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Article
Climate Change, Farming, and Gardening in Alaska:
Cultivating Opportunities
Nancy Fresco 1, * , Alec Bennett 2, Peter Bieniek 1and Carolyn Rosner 1
Citation: Fresco, N.; Bennett, A.;
Bieniek, P.; Rosner, C. Climate
Change, Farming, and Gardening in
Alaska: Cultivating Opportunities.
Sustainability 2021,13, 12713. https://
doi.org/10.3390/su132212713
Academic Editors: Stephanie Pfirman,
Gail Fondahl, Grete K. Hovelsrud
and Tero Mustonen
Received: 30 September 2021
Accepted: 8 November 2021
Published: 17 November 2021
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1International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;
pbieniek@alaska.edu (P.B.); crosner@alaska.edu (C.R.)
2School of Management, University of Alaska Fairbanks, Fairbanks, AK 99775, USA; apbennett@alaska.edu
*Correspondence: nlfresco@alaska.edu; Tel.: +1-907-474-2405
Abstract:
Ongoing climate change and associated food security concerns are pressing issues globally,
and are of particular concern in the far north where warming is accelerated and markets are remote.
The objective of this research was to model current and projected climate conditions pertinent to
gardeners and farmers in Alaska. Research commenced with information-sharing between local
agriculturalists and climate modelers to determine primary questions, available data, and effective
strategies. Four variables were selected: summer season length, growing degree days, temperature
of the coldest winter day, and plant hardiness zone. In addition, peonies were selected as a case
study. Each variable was modeled using regional projected climate data downscaled using the delta
method, followed by extraction of key variables (e.g., mean coldest winter day for a given decade).
An online interface was developed to allow diverse users to access, manipulate, view, download,
and understand the data. Interpretive text and a summary of the case study explained all of the
methods and outcomes. The results showed marked projected increases in summer season length and
growing degree days coupled with seasonal shifts and warmer winter temperatures, suggesting that
agriculture in Alaska is undergoing and will continue to undergo profound change. This presents
opportunities and challenges for farmers and gardeners.
Keywords:
climate change; agriculture; Alaska; growing degree days; seasonality; plant hardi-
ness zone
1. Introduction
The relationship between agricultural production and climate change is of particular
interest and pertinence in Alaska for several reasons. These include the accelerated pace
of climate change in Alaska, the state’s current low agricultural food production and
associated vulnerability to supply disruptions, remoteness, the lack of diversity in Alaska’s
highly oil-dependent economy, and the potential for agricultural expansion.
Alaska’s high-latitude setting places it at the front lines of environmental change [
1
,
2
].
Due in large part to polar amplification [
3
], the climate is warming in the far north at as
much as three times the rate of other regions of the world [4].
Food security is an issue of particular concern in Alaska, in part because the state
is remote from the contiguous United States and other agricultural regions; of all the
agriculturally-produced food consumed in the state, only five percent is locally grown [
5
].
Alaska also has many communities which are inaccessible by road, and are thus vulnerable
to interruptions in food supply [
6
]. Alaska has ample arable land and fresh water, and yet
lags far behind northern European nations in terms of agricultural self-sufficiency, which
places it at a high risk for catastrophic disruptions to supply chains [7].
Rising temperatures, altered precipitation regimes and associated shifts in growing
degree days, summer season length, and the timing of spring thaw and autumn frost are
among the factors that are rapidly altering natural ecosystems and agricultural opportuni-
ties [
2
,
8
]. The ability of Alaskans to predict these changes will profoundly affect their ability
Sustainability 2021,13, 12713. https://doi.org/10.3390/su132212713 https://www.mdpi.com/journal/sustainability
Sustainability 2021,13, 12713 2 of 13
to adapt. The State of Alaska recognizes the scope and magnitude of these changes and has
made it a priority to ensure Alaskan communities and managers incorporate anticipated
change into local and regional planning. This is also a goal of the University of Alaska
(UA), which seeks to apply and advance its expertise in climate science and landscape
ecology to better understand the manner by which these changes affect ecosystems, food
webs and human populations.
Thus far, adaptation to climate change in the agricultural sector has been slow, and
studies suggest that such adaptation does not occur until farmers perceive the importance
and immediacy of climate change [
9
]. Farmers’ perceptions of climate change have been
identified as an important factor for adaptation to take place [
10
–
12
] as it triggers the
necessary changes that are needed for action, in addition to other factors [13].
Currently, agriculture in Alaska is climate-limited. Sparrow et al. [
14
] note that
primary limitations include low heat energy, short growing seasons, and cold winters that
prevent survival of perennial crops. Considerable research (e.g., [
15
–
17
]) has assessed how
to overcome some of these limitations, particularly in the context of food security.
The scientific consensus suggests that climate change is already altering the equation,
and will continue to do so [
18
]. Hatch [
19
] found that climate projections show that future
growing conditions in the Fairbanks North Star Borough may be more similar to northern
prairies in the lower 48 states. Sparrow [
16
] found that increases in growing degree days
(GDD) could cause crop production to advance northward throughout the century, with
increases in yields and new varieties becoming viable. Meanwhile, for some crops, climate
change may not be positive. For example, burgeoning peony markets are dependent on
Alaska’s relatively cool climate and late summer season.
