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水稻生产系统中的土壤质量评估:建立最小数据集。

Soil quality assessment in rice production systems: establishing a minimum data set.

作者信息

Rodrigues de Lima Ana Cláudia, Hoogmoed Willem, Brussaard Lijbert

机构信息

Wageningen Univ., Farm Technology Group, Wageningen, The Netherlands.

出版信息

J Environ Qual. 2008 Mar-Apr;37(2):623-30. doi: 10.2134/jeq2006.0280.

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.

摘要

土壤质量作为衡量土壤功能的指标,可以通过基于物理、化学和生物学特性的指标来评估。在此,我们报告了巴西南里奥格兰德州卡马夸地区四种土壤质地类型上,三种水稻生产系统(半直播、催芽和传统)下21块稻田土壤质量的评估情况。我们研究的目的是:(i)确定能够区分管理系统和土壤质地类型的土壤质量指标;(ii)建立土壤质量指标的最小数据集;(iii)检验这个最小数据集是否与产量相关。评估了29种土壤生物学、化学和物理特性以表征区域土壤质量。土壤质量评估基于因子分析和判别分析。容重、有效水分和微量营养素(铜、锌和锰)是区分不同土壤质地类型最有效的土壤特性。有机质、蚯蚓、微量营养素(铜和锰)以及平均重量直径是评估水稻管理系统间土壤质量差异最有效的土壤特性。锰是与产量相关性最强的特性(调整后的r2 = 0.365,P = 0.001)。特别讨论了根据质地细分样本的优点以及土壤质量指标、土壤功能、作物表现和土壤管理选项之间的联系。

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