Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.

中文翻译：

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比较通过工业分析和 13C NMR 光谱评估的植物凋落物分子多样性


在生态系统规模模型中准确表示凋落物的化学多样性对于改善植物材料分解速率和稳定性的预测至关重要。在这篇文章中，我们进行了系统综述，以评估使用近似分析的传统植物凋落物质量表征与使用 13 C NMR 光谱的分子尺度表征的比较。使用分子混合模型，我们将 NMR 的化学位移区域转换为五类有机化合物中的碳 (C) 分数，这些有机化合物是植物材料的主要成分：碳水化合物、蛋白质、木质素、脂质和羰基化合物。我们发现酸溶部分和碳水化合物之间以及酸不溶部分和木质素之间存在正相关性。然而，酸溶部分低估了碳水化合物，而酸不溶部分则高估了木质素 243%。我们确定了两个不确定性来源：i）基于水解性的凋落物化学成分与从 NMR 获得的实际化学成分之间的差异，以及 ii）将近似分数转换为有机成分的转换因子。这两种不确定性都很重要，可能会导致垃圾分解模型中衰减率的误解。因此，我们建议在下一代凋落物分解模型中包含明确的底物化学数据。