Lader et al. [
20
] investigated some of the potential impacts of climate warming on
northern agriculture. Their research used climate projections based on regional dynamical
downscaling using the Weather Research and Forecasting (WRF) Model. They used these
model outputs to assess changes in growing season length (GSL), spring planting dates,
and potential occurrences of plant heat stress for five regions in Alaska. Expanding the
use of these data by adding additional variables and the full spatial extent of the state, this
project’s goal was to provide real-world tools that stakeholders around Alaska can use to
plan for and adapt to agricultural change.
Currently available tools are limited in their ability to help Alaska’s farmers adjust to
climate change. USDA hardiness zones are relatively fine-scale within Alaska, but are based
only on extreme winter temperature; thus, they serve as a reliable metric only for plants
affected and limited by winter extremes. Indicator plants, as defined by the USDA, help to
capture some of the nuances of range limits. However, with ongoing climate change, both
winter extremes and indicator species may shift and change. Moreover, for many species,
particularly annual crops, other climate indicators are likely to provide more pertinent
hardiness information. Communication with stakeholders can aid researchers in creating
climate change assessments that address real-world concerns. Moreover, a meaningful
assessment must take into account both positive and negative potential changes, including
new opportunities and new stressors.
Peony farming serves as an excellent case study for research on climate change and
agriculture in Alaska, because peonies represent a burgeoning niche market, and are a crop
that is uniquely lucrative in Alaska for reasons linked directly to the climate. Peonies bloom
in Alaska in July, August, and September and are available commercially nowhere else in
the world during this time. Commercial peony farming has seen considerable growth in
recent years. There are over 100,000 peony roots in the ground on peony farms in Alaska
and farmers are continuing to add roots at over 30,000 roots per year, with gross sales of
well over a million dollars. Peony growers engaged in the project expressed concern about
seeing shifts toward earlier blooming times, which puts Alaska’s peonies in more direct
competition with other markets.
The shifts in the Arctic climate will likely produce a range of impacts on different
crop species, but through the development of decision support tools, those affected have
Sustainability 2021,13, 12713 3 of 13
a greater ability to prepare for changes proactively. This project drew upon local knowl-
edge and the best available climate modeling techniques to build user-friendly tools that
deliver practical information to farmers, ranchers, forest landowners, and Alaska Native
communities to help them to adapt to climate change. The project demonstrated that over
the coming years and decades profound shifts are likely in growing season length, growing
degree days, and winter temperatures, and it linked these changes with potential shifts in
key crops.
2. Materials and Methods
The project included four major stages:
1.
collaborative research and information-sharing in order to determine primary ques-
tions, available data, and effective strategies;
2. development of datasets, models, and tools, to address these primary questions;
3.
interpretation and refinement of models and tools in order to maximize their utility
and effectiveness; and
4.
final interpretation and dissemination of results and outcomes to project partners and
to the public.
In the first stage, we fully reviewed the existing literature and the potential appli-
cations for existing downscaled climate projections. We then met (in person, via video
conference, and by email) with collaborative partners from around Alaska, represent-
ing family-owned community-sustained farms (Calypso Farm and Ecology Center and
Spinach Creek Farm), small-scale commercial peony growers (Arctic Alaska Peonies and
The Alaska Peony Growers’ Association), a knowledge-sharing program between UAF
and rural Alaska communities (Community Partnerships for Self-Reliance) and a Tribal
conservation organization dedicated to the wise use of natural resources (Tyonek Tribal
Conservation District). These partners had all indicated interest in the project prior to
it being successfully funded, and had provided letters of support. Some, including the
peony growers, had previously approached us with their questions, while others were
networking contacts with known interest in climate change research, sustainability, and
community self-reliance.
Given the collaborative, rather than top-down, nature of the research, discussions were
open-ended. We discussed the development of user-friendly tools that could be developed
for the project. However, we did also seek each collaborators’ specific thoughts regarding
the greatest climate-related factors related to successful farming, with the expectation that
these answers would vary by region and expertise but would reveal key patterns.
From these interactions we determined the following priorities. Those marked by
a star (1, 2, and 7) were selected for modeling, based on the availability of appropriate
climate data. Those not examined in this study may be pursued in future research.
1.
Growing degree days (GDD) are an important variable for determining crop viability.
It is optimal to map GDD spatially, given that elevation, slope, and aspect are all
key. Peonies and brassica (broccoli/cabbage family) are more successful with cooler
temperatures (lower GDD), while other flowers and vegetables (e.g., squash) are more
successful with higher GDD. *
2.
Total season length and/or the date of the first and last frost are crucial. This affects
not only crops that need a long growing season (e.g., squash) but also crops that
farmers like to stagger with multiple plantings, and plants for which the timing of
harvest is key in order to compete in markets. *
3. Soil temperature, particularly in spring, is very important.
4. Cold spring temperatures in general are difficult for farmers.
5. Drought or constant rain are problematic.
6. For perennials, the timing of when snow arrives is an important factor.
7.
The coldest monthly mean temperature (often January) and coldest winter tempera-
ture are of interest for perennials. *
Sustainability 2021,13, 12713 4 of 13
The selected questions led directly to the data analysis, modeling, and tool creation
described below.
