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      Organic farming enhances soil microbial abundance and activity—A meta-analysis and meta-regression

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          Abstract

          Population growth and climate change challenge our food and farming systems and provide arguments for an increased intensification of agriculture. A promising option is eco-functional intensification through organic farming, an approach based on using and enhancing internal natural resources and processes to secure and improve agricultural productivity, while minimizing negative environmental impacts. In this concept an active soil microbiota plays an important role for various soil based ecosystem services such as nutrient cycling, erosion control and pest and disease regulation. Several studies have reported a positive effect of organic farming on soil health and quality including microbial community traits. However, so far no systematic quantification of whether organic farming systems comprise larger and more active soil microbial communities compared to conventional farming systems was performed on a global scale. Therefore, we conducted a meta-analysis on current literature to quantify possible differences in key indicators for soil microbial abundance and activity in organic and conventional cropping systems. All together we integrated data from 56 mainly peer-reviewed papers into our analysis, including 149 pairwise comparisons originating from different climatic zones and experimental duration ranging from 3 to more than 100 years. Overall, we found that organic systems had 32% to 84% greater microbial biomass carbon, microbial biomass nitrogen, total phospholipid fatty-acids, and dehydrogenase, urease and protease activities than conventional systems. Exclusively the metabolic quotient as an indicator for stresses on microbial communities remained unaffected by the farming systems. Categorical subgroup analysis revealed that crop rotation, the inclusion of legumes in the crop rotation and organic inputs are important farming practices affecting soil microbial community size and activity. Furthermore, we show that differences in microbial size and activity between organic and conventional farming systems vary as a function of land use (arable, orchards, and grassland), plant life cycle (annual and perennial) and climatic zone. In summary, this study shows that overall organic farming enhances total microbial abundance and activity in agricultural soils on a global scale.

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          An extraction method for measuring soil microbial biomass C

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            Soil biodiversity and soil community composition determine ecosystem multifunctionality.

            Biodiversity loss has become a global concern as evidence accumulates that it will negatively affect ecosystem services on which society depends. So far, most studies have focused on the ecological consequences of above-ground biodiversity loss; yet a large part of Earth's biodiversity is literally hidden below ground. Whether reductions of biodiversity in soil communities below ground have consequences for the overall performance of an ecosystem remains unresolved. It is important to investigate this in view of recent observations that soil biodiversity is declining and that soil communities are changing upon land use intensification. We established soil communities differing in composition and diversity and tested their impact on eight ecosystem functions in model grassland communities. We show that soil biodiversity loss and simplification of soil community composition impair multiple ecosystem functions, including plant diversity, decomposition, nutrient retention, and nutrient cycling. The average response of all measured ecosystem functions (ecosystem multifunctionality) exhibited a strong positive linear relationship to indicators of soil biodiversity, suggesting that soil community composition is a key factor in regulating ecosystem functioning. Our results indicate that changes in soil communities and the loss of soil biodiversity threaten ecosystem multifunctionality and sustainability.
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              Improved tests for a random effects meta-regression with a single covariate.

              The explanation of heterogeneity plays an important role in meta-analysis. The random effects meta-regression model allows the inclusion of trial-specific covariates which may explain a part of the heterogeneity. We examine the commonly used tests on the parameters in the random effects meta-regression with one covariate and propose some new test statistics based on an improved estimator of the variance of the parameter estimates. The approximation of the distribution of the newly proposed tests is based on some theoretical considerations. Moreover, the newly proposed tests can easily be extended to the case of more than one covariate. In a simulation study, we compare the tests with regard to their actual significance level and we consider the log relative risk as the parameter of interest. Our simulation study reflects the meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis originally discussed in Berkey et al. The simulation study shows that the newly proposed tests are superior to the commonly used test in holding the nominal significance level. Copyright 2003 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                12 July 2017
                2017
                : 12
                : 7
                : e0180442
                Affiliations
                [1 ] Department of Soil Sciences, Research Institute of Organic Agriculture (FiBL), Frick, Switzerland
                [2 ] Karl-Glöckner-Str. 21 C, Justus-Liebig University Giessen, Giessen, Germany
                [3 ] Department of Soil Quality, Wageningen University, Wageningen, The Netherlands
                USDA Agricultural Research Service, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: ML AG PM SS.

                • Data curation: ML AG.

                • Formal analysis: ML AG.

                • Funding acquisition: AG PM.

                • Investigation: ML.

                • Methodology: ML AG PM.

                • Project administration: ML AG SS.

                • Resources: ML AG PM.

                • Supervision: AG SS PM.

                • Validation: AG.

                • Visualization: ML AG.

                • Writing – original draft: ML.

                • Writing – review & editing: ML SS AG GD PM.

                Author information
                http://orcid.org/0000-0001-5385-9159
                Article
                PONE-D-16-50213
                10.1371/journal.pone.0180442
                5507504
                28700609
                71ebd57b-fdd0-4d6e-b329-bac2bab3e497
                © 2017 Lori et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 December 2016
                : 15 June 2017
                Page count
                Figures: 4, Tables: 2, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                This study was funded by the Swiss Science Foundation (40FA40_158392, www.snf.ch) through the Eco-serve project funded under the 2013–2014 BiodivERsA/FACCE‐JPI joint call ( http://www.biodiversa.org/578) for research proposals on “Promoting synergies and reducing trade-offs between food supply, biodiversity and ecosystem services“. The authors acknowledge Horizon 2020 project iSQAPER “Interactive Soil Quality Assessment in Europe and China for Agricultural Productivity and Environmental Resilience” for support. GB De Deyn acknowledges NWO-ALW Vidi for the support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Agriculture
                Agricultural Methods
                Organic Farming
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Meta-Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Meta-Analysis
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Ecology and Environmental Sciences
                Soil Science
                Agricultural Soil Science
                Biology and Life Sciences
                Agriculture
                Agrochemicals
                Fertilizers
                Biology and Life Sciences
                Agriculture
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Biology and Life Sciences
                Organisms
                Plants
                Legumes
                Biology and Life Sciences
                Agriculture
                Agricultural Methods
                Custom metadata
                All relevant data are within the paper and its Supporting Information file.

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                Uncategorized

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