Modeling and mapping potential epidemics of rice diseases globally

Crop Protection 34 (2012) 6e17

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Modeling and mapping potential epidemics of rice diseases globally Serge Savary a, *,1, Andrew Nelson b, Laetitia Willocquet a,1, Ireneo Pangga a, 2, Jorrel Aunario b a b

Plant Breeding, Genetics and Biotechnology Division, International Rice Research Institute, Los Baños, Philippines Social Sciences Division, Geographic Information System Unit, International Rice Research Institute, Los Baños, Philippines

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 April 2011 Received in revised form 24 October 2011 Accepted 5 November 2011

Prioritizing research for crop health management is critical in times of rapid agricultural changes. A generic model for plant diseases, EPIRICE, was developed and coupled to a geographic information system (GIS) in order to map simulated potential epidemics in rice globally. EPIRICE encompasses different hierarchy levels of a growing crop canopy: disease-sites on a leaf, whole leaves, tillers, plants, crop stand, world regions, and world. Five widely different diseases were considered, caused by fungi, bacteria, and viruses, which have different disease-site and epidemiological attributes. EPIRICE is a simple Suscept-ExposedInfectious-Removed (SEIR) model, parameterized using literature data for each of the five diseases, and combined with a few, simplified, characteristics of the growing crop (establishment, growth, senescence). Simulated outputs were tested against published disease progress curves, using visual and statistical, parametric and non-parametric, methods. Despite its simplicity, the model generates potential disease progress curves that concur with the literature. The model was linked to a GIS involving crop establishment date and daily climate data over a 12-year period of time. Successive epidemics were simulated at locations across the world, and their means and variances at each location were analyzed. Using rice as a model crop system, our results provide a proof of concept that it is possible to (1) use the same model (2) at different levels of plant and crop hierarchy, in order to (3) simulate and map potential plant disease epidemics globally. Further developments of this approach are discussed. The study also underscores the dearth of publicly available field data. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Botanical epidemiology Potential epidemics Simulation modeling Variance-to-mean analysis Geographic information system Chronic disease Acute disease Emerging disease

1. Introduction Many diseases caused by a wide range of pathogens (Ou, 1987; Mew, 1991) affect rice, the world’s first food crop (Zeigler and Barclay, 2008). Collectively, rice diseases result in yield reductions in the range of 10e15% in tropical Asia (Savary et al., 2006). There are several reasons to explain the wide range in harmfulness of crop diseases, rice in particular. One of them is that some diseases may be (i) chronic, affecting large acreage every cropping season, yet not causing massive losses, (ii) acute, affecting comparatively smaller areas, for fewer consecutive seasons, where they are responsible for heavy losses, or (iii) emerging, affecting small areas, with very variable outcomes, sometimes disastrous (Savary et al., 2011a,b). Such a simplified categorization will not apply to all diseases, or to

* Corresponding author. Tel.: þ33 5 6128 5567; fax: þ33 5 6176 5537. E-mail address: [email protected] (S. Savary). 1 Current address: INRA, UMR AGIR, Chemin de Borderouge, BP52627 Auzeville, 31326 Castanet Tolosan Cedex, France. 2 Current address: Crop Protection Cluster, College of Agriculture, University of the Philippines, Los Baños, College, Laguna 4031, Philippines. 0261-2194/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.cropro.2011.11.009

all locations: while a given disease is chronic (endemic; Putter, 1978) in some locations, it is acute elsewhere, occurring irregularly, and if so, causing massive losses. The purpose of this article is to present an approach to address this type of diversity in a proof of concept form, which later on may be useful for formal pest risk assessment. To that purpose, we chose five extremely different rice diseases only, which: (i) affect different levels of hierarchy (Allen and Starr, 1982) on a crop canopy, from individual leaf lesions (brown spot and blast), to entire leaves (bacterial blight), entire tillers (sheath blight), and to entire plants (rice tungro disease), (ii) are caused by bacteria, fungi or viruses, (iii) are associated with extremely different environmental conditions (Ou, 1987; Mew, 1991), and (iv) seem to exhibit the patterns indicated above e chronic, acute, or emerging. With the aim of providing a basis for future progress, while emphasizing the links among methods and approaches, the work reported here is based on a simple, and thus generic, model structure that can easily be linked to climate data in a geographic information system (GIS). The objective of this work is based on the current need to develop tools that enable scoping the potential importance of plant diseases, in rice in particular. While facilitating research

