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Diagnosis of forest soil sensitivity to harvesting residues removal - A transfer study of soil science knowledge to forestry practitioners

机译:森林土壤对收获残留物去除敏感性的诊断-土壤科学知识向林业从业者的转移研究

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摘要

Forest biomass is a source of renewable energy that can contribute to meeting international targets for reducing greenhouse gas emissions. However, removing forest harvesting residues may cause important nutrient losses. Because negative effects of increased nutrient removal are not systematic, forest managers need tools for soil sensitivity assessment, to decide whether they can or not increase biomass harvesting without impairing long term forest productivity and health. This study follows two goals: (i) define forest ecosystem sensitivity indicators derived from soil physico-chemical analyses and (ii) build and test a simplified tool that predicts such soil sensitivity. After screening international literature, nutrient concentration in the topsoil was chosen as the simplest and currently most accurate indicator of soil sensitivity. With a consolidated database on French forest soils, we built diagnostic keys that predict soil sensitivity using only five parameters: humus form, topsoil texture, depth of CaCO3 apparition, ecological region, and rooting depth. We performed a statistical evaluation of the simplified tool on independent data sets and evaluated it in the field with potential users. As compared with the existing French forest soils sensitivity indicator, our diagnosis tool displayed lower high and low sensitivities classification errors and allowed to differentiate sensitivity into five elemental ones (Ca, Mg, K, P and N). All participating end users agreed with the necessity of such indicator and appreciated the simplicity of diagnosis with our tool. This study shows a complete research and development process, from the translation of scientific knowledge into an indicator of sustainable forest management to the simplification for assimilation.
机译:森林生物量是可再生能源的来源,可有助于实现减少温室气体排放的国际目标。但是,清除森林砍伐残留物可能会导致重要的养分流失。由于增加的养分去除带来的负面影响不是系统性的,因此森林管理者需要用于土壤敏感性评估的工具,以决定他们是否可以增加生物量的收获而又不损害森林的长期生产力和健康。这项研究遵循两个目标:(i)定义从土壤理化分析得出的森林生态系统敏感性指标,以及(ii)建立和测试可预测这种土壤敏感性的简化工具。在筛选国际文献之后,表层土壤中的养分浓度被选为最简单且目前最准确的土壤敏感性指标。利用有关法国森林土壤的综合数据库,我们建立了仅使用五个参数即可预测土壤敏感性的诊断关键字:腐殖质形式,表土质地,CaCO3分布深度,生态区和生根深度。我们对简化工具的独立数据集进行了统计评估,并在野外与潜在用户进行了评估。与现有的法国森林土壤敏感性指标相比,我们的诊断工具显示出较低的高敏感度和低敏感度分类错误,并允许将敏感度区分为五种元素(钙,镁,钾,磷和氮)。所有参与的最终用户都同意这种指标的必要性,并赞赏使用我们的工具进行诊断的简便性。这项研究显示了完整的研究和开发过程,从将科学知识转化为可持续森林管理的指标到简化同化过程。

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