Climate station data is limited in Alaska, necessitating the use of downscaled gridded
reanalysis data for both historical and future projections. The climate projections used
in this project were derived from regional dynamically downscaled data produced with
the Advanced Research core of the Weather Research and Forecasting Model (WRF) [
21
].
The model simulations were driven by multiple climate datasets for past time periods,
including ERA-Interim data [
22
], the Geophysical Fluid Dynamics Laboratory Climate
Model (GFDL), version 3 [
23
], and the National Center for Atmospheric Research (NCAR)
Community Climate System Model, version 4. Two different sets of modeled data were
used in this project, referred to as GFDL and NCAR, based on these two different Global
Circulation Models, in order to represent the range and uncertainty associated with use of
climate projections. While both models have been shown to be highly valid in northern
latitudes [
1
], GFDL data tend to project greater changes in temperature, while NCAR
outputs are more conservative. The full dynamical downscaling methodology and WRF
configuration are described in Bieniek et al. [
24
] and Lader et al. [
20
]. Data were downscaled
to 20 km spatial resolution; thus, all community-specific data described in this analysis
can be understood to represent the 20 km grid cell that best represents the community
locations. The projected data used the 8.5 RCP (Representative Concentration Pathway)
of phase 5 of the Coupled Model Intercomparison Project (CMIP5) [
25
], as defined by the
IPCC. The limitations imposed by spatial resolution and choice of RCP are described in the
Discussion section.
Temperature and precipitation data were produced at hourly time resolution. This
allowed for fine-scale identification of some of the key variables that were identified by
stakeholders. Because we were aiming to highlight climate trends over long periods of time
(decades or longer) rather than to accentuate model variability at the annual or sub-annual
level, we used decadal means and multi-decadal means in our data visualization tools.
Separate tool interfaces within a dashboard-based website were developed for each
selected variable, including one for GDD, one for season length, and two separate tools
for visualizing cold conditions. All four resulting tools are available online–in separate
tabs—at https://www.snap.uaf.edu/tools/gardenhelper/ (accessed on 15 September 2021)
(see Supplementary Materials) with an accompanying explanatory text aimed at a wide
range of stakeholders from the general public.
The first tool in the online interface was designed to provide gardeners with past,
current, and projected future data estimating growing season length, as defined by the
longest time period during which the temperature never drops below a selected threshold
Fahrenheit degrees were used throughout the tool, based on user familiarity. This famil-
iarity is also reflected by the fact that Fahrenheit degrees are more commonly referenced
in the corresponding agricultural literature. Thus, in the tool these thresholds are defined
as “hard frost, 28
◦
F” (
−
2.2
◦
C); “light frost, 32
◦
F” (0
◦
C); “Cold crops, 40
◦
F” (4.4
◦
C);
or “Warm crops, 50
◦
F” (10
◦
C). In order to make the tool locally pertinent and accessible,
we created drop-down menus to offer users a choice of hundreds of Alaska communities,
each linked to the appropriate latitudinal and longitudinal location in the database; a radio-
button choice of the NCAR or GFDL model, and a drop-down menu choice of temperature
thresholds. The accompanying text explains these choices and interprets the outputs. Based
on the literature, gardeners are offered a table with appropriate threshold values and the
approximate number of days necessary to produce 24 different annual crops, including
a range of popular vegetables and grains. The interpretation includes an explanation for
why the data appear variable, even when averaged by decade, as well as an explanation
for the use of two different GCMs.
The second component of the tool focuses on daily minimum temperatures—estimates
of record-breaking cold. We created an interface such that for user-selected locations and a
user-selected model, as described above, a graph is generated showing the modeled data
based on the coldest temperature ever recorded or projected for a chosen location, date (e.g.,
Sustainability 2021,13, 12713 5 of 13
12 February or 18 July) and time period. For the purposes of this interface, we aggregated
the data into color-coded thirty-year ranges to represent climatologies: 1980–2009, 2010–
2039, 2040–2069, and 2070–2099. This allows users to see the clear distinctions between
past, current, near-future, and far-future projections. The accompanying text explains how
to use and interpret the interface.
The third tool interface calculates GDD, a metric commonly used to estimate how
much heat is available and useable to crops. There are several methods for calculating
GDD. Based on the available modeled data, and in order to create a user-friendly online
tool with the greatest possible flexibility and clarity, we used a method in which we took
the average of the daily high and daily low temperatures, and subtracted the user-selected
baseline value from that average. In other words, if a user selected a baseline of 50
◦
F
(10
◦
C), and if the daily high for a particular day was 70
◦
F (21
◦
C) and the low was 60
◦
F
(16
◦
C), the GDD value for that day would be ((70
◦
F + 60
◦
F)/2)
−
50
◦
F = 15
◦
F or in
SI units ((21
◦
C + 16
◦
C)/2)
−
10
◦
C = 8.5
◦
C. As in the season length tool, we offer four
possible baselines: 28
◦
F (
−
2.2
◦
C), 32
◦
F (0
◦
C), 40
◦
F (4.4
◦
C), and 50
◦
F (10
◦
C). Daily
values are cumulatively summed across the summer season, creating graphical outputs.
