![A mosaic plot comparing a previous global breadfruit model by Lucas and Ragone [62] on the x-axis in categories of unsuitable (0), suitable (1), and best (2), and our fuzzy set model on the y-axis represented by percentage total of each score. The width of the x-axis indicates the relative total area of that category, while the y-axis indicates the percentage of that area occupied by each suitability score. https://doi.org/10.1371/journal.pone.0228552.g003](https://www.researchgate.net/profile/Noa-Lincoln/publication/341580009/figure/fig3/AS:894105508921349@1590182773352/A-mosaic-plot-comparing-a-previous-global-breadfruit-model-by-Lucas-and-Ragone-62-on_Q320.jpg)
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RESEARCH ARTICLE
Cultivation potential projections of breadfruit
(Artocarpus altilis) under climate change
scenarios using an empirically validated
suitability model calibrated in Hawai’i
Kalisi MausioID
1☯
, Tomoaki Miura
2
, Noa K. LincolnID
1☯
*
1Tropical Plant and Soil Sciences, University of Hawai’i at Manoa, Honolulu, HI, Unites States of America,
2Natural Resources and Environmental Management, University of Hawai’i at Manoa, Honolulu, HI, Unites
States of America
☯These authors contributed equally to this work.
*nlincoln@hawaii.edu
Abstract
Humanity faces significant challenges to agriculture and human nutrition, and changes in cli-
mate are predicted to make such challenges greater in the future. Neglected and underuti-
lized crops may play a role in mitigating and addressing such challenges. Breadfruit is a
long-lived tree crop that is a nutritious, carbohydrate-rich staple, which is a priority crop in
this regard. A fuzzy-set modeling approach was applied, refined, and validated for breadfruit
to determine its current and future potential productivity. Hawai’i was used as a model sys-
tem, with over 1,200 naturalized trees utilized to calibrate a habitat suitability model and 56
producer sites used to validate the model. The parameters were then applied globally on 17
global climate models at the RCP 4.5 and RCP 8.5 global climate projections for 2070. Over-
all, breadfruit suitability increases in area and in quality, with larger increases occurring in
the RCP 8.5 projection. Current producing regions largely remain unchanged in both projec-
tions, indicating relative stability of production potential in current growing regions. Bread-
fruit, and other tropical indigenous food crops present strong opportunities for cultivation
and food security risk management strategies moving forward.
Introduction
Humanity faces multiple challenges for the future of food production. By mid-21st century,
the world population is expected to reach nine billion people with associated pressure on
resources [1], increasing demands on food and nutrition while rates of hunger and malnutri-
tion are on the rise [2]. Changes in global weather are expected to negatively impact food
yields, especially the major commodity crops that provide much of the global food supply
[3,4], and nutrient quality and density of crops [5,6]. Global hunger and malnourishment,
largely correlated to poverty and insufficient access to enough nutritious food is increasing
[2,7]. Some 2 billion people are suffering from micronutrient malnutrition [8,9]. At the other
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OPEN ACCESS
Citation: Mausio K, Miura T, Lincoln NK (2020)
Cultivation potential projections of breadfruit
(Artocarpus altilis) under climate change scenarios
using an empirically validated suitability model
calibrated in Hawai’i. PLoS ONE 15(5): e0228552.
https://doi.org/10.1371/journal.pone.0228552
Editor: Ahmet Uludag, Canakkale Onsekiz Mart
University, TURKEY
Received: September 24, 2019
Accepted: January 18, 2020
Published: May 22, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0228552
Copyright: ©2020 Mausio et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All model codes and
data layer files are available at GitHub for public
access: https://github.com/nlincoln2017/
Breadfruit-Suitability-Model
end of the spectrum, obesity—which is often attributed to global commodities that lack essen-
tial micro and macro-nutrients—is also on the rise [2].
Increased pressure on food markets is expected to exacerbate existing issues of food secu-
rity, nutrition, social equity, and economies [1,3]. In order to achieve the goals of eliminating
hunger, improving food security and nutrition and promoting sustainable agriculture, many
advocates for transforming and developing food systems that respond to or anticipate climate
change [10]. With existing problems in our current food supply and compounding factors on
the horizon, developing food production systems that emphasize resilience and nutrition is an
essential strategy for mitigating these issues.
Of the approximately 250–300,000 flowering plant species, at least 50,000 are edible [11,12].
Of these, about 3,000 species are regularly exploited for food [13]. Yet only three crops provide
60% of the world’s calories [14] and 103 crops provide some 90% of the world’s food produc-
tion [15]. This gap between the 3,000 species regularly utilized for food and the 103 dominant
crops of the planet make up a vast, relatively untapped resource of crops commonly referred to
as neglected and underutilized species (NUS). Such crops are often ignored by researchers,
policy makers and breeders [13,16]. NUS offer opportunities for adaptation because they have
a wide range of genetic diversity that can enhance resilience to stressors related to climate
change [17].
Although emphasis on NUS will undoubtedly help to mitigate climate impacts to current
food systems and positively impact food and nutrition security, multiple barriers exist to their
widespread adoption. These barriers include loss of genetic diversity and traditional knowl-
edge, undervaluation through lack of knowledge and research, poor competitiveness, and lack
of infrastructure, policy support and investments [16]. Therefore, more attention must be
given to NUS on all levels—from cultivation to gastronomic uses—if they are to be useful in
creating sustainable food systems for the future.
Breadfruit (Artocarpus altilis) is an underutilized tree crop that grows in the tropics and
sub-tropics, originally cultivated from the wild species ancestor bread nut (Artocarpus
camansi) in Papua New Guinea [18,19]. The crop is listed as one of the thirty-five priority
crops in the International Treaty on Plant Genetic Resources for its potential to target food
security and interdependence [20]. The trees are a high yielding starchy staple crop, producing
up to 10 t/ha of fruit that is rich in carbohydrates, essential amino acids, fiber, vitamins, miner-
als including micronutrients such as iron and potassium [21–24]. Although exact fruit yield
estimations are variable (see [25] for a summary of yield), it is evident that breadfruit has the
potential to target issues surrounding food and nutrition security if specific cultivar and grow-
ing conditions are optimized.
