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Farmers' willingness to adopt silvopastoral systems: investigating cattle producers' compensation claims and attitudes using a contingent valuation approach

Publication: Contribution to journalJournal articlepeer-review

Abstract

Intensive cattle production systems are currently a major contributor to CO2 emissions and biodiversity loss. Silvopastoral systems that combine foraging pastures and trees into an integrated system for raising livestock have been suggested a promising avenue to store carbon and preserve farmland biodiversity. However, investments and maintenance costs for these improvements are paid by producers, who reap few of the environmental benefits. The objective of the present study was to assess farmers' willingness to adopt silvopastoral systems by reforesting treeless pastures, their compensation claims related to adoption, and how both are affected by their attitudes towards silvopastoral systems. This study was based on a contingent valuation approach coupled with exploratory factor analysis to obtain measures of attitudinal constructs derived from the Theory of Planned Behavior. Results indicate that 52% of respondents were willing to adopt silvopastoral systems and the mean compensation claim per year per hectare is estimated at SEK 3107.17 (308(sic)). Adoption decision is positively correlated with attitudes towards silvopastoral systems, suggesting that decision-making is not solely driven by profit maximization through concerns related to pecuniary factors.
Original languageEnglish
Pages (from-to)133-149
Number of pages17
JournalAgroforestry Systems
Volume97
Issue number1
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Cattle production
  • Silvopastoral systems
  • Willingness to adopt
  • Compensation claims
  • Contingent valuation method
  • Theory of planned behavior

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