In order to smooth the data and create a reasonable number of future projections, data
are averaged by decade. Because heat stress is rare in Alaska, we did not include upper
GDD thresholds in our calculations. GDD is not a familiar concept or calculation for many
stakeholders, and as such we included adequate explanation in the tool interface to allow
for appropriate interpretation. This included tables of sample crops identified by their
growth thresholds and necessary GDD values (ten species with a threshold of 32
◦
F, four at
40
◦
F, and six at 50
◦
F; no species were identified with a threshold below 32
◦
F, but 28
◦
F
was included in the dropdown interface to provide continuity with the growing season
length tool.)
In the fourth and final tool interface, we created maps using metrics similar to those
used by the USDA to define Plant Hardiness Zones. Here, users do not need to select a
location, because all maps cover the full statewide spatial domain. However, users are
offered the option of downloading high-resolution individual maps for four current and
future time periods, or viewing all four simultaneously. For the purposes of this interface,
we aggregated the data into the same thirty-year ranges that were used for the minimum
temperature tool: 1980–2009, 2010–2039, 2040–2069, and 2070–2099. However, rather than
being based on absolute coldest daily temperatures, hardiness maps are based on the
average annual minimum winter temperature. In order to aid user interpretation, we
matched map colors and labeling schemes to those used in USDA maps.
Finally, in addition to the creation of the above tool components, we assessed the
potential changes to peony crops in Alaska, based on our climate projections coupled
with data available from the literature and insights on agricultural research on peonies—
particularly research conducted in Alaska by Patricia Holloway and others at UAF’s
Georgeson Botanical Garden. This included data on dormancy, stem growth, flowering,
and seasonal timing.
3. Results
3.1. Length of Growing Season
An example of the tool outputs for the length of growing season is shown in Figure 1.
Although this figure shows only a single location (Fairbanks, the location of the researchers
and several stakeholders engaged in this project), threshold (32
◦
F) and model (GFDL), it
is typical of the full range of results in several ways. First, it clearly demonstrated, even
to a casual viewer, that the summer growing season is projected to get longer over time.
Second, it shows that this increase is likely to occur at both ends of the season, with earlier
springs and later autumns. Finally, the results demonstrate that this shift, while obvious at
the scale of a century, is somewhat variable or unpredictable, even with decadal averaging.
Sustainability 2021,13, 12713 6 of 13
Sustainability 2021, 13, x FOR PEER REVIEW 6 of 13
over time. Second, it shows that this increase is likely to occur at both ends of the season,
with earlier springs and later autumns. Finally, the results demonstrate that this shift,
while obvious at the scale of a century, is somewhat variable or unpredictable, even with
decadal averaging.
Figure 1. Sample of Growing Season Length tool output. Location selected was Fairbanks, temper-
ature threshold selected was 32 °F, and model selected was GFDL.
This tool can return data that, although technically realistic (reflecting likely real-
world conditions), may be confusing and not useful to end users. If users select a particu-
larly high temperature threshold, and/or live in a very cold region, the results may appear
to be short and uneven, as in Figure 2. This is because the tool finds the longest consecutive
period during which the daily minimum temperature never drops below the selected tem-
perature. This time period may be extremely short, and is unlikely to be helpful in deter-
mining when to plant crops. Users are cautioned to be sure to select thresholds that make
sense for their area. In future tool iterations, feedback from users may help create visuali-
zations that avoid this issue altogether.
Figure 2. Sample of Growing Season Length tool output. Location selected was Nome, temperature
threshold selected was 50 °F, and model selected was GFDL.
3.2. Annual Minimum Temperature (AMT)
Sample output from the AMT interface is shown in Figure 3. In this case, Anchorage
(the largest population center in Alaska) and the NCAR model, which tends to project less
extreme climate change than the GFDL model, were selected. However, outputs for other
locations and for the GFDL model show similar patterns. While variability is high, as can
Figure 1.
Sample of Growing Season Length tool output. Location selected was Fairbanks, temperature threshold selected
was 32 ◦F, and model selected was GFDL.
This tool can return data that, although technically realistic (reflecting likely real-world
conditions), may be confusing and not useful to end users. If users select a particularly high
temperature threshold, and/or live in a very cold region, the results may appear to be short
and uneven, as in Figure 2. This is because the tool finds the longest consecutive period
during which the daily minimum temperature never drops below the selected temperature.
This time period may be extremely short, and is unlikely to be helpful in determining
when to plant crops. Users are cautioned to be sure to select thresholds that make sense for
their area. In future tool iterations, feedback from users may help create visualizations that
avoid this issue altogether.
Sustainability 2021, 13, x FOR PEER REVIEW 6 of 13
over time. Second, it shows that this increase is likely to occur at both ends of the season,
with earlier springs and later autumns. Finally, the results demonstrate that this shift,
while obvious at the scale of a century, is somewhat variable or unpredictable, even with
decadal averaging.
Figure 1. Sample of Growing Season Length tool output. Location selected was Fairbanks, temper-
ature threshold selected was 32 °F, and model selected was GFDL.