Recent advances in propagation methods, scientific research and promotion of breadfruit
have made breadfruit trees widespread throughout the tropics however, they are still underuti-
lized due to the range of factors identified for NUS in general [18,25]. As a perennial tree,
breadfruit has significant potential to contribute to building climate resilient food systems
[26,27] in addition to their ability to sequester carbon, provide shade, stabilize the soil, benefit
watersheds, and provide a multitude of invaluable environmental benefits [28,29].
GIS-based land-use suitability analysis attempts to identify the most appropriate spatial pat-
tern for land uses according to specific requirements, preferences, or predictors of some activ-
ity [30], including suitability of land for agricultural activities [31]. A considerable amount of
research has been conducted on assessing the relationship between crop response/productivity
and climate using simulation models [32,33]. Recently this approach has also been utilized as a
method with which to assess crop response to climate change [34–37]. However, most of these
models are fine-tuned towards predicting crop yields for global commodity crops (e.g. corn,
wheat, soybean, rice), for which widespread cultivation offer ample opportunities for model
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Funding: NKL have received McIntire-Stennis
(8038-MS), HATCH (8035-H) and Western SARE
(SW17-050) funding that has supported this
project. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
calibration and validation. The methods are quite adaptable to different crop types as long as
basic information about the crop’s environmental range is known.
GIS-based suitability analysis can address multiple-criteria decision-making problems and
can incorporate fuzzy logic techniques [31]. Fuzzy logic [38] involves the concept of member-
ship function in which a given element is numerically represented by the degree to which it
belongs to a set. In this way, a measurement within a criterion can have degrees of membership
between unsuitable (0) and perfectly suitable (1). Because geographical phenomena tend to
exhibit continuous spatial variability, Burrough and McDonnell [39] suggest that fuzzy mem-
bership more accurately captures boundaries between land suitability classes than binary or
categorical approaches. Such application in crop modeling has been demonstrated more
recently [37].
Different approaches to modeling have been established, demonstrating methodologies
that can be applied to a wide range of crops. For instance, AquaCrop is a water-driven crop
growth model that utilizes linear proportionality to transpiration, with crop/cultivar specific
scalar parameters [40,41]. Other models, such as CropSyst, use several dozen crop input
parameters to simulate the production rate [42,43]. Driven by daily weather inputs, such mod-
els can be highly accurate when properly calibrated and have recently been applied to NUS
[44–48]. However, such calibration typically requires experimental investigation over the life-
time of a crop, which in the case of long-lived tree crops, and in particular neglected and
underutilized tree crops, can be prohibitive. In the case of trees, landscape-level approaches
utilizing natural experiments may be better suited to understanding patterns of potential
productivity.
Traditionally, breadfruit in Hawai’i was cultivated as a major staple [49–51] in a range of
cropping systems, from massive arboriculture developments, to mixed agroforestry, to individ-
ual and backyard trees [52]. Following European colonization, a dramatic shift away from tra-
ditional crops occurred [53], although many pockets of traditional agriculture and associated
practices remained [54] and remnant trees and production systems persisted [50, Lincoln in
press]. Over the past 20 years, significant efforts have occurred to revitalize breadfruit in
Hawai’i. Such efforts have included large-scale tree giveaways [55], restoration of traditional
agricultural systems [54], a growing local food producing sector [56], and consumer education
such as outreach, chef campaigns and festivals [57]. Such initiatives have resulted in significant
increases in production at multiple levels including backyard trees, small diversified farm
plantings, and larger mono-cropped orchards [55,56].
Hawai’i is an excellent location to tune habitat distribution models, including for agricul-
tural species and activities. Hawaii provides a “model system” for ecological investigations [58]
due to its consistent geology and wide, well-defined variation in climate and substrate age that
allow for a degree of precision that cannot be duplicated elsewhere. Environmental gradients
in mean annual temperature (from <10–24 C), annual precipitation (from <200->10,000
mm), and substrate age (from hot rock to >4,000 kyr) are among the clearest, broadest, and
most orthogonal on Earth [59]. The matrix of environmental gradients creates among the
densest concentration of ecosystems on the planet [60], and correspondingly dense variation
in agricultural habitats and opportunities.
The purpose of this paper is to develop an empirically validated, fuzzy-set model for bread-
fruit production that incorporates both climate and soil data, and to explore the global poten-
tial for breadfruit cultivation in current and future climate scenarios. This builds upon prior
work that conducted a two-tiered suitability model for breadfruit based on rainfall and tem-
perature [61]. The developed model is utilized to further assess potential changes in breadfruit
production over time with anticipated climate scenarios to understand global and regional
changes in productive potential.
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Methods
Modeling approach
The crop model methodology in this study utilizes a basic mechanistic approach in which 1)
environmental criteria deemed important to the crop’s success are selected, 2) suitable envi-
ronmental ranges for the crop are determined for each of the selected criteria, 3) fuzzy sets are
constructed for each criteria based on the environmental ranges, representing the approximate
niche of the crop, 4) a crop suitability score is calculated based on how closely the current or
future environmental conditions match the constructed fuzzy sets, and 5) model is validated
and refined in an iterative process as required. This approach allows flexibility within the
model and the ability to add new evaluation criteria.
The initial environmental ranges for breadfruit were obtained from the EcoCrop database
[62] which contained an optimum range and an absolute range for each parameter. Environ-
mental criteria with values within the optimal range represent perfect suitability while values
outside the absolute range are considered unsuitable. Fuzzy sets were constructed using the
methodology adapted from Ramirez-Villegas et al. [37] which utilized a linear algorithm to
systematically construct the fuzzy set used to derive scores (0–100) between suitable and
unsuitable:
SUITi¼if ½Pi<ABSmin;0
ABSmin Pi<OPTmin;ðPiABSminÞ=ðOPTmin ABSmin Þ 100
OPTmin Pi<OPTmax;100
OPTmax Pi<ABSmax;ð1 ðPiOPTmaxÞ=ðABSmax OPTmax ÞÞ 100
Pi>ABSmax;0Þ
where SUIT is the suitability score, and P is the measured environmental criterion at the site
(i.e., each pixel), and ABS
min
, ABS
max
, OPT
min
, and OPT
max
are the absolute and optimal envi-
ronmental ranges for the criterion of a particular crop.
