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Plant litter decomposition
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40 2019 ND-CP 413905
- Điều hướng trang này:
- ENVIRONMENTAL MODELS Soil erosion
| Plant litter decomposition
Plant litter decomposition depends on temperature, soil moisture, type of organic matter and to a lesser extent on soil cover. The major driver for the plant litter decomposition module is temperature. Four major steps are identified in the plant litter decomposition and soil organic matter module: 1. pool definition, 2. calculation of decay factors, 3. calculation of decay and 4. redistribution of organic matter pool contents. Several soil organic matter pools can be discerned; the Rothamsted-C model describes five different pools (Coleman and Jenkinson 1999). They are resistant plant material (RPM), decomposable plant material (DPM), soil microbial biomass (BIO), humified organic matter (HUM), and inert organic matter (IOM). Each soil organic matter pool decays at its own rate. The formula for calculating the decay rate takes into account the nature of the soil organic matter pool and envisages an exponential decay with time based on the decay rate factors. The most important decay factor is related to temperature. This temperature factor is multiplied by a moisture factor, a plant cover factor and a decay rate factor specific to the soil organic pool. The final step of the model is to redistribute the decayed organic matter over the different pools. The ratio of decomposable to resistant plant material is set at 1.44 for annual crops; the ratio of humified organic matter to soil microbial biomass is set at 1.17. The ratio of CO 2 to the sum of humified organic matter and soil microbial biomass depends on the clay content of the soil. ENVIRONMENTAL MODELS Soil erosion Soil erosion is a natural process, occurring over geological time. Most concerns about erosion are related to accelerated erosion, where the natural rate has been significantly increased by human activities such as changes in land cover and management. Accelerated soil erosion poses severe limitations to sustainable agricultural land use, as it reduces on-farm soil productivity and causes the accumulation of sediments and chemical pollutants in waterways. Runoff is the most important direct driver of severe soil erosion. Processes that influence runoff therefore must play an important role in the analysis of soil erosion intensity, and measures that reduce runoff are critical to effective soil conservation. However, most erosion models are designed to assess soil erosion at very detailed scales, and are not very useful in the development of regional soil conservation measures. Soil erosion is widely recognized to be patchy both in time and in space. A major event may occur in a day, followed by some years of quiet, or may hit one Annex 3 – Tools for land evaluation 95 area but leave adjacent areas untouched. In addition the lack of widespread soil loss measurements hampers effective interpolation between the limited sites available. Soil loss measurements or observations are typically limited to a period of a few years, which makes extrapolation over longer periods difficult. The lack of data and the patchy nature of soil erosion also make model development a difficult process. Ultimately, the area affected by soil erosion and an estimate of the expected severity in a particular area have to be known for land evaluation. Some methods for carrying out regional assessments, not using formal models, are based on the collection of distributed field observations. Methods based on questionnaire surveys, such as GLASOD (Oldeman et al., 1991) or ASSOD (Van Lynden & Oldeman 1997), and methods based on erosion measurement sites, such as the Hot Spots map (Turner et al., 2001) are likely to be inadequate on their own. In addition, differences between expert assessments and measurements reduce the comparability between the limited data available. However, the GLASOD map is still the only readily available information on the worldwide distribution and severity of soil erosion. Methods based on an assessment of factors and combinations of factors that influence erosion rates have the immediate benefit of using distributed data sources. All of the mapping methods appear to use at least some indicators, particularly soil classifications, and are based on the Universal Soil Loss Equation (USLE), which is no longer considered as state of the art. Despite this, the most commonly used factor-based assessment of regional soil erosion is still based on a simplification of the Revised Universal Soil Loss Equation (RUSLE; Renard et al., 1997), a regression-based model for which there is a massive database for US conditions. However, there are few systematic data for it elsewhere in the world. The RUSLE is intended to provide an estimate of average annual erosion loss in tons per unit area, derived from soil erodibility, rainfall erosivity, slope length, vegetation cover and crop management. Its defects and limitations have been discussed in the section Types of models. Examples of RUSLE using regional geographic data in Europe are provided by CORINE (1992), RIVM (1992) and Van der Knijff et al., (2000). Elwell (1981) developed SLEMSA, a variant of USLE adapted to southern African conditions. The third method for regional soil erosion assessment is the application of a process model. Process modelling methods allow for a more quantitative forecast, which is important as a critical control on soil erosion. The PESERA model, for example, produces a quantitative forecast of soil erosion and plant growth (Kirkby et al., 2000). The strong and weak points of process models, and their integration with a GIS, have been discussed in the section Types of models. All of these regional erosion assessment models require calibration and validation against erosion measurements, although the type of validation needed is different for each method. There are also differences in the extent to which the assessment methods identify past erosion and an already degraded soil resource, as opposed to risks of future erosion under present climate and land use or under scenarios of global change. tải về 0.57 Mb. Chia sẻ với bạn bè của bạn:
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