S. Savary et al. / Crop Protection 34 (2012) 6e17

prioritization, such a scoping work may become helpful to both assess the risk of epidemics occurring, and to measure the progress that has been made in controlling plant diseases. 2. Materials and methods 2.1. Rationale for developing EPIRICE The large number of diseases in rice makes rice and its diseases an ideal crop model for conducting comparative epidemiology and developing generic modeling approaches. A number of rice disease simulation models have been developed to understand, predict, and manage rice diseases (Teng, 1990, 1994a; Teng and Savary, 1992). These models enter with varying degree of detail in the biology of rice diseases. In declining order of research effort, three rice diseases have been the focus of epidemiological modeling work: leaf blast (e.g., Teng, 1994b), rice tungro disease (e.g., Azzam and Chancellor, 2002), and rice sheath blight (e.g., Savary et al., 1997). The modeling approaches used in these different pathosystems differ widely because of profound differences in the details of the mechanisms underpinning the epidemiological dynamics. Five rice diseases were chosen to develop EPIRICE: brown spot, leaf blast, bacterial blight, sheath blight, and rice tungro disease. This is because these diseases differ with respect to the pathogens involved, the hierarchy levels they affect, and their epidemiological characteristics. Brown spot and leaf blast cause lesions on the leaf blades, which produce propagules that spread the disease. Bacterial blight also causes lesions on the leaf blades, which expand rapidly in the case of compatible host-pathogen interactions, causing the entire leaf to die, and which are also a source of inoculum. Sheath blight is a (primarily) soil-borne disease, which affects entire tillers. Unlike the four other diseases, the sheath blight pathogen does not produce propagules per se; instead hyphal strands of the fungus progress over the growing canopy, causing canopy-borne epidemics. Rice tungro disease is caused by two different viral species, and infection by both the viruses is necessary to cause disease; the viruses are semi-persistently transmitted by the green leafhopper (GLH; Azzam and Chancellor, 2002). Thus, the five diseases were chosen because they affect a hierarchy of plant tissues in a growing crop canopy: fractions of leaf areas in the case of brown spot and leaf blast, entire leaves for bacterial blight, entire tillers for sheath blight, and entire plants for tungro. Conversely, the five diseases were chosen because they represent sites (i.e., entities which, when occupied by the pathogen may become infectious, Zadoks, 1971) that differ widely. This is also reflected by the ways disease in these different pathosystems is measured: severity (% leaf area affected) for brown spot and blast, incidence at the leaf level (% leaves diseased) for bacterial blight, incidence at the tiller level (% tillers diseased) for sheath blight, and incidence at the plant level (% plants infected) for tungro. This choice was considered a good basis for the proof of concept intended in this work. EPIRICE was developed as a general model framework to address any rice (or plant) disease. Therefore its structure was designed to remain as simple as possible, which also enabled its linkage with other applications, especially GIS. Yet, the EPIRICE structure was made flexible enough to (1) capture some of the key epidemiological features of each disease and (2) allow further improvements.

7

model epidemics of infectious diseases of plants (Scherm et al., 2006), as well as of animal and humans (Mollison, 1995). The system considered is 1 m2 of a rice crop stand, and epidemics are simulated over a 120-day duration using a daily time step. It involves four state variables of a crop stand: healthy (H), latent (L), infectious (I), and post-infectious sites (P). Three components have been added to the structure: (1) the spatial aggregation of disease (Waggoner and Rich, 1981; Jeger, 1983; Hughes and Madden, 1992; McRoberts et al., 1996); (2) crop growth (Berger, 1977); and (3) the senescence of plant tissues, whether disease-induced or physiological, as a major determinant of the decline of epidemics (Kranz, 1976; Berger et al., 1995). The list of the model variables is given in Table 1. A central element of the model is the rate of infection, RI, which is written as:

dL=dt ¼ RI ¼ Rc IC a

(1)

where the rate of variation of the infected-latent sites L is proportional to (1) the number of infectious sites I, (2) a power function of the proportion C of sites that are healthy relative to the total number of sites in the system, and (3) Rc, the basic infection rate corrected for removals (Van der Plank, 1963). The basic reproduction number R0 has been borrowed from medical epidemiology in botanical epidemiology (Madden, 2006; Madden et al., 2007). If Rc is constant over i, a simple relationship between R0 and Rc is: R0 ¼ Rci. If Rc varies over i: R0 ¼ !Rcdt from t ¼ p to t ¼ p þ i, where p is the latency period duration and i is the infectious period. The value of the exponent parameter a is 1 assuming there is no aggregation of disease, i.e., new infections occur at random among the population of healthy sites. a is larger than 1 when the horizon of infection (Van der Plank, 1963) of the disease is such that propagules cannot access the entire population of healthy sites, resulting into aggregation (Waggoner and Rich, 1981). With a ¼ 1, equation (1) is Van der Plank’s (1963) model, where all healthy sites (H) are equally exposed to infection, which is a simplification of reality (Campbell and Madden, 1990; Madden et al., 2007). When a > 1, equation (1) is a simplification of the spatial heterogeneity in Table 1 Description of EPIRICE variables.a Acronym

Variable type

Variable meaning

Dimension

H L I P

State State State State

Number of healthy sites Number of latent sites Number of infectious sites Number of post-infectious (removed) sites