This tool can return data that, although technically realistic (reflecting likely real-
world conditions), may be confusing and not useful to end users. If users select a particu-
larly high temperature threshold, and/or live in a very cold region, the results may appear
to be short and uneven, as in Figure 2. This is because the tool finds the longest consecutive
period during which the daily minimum temperature never drops below the selected tem-
perature. This time period may be extremely short, and is unlikely to be helpful in deter-
mining when to plant crops. Users are cautioned to be sure to select thresholds that make
sense for their area. In future tool iterations, feedback from users may help create visuali-
zations that avoid this issue altogether.
Figure 2. Sample of Growing Season Length tool output. Location selected was Nome, temperature
threshold selected was 50 °F, and model selected was GFDL.
3.2. Annual Minimum Temperature (AMT)
Sample output from the AMT interface is shown in Figure 3. In this case, Anchorage
(the largest population center in Alaska) and the NCAR model, which tends to project less
extreme climate change than the GFDL model, were selected. However, outputs for other
locations and for the GFDL model show similar patterns. While variability is high, as can
Figure 2.
Sample of Growing Season Length tool output. Location selected was Nome, temperature threshold selected was
50 ◦F, and model selected was GFDL.
3.2. Annual Minimum Temperature (AMT)
Sample output from the AMT interface is shown in Figure 3. In this case, Anchorage
(the largest population center in Alaska) and the NCAR model, which tends to project less
extreme climate change than the GFDL model, were selected. However, outputs for other
locations and for the GFDL model show similar patterns. While variability is high, as can
be seen from the scattering of a few extreme values, and while there is considerable overlap
between time periods, even with thirty-year time intervals, the overall pattern of projected
warming is clear from time period to time period. Also of note is the fact that although
warming is projected across all seasons, winter warming is likely to be much greater than
Sustainability 2021,13, 12713 7 of 13
summer warming. In this example, by late this century (2070–2099), very few days are
expected to be below 10
◦
F in Anchorage, which is a stark departure from past extreme
lows. In future iterations of this tool, greater contrast in dot colors may improve readability.
Sustainability 2021, 13, x FOR PEER REVIEW 7 of 13
be seen from the scattering of a few extreme values, and while there is considerable over-
lap between time periods, even with thirty-year time intervals, the overall pattern of pro-
jected warming is clear from time period to time period. Also of note is the fact that alt-
hough warming is projected across all seasons, winter warming is likely to be much
greater than summer warming. In this example, by late this century (2070–2099), very few
days are expected to be below 10 °F in Anchorage, which is a stark departure from past
extreme lows. In future iterations of this tool, greater contrast in dot colors may improve
readability.
Figure 3. Sample of Daily Minimum Temperature tool output. Location selected was Anchorage
and model selected was NCAR.
3.3. Growing Degree Days (GDD)
Sample results for the GDD tool are shown in Figure 4. These outputs are for Igiugig,
a small remote village with an active community garden. Again, this example shows many
features common to outputs from this tool. Heat units are added up day by day to create
a cumulative total. Totals increase from 1875 °F (1042 °C) for the earliest baseline decade
(1980–1989) to 4454 °F (2474 °C) for the most distant projected decade (2090–2099). The
layout of the graph is designed to make the approximate magnitude of this shift clear even
to users who are unfamiliar with GDD.
Figure 4. Sample of Daily Minimum Temperature tool output. Location selected was Igiugig, thresh-
old was 40 °F and model selected was GFDL.
Plants reach particular growth stages when cumulative GDD reaches the necessary
values. However, the minimum GDD necessary for growth and development varies by
species and, as such, different lower thresholds are used for the calculation of GDD. Many
Alaskan wild plants and cultivated crops are cold-hardy, and can take advantage of all
Figure 3.
Sample of Daily Minimum Temperature tool output. Location selected was Anchorage and model selected was
NCAR.
3.3. Growing Degree Days (GDD)
Sample results for the GDD tool are shown in Figure 4. These outputs are for Igiugig,
a small remote village with an active community garden. Again, this example shows many
features common to outputs from this tool. Heat units are added up day by day to create a
cumulative total. Totals increase from 1875
◦
F (1042
◦
C) for the earliest baseline decade
(1980–1989) to 4454
◦
F (2474
◦
C) for the most distant projected decade (2090–2099). The
layout of the graph is designed to make the approximate magnitude of this shift clear even
to users who are unfamiliar with GDD.
Sustainability 2021, 13, x FOR PEER REVIEW 7 of 13
be seen from the scattering of a few extreme values, and while there is considerable over-
lap between time periods, even with thirty-year time intervals, the overall pattern of pro-
jected warming is clear from time period to time period. Also of note is the fact that alt-
hough warming is projected across all seasons, winter warming is likely to be much
greater than summer warming. In this example, by late this century (2070–2099), very few
days are expected to be below 10 °F in Anchorage, which is a stark departure from past
extreme lows. In future iterations of this tool, greater contrast in dot colors may improve
readability.
Figure 3. Sample of Daily Minimum Temperature tool output. Location selected was Anchorage
and model selected was NCAR.
