Xu Dong-mei, Wang Kun
Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Oct;27(10):2013-6.
Vegetation is a major index for monitoring the grassland condition and productivity. The change in vegetation directly reflects degradation and restoration of grassland ecosystem. It is important to monitoring the information of vegetation changes to prevent degradation and realize sustainable development of grassland. Predication of vegetation was often completed by field investigation and laboratory analysis in the past, and could not satisfy the needs for inspecting of grassland degradation and restoration. Near-infrared spectroscopy (NIRS) is a rapid, convenient, high-efficiency, non-destructive and low-cost analytical technique, and has been widely used in various fields for quantitative and qualitative analysis. It has been one of the most important techniques for monitoring the succession of grassland ecosystem, and has great potential for applying in natural grassland management. Botanical composition is a major index of the vegetation community structure. The change in botanical composition indicates the developing stage of the plant community. Determining the botanical composition during vegetation succession can provide sound basis for establishing feasible measure of grassland management. NIRS can be successfully used as a rapid method to predict the grass, legume and other plant proportion in natural or semi-natural grasslands. The legume content in multi-species mixtures and the species composition in root mixtures can accurately be estimated by means of NIRS. Leaf/stem ratio of grass stands is an important factor affecting forage quality, diet selection, intake, and the intensity of photosynthesis. Estimates of leaf/stem ratios commonly are based on a labor intensive process of hand separating leaf and stem fractions. NIRS can be used successfully to predict leaf/stem ratios and mineral contents. The results of NIRS technique were well correlated with labor separating method. The decomposition of litter in grasslands is an important aspect of material cycle in grassland ecosystem. To study material cycling, especially mineral cycling in grassland ecosystems, it is essential to know the decomposition rate of the litter. NIRS technique can accurately predict the decomposition status of litter and the change of lignin, cellulose, nitrogen, ash and other nutrient contents during the decomposition of litter. NIRS has potential to provide rapid and effective estimates of material cycling in grassland ecosystems to assist managers in establishing application rates of grasslands that fall within productive and environmentally safe levels. The chemical composition of the plants in grassland is an important factor affecting herbivorous intake and material cycle, and is an important parameter in determining the status of degradation and restoration of grassland ecosystems. NIRS has been confirmed as a technique for reliably and accurately determining the dry matter, crude protein, acid detergent fibre, neutral detergent fibre, and certain microelement contents. With the development of spectral technique, the NIRS method will be more widely used in vegetation management.
植被是监测草地状况和生产力的主要指标。植被变化直接反映了草地生态系统的退化和恢复情况。监测植被变化信息对于防止草地退化和实现草地可持续发展至关重要。过去,植被预测通常通过实地调查和实验室分析来完成,无法满足草地退化和恢复监测的需求。近红外光谱(NIRS)是一种快速、便捷、高效、无损且低成本的分析技术,已广泛应用于各个领域进行定量和定性分析。它已成为监测草地生态系统演替的最重要技术之一,在天然草地管理中具有巨大的应用潜力。植物组成是植被群落结构的主要指标。植物组成的变化表明了植物群落的发展阶段。确定植被演替过程中的植物组成可为制定可行的草地管理措施提供可靠依据。NIRS可成功用作预测天然或半天然草地中禾本科、豆科及其他植物比例的快速方法。通过NIRS可以准确估计多物种混合物中的豆科植物含量和根系混合物中的物种组成。草群的叶/茎比是影响牧草质量、采食选择、采食量和光合作用强度的重要因素。叶/茎比的估计通常基于手工分离叶和茎部分的劳动密集型过程。NIRS可成功用于预测叶/茎比和矿物质含量。NIRS技术的结果与手工分离法相关性良好。草地凋落物的分解是草地生态系统物质循环的一个重要方面。为了研究物质循环,特别是草地生态系统中的矿物质循环,了解凋落物的分解速率至关重要。NIRS技术可以准确预测凋落物的分解状态以及凋落物分解过程中木质素、纤维素、氮、灰分和其他养分含量的变化。NIRS有潜力提供草地生态系统物质循环的快速有效估计,以帮助管理者确定处于生产性和环境安全水平范围内的草地施肥量。草地植物的化学成分是影响草食动物采食量和物质循环的重要因素,也是确定草地生态系统退化和恢复状况的重要参数。NIRS已被确认为一种可靠且准确地测定干物质、粗蛋白、酸性洗涤纤维、中性洗涤纤维和某些微量元素含量的技术。随着光谱技术不断发展,NIRS方法将在植被管理中得到更广泛的应用。