摘要: Spatially accurate and reliable estimates from fast-growing plantations are a key factor for planning energy supply. This study aimed to estimate the yield of biomass from short rotation willow plantations in northern Europe. The data were based on harvesting records from 1790 commercial plantations in Sweden, grouped into three ad hoc categories: low, middle and high performance. The predictors included climatic variables, allowing the spatial extrapolation to nearby countries. The modeling and spatialization of the estimates used boosted regression trees, a method based on machine learning. The average RMSE for the final models selected was 0.33, 0.39 and 1.91 (corresponding to R2 = 0.77, 0.88 and 0.45), for the low, medium and high performance categories, respectively. The models were then applied to obtain 1×1 km yield estimates in the rest of Sweden, as well as for Norway, Denmark, Finland, Estonia, Latvia, Lithuania and the Baltic coast of Germany and Poland. The results demonstrated a large regional variation. For the first rotation under high performance conditions, the country averages were as follows: >7 odt ha−1 yr−1 in the Baltic coast of Germany, >6 odt ha−1 yr−1 in Denmark, >5 odt ha−1 yr−1 in the Baltic coast of Poland and between 4–5 odt ha−1 yr−1 in the rest. The results of this approach indicate that they can provide faster and more accurate predictions than previous modeling approaches and can offer interesting possibilities in the field of yield modeling.
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