The case for incorporating soil nutrient availability into large-scale forest management models

17456 11 95376 4 293828 document 95376 YSSPreport_Kevin van Sundert.pdf application/pdf 2a44f80893c117889fe0d223c53eca12 MD5 1774059 2021-10-01 12:18:37 https://pure.iiasa.ac.at/id/eprint/17456/1/YSSPreport_Kevin%20van%20Sundert.pdf 17456 1 1 application/pdf text en public cc_by_nc_4

YSSPreport_Kevin van Sundert.pdf

published 95383 2 293846 document 95383 lightbox.jpg image/png b31ce04a90a109e1dd2b28bb582ab441 MD5 26380 2021-10-01 12:28:47 https://pure.iiasa.ac.at/id/eprint/17456/2/lightbox.jpg 17456 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en staffonly

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/95376 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/95376 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/95376 archive 2 disk0/00/01/74/56 2021-10-01 12:20:40 2022-04-01 10:52:23 2021-10-01 12:20:40 monograph show 1 Van Sundert K. 9404 The case for incorporating soil nutrient availability into large-scale forest management models pub BNR prog_ysp The availability of nutrients exerts major control on ecosystem structure, functioning and responses to global change. Process-based ecosystem models therefore increasingly incorporate nutrient cycles, but forest and other ecosystem modules in integrated assessment models, used to advise policy making based on trade-offs and feedbacks within and among economy, agriculture, forestry etc., nutrient availability is usually poorly accounted for. Here, we explored whether in a statistical random forest model predicting site productivity, replacing soil type by key soil properties (organic layer C:N ratio, upper soil organic carbon concentration (SOC), organic layer pH) would improve predictions across Swedish and European forests. We found substantial variation in the key soil properties and a nutrient availability metric (which à priori integrated the same soil properties), both among and within soil types. Because of the within-soil type variation in nutrient availability, both random forest models using soil properties and these using the nutrient availability metric predicted significantly better forest site productivity than the soil type-fed models across Sweden and Europe. We recommend the inclusion of often available, resource-use related soil properties such as C:N, SOC and pH into random forests that feed into integrated assessment models. Substituting individual soil properties by an à priori defined nutrient availability metric can reduce overfitting in statistical random forest models. 2021-09-28 published IIASA 3357 public yssp Laxenburg, Austria FALSE no info:eu-repo/semantics/book