Five environmental criteria essential to breadfruit growth were selected to evaluate bread-
fruit crop suitability based on the EcoCrop parameters, availability of data, and the relationship
to tree distribution. These criteria were rainfall, average temperature, solar radiation, soil
drainage, and soil pH. The overall suitability score for each crop was calculated on a per pixel
basis using the minimum value of the sets the cell location belongs to:
SUIToverall ¼minðCSUIT 1;CSUIT 2;...;CSUIT nÞ
where nis the number of criteria used in the evaluation. This conservative approach is based
on the law of the minimum [63], in which crop yield is proportional to the most limiting nutri-
ent; the same idea can be applied to environmental conditions and has been utilized in similar
GIS crop-suitability analysis [64].
Spatial data layers of the environmental parameters were obtained from the Hawaii Rainfall
Atlas [65], the Hawaii Evapotranspiration Atlas [66] and the SSURGO database [67]. All calcu-
lations were performed in R Studio (RStudio, Inc., Boston, MA) [68] at a mapping unit of 50m
by 50m. The R Studio “raster” and “rgdal” packages were used to generate the equations within
and between the spatial layers. The scripts, layers, and imagery are available online at https://
github.com/nlincoln2017/breadfruit-suitability-model.
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Model refinement and validation
Based on previous modeling of breadfruit habitat [62,57], the EcoCrop environmental param-
eters were known to not be accurate. We addressed this by overlaying the habitat suitability
maps generated from the EcoCrop parameters with a map of 1,200 naturalized breadfruit trees
from systematic surveys of breadfruit on four islands (Kaua’i, Molokai, O’ahu, and Hawai’i)
(e.g., [50,55]); and convening a panel of experts to discuss and refine the optimal and absolute
levels for each of the environmental parameters. The model was regenerated with adjusted
absolute and optimal environmental parameters for two iterations at which point the panel of
experts agreed upon the ranges and the resulting model closely aligned with the mapped tree
distributions.
To validate the new model, yield and productivity data from 56 producer sites were used
(see [69] for details on producer sites and methods of quantifying productivity). These pro-
ducer sites were categorized as follows: 11 both irrigated and amended soils, 15 irrigated only,
4 amended soils only, and 27 neither irrigated nor amended soils. Model validation was con-
ducted by comparing the modeled habitat suitability to the observed breadfruit productivity,
both utilizing a scale of 0–100.
The model efficacy was evaluated using Root Mean Square Error-observations standard
deviation ratio (RSR), Nash-Sutcliff Model Efficiency coefficient (ME) [69], and Willmott’s
Index of Agreement (IA) [70] using the following equations:
RSR ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1Xn
i¼1ðMiSiÞ2
Xn
i¼1ðMi�
MÞ2
2
v
u
u
t
ME ¼1Xn
i¼1ðMiSiÞ2
Xn
i¼1ðMi�
MÞ2
IA ¼1Xn
i¼1ðSiMiÞ2
Xn
i1ðjSi�
Mj þ jMi�
MjÞ2
Where S
i
and M
i
are the simulated and measured values of yield and �
Mis the average of M
i
values of nmeasured values. The RSR is an indicator of the distance between the observed and
simulated values; the closer the value is to zero the better the model simulation. The ME mea-
sures the departure of the model compared to the observed variance, where ME = 1 indicates a
perfect model fit and ME = 0 means that the observed mean value is as good a predictor as the
model. The IA measures the ratio of the mean square error to the total potential error, with
IA = 1 indicates a perfect fit and IA = 0 represents the worst possible model. Values were calcu-
lated for all sites against corresponding model output of combined environmental parameters.
For instance, modeled outputs compared to irrigated sites did not include the rainfall parame-
ter since this is not a limiting parameter for the irrigated sites. The same concept was applied
for the soil pH parameter and sites that used soil amendments. At sites where trees were nei-
ther irrigated nor amended, the model output that used all environmental parameters was
applied.
Following the development, refinement, and validation of the model in Hawai’i, the derived
ranges of optimal and absolute environmental conditions were used to run the model at a
global level. The environmental layers of mean annual temperature, rainfall and solar radiation
were acquired from WorldClim [71] and the global soil data of pH and drainage class from the
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WISE database [72,73]. A comparison of the global model to a previous model developed by
Lucas and Ragone [62] was conducted by spatially overlaying the two datasets and extracting
spatially corresponding suitability scores. A mosaic plot was generated to anecdotally examine
the similarities and differences in predicted extent and quality of breadfruit habitat between
the two models.
Future projections
Global average annual temperature and rainfall projection data from the WorldClim database
were applied to our model to create future suitability scores. These datasets represent the 2060–
2080 average using global climate models (GCM) from the CMIP5 of the IPPC Fifth Assessment
that were downscaled to a 30 arc second (~1km at the equator) spatial resolution using current
WordClim 1.4 as baseline current (1950–2000) data [74]. All 17 GCMs which had projections of
the environmental parameters for both Representative Concentration Pathway (RCP) 8.5 and 4.5
—representing extreme and intermediate Greenhouse Gas scenarios–were applied. Each set of
projections were used to generate breadfruit suitability using our validated breadfruit model. The
17 model outputs from the GCM scenarios were averaged to represent 2070 breadfruit suitability
for the two RCPs. Finally, each RCP scenario was compared to the current global suitability out-
put to determine how much suitability would increase or decrease in the next 50 years by spatially
overlaying the two datasets and extracting corresponding suitability scores.
Results and discussion
Hawai’i model and validation
The initial breadfruit model that utilized parameters defined by EcoCrop proved to be highly
restrictive compared to the distribution of naturalized trees surveyed in Hawai’i especially in
terms of rainfall and soil drainage (Table 1). For instance, the absolute maximum rainfall as
defined by EcoCrop was 3,500 mm/yr, but tree mapping in Hawai’i demonstrated naturalized
trees in substantially wetter areas and experts provided numerous contrary examples from
across the Pacific. Similarly, there were ample trees documented in Hawai’i growing on higher
and lower drainage class than reported by EcoCrop. Less significant changes were recom-
mended for temperature and pH, and no changes to solar radiation were suggested. Final
parameters utilized were in all cases less restrictive than provided by EcoCrop (Table 1).
