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Methods of Estimating Belowground Net Primary Production

  • William K. Lauenroth

Abstract

Despite much recent attention over the past 30 years, belowground net primary production (BNPP) remains one of the poorest understood attributes of terrestrial ecosystems (Milchunas and Lauenroth 1992; Nadelhoffer and Raich 1992). Estimates of the ratio of BNPP to net primary production (NPP) across ecosystem types range from less than 0.20 to more than 0.80 (Bray 1963, Coleman 1976). Part of this range can be explained by differences among ecosystems, but a portion of it is the result of differences among methods. For instance, Aber et al. (1985) reported ratios of fine root production by two different methods for the same sites ranging from close to 1 to more than 10. In addition to a wide range of estimates of the importance of BNPP across ecosystem types, estimates within ecosystems also vary widely. For instance, Sims and Singh (1978) found that BNPP accounted for 24 to 87% of NPP over a range of grassland ecosystems and Vogt (1991) reported that fine root production accounted for 7 to 76% of NPP over a range of forest ecosystems. How much of this variability is the result of differences among ecosystems and how much is the result of differences among methods? While there is no agreement on the answer to this question, there is abundant evidence that all of the current methods have important strengths and limitations. The objective of this chapter is to describe the most frequently used methods to estimate BNPP and their strengths and weaknesses. The first section provides definitions of terms and descriptions of concepts. The next section describes the methods, comments on their strengths and weaknesses, and provides references in which they have been applied. The last section evaluates the degree to which uncertainty (variability) in input data influences the uncertainty of the resulting estimate of BNPP.

Keywords

Fine Root Fine Root Production Ingrowth Core Dead Fine Root Belowground Carbon Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 2000

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  • William K. Lauenroth

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