[NSites] [NSites] [NSites] [NSites]

a i p RcOpt

Parameter Parameter Parameter Parameter

RcA RcT RcW RRG

Parameter Parameter Parameter Parameter

Aggregation coefficient Duration of infectious period Duration of latent period Potential basic infection rate corrected for removals Modifier for Rc for crop age Modifier for Rc for temperature Modifier for Rc for wetness Relative rate of growth

RRS

Parameter

Relative rate of senescence

2.2. Structure of EPIRICE

RP

Parameter

The model structure is based on Van der Plank’s concepts (1963), using the systems representation of Forrester (1961), translated to botanical epidemiology by Zadoks (1971). The structure is a SEIR model (susceptible-exposed-infectious-removed; Kermack and McKendrick, 1927; Madden, 2006), which has been widely used to

Sx

Parameter

Rate of senescence induced by disease Maximum number of sites

[e] [day] [day] [NSites NSites1day1] [e] [e] [e] [NSites NSites1day1] [NSites NSites1day1] [NSites day1]

Rc

Variable

TS

Variable

a

variable variable variable variable

Basic infection rate corrected for removals Total number of sites

The system modeled is 1 m2 of a rice crop stand.

[NSites] [NSites NSites1day1] [NSites]

8

S. Savary et al. / Crop Protection 34 (2012) 6e17

disease distribution. The latter simplification is two-fold, because a is dimensionless (and thus, not process-related; Savary et al., 1997) and because a may not be constant throughout the course of an epidemic (Campbell and Madden, 1990). The approach however has proven to be suitable in several cases (Waggoner and Rich, 1981; Savary et al., 1997; Allorent et al., 2005). The growth of the host population is modeled in a very simple, logistic manner:

dH=dt ¼ RG ¼ RRGHð1  ðTS=SxÞÞ

(2)

where RRG is the relative rate of growth, H is the amount of healthy sites, TS is the total number of sites, and Sx is the maximum number of sites a crop stand can produce. The rate of senescence is first described as a simple feed-forward, physiological process, whereby the amount of senesced sites, S, increases over time proportionally to the amount of (healthy) tissue produced, with a constant relative rate of physiological senescence, RRS:

dS=dt ¼ RS ¼ RRSH

(3)

Many diseases are responsible for accelerated senescence of diseased tissues. This is particularly true in rice for necrotrophic pathogens such as brown spot (Klomp, 1977) and sheath blight (Savary et al., 1997), but also hemi-biotrophic ones such as blast (Bastiaans, 1993). Equation (3) thus becomes:

dS=dt ¼ RS ¼ RP þ RRSH

(4)

where RP is the rate of transition from the infectious to the postinfectious state of a diseased site. This equation assumes that (i) latent (L) and infectious (I) sites are not affected by senescence, and (ii) that age-induced and disease-induced senescence are additive. The first hypothesis is acceptable if one considers that (i) latency and infectiousness are transitory stages only, (ii) senescence takes place at a later stage of epidemic and of crop dynamics, which (iii) will affect accumulating post-infectious sites. The second hypothesis is a simplified way to capture the indirect effect of disease on the senescence of healthy tissues. This equation also assumes that disease-induced senescence equally affects healthy and diseased (post-infectious) sites, which further supposes a random distribution of disease amongst host sites.

only one rice crop per year is grown. Second, tungro intensity is often much greater in rainy seasons than in dry seasons (Azzam and Chancellor, 2002). This is due to a number of reasons, including the larger density of the vector population at the early stages of crop, which depends on rainfall patterns. Analyses of the dynamics of GLH populations during three rainy seasons and two dry seasons in lowland tropical rice (Cook and Perfect, 1989), and of the corresponding weather data led to the development of a simple rule, where the history of rainfall pattern during the early stages of the growing crop is summarized by the Previous Rainfall Index (PRI; Chevallier, 1983):

PRIt ¼ ðPRIt1 þ Rt Þek

(5)

where Rt is the daily rainfall and k is an extinction parameter, set to 0.025 (Chevallier, 1983). Setting PRI to 100 mm at the date of crop establishment, conditions favorable to the vector population and to epidemic initiation were translated as PRI > 100 mm at 20 days after crop establishment (DACE), with a corresponding initiation of epidemics at 25 DACE. On the contrary, PRI < 100 mm at 20 DACE corresponded to less favorable conditions, and were translated by setting the initiation of tungro epidemics at 40 DACE. Temperature is also a well-known factor influencing epidemiological processes in many plant diseases. Temperature, in particular, affects the basic infection rate corrected for removals, Rc, as well as the latent (p) and infectious (i) period durations. Here again, simplicity was sought: p and i were considered fixed in the course of a given epidemic, and thus only Rc was made a function of temperature. Plant age, too, can have very strong effects on epidemiological processes (including Rc, i, p). Again, only the effect of age on Rc was considered in the framework used here. The effects of plant age, temperature, and wetness, were incorporated in the model by three modifiers (Loomis and Adams, 1983), respectively RcA, RcT, and RcW. These modifiers are strictly bound between 0 and 1, which are multiplied to a reference, potential, value of the basic infection rate corrected for removals, RcOpt, which is derived from published data. The running value of Rc is therefore modeled as the product of the four terms:

