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Litter decomposition affected by climate and litter quality - Testing the Yasso model with litterbag data from the Canadian intersite decomposition experiment

机译:受气候和垃圾质量影响的垃圾分解-使用来自加拿大站点间分解实验的垃圾袋数据测试Yasso模型

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摘要

Litterbag experiments provide valuable data for testing the accuracy of predictions of decomposition from soil carbon models. The soil carbon model Yasso describes litter decomposition based on basic climate and litter quality information, and was calibrated using European litterbag data. In this study, we tested the predictive capabilities of Yasso using independent litterbag data for 10 foliage litter types decomposed for 6 years at 18 upland forest sites across Canada (CIDET). The model underestimated mass of leaf litters remaining on CIDET sites, with only a small systematic error in predicting the effects of climate when effective temperature sum was used as the temperature variable in the model. The overall rate of decomposition was predicted correctly when mean annual temperature was used as the temperature variable, but then the model substantially overestimated climatic effects. The model correctly predicted differences in decomposition rates among litter types in the early years of decomposition, but underestimated them in later years. The decomposition rate of the litter type richest in phenolic compounds (larch needles) was systematically overestimated, and that of the litter type richest in O-alkyl compounds (grass leaves) was systematically underestimated. Accounting for these factors would improve the general applicability of the model. However, accounting for the initial nitrogen concentration of litter did not improve the accuracy of the model unless the initial lignin (i.e., acid unhydrolyzable residue) content was also taken into account. We conclude that the model Yasso accounts for most of the effects of climate and initial litter quality on the decomposition of a range of foliage litter types under varying climate conditions. Recalibration of the reference decomposition rates used in the model may improve the accuracy when applying the model outside of Europe.
机译:垃圾袋实验提供了宝贵的数据,可用于测试土壤碳模型分解预测的准确性。土壤碳模型Yasso基于基本的气候和垃圾质量信息描述了垃圾分解,并使用欧洲垃圾袋数据进行了校准。在这项研究中,我们使用独立的垃圾袋数据测试了Yasso的预测能力,该数据用于在加拿大18个山地森林站点(CIDET)分解了6年的10种树叶凋落物。该模型低估了CIDET站点上残留的凋落物质量,当将有效温度总和用作模型中的温度变量时,在预测气候影响方面只有很小的系统误差。当使用年平均温度作为温度变量时,可以正确预测总分解速率,但随后该模型大大高估了气候影响。该模型正确预测了分解初期各类型凋落物的分解速率差异,但在后期却低估了它们的差异。系统地高估了酚类化合物(落叶松针)最丰富的凋落物类型的分解率,而系统地低估了O-烷基化合物(草叶)最丰富的凋落物类型的分解率。考虑这些因素将改善模型的一般适用性。但是,除非还考虑了初始木质素(即酸不可水解残基)的含量,否则计算垫料的初始氮浓度并不能提高模型的准确性。我们得出结论,亚索模型考虑了气候和初始凋落物质量在不同气候条件下对一系列树叶凋落物类型分解的大部分影响。在欧洲以外地区应用模型时,对模型中使用的参考分解率进行重新校准可能会提高准确性。

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