Understanding how climate variability influences ecological processes is a key approach to provide recommendations for climate adaptation and biodiversity conservation. climatrends provides methods to compute precipitation and temperature indices that serves as input for crop modelling, ecological modelling and trends in climate change. The climate variables available in climatrends were previously used to identify climate patterns in crop growing cycles1, regional climate change assessment2–5, crowdsourcing citizen science in agriculture6 and trends in climate change compared with farmers’ perceptions7.
Reproducibility, the ability to repeat the analysis, and
Replicability, the ability to repeat an experiment are key to perform
collaborative scientific research. This is still a gap in most of the
studies in agriculture and ecology. climatrends addresses
this specific issue. The package originates from a set of scripts to
compute climate indices in our previous studies. Building up on the
interest in expanding the analysis to other regions and to enable
reproducible and replicable studies we developed
climatrends. Most of the package functions take into
account the heterogeneity of testing sites (locations), dates and
seasons, a common characteristic of decentralized agricultural trials.
Further development was made to enable time series analysis with fixed
periods of time and locations. The package climatrends
computes temperature, precipitation, crop growing and crop stress
indices that were validated by previous studies on climatology and crop
science. Currently climatrends is part of the CRAN Task
View in Agriculture (https://cran.r-project.org/web/views/Agriculture.html).
By default, the functions in climatrends requires a
vector with climate data, and optionally a vector of dates that can be
linked to the data for time series analysis. The package also provides
methods for objects of classes array (or
matrix), sf and data.frame, where
the last two are designed to be used to request remote data from
R packages that uses API clients, such as nasapower8. This vignette presents the main
functions and the methods available in climatrends.
Overall, these applications can be generalized to all functions.
Here we compute temperature indices for the first semester of 2019 in the Innlandet county in Norway:
library("climatrends")
data("innlandet", package = "climatrends")
temp1 <- temperature(innlandet$tmax, innlandet$tmin)
temp1## maxDT minDT maxNT minNT DTR SU TR CFD WSDI CSDI T10p T90p
## <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <dbl> <dbl>
## 1: 15.13 -14.86 6.77 -19.25 6 0 0 115 4 5 -15.81 9.09With the argument timeseries = TRUE, temperature()
returns the indices for a time series based on intervals.
Here we compute the indices for intervals of 30 days:
temp2 <- temperature(innlandet$tmax, innlandet$tmin,
dates = innlandet$dates,
timeseries = TRUE,
intervals = 30)
temp2## id date index value
## <int> <date> <chr> <dbl>
## 1: 1 2019-01-01 maxDT -0.15
## 2: 1 2019-01-01 minDT -14.86
## 3: 1 2019-01-01 maxNT -3.41
## 4: 1 2019-01-01 minNT -18.67
## 5: 1 2019-01-01 DTR 4.35
## ---
## 68: 1 2019-05-31 CFD 3.00
## 69: 1 2019-05-31 WSDI 2.00
## 70: 1 2019-05-31 CSDI 3.00
## 71: 1 2019-05-31 T10p 0.20
## 72: 1 2019-05-31 T90p 11.14Growing degree-days is a heuristic tool in phenology, it is a measure
of heat accumulation used to predict plant and animal development
rates9. With the function
GDD() we can compute the growing degree-days over a time
series. In this case we apply an adjusted equation designed for cold
areas with the argument equation:
## gdd
## <dbl>
## 1: 0.00
## 2: 0.00
## 3: 0.00
## 4: 0.00
## 5: 0.00
## ---
## 178: 143.04
## 179: 147.38
## 180: 153.89
## 181: 162.18
## 182: 164.89Additionally, the function may return the daily values of degree-days
or the number of days that a given organism required to reach a certain
number of accumulated degree-days. These values are defined by ‘daily’
or ‘ndays’ and can be adjusted using the argument
return.as. The required accumulated gdd is defined with
argument degree.days. For example, the Korean pine
(Pinus koraiensis) requires 105 ∘C accumulated gdd to
onset of photosynthesis10. In
that case, the function will calculate the growing degree-days and sum
up the values until it reaches the defined gdd (105 ∘C) and return the number
of days needed in the given season, as follows.
lonlat <- data.frame(lon = 129.19,
lat = 36.39)
GDD(lonlat,
day.one = "2019-04-01",
last.day = "2019-10-01",
degree.days = 150,
return.as = "ndays")This means that 45 days were required to reach 150 degree-days from April 2019.
Late frost is a freezing event occurring after a substantial
accumulation of warmth. These events can be tracked using the function
late_frost(). The function returns a data.frame with the
duration and gdd accumulated during the events of frost,
latency (where there is no frost event, but also there is no GDD), and
warming (where GDD is accumulated).
## date gdd event duration
## <date> <dbl> <fct> <int>
## 1: 2019-01-01 0.00 frost 108
## 2: 2019-04-19 0.00 latent 1
## 3: 2019-04-20 0.00 frost 1
## 4: 2019-04-21 0.00 latent 2
## 5: 2019-04-23 0.00 frost 2
## 6: 2019-04-25 0.98 warming 6
## 7: 2019-05-01 0.00 frost 14
## 8: 2019-05-15 19.14 warming 11
## 9: 2019-05-26 0.00 frost 6
## 10: 2019-06-01 82.48 warming 31Precipitation indices are computed in the same way as the
temperature() indices using the function
rainfall(). But here, we fetch data from NASA POWER using
the nasapower
package passed to the data.frame method.
These indices are designed to capture variability in sensitive stages
of crop development3,4 and can
be computed using the function crop_sensitive(). Here we
use the sf method with 5 random points across the Sinop
municipality in Brazil.
library("sf")
data("lonlatsf", package = "climatrends")
crop_sensitive(lonlatsf,
day.one = "2018-12-01",
last.day = "2019-01-31",
as.sf = FALSE)Additionally the thresholds for each index can be adjusted using the
argument *.threshold (where * is replaced by the index
abbreviation).
Evapotranspiration can be defined as the sum of evaporation and plant
transpiration from the Earth’s surface to the atmosphere. This is also
an important index to include in models for ecological interactions. In
**climatrends* this index can be calculate using the function
ETo() which computes the evapotranspiration based on the
Blaney-Criddle method11, an
ideal equation when only air-temperature data sets are available for a
site. Here we use the array method to compute the reference
evapotranspiration. And the duration of the time series in each row is
adjusted with the argument span based on the duration of
the anthesis period in the latitude 25N, adjusted with argument
lat.
data("temp_dat", package = "climatrends")
eto <- ETo(temp_dat,
day.one = "2013-10-28",
span = c(9, 10, 11, 12, 8, 10, 11, 11, 12, 10),
lat = rep(25, 10),
Kc = 0.92)
eto## ETo
## <dbl>
## 1: 4.21
## 2: 4.34
## 3: 4.13
## 4: 3.50
## 5: 4.02
## 6: 3.75
## 7: 4.02
## 8: 3.89
## 9: 3.91
## 10: 3.65