Influence of soil properties on crop yield: a multivariate statistical approach
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Department of Soil Science and Water Management, Corvinus University of Budapest, 29-43 Villányi St., H-Budapest, Hungary
Department of Physical Geography and Geoinformatics, University of Debrecen, 1 Egyetem tér, H-4032 Debrecen, Hungary
Department of Biometrics and Agricultural Informatics, Corvinus University of Budapest, 29-43 Villányi St., H-Budapest, Hungary
Publication date: 2015-10-29
Int. Agrophys. 2015, 29(4): 433-440
The aim of the study was to reveal the relationship between soil properties and grain yields in an East Hungarian region in regard to weather conditions. Soil pH, EC, carbonate content, soluble and exchangeable Na+, texture, organic carbon, and nutrient contents were analyzed. Yield data (maize, winter wheat, sunflower) from 10 years were standardized using calculated relative yield and yield variability. Weather conditions were characterized by the Pálfai Drought Index. Hydrological and topographical conditions were characterized by the mean altitude of plots. The ranged pedological variables were analyzed using principal component analysis with Varimax rotation. The principal component analysis showed that three principal components with eigenvalues greater than one explained more than 84% of the variability of soil properties. The multiple stepwise principal regression analysis showed that the mean relative yield was linearly correlated with all the three principal component factors (R2 = 0.49, p < 0.01). In droughty years, the sodification, salinization, soil texture, and nutrient contents determined the yields (R2 = 0.30, p < 0.05). In humid years, the lower topographical position, soil organic matter, and nutrient contents were the main limiting factors (R2 = 0.40, p < 0.01). Consequently, the variables can effectively explain the yield variability together with other variables as linear combinations.
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