Rc ¼ RcOpt RcA RcT RcW

(6)

2.3. Modelling the effects of wetness, temperature, and crop age

2.4. Parameterization of EPIRICE

Canopy moisture is an important factor in numerous plant diseases. The effects of physical factors on moisture formation and duration in a rice canopy have for instance been studied in depth (Luo, 1996). The translation of physical variables into the actual moisture of a canopy is a research area of its own (Huber and Gillespie, 1992) in which we do not enter here. For the sake of simplicity, we applied a simple rule to represent canopy moisture: when the maximum daily relative humidity is higher than 90%, or when daily rainfall exceeds 5 mm, some moisture is assumed to occur in the canopy (Savary et al., 1990; Luo, 1996). This approach was used in the case of all diseases, except tungro. Tungro infection per se was not made dependent on wetness, as infection results from transmission by the insect vector (GLH). The literature does not suggest that virus transmission is affected by canopy moisture. Two specific elements for modeling tungro epidemics were included. First, the likelihood of occurrence of tungro epidemics is very low in areas where rice is preceded by fallow or by non-rice crops. This is due to the semi-persistent transmission mechanism of the viruses by the green leafhopper vector (Chancellor, 1995). This was accounted for by not initiating epidemics in areas where

The model parameters for each of the five rice diseases addressed have been derived from the literature, and are given in Table 2. The derivation of parameters from literature is straightforward in most of cases. In the case of RcOpt, however, estimation from experimental data requires several steps. The quantity iRc represents the amount of effective propagules (Rc) produced by an infectious site (I) during the infectious period i. If this quantity is smaller than 1, the population of infected sites (L, I, and R) will decline, and the epidemic will stop. Several approaches to estimate Rc have been proposed (Van der Plank, 1963; Campbell and Madden, 1990; Sun and Zeng, 1993; Segarra et al., 2001). In the early stage of an epidemic:

rl ¼ lnðx2 =x1 Þ=ðt2  t1 Þ

(7)

where x1 and x2 are diseased fractions at two successive dates t1 and t2, and rl is the apparent rate of disease increase. Rc can then be estimated from:

Rc ¼ rl =fexpð  rl pÞ  expð  rl ½p þ iÞg

(8)

S. Savary et al. / Crop Protection 34 (2012) 6e17

9

Table 2 EPIRICE parameter values and references. System’s attribute

Parametera

Leaf blast

Brown spot

Bacterial blight

Sheath blight

Tungro

Sites

Site size Sx Referencesb

45 mm2 of a leaf 30,000 (1)

10 mm2 of a leaf 100,000 (2)

1 leaf 3200 (3,4)

1 tiller 800 (3,4)

1 plant 100 (5)

Crop growth

RRG RRS Referencesb

0.1 0.01 (3,4,6)

0.1 0.01 (3,4,6)

0.1 0.01 (3,4,6)

0.2 0.005 (3,4)

0 0 (7)

Epidemic onset

Date Referencesb

15 DACE (8)

20 DACE (9)

20 DACE (10)

30 DACE (3)

25 DACE (7)

Residence times

p i Referencesb

5 20 (11,12)

6 19 (13,14)

5 30 (15)

3 120 (16)

6 120 (17)

Infection rate

rl Rc (calculated) Referencesb

0.28 1.14 (8)

0.19 0.61 (18)

0.25 0.87 (19)

0.23 0.46 (20)

0.10 0.18 (21)

Age effect

RcA Referencesb

(Strong) decrease with plant age (22)

(Strong) increase with plant age (23)

Decrease with plant age (24)

(Slight) increase over age (25)

(Strong) decrease with plant age (26)

Temperature effect

RcT Referencesb

Optimum: 25  C (27)

Optimum: 20  C (13)

Optimum: 28  C (28)

Optimum: 28  C (29)

Optimum: 31  C (30)

Wetness effect

RcW

1 if canopy wet, 0 otherwise (31)

1 if canopy wet, 0 otherwise (31)

1 if canopy wet, 0 otherwise (32)

1 if canopy wet, 0 otherwise (33)

Unaffected

1

1

1

2.8 (34)

