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Quantification and Prediction of Biomass Yield of Temperate Low-Input High-Diversity Ecosystems

机译：温带低投入高多样性生态系统生物量产量的量化和预测

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摘要

Little is known about the biomass production and bioenergy potential of low-input high-diversity (LIHD) systems in temperate nonforest conservation areas. In order to assess the potential of the biomass for energetic or other purposes, accurate yield data from LIHD systems are needed. We quantified the biomass yield in a wide range of seminatural systems (grasslands, marshes, tall-herb vegetation, and heathlands). Our results show a considerable variation in annual biomass yield ranging between 0.69 and 6.49 tDM ha鈭?聽year鈭?. In addition, we provide an accurate method to determine the standing stock of harvestable biomass in the field. We developed four predictive models: one multiple linear regression (MLR) model and three boosted regression tree (BRT) models: (i) a vegetation model with variables that are easy to measure in the field, (ii) a soil model with soil physical and chemical variables, and (iii) a vegsoil model with all available variables. Due to its ability to fit nonlinear response functions and threshold values, the boosted regression tree technique outperformed the classical multiple linear regression. The vegetation model is the preferred model because it combines a good predictive performance (R2adj鈥?鈥?.75 and R2adjCV鈥?鈥?.51) with a relatively simple application.
机译：在温带非森林保护区，低投入高多样性（LIHD）系统的生物量生产和生物能源潜力知之甚少。为了评估生物质用于能源或其他目的的潜力，需要来自LIHD系统的准确产量数据。我们对广泛的半自然系统（草地，沼泽，高草植物和荒地）的生物量产量进行了量化。我们的结果表明，每年的生物量产量在0.69至6.49 tDM ha·年间变化很大。此外，我们提供了一种准确的方法来确定田间可收获生物量的固定存量。我们开发了四个预测模型：一个多元线性回归（MLR）模型和三个增强回归树（BRT）模型：（i）具有易于在野外测量的变量的植被模型，（ii）具有土壤物理特性的土壤模型和化学变量，以及（iii）具有所有可用变量的蔬菜模型。由于其能够拟合非线性响应函数和阈值，因此增强回归树技术的性能优于经典多元线性回归。植被模型是首选模型，因为它具有良好的预测性能（R2adj'-。75和R2adjCV'-.. 51），并且具有相对简单的应用。

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