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PERSPECTIVE
The persistent threat of emerging plant disease
pandemics to global food security
Jean B. Ristaino
a,1
, Pamela K. Anderson
b,c
, Daniel P. Bebber
d
, Kate A. Brauman
e
, Nik J. Cunniffe
f
,
Nina V. Fedoroff
g
, Cambria Finegold
h
, Karen A. Garrett
i,j
, Christopher A. Gilligan
f
,
Christopher M. Jones
k
, Michael D. Martin
l
, Graham K. MacDonald
m
, Patricia Neenan
n
,
Angela Records
o
, David G. Schmale
p
, Laura Tateosian
k
, and Qingshan Wei
q
Edited by Barbara Valent, Kansas State University, Manhattan, KS, and approved April 7, 2021 (received for review November 30, 2020)
Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the
world. Now a global human pandemic is threatening the health of millions on our planet. A stable,
nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant
diseases, both endemic and recently emerging, are spreading and exacerbated by climate change,
transmission with global food trade networks, pathogen spillover, and evolution of new pathogen
lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and
improved detection technologies including pathogen sensors and predictive modeling and data analytics
are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could
help mitigate future plant disease pandemics.
emerging plant disease
|
plant pathology
|
food security
The United Nations declared 2020 the International
Year of Plant Health. It is estimated that food produc-
tion will need to increase by 60% by 2050 to feed the
estimated 10 billion people expected on Earth (1, 2).
An increase in production along with a reduction in
food loss due to pests and pathogens and food waste
will be needed to meet demand (3, 4). Global yield
losses due to crop pests and diseases on food crops
are large, with mean losses ranging from 21.5% (10.1
to 28.1%) in wheat, 30.3% (24.6 to 40.9%) in rice,
22.6% (19.5 to 41.4%) in maize, 17.2% (8.1 to 21%)
in potato, and 21.4% (11 to 32.4%) in soybean (3). In
some regions of the world, plant diseases also cause
significant preharvest losses for smallholder farmers,
with nearly 50% of beans and maize farmers surveyed
in Central America and nearly 50% of potato farmers
surveyed in South America experiencing loss (4) (SI
Appendix, Fig. S1). Plant diseases cause significant
losses in food crop production that lead not only to
lower yields but also to loss of species diversity, mitiga-
tion costs due to control measures, and downstream
impacts on human heath (3).
The National Academy of Sciences recently pub-
lished an ambitious agricultural research agenda that
emphasized the need for breakthrough technology for
the early and rapid detection and prevention of plant
diseases (5). Emerging plant diseases are diseases that
1) have increased in either incidence, geographical, or
host range; 2) have changed pathogenesis; 3) have
newly evolved; or 4) have been discovered or newly
a
Emerging Plant Disease and Global Food Security Cluster, Department of Entomology and Plant Pathology, North Carolina State University,
Raleigh, NC 27695;
b
International Potato Center, 1558 Lima, Peru;
c
Board for International Food and Agricultural Development, United States
Agency for International Development, Washington, DC 20523;
d
Biosciences, Exeter University, Exeter EX4 4QD, United Kingdom;
e
Global Water
Initiative, Institute on the Environment, University of Minnesota, St. Paul, MN 55108;
f
Department of Plant Sciences, University of Cambridge,
Cambridge CB2 3EA, United Kingdom;
g
Huck Institute of the Life Sciences, Pennsylvania State University, University Park, PA 16801;
h
Digital
Development, CABI, Wallingford OX10 8DE, United Kingdom;
i
Institute for Sustainable Food Systems, University of Florida, Gainesville, FL 32611;
j
Plant Pathology Department, University of Florida, Gainesville, FL 32611;
k
Center for Geospatial Analytics, North Carolina State University, Raleigh,
NC 27695;
l
Department of Natural History, Norwegian University of Science and Technology University Museum, Norwegian University of Science
and Technology, 7491 Trondheim, Norway;
m
Department of Geography, McGill University, Montreal, QC, Canada H3A 0B9;
n
Strategic
Partnerships, the Americas, CABI, Wallingford OX10 8DE, United Kingdom;
o
Bureau for Food Security, United States Agency for International
Development, Washington, DC 20523;
p
School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg,
VA 24061; and
q
Emerging Plant Disease and Global Food Security Cluster, Department of Chemical and Biomolecular Engineering, North Carolina
State University, Raleigh, NC 27695
Author contributions: J.B.R. designed research; J.B.R. and Q.W. contributed new reagents/analytic tools; and J.B.R., P.K.A., D.P.B., K.A.B., N.J.C.,
N.V.F., C.F., K.A.G., C.A.G., C.M.J., M.D.M., G.K.M., P.N., A.R., D.G.S., L.T., and Q.W. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
Published under the PNAS license.
1
To whom correspondence may be addressed. Email: jean_ristaino@ncsu.edu.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2022239118/-/DCSupplemental.
Published May 21, 2021.
PNAS 2021 Vol. 118 No. 23 e2022239118 https://doi.org/10.1073/pnas.2022239118
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PERSPECTIVE
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recognized (6). Emerging plant disease and pest outbreaks affect
food security, national security, and human health, with serious
economic implications for agriculture (6). Emerging plant diseases
have already become more frequent, and in coming decades it is
expected that shifts in the geographic distributions of pests and
pathogens in response to climate change and increased global
commerce will make them both more frequent and severe (7–9).
Plant diseases influence many components of food security, and
effective management will lead to both improved crop production
and human health (10). Herein, we discuss the impacts of emerg-
ing plant diseases on food production and food security, discuss
why plant pathogens emerge, and recommend an integrated re-
search agenda that can be implemented by global science and
development agencies to help prevent emerging plant diseases
and improve mitigation strategies when plant disease outbreaks
do occur.
Plant Diseases Impact Food Security
During the global food crisis of 2007/2008 food price spikes oc-
curred, pushing millions into hunger (11, 12). These price spikes
were subsequently associated with political unrest and food riots
in the Middle East and Africa (2, 12). This prompted the former US
Secretary of State, John Kerry, to remark that “access to food and
agriculture needs to be the cornerstone of development strategy”
(13). The climate crisis is expected to disrupt food supply chains
and increase plant diseases, leading to both lower crop yields and
reductions in food stockpiles within countries (12).
