JEQ Journal of Natural Resources and Life Sciences Education
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Published online 20 February 2008
Published in J Environ Qual 37:623-630 (2008)
DOI: 10.2134/jeq2006.0280
© 2008 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
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ENVIRONMENTAL ISSUES

Soil Quality Assessment in Rice Production Systems: Establishing a Minimum Data Set

Ana Cláudia Rodrigues de Limaa,b,*, Willem Hoogmoeda and Lijbert Brussaardb

a Wageningen Univ., Farm Technology Group, P.O. Box 17, 6700 AA Wageningen, The Netherlands
b Wageningen Univ., Dep. of Soil Quality, P.O. Box 47, 6700 AA Wageningen, The Netherlands

* Corresponding author (ana.lima{at}wur.nl; anacrlima{at}hotmail.com).

Received for publication July 19, 2006. ABSTRACT

Soil quality, as a measure of the soil's capacity to function, can be assessed by indicators based on physical, chemical, and biological properties. Here we report on the assessment of soil quality in 21 rice (Oryza sativa) fields under three rice production systems (semi-direct, pre-germinated, and conventional) on four soil textural classes in the Camaquã region of Rio Grande do Sul, Brazil. The objectives of our study were: (i) to identify soil quality indicators that discriminate both management systems and soil textural classes, (ii) to establish a minimum data set of soil quality indicators and (iii) to test whether this minimum data set is correlated with yield. Twenty-nine soil biological, chemical, and physical properties were evaluated to characterize regional soil quality. Soil quality assessment was based on factor and discriminant analysis. Bulk density, available water, and micronutrients (Cu, Zn, and Mn) were the most powerful soil properties in distinguishing among different soil textural classes. Organic matter, earthworms, micronutrients (Cu and Mn), and mean weight diameter were the most powerful soil properties in assessing differences in soil quality among the rice management systems. Manganese was the property most strongly correlated with yield (adjusted r2 = 0.365, P = 0.001). The merits of sub-dividing samples according to texture and the linkage between soil quality indicators, soil functioning, plant performance, and soil management options are discussed in particular.







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