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Using simple environmental variables to estimate below‐ground productivity in grasslands
Author(s) -
Gill R. A.,
Kelly R. H.,
Parton W. J.,
Day K. A.,
Jackson R. B.,
Morgan J. A.,
Scurlock J. M. O.,
Tieszen L. L.,
Castle J. V.,
Ojima D. S.,
Zhang X. S.
Publication year - 2002
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1046/j.1466-822x.2001.00267.x
Subject(s) - biomass (ecology) , productivity , primary production , grassland , environmental science , temperate climate , ecology , atmospheric sciences , ecosystem , biology , geology , economics , macroeconomics
Abstract In many temperate and annual grasslands, above‐ground net primary productivity (NPP) can be estimated by measuring peak above‐ground biomass. Estimates of below‐ground net primary productivity and, consequently, total net primary productivity, are more difficult. We addressed one of the three main objectives of the Global Primary Productivity Data Initiative for grassland systems to develop simple models or algorithms to estimate missing components of total system NPP. Any estimate of below‐ground NPP (BNPP) requires an accounting of total root biomass, the percentage of living biomass and annual turnover of live roots. We derived a relationship using above‐ground peak biomass and mean annual temperature as predictors of below‐ground biomass ( r 2 = 0.54; P = 0.01). The percentage of live material was 0.6, based on published values. We used three different functions to describe root turnover: constant, a direct function of above‐ground biomass, or as a positive exponential relationship with mean annual temperature. We tested the various models against a large database of global grassland NPP and the constant turnover and direct function models were approximately equally descriptive ( r 2 = 0.31 and 0.37), while the exponential function had a stronger correlation with the measured values ( r 2 = 0.40) and had a better fit than the other two models at the productive end of the BNPP gradient. When applied to extensive data we assembled from two grassland sites with reliable estimates of total NPP, the direct function was most effective, especially at lower productivity sites. We provide some caveats for its use in systems that lie at the extremes of the grassland gradient and stress that there are large uncertainties associated with measured and modelled estimates of BNPP.