Plant diseases are of global concern and exact a heavy toll on
food crop production and the social and political stability of na-
tions. A classic example is the 19th century Irish potato famine. In
1845, Phytophthora infestans destroyed the potato crop and in
subsequent years led to the Irish famine with over 2 million deaths
and mass migration of people from Ireland (14) (Fig. 1A). The
pathogen first emerged in the United States in 1843 with less-
severe consequences than in Ireland where dependence on a
single food crop, the lack of social and political will, and the delay
by the British government to address the hunger problem led to
dire food insecurity for the Irish people (14). Conflict, poverty, and
the British rule during World War II made the Bengal famine worse
as Cochliobolus miyabeanus, the cause of brown spot on rice,
spread, resulting in the death of over 2 million Bengalese people.
Rice production dropped by 25% and the country’s rice supplies
were shifted to feed troops (15). The recent coffee rust outbreaks
caused by Hemileia vastatrix in Central America provide yet an-
other example of the displacement of people due to an emerging
endemic plant disease and climate change (Fig. 1B) (16). Yield
losses in coffee greater than 50% occurred in some regions of
Central America and over 400,000 coffee workers lost their live-
lihoods in the coffee sector in Honduras, El Salvador, and Gua-
temala, leading to hunger, poverty, and increased migration (17).
Monoculture of susceptible coffee varieties, low coffee prices, and
climate change in Central America led to the spread of coffee rust
to higher elevations where growers were ill-prepared and lacked
access to fungicides to control the disease (16).
Unlike endemic disease that is usually managed, emerging
plant diseases can pose shocks to agricultural productivity, and
thus we risk undercutting other input investments in agriculture
that alleviate hunger unless the deleterious impacts of plant dis-
eases on crop production are considered in policy discussions (1,
4, 18). Food security exists when all people, at all times, have
physical, social, and economic access to sufficient, safe, and nu-
tritious food to meet their dietary needs and food preferences for
an active and healthy life (1). The four components of food security
include access, availability, utilization, and stability (3), and plant
diseases affect these components (Table 1). Fusarium oxysporum
f. sp. cubense tropical race 4 (TR4) that causes Panama disease of
banana threatens to reduce the availability of banana in some areas
of the world (19) (Fig. 1C) (Table 1). The TR4 strain has moved from
Asia into Mozambique, Jordan, and, in 2019, Colombia (20). The
susceptible cultivar Cavendish is grown in monoculture in Central
America and has no resistance to TR4. The disease threatens farms
of both subsistence farmers in Asia and Africa and major banana
plantations. Thus, the availability of banana may be greatly reduced
(19). Cassava is a major staple in the diet of millions of East Africans
(Table 1). Cassava viruses (21) threaten access to a stable supply of
cassava. The onset of cassava mosaic disease in Uganda in the
1980s led to precipitous drops in cassava production, thus limiting
access to this important food source (21). Cassava mosaic virus
(CMV) causes over $1 billion in losses in East Africa, and cassava
yields have been reduced by 13 million tons annually (Fig. 1D)(21).
Conflict, civil war, and drought also exacerbated the problem. The
Fig. 1. Several important emerging plant diseases that threaten food
security, including (A) late blight of potato caused by P. infestans,(B)
coffee rust caused by H. vastatrix,(C) Panama disease caused by F.
oxysporum f. sp. cubense (TR4) on banana, and (D) cassava mosaic
disease caused by East African CMV.
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utilization of corn and groundnuts (peanut) as sources of nutri-
tion in Africa has been severely compromised by aflatoxins
produced primarily by Aspergillus flavus and other fungi (Ta-
ble 1) (22). When contaminated corn is consumed, stunting of
growth in children and impairment of health, including cancer,
can occur. The levels of aflatoxins in corn in East Africa are high,
and the lack of uniform standards in Africa for detection of
aflatoxins and risk assessment hinders food safety regulations
(22). The stability of the premium and fair-trade coffee supply in
Central America was severely disrupted and reduced by coffee
rust (16, 17) (Fig. 1Band Table 1). Over 120,000 smallholder
farmers in Guatemala were impacted by increased production
costs of fungicides to control coffee rust, and many were left in
poverty due to lack of income from coffee and were forced to
emigrate (17).
Why Do Plant Pathogens Emerge?
Plant diseases emerge by a range of means (6). An increase in
incidence, geography, or host range of a pathogen can occur by
movement of pathogens in infected plant material (9), as was the
case in the recent introductions of wheat blast into Bangladesh in
infected seed (23). Extreme weather events, such as hurricanes,
can transport pathogen spores over continents, as illustrated
by soybean rust movement from Brazil to the United States via
Hurricane Ivan (24). Years after the introduction of citrus tristeza
virus in South America, an insect vector was introduced that led to
wider geographic spread of the disease (6). Plant pathogens may
shift hosts and gain the ability to infect new hosts when intro-
duced into new regions. For example, CMV does not naturally
occur on cassava in South America but moved into cassava in
Africa from an unknown host following uptake of cassava as a
major food crop there (21). A change in pathogenesis or virulence
of an endemic strain can occur, as exemplified by the recent alarm
over a new race of the wheat stem rust pathogen in Ethiopia (race
TTKTT) with a very high combination of virulence genes and a new
race in Sicily that could overcome resistance in a widely grown
European cultivars of wheat (25, 26). Newly evolved plant path-
ogenic species can also occur through interspecific hybridization
or mutations within existing pathogen lineages, as illustrated by
the emergence of Phytophthora andina in South America and
Phytophthora alni in the United Kingdom (27, 28).
A Convergence Research Agenda to Manage Emerging
Plant Diseases
Convergence science is the integration of knowledge, methods,
and expertise from different disciplines to form novel frameworks
to catalyze scientific discovery and innovation. Science innovation
is needed to tackle emerging plant diseases and will require in-
terdisciplinary teams of researchers that work at multiple scales
from the molecular to the landscape level (Fig. 2). Fundamental
research onpathogen biologyis needed to dissect the complexities
of disease transmission and emergence of novel pathogen line-
ages. The integration of such knowledge can be mobilized and
accelerated by convergence science. Predictive data such as
transportation and trade networks, geography, weather and cli-
mate parameters, and early pathogen detection data from DNA
sequences, incidence records, disease sensors, citizen science,
data mining, and web scraping tools and disease identification
applications can be input into models to develop dynamic dis-
ease surveillance networks. These data can be used to model and
predict future outbreak hotspots on the landscape level (Fig. 2).
