基于大比例尺数据库的福建省耕地土壤固碳速率和潜力研究

明确土壤固碳速率和潜力是制定耕地固碳减排措施的基础。以我国典型亚热带地区—福建省不同地理位置的闽侯、浦城、同安、武平和永定5个县为研究区,运用生物地球化学过程模型DNDC（DeNitrification and Decomposition）,模拟这5个县在目前区域尺度最详细的1:5万土壤数据库下1980─2009年和2010─2039年有机碳动态变化,并运用尺度上推的方法估算出全省耕地土壤固碳速率和潜力。结果表明,福建省耕地土壤1980─2009年的固碳总量为7.37 Tg,而2010─2039年的固碳潜力为7.04 Tg,两个时段的年均固碳速率分别为：190 kg·hm-2和176 kg·hm-2,说明目前的农田管理措施有利于研究区长期固碳。其中,水稻土和盐渍水稻土分别在土类和亚类级别中固碳速率最大,不同时段均大于175 kg·hm-2·a-1;而红壤在土类和亚类级别中固碳速率皆最小,不同时段均小于3 kg·hm-2·a-1。总体来看,1980─2009年和2010─2039年水稻土的固碳总量均占全省耕地固碳总量的92%以上,是今后制定固碳减排措施的重点。     

Carbon stocks and sequestration potentials of agricultural soils in the federal state of Baden‐Württemberg,SW Germany

Soil organic carbon (SOC) inventories are important tools for studying the effects of land‐use and climate change and evaluating climate‐change policies. A detailed inventory of SOC in the agricultural soils of the federal state of Baden‐Württemberg was therefore prepared based on the highest‐resolution geo‐referenced soil, land‐use, and climate data (BÜK200 inventory). In order to estimate the quality of different approaches, C inventories of the region were also prepared based on data from the National Inventory Report (UBA, 2003) and by applying the IPCC (1997) method to the two data sets. Finally, the BÜK200 inventory was used to estimate potentials of no‐tillage agriculture (NT) and peatland restoration to contribute to C sequestration and greenhouse‐gas (GHG)‐emission mitigation since both measures are discussed in this context. Scenario assumptions were change to NT on 40% of the cropland and restoration of 50% of cultivated peatlands within 20 years. On average, grasslands contained 9.5 kg C m^–2 to 0.3 m depth as compared to only 6.0 kg C m^–2 under cropland, indicating strong land‐use effects. The SOC content depended strongly on waterlogging and elevation, thus reflecting reduced C mineralization under aquic moisture regimes and low temperatures. Comparison of the BÜK200 inventory with the approach used for UBA (2003) showed high inconsistencies due to map resolution and SOC contents, whereas the IPCC method led to fairly good agreements. Results on the simulated effects of NT and peatland restoration suggested that 5%–14% of total agricultural GHG emissions could be abated with NT whereas peat restoration appeared to have a minor mitigation potential (0.2%–2.7%) because the total area of cultivated organic soils was too small to have larger impact.     

Combination of electromagnetic induction and gamma spectrometry using K‐means clustering: A study for evaluation of site partitioning

Today rapid survey methods of proximal soil sensing (PSS) provide an increasing number of different and highly resolved data. These multidimensional data sets can lead to multilayered and complex maps of parameters which are only indirectly related to soil properties and soil functions. However, in applications usually just one clear elementary map is required. It is of increasing importance to tackle this problem utilizing a cluster algorithm for the synthesis and reduction of multidimensional input variables. The cluster algorithm provides a partitioning of the investigated site whereby the units are characterized by the statistics of the PSS data. Therefore, the question that arises is how suitable is the suggested partitioning in terms of the delineation of different soil units. In this study, we investigate the suitability of cluster partitioning through a case study at a medium‐scale test site (≈ 50 000 m²). Two common PSS methods: electromagnetic induction (EMI) and gamma spectrometry (GS) will be employed to create a data set for partitioning by a K‐means cluster. The result of the cluster analysis is a delineation of three different parts. In contrast to previous studies, we evaluate the generated partitions by independent soil properties such as grain size, horizon thickness, and color of stratified randomly taken soil samples. The soil analyses show that one of three clusters significantly differs from the others in terms of grain‐size distribution and horizon thickness. The partitioning of the other two clusters could not be confirmed by the considered soil parameters. Nevertheless, the case study demonstrates the combination of different PSS data by K‐means clustering as a potential approach for site partitioning. An evaluation of the results of the cluster analysis through the collection and analysis of soil samples is highly recommended.