Bioenergy crop models: Descriptions, data requirements, and future challenges

Sujithkumar Surendran Nair, Shujiang Kang, Xuesong Zhang, Fernando E. Miguez, R. Cesar Izaurralde, Wilfred M. Post, Michael C. Dietze, Lee R. Lynd, Stan D. Wullschleger

Research output: Contribution to journalReview articlepeer-review

62 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)620-633
Number of pages14
JournalGCB Bioenergy
Volume4
Issue number6
DOIs
StatePublished - Nov 2012

Keywords

  • Biomass
  • Climate change
  • Crop models
  • Data management
  • Land use
  • Productivity
  • Sustainability

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