Breakthroughs in novel technology development such as rapid
pathogen detection sensors and artificial intelligence to analyze
multiple data streams will enable disease surveillance to be
scaled, thus greatly expanding the input datasets that inform
disease prediction models. Thus, more rapid responses by
stakeholders to contain disease outbreaks and improved farm
economies can occur (Fig. 2).
The Need for Emerging Plant Disease Surveillance and
Monitoring Systems
Microbial threats to human health and food safety are in the daily
view of the public and policymakers, as illustrated by the recent
pandemic caused by COVID-19. The risks posed by emerging
plant disease outbreaks are equally important yet often under-
reported (3, 6, 29). Coordinated global monitoring and surveil-
lance of emerging human diseases was begun in 1994 by the
Program for Monitoring Emerging Diseases (ProMED) (6). ProMED
rapidly disseminates written reports of global outbreak information
Table 1. Impact of emerging plant diseases on four components of food security on key food and subsistence crops
Component Definition Example of a plant disease Consequence
Availability The existence of food in
a particular place and time
Panama disease Cavendish banana, a key food source for many smallholder
farmers, is threatened by TR4 race of Fusarium oxysporum
f. sp. cubense that migrated from southeast Asia to
Mozambique (19, 20). The disease could eliminate
production of the crop in some areas of the world.
F. odoratissimum (TR4)
Access The ability of a person or
group to obtain food
Cassava mosaic disease
caused by East African
CMV (EACMV-!UG2)
A strain of a CMV caused huge losses and cassava fields were
abandoned in sub-Saharan Africa. Food shortages and
famine-related deaths occurred in Uganda due to
dependence on cassava (21).
Utilization The ability to use and obtain
nourishment from food
(includes food nutritional value
assimilation of nutrients)
Mycotoxins on corn caused
by A. flavus and Fusarium
species
Eighty-seven percent of East Kenyan corn mills had over
the legal limit of fumonisins in corn (22). Consuming
fumonosin-affected corn affects nutritional value of corn
and is carcinogenic.
Stability The absence of significant
fluctuation in availability,
access and utilization
Coffee rust Coffee yields reduced by 16 to 31% in Central and South
America. Low price of coffee and lack of inputs such as new
varieties and fungicides exacerbated disease (16, 17).
Smallholder income was lost for food purchases and
stability of the commodity in the region was threatened.
H. vastatrix
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on infectious diseases in humans and animals, and since 1995 in
plants, via email and reaches 70,000 subscribers in 185 countries. A
team of science experts moderates and reviews ProMed reports
before posting to the network. Information is dispersed in email
reports. Notably, ProMED monitoring first revealed the Wuhan
outbreak of COVID-19 in December 2019 (https://promedmail.
org/promed-post/?id=20191230.6864153).
Although there are many national and regional plant protec-
tion agencies that monitor disease, a coordinated global report-
ing mechanism is still lacking (30). The challenges with monitoring
and surveillance for emerging plant diseases are that plant dis-
eases are underreported, and reports are overly influenced by
the sociology of science (science as a social activity), in addition to
the politics and economics of nations and international agriculture
(6, 30). There are political considerations around “who should
legitimately report”disease outbreaks and who has access to the
information (6, 30). Countries are often reluctant to report plant
disease outbreaks because of the potential economic conse-
quences, including barriers for export markets. Oftentimes,
maps of emerging plant disease are as much an artifact of where
projects and scientists are active as where disease is actually
emerging (29–31). The areas of highest detected plant disease
emergence are often in the developed world, simply due to more
funding to conduct and publish work in these areas (8, 31). Poli-
cymakers need to be made aware that global plant disease sur-
veillance systems are important, as agricultural food production is
inextricably linked to both human and animal health. There is a
need to increase data sharing and real-time surveillance of
emerging plant diseases to improve the timeliness and accuracy
of reporting of plant disease outbreaks globally and improve the
food security of all reporting nations.
Geospatial Analytics to Monitor and Understand
Outbreaks
Geographic information systems (GIS) are an essential tool for
mapping and analyzing data about healthy and diseased hosts in
order to respond to potential disease threats in landscapes and
agricultural systems (32, 33). Many forecasts of disease in food
production regions are based on routine pest and pathogen
monitoring via sentinel plots and weather monitoring and fore-
casting (24). Real-time web-based GIS applications allow users to
see reported crop disease outbreaks within the regions of interest.
Unfortunately, only a few plant pathogens are actively monitored
globally using disease surveillance systems (30). For example, the
disease alert and forecasting systems for late blight in the United
States (https://usablight.org/) and in Europe (https://agro.au.dk/
forskning/internationale-platforme/euroblight/) uses real-time
monitoring to map outbreaks, genotype lineages, and detect
fungicide sensitivity of pathogen lineages (32, 33). USABlight.org
sends alerts to growers nearby when the pathogen is detected via
text or email (32, 33) (Fig. 3). Use of a late blight decision support
tool to target fungicide sprays saved growers from $3,706 to
$4,201 per hectare depending on the variety planted (34). An-
other system, the Global Rust Reference Center, tracks the yearly
outbreak of yellow rust and stem rust on wheat and provides
genotyping and race identification to guide breeding efforts and
deployment of resistant varieties (35). There is a need for global
plant disease reporting on geographic scales so comparisons of
disease can be made across continents and countries. Pathogen
detection by citizen scientists using mobile applications and
geotagged cell phone images could be used in addition to pre-
dictive models to map outbreak risks for many plant diseases (36).
