http://orcid.org/0000-0003-4621-6926
Correction
28 Oct 2020: The PLOS ONE Staff (2020) Correction: Cultivation Potential Projections of Breadfruit (Artocarpus altilis) Under Climate Change Scenarios Using an Empirically Validated Suitability Model Calibrated in Hawai’i. PLOS ONE 15(10): e0241547. https://doi.org/10.1371/journal.pone.0241547 View correction
Figures
Abstract
Humanity faces significant challenges to agriculture and human nutrition, and changes in climate are predicted to make such challenges greater in the future. Neglected and underutilized 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 system, 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. Overall, 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 projections, indicating relative stability of production potential in current growing regions. Breadfruit, and other tropical indigenous food crops present strong opportunities for cultivation and food security risk management strategies moving forward.
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
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: All model codes and data layer files are available at GitHub for public access: https://github.com/nlincoln2017/Breadfruit-Suitability-Model
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.
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 malnutrition 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 end of the spectrum, obesity—which is often attributed to global commodities that lack essential micro and macro-nutrients—is also on the rise [2].
Increased pressure on food markets is expected to exacerbate existing issues of food security, 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 production [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 knowledge, 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, minerals 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 growing 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 underutilized 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 pattern for land uses according to specific requirements, preferences, or predictors of some activity [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 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 membership 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 membership 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 models 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 lifetime 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 individual and backyard trees [52]. Following European colonization, a dramatic shift away from traditional 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 agricultural 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 breadfruit production that incorporates both climate and soil data, and to explore the global potential 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 temperature [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.
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 environmental 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. Environmental 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:
where SUIT is the suitability score, and P is the measured environmental criterion at the site (i.e., each pixel), and ABSmin, ABSmax, OPTmin, and OPTmax are the absolute and optimal environmental ranges for the criterion of a particular crop.
Five environmental criteria essential to breadfruit growth were selected to evaluate breadfruit 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:
where n is 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 nutrient; 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 calculations 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.
Model refinement and validation
Based on previous modeling of breadfruit habitat [62, 57], the EcoCrop environmental parameters 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 producer 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 conducted 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:
Where Si and Mi are the simulated and measured values of yield and
is the average of Mi values of n measured 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 measures 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 calculated for all sites against corresponding model output of combined environmental parameters. For instance, modeled outputs compared to irrigated sites did not include the rainfall parameter 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 neither 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 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 output 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 recommended 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 visible, with greater potential on the wetter windward (northeastern) sides of the islands and near 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).
Validation sites are marked by the black crosshairs.
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 (r2 0.91, p<0.001). Using all sites and their corresponding simulated suitability the relationship weakens but remains strong (r2 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 performing substantially stronger.
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 production potential (Fig 2).
We compared our empirically validated model against the only previous global breadfruit 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 km2 of suitable habitat (~14,800,000 km2 of “best” and ~12,100,000 of “suitable”), and our model suggesting ~35,100,000 km2 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 km2 of “suitable” lands and 700,000 km2 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.
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.
Future projections
Future breadfruit suitability was assessed using our fuzzy set model using climate projections 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).
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 suitability, including total loss of cultivation (Fig 5). In particular, losses in suitability occur in currently 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 scenario, the area that does not change is 43% of the total modelled area (~16,700,000 km2), 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 km2). 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 km2). 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 km2).
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 indicated conditions suitable for breadfruit. Further examination into the inter-GCM model variability 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 observation of validation sites, we noted that suitability of 30 or below represented very poor producing 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 (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 particularly 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 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 interactions. 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 globally available data or high-resolution data is lacking, site specific, fine-scaled models may serve to fill the gap especially for seasonally variable areas.
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 agriculturalist 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 supporting 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.
References
- 1. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, et al. Food security: the challenge of feeding 9 billion people. Science. 2010;327(5967): 812–818. 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 Production 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 Intergovernmental 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 Environmental Research and Public Health 2016;13 (1). Multidisciplinary Digital Publishing Institute: 107. 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
- 13.
Padulosi S, Hodgkin T, Williams JT, and Haq N, Underutilized crops: trends, challenges and opportunities in the 21st Century. In: Engels J, Rao VR, Jackson Meditors. Managing plant genetic diversity. Wallingford, 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, Gómez MEF, Méndez 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.
- 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): Implications 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 Agriculture. 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 Breadfruit (Artocarpus, Moraceae): Identification of Elite Cultivars for Food Security. J of Food Comp and Anal. 2011;24 (8): 1091–1102.
- 22. Liu Y, Ragone D, Murch SJ. Breadfruit (Artocarpus altilis): a source of high-quality protein for food security and novel food products. Amino acids. 2015 Apr 1;47(4):847–56. pmid:25588988
- 23. Jones AMP, Murch SJ, Wiseman J, Ragone D. Morphological Diversity in Breadfruit (Artocarpus, Moraceae): Insights into Domestication, Conservation, and Cultivar Identification. Gen Resourc and Crop Evol. 2013;60 (1).
- 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.
- 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 biodiversity 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.
- 36. Nisar Ahamed T, Gopal Rao K, Murthy J, 2000. GIS-based fuzzy membership model for crop-land suitability 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.
- 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. Stöckle 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 response to 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., Stöckle 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 Encyclopedias: Environ Sci. March;2017.
- 53. Kagawa-Viviani A, Levin P, Johnson E, Kantar M, Ooka J, Baker JD, et al. I ke ēwe ‘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 Environmental Science, University of Copenhagen, Copenhagen, Denmark. 2018.
- 56. Langston B, Lincoln N. The role of breadfruit in biocultural restoration and sustainability in Hawai‘i. Sustainability, 2018;10(11), 3965.
- 57. Ragone D, Elevitch C, Dean A. Revitalizing breadfruit in Hawaii—A model for encouraging the cultivation 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 Innovative Food Crop. PIOJ Sustainable Development & Regional Planning Division 2012;5(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 Hawaiʻi. 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. Evapotranspiration of Hawaii. Final report submitted to the U.S. Army Corps of Engineers-Honolulu District, and the Commission on Water Resource Management, State of Hawaiʻi, 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.
- 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 surfaces 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 climate change impacts on smallholder agriculture and ecosystems in Central America. Climatic Change. 2017;141; 29–45 (2017)
- 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