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Title:   Author  item
                                            RYSCHAWY, J - Institut National De La Recherche Agronomique (INRA)item
                                            Liebig, Markitem
                                            Kronberg, Scottitem
                                            Archer, Daviditem
                                            Hendrickson, John
  Submitted to: Applied and Environmental Soil Science
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 5/22/2017
                                    Publication  Date: 6/22/2017
                                    Citation: Ryschawy, J., Liebig, M.A., Kronberg, S.L., Archer, D.W., Hendrickson, J.R. 2017. Integrated crop-livestock management effects on soil quality dynamics in a semiarid region: A typology of soil change over time. Applied and Environmental Soil Science. doi:10.1155/2017/3597416.Interpretive Summary: Traditional statistical and index-based approaches for assessing soil quality may provide a limited assessment of soil property dynamics.  Multivariate statistical analyses such as multiple factor analysis and cluster analysis provide a potentially useful method for screening a diverse collection of functionally-relevant soil properties to identify data trends.  In this study, multivariate analyses were used to assess variation in 10 soil properties measured in a long-term integrated crop-livestock experiment near Mandan, ND.  Potential data trends were evaluated across three years (2002, 2005, 2008) within two production systems (annual cropped, perennial grass) and annually cropped systems with and without grazing.  Use of multivariate statistical analyses revealed trends in data not previously detected using traditional statistical and index approaches for assessment of soil quality.  Principal component and hierarchical cluster analyses provided a helpful means to visually discriminate between annually cropped and perennial grass treatments, while concurrently identifying a clear trend toward greater dispersion in the data over time.  From a soil management perspective, outcomes from the cluster analysis underscored the value of perennial grass systems to enhance soil quality through increased accrual of soil carbon and nitrogen, while minimizing levels of nutrients susceptible to loss.  Based on the approach used in this evaluation, multivariate statistical techniques represented a valuable tool for analysis of data collected at different time periods where changes in responses variables occur slowly.
                                Technical Abstract: Integrated crop-livestock systems can have subtle effects on soil quality over time, particularly in semiarid regions where soil responses to management occur slowly.  This evaluation was conducted to determine if multivariate statistical analyses could detect important trends in soil quality data which were not detected using traditional statistical and index approaches.  Multivariate analyses (multiple factor analysis and cluster analysis) were used to assess variation in 10 soil properties at three sampling times within two production systems (annual cropped, perennial grass) and annually cropped systems with and without grazing.  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.  Multivariate analyses detected trends in data not found using traditional statistical and index approaches for assessment of soil quality.  Accordingly, multivariate statistical techniques serve as a valuable tool for analyzing data where responses to management are subtle or anticipated to occur slowly.


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