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An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes.

ABSTRACT: Quantifying soil organic carbon (SOC) changes is a fundamental issue in ecology and sustainable agriculture. However, the algorithm-derived biases in comparing SOC status have not been fully addressed. Although the methods based on equivalent soil mass (ESM) and mineral-matter mass (EMMM) reduced biases of the conventional methods based on equivalent soil volume (ESV), they face challenges in ensuring both data comparability and accuracy of SOC estimation due to unequal basis for comparison and using unconserved reference systems. We introduce the basal mineral-matter reference systems (soils at time zero with natural porosity but no organic matter) and develop an approach based on equivalent mineral-matter volume (EMMV). To show the temporal bias, SOC change rates were recalculated with the ESV method and modified methods that referenced to soils at time t1 (ESM, EMMM, and EMMV-t1) or referenced to soils at time zero (EMMV-t0) using two datasets with contrasting SOC status. To show the spatial bias, the ESV- and EMMV-t0-derived SOC stocks were compared using datasets from six sites across biomes. We found that, in the relatively C-rich forests, SOC accumulation rates derived from the modified methods that referenced to t1 soils and from the EMMV-t0 method were 5.7%-13.6% and 20.6% higher than that calculated by the ESV method, respectively. Nevertheless, in the C-poor lands, no significant algorithmic biases of SOC estimation were observed. Finally, both the SOC stock discrepancies (ESV vs. EMMV-t0) and the proportions of this unaccounted SOC were large and site-dependent. These results suggest that although the modified methods that referenced to t1 soils could reduce the biases derived from soil volume changes, they may not properly quantify SOC changes due to using unconserved reference systems. The EMMV-t0 method provides an approach to address the two problems and is potentially useful since it enables SOC comparability and integrating SOC datasets.

SUBMITTER: Zhang W

PROVIDER: S-EPMC6636197 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes.

Zhang Weixin W Chen Yuanqi Y Shi Leilei L Wang Xiaoli X Liu Yongwen Y Mao Rong R Rao Xingquan X Lin Yongbiao Y Shao Yuanhu Y Li Xiaobo X Zhao Cancan C Liu Shengjie S Piao Shilong S Zhu Weixing W Zou Xiaoming X Fu Shenglei S

Ecology and evolution 20190530 13

Quantifying soil organic carbon (SOC) changes is a fundamental issue in ecology and sustainable agriculture. However, the algorithm-derived biases in comparing SOC status have not been fully addressed. Although the methods based on equivalent soil mass (ESM) and mineral-matter mass (EMMM) reduced biases of the conventional methods based on equivalent soil volume (ESV), they face challenges in ensuring both data comparability and accuracy of SOC estimation due to unequal basis for comparison and ...[more]

PMID: 31346424

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Project description:Manure application to agricultural soils is widely considered as a source of nutrients and a method of maintaining levels of soil organic carbon (SOC) to mitigate climate change. At present, it is still unclear which factors are responsible for the SOC stock dynamics. Therefore, we analyzed the relationship between SOC stock changes and site characteristics, soil properties, experiment characteristics and manure characteristics. Overall, we included 101 studies with a total of 592 treatments. On average, the application of manure on agricultural soils increased SOC stocks by 35.4%, corresponding to 10.7 Mg ha-1. Manure applications in conventional tillage systems led to higher SOC stocks (+ 2.2 Mg ha-1) than applications under reduced tillage. Soil organic carbon increase upon manure application was higher in soils under non-tropical climate conditions (+ 2.7 Mg ha-1) compared to soils under sub-tropical climate. Larger SOC increases after manure application were achieved in intermediate and shallow topsoils (in 0-15 cm by 9.5 Mg ha-1 and in 16-20 cm by 13.6 Mg ha-1), but SOC stocks were also increased in deeper soils (> 20 cm 4.6 Mg ha-1), regardless of the tillage intensity. The highest relative SOC increase (+ 48%) was achieved if the initial SOC was below 1% but the absolute SOC increased with increasing initial SOC. Clay soils showed higher SOC increase rates compared to sandy soils (+ 3.1 Mg ha-1). Acidic soils showed comparable relative effects but a higher stock difference than neutral (+ 5.1 Mg ha-1) and alkaline soils (+ 5.1 Mg ha-1). The application of farmyard-, cattle- and pig manure showed the highest SOC increases (50%, 32% and 41%, respectively), while green manure and straw showed only minor effects. If manure applications were combined with additional mineral fertilizer, the SOC increases were higher (+ 1.7 Mg ha-1) compared to manure alone. Higher applied amounts generally led to higher SOC stocks. However the annually applied amount is only important under conventional tillage, non-tropical climate conditions, and pH-neutral as well as SOC-rich or SOC-depleted soils and if no additional mineral fertilization is applied. Further studies should focus on the SOC dynamics under tropical climate conditions and factors influencing a potential carbon saturation. In both cases, the number of data was too small. For this reason, additional field studies should be conducted primarily in the tropics. On the other hand, long-term field trials should be re-assessed or newly established to specifically investigate potential saturation effects and long-term (> 20 years) fertilizer effects and carbon sequestration.

Project description:Temperate grassland soils store significant amounts of carbon (C). Estimating how much livestock grazing and manuring can influence grassland soil organic carbon (SOC) is key to improve greenhouse gas grassland budgets. The Rothamsted Carbon (RothC) model, although originally developed and parameterized to model the turnover of organic C in arable topsoil, has been widely used, with varied success, to estimate SOC changes in grassland under different climates, soils, and management conditions. In this paper, we hypothesise that RothC-based SOC predictions in managed grasslands under temperate moist climatic conditions can be improved by incorporating small modifications to the model based on existing field data from diverse experimental locations in Europe. For this, we described and evaluated changes at the level of: (1) the soil water function of RothC, (2) entry pools accounting for the degradability of the exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) the month-on-month change in the quality of C inputs coming from plant residues (i.e above-, below-ground plant residue and rhizodeposits), and (4) the livestock trampling effect (i.e., poaching damage) as a common problem in areas with higher annual precipitation. In order to evaluate the potential utility of these changes, we performed a simple sensitivity analysis and tested the model predictions against averaged data from four grassland experiments in Europe. Our evaluation showed that the default model's performance was 78% and whereas some of the modifications seemed to improve RothC SOC predictions (model performance of 95% and 86% for soil water function and plant residues, respectively), others did not lead to any/or almost any improvement (model performance of 80 and 46% for the change in the C input quality and livestock trampling, respectively). We concluded that, whereas adding more complexity to the RothC model by adding the livestock trampling would actually not improve the model, adding the modified soil water function and plant residue components, and at a lesser extent residues quality, could improve predictability of the RothC in managed grasslands under temperate moist climatic conditions.

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An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes.

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An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes.

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