests. RDA7 was positively correlated with silt and clay content, but negatively correlated with organic matter and sand content. This axis was positively correlated with the proportion of agricultural land and negatively correlated with the proportion of broadleaf, coniferous and mixed forests. RDA8 was positively correlated with the silt content of the soil. This axis was positively correlated with the proportion of agricultural land and negatively correlated with the proportion of coniferous and mixed forests. The temporal modelling of precipitation dynamics over more than 60 years can be carried out using eight AEM predictors, which represent temporal patterns of different frequencies and variable amplitudes over time. If we assume that the established oscillatory dynamics will continue in the coming decades, then these AEM predictors can be extended for the time of interest and a regression model can be used to obtain a forecast of precipitation dynamics in the near future. The forecast indicates a downward trend in precipitation, mainly in areas with the most developed agriculture.References
Augustine, D. J. (2010). Spatial versus temporal variation in precipitation in a semiarid ecosystem. Landscape Ecology, 25(6), 913–925.
Baho, D. L., Futter, M. N., Johnson, R. K., & Angeler, D. G. (2015). Assessing temporal scales and patterns in time series: Comparing methods based on redundancy analysis. Ecological Complexity, 22, 162–168.
Benziane, S. (2024). Survey: Rainfall prediction precipitation, review of statistical methods. Wseas Transactions on Systems, 23, 47–59.
Blanchet, F. G., Legendre, P., & Borcard, D. (2008). Modelling directional spatial processes in ecological data. Ecological Modelling, 215(4), 325–336.
Blyth, E. M., Evans, J. G., Finch, J. W., Bantges, R., & Harding, R. J. (2006). Spatial variability of the English agricultural landscape and its effect on evaporation. Agricultural and Forest Meteorology, 138(1–4), 19–28.
Bougara, H., Hamed, K. B., Borgemeister, C., Tischbein, B., & Kumar, N. (2020). Analyzing trend and variability of rainfall in the Tafna basin (Northwestern Algeria). Atmosphere, 11(4), 347.
Butler, E. E., & Huybers, P. (2013). Adaptation of US maize to temperature variations. Nature Climate Change, 3(1), 68–72.
Charles, A. C., Armstrong, A., Nnamdi, O. C., Innocent, M. T., Obiageri, N. J., Begianpuye, A. F., & Timothy, E. E. (2024). Review of spatial analysis as a geographic information management tool. American Journal of Engineering and Technology Management, 9(1), 8–20.
Chen, J., Franklin, J. F., & Lowe, J. S. (1996). Comparison of abiotic and structurally defined patch patterns in a hypothetical forest landscape. Conservation Biology, 10(3), 854–862.
Chen, J., Franklin, J. F., & Spies, T. A. (1993). Contrasting microclimates among clearcut, edge, and interior of old-growth Douglas-fir forest. Agricultural and Forest Meteorology, 63(3–4), 219–237.
Claußnitzer, A., & Névir, P. (2009). Analysis of quantitative precipitation forecasts using the Dynamic State Index. Atmospheric Research, 94(4), 694–703.
Dorninger, M., Schneider, S., & Steinacker, R. (2008). On the interpolation of precipitation data over complex terrain. Meteorology and Atmospheric Physics, 101(3–4), 175–189.
Dray, S., Legendre, P., & Peres-Neto, P. R. (2006). Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196(3–4), 483–493.
Ellouze, M., Azri, C., & Abida, H. (2009). Spatial variability of monthly and annual rainfall data over Southern Tunisia. Atmospheric Research, 93(4), 832–839.
Emmanuel, I., Andrieu, H., Leblois, E., & Flahaut, B. (2012). Temporal and spatial variability of rainfall at the urban hydrological scale. Journal of Hydrology, 430–431, 162–172.
Forman, R. (1995). Land mosaics. The ecology of landscapes and regions. Cambridge University Press, Cambridge.
Guimarães Nobre, G., Hunink, J. E., Baruth, B., Aerts, J. C. J. H., & Ward, P. J. (2019). Translating large-scale climate variability into crop production forecast in Europe. Scientific Reports, 9(1), 1277.
