Abstract
The 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 (δ15N) 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 (~ 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 δ15N 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 δ15N enrichment factor (δ15Nfoliage − δ15Nsoil) showing more than 50% relative importance to the model. Tree growth rates generally decreased with more negative δ15N 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 δ15N 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.
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