Disease surveillance systems are used in developed countries,
but often the information is not shared 1) because it can confer a
competitive economic advantage or 2) because what happens in
one region is not viewed as important by growers in another re-
gion. More funding by larger intergovernmental organizations
such as the Consultative Group for International Agriculture Re-
search (CGIAR), Food and Agriculture Organization of the United
Nations, or the World Bank would enable scaling plant disease
surveillance (30). This information could be used both by regula-
tors within countries and decision-makers on the ground so that
Fig. 2. Critical components and data analytics needed for an emerging plant disease surveillance network. Data include predictive data such as
transportation and trade networks, geography, weather and climate parameters, and early detection data such as DNA sequence data, pathogen
detection from sensors, text mining of historical and social media data, citizen science data, and identification applications. These data can be
used to model spread of plant diseases and predict future spread. Enhanced monitoring and proactive mitigation strategies can be deployed in
forecasted future hotspots. Early detection of plant diseases leads to more timely deployment of mitigation strategies.
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appropriate mitigation strategies can be deployed to prevent
pathogen spread once an outbreak is identified. This will require
long-term investment by intergovernmental agencies since many
surveillance systems are now fragmented and firewalled (30).
Greater interagency and international collaboration with the goal
of shared access to data for research and disease mitigation could
expand disease surveillance. Geospatial surveillance systems
funded by intergovernmental agencies that provide shared data
on disease outbreaks and that are active in real time are needed
by stakeholders to mitigate threats.
Pathogen Risk Assessment and Modeling to Predict
Outbreak Spread
Epidemiological tools can be used to predict risk of outbreaks.
Models can vary from simple deterministic mathematical models
to spatially explicit stochastic simulations and decision support
systems (37). Meteorologically driven dispersal models can iden-
tify the risks of long-distance movement of spores to show which
countries are at risk. For example, five major zones for exchange
of rust spores of wheat stem rust throughout Africa, the Middle
East, and South Asia were identified. Predominant donor coun-
tries, notably Yemen, acted as a stepping stone for wheat rust into
and out of Africa, thus identifying sources and countries that were
at risk (38). Near-real-time early-warning systems in Ethiopia for
within-country spread of wheat stem and stripe rust have been
developed that enable growers to apply fungicide effectively to
reduce crop losses (39). Critical parameters for dispersal and
transmission within the landscapes must be estimated under
prevailing environmental conditions for many pathogens, allow-
ing for discontinuities in landscape connectedness (40).
Bayesian statistical methods allow any prior knowledge to in-
form parameter estimates based on emerging patterns of spread,
as well as estimates that become progressively more resolved
over time as an outbreak progresses (41). Probability distributions
of estimated values may be used to parameterize stochastic,
spatiotemporal epidemic models to predict the likely spread of
disease across the landscape. This can highlight uncertainties in
current knowledge, as well as the variability of epidemic spread
driven by both climate as well as the inherent unpredictability of
epidemics. Models are also used to inform surveillance programs
(42), to optimize disease control (37), and for web-based decision
support via “serious games”to help inform stakeholders and
policymakers about options for disease control (43). Linking epi-
demiological with economic models allows additional insights
into how to deploy control when resources are limited (44). Open-
source code and software that is scalable to multiple projects and
shared knowledge across animal, plant, and human systems could
help build capacity more rapidly for resource-poor regions (37).
Modeling to accurately predict both the potential for spread of
plant diseases and translation to stakeholders’communities is
needed to inform policy and control strategies.
Emerging Pathogen Risks through Trade
International trade has a growing role in providing national food
supplies across the globe (45, 46). Just seven countries form the
backbone of the global agricultural trade network, each trading
with greater than 77% of countries worldwide (47). Thus, patho-
gen movement risk is concentrated where trade is prevalent (48).
For example, the United States is a dominant player in global
agricultural trade, and its total agricultural trade activity (mass and
trade value of imports +exports) surpassed that of any other
country in recent years (49). Major trade partners include China,
Europe, Canada, and Mexico. Thus, monitoring pest and disease
outbreaks from the major trading export country partners is likely
to result in greater likelihood of detecting pests or diseases (49).
New epidemiological tools that utilize dynamic network anal-
ysis to study the key drivers of pathogen movement with trade are
needed (Fig. 2). Dynamic network models (DNMs) can represent a
landscape as a system of interconnected suitable patches, de-
fined as nodes, and consider links between nodes based on the
Fig. 3. Reports of late blight caused by P. infestans on potato and tomato in the United States mapped using geospatial analytics. Data are
analyzed from USABlight (2011 to 2019). The total number of outbreaks and the number of states and counties reporting over 9 y are shown.
Most of the reports were from tomato and the top 10 states that were hot spots of disease in the eastern United States are indicated.
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probability of movement of pathogens or pests (50). The inte-
gration of risk components based on socioeconomic, weather, or
trade factors can offer a more complete system perspective.
Several studies have begun to use DNMs to examine the structure
of global crop breeding and seed systems networks to examine
the spread of crop resistance genes at the landscape level
(51–53). Networks of postharvest transportation were used to
determine risk of pathogen and mycotoxin movement in stored
grains (54). By tracing the transport of stored wheat by rail through
states using network analysis, hub locations were identified that
provide higher risk of transporting pests and pathogens and pri-
ority control points for inspection and controls (54). Social network
models were used to analyze patterns of maize trade among na-
tions and to determine where vulnerabilities in food security might
arise if maize availability were decreased from shortages, trade
tariffs, or diversion to nonfood uses (55). Dynamic and social
network models and analysis that include trade data to predict
plant disease spread are needed and can be used to identify the
critical control points where potential introduction risks of
emerging pathogens could be stopped or mitigated more quickly
to reduce the costs of mitigation.
Earth Observations to Assess Climate-Driven Disease
Spread
Along with increased trade connectivity among countries, warm-
ing climates may facilitate the spread of new pathogens, since
increased climate suitability may expand the range of plant
pathogens and their vectors and hosts into uncolonized areas (7,
8). Weather and climate also play an integral role in determining if
conditions are suitable for disease at a particular place and time.
However, risk analysis requires pest and pathogen occurrence
data along with weather modeling, thus emphasizing the impor-
tant need for surveillance and detection networks (56) (Fig. 2).
Decision support systems are under development that use Earth
observation data. One such example is the Pest Risk Information
Services (PRIZE) that uses environmental data and land-surface
temperature and rainfall combined with models of pest life cycles
to make pest risk assessments and was recently used to predict and
reduce impact of fall armyworm in corn in east Africa (57).