Harbar, O., Moroz, V., Harbar, D., Vyskushenko, D., & Kratiuk, O. (2024). Spatial variation of earthworm communities in the motorway proximity. Studia Biologica, 18(2), 157–168.
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.
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, 1(1), 1–14.
Hird, J. N., & McDermid, G. J. (2009). Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113(1), 248–258.
Jonsson, P., & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1824–1832.
Kassaye, S. M., Tadesse, T., Tegegne, G., & Tadesse, K. E. (2022). The sensitivity of meteorological dynamics to the variability in catchment characteristics. Water, 14(22), 3776.
Kraan, C., Dormann, C. F., Greenfield, B. L., & Thrush, S. F. (2015). Cross-scale variation in biodiversity-environment links illustrated by coastal sandflat communities. PLoS One, 10(11), e0142411.
Kukol, K., Pukhtaievych, P., Vorobey, N., & Kots, T. (2024). Impact of biological fungicides on the formation and functioning of symbiotic system soybean–Bradyrhizobium japonicum. Studia Biologica, 18(2), 97–110.
Kunah, O. M., Pakhomov, O. Y., Zymaroieva, А. А., Demchuk, N. I., Skupskyi, R. M., Bezuhla, L. S., & Vladyka, Y. P. (2018). Agroeconomic and agroecological aspects of spatial variation of rye (Secale cereale) yields within Polesia and the Forest-Steppe zone of Ukraine: The usage of geographically weighted principal components analysis. Biosystems Diversity, 26(4), 276–285.
Kunakh, O. M., Volkova, A. M., Tutova, G. F., & Zhukov, O. V. (2023). Diversity of diversity indices: Which diversity measure is better? Biosystems Diversity, 31(2), 131–146.
Kunakh, O. M., Yorkina, N. V., Zhukov, O. V., Turovtseva, N. M., Bredikhina, Y. L., & Logvina-Byk, T. A. (2020). Recreation and terrain effect on the spatial variation of the apparent soil electrical conductivity in an urban park. Biosystems Diversity, 28(1), 3–8.
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.
Latif, S. D., Alyaa Binti Hazrin, N., Hoon Koo, C., Lin Ng, J., Chaplot, B., Feng Huang, Y., El-Shafie, A., & Najah Ahmed, A. (2023). Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches. Alexandria Engineering Journal, 82, 16–25.
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), 044027.
Lewis-Beck, C., Walker, V. A., Niemi, J., Caragea, P., & Hornbuckle, B. K. (2020). Extracting agronomic information from SMOS vegetation optical depth in the us corn belt using a nonlinear hierarchical model. Remote Sensing, 12(5), 827.
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.
Mughal, M. O., Kubilay, A., Fatichi, S., Meili, N., Carmeliet, J., Edwards, P., & Burlando, P. (2021). Detailed investigation of vegetation effects on microclimate by means of computational fluid dynamics (CFD) in a tropical urban environment. Urban Climate, 39, 100939.
Mykhailyuk, T., Lisovets, O., & Tutova, H. (2023). The importance of terrain factors in the spatial variability of plant cover diversity in a steppe gully. Biosystems Diversity, 31(4), 470–483.
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.
Papalaskaris, T., Panagiotidis, T., & Pantrakis, A. (2016). Stochastic monthly rainfall time series analysis, modeling and forecasting in Kavala City, Greece, North-Eastern Mediterranean Basin. Procedia Engineering, 162, 254–263.
Pham, B. T., Le, L. M., Le, T.-T., Bui, K.-T. T., Le, V. M., Ly, H.-B., & Prakash, I. (2020). Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research, 237, 104845.
Qingming, W., Shan, J., Jiaqi, Z., Guohua, H., Yong, Z., Yongnan, Z., Xin, H., Haihong, L., Lizhen, W., Fan, H., & Changhai, Q. (2022). Effects of vegetation restoration on evapotranspiration water consumption in mountainous areas and assessment of its remaining restoration space. Journal of Hydrology, 605, 127259.
Roldán-Valcarce, A., Jato-Espino, D., Manchado, C., Bach, P. M., & Kuller, M. (2023). Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities. International Journal of Disaster Risk Reduction, 95, 103894.
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, 124289.
