Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

A. Salaberria, G. García-Baquero, I. Odriozola, A. Aldezabal


Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage. In this work the aboveground net primary production (ANPP) of an Atlantic mountain grassland system was modelled in order to attempt producing short-term forecasts. Since grazing influences productivity, two treatment levels (grazing and exclusion) were experimentally applied in each of three field sites. Monthly ANPP data were then collected over three consecutive vegetative periods (2006-2008), thereby obtaining six time series (one per plot). Since no significant differences among sites (within treatments) were found, these six series were later reduced through averaging to only two series (one per treatment level). Two kinds of statistical models were then used to attempt monthly ANPP forecasting: exponential smoothing methods and ARIMA models. Both methodologies turned out to produce inadequate forecasts due to the presence of marked local features (innovative outliers) in our relatively short time-series data. Nonetheless, useful information for a more innovative shepherding management was revealed (e.g. the presence of within-year variation in ANPP, and differences between the grazing and exclusion treatments). Longer data series, which would require a more demanding effort in sampling investment, are likely necessary in order to obtain adequate forecasts using these time series methodologies.


ARIMA models; net primary production; forecasting; Holt-Winters methods; livestock exclusion; manipulative experiment; Atlantic mountains

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