Abstract

In a changing world, identifying the drivers of vulnerability, resilience and robustness (VRR) of agricultural systems has become a major issue. We developed and applied a generic analytical framework to perform a systematic review of studies that quantitatively assess VRR of agricultural systems in temperate climate zones. After a thorough selection process based on visualizing bibliometric networks in the Web of Science database of peer-reviewed articles in English, we show that the core set of the 37 selected studies addressed mainly the effect of climate change on the yield of grassland and crop systems. Synthesis of the studies' results yields some main conclusions, but also reveals differences in the influence of diversity and intensification level. First, the synthesis shows that diversity enhances (i) resilient crop yield dynamics and (ii) levels of grassland biomass, but (iii) its effect on grassland biomass dynamics is unclear. In addition, the effect of crop intensification on yield dynamics depends on (iv) the intensification practice considered (e.g. irrigation, fertilisation), (v) its combination with other practices and (vi) the soil and climate. Transversally, the synthesis reveals that (vii) the nature of species in grasslands or crop rotations (e.g. presence of a legume) and (viii) the nature of farms in a region (e.g. economic size distribution) are strong drivers of VRR, i.e. composition has a strong effect. Furthermore, (ix) the frequency of climate events, (x) short- vs. long-term analysis, (xi) nonlinear effects and (xii) the relative impact of disturbances in comparison with the impact of agricultural practices are all elements that make it difficult to draw clear conclusions about the influence of diversity and intensification. Finally, our results highlight that a functional approach should be used to decipher diversity-productivity relationships, and that agricultural practices and their appropriateness to the pedoclimatic context and local resources must be characterised finely when analysing drivers of VRR.

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