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

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.

Nitrogen Addition Decreased Respiration and Heterotrophic Respiration but Increased Autotrophic Respiration in a Cabbage (Brassica pekinensis Rupr) Experiment in the Northeast Plains

“…Secondly, we utilized the random forest method to determine the relative im portance of regulating biotic and abiotic factors on Ra and Rh. This powerful machine learning technique is robust to multicollinearity and overfitting [36]. This study ran 100 times with evaluation based on the increased mean square error (lnMSE).…”

Stable isotopes for the study of soil C and N under global change

“…Though N processes and N 2 O emission were quite variables with the treatments, saltwater tended to enhance N mineralization and thus loss of N, indicating that saltwater incursion into freshwater wetlands might reduce the N sink capacity of the coastal wetlands. In forests, Garrett et al (2023) showed that the δ 15 N can be used as an indicator of N availability status and thus as a predictor of site productivity responses to N fertilizer in New Zealand. Though the δ 15 N has been widely used to estimate the degree of N saturation of forests, this study is unique as it extended the use of δ 15 N in predicting forest responses to N fertilization.…”