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    Author(s): Thomas P. HolmesDavid E. Calkin
    Date: 2013
    Source: International Journal of Wildland Fire. 22: 246-255.
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: Download Publication  (127.59 KB)

    Related Research Highlights

    Analyzing How to Increase Fireline Production Efficiency


    In this paper, we use operational data collected for large wildland fires to estimate the parameters of economic production functions that relate the rate of fireline construction with the level of fire suppression inputs (handcrews, dozers, engines and helicopters). These parameter estimates are then used to evaluate whether the productivity of fire suppression inputs during extensive fire suppression efforts are similar to productivity estimates derived from direct observation and used as standard rates by the US Forest Service. The results indicated that the production rates estimated with operational data ranged from ~14 to 93% of the standard rates. Further, the econometric models indicated that the productivity of all inputs taken together increases more than proportionally as their use is increased. This result may indicate economies of scale in fire suppression or, alternatively, that fire managers learn how resources may be deployed more productively over the course of a fire. We suspect that the identified productivity gaps are primarily due to unobserved factors related to fire behaviour, other resources at risk, firefighter fatigue, safety considerations and managerial decision-making. The collection of more precise operational data could help reduce uncertainty regarding the relative importance of factors that contribute to productivity shortfalls.

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    Holmes, Thomas P.; Calkin, David E. 2013. Econometric analysis of fire suppression production functions for large wildland fires. International Journal of Wildland Fire. 22: 246-255.


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    efficiency, fireline productivity, fractal dimension, random parameters, returns to scale, selective rationality

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