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introduction
objectives
performance-assessment
house-rules
the-tools-we-use
r-and-rstudio
git-and-github
rmarkdown
tree-dormancy
learning-goals-for-this-lesson
introduction-to-dormancy
dormancy-physiology
experimental-and-statistical-determination-of-the-chilling-and-forcing-periods
phenology-record-and-bbch-scale
climate-change-and-impact-projection
the-drivers-of-climate-change
what-weve-already-seen
future-scenarios
impact-projection-approaches
winter-chill-projections
winter-chill-projections-1
winter-chill-in-oman
chill-model-sensitivity
winter-chill-in-california
winter-chill-ratios
a-global-projection-of-future-winter-chill
winter-chill-in-germany
winter-chill-in-tunisia
winter-chill-in-chile
chill-projection-for-patagonia
chill-model-comparison
chill-projection-for-all-of-tunisia
revisiting-chill-accumulation-in-oman
future-studies
manual-chill-analysis
computing-chilling-hours-from-hourly-temperature-data
our-first-chill-function
chill-models
chill-models-in-chillr
making-hourly-temperatures
generating-hourly-temperatures
idealized-daily-temperature-curves
empirical-daily-temperature-curves
some-useful-tools-in-r
an-evolving-language---and-a-lifelong-learning-process
the-tidyverse
the-ggplot2-package
the-tibble-package
the-magrittr-package---pipes
the-tidyr-package
pivot_longer
pivot_wider
select
filter
mutate
arrange
loops
for-loops
while-loops
apply-functions
sapply
lapply
apply
get_temp_data
temperature-data-needs
the-global-summary-of-the-day-database
actionlist_stations
actiondownload_weather
downloaded-weather-as-action-argument
filling-gaps-in-temperature-records
gaps
filling-short-gaps-in-daily-records
filling-long-gaps-in-daily-records
bias-correction-for-shorter-intervals
saving-the-data-for-later
filling-gaps-in-hourly-records
accuracy-assessment
computing-agroclimatic-metrics
generating-temperature-scenarios
chill-scenarios
risk-assessment-in-orchard-planning
weather-generators
weather-generation-in-chillr
saving-and-loading-data-and-hiding-this-in-markdown
learning-goals-for-this-lesson-1
saving-and-loading-data
hiding-all-this-from-the-readers-of-our-markdown-file
historic-temperature-scenarios
climate-change-scenarios
making-historic-temperature-scenarios
future-temperature-scenarios
impacts-of-future-climate-change
future-climate-scenarios
some-background-on-climate-models-and-warming-pathways
making-cmip6-scenarios
accessing-gridded-climate-data-from-the-copernicus-climate-data-store
downloading-future-climate-data
generating-change-scenarios
extracting-data-from-the-grids
baseline-adjustment
making-cmip5-scenarios-with-the-climatewizard
using-the-climatewizard
plot_future
making-attractive-plots
the-results
chill_model_comparison
safe-winter-chill
chill-metrics
an-animated-plot-of-relative-changes-in-safe-winter-chill
simple_phenology
phenology-analysis
time-series-analysis
p-hacking
the-process-that-generates-the-data
an-ecological-theory-to-guide-our-analysis
temperature-correlations
pls_intro
detecting-temperature-response-phases
finding-signals-in-hyperspectral-remote-sensing-data
pls-regression-for-phenology-analysis
a-note-of-caution-about-pls-results
pls-analysis-of-alexander-lucas-pears-in-klein-altendorf
pls_reflection
pls-regression
pls-examples
grasslands-on-the-tibetan-plateau
deciduous-trees
why-were-not-seeing-the-chilling-phase
pls_chillforce
adjusting-pls-for-use-with-non-monotonic-relationships
pls-analysis-with-chilling-and-forcing-data
delineating-chilling-and-forcing-periods
ggplotting-the-results
pls_chillforce_ex
pls-regression-across-species-and-agroclimatic-contexts
chestnut-jujube-and-apricot-in-beijing
apples-in-shaanxi-province-china
cherries-in-klein-altendorf
apricots-in-the-uk
grapevine-in-croatia
walnuts-in-california
almonds-in-tunisia
pistachios-in-tunisia
pls_chillforce_reflection
disappointing-pls-performance
what-pls-regression-can-find
chill-model-sensitivity-vs.-observed-temperature
beijing-china
klein-altendorf-germany
davis-california
sfax-tunisia
heat-model-sensitivity-vs.-observed-temperature
pls_eval_PLS
chilling-and-forcing-requirements
response-to-seasonal-temperature
applying-our-functions-to-california-walnuts
pls_chillforce_relimp
chilling-vs.-forcing-temperatures
patterns-in-temperature-responses
the-warm-end-of-the-spectrum
implications-of-our-hypothesis
exp_PLS
enhanced-phenology-data
model_validity
the-modeling-challenge
validating-models
mapping-validity-domains
one-last-thought-on-model-validity
phenoflex
ingredients-of-this-lesson
towards-credible-phenology-models
the-phenoflex-framework
running-phenoflex
parameterizing-phenoflex
conclusions-on-phenoflex
phenoflex2
some-basic-diagnostics-for-our-phenoflex-fit
temperature-response-of-the-phenoflex-components
overall-impression
caveats-and-assumptions
outlook
phenoflex3
the-motivation
using-experimental-phenology-data-for-assessing-the-performance-of-phenoflex
validation
conclusions
frost-risk-analysis
spring-frost
phenology-trend-analysis
frost-risk
a-robust-method-to-estimate-future-frost-risks
frost-risk-for-future-scenarios
validation-1
exercises-for-this-chapter
major_concepts
key-insights
tree-dormancy-1
climate-change
phenology-modeling
phenology-responses-to-global-warming
the-phenoflex-modeling-framework
major-concepts-weve-encountered
reproducibility-and-transparency
tools
automate-and-move-on
the-power-of-r
curiosity-and-interdisciplinarity
uncertainty
ensembles
remaining-uncertainties
p-hacking-1
the-dangers-of-machine-learning
rationalizing
overfitting
the-process-that-generated-the-data
the-importance-of-theory
conceptual-modeling
consistency-with-existing-theory-and-prior-knowledge
model-validity-and-model-validation
output-vs.-process-validation
validity-domains
validation-for-purpose
our-role-in-research