1

References Aggregation

b

a Referencesb

Disease

a

See Table 1 for acronym meaning. References: (1): Pinnschmidt et al., 1995; (2): Dasgupta and Chattopadhyay, 1977; (3): Willocquet et al., 2000 (4): Willocquet et al., 2004; (5): Azzam and Chancellor, 2002; (6): Yoshida, 1981; (7): Chancellor, 1995; (8): Hwang et al., 1987; (9): Pannu et al., 2005; (10): Adhikari et al., 1999; (11): Hemmi et al., 1936; (12): Kato and Kozaka, 1974; (13): Sarkar and Sen Gupta, 1977; (14): Levy and Cohen, 1980; (15): Nayak et al., 1987; Oña, pers.com. (16): Castilla et al., 1996; (17): Rivera and Ou, 1965; (18): Klomp, 1977; (19): Adhikari, 1991; (20): Savary et al., 2001; (21): Tiongco et al., 1993; (22): Torres, 1986; (23): Padmanabhan and Ganguli, 1954; (24): Baw and Mew, 1988; (25): Sharma et al., 1990; (26): Ling and Palomar, 1975; (27) El-Refaei, 1977; (28): Horino et al., 1982; (29): Tu et al., 1979; (30): Ling and Tiongco, 1977; (31): Luo, 1996; (32): Mew et al., 1992; (33): Hashiba and Ijiri, 1989; (34): Savary et al., 1997. b

Equation (8) was used to estimate RcOpt from rl, estimated from published disease progress curves under near-optimum conditions for all five diseases. The values used for p and i are very different from one disease to another. For leaf blast and brown spot, we use values reported in the literature, which are not very different from one disease to another. As for the other diseases, these values correspond to nearoptimal conditions, including maximum host susceptibility. In the case of bacterial blight, the value for p is derived from the literature, too, whereas the value for i corresponds to the survival duration of an infected, infectious, site (in the case of this disease, an entire leaf); the i value for bacterial blight therefore is much longer than for the two first diseases. Similarly, in the case of sheath blight, the latency period is short, but the infectious period is prolonged to its maximum possible duration. The underlying hypothesis is that an infected tiller remains infectious throughout the epidemic and does not become post-infectiousddestruction of tillers by sheath blight does occur (Savary et al., 1997), but is not considered here. A same approach is used for tungro, with a short latency period, and a prolonged infectious period; again, at the plant level, an infected, infectious site remains so throughout the epidemic, and plant death from tungro is ignored. RcA, RcW, and RcT functions are not discussed in detail here, but very large difference exist among diseases, of course. For instance there is a sharp contrast between leaf blast, where RcA decreases strongly as crop ages (Torres, 1986), and brown spot (Padmanabhan and Ganguli, 1954), where it is the opposite. There also is a decrease in RcA with age in the case of tungro (Ling and Palomar, 1975).

2.5. Evaluation of EPIRICE Given the scope of the modeling work, which was aimed at addressing potential epidemics, and their intensity distribution at a global scale, a formal quantitative model evaluation (Teng, 1981; Hughes et al., 1997) was considered extremely difficult to achieve, since data on potential epidemics across different locations and years do not exist. Furthermore, surprisingly, the number of rice disease epidemics that are quantitatively reported in the literature is limited. We therefore chose to evaluate EPIRICE by comparing simulated epidemics with a set of observed epidemics reported in the literature. Data on observed epidemics were first selected from the literature, representing reported optimum conditions of epidemics in terms of weather and of host-pathogen compatibility for each of the five diseases. This corresponded to conditions as close as possible to potential epidemic contexts. Conversely, simulations were run for each disease with daily temperature of 25, 20, 28, 28, and 31  C for brown spot, leaf blast, bacterial blight, sheath blight, and tungro, respectively, corresponding to optimum values for Rc (Table 2). Daily relative humidity and rainfall were also set to achieve maximal Rc values. For each disease, all other parameters were as indicated in Table 2. All simulations were run on a 120-day period. In a first stage, simulations were compared with available quantitative disease progress curves: the shape of the five groups of disease progress curves (delays in disease onset, slopes, and possible declines) was considered (Penning de Vries, 1977) a strong element to decide whether EPIRICE was producing satisfactory outputs. Two complementary approaches were then implemented

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S. Savary et al. / Crop Protection 34 (2012) 6e17

