CGIAR Research Program on Forests, Trees and Agroforestry - Plan of Work and Budget 2020

There were no significant changes in 2019 to FTA’s theory of change1. FTA plans all its work on the basis of its operational priorities. These, in turn, focusresearch towards major development demands and knowledge gaps, orienting FTA towards the implementation of the SDGs and other global commitments. Three operational priorities were added in 2020 (see list in Appendix 1) to better delineate pre-existing research areas addressing development bottlenecks needing dedicated investment and visibility: smallholder tree-crop commodities, tree seeds and seedlings delivery systems, and foresight. FTA organized in 2019, at the request of its ISC, a joint ISC-FTA workshop on impact assessment methods for the program. Based on the outcomes of this workshop FTA will, inter alia, revisit in 2020 its impact pathways and end of programme outcomes, and if need be, corresponding adjustments to the ToC of FTA and/or of its FPs will be made

CGIAR Research Program on Forests, Trees and Agroforestry - Plan of Work and Budget 2020

There were no significant changes in 2019 to FTA’s theory of change1. FTA plans all its work on the basis of its operational priorities. These, in turn, focusresearch towards major development demands and knowledge gaps, orienting FTA towards the implementation of the SDGs and other global commitments. Three operational priorities were added in 2020 (see list in Appendix 1) to better delineate pre-existing research areas addressing development bottlenecks needing dedicated investment and visibility: smallholder tree-crop commodities, tree seeds and seedlings delivery systems, and foresight. FTA organized in 2019, at the request of its ISC, a joint ISC-FTA workshop on impact assessment methods for the program. Based on the outcomes of this workshop FTA will, inter alia, revisit in 2020 its impact pathways and end of programme outcomes, and if need be, corresponding adjustments to the ToC of FTA and/or of its FPs will be made

Risk bounds for purely uniformly random forests

Random forests, introduced by Leo Breiman in 2001, are a very effective statistical method. The complex mechanism of the method makes theoretical analysis difficult. Therefore, a simplified version of random forests, called purely random forests, which can be theoretically handled more easily, has been considered. In this paper we introduce a variant of this kind of random forests, that we call purely uniformly random forests. In the context of regression problems with a one-dimensional predictor space, we show that both random trees and random forests reach minimax rate of convergence. In addition, we prove that compared to random trees, random forests improve accuracy by reducing the estimator variance by a factor of three fourths