David LeBauer dlebauer

## srer_priors.tsv

          
            pft
            variable
            phylogeny
            distn
            parama
            paramb
            n
            citation

            
              forb_priors
              mort2
              plants
              gamma
              1.47
              0.0578
              0
              LeBauer 2010 Expert Prior

            
              forb_priors
              growth_resp_factor
              grass
              beta
              2.63
              6.52
              0
              LeBauer 2010 Expert Prior

            
              forb_priors
              fineroot2leaf
              grass
              lnorm
              0.811
              0.843
              0
              Chapin 2002 Principles of Terrestrial Ecosystem Ecology

            
              forb_priors
              root_turnover_rate
              grass
              weibull
              1.67
              0.657
              66
              Gill 2000 Global patterns of root turnover for terrestrial ecosystems

            
              forb_priors
              seedling_mortality
              monocots
              beta
              3.61
              0.433
              0
              LeBauer 2010 Expert Prior

            
              forb_priors
              Vcmax
              
              lnorm
              4.51
              0.64
              19
              Wullschleger 1993 Biochemical Limitations to Carbon Assimilation in C3 Plants: A Retrospective Analysis of the A/Ci Curves from 109 Species

            
              forb_priors
              root_respiration_rate
              angiosperm
              gamma
              4.95
              0.762
              36
              George 2003 Fine-root respiration in a loblolly pine and sweetgum forest growing in elevated CO2

            
              forb_priors
              r_fract
              plants
              beta
              2
              4
              0
              LeBauer 2010 Expert Prior

            
              forb_priors
              c2n_leaf
              grass
              gamma
              4.18
              0.13
              95
              Wright 2004 The world-wide leaf economics spectr

## README.md

      
              2 files
            
          
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                dlebauer
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              Last active
              March 18, 2022 21:16
            
              
                UA Drone Day 2022 flight plan
              
          
## model_output_munging.R
sum_out <- broom.mixed::tidyMCMC(coda_out, conf.int = TRUE, conf.level = 0.95) %>%
  rename(mean = estimate, sd = std.error, pc2.5 = conf.low, pc97.5 = conf.high) %>%
  mutate(sig = if_else(pc2.5 * pc97.5 > 0, TRUE, FALSE)) # test if UCL and LCL both have the same sign

years <- data.frame(year_index = 1:length(unique(x$year)), year = unique(x$year))
states <- data.frame(state_index = 1:length(unique(x$state)), state = unique(x$state))

sum_rep <- broom.mixed::tidyMCMC(coda_rep, conf.int = TRUE, conf.level = 0.95) %>%
  rename(mean = estimate, sd = std.error, pc2.5 = conf.low, pc97.5 = conf.high) %>%
  separate(term, sep = '\\[|\\]|,', into = c('term', 'i', 'j', 'other'), convert = TRUE) %>%

## organic_ag_data_nass_quickstats.R
#CSV downloaded from https://quickstats.nass.usda.gov/results/12827E57-2B20-368C-A91F-1B97FF8F7B35#E2E0BF24-9F88-35DB-8526-8E47D941FDBF
library(dplyr)
library(tidyr)
nass_data <- readr::read_csv("raw_data/E2E0BF24-9F88-35DB-8526-8E47D941FDBF.csv",na = '(D)',
                             skip = 1,

                             col_names = c(
                               "program", "period", "week_ending", "geo_level", "state", "state_ansi",
                               "ag_district", "ag_district_code", "county", "county_ansi", "zip_code",
                               "region", "watershed_code", "watershed", "commodity", "data_item",

## download_terraref_from_dryad.R
library(ggplot2)
library(dplyr)

download.file(url = 'https://datadryad.org/stash/downloads/file_stream/624637', destfile = 'trait_data.zip', mode = 'wb')

unzip(zipfile = 'trait_data.zip', unzip = 'unzip')
s6heights <- readr::read_csv('traits/season_6_traits/season_6_canopy_height_sensor.csv')
s4heights <- readr::read_csv('traits/season_4_traits/season_4_canopy_height_sensor.csv')

heights <- bind_rows(s4heights, s6heights)

## after.sql
select
  t.treatment_id :: text as treatmentDbId,
	t.site_id :: text as observationUnitDbId,
  t.variable_id :: text as observationVariableDbId,
  v.name as observationVariableName,
  t.id :: text as observationDbId,
  t.mean :: text as value,
  t.date as observationTimeStamp,
  s.sitename as observationUnitName,
  t.cultivar_id :: text as germplasmDbId,

## MAB.R
n_sim <- 10000

mu <- -10
sd <- 2
n  <- 30

z <- array(NA, c(3,n_sim))
for(i in 1:n_sim){
  x <- rnorm(n, mu, sd)
  z[1, i] <- mean(x)

## README.md

      
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                dlebauer
                / README.md
            
            
              Created
              February 24, 2021 04:05
            
              
                simple PEcAn workflow; variations on https://github.com/az-digitalag/model-vignettes/pull/60
              
          
    Environment comparisons

This folder contains code used to compare setaria growth in different environments.
Contents:


inputs/


scripts/ folder:


