Analyzing cereal and grain legumes (pulses) yields patterns in the forest and forest-steppe zones of Ukraine using geographically weighted principal components analysis

Anastasiia ZYMAROIEVA, Oleksandr ZHUKOV

Abstract


This paper aims to explore spatial heterogeneity present in the crop yields data collected from 170 administrative districts in the forest and forest-steppe zones of Ukraine for 27 years using the PCA and GWPCA methods. As a result of the principal component analysis of cereal and grain legumes (pulses) yields variability seven principal components were determined which together explain 66.8 % of the overall yields variability. The global PCA revealed the presence of dynamic processes of the cereal and grain legumes yields variation which have the oscillatory nature with different frequencies. We associate oscillatory processes of the varying frequency with causes of a different nature. The oscillating processes with a period of ten years or more may be of climatic origin. The oscillatory process with the longest period (13 years) is characteristic for the principal component 1, which explains the largest part of cereal and grain legumes yields variability (22.6 %). It is possible to assume that among agroecological factors climate change mostly affects crop productivity. The cluster analysis of administrative districts was conducted based on the cereal and leguminous yield dynamics. The clusters are geographically defined administrative districts that together forming spatially connected areas, which we identified as agroecological zones.


Keywords


yield; cereals; leguminous crops; spatial and temporal variability; geographically weighted principal components analysis

Full Text:

PDF

References


Anselin, L.,Syabri, I., Kho, Y. (2005). GeoDa: An Introduction to Spatial Data Analysis. Geographical Analysis, 38 (1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x

Comber, A.J., Harris, P., Tsutsumida, N. (2016). Improving land cover classification using input variables derived from a geographically weighted principal components analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 347-360. ISSN0924-2716. https://doi.org/10.1016/j.isprsjprs.2016.06.014

Filho, O. G., Vieira, S. R., Chiba, M. K., Nagumo, C. H., Dechen, S. C. F. (2010). Spatial and temporal variability of crop yield and some Rhodic Hapludox properties under no-tillage. Revista Brasileira de Ciência do Solo, 34 (1). http://dx.doi.org/10.1590/S0100-06832010000100001

Fotheringham, A.S., Brunsdon, C.,Charlton, M. (2002). Geographically Weighted Regression the analysis of spatially varying relationships. Wiley, Chichester, 284 p. ISBN: 978-0-471-49616-8.

Frieler, K., Schauberger, B., Arneth, A., Balkovi, J., Chryssanthacopoulos, J., Deryng, D., … Leverman, A. (2017). Understanding the weather signal in national crop-yield variability. Earth’s Future, 5, 605-616. http://dx.doi.org/10.1002/2016EF000525

Gollini, I., Lu, B., Charlton, M., Brunsdon, Ch., Harris, P. (2013). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(17), 1–52.https://doi.org/10.18637/jss.v063.i17

Hammond, M. P. & Kolasa, J. (2014). Spatial variation as a tool for inferring temporal variation and diagnosing types of mechanisms in ecosystems. PloS one, 9(2), e89245. https://doi.org/10.1371/journal.pone.0089245

Harris, P., Brunsdon, C. & Charlton, M. (2011). Geographically Weighted Principal Components Analysis. International Journal of Geographical Information Science. 25(10), 1717–1736.

http://dx.doi.org/10.1080/13658816.2011.554838.

Harris, P., Clarke, A., Juggins, S., Brunsdon, C., Charlton, M. (2015). Enhancements to a Geographically Weighted Principal Component Analysis in the Context of an Application to an Environmental Data Set. Geographical analysis, 47 (2), 146-172. http://dx.doi.org/10.1111/gean.12048

Hatzinger, R., Hornik, K., Nagel, H., Maier, M. J. (2014). R: Einführung durch angewandte Statistik (2nd ed.). München: Pearson Studium.

Horn, J.L. (1965). A rationale and a test for the number of factors in factor analysis. Psychometrika, 30, 179–185. http://dx.doi.org/10.1007/BF02289447

Iqbal, J., Thomasson, J.A., Jenkins, J.N., Owens, P.R., Whisler, F .D. (2005). Spatial variability analysis of soil physical properties of alluvial soils. Soil Science Society America journal, 69(4), 1338-1350. http://dx.doi.org/10.2136/sssaj2004.0154

Kaiser, H. F. (1974). An Index of Factorial Simplicity. Psychometrika, 39 (1), 31–36.https://doi.org/10.1007/BF02291575

Kaspari, M. & Yanoviak, S. (2009). Biogeochemistry and the Structure of Tropical Brown Food Webs. Ecology, 90, 3342–51.https://doi.org/10.1890/08-1795.1

Khosla, R., Fleming, K., Delgado, J.A., Shaver, T., Westfall, D.G. (2002). Use site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation, 57, 513-518.

Koenker, R. & Bassett,G. Jr. (1978). Regression Quantiles. Econometrica, 46 (1), 33-49.