Weather conditions are routinely monitored across the globe,
with reliable forecasts days in advance. The wheat rust early
warning system in Ethiopia uses 1- to 7-d weather forecast data
from the UK Meteorological Office in combination with spore
dispersal models and ground surveillance data (35). A shift in the
distribution of plant pests and pathogens, and a movement
poleward, has been documented using structured archival pub-
lished datasets from CABI and climate data (7). Latitudinal trends
in the data suggest climate impacts on disease.
However, data are limited on measuring actual disease oc-
currence on whole plants using satellite imagery because it is
difficult to resolve specific diseases. Innovation in hyperspectral
imaging is now overcoming this barrier (58). The ability to track the
movement of plant pathogens in the atmosphere is also important
for establishing effective quarantine measures and forecasting
disease spread (59). Drones (unmanned aerial vehicles, UAVs)
have been used to study the transport of plant pathogens tens to
hundreds of meters above crop fields. Sporangia of P. infestans
have been detected 90 m and higher above potato fields using
UAVs, showing the potential of drones for atmospheric sampling
of plant pathogens (59). Images from drones combined with re-
mote sensing are being used to detect plant water stress and
monitor disease spread (58). As resolution from remote sensing
and satellite imagery improves, disease at the individual plant or
field scale will be possible with both UAV and imagery data. In-
formation can be used in conjunction with models to make appro-
priate plant disease management decisions based on anticipated
patterns of spread of pathogens (42, 59). The expanded use of high-
resolution earth observation and weather data including hyper-
spectral and satellite imagery and drones can help identify and
discern impacts of specific plant diseases on crops.
Sensors for Detection of Plant Diseases
Research is rapidly expanding in the development of in-field
sensors for detection of plant pathogens on food crops (60–62)
(SI Appendix, Fig. S2). For example, a microneedle molecular
diagnostic platform was developed that couples a quick micro-
needle nucleic acid extraction with a loop-mediated isothermal
amplification–based sensor to detect coinfections in tomato by
P. infestans and tomato spotted wilt virus. A smartphone-based
plant volatile organic compound (VOC) sensing platform has also
recently been developed that uses multiplexed nanoplasmonic and
organic dye sensor arraysprepared on low-cost paper substrates for
early detection of late blight (P. infestans), early blight, and Septoria
blight on tomato (SI Appendix,Fig.S2) (61). The VOC sensors
detect disease before symptoms occur. Wearable, wireless sensors
for detection of plant disease are on the horizon.
Development and deployment of new information and com-
munication technologies through smartphone applications that
incorporate pathogen-specific sensors could help facilitate risk
mitigation strategies. Timely and accurate plant disease diagnosis
by field clinicians using sensors, particularly in resource-poor areas
that lack extension and plant disease clinics, will help expand
collection of data on regional and country levels that can be fed
into surveillance and prediction models and improve detection
and reporting of newly emerged pathogens. Expanded devel-
opment of electronic and wearable plant disease sensors for rapid
detection of plant pathogens will enable more rapid tracking of
emerging pathogens.
Data Mining and Big Data Analytics for Geographic
Monitoring
Data mining technologies can be used to examine potential
pathogen threats to major food crops. Archival data and recently
available “big data”streams from multiple structured (published
papers, archival printed documents, PubMed and CAB Abstracts,
and Google Ngram) and unstructured data sources (social media,
image posts, and Twitter feeds) can be used with natural-language
processing tools to monitor and map infectious plant diseases and
pathogens (63–65). Data mining tools may be used that periodically
scan the internet and social media for plant diseases. New infor-
mation critical to decision-makers can be obtained from “big data”
for efficient management of crop diseases and to examine po-
tential disruptions that could make food supplies more resilient
to outbreaks. By identifying the control points we may be able to
predict the resilience of cropping systems in specific geographic
areas and identify hot spots of disease outbreaks (31).
Few large-scale studies using data mining to study the spread
of emerging plant disease have been done (65–67). Natural-
language processing tools and geoparsing were used to track
the 19
th
-century spread of late blight in the United States around
the ports of New York and Philadelphia and revealed methods
used to control the disease and speculations on the source of
outbreaks (66). In another example, over 12,500 disease reports
were mined and collated to examine the genus-wide distribution
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and invasiveness of Phytophthora species (67). Countries at risk for
Phytophthora outbreaks and the invasive potentials of dominant
species were predicted. Data mining techniques using natural-
language processing and geoparsing, text mining, and crowd
sourcing of social media data should be harnessed as a resource for
monitoring past and predicting future plant pathogen outbreaks.
This will improve global food security vigilance, policy planning, re-
search, and implementation of phytosanitary plant health services.
Population Genomic Surveillance for Monitoring Emerging
Plant Pathogens
Currently, with few exceptions, most plant disease surveillance
tracking systems do not incorporate molecular genotyping due to
expense and lack of infrastructure to process large numbers of
samples (33, 35, 68, 69). Plant disease alert and surveillance sys-
tems that track pathogen genotypes and races and use bio-
informatic predictive tools to identify novel strains of pathogen
lineages can help reveal the source of the outbreak strains. The
next-generation sequencing revolution introduces exciting op-
portunities for using low-cost, evolutionary genetic discovery to
track movement of plant pathogens. Genomic and bioinformatic
approaches can be applied to elucidate the origins, ancestry, and
functional evolution of important plant pathogens and lineages,
especially when historical collections are available (70). The use of
natural history collections including mycological collections has
been especially useful for understanding historical outbreaks,
identifying pathogen lineages and sources of new outbreaks, and
determining whether an outbreak lineage is new or reproducing
sexually (28, 33, 68–70). Enhanced digitization of herbarium
specimens and data mining can be used to produce baseline re-
ports of outbreaks for major food crops (70). This is particularly
exciting in the context of parameterizing pathogen spread mod-
els, for which methods have emerged to infer dispersal from
successive spatial snapshots (71). Genomic data can be used to
constrain chains of transmission more tightly, thereby allowing
better model parameterization.