## pecan.xml
<?xml version="1.0"?><pecan>
  <info>
    <notes/>
    <userid>-1</userid>
    <username/>
    <date>2021/01/28 19:56:08 +0000</date>
  </info>
  <outdir>/data/workflows/PEcAn_99000000009</outdir>
  <database>
    <bety>

## simple_purple_shaker.py
import pgzrun
import time
import random

WIDTH = 400
HEIGHT = 400

guy = Actor("purple_guy")
guy.pos = (200, 200)
guy.angle = -25
pft	variable	phylogeny	distn	parama	paramb	n	citation
forb_priors	mort2	plants	gamma	1.47	0.0578	0	LeBauer 2010 Expert Prior
forb_priors	growth_resp_factor	grass	beta	2.63	6.52	0	LeBauer 2010 Expert Prior
forb_priors	fineroot2leaf	grass	lnorm	0.811	0.843	0	Chapin 2002 Principles of Terrestrial Ecosystem Ecology
forb_priors	root_turnover_rate	grass	weibull	1.67	0.657	66	Gill 2000 Global patterns of root turnover for terrestrial ecosystems
forb_priors	seedling_mortality	monocots	beta	3.61	0.433	0	LeBauer 2010 Expert Prior
forb_priors	Vcmax		lnorm	4.51	0.64	19	Wullschleger 1993 Biochemical Limitations to Carbon Assimilation in C3 Plants: A Retrospective Analysis of the A/Ci Curves from 109 Species
forb_priors	root_respiration_rate	angiosperm	gamma	4.95	0.762	36	George 2003 Fine-root respiration in a loblolly pine and sweetgum forest growing in elevated CO2
forb_priors	r_fract	plants	beta	2	4	0	LeBauer 2010 Expert Prior
forb_priors	c2n_leaf	grass	gamma	4.18	0.13	95	Wright 2004 The world-wide leaf economics spectr
	sum_out <- broom.mixed::tidyMCMC(coda_out, conf.int = TRUE, conf.level = 0.95) %>%
	rename(mean = estimate, sd = std.error, pc2.5 = conf.low, pc97.5 = conf.high) %>%
	mutate(sig = if_else(pc2.5 * pc97.5 > 0, TRUE, FALSE)) # test if UCL and LCL both have the same sign

	years <- data.frame(year_index = 1:length(unique(x$year)), year = unique(x$year))
	states <- data.frame(state_index = 1:length(unique(x$state)), state = unique(x$state))

	sum_rep <- broom.mixed::tidyMCMC(coda_rep, conf.int = TRUE, conf.level = 0.95) %>%
	rename(mean = estimate, sd = std.error, pc2.5 = conf.low, pc97.5 = conf.high) %>%
	separate(term, sep = '\\[\|\\]\|,', into = c('term', 'i', 'j', 'other'), convert = TRUE) %>%
	#CSV downloaded from https://quickstats.nass.usda.gov/results/12827E57-2B20-368C-A91F-1B97FF8F7B35#E2E0BF24-9F88-35DB-8526-8E47D941FDBF
	library(dplyr)
	library(tidyr)
	nass_data <- readr::read_csv("raw_data/E2E0BF24-9F88-35DB-8526-8E47D941FDBF.csv",na = '(D)',
	skip = 1,

	col_names = c(
	"program", "period", "week_ending", "geo_level", "state", "state_ansi",
	"ag_district", "ag_district_code", "county", "county_ansi", "zip_code",
	"region", "watershed_code", "watershed", "commodity", "data_item",
	library(ggplot2)
	library(dplyr)

	download.file(url = 'https://datadryad.org/stash/downloads/file_stream/624637', destfile = 'trait_data.zip', mode = 'wb')

	unzip(zipfile = 'trait_data.zip', unzip = 'unzip')
	s6heights <- readr::read_csv('traits/season_6_traits/season_6_canopy_height_sensor.csv')
	s4heights <- readr::read_csv('traits/season_4_traits/season_4_canopy_height_sensor.csv')

	heights <- bind_rows(s4heights, s6heights)
	select
	t.treatment_id :: text as treatmentDbId,
	t.site_id :: text as observationUnitDbId,
	t.variable_id :: text as observationVariableDbId,
	v.name as observationVariableName,
	t.id :: text as observationDbId,
	t.mean :: text as value,
	t.date as observationTimeStamp,
	s.sitename as observationUnitName,
	t.cultivar_id :: text as germplasmDbId,
	n_sim <- 10000

	mu <- -10
	sd <- 2
	n <- 30

	z <- array(NA, c(3,n_sim))
	for(i in 1:n_sim){
	x <- rnorm(n, mu, sd)
	z[1, i] <- mean(x)
	<?xml version="1.0"?><pecan>
	<info>
	<notes/>
	<userid>-1</userid>
	<username/>
	<date>2021/01/28 19:56:08 +0000</date>
	</info>
	<outdir>/data/workflows/PEcAn_99000000009</outdir>
	<database>
	<bety>
	import pgzrun
	import time
	import random

	WIDTH = 400
	HEIGHT = 400

	guy = Actor("purple_guy")
	guy.pos = (200, 200)
	guy.angle = -25