Department of Bioenergy and Department of Soil System Science, Helmholtz Centre for Environmental Research-UFZ, Halle, Germany.
Department of Ecology, Chair of Soil Science, Technische Universität Berlin, Berlin, Germany.
PLoS One. 2018 Oct 12;13(10):e0204121. doi: 10.1371/journal.pone.0204121. eCollection 2018.
A variety of biogas residues (BGRs) have been used as organic fertilizer in agriculture. The use of these residues affects the storage of soil organic matter (SOM). In most cases, SOM changes can only be determined in long-term observations. Therefore, predictive modeling can be an efficient alternative, provided that the parameters required by the model are known for the considered BGRs. This study was conducted as a first approach to estimating the organic matter (OM) turnover parameters of BGRs for process modeling. We used carbon mineralization data from six BGRs from an incubation experiment, representing a range of substrate inputs, to calculate a turnover coefficient k controlling the velocity of fresh organic matter (FOM) decay and a synthesis coefficient η describing the SOM creation from FOM. An SOM turnover model was applied in inverse mode to identify both parameters. In a second step, we related the parameters k and η to chemical properties of the corresponding BGRs using a linear regression model and applied them to a long-term scenario simulation. According to the results of the incubation experiment, the k values ranged between 0.28 and 0.58 d-1 depending on the chemical composition of the FOM. The estimated η values ranged between 0.8 and 0.89. The best linear relationship of k was found to occur with pH (R2 = 0.863). Parameter η is related to the Ct/Norg ratio (R2 = 0.696). Long-term scenario simulations emphasized the necessity of specific k and η values related to the chemical properties for each BGR. However, further research is needed to validate and improve these preliminary results.
各种沼气残留物 (BGR) 已被用作农业中的有机肥料。这些残留物的使用会影响土壤有机质 (SOM) 的储存。在大多数情况下,只能通过长期观察来确定 SOM 的变化。因此,预测模型可以是一种有效的替代方法,前提是模型所需的参数对于所考虑的 BGR 是已知的。本研究旨在首次估计 BGR 的有机质 (OM) 转化参数,以用于过程建模。我们使用来自一项孵育实验的六个 BGR 的碳矿化数据来计算控制新鲜有机质 (FOM) 衰减速度的转化系数 k 和描述 FOM 向 SOM 转化的合成系数 η。使用 SOM 转化模型以反演模式识别这两个参数。在第二步中,我们使用线性回归模型将参数 k 和 η 与相应 BGR 的化学性质相关联,并将它们应用于长期情景模拟。根据孵育实验的结果,k 值的范围取决于 FOM 的化学组成,在 0.28 到 0.58 d-1 之间。估计的 η 值在 0.8 到 0.89 之间。发现 k 与 pH 的最佳线性关系 (R2 = 0.863)。参数 η 与 Ct/Norg 比相关 (R2 = 0.696)。长期情景模拟强调了对于每个 BGR,需要特定的 k 和与化学性质相关的 η 值。然而,需要进一步的研究来验证和改进这些初步结果。