A tool for identifying potential Eucalyptus nitens seed orchard sites based on climate and topography

Abstract

Shy seed production in orchards of Eucalyptus nitens is a major barrier to the deployment of genetic gain in South African plantations. A machine learning method was used to identify optimal sites for the establishment of E. nitens seed orchards within the plantation forestry landscape of the summer rainfall region of South Africa. The ensemble classifier random forests (RF) was used to identify the environmental factors conducive to E. nitens floral bud production, and, based on these, build a predictive model deployable to the plantation forestry landscape for identifying suitable areas for E. nitens seed orchards. The RF model predicted site suitability likelihood for floral bud production with a high level of accuracy (area under the receiver operating characteristic curve = 0.83). Within the climatically optimal range for growing E. nitens, flower bud production was more abundant and consistent on cold slopes, i.e. sites experiencing lower minimum air temperatures during spring and autumn. The model was applied to the commercial plantation forestry landscape for the purpose of indicating sites climatically optimal for floral bud production in E. nitens and the establishment of breeding and seed production orchards of the same species.

Keywords: machine learning, predicting flowering, random forests, temperate eucalypts