Temperate agroforestry for tree carbon storage in Switzerland: 10 years of biophysical and social monitoring

Agroforestry, the integration of woody structures in agricultural land, has high potential for climate protection and resilience, since trees are active carbon sinks. Yet, there is only limited empirical evidence on the actual performance of temperate agroforestry systems in this respect, nor on its acceptance by farmers. We monitored four silvoarable agroforestry systems in Switzerland (apple, sour cherry, poplar, wild cherry) over ten years and measured tree growth and carbon storage performances. We compared the measured data to outcomes of the Yield-SAFE model. We regularly interviewed farmers on their observations of their agroforestry systems. Individual growth of agroforestry trees varied between species and location, with differences between the smallest and largest tree ranging from 44 ​% to 97 ​%. Consequently, the carbon sequestration potential varied substantially between 0.4 ​and ​2.5 ​t CO 2eq ​per year and hectare. The modelling approach showed a good fit for apples and wild cherries and ​– ​after (re)calibration with local data ​– ​also for poplars and sour cherries. Tree mortality was up to 20 ​% in the first years but if replaced, this did not influence the overall outcome after ten years. Farmers' evaluations differed, depending on the motivation of individual farmers. They changed only slightly with time, indicating that their expectations had been realistic. The study highlights the usefulness of long-term empirical data for model calibration and of monitoring farmers' satisfaction. Realistic model predictions and management of farmers' expectations will facilitate the implementation of agroforestry. • After ten years, four Swiss silvoarable agroforestry systems stored between 0.4 and 2.5 ​t CO 2eq ha −1 yr −1 . • The Yield-SAFE Model accurately reflects tree growth, but requires calibration with local data. • Farmers' perceptions vary according to their expectations, but tend to remain stable over time. • More empirical data will improve agroforestry model predictions and increase farmers' acceptance.