Following refinement of the model parameters, a fuzzy set model was produced for Hawai’i
(Fig 1). The effects of Hawai’i’s substantial gradients in temperature and rainfall are clearly vis-
ible, with greater potential on the wetter windward (northeastern) sides of the islands and near
Table 1. Environmental parameters initially obtained from the EcoCrop database and the refined parameters applied to the habitat suitability model.
Abs Min Abs Max Opt Min Opt Max
Final Model
Parameters
Temp (˚C) 17 40 21 33
Rain (mm/yr) 750 8000 1500 4000
Solar Rad. (W/m
2
) 20 295 50 197
pH 4 8.7 5 6.5
Drainage Class 2 7 4 6
EcoCrop
Parameters
Temp (˚C) 16 40 21 33
Rain (mm/yr) 1000 3500 1500 3000
Solar Rad. (W/m
2
) 20 295 50 197
pH 4.3 8.7 5.5 6.5
Drainage Class 4 6 4 6
https://doi.org/10.1371/journal.pone.0228552.t001
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the warmer coasts. The textured breaks in the model represent the soil parameters driven by
different aged lava flows on the younger (southern) islands and valley topography on the older
(northern) islands. Leveraging the diversity across the archipelago, validation was conducted
using sites located on five islands (Fig 1).
The sites used for validation differed substantially in both their actual productivity and
their modeled suitability. A linear regression between measured productivity of “natural” sites
(no irrigation and no soil amendments) and corresponding simulated suitability demonstrated
a strong, significant relationship (r
2
0.91, p<0.001). Using all sites and their corresponding
simulated suitability the relationship weakens but remains strong (r
2
0.84, p<0.001). Model
validation statistics indicated that overall our model performs very well with moderately high
model efficiency (ME) and very high index of agreement (IA) (Table 2). It is suggested that
models perform satisfactorily if ME >0.5 and the RSR is below 0.7 [75], with our model per-
forming substantially stronger.
Fig 1. Suitability for productive capacity of breadfruit in Hawai’i, with scores ranging from 0 (white, cannot cultivate) to green (100, ideal cultivation) based on 5
climate and soil parameters. Validation sites are marked by the black crosshairs.
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Global model and comparison to previous model
Following the development, refinement, and validation of the model using Hawai’i as a model
system, the parameters were applied to global data layers to make global predictions of produc-
tion potential (Fig 2).
We compared our empirically validated model against the only previous global bread-
fruit suitability model published (Fig 3), which applied only rainfall and temperature to
define two classes of breadfruit habitat: “suitable” and “best” [62]. Overall our model is
more inclusive, with the model by Lucas and Ragone [62] suggesting ~26,900,000 km
2
of
suitable habitat (~14,800,000 km
2
of “best” and ~12,100,000 of “suitable”), and our model
suggesting ~35,100,000 km
2
of suitable habitat–an ~30% increase in total cultivable area.
This is likely due to our more inclusive thresholds for rainfall and temperature as defined in
the model fitting process. Since our model applies the law of minimums, the inclusion of
additional criteria should have further restricted the extent of breadfruit habitat. Of the
lands excluded by Lucas and Ragone [62] and included by our model, ~70% of those lands
score 50 or below on the fuzzy set; however, ~10% do show exceptional suitability, scoring
90 or above. Of the lands included by Lucas and Ragone [62], our model excludes
~1,300,000 km
2
of “suitable” lands and 700,000 km
2
of “best” lands. A brief examination of
excluded pixels indicates that these exclusions primarily resulted from the consideration of
soil parameters in our model. Overall, however, the two models show fairly good alignment.
Of the “best” lands identified by Lucas and Ragone [62], ~90% of the points demonstrate
suitability values of 90 or above by our model. The average simulated suitability by our
model of the “best” lands in Lucas in Ragone [62] is 89, for “suitable” lands 61, and for
“unsuitable” lands 0.6.
Future projections
Future breadfruit suitability was assessed using our fuzzy set model using climate projec-
tions of precipitation and temperature for the RCP 4.5 and RCP 8.5 climate scenarios (Fig
4). The results demonstrate successive increase in suitability in the RCP 4.5 and RCP 8.5
scenarios compared to the current suitability (Table 3). At all levels of suitability, our
model predicts an increase of total cultivable land (an increase of 27% and 89% for RCP
4.5 and 8.5 respectively) as well as a total increase in average suitability (an increase of
14% and 45% respectively). While a large portion of the total increase in habitat occurred
under marginal suitability (<30), increases in the cultivable area of all suitability classes is
clear (Table 3).
Table 2. Summary of validation site statistics in terms of measured and simulated productivity and their coefficient of variation, and model accuracy assessment
for only sites without irrigation or fertilization and for all sites.
Irrigated Fertilized n Meas.
Prod.
Meas.
CV
Sim.
Prod.
Sim.
CV
Model Used
N N 27 65.7 28.9 63.2 38.4 All model parameters
N Y 15 78.7 34.4 81.9 50.2 Removed rainfall consideration
Y N 4 80.0 24.8 59.5 29.1 Removed pH consideration
Y Y 11 92.7 25.3 99.5 10.7 Removed rainfall and pH consideration
Y/N Y/N 57 75.4 8.5 74.9 1.7 Relevant model for each site
Irrigated Fertilized n RMSE St Dev ME IA Model Used
N N 27 12.2 22.6 0.70 0.95 All model parameters
Y/N Y/N 57 12.5 21.7 0.67 0.94 Relevant model for each site
https://doi.org/10.1371/journal.pone.0228552.t002
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While our model suggests a net increase in total suitable land and general suitability over
the next 50 years, the changes are not uniform and some areas do show a decrease in suitabil-
ity, including total loss of cultivation (Fig 5). In particular, losses in suitability occur in cur-
rently suitable areas of Central and South America, while large gains are seen in southeast
Asia, southeastern United States, and southeastern parts of South America.