3.3. Growing Degree Days (GDD)
Sample results for the GDD tool are shown in Figure 4. These outputs are for Igiugig,
a small remote village with an active community garden. Again, this example shows many
features common to outputs from this tool. Heat units are added up day by day to create
a cumulative total. Totals increase from 1875 °F (1042 °C) for the earliest baseline decade
(1980–1989) to 4454 °F (2474 °C) for the most distant projected decade (2090–2099). The
layout of the graph is designed to make the approximate magnitude of this shift clear even
to users who are unfamiliar with GDD.
Figure 4. Sample of Daily Minimum Temperature tool output. Location selected was Igiugig, thresh-
old was 40 °F and model selected was GFDL.
Plants reach particular growth stages when cumulative GDD reaches the necessary
values. However, the minimum GDD necessary for growth and development varies by
species and, as such, different lower thresholds are used for the calculation of GDD. Many
Alaskan wild plants and cultivated crops are cold-hardy, and can take advantage of all
Figure 4.
Sample of Daily Minimum Temperature tool output. Location selected was Igiugig, threshold was 40
◦
F and
model selected was GFDL.
Plants reach particular growth stages when cumulative GDD reaches the necessary
values. However, the minimum GDD necessary for growth and development varies by
species and, as such, different lower thresholds are used for the calculation of GDD. Many
Alaskan wild plants and cultivated crops are cold-hardy, and can take advantage of all
above-freezing days, so for these species GDD can be calculated with a baseline of 0
◦
C
(32
◦
F). Most crops in other regions have higher baseline temperatures, e.g., 5
◦
C (about
Sustainability 2021,13, 12713 8 of 13
40
◦
F) for crops considered suitable for cool climates, such as barley and oats, or 10
◦
C
(about 50 ◦F) for so-called warm-climate crops, such as corn and tomatoes.
Because many tool users may be unfamiliar with GDD and with the thresholds and
total GDD needed for crop growth and maturity, we provided an explanatory text, noting
that, “plants can grow when the temperature is above some minimum value, which varies
by species. Many Alaska plants are cold-hardy and can grow on all above-freezing days.
For these, GDD can be calculated with a baseline of 32
◦
F. Most crops in other regions
have higher baseline temperatures, such as 40
◦
F for barley and oats, or 50
◦
F for corn and
tomatoes. Choose a threshold based on what crop you plan to grow.” We also provided
sample tables, based on the literature. These are shown in Table 1, which also shows data
on days to maturity. In the online interface, these data are shown in two separate tables
associated with the two different tools, in order to avoid confusion.
Table 1.
Common crops and their associated minimum temperature thresholds, days to maturity,
and GDD.
Baseline
Temperature
Threshold, ◦F
Species or Variety Minimum Number
of Days to Maturity
Growing Degree
Days to Maturity, ◦F
32 Wheat (hard red) 90–100 2800–3029
32 Barley 60–90 2316–2771
32 Oat 85–88 2701–3160
32 Canary Seed 95–105 2447–2795
32 Flax 85–100 2917–3273
32 Canola (B. rapa) 73–102 2280–2519
32 Mustard (S. juncea) 85–95 2748–2930
32 Chickpea N/A 3054–3277
32 Lentil 85–100 3164–3408
32 Sunflower 80–120 3236–3581
40 Wheat (Indiana) N/A 2100–2400
40 Broccoli from starts 46 1623–1702
40 Beets 40 N/A
40 Brussels sprouts 90 N/A
40 Cabbage 45 1623–1702
40 Carrots 60 N/A
40 Cauliflower 45 1623–1702
40 Radish 25 N/A
40 Spinach 39 N/A
40 Kale 25 N/A
40 Peas 60 N/A
50 Sorghum 90–120 1690–1944
50 Soybeans 100 1679–1992
50 Cucumber 60 682–952
50 Sweet corn 80 1134–1522
50 Tomatoes 60 1700 +
3.4. Hardiness Zones
The USDA uses Plant Hardiness Zones as the standard by which growers can de-
termine which plants are likely to thrive at a given location. Many seed manufacturers
reference these zones. Hardiness maps are based on the average annual minimum winter
temperature. These zones are only a rough guide. Because they are based on winter
temperatures, they are of greatest importance for perennials, such as fruit trees or peonies.
The four maps shown in Figure 5use nomenclature and color ramps similar to those used
in USDA maps in order to render them more familiar to gardeners and farmers who are
accustomed to the USDA zone delineations. These maps represent current estimates of
hardiness zones in Alaska, plus projections of how these zones may look in three future
time periods, as described in the Methods section.
Sustainability 2021,13, 12713 9 of 13
Sustainability 2021, 13, x FOR PEER REVIEW 9 of 13
four maps shown in Figure 5 use nomenclature and color ramps similar to those used in
USDA maps in order to render them more familiar to gardeners and farmers who are
accustomed to the USDA zone delineations. These maps represent current estimates of
hardiness zones in Alaska, plus projections of how these zones may look in three future
time periods, as described in the Methods section.
Figure 5. Alaska Hardiness Maps.
3.5. Peony Case Study
This case study was shared directly with project partners and was summarized for
the public and made available here online [26]. The summary highlights some clear con-
tinuing advantages for Alaska peony growers, including the fact that although Alaska
winters are likely to remain cool enough for peony dormancy, the same may not be true
for growers elsewhere. However, it also highlights challenges, particularly for growers in
the parts of the state with the warmest summers, as increased spring heat spurs earlier
blooming and diminishes the late-summer niche enjoyed by Alaska growers.