to derive quantitative assessments for comparing the available data and the simulated outputs. All data (observed and simulated disease intensities) were first standardized to the maximum levels of epidemics (i.e., 100%). Comparisons of the simulated outputs (over 120 days) also implied normalization of the simulated to the observed, reported values, which may cover varying time spans, depending on the rice varieties. For instance, the reference brown spot epidemic we used (Pannu et al., 2005) covers a period of 145 days, and thus, the original values were normalized to a 120day season. To that aim, the daily severity values for an actual 145-day period of time were linearly weighed to daily values on a 120-day period of time. The reported blast epidemic (the reference disease progress curve was Jigheung, representing a moderately strong epidemic; Hwang et al., 1987) was considered for the 83 first days of the cropping season; and the reported sheath blight (Willocquet et al., 2000) and tungro epidemics (Chancellor, 1995) were considered for the 84 and the 63 first days of the cropping seasons, respectively. No transformation was required for the reported bacterial blight epidemic (Adhikari et al., 1999), which covered a period of 120 days. The resulting standardized (over 120 days) and normalized (observed and simulated) sets of daily data were assessed in two ways. First, chi-square tests were performed between the observed and simulated data, using three categories of intensities that were chosen in order to generate sufficiently high expected values, and valid chi-square tests (Sokal and Rohlf, 1981; Savary et al., 1995): below 1%, below 30%, and up to 100% of the maximum possible disease intensity. Second, ordinary least-square regressions were performed on the data points for each pair of observed and simulated values corresponding to each of the five diseases, R2 and F-values were considered, the regression parameters analyzed, and the residuals of regressions examined (Draper and Smith, 1981; Teng, 1981). 2.6. Climate data description Contemporary daily climate data was extracted from the NASA POWER agroclimatology dataset (Chandler et al., 2004), which contains daily estimates of precipitation, mean, minimum and maximum temperature, relative humidity, dew point, solar radiation and wind speed with global coverage at one degree resolution (approximately 111 km at the equator). The agroclimatology data covers the period Jan-1983 to near present but the precipitation data is limited to the period Jan-1997 Aug-2009, thus 12 complete years of data were available for this analysis (1997e2008). The entire agroclimatology dataset for this period was downloaded from http://power.larc.nasa.gov/ and stored in a MySQL database (version 5.0.32-Debian_7etch6) to allow easy retrieval from GIS or statistical software packages. The NASA POWER agroclimatology data are derived from various sources; solar radiation from satellite observations, meteorological data from the Goddard Earth Observing System global assimilation model version 4 (GEOS-4), and precipitation from the Global Precipitation Climate Project and Topical Rainfall Measurement Mission. A full description can be found at http://power.larc.nasa.gov/ common/MethodologySSE6/POWER_Methodology_Content.html. 2.7. Rice crop establishment dates and rice cropping information The rice establishment date was derived from a crop model simulation to determine the optimum date of crop establishment under rainfed conditions as used by Hijmans and Serraj (2009). In brief, a point based rice growth model, Oryza2000 (Kropff et al., 1994; Bouman et al., 2001) was coupled with the agroclimatology data within a GIS environment to enable the crop growth model to

run on each one degree cell. The simulation was run e with parameters applicable to the IR64 rice variety e over nine years (1997e2005) with a rainfed rice crop planted at two-week intervals. The fortnight that most frequently gave the highest yield over the nine years was designated as the planting period. The resulting map can be interpreted as a map of the optimum potential establishment date if rice were to be cultivated within that one-degree cell. The Tungro model requires an additional input parameter, which is simply whether rice is cropped more than one time per year in a given location. This was derived from the Huke and Huke database on rice systems in Asia (Huke and Huke, 1997) resulting in a one degree map of three classes, no rice crop, single rice crop, double/triple rice crop. In other regions, areas with more than one rice crop per year are limited to the humid and sub humid tropics where there is sufficient water supply throughout the year and no distinct cold season. 2.8. EPIRICE runs, areas under progress curve, and implementation in a GIS with climate data The EPIRICE model was run for each one degree cell using the date of crop establishment and daily climate data for each year, resulting in maps showing annual simulated potential epidemic (represented as the area under the disease progress curve, AUDPC; Campbell and Madden, 1990) for the five diseases. EPIRICE was translated from the STELLA modeling language (v 9) to the R language (v 2.11.1; http://www.r-project.org). R is a multiplatform, free, statistical programming environment, which can be easily linked to database and GIS softwares. The following R packages were used to achieve the required functionality; raster (Hijmans and Van Etten, 2010), rgdal (Keitt et al., 2010), cropsim (Hijmans et al., 2010), weather (Hijmans, 2010), and rodbc (Ripley and Lapsley, 2010). The resulting maps of AUDPC are in GeoTIFF format and can be viewed directly in R, but we used ArcGIS (v 10) to prepare the maps for publication purposes. 2.9. Disease maps Twelve years of climate data results in 60 outputs across the five diseases. These were summarized for each disease by computing the mean and standard deviation of the potential AUDPC for each cell. In all cases, the maps show a measure of the potential overall intensity for a particular disease throughout a cropping season (AUDPC). The term potential is used here in the sense that it (1) represents what could occur if no plant protection measures were taken to prevent or reduce disease and (2) shows areas where the climate is conducive to rice cultivation (and possibly the diseases considered), not necessarily where rice is actually cultivated. 3. Results 3.1. Evaluation of EPIRICE The patterns of simulated disease epidemics differed between the five diseases with respect to epidemic onset, shape (exponential, sigmoid, sigmoid followed by a decline), and speed (Fig. 1). These patterns corresponded in general to the respective patterns of observed disease epidemics. The simulated brown spot epidemic started late (90 days after crop establishment; DACE) in comparison with the observed data, was very fast, and showed an exponential increase. The observed epidemic had the same shape, but with earlier onset and lower slope. The simulated leaf blast epidemic started early (35 DACE), increased until 70 DACE, and then declined progressively. The observed reference epidemic (cvr. Jingheung) was very similar in shape. The simulated bacterial blight epidemic

