Unfolding the potential of wheat cultivar mixtures : a meta-analysis perspective and identification of knowledge gaps

Increasing the biodiversity of cropped plants is a key leverage for agroecology, aiming to replace chemical inputs by ecological processes and regulations. Cultivar mixtures are a straightforward way to increase within-crop diversity, but they have so far been poorly used by farmers and they are not encouraged by advisory services. Based on the methodology developed by Kiaer et al. (2009), we achieved a meta-analysis of cultivar mixtures in wheat. Among the 120 publications dedicated to wheat, we selected 32 studies to analyze various factors that may condition the success or failure of wheat mixtures by calculating overyielding, i.e. the difference in productivity of a variety mixture compared with the weighted mean of its component varieties in pure stand. The analysis highlighted a significant global overyielding of 3.5%, which reached 6.2% in condition of high disease pressures. Overyielding was not affected by seeding density or plot size. Under high disease pressure, over yielding increased by 3.2% point per added component variety. Overyielding was respectively 5.3% and 3.3% higher for mixtures heterogeneous in disease resistance or phenology than for homogeneous ones, and did not vary when considering height. Overyielding reached its highest values in the 1980s and 1990s, which reflects the predominance of disease-focused studies during this period. Our results confirm that cultivar mixtures are a potential way to increase yield relatively to pure varieties, especially under low pesticide cropping systems. Literature suggests that mixture practice is impeded by the lack of general rules that could help to mixing varieties. To design such rules it is needed to (1) achieve new experiments manipulating the heterogeneity in variety traits, (2) determine experimentally the ecological mechanisms underlying mixture performance and (3) develop new models allowing testing and analyzing these mechanisms