In general, though, currently productive areas remain largely unchanged. Under the 4.5 sce-
nario, the area that does not change is 43% of the total modelled area (~16,700,000 km
2
), and
the area that increases/decreases by a suitability of up to 10 is a further 35%, making the total
area that remains unchanged or minimally unchanged 78% of the total modeled area
(~27,400,000 km
2
). Under the RCP 8.5 climate scenario, the area that will remain unchanged
over the next 50 years is 43% of the totaled modelled area (~15,200,000 km
2
). The area that
will minimally change (+/- 10 suitability score), will be another 30%, to make the total area
unchanged or minimally unchanged 73% of the total modeled area (~25,500,000 km
2
).
Fig 2. Current global breadfruit suitability extrapolated from the environmental parameters derived in Hawai’i.
https://doi.org/10.1371/journal.pone.0228552.g002
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Future opportunities
There is substantial potential for future breadfruit production based on the large increases in
total cultivable area and average suitability of that area under climate change projections. This
is encouraging given that many current staple crops are expected to decline in suitability with
projected future climate scenarios [34,76–78]. Furthermore, most currently producing regions
are not negatively affected, providing some security and stability in the face of projected
changes.
The vast growth in very low suitability in our model is a facet of outlier GCM models. All of
the very low (<1) suitability numbers result from pixels where only one of the 17 GCMs indi-
cated conditions suitable for breadfruit. Further examination into the inter-GCM model vari-
ability would provide stronger confidence in future habitat and risk mitigation investment.
This is particularly important for a neglected crop species as there is already substantial risk
because of the large scale-lack in agronomic research, post-harvest research, and market
development.
As mentioned, our model indicates expansion of area in all bins of suitability. In our obser-
vation of validation sites, we noted that suitability of 30 or below represented very poor pro-
ducing trees–the plants would grow and bear fruit but only on the order of 10% of what trees
in very high suitability are able to produce. In this light, we propose excluding the sites with
suitability scores less than 30. Similarly, at the upper end of the spectrum, sites with suitability
of >70 appear to all be highly productive, with only slight changes in relatively high yields.
Therefore, a more tempered approach might be to think of “moderate” (31–70) and “high”
(71–100) quality habitat for breadfruit. Such an approach would also inherently eliminate the
vast areas of very low suitability (<10) caused by model outliers. Applying this breakdown
Fig 3. A mosaic plot comparing a previous global breadfruit model by Lucas and Ragone [62] on the x-axis in
categories of unsuitable (0), suitable (1), and best (2), and our fuzzy set model on the y-axis represented by
percentage total of each score. The width of the x-axis indicates the relative total area of that category, while the y-axis
indicates the percentage of that area occupied by each suitability score.
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(Table 3), we see a 10% and 30% increase in high suitability sites for RCP 4.5 and RCP 8.5
respectively, and a corresponding increase of 21% and 90% in moderate suitability.
A current shortcoming to the model is that average annual data was used. This may be par-
ticularly problematic in areas that get below freezing temperatures, as frost has been reported
to kill breadfruit trees. While the absolute minimum average annual temperature was set at
17˚C, freezing temperatures for short periods of time could still occur and be offset by much
higher temperatures. Likewise, this could apply to seasonality of rainfall as there is not a clear
Fig 4. Modeled future global suitability in 2070 for RCP 4.5 (top) and RCP 8.5 (bottom).
https://doi.org/10.1371/journal.pone.0228552.g004
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understanding of how much prolonged drought breadfruit trees can tolerate. Furthermore,
there may be temporal aspects of such variations in weather that further complicate the inter-
actions. For instance, local seasonal drought during the season of vegetative growth may have
different impacts than if it occurred during the fruiting season. Unfortunately, there currently
exists a drastic shortage of such observations, with the limited observations tending to exist as
anecdotal evidence rather than quantifiable parameter thresholds. However, with increased
plantings globally and increased focus on the crop, the necessary data to move towards more
refined models driven by increasingly specific criteria can be generated. Any future modeling
efforts should certainly take an approach that considers monthly extremes in temperature and
rainfall as well as rainfall seasonal distributions. However, for the tropical and subtropical
regions of the world this shortcoming is not expected to impact the results. Other extreme
events, such as heat waves, prolonged droughts, damaging floods and hurricanes, etc. are also
not considered in examining suitability in this way.
Conclusion
Our study highlights breadfruit as a highly resilient tree crop, suitable for investment and
incorporation into climate adaptation and regional land-use planning. The dramatic increases
in global suitability shown by our model for breadfruit also begs the question of what other
NUS crops may flourish under future climate conditions, and can they do this synergistically
in an integrated, adaptive food forest. To plan for future adaptation, they must be identified
and nurtured now, and supported with technical and infrastructural resources. Approaches
such as validated models can be a first step in this direction, providing an increased degree of
security and investor confidence to develop the plantings and infrastructure needed. Further
modelling that integrates environmental variability will assist in this capacity and where glob-
ally available data or high-resolution data is lacking, site specific, fine-scaled models may serve
to fill the gap especially for seasonally variable areas.
Table 3. Area of breadfruit suitability in millions of km2 for current and future climate projections, presented as
total and individual bins as discussed.
Current RCP 4.5 RCP 8.5
Total 32.66 41.62 61.71
Bin
<1 2.59 3.53 9.35
1–10 2.11 4.68 10.38
11–20 1.97 3.62 6.20
21–30 1.83 2.90 5.28
31–40 2.03 2.50 4.62
41–50 3.27 3.74 5.17
51–60 1.64 1.90 3.16
61–70 1.42 1.96 2.93
71–80 1.63 2.12 4.17
81–90 4.25 5.25 6.22
91–100 12.50 12.94 13.59
Group
<1 TO 30 8.51 14.73 31.21
31 TO 70 8.36 10.11 15.88
71 TO 100 18.38 20.31 23.97
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Bringing an underutilized crop into the market as a major food source is a difficult task that
face many hurdles but is a critical component for addressing mounting needs for food security
and good nutrition in a changing world. Our extensive regional work with breadfruit agricul-
turalist shows a strong need to shift consumer preferences, grow peoples’ palette for new
crops, and change perception and markets. Large scale marketing efforts may do well support-
ing this up-and-coming “superfood”, especially in light of its ability to persist in the future
along with the multitude of economic and environmental co-benefits that may ensue from the
farming of sustainable, breadfruit forests.
