Biodiesel from Oilseeds in the Canadian Prairies and Supply-Chain Models for Exploring Production Cost Scenarios: A Review. (20th May 2012)

• Record Type: Journal Article
• Authors: Newlands, Nathaniel K.; Townley-Smith, Lawrence
• Other Names: Ferrarese-Filho O. Academic Editor.; Hatfield J. Academic Editor.; Kindiger B. Academic Editor.
• Is Part Of: ISRN agronomy. Volume 2012(2012)
• Publication Date: 2012-05-20
• Subjects: Agronomy -- Periodicals; Agronomy; Agricultural Technology; Periodicals; Electronic journals; 630
• DOI: 10.5402/2012/980621
• Languages: English
• ISSNs: 2090-7656

Abstract

Abstract : Canada recently implemented a federal mandate of 2% of renewable content in diesel fuel and heating oil. Federal-level biofuel strategy is currently more geared to bioethanol, as nonfood oils continue to be more cost-competitive and canola seeded area is forecast to increase 10% as a new record due to strong prices and high expected yields. Increasing focus is therefore being placed on alternative oilseeds as nonfood crops for biodiesel and their ability to adapt to the semiarid conditions of the Canadian Prairies and provide benefits in nutrient and water-use efficiency when introduced into the crop rotation. Systems engineering and supply-chain modeling and optimization will have an increasingly important role in decision making for designating supply units, the linkage of processes and chains, and biorefinery system design. However, current models require further enhancement to address current challenging questions: (1) changing spatial considerations (e.g., land use and suitability for feedstocks), (2) changing temporal dynamics of supply and risk of climate extreme impacts on transportation networks (road, rail, pipeline), price volatility, changes in policy targets and subsidy regimes, process technological change, and multigenerational biorefinery systems engineering advancements. Greater integration internationally in model development and testing would improve sensitivity and reliability in their system-level predictions and forecasts.