Genomic mining of both archival and present data outbreak
strains promises to help trace and time the presence of a particular
plant disease and can help identify migration routes of past out-
breaks strains (28, 70, 72). Genomic mining can trace the evolu-
tion and geographic origins of pathogen species, identify where
highest diversity and greatest threat to crop populations may
exist, and reveal the particular dynamics of virulence of past
outbreaks. New methods that exploit low-depth sequencing (e.g.,
“genome skimming”) hold power to monitor both extinct and
extant plant diseases. Low-depth sequencing of museum speci-
mens, grouped in time and space to form “populations,”could be
used for population-level tracking of past plant pathogen outbreaks
and push back the dates of first reports in countries (72, 73). Rapid
in-field sequencing technologies using Minion sequencers have
been used to reduce time and cost to identify strains of rust fungi
(74). Nextstrain is currently being used to track COVID-19 outbreak
strains and provide real-time phylogenies and open sequence data.
These open phylogenetic tools could be used to track plant disease
outbreak strains and reduce time of discovery of novel lineages(75,
76). Additional reference genome assemblies for emerging plant
pathogens, diverse sequenced panels of globally diverse plant
pathogens, and open sequence reporting platforms are needed to
fully exploit rapidly advancing population genomics technologies
for tracking outbreak strains.
Extension and Digital Advisory Services for Smallholders
to Identify Plant Diseases
Lack of access to timely, appropriate, and actionable extension
advice is a major cause of poor productivity and crop loss for
smallholders in the developing world. Pest and disease outbreaks
can flare up unpredictably and cross borders quickly, and often
there is no mechanism to rapidly identify the problem and suggest
effective, pragmatic solutions. Work in the developing world by
organizations such as CABI’s Plantwise clinics has helped support
establishment of local “plant clinics”staffed by trained “plant
doctors”where smallholder farmers get practical advice (77).
Plant disease clinics and digital disease surveillance needs to be
expanded in the developing world (30). The African Crop Epi-
demiology System (ACES) initiative started by the Bill & Melinda
Gates Foundation and a set of UK international development
partners aims to develop an early-warning plant health system for
East Africa on a set of targeted pathogens and crops. Reductions
in crop losses can occur by timely identification of problems and
implementation of appropriate management practices (30). Plant
disease clinics need to be expanded in developing countries so
plant health information can be used more efficiently by regional
regulatory agencies. Information can then be disseminated to
stakeholders in the form of factsheets, distribution maps, diag-
nostic tools, and pest management information. Expanded plant
disease clinics in the developing world networked with electronic
data collection devices to record/report pathogen data in a timely
fashion to authorized stakeholders will provide opportunities for
early disease warning for mitigation response.
Prospects
Plant diseases do not recognize political borders, yet country
borders are often used to stop the sharing of information on plant
disease outbreaks (31). The migration of plant pathogens with
trade is likely to increase with the emergence of new trade rela-
tionships, compounding the impacts of climate change on agri-
cultural productivity in some regions of the world (29). More
comprehensive surveillance strategies for plant diseases that in-
clude strategic partnerships among research universities, devel-
opment agencies, nongovernmental organizations, the private
sector, and CGIAR are needed. Research programs focused on
disease surveillance and epidemiology for the high-impact plant
pathogens on major food crops such as wheat, potato, cassava,
banana, corn, and rice are needed to prevent plant pathogen
spread and predict the global burden of plant disease.
Currently, disease surveillance is woefully underfunded and is
only done on a global scale for wheat rusts and potato late blight
(30, 33, 35). Modeling, surveillance, and bioinformatics tools in
many cases are ready to be deployed on larger landscape-level
scales by research, phytosanitary, regulatory, and development
agencies, but an organized strategy is needed (30). The open
sharing of datasets by researchers and policymakers is needed as
well as threat scenario training so coordinated responses can be
made to control plant diseases that affect crop yields. New out-
breaks can be predicted with disease surveillance systems and
data can be used to determine the origin of outbreak strains and
calculate rates of spread. Global hot spots of new emerging dis-
eases can be identified (Fig. 2). This information will be useful to
deploy resistant germplasm, fungicides, or other eradication
methods at the landscape level. The time for strategic thinking is
now as the food security of resource poor countries is threatened
by both plant diseases and the COVID-19 pandemic that has
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impacted food production, access, supply chains, and the health
of agricultural workers.
Data Availability. All study data are included in the article and/or
SI Appendix.
Acknowledgments
J.B.R. thanks Dr. Fred Gould, member of the National Academy of Sciences, for
his useful comments on this manuscript. The National Academies Keck Futures
Initiative Grant (NAKFIES9) and the Rockefeller Foundation provided funding for
a Bellagio Conference Grant (88220) on “Emerging Infectious Diseases of Africa
in the Context of Ecosystem Services.”Appreciation is also expressed to the
North Carolina State University Provost’s Office; The Nusbaum Foundation; The
Kenan Institute; The College of Agriculture and Life Sciences, the College of
Engineering, and the College of Sciences at North Carolina State University; and
Research Triangle International and Novozymes for funding of a conference
on “Emerging Plant Disease and Global Food Security,”where ideas were gen-
erated for this work (https://globalfoodsecurity.ncsu.edu/). We thank Shannon
Jones for preparing Fig. 2, Xingli Ma for preparing Fig. 3, and Amanda Saville for
formatting all figures. The views and opinions expressed in this paper are those
of the authors and not necessarily the views and opinions of the United States
Agency for International Development or any of the authors’institutions.
1Food and Agriculture Organization of the United Nations, The State of Food and Agriculture 2019: Moving Forward on Food Loss and Waste Reduction (Food and
Agricultural Organization, Rome, Italy, 2019).
2N. V. Fedoroff, Food in a future of 10 billion. Agric. Food Secur. 4, 11 (2015).
3S. Savary et al., The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3, 430–439 (2019).
4L. Delgado, M. Schuster, M. Torero, “The reality of food losses: A new measurement methodology”(IFPRI Discussion Paper 1686, International Food Policy
Research Institute, Washington, DC, 2017). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/131530.
5National Academies of Sciences, Engineering, and Medicine, Science Breakthroughs to Advance Food and Agricultural Research by 2030 (National Academies
Press, Washington, DC, 2019).