Fig 5. Changes (increase/decrease) in breadfruit suitability over the next 50 years under (top) RCP 4.5 and
(bottom) RCP 8.5.
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Author Contributions
Conceptualization: Kalisi Mausio, Tomoaki Miura, Noa K. Lincoln.
Data curation: Kalisi Mausio.
Formal analysis: Kalisi Mausio, Noa K. Lincoln.
Funding acquisition: Noa K. Lincoln.
Investigation: Kalisi Mausio, Tomoaki Miura, Noa K. Lincoln.
Methodology: Kalisi Mausio, Tomoaki Miura, Noa K. Lincoln.
Project administration: Noa K. Lincoln.
Resources: Noa K. Lincoln.
Software: Kalisi Mausio.
Supervision: Noa K. Lincoln.
Validation: Noa K. Lincoln.
Visualization: Kalisi Mausio, Noa K. Lincoln.
Writing – original draft: Kalisi Mausio, Noa K. Lincoln.
Writing – review & editing: Kalisi Mausio, Tomoaki Miura, Noa K. Lincoln.
References
1. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, et al. Food security: the chal-
lenge of feeding 9 billion people. Science. 2010; 327(5967): 812–818. https://doi.org/10.1126/science.
1185383 PMID: 20110467
2. Food and Agriculture Organization; International Fund for Agricultural Development; United Nations
International Children’s Emergency Fund; World Food Program; World Health Organization. The State
of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome,
Italy: 2017.
3. Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, et al. Food Security and Food Pro-
duction Systems. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and
Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovern-
mental Panel on Climate Change. https://doi.org/10.1111/j.1728-4457.2009.00312.x
4. Porter JR, Howden M, Smith P. Considering agriculture in IPCC assessments. Nature Climate Change.
2017; 7(10): 680.
5. Porter JR, Semenov MA. Crop responses to climatic variation. Philosophical Transactions of the Royal
Society B: Bio Sciences, 2005; 360(1463): 2021–2035.
6. Wang Y, Frei M. Stressed food–The impact of abiotic environmental stresses on crop quality. Ag Eco &
Env. 2011; 141(3–4): 271–86.
7. Mabhaudhi T, Chibarabada T, Chimonyo VGP, Modi A. Water-Food- Nutrition-Health Nexus: Linking
Water to Improving Food, Nutrition and Health in Sub- Saharan Africa. International Journal of Environ-
mental Research and Public Health 2016; 13 (1). Multidisciplinary Digital Publishing Institute: 107.
https://doi.org/10.3390/ijerph13010107 PMID: 26751464
8. Buyck M. The international community’s commitment to combating micronutrient deficiencies. J of Food
Nutr and Agri. 1993; 7: 2–7.
9. Ramalingaswami V. New global perspectives on overcoming malnutrition. American J of Clinical Nutri.
1995; 61: 259–263.
10. United Nations. Sustainable Development Goals: 2015. https://www.un.org/sustainabledevelopment/
sustainable-development-goals/.
11. Food and Agriculture Organization. Global plan of action for the conservation and sustainable utilization
of plant genetic resources for food and agriculture. Leipzig, Germany: 1996.
12. Food and Agriculture Organization. Agricultural Biodiversity, Multifunctional Character of Agriculture
and Land Conference, Background Paper 1. Maastricht, Netherlands: September 1999
PLOS ONE
Growing global potential of breadfruit cultivation under climate change scenarios
PLOS ONE | https://doi.org/10.1371/journal.pone.0228552 May 22, 2020 14 / 17
13. Padulosi S, Hodgkin T, Williams JT, and Haq N, Underutilized crops: trends, challenges and opportuni-
ties in the 21st Century. In: Engels J, Rao VR, Jackson Meditors. Managing plant genetic diversity. Wal-
lingford, UK. CAB International: 2002. p. 323–338.
14. Food and Agriculture Organization. Biodiversity: Plants. Retrieved June 26, 2018, from http://www.fao.
org/biodiversity/components/plants/en/
15. Prescott-Allen R, Prescott-Allen C. How many plants feed the world?. Conservation Biology, 1990; 4(4):
365–374.
16. Padulosi S, Thompson J, Rudebjer P. Fighting Poverty, Hunger and Malnutrition with Neglected and
Underutilized Species: Needs, Challenges and the Way Forward. Biodiversity International. 2013.
www.bioversityinternational.org.
17. Bacon CM, Sundstrom WA, Go
´mez MEF, Me
´ndez VE, Santos R, Goldoftas B, et al. Explaining the
‘Hungry Farmer Paradox’: Smallholders and Fair-Trade Cooperatives Navigate Seasonality and
Change in Nicaragua’s Corn and Coffee Markets. Global Environ Change 2014; 25(1): 133–49. https://
doi.org/10.1016/j.gloenvcha.2014.02.005
18. Ragone D. 1997. Breadfruit (Artocarpus Altilis (Parkinson) Fosberg): Promoting the Conservation And
Use of Underutilized and Neglected Crops. International Pant Genetic Resources Institute, 1997:77.
http://medcontent.metapress.com/index/A65RM03P4874243N.pdf.
19. Zerega NJC, Ragone D, Motley TJ. Complex Origins of Breadfruit (Artocarpus Altilis, Moraceae): Impli-
cations for Human Migrations in Oceania. Am J of Bot. 2004; 91(5): 760–66.
20. Food and Agriculture Organisation. International Treaty on Plant Genetic Resources for Food and Agri-
culture. 2009;56. http://www.fao.org/3/a-i0510e.pdf.
