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empirical_yield_model's Introduction

Application of machine learning algorithm in understanding growth pattern of miscanthus

/paper/ contains two papers, describing growth pattern of miscanthus in the paper folder. one paper on meta decision tree.

./mxgdata/ contains a simple csv file wit observed miscanthus data

./Rscripts/ contains a simple R script to visualize miscanthus data

./figures/ contains plots/visualization of miscanthus data.

Research Questions:

  1. Can we come up with an machine learning alternative for meta analyses performed in the two papers with improved prediction capabity and classifier definition.
  2. Can machine learning help us to identify when miscanthus will respond to Nitrogen and when it does not? given the input data set, independent variables could be species, growth stange, site (N deposition rate), irrigation, etc.

Methodology

Daily Climate Data for each site

Deepak will fill in this part.

Constructing Classifiers

Deepak, David, and Jiarui will perform this task

Selection of models from two papers

Deepak, David and Jiarui will perform this task

Identifying best combination of models and classifiers

Jiarui will perform this task

Conclusions for questions 1 and 2.

Deepak, David, and Jiarui will write this.

empirical_yield_model's People

Contributors

djaiswal avatar dlebauer avatar sidewallme avatar

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sidewallme

empirical_yield_model's Issues

Define a modeling approach

Define models, method of evaluation (e.g. regression tree, ANN, linear regression), and model skill metrics (e.g. RMSE, r2, sd_model/sd_mean, Taylor Skill, etc.)

Write methods (and revise / update code)

  • rename tree.Rmd to methods-and-results.Rmd, or else move content to manuscript.Rmd
  • code updated to use both swithcgrass and miscanthus
  • update the methods section
  • update the results section
  • propose new analyses as github issues

Download climate data for each location using R raster package

@sidewallme

Download each fof the 19 BioClim variables listed here http://www.worldclim.org/bioclim using the getData function of the raster package

other variables: http://www.worldclim.org/formats

library(raster)
x <- getData('worldclim', var='tmin', res=0.5, lon=5, lat=45)

note that bioclim are long-term climate means; also would be useful to find precip. and temp during harvest year (e.g. 'year' field in miscanthus.csv from #2).

Try out this script for downloading dayment: https://bitbucket.org/khufkens/daymetr

What other species are good biofuel crops?

@phenolphtalein

How might you answer the following ...

Given the Miscanthus and Switchgrass yield data that @sidewallme has used in his analysis, plus any other data in the yields table, determine:

Are there species that haven't been grown in agronomic trials (i.e. we don't have yield data in betydb.org/yields.csv) that might be good candidates for high yielding crops?

Notes:

  • in the plants table (betydb.org/species.csv) is full of categorical variables - for all species in plants.usda.gov
  • there are also many sparse rows that we have created, mostly for poplar and willow hybrids (bred across species) and non-US plants.

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