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Assessment of land levelling effects on lowland soil quality indicators and water retention evaluated by multivariate and geostatistical analyses

Bibliographic Data

ID21648033
AuthorsLuís Carlos Timm (0000-0003-2916-8125, Department of Rural Engineering, Faculty of Agronomy Federal University of Pelotas Capão do Leão Rio Grande do Sul Brazil, corresponding author), Luiz F Pires (0000-0001-5073-5900, Universidade Estadual de Ponta Grossa), Luana Nunes Centeno (0000-0001-9398-9863, Water Resources Post‐Graduate Program, Center of Technological Development Federal University of Pelotas Pelotas Rio Grande do Sul Brazil), Dioni Glei Bonini Bitencourt (0000-0002-2837-4827, Department of Aquaculture Federal University of Pampa Uruguaiana Rio Grande do Sul Brazil), José Maria Barbat Parfitt (0000-0003-0869-0360, EMBRAPA Clima Temperado—Brazilian Agricultural Research Corporation Pelotas Rio Grande do Sul Brazil), Alexssandra Dayanne Soares de Campos (Soil and Water Management and Conservation Post‐Graduate Program, Faculty of Agronomy Federal University of Pelotas Capão do Leão Rio Grande do Sul Brazil)
Year2020
Volume31
Issue8
Pages959-974
Publication date2020-05-15
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.3529
OpenAlexW2994611184
LanguageEN
References cited50

Studies focusing on the impact of levelling on the management of available water for rice growth and on soil hydro‐physical quality indicators cultivated in lowland soils under aerobic conditions are still scarce. The objective of this study was to evaluate the levelling impact on soil water retention and soil quality indicators by multivariate and geostatistical analyses. A 10 × 10 m grid was used to sample the 0‐ to 20‐cm layer in a 1.0‐ha experimental field, before and after levelling. Sand and clay contents, macroporosity, bulk density (Bd), organic matter (OM), weighted average diameter of aggregates, and the water retention curve were measured. Available water capacity, structural stability, and S indexes and relative field capacity were calculated. Descriptive statistics, Kolmogorov–Smirnov test, and geostatistical analysis were used. Maps of all data sets significantly correlated with Bd and OM after levelling were constructed using ordinary and indicator kriging. Kaiser–Meyer–Olkin and Bartlett tests were performed for factorial and principal component analyses which were used to reduce the dimensionality of data and of main principal component maps. Levelling negatively affected the majority of soil quality indicators, caused an increase of their spatial range, and an improved the goodness of fit of the semivariogram models used. The majority of variables were best fitted by the Gaussian model after levelling. Bd and OM probability maps were found to be useful tools for farmers wanting to utilize different strategies for topsoil management, with the goal of improving soil quality of levelled areas for future land recuperation operations

Geography · Geotechnical engineering · Kriging · Levelling · Multivariate statistics · Principal component analysis · Soil Quality · Soil water · Statistics · Topsoil · Water quality · Environmental Science · Mathematics · Soil and Unsaturated Flow · Soil erosion and sediment transport · Soil Geostatistics and Mapping · Geology · Soil Science

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Highly citedNo

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