Predictive model of soil molecular microbial biomass

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• 作者：Walid Horrigue^a ; ^b ; Samuel Dequiedt^a ; Nicolas Chemidlin Pré ; vost-Bouré ; ^c ; Claudy Jolivet^d ; Nicolas P.A. Saby^d ; Dominique Arrouays^d ; Antonio Bispo^e ; Pierre-Alain Maron^b ; Lionel Ranjard^b ; ^{ranjard@dijon.inra.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Predictive modeling ; Molecular microbial biomass ; Bioindicator ; Polynomial model ; Land use ; Diagnostic
• 刊名：Ecological Indicators
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：64
• 期：Complete
• 页码：203-211
• 全文大小：922 K

文摘

Preservation and sustainable use of soil biological communities represent major challenges in the current agroecological context. However, to identify the agricultural practices/systems that match with these challenges, innovative tools have to be developed to establish a diagnosis of the biological status of the soil. Here, we have developed a statistical polynomial model to predict the molecular biomass of the soil microbial community according to the soil physicochemical properties. For this, we used a dataset of soil molecular microbial biomass estimates and pedoclimatic properties derived from analyses of samples collected in the context of the “French monitoring soil quality network = Réseau de Mesures de la qualité des Sols” (RMQS). This sampling network has provided 2115 soil samples covering the range of variability of soil type and land use at the scale of France. The best model obtained from the data showed that soil organic carbon content, clay content, altitude, and pH were the best explanatory variables of soil microbial biomass while other variables such as longitude, latitude and annual temperature were negligeable. Based on these variables, the multilinear model developed allowed very accurate prediction of the soil microbial biomass, with an excellent adjusted coefficient of determination class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S1470160X15007062&_mathId=si1.gif&_user=111111111&_pii=S1470160X15007062&_rdoc=1&_issn=1470160X&md5=2c859c00b5173d527741b18214b53f46">class="imgLazyJSB inlineImage" height="19" width="25" alt="View the MathML source" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S1470160X15007062-si1.gif">class="mathContainer hidden">class="mathCode">$R_{\text{adj}}^2$ of 0.6772 (P < 10⁻³). In addition to class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S1470160X15007062&_mathId=si1.gif&_user=111111111&_pii=S1470160X15007062&_rdoc=1&_issn=1470160X&md5=2c859c00b5173d527741b18214b53f46">class="imgLazyJSB inlineImage" height="19" width="25" alt="View the MathML source" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S1470160X15007062-si1.gif">class="mathContainer hidden">class="mathCode">$R_{\text{adj}}^2$, the model was further validated by results from cross validation and sensitivity analyses. The model provides a reference value for microbial biomass for a given pedoclimatic condition, which can then be compared with the corresponding measured data to provide for the first time a robust diagnosis of soil quality. Application of the model to a set of soil samples obtained at the scale of an agricultural landscape is presented and discussed, showing the suitability of the model to diagnose of the impact of particular agricultural practices such as tillage and catch crops in field conditions, at least over the French nation.