Koenker, R. & Bassett,G. Jr. (1982). Robust Tests for Heteroscedasticity Based on Regression Quantiles Econometrica, 50 (1), 43-61.https://doi.org/10.2307/1913643

Kong, L.Q., Zheng, H., Rao, E.M., Xiao, Y., Ouyang, Z.Y., Li, C. (2018). Evaluating indirect and direct effects of eco-restoration policy on soil conservation service in Yangtze River Basin. Science of the total environment, 631-632, 887-894. http://dx.doi.org/10.1016/j.scitotenv.2018.03.117

Kumar, S., Lal, R., Lloyd, C. D. (2012). Assessing spatial variability in soil characteristics with geographically weighted principal components analysis. Computational Geosciences,16 (3), 827-835. http://dx.doi.org/10.1007/s10596-012-9290-6

Lauzon, J. D., Fallow, D. J., O’Halloran, O. P., Gregory, S. D. L., Bertoldi, A. P. (2005). Assessing the temporal stability of spatial patterns in crop yields using combine yield monitor data. Canadian journal of soil science, 85(3), 439-451. https://doi.org/10.4141/S04-067

Lazarenko, P.I. (1995). Ecological and biological bases of agricultural zoning areas (Dnipropetrovsk region as an example). Kyiv, 476 p.

Legendre, P. & Gallagher, E. (2001). Ecological Meaningful Transformations for Ordination of Species Data. Oecologia, 129, 271–80.https://doi.org/10.1007/s004420100716

Li, Y.S. & Huang, M.B. (2008). Pasture yield and soil water depletion of continuous growing alfalfa in the Loess Plateau of China. Agriculture, Ecosystem & Environment, 124(1–2), 24–32. https://doi.org/10.1016/j.agee.2007.08.007

Li, Z.,Cheng, J., Wu, Q. (2015). Analyzing regional economic development patterns in a fast-developing province of China through geographically weighted principal components analysis. Letters in Spatial and Resource Science, 9 (3), 233-245. https://doi.org/10.1007/s12076-015-0154-

Liu, X., Zhu, X. H., Qiu, P., Chen, W. (2012). Correlation-Matrix-Based Hierarchical Clustering Method for Functional Connectivity Analysis. Journal of Neuroscience Methods, 211 (1), 94-102. http://dx.doi.org/10.1016/j.jneumeth.2012.08.016

Lloyd, C.D. (2010). Analysing population characteristics using geographically weighted principal components analysis: a case study of Northern Ireland in 2001. Computers, Environment and Urban System, 34(5), 389–399. https://doi.org/10.1016/j.compenvurbsys.2010.02.005

Lobell, D. B., Hammer, G. L., McLean, C., Roberts, M. J., Schlenker, W. (2013). The critical role of extreme heat for maize production in the United States. Nature Climat Change, 3(5), 497–501. https://doi.org/10.1038/nclimate1832

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2, 17–23. https://doi.org/10.2307/2332142

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(7–12), 559–572. https://doi.org/10.1080/14786440109462720

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrived from URL https://www.R-project.org/

State Statistics Service of Ukraine. Retrieved from http://www.ukrstat.gov.ua/

Zhukov, O. V. & Ponomarenko, S. V. (2018). Spatial-time dynamics of cereals of grain and grain crops in Poltava region. Bulletin of Poltava State Agrarian Academy, 1, 55–62.

Zhukov, O.V., Pelina, T.O., Demchuk, O. M., Demchuk, N. I., Koberniuk, S.O. (2018). Agroecological and agroeconomic aspects of the grain and grain legumes (pulses) yield dynamic within the Dnipropetrovsk region (period 1966-2016). Biosystems Diversity, 26(2), 3–10. ttps://doi.org/10.15421/011826

Zymaroieva, A., Zhukov, O., Fedonyuk, T., Pinkin, A. (2019 a). 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. https://doi.org/10.15159/AR.19.208

Zymaroieva, A., Zhukov O., Romanchuck, L., Pinkin A. (2019 b). Spatiotemporal dynamics of cereals grains and grain legumes yield in Ukraine. Bulgarian Journal of Agricultural Science, 25 (6), 1107–1113.

Zymaroieva, A., Zhukov, O., Romanchuck, L. (2020a). 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. https://doi.org/10.5513/JCEA01/21.2.2402

Zymaroieva A., Zhukov O., Fedonyuk T., Pinkina T. (2020b). The spatio-temporal trend of rapeseed yields in Ukraine as a marker of agro-economic factors influence. Agronomy Research, 18(S2), 1584–1596. https://doi.org/10.15159/AR.20.119




DOI: http://dx.doi.org/10.14720/aas.2020.116.2.873

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Anastasiia Zymaroieva, Oleksandr Zhukov

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

 

Acta agriculturae Slovenica is an Open Access journal published under the terms of the Creative Commons CC BY-NC-ND 4.0 License.

                            


eISSN 1854-1941