60

30

0

100

0

0

150

30

10

16

8

0

0

0

24

60

0

90

30

DACE

10

30

100

60 DACE

90

0

0

120

30

90

120

DACE

Number of sites

Sheath blight

75 50 25

90

60

30

0

0

0

0

30

60

90

0

30

Number of sites

Tungro

40

20

0

25

50 DACE

75

120

60

30

0

0

0

90

100

60

60 DACE

50

DACE

Disease intensity (%)

60

900

30

600

0

Disease intensity (%)

60

0

0

2400

Laxmi

1600

Number of sites

Sabitri

90

800

IR-24

20

120

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0

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Fig. 1. Observed and simulated disease dynamics using EPIRICE. Left: Shapes of progress curves derived from the literature for five major rice diseases: leaf blast (Hwang et al., 1987), brown spot (Pannu et al., 2005), bacterial blight (Adhikari et al., 1999), sheath blight (Willocquet et al., 2000), and tungro (Chancellor, 1995). Right: simulated dynamics of sites: thick dashed lines: healthy (H); thick solid lines: proportion of total infected sites; thin dotted lines: post-infectious sites (P); thin continuous lines: infectious sites (I); thin dot-and-dash lines: latent sites (L). The observed dynamics of the five diseases (left) may be compared with the solid thick lines (right).

started at 45 DACE, then followed a sigmoid shape until 95 DACE, tapered off, and slightly declined at 110 DACE onwards. The observed reference epidemic (Sabitri) had a very similar shape. The simulated sheath blight epidemic had a sigmoid shape, which started at 45 DACE, and was very similar to the reference curve, except for a later onset of the simulated dynamics. The simulated tungro epidemic started at about 30 DACE, increased progressively, and tapered off around 90 DACE. The observed epidemic was monitored until 64 DACE only. Over this period, the shape of observed epidemic was similar to the simulated one, except that observed maximum intensity was higher (50%) than the simulated one (30%). In two instances (leaf blast and bacterial blight), several curves are shown to indicate how the intensity of disease may vary in the same location, depending on host-pathogen compatibility. Emphasizing on disease

progress curve shapes, rather than numerically observed epidemics, we chose to use the intermediate reported curves as references for comparison with simulated outputs. Chi-square tests indicated a good fit between the observed and simulated curves, with c2 values of 58.4, 100.0, 98.6, 80.1, 28.6 (4 d.f., P < 0.001) for brown spot, leaf blast, bacterial blight, sheath blight, and tungro, respectively, leading to rejection of the null hypothesis of independence of the distribution frequencies of categorized observed and simulated disease levels. The respective regression analyses yielded R2 values of 0.786, 0.861, 0.984, 0.847, and 0.994, with associated F-ratios on the associated analyses of variances of 437.5, 234.7, 3741.7, 211.5, and 5394.0, for the five diseases in the same order, respectively. Examination of residuals did not show bias in the case of leaf blast and bacterial blight. In the

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Fig. 2. Simulated global maps of rice disease epidemics. Mean (left) and standard deviation (right) values of the potential AUDPC for years 1997e2008 for the five diseases: brown spot (a and b), leaf blast (c and d), bacterial blight (e and f), sheath blight (g and h), and tungro (i and j). Legend classes are approximately quantiles.

cases of brown spot, sheath blight, and tungro, residuals tended to increase with estimates. Slope values indicated that EPIRICE tended to underestimate brown spot severity by a factor of approximately 0.3, that leaf blast was correctly estimated, that bacterial blight was slightly over-estimated (by a factor of about 0.1), that sheath blight was under-estimated by a factor of 0.3, and that tungro was slightly under-estimated by a factor of about 0.1.

3.2. Spatial distribution of potential epidemics The general patterns for mean potential epidemics expressed as AUDPCs are shown in the left hand side of Fig. 2. Potential rice disease epidemics were predicted with large differences amongst diseases in the tropical world and in some parts of the sub-tropical and temperate world (e.g., southern Brazil, North America,