21. Jones AMP, Ragone D, Aiona K, Lane WA, Murch SJ. Nutritional and Morphological Diversity of Bread-
fruit (Artocarpus, Moraceae): Identification of Elite Cultivars for Food Security. J of Food Comp and
Anal. 2011; 24 (8): 1091–1102. https://doi.org/10.1016/j.jfca.2011.04.002
22. Liu Y, Ragone D, Murch SJ. Breadfruit (Artocarpus altilis): a source of high-quality protein for food secu-
rity and novel food products. Amino acids. 2015 Apr 1; 47(4):847–56. https://doi.org/10.1007/s00726-
015-1914-4 PMID: 25588988
23. Jones AMP, Murch SJ, Wiseman J, Ragone D. Morphological Diversity in Breadfruit (Artocarpus, Mora-
ceae): Insights into Domestication, Conservation, and Cultivar Identification. Gen Resourc and Crop
Evol. 2013; 60 (1). https://doi.org/10.1007/s10722-012- 9824–8
24. Turi CE, Liu Y, Ragone D, and Murch SJ. Breadfruit (Artocarpus Altilis and Hybrids): A Traditional Crop
with the Potential to Prevent Hunger and Mitigate Diabetes in Oceania. Trends in Food Sci & Tech.
2015; 45 (2). Elsevier Ltd: 264–72. https://doi.org/10.1016/j.tifs.2015.07.014
25. Lincoln NK, Ragone D, Zerega N, Roberts-Nkrumah LB, Merlin M, and Jones AM. Grow us our daily
bread: A review of breadfruit cultivation in traditional and contemporary systems. Hort Reviews. 2018a;
46: 299–384.
26. McGregor AM, Tora L, Lebot V. Planting breadfruit orchards as a climate change mitigation strategy for
the Pacific islands. Paper presented at XXIX Intl Hort Congress. Brisbane. 2014
27. Bell J, Taylor M. Building climate-resilient food systems for Pacific Islands. WorldFish. Program Rep:
2015–15. Penang, Malaysia.
28. Jose S. Agroforestry for ecosystem services and environmental benefits: an overview. Agroforestry
Syst, 2009; 76(1), 1–10.
29. Nair PR, Nair VD, Kumar BM, Showalter JM. Carbon sequestration in agroforestry systems. Advances
in Agronomy 2010; 108: 237–307.
30. Collins WW, Hawtin GC. Conserving and Using Crop Plant Biodiversity in Agroecosystems. 1999. In:
Collins W.W. and Qualset C.O., editors. Biodiversity in Agroecosystems. CRC Press, Boca Raton,
Washington: 1999. p. 267–281
31. Malczewski J. GIS-based land-use suitability analysis: a critical overview. Progress in Planning. 200; 62
(1): 3–65.
32. Challinor A, Wheeler T, Craufurd P, Slingo J. Simulation of the impact of high temperature stress on
annual crop yields. Agri and Forest Meteor. 2005; 135(1): 180–189.
33. White JW, Hoogenboom G, Kimball BA, Wall GW. Methodologies for simulating impacts of climate
change on crop production. Field Crops Research, 2011; 124(3): 357–368.
34. Lane A, Jarvis A. Changes in climate will modify the geography of crop suitability: agricultural biodiver-
sity can help with adaptation. Journal of Semi-arid Tropical Agricultural Research, 2007; 4(1): 1–12.
35. Mathur P, Ramirez-Villegas J, Jarvis A. The Impacts of Climate Change on Tropical and Sub-tropical
Horticultural Production. Trop Fruit Tree Species and Clim Change. 2012;27.
PLOS ONE
Growing global potential of breadfruit cultivation under climate change scenarios
PLOS ONE | https://doi.org/10.1371/journal.pone.0228552 May 22, 2020 15 / 17
36. Nisar Ahamed T, Gopal Rao K, Murthy J, 2000. GIS-based fuzzy membership model for crop-land suit-
ability analysis. Agri Syst. 2000; 63(2): 75–95.
37. Ramirez-Villegas J, Jarvis A, Laderach P, Empirical approaches for assessing impacts of climate
change on agriculture: The EcoCrop model and a case study with grain sorghum. Agri and Forest
Meteor. 2013; 170: 67–78.
38. Zadeh LA, 1965. Fuzzy sets. Information and Control. 8(3): 338–353.
39. Burrough PA, McDonnell R. Principles of Geographical Information Systems, 333. Oxford University
Press; 1998.
40. Steduto P, Hsiao TC, Raes D, Fereres E. AquaCrop—The FAO Crop Model to Simulate Yield
Response to Water: I. Concepts and Underlying Principles. Agron J. 2009; 101:426–437. https://doi.
org/10.2134/agronj2008.0139s
41. Raes D., Steduto P., Hsiao T. C., & Fereres E. (2009). AquaCrop—the FAO crop model to simulate
yield response to water: II. Main algorithms and software description. Agron J. 101(3), 438–447.
42. Stockle CO, Martin SA, Campbell GS. CropSyst, a cropping systems simulation model: water/nitrogen
budgets and crop yield. Agri Syst. 1994; 46(3), 335–359.
43. Sto
¨ckle CO, Donatelli M, Nelson R. CropSyst, a cropping systems simulation model. European J of
Agron. 2003; 18(3–4), 289–307.
44. Araya A., Keesstra S. D., & Stroosnijder L. (2010). Simulating yield responseto water of Teff (Eragrostis
tef) with FAO’s AquaCrop model. Field Crops Research. 116(1–2), 196–204.
45. Geerts S., Raes D., Garcia M., Miranda R., Cusicanqui J.A., Taboada C., et al., 2009. Simulating yield
response of quinoa to water availability with AquaCrop. Agron. J. 101(3), pp.499–508.
46. Steduto P., Hsiao T. C., Raes D., & Fereres E. (2009). AquaCrop—The FAO crop model to simulate
yield response to water: I. Concepts and underlying principles. Agron J. 101(3), 426–437.
47. Todorovic M., Albrizio R., Zivotic L., Saab M. T. A., Sto
¨ckle C., & Steduto P. (2009). Assessment of
AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water
regimes. Agron J. 101(3), 509–521.
48. Zeleke K. T., Luckett D., & Cowley R. (2011). Calibration and testing of the FAO AquaCrop model for
canola. Agron J. 103(6),
49. Meilleur BA, Jones RR, Titchenal CA, Huang AS. Hawaiian Breadfruit: Ethnobotany, Nutrition, and
Human Ecology. CTAHR, University of Hawaii, Honolulu, HI;2004.
50. Lincoln NK, Ladefoged TN. Agroecology of pre-contact Hawai’ian dryland farming: the spatial extent,
yield and social impact of Hawaiian breadfruit groves in Kona, Hawai’i. J. Archaeol Sci. 2014; 49:192–
202.
