Garrett, Loretta G.Lin, YueMatson, Amanda L.Strahm, Brian D.2023-05-022023-05-022022-10http://hdl.handle.net/10919/114882The fertiliser growth response of planted forests can vary due to differences in site-specific factors like climate and soil fertility. We identified when forest stands responded to a standard, single application of nitrogen (N) fertiliser and employed a machine learning random forest model to test the use of natural abundance stable isotopic N (delta N-15) to predict site response. Pinus radiata growth response was calculated as the change in periodic annual increment of basal area (PAI BA) from replicated control and treatment (similar to 200 kg N ha(-1)) plots within trials across New Zealand. Variables in the analysis were climate, silviculture, soil, and foliage chemical properties, including natural abundance delta N-15 values as integrators of historical patterns in N cycling. Our Random Forest model explained 78% of the variation in growth with tree age and the delta N-15 enrichment factor (delta N-15(foliage) - delta N-15(soil)) showing more than 50% relative importance to the model. Tree growth rates generally decreased with more negative delta N-15 enrichment factors. Growth response to N fertiliser was highly variable. If a response was going to occur, it was most likely within 1-3 years after fertiliser addition. The Random Forest model predicts that younger stands (< 15 years old) with the freedom to grow and sites with more negative delta N-15 isotopic enrichment factors will exhibit the biggest growth response to N fertiliser. Supporting the challenge of forest nutrient management, these findings provide a novel decision-support tool to guide the intensification of nutrient additions.application/pdfenCreative Commons Attribution 4.0 InternationalGrowth responseFertiliserNitrogenNatural abundance nitrogen isotopeMachine learningPinus radiataArticle - Refereedhttps://doi.org/10.1007/s00374-022-01671-81432-0789