6P. K. Anderson et al., Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19,535–544
(2004).
7D. P. Bebber, M. A. T. Ramotowski, S. J. Gurr, Crop pests and pathogens move polewards in a warming world. Nat. Clim. Chang. 3, 985–988 (2013).
8D. P. Bebber, Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).
9C. M. Brasier, The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathol. 57, 792–808 (2008).
10 B. J. Larkin, Emerging Technologies to Benefit Farmers in Sub-Saharan Africa and South Asia (National Academies Press, Washington, DC, 2009).
11 Food and Agriculture Organization of the United Nations, “The state of food insecurity in the World: The multiple dimensions of food security”(Food and
Agriculture Organization, Rome, Italy, 2013).
12 International Food Policy Research Institute, “Global food policy report: 2016”(International Food Policy Research Institute, Washington, D.C., 2016).
13 J. Kerry, “Alleviating global hunger: Challenges and opportunities for US leadership, United States Foreign Relations Committee”(United States Government
Printing Office, Washington DC, 2009).
14 P. M. A. Bourke, Emergence of potato blight, 1943-46. Nature 203, 805–808 (1964).
15 S. Y. Padmanabhan, The Great Bengal famine. Annu. Rev. Phytopathol. 11,11–24 (1973).
16 J. Avelino et al., The coffee rust crises in Colombia and Central America (2008–2013): Impacts, plausible causes and proposed solutions. Food Secur. 7,303–321
(2015).
17 K. Sieff, The migration problem is a coffee problem. Washington Post, 11 June 2019. https://www.washingtonpost.com/world/2019/06/11/falling-coffee-prices-
drive-guatemalan-migration-united-states/. Accessed 7 October 2019.
18 J. A. Foley et al., Solutions for a cultivated planet. Nature 478, 337–342 (2011).
19 E. Stokstad, Devastating banana disease may have reached Latin America, could drive up global prices. Science 365, 207–208 (2019).
20 F. A. Garc´
ıa-Bastidas et al., First report of Fusarium wilt Tropical Race 4 in Cavendish bananas caused by Fusarium odoratissimum in Colombia. Plant Dis. 104
(2019).
21 J. P. Legg et al., Cassava virus diseases: Biology, epidemiology, and management. Adv. Virus Res. 91,85–142 (2015).
22 S. K. Mutiga, V. Hoffmann, J. W. Harvey, M. G. Milgroom, R. J. Nelson, Assessment of aflatoxin and fumonisin contamination of maize in western Kenya.
Phytopathology 105, 1250–1261 (2015).
23 Y. Inoue et al., Evolution of the wheat blast fungus through functional losses in a host specificity determinant. Science 357,80–83 (2017).
24 S. A. Isard, J. M. Russo, A. Ariatti, The integrated aeriobiology modeling system applied to the spread of soybean rust into the Ohio Valley during September
2006. Aerobiologia 23, 271–282 (2007).
25 S. Battacharya, Wheat rust back in Europe. Nature 542, 145–146 (2017).
26 N. B. Hei et al., First report of Puccinia graminis f. sp. tritici race TTKTT in Ethiopia. Plant Dis. 103, 982 (2019).
27 P. Gladieux et al., The population biology of fungal invasions. Mol. Ecol. 24, 1969–1986 (2015).
28 M. D. Martin et al., Genomic characterization of a South American Phytophthora hybrid mandates reassessment of the geographic origins of Phytophthora
infestans.Mol. Biol. Evol. 33, 478–491 (2016).
29 D. P. Bebber, T. Holmes, D. Smith, S. J. Gurr, Economic and physical determinants of the global distributions of crop pests and pathogens. New Phytol. 202,
901–910 (2014).
30 M. Carvajal-Yepes et al., A Global surveillance system for crop diseases: Global preparedness minimizes the risk to food supplies. Science 364, 137–1239 (2019).
31 H. Scherm, C. S. Thomas, K. A. Garrett, J. M. Olsen, Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology.
Annu. Rev. Phytopathol. 52, 453–476 (2014).
32 W. E. Fry et al., The 2009 late blight pandemic in the eastern United States - Causes and results. Plant Dis. 97, 296–306 (2013).
33 J. B. Ristaino, D. E. L. Cooke, I. Acu~
na, M. Mu~
noz, “The threat of late blight to global food security”in Emerging Plant Disease and Global Food Security,J.B.
Ristaino, A. Records, Eds. (American Phytopathological Society Press, 2020), pp. 403–420.
34 Y. Liu et al., A risk analysis of precision agriculture technology to manage tomato late blight. Sustainability 10, 3108 (2018).
35 R. Park et al., International surveillance of wheat rust pathogens: Progress and challenges. Euphytica 179,109–117 (2011).
36 R. K. Meentemeyer, M. A. Dorning, J. B. Vogler, D. Schmidt, M. Garbelotto, Citizen science helps predict risk of emerging infectious disease. Front. Ecol. Environ.
13, 189–194 (2015).
37 R. N. Thompson, E. Brooks-Pollock, Detection, forecasting and control of infectious disease epidemics: Modelling outbreaks in humans, animals and plants.
Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20190038 (2019).
38 M. Meyer et al., Quantifying airborne dispersal routes of pathogens over continents to safeguard global wheat supply. Nat. Plants 3, 780–786 (2017).
39 C. Allen-Sader et al., An early warning system to predict and mitigate wheat rust diseases in Ethiopia. Environ. Res. Lett. 14, 115004 (2019).
40 R. K. Meentemeyer et al., Application of stochastic epidemiological models to realistic landscapes: Spread of the sudden oak death pathogen in California.
Ecosphere 2,1–24 (2011).
41 R. N. Thompson, C. A. Gilligan, N. J. Cunniffe, Control fast or control smart: When should invading pathogens be controlled? PLOS Comput. Biol. 14, e1006014
(2018).
8of9
|
PNAS Ristaino et al.
https://doi.org/10.1073/pnas.2022239118 The persistent threat of emerging plant disease pandemics to global food security
Downloaded at University of Minnesota Libraries on May 27, 2021
42 S. Parnell, F. van den Bosch, T. Gottwald, C. A. Gilligan, Surveillance to inform control of emerging plant diseases: An epidemiological perspective.Annu. Rev.
