Nitrogen isotope enrichment predicts growth response of Pinus radiata in New Zealand to nitrogen fertiliser addition


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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 (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.



Growth response, Fertiliser, Nitrogen, Natural abundance nitrogen isotope, Machine learning, Pinus radiata