Sariş, F., Hannah, D. M., & Eastwood, W. J. (2010). Spatial variability of precipitation regimes over Turkey. Hydrological Sciences Journal, 55(2), 234–249.
Sayemuzzaman, M., & Jha, M. K. (2014). Seasonal and annual precipitation time series trend analysis in North Carolina, United States. Atmospheric Research, 137, 183–194.
Seo, S. B., Das Bhowmik, R., Sankarasubramanian, A., Mahinthakumar, G., & Kumar, M. (2019). The role of cross-correlation between precipitation and temperature in basin-scale simulations of hydrologic variables. Journal of Hydrology, 570, 304–314.
Steele, B. M., Reddy, S. K., & Nemani, R. R. (2005). A regression strategy for analyzing environmental data generated by spatio-temporal processes. Ecological Modelling, 181(2–3), 93–108.
Wagner, H. H. (2013). Rethinking the linear regression model for spatial ecological data. Ecology, 94(11), 2381–2391.
Wallis, K. F. (2014). The two-piece normal, binormal, or double gaussian distributeon: Its origin and rediscoveries. Statistical Science, 29(1), 106–112.
Wei, J., & Dirmeyer, P. A. (2019). Sensitivity of land precipitation to surface evapotranspiration: A nonlocal perspective based on water vapor transport. Geophysical Research Letters, 46(21), 12588–12597.
Wu, J., & David, J. L. (2002). A spatially explicit hierarchical approach to modeling complex ecological systems: Theory and applications. Ecological Modelling, 153(1–2), 7–26.
Zymaroieva, A. (2019a). Assessment of the climate changes impact on the productivity of maize within the Polissya and Forest Steppe ecoregions within Ukraine. Scientific Horizons, 84(11), 113–120.
Zymaroieva, A. (2019b). Spatio-temporal patterns of maize yield variation within Ukraine. Scientific Horizons, 75(2), 58–66.
Zymaroieva, A. (2019c). The perspectives of the application of geographically weighted principal components analysis for estimation of maize yields spatial variability. Scientific Horizons, 83(10), 20–27.
Zymaroieva, A., & Zhukov, O. (2020). Analyzing cereal and grain legumes (pulses) yields patterns in the forest and forest-steppe zones of Ukraine using geographically weighted principal components analysis. Acta Agriculturae Slovenica, 116(2), 287.
Zymaroieva, A., Pinkina, T., Ivanyuk, T., & Tyshkovskyy, V. (2019). Evaluation of correlation between maize yield parameters and landscape diversity indicators. Scientific Horizons, 86(1), 29–38.
Zymaroieva, A., Zhukov, O., & Romanchuck, L. (2020). The spatial patterns of long-term temporal trends in yields of soybean (Glycine max (L.) Merril) in the Central European Mixed Forests (Polissya) and East European Forest Steppe ecoregions within Ukraine. Journal of Central European Agriculture, 21(2), 320–332.
Zymaroieva, A., Zhukov, O., Fedoniuk, T., Pinkina, T., & Hurelia, V. (2021). The relationship between landscape diversity and crops productivity: Landscape scale study. Journal of Landscape Ecology, 14(1), 39–58.
Zymaroieva, A., Zhukov, O., Fedoniuk, T., Pinkina, T., & Vlasiuk, V. (2021). Edaphoclimatic factors determining sunflower yields spatiotemporal dynamics in Northern Ukraine. Oilseeds and Fats, Crops and Lipids, 28, 26.
Zymaroieva, A., Zhukov, O., Fedonyuk, T., & Pinkin, A. (2019). Application of geographically weighted principal components analysis based on soybean yield spatial variation for agro-ecological zoning of the territory. Agronomy Research, 17(6), 2460–2473.
Zymaroieva, A., Zhukov, O., Fedonyuk, T., & Pinkina, T. (2020). The spatio-temporal trend of rapeseed yields in Ukraine as a marker of agro-economic factors influence. Agronomy Research, 18(S2), 1584–1596.
Zymaroieva, A., Zhukov, O., Romanchuck, L., & Pinkin, A. (2019). Spatiotemporal dynamics of cereals grains and grain legumes yield in Ukraine. Bulgarian Journal of Agricultural Science, 25(6), 1107–1113.
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