Phytopathol. 55, 591–610 (2017).
43 N. J. Cunniffe, R. O. J. H. Stutt, R. E. DeSimone, T. R. Gottwald, C. A. Gilligan, Optimizing and communicating options for the control of invasive plant disease
when there is epidemiological uncertainty. PLoS Comput. Biol. 11, e1004211 (2015).
44 G. A. Forster, C. A. Gilligan, Optimizing the control of disease infestations at the landscape scale. Proc. Natl. Acad. Sci. U.S.A. 104, 4984–4989 (2007).
45 M. Porkka, M. Kummu, S. Siebert, O. Varis, From food insufficiency towards trade dependency: A historical analysis of global food availability. PLoS One 8, e82714
(2013).
46 Z. Mehrabi, N. Ramankutty, Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).
47 M. Ercsey-Ravasz, Z. Toroczkai, Z. Lakner, J. Baranyi, Complexity of the international agro-food trade network and its impact on food safety. PLoS One 7, e37810
(2012).
48 B. A. Sikes et al., Import volumes and biosecurity interventions shape the arrival rate of fungal pathogens. PLoS Biol. 16, e2006025 (2018).
49 G. K. MacDonald et al., Rethinking agricultural trade relationships in an era of globalization. Bioscience 65,275–289 (2015).
50 M. W. Shaw, M. Pautasso, Networks and plant disease management: Concepts and applications. Annu. Rev. Phytopathol. 52,477–493 (2014).
51 K. A. Garrett et al., Resistance genes in global crop breeding networks. Phytopathology 107, 1268–1278 (2017).
52 C. E. Buddenhagen et al., Epidemic network analysis for mitigation of invasive pathogens in seed systems: Potato in Ecuador. Phytopathology 107, 1209–1218
(2017).
53 M. Pautasso et al., Seed exchange networks for agrobiodiversity conservation. Agron. Sustain. Dev. 33, 151–175 (2013).
54 J. F. Hernandez Nopsa et al., Ecological networks in stored grain: Key postharvest nodes for emerging pests, pathogens, and mycotoxins. Bioscience 65,
985–1002 (2015).
55 F. Wu, H. Guclu, Global maize trade and food security: Implications from a social network model. Risk Anal. 33, 2168–2178 (2013).
56 C. Rosenzweig et al., Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci.
U.S.A. 111, 3268–3273 (2014).
57 F. Mbugua, M. Bundi, C. Day, T. Beale, F. Williams, “PRISE-PAD fall armyworm SMS alert pilot results”(CABI Study Brief No. 35, CABI, Wallingford, UK, 2020).
58 A. K. Mahlein, Plant disease detection by imaging sensors–Parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100,
241–251 (2016).
59 D. G. Schmale III, S. D. Ross, Highways in the sky: Scales of atmospheric transport of plant pathogens. Annu. Rev. Phytopathol. 53, 591–611 (2015).
60 R. Paul et al., Extraction of plant DNA by microneedle patch for rapid detection of plant diseases. ACS Nano 13, 6540–6549 (2019).
61 Z. Li et al., Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles. Nat. Plants 5, 856–866 (2019).
62 R. Jackson et al., How remote sensing is offering complementing and diverging opportunities for precision agriculture users and researchers. Adv. Anim. Biosci. 8,
383–387 (2017).
63 N. Collier, Uncovering text mining: A survey of current work on web-based epidemic intelligence. Glob. Public Health 7, 731–749 (2012).
64 C. S. Thomas, N. P. Nelson, G. C. Jahn, T. Niu, D. M. Hartley, Use of media and public-domain Internet sources for detection and assessment of plant health
threats. Emerg. Health Threats J. 4, 7157 (2011).
65 J. O’Shea, Digital disease detection: A systematic review of event-based internet biosurveillance systems. Int. J. Med. Inform. 101,15–22 (2017).
66 L. Tateosian, R. Guenter, Y. Yang, J. Ristaino, “Tracking 19th century late blight from archival documents using text analytics and geoparsing”in Free and Open
Source Software for Geospatial (FOSS4G) Conference Proceedings, C. M. Schwelk, M. Zia, A. Anderson, Eds. (University of Massachusetts, Amherst, MA, 2017),
pp. 146–155.
67 P. Scott, M. Bader, T. Burgess, G. Hardy, N. Williams, Global biogeography and invasion risk of the plant pathogen genus Phytophthora.Environ. Sci. Policy 101,
175–182 (2019).
68 A. C. Saville, M. D. Martin, J. B. Ristaino, Historic late blight outbreaks caused by a widespread dominant lineage of Phytophthora infestans (Mont.) de Bary. PLoS
One 11, e0168381 (2016).
69 M. T. Islam et al., Emergence of wheat blast in Bangladesh was caused by a South American lineage of Magnaporthe oryzae. BMC Biol. 14, 84 (2016).
70 J. B. Ristaino, The importance of mycological and plant herbaria in tracking a plant killer. Front. Ecol. Evol. 7, 521 (2020).
71 T. Jombart et al., Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Comput. Biol. 10, e1003457 (2014).
72 V. C. Bieker, M. D. Martin, Implications and future prospects for evolutionary analyses of DNA in historical herbarium collections. Bot. Lett. 165, 409–418 (2018).
73 A. C. Saville, J. B. Ristaino, Global historic pandemics caused by the FAM-1 genotype of Phytophthora infestans.BioRxiv [Preprint] (2021). https://doi.org/10.
1101/2020.08.25.266759 (Accessed 5 May 2021).
74 G. V. Radhakrishnan et al., MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens. BMC Biol. 17, 65 (2019).
75 J. L. Gardy, N. J. Loman, Towards a genomics-informed, real-time, global pathogen surveillance system. Nat. Rev. Genet. 19,9–20 (2018).
76 J. Hadfield et al., Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
77 D. Romney et al., Plantwise: Putting innovation systems principles into practice. Agric. Dev. 18,27–31 (2013).
Ristaino et al. PNAS
|
9of9
The persistent threat of emerging plant disease pandemics to global food security https://doi.org/10.1073/pnas.2022239118
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