Previous studies show that relatively cool winter temperatures are necessary for pe-
ony roots to achieve dormancy. However, provided that temperatures are consistently
below 6 °C (43 °F) for seventy days, dormancy will be achieved [27]—a condition easily
met in most of Alaska. Plants break dormancy as soon as temperatures rise above freezing
[28–30]. Byrne and Halevy [27] report that flowering can occur in only about 50 days in
greenhouse conditions, but suggests that slower growth in cooler temperatures results in
less atrophy of buds. Indeed, Kamenetsky et al. [31] found that moderate temperatures
with highs of 72 °F and lows of 50 °F were best for enhancing stem length and flowering.
When daily highs and lows were 82 °F and 72 °F, flowering was drastically reduced. Hall
[27] similarly found that temperatures over 77 °F resulted in reduced blooms. Holloway
et al. [28–30] found that flowers bloomed in all cases when cumulative GDD above a 32
°F threshold reached between 1734 and 2313. In contrast, the number of days from bud
emergence to first cutting ranged from 32 in Fairbanks to 79 in much cooler Kenai.
A further case study linking tool outputs to existing or planned community gardens
would offer an excellent area for future investigation.
Figure 5. Alaska Hardiness Maps.
3.5. Peony Case Study
This case study was shared directly with project partners and was summarized for the
public and made available here online [
26
]. The summary highlights some clear continuing
advantages for Alaska peony growers, including the fact that although Alaska winters are
likely to remain cool enough for peony dormancy, the same may not be true for growers
elsewhere. However, it also highlights challenges, particularly for growers in the parts of
the state with the warmest summers, as increased spring heat spurs earlier blooming and
diminishes the late-summer niche enjoyed by Alaska growers.
Previous studies show that relatively cool winter temperatures are necessary for peony
roots to achieve dormancy. However, provided that temperatures are consistently below
6
◦
C (43
◦
F) for seventy days, dormancy will be achieved [
27
]—a condition easily met in
most of Alaska. Plants break dormancy as soon as temperatures rise above freezing [
28
–
30
].
Byrne and Halevy [
27
] report that flowering can occur in only about 50 days in greenhouse
conditions, but suggests that slower growth in cooler temperatures results in less atrophy
of buds. Indeed, Kamenetsky et al. [
31
] found that moderate temperatures with highs of
72
◦
F and lows of 50
◦
F were best for enhancing stem length and flowering. When daily
highs and lows were 82
◦
F and 72
◦
F, flowering was drastically reduced. Hall [
27
] similarly
found that temperatures over 77
◦
F resulted in reduced blooms. Holloway et al. [
28
–
30
]
found that flowers bloomed in all cases when cumulative GDD above a 32
◦
F threshold
reached between 1734 and 2313. In contrast, the number of days from bud emergence to
first cutting ranged from 32 in Fairbanks to 79 in much cooler Kenai.
A further case study linking tool outputs to existing or planned community gardens
would offer an excellent area for future investigation.
4. Discussion
These tools have already been discussed, shared, and used as teaching and presenta-
tion materials within the Alaska agricultural community, particularly by project partners
Sustainability 2021,13, 12713 10 of 13
and participants associated with Cooperative Extension Services and/or the peony grow-
ing industry. The results of the peony case study were presented at the annual meeting of
the Alaska Peony Growers’ Association in 2020. Outcomes were included in a presentation
in June 2021 by Dr. Glenna Gannon and Shannon Powers which focused on Variety Trials
in the Matanuska Susitna region. In addition, the Fairbanks Daily Newsminer ran a feature
on the tool [32].
Given the goals of this project in relation to stakeholder needs, small-scale gardens
and farms, and food security, all of the results must be interpreted within the simultaneous
contexts of users’ ability to successfully access the information, correctly understand and
interpret the information, and apply the information.
One overarching aspect of model transparency is the clear explanation of data un-
certainties. With this in mind, uncertainties are clearly explained in plain language in
conjunction with all outputs, including online tools and fact sheets in order to avoid misin-
terpretation and misapplication. Across all outputs, some uncertainties can be attributed
to underlying differences in the complex atmospheric modeling used in the GCMs. By
offering two models, we gave users a chance to explore a model that tends to produce
more extreme results, and a model that tends to produce conservative results. Additional
uncertainty stems from spatial limitations. As noted, all model outputs are at 20 km resolu-
tion. Especially in areas of complex topography, growing conditions can vary enormously
across areas of this size. As such, users are reminded to consider their local microclimate.
Uncertainties inherent to short-term variability in weather are inherent to agriculture.
While averaging across decadal or multi-decadal time periods helps smooth data for the
purposes of highlighting long-term trends, users are cautioned that short-term variability
will nonetheless play a large role in year-to-year gardening and farming outcomes.
