The Predicting Tree Growth App: an algorithmic approach to modelling individual tree growth

PredictingTreeGrowth is free and open-source application software written in Python 3.7 that allows easy and fast development of predictive models using the Recurrent Neural Network (RNN)/Long Short-Term Memory (LSTM) framework. RNNs have an upgraded architecture able to capture tree growth mechanisms related to time ordering and size dependence. The motivation for this App is to demystify the use of Machine Learning algorithms and allow accessibility of Machine Learning algorithms by the scientific community. Its simple graphical user interface (GUI) provides straightforward tools for building predictive models with the RNN algorithm.Fil: Magalhaes, Juliana G. de S.. University of British Columbia; CanadáFil: Polinko, Adam P.. Mississippi State University.; Estados UnidosFil: Amoroso, Mariano Martin. Universidad Nacional de Río Negro. Sede Andina. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kohli, Gursimran S.. University Fraser Simon; CanadáFil: Larson, Bruce C.. University of British Columbia; Canad

Biochar and Microbial Soil Amendment Effects on Post-mined Soil for Pinus echinata Restoration in a Changing Climate

In the face of climate uncertainty, there is a need to understand how the success of current restoration efforts may be impacted in the future. Combinations of biochar and microbial soil amendments were used in a greenhouse study to quantify potential benefits for soil health, water quality, and tree growth parameters in post-mined soil. This dataset represents a comprehensive 6-month greenhouse experiment for shortleaf pine restoration with consideration to climate change under dry, average, and wet moisture regimes. Soil amendments were applied to one year old seedlings replicated across moisture treatments including: biochar, microbial, mixture of biochar and microbial, unamended control, or control with no tree