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Integrated Crop-Livestock Management Effects on Soil Quality Dynamics in a Semiarid Region: A Typology of Soil Change Over Time

机译:农牧业综合管理对半干旱地区土壤质量动态的影响:土壤随时间变化的类型

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摘要

Integrated crop-livestock systems can have subtle effects on soil quality over time, particularly in semiarid regions where soil responses to management occur slowly. We tested if analyzing temporal trajectories of soils could detect trends in soil quality data which were not detected using traditional statistical and index approaches. Principal component and cluster analyses were used to assess the evolution in ten soil properties at three sampling times within two production systems (annually cropped, perennial grass). Principal component 1 explained 33% of the total variance of the complete dataset and corresponded to gradients in extractable N, available P, and C: N ratio. Principal component 2 explained 25.4% of the variability and corresponded to gradients of soil pH, soil organic C, and total N. While previous analyses found no differences in Soil Quality Index (SQI) scores between production systems, annually cropped treatments and perennial grasslands were clearly distinguished by cluster analysis. Cluster analysis also identified greater dispersion between plots over time, suggesting an evolution in soil condition in response to management. Accordingly, multivariate statistical techniques serve as a valuable tool for analyzing data where responses to management are subtle or anticipated to occur slowly.
机译:随着时间的流逝,农作物-畜牧业综合系统会对土壤质量产生微妙影响,特别是在半干旱地区,土壤对管理的反应缓慢。我们测试了分析土壤的时间轨迹是否可以检测到土壤质量数据的趋势,而使用传统的统计和指数方法则无法检测到这种趋势。使用主成分和聚类分析来评估两个生产系统(一年生,多年生草)中三个采样时间的十种土壤特性的演变。主成分1解释了完整数据集总方差的33%,并对应于可提取N,可用P和C:N比的梯度。主要成分2解释了25.4%的变异性,并对应于土壤pH值,土壤有机碳和总氮的梯度。虽然先前的分析发现生产系统之间的土壤质量指数(SQI)评分没有差异,但每年种植的作物和多年生草地通过聚类分析清楚地区分。聚类分析还发现,随着时间的流逝,各样地之间的分散程度更大,这表明土壤条件随管理的发展而变化。因此,多元统计技术可作为分析数据的有价值的工具,在这些数据中,对管理的响应微妙或预期缓慢发生。

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  • 来源
    《Applied and environmental soil science》 |2017年第2017期|38-47|共10页
  • 作者单位

    Universite de Toulouse, INRA, INP-ENSAT, UMR 1248 AGIR, 31324 Castanet-Tolosan, France;

    USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND 58554-0459, USA;

    USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND 58554-0459, USA;

    USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND 58554-0459, USA;

    USDA-ARS, Northern Great Plains Research Laboratory, P.O. Box 459, Mandan, ND 58554-0459, USA;

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