Bioenergy crop models: descriptions, data requirements, and future challenges
Bioenergy crop models: descriptions, data requirements, and future challenges
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DOI：
10.1111/j.1757-1707.2012.01166.x
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
Sujithkumar Surendran Nair;Shujiang Kang;Xuesong Zhang;F. Miguez;R. Izaurralde;W. Post;M. Dietze;L. Lynd;S. Wullschleger
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文献类型：
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作者：
Sujithkumar Surendran Nair;Shujiang Kang;Xuesong Zhang;F. Miguez;R. Izaurralde;W. Post;M. Dietze;L. Lynd;S. Wullschleger
将木质纤维素生物质生产作为可再生能源的实地研究为开发生物能源作物模型提供了关键数据。通过文献调查发现，目前已有14种模型用于模拟草本和木本生物能源作物，以及模拟天冬氨酸酸代谢（CAM）作物。这些模型模拟了柳枝稷（ALMANAC、EPIC和Agro - BGC）、芒草（MISCANFOR、MISCANMOD和WIMOVAC）、甘蔗（APSIM、AUSCANE和CANEGRO）以及杨树和柳树（SECRETS和3PG）的田间规模生物量生产。Agro - IBIS和LPJmL两个模型是对全球动态植被模型的改进，在区域尺度上模拟了芒草和甘蔗的生物量产量。虽然环境生产力指数（EPI）缺乏其他生物能源作物模型的复杂性，但它是唯一用于估算CAM（龙舌兰和奥普蒂亚）植物生物量产量的模型。除EPI模型外，所有模型都包含叶面积动态、物候、辐射截获和利用、生物量生产以及生物量向根和芽的分配。一些模型模拟了土壤水分、养分和碳循环动态，使它们在评估与大规模种植生物能源作物相关的环境后果（如侵蚀和养分损失）方面特别有用。能源作物模拟模型使用的迅速增加令人鼓舞；然而，关于气候、土壤和作物管理做法对生物质生产的影响的详细信息很少。因此，关于基于过程的生物能源作物模型的参数化和验证，还有大量的工作要做；为模型开发和验证生成和分发高质量的现场数据；并实施一个综合框架，用于规划可持续生物能源系统的高效、高分辨率生物质生产模拟。
Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field‐scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro‐BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro‐IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large‐scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process‐based models for bioenergy crops; generation and distribution of high‐quality field data for model development and validation; and implementation of an integrated framework for efficient, high‐resolution simulations of biomass production for use in planning sustainable bioenergy systems.