All of the tools developed during this research were intended to improve the current
state of information readily and easily available to Alaska gardeners. For example, with
regard to the season length tool, many seeds offer estimates of how many days the crop
may take to mature. Typically, planting guides refer to “last frost” in spring and “first
frost” in fall, implying daily minimum temperatures of 0
◦
C (32
◦
F). By offering additional
thresholds, our tool allows for more flexibility in considering cold-hardy crops that may
be harvested only when a hard frost is reached (28
◦
F), or more delicate crops that cannot
effectively grow when temperatures are below a higher threshold. Such plants might be
kept as starts in a greenhouse until a later planting date, and harvested earlier. Moreover,
the results show that season length is increasing statewide. In many regions, longer frost-
free seasons may make it possible to plant crops that were not previously suitable for the
region.
Very little information on GDD is currently available to gardeners who do not read
the scientific literature. Understanding GDD and knowing the approximate number of
growing degree days that can be expected in an area, for a given baseline temperature, can
help gardeners plan what to plant, and what not to plant, especially when the length of
the frost-free season does not provide enough information. For example, with a baseline
temperature of 50
◦
F and over 2000 GDD necessary for maturation, corn is not likely to be
successful in most parts of Alaska, even though many varieties can mature in only 60–80
days, given enough heat. However, the results indicate that GDD is shifting rapidly and
dramatically statewide, with values projected to double or even triple by the end of the
century. This may prove a productive avenue for additional study and seed trials.
Both the Annual Minimum Temperature tool and the Hardiness Zone Maps offer
important information for those who are interested in perennials, such as fruit trees and
shrubs, which have to be hardy to survive Alaska winters. Many cannot withstand tem-
peratures below certain thresholds—but model results make it clear that these thresholds
are changing rapidly statewide. The results suggest that many perennials that were not
suitable to Alaska may soon become potential crops in large areas of the state. This may
prove to be an important area for further research and experimentation. However, tool
users are reminded that “cold hardiness” is just one gauge of whether a crop is suitable to
Sustainability 2021,13, 12713 11 of 13
a particular region. Many other factors affect winter survival, such as the insulating value
of snow, the moisture content of the ground, the presence or absence of permafrost, and
the number of freeze–thaw cycles that occur. Future versions of this tool may include some
of these factors.
Alaska’s peony growers may see both gains and losses due to climate change. Winters
are likely to remain cool enough for peony dormancy, while growers in other parts of the
world may find challenges in this regard. This may provide some local advantage. Using
the Growing Season tool to look at the 32
◦
F threshold can help to provide an estimate of
when peonies are likely to break dormancy in the future in communities around Alaska.
However, late springs and cool summer weather are better for Alaska’s peony growers
for two reasons: first, because such conditions promote healthier flowering, and second,
because they promote later flowering, which allows Alaska to capture the late-season niche
market. Given the results obtained by Holloway et al. [
28
–
30
], using the Growing Degree
Days tool to plan for peony growth once buds have emerged is likely to be more effective
than using the Growing Season tool. For peony farming, as late springs and cool summers
become more elusive, growers may need to adapt. This may be hardest for those who
already farm in regions of the state that are warmest in the summer, such as Fairbanks.
For new growers who have yet to invest in land, picking cooler parts of the state may be
practical if relocation is possible, or selecting cooler sites within a community—such as
north-facing slopes—might aid at the local level. New storage methods for cut blooms can
also extend the season for sales.
Taken together, the outcomes of this research, as well as the feedback received by tool
users, point toward potential refinements in tool development as well as many new possible
avenues for expansion of Alaska’s agricultural potential at the local scale. Especially in the
context of climate-related agricultural uncertainty, challenges in other regions and possible
climate-related needs for greater local autonomy (due to disruptions in supply chains
and/or reduced use of fossil fuels for transportation of crops), and the need to diversify
Alaska’s economy, such new opportunities for farms and gardens, may prove important
areas for further study and development.
Supplementary Materials:
The Alaska Garden Helper tool described in this article is available online
at https://www.snap.uaf.edu/tools/gardenhelper/ (accessed 3 October 2021).
Author Contributions:
Conceptualization, N.F. and A.B.; methodology, N.F. and A.B.; software, A.B.;
validation, N.F., A.B., P.B., and C.R.; formal analysis, N.F. and A.B.; investigation, N.F.; resources,
N.F., A.B., P.B. and C.R.; data curation, A.B. and P.B.; writing—original draft preparation, N.F.;
writing—review and editing, N.F., A.B., P.B. and C.R.; visualization, A.B. and C.R.; supervision,
N.F.; project administration, N.F.; funding acquisition, N.F. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by the United States Geological Survey (USGS) via the Alaska
Climate Adaptation Science Center. The APC was also funded by the USDA via the Alaska Climate
Adaptation Science Center.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are openly available in the Scenarios
Network for Alaska and Arctic Planning (SNAP) Data Portal at http://ckan.snap.uaf.edu/dataset
(accessed 3 October 2021).
Acknowledgments:
The authors would like to acknowledge the support of the faculty and staff at
UAF’s International Arctic Research Center. All work on this project was built upon past efforts
in model selection, model downscaling and data analysis. We would also like to thank all the
agriculturalists who contributed their expertise and advice, including the Alaska Peony Growers
Association, Community Partnerships for Self-Reliance, Calypso Farm and Ecology Center, Spinach
Creek Farm, Arctic Alaska Peonies Co-op, and Tyonek Tribal Conservation District.
Sustainability 2021,13, 12713 12 of 13
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
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