Experiential Knowledge Complements an LCA-Based Decision Support Framework

Sustainability

Heng Yi Teah¹  Yasuhiro Fukushima²  Motoharu Onuki¹ 

[1] Graduate Program in Sustainability Science, Global Leadership Initiative (GPSS-GLI), Division of Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 332 Building of Environmental Studies, 5-1-5 Kashiwanoha, Kashiwa City, Chiba 277-8563, Japan; E-Mail:;Department of Chemical Engineering, Graduate School of Engineering, Tohoku University, 6-6-07, Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan; E-Mail:

关键词: life cycle assessment;    decision support framework;    experiential knowledge;    shrimp farming;    farmer-based innovation;    indifference curves;    mixing triangle;   

DOI  :  10.3390/su70912386

来源: mdpi

【 摘 要 】

A shrimp farmer in Taiwan practices innovation through trial-and-error for better income and a better environment, but such farmer-based innovation sometimes fails because the biological mechanism is unclear. Systematic field experimentation and laboratory research are often too costly, and simulating ground conditions is often too challenging. To solve this dilemma, we propose a decision support framework that explicitly utilizes farmer experiential knowledge through a participatory approach to alternatively estimate prospective change in shrimp farming productivity, and to co-design options for improvement. Data obtained from the farmer enable us to quantitatively analyze the production cost and greenhouse gas (GHG) emission with a life cycle assessment (LCA) methodology. We used semi-quantitative graphical representations of indifference curves and mixing triangles to compare and show better options for the farmer. Our results empower the farmer to make decisions more systematically and reliably based on the frequency of heterotrophic bacteria application and the revision of feed input. We argue that experiential knowledge may be less accurate due to its dependence on varying levels of farmer experience, but this knowledge is a reasonable alternative for immediate decision-making. More importantly, our developed framework advances the scope of LCA application to support practically important yet scientifically uncertain cases.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.