Explore the influence of soil quality on crop yield using statistically-derived pedological indicators

The aim of this paper is (1) to find and discuss the best multivariate statistical method in exploring the soil productivity function in an East-Hungarian region; (2) to evaluate and interpret the edaphic indicators and Hungarian soil quality index (HSQI); and (3) to identify the main determinant fa...

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Bibliographic Details
Published in:Ecological indicators 2016-04, Vol.63, p.366-373
Main Authors: Juhos, Katalin, Szabó, Szilárd, Ladányi, Márta
Format: Article
Language:English
Subjects:
Chernozems
clay
corn
crop quality
crop yield
Crops
data collection
Ecology
Eigenvalues
Helianthus
Helianthus annuus
humus
Indicators
linear models
Maize
multivariate analysis
phosphorus
polymerase chain reaction
potassium
principal component analysis
Principal component regression analysis
Regression analysis
sodium
Soil (material)
soil pH
soil productivity
Soil productivity function
soil quality
Soil quality index
Solonetz
Statistical methods
Triticum aestivum
Variance
Winter wheat
Zea mays
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Summary:The aim of this paper is (1) to find and discuss the best multivariate statistical method in exploring the soil productivity function in an East-Hungarian region; (2) to evaluate and interpret the edaphic indicators and Hungarian soil quality index (HSQI); and (3) to identify the main determinant factors and indicators in this region. Soil pH, carbonate content, soluble and exchangeable Na+, clay, humus, available phosphorus and potassium content were analyzed. Topographical position and HSQI were evaluated as well. Yield data (maize, winter wheat, sunflower) of 10 years were standardized using calculated relative yield of each crop. Having simple indicators, stepwise linear regressions for mean relative yield were inadequate for choice uncorrelated indicators which have significant influence on yields. The variables were analyzed using principal component analysis (PCA) with Varimax rotation. According to the eigenvalues greater than 1, the PCA yielded three principal components (PCs) explaining a total of 89.471% of the variance for the entire data set. These factors could be well interpreted as derived complex indicators. Having the three PCs, a stepwise linear regression process (PCR) was conducted with dependent variables mean relative yield. The explained variance for mean relative yield was as high as adjusted R2=0.771 (p
ISSN:1470-160X
1872-7034
DOI:10.1016/j.ecolind.2015.12.029