A Global Short Rotation Coppice (<scp>SRC</scp>) Willow Dataset for the Bioeconomy: Implications for the Yield in the <scp>United Kingdom</scp>

ABSTRACT Short rotation coppice (SRC) willow is a second‐generation lignocellulosic energy crop with a background of research and breeding programmes carried out globally for more than three decades. While commercial standards include planting in mixtures of 6–8 willow genotypes of genetic diversity, much research to date has focused on monoculture trials. Research has found significant differences in willow performance through different management methods, soil properties and environmental interactions (GxE), when applied locally. However, global analysis of these interactions remains a challenge. We present a global SRC willow dataset to facilitate researchers and growers with a resource not available to date to help in closing the gap between research and industry. Data has been collected through literature review and personal communications with key researchers on willow in the United Kingdom. Global annual average yield is 9 Mg Dry Matter (DM) ha −1 year −1 with 17 genotypes, including two types of mixtures, above the economic threshold of 10 Mg DM ha −1 year −1 . Canada and the United States are the best and worst performers with 10.6 and 6.7 Mg DM hr −1 year −1 , respectively. We expect this dataset to provide an efficient way of estimating yields at a smaller scale by multiple combinations of GxE interactions. Biomass production from 1‐year‐old stems in the first harvest cycle is significantly lower than for the second and third year of the first harvest cycle (ANOVA, p &lt; 0.001). Harvest cycles of 2 and 3 years did show significant but small differences in final yield ( t = 3.87, p &lt; 0.001). A random forest statistical procedure was applied to test for the association of the predictor variables with biomass production. The model explained up to 63.65% of the variance observed in yield for all genotypes and sites, with genetic diversity among the most important variables.