Integrating environmental and economic performance to assess modern silvoarable agroforestry in Europe

J. Palma, A.R. Graves, P.J. Burgess, W. van der Werf, F. Herzog

Research output: Contribution to journalArticleAcademicpeer-review

43 Citations (Scopus)

Abstract

The environmental and economic performance of silvoarable agroforestry in Europe is highly variable. Multi-criteria analysis, using the PROMETHEE outranking approach, was used to evaluate the integrated performance of silvoarable agroforestry on hypothetical farms in nineteen landscape test sites in Spain, France, and The Netherlands. The silvoarable scenarios allocated a proportion of the hypothetical farms (10 or 50%) to silvoarable agroforestry at two different tree densities (50 or 113 trees ha¿ 1) on two different qualities of land (best or worst quality land). The status quo (conventional arable farming) was also assessed for comparison. The criteria used in the evaluation (soil erosion, nitrogen leaching, carbon sequestration, landscape biodiversity, and infinite net present value) were assessed at each landscape test site; infinite net present value was assessed under six levels of government support. In France, the analysis showed, assuming equal weighting between environmental and economic performance, that silvoarable agroforestry was preferable to conventional arable farming. The best results were observed when agroforestry was implemented on 50% of the highest quality land on the farm; the effect of tree density (50¿113 trees ha¿ 1) was small. By contrast, in Spain and The Netherlands, the consistently greater profitability of conventional arable agriculture relative to the agroforestry alternatives made overall performance of agroforestry systems dependent on the proportion of the farm planted, and the tree density and land quality used.
Original languageEnglish
Pages (from-to)759-767
JournalEcological Economics
Volume63
Issue number4
DOIs
Publication statusPublished - 2007

Keywords

  • promethee method
  • nutrient loss
  • systems

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