51. Winter KB, Lincoln NK, Berkes F. The socio-ecological keystone concept: A quantifiable metaphor for
understanding the structure, function, and resilience of a biocultural system. Sustainability. 2018; 10(9):
3294.
52. Lincoln NK, Vitousek PM. Indigenous Polynesian Agriculture in Hawai‘i. Oxford Research Encyclope-
dias: Environ Sci. March;2017. https://doi.org/10.1093/acrefore/9780199389414.013.376
53. Kagawa-Viviani A, Levin P, Johnson E, Kantar M, Ooka J, Baker JD, et al. I ke e¯we ‘āina o ke kupuna:
Hawaiian ancestral crops in perspective. Sustainability 2018; 10(12): 4607.
54. Lincoln NK, Rossen J, Vitousek P, Kahoonei J, Shapiro D, Kalawe K., et al. Restoration of ‘āina malo‘o
on Hawai‘i Island: Expanding Biocultural Relationships. Sustainability, 2018b; 10(11), 3985.
55. Lysak M. Improving Food and Nutrition Security through Exploration of Market Potentials by Revitalizing
Breadfruit as a Strategy for Adapting to Climate Change. Ph.D. Thesis, Department of Plant and Envi-
ronmental Science, University of Copenhagen, Copenhagen, Denmark. 2018.
56. Langston B, Lincoln N. The role of breadfruit in biocultural restoration and sustainability in Hawai‘i. Sus-
tainability, 2018; 10(11), 3965.
57. Ragone D, Elevitch C, Dean A. Revitalizing breadfruit in Hawaii—A model for encouraging the cultiva-
tion and use of breadfruit in the tropics. Trop. Agric. 2016; 93: 213–224.
58. Vitousek PM. The Hawaiian Islands as a Model System for Ecosystem Studies. Pac Sci. 1995; 49: 2–
16.
59. Juvik SP, Juvik JO. Atlas of Hawaii, University of Hawaii Press, Honolulu, HI; 1998
60. Asner GP, Elmore AJ, Hughes RF, Warner AS, Vitousek PM. Ecosystem structure along bioclimatic
gradients in Hawaii from imaging spectroscopy, Remote Sensing of Environ. 2005; 96(3–4): 497–508.
61. Lucas MP, Ragone D. Will Breadfruit Solve the World Hunger Crisis? New Developments in an Innova-
tive Food Crop. PIOJ Sustainable Development & Regional Planning Division 2012; 5(17).
PLOS ONE
Growing global potential of breadfruit cultivation under climate change scenarios
PLOS ONE | https://doi.org/10.1371/journal.pone.0228552 May 22, 2020 16 / 17
62. Food and Agriculture Organization. Crop Ecological Requirements Database (ECOCROP); 2000.
http://ecocrop.fao.org/ecocrop/srv/en/home
63. Paris Q. The Return of von Lie’ig’s “Law of the Minimum”. Agron J. 1992; 84(6): 1040–1046.
64. Bowen RC, Hollinger SE. Alternative Crops Web Site, Illinois State Water Survey. Champaign Illinois;
2002. http://www.sws.uiuc.edu/data/altcrops/.
65. Giambelluca TW, Chen Q, Frazier AG, Price JP, Chen YL, Chu PS, et al. Online rainfall atlas of Hawai‘i.
Bull. Of the Am. Meteorol. Soc. 2013; 94(3):313–316. https://doi.org/10.1175/BAMS-D-11-00228.1
66. Giambelluca TW, Shuai X, Barnes ML, Alliss RJ, R.J. Longman, T. Miura, et al. 2014. Evapotranspira-
tion of Hawaii. Final report submitted to the U.S. Army Corps of Engineers-Honolulu District, and the
Commission on Water Resource Management, State of Hawai‘i, USA.
67. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture.
Soil Survey Geographic (SSURGO) Database; 2018. Available online at https://sdmdataaccess.sc.
egov.usda.gov. Accessed [02/22/1999].
68. RStudio Team. RStudio: Integrated Development for R. RStudio, Inc., Boston, MA; 2015. http://www.
rstudio.com/.
69. Lincoln NK, Radovich T, Acosta K, Isele E, Cho A. Towards standardization of foliar tissue sampling of
breadfruit. Hort Tech. 2019; 29(4), 443–449
70. Nash J.E., and Sutcliffe J.V. 1970. River flow forecasting through conceptual models: A discussion of
principles. J of Hydrol. 10: 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
71. Fick SE, Hijmans RJ. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas.
Internat J of Climatol.; Oct 2017; 37(12):4302–15.
72. Batjes NH. World soil property estimates for broad-scale modelling (WISE30sec) (No. 2015/01). ISRIC-
World Soil Information.
73. Batjes NH. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of
global soil carbon stocks. Geoderma. 2016; 269: 61–68.
74. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high-resolution interpolated climate sur-
faces for global land areas. Internat J of Climatol. 2005; 25: 1965–1978
75. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL. Model evaluation guidelines
for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007;
50(3): 885–900.
76. Hannah L, Ikegami M, Hole DG, Seo C, Butchart SHM, Peterson AT, et al. Global Climate Change
Adaptation Priorities for Biodiversity and Food Security. PLoS ONE. 2013; 8(8): e72590. https://doi.org/
10.1371/journal.pone.0072590 PMID: 23991125
77. Hannah L., Donatti CI, Harvey CA, Alfaro E, Rodrigues DA, Bouroncle C. et al. Regional modeling of cli-
mate change impacts on smallholder agriculture and ecosystems in Central America. Climatic Change.
2017; 141; 29–45 (2017) https://doi.org/10.1007/s10584-016-1867-y
78. Zabel F, Putzenlechner B, Mauser W. Global Agricultural Land Resources–A High Resolution Suitability
Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLoS ONE. 2014; 9(9):
e107522. https://doi.org/10.1371/journal.pone.0107522 PMID: 25229634
PLOS ONE
Growing global potential of breadfruit cultivation under climate change scenarios
PLOS ONE | https://doi.org/10.1371/journal.pone.0228552 May 22, 2020 17 / 17