S. Savary et al. / Crop Protection 34 (2012) 6e17

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Southern Africa, North-East China, Japan, and Korea). In these areas, the standard deviation of potential epidemic AUDPCs (Fig. 2, right hand side) often showed a large spatial variation and differing patterns from one disease to another. The AUDPC of brown spot severity (Fig. 2a) ranged from 0 to 902%.day, corresponding to potential epidemics with terminal disease severities of 0e42%, i.e., to a crop that would theoretically suffer a severity of up to 7.5% from crop establishment to harvest. Largest values (122e902%.day) were predicted in tropical South America, tropical western Africa, and some areas of South and South-East Asia. Values between 23 and 122%.day corresponded to maximum severity between 1 and 5%. Such ranges were simulated in, e.g., south of Brazil, central Africa, the Southeast coast of Africa, Madagascar, South Asia and the Eastern coast of China. Standard deviations (Fig. 2b) mostly corresponded to variation in means. In some areas, a negative relationship was however indicated, as, for example in the eastern part of India (large means, low variance) vs. the western part of India and Pakistan (low means, large variance). The maximum range of the simulated potential AUDPC for leaf blast severity, 58e361%.day (Fig. 2c), corresponded to a maximum severity of 1.6e9.7%. Such areas were mapped in South America (e.g., on the Northeastern coast, and on the Southeastern coast of Brazil), Africa (e.g., Madagascar, West Africa, and the Ethiopian highlands), South and South-East Asia (e.g., the Himalayan foot hills, the northern part of the Indo-China peninsula, southern China), and East Asia (e.g., Korea, Japan). Standard deviations (Fig. 2d) were large in areas with large means, but also in some areas with intermediate to average means (e.g., Central North-America and North-East China). The highest range of AUDPC for bacterial blight leaf incidence (Fig. 2e) was 1681e2677%.day, corresponding to a maximum incidence range of 32e52%, and was mapped in the northern half of South America, North and East coasts of the African gulf of Guinea, East India, the Indo-China peninsula, Eastern China, and South-East Asia islands. Highest standard deviations (Fig. 2f) were simulated in Southern Brazil, the Sahel, West India, and patches across East and South East Asia. The simulated potential sheath blight epidemics (Fig. 2g) had spatial patterns similar to bacterial blight, except that high ranges were also simulated in southern Africa and in Madagascar. These highest ranges, 3783 to 4282%.day, corresponded to maximum % of diseases tillers ranging between 77 and 87% (i.e., 33% of the tillers infected with sheath blight from crop establishment till harvest). The largest standard deviations (Fig. 2h) were simulated in, e.g., North America, South America, southern Africa, the Sahel, and North-West India. Tungro potential epidemics (Fig. 2i) were simulated in areas where more than one crop of rice is grown per year, i.e., South and South-East Asia, Madagascar, in limited areas of Africa, and in Guyana and Surinam. In these areas, the highest potential epidemics were simulated in North-East India, Bangladesh, the Indochina peninsula, South and East China, and patches in the Philippines. These corresponded to maximum % of diseased plants of 22.7e24.0%. Standard deviations (Fig. 2j) were in general high. 3.3. Inter-annual variation of potential epidemics The relationships between AUDPC means and standard deviations of potential disease intensities are shown in Fig. 3. In each of these graphs, one dot represents one location over the 12 simulated years. In the case of brown spot, the envelope of the dots had an overall moon-crescent shape. Most dots accumulated at low mean and standard deviation values. As mean AUDPC values increased, dots

Fig. 3. Plots of the mean values (horizontal axis) to standard deviations (vertical axis) of the simulated potential AUDPCs for each of the five diseases (a: brown spot, b: leaf blast, c: bacterial blight, d: sheath blight, and e: tungro). Each dot represents a location where 12 climate-year simulations were run.

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scattered on the graph, with widely ranging standard deviation (100e350%.day). With mean potential AUDPC approaching 900%.day, the cloud of dots progressively converged toward very small standard deviation. As a result, the vertical (standard deviation) dispersion of dots was highest for intermediate mean (horizontal axis) AUDPC values. Leaf blast showed a somewhat similar pattern, except that the third part (high mean, low standard deviation) of the cloud of points was represented by a much smaller number of dots. In general, the standard deviation increased with simulated mean (severity) AUDPCs, and there were very few dots corresponding to high mean and small standard deviation of AUDPC. In the case of bacterial blight, a pattern very similar to that of brown spot was found, with larger number of dots occurring at large values (2500e2700%.day) of mean AUDPC (% diseased leaves), and larger number of dots corresponding to intermediate mean and low standard deviation. The graph for sheath blight also shows a clear crescent moon pattern, with large number of dots corresponding to low and high values of mean AUDPC (% diseased tillers). Tungro, by contrast, exhibited a distinct pattern, where eight clusters of dots may be distinguished. Many simulation outputs corresponded to 0% AUDPC (plant incidence), and thus to a corresponding 0 standard deviation, i.e., locations where tungro was not predicted. Another cloud of dots, corresponded to high mean AUDPC values (above 1100%.day) and very low standard deviations (below 80%.day). Further, a series of four groups of dots may be distinguished, each of them approximately corresponding to linear increases of standard deviation with increasing AUDPC means, with increasing slopes, as the standard deviation increases. Lastly, three additional series may further be detected with even higher slopes (and higher standard deviations), which are less apparent on the graph. These seven linear trends corresponded to comparatively high values of standard deviation (above 200%.day), and mean values ranging from 600 to 1300%.day. Yet, looking at the overall structure of these groups of location/dots for tungro, the crescent structure observed in the plots for the four other diseases was discernible too in the case of tungro: a 0e0 starting point, an increase in both AUDPC means and standard deviation (which increases with the means, and in the case of tungro, exhibit specific patterns), and a (general) decline in both means and standard deviations. 4. Discussion 4.1. Assessing the proof of concept The results obtained indicate that it is possible to use a generic model structure to simulate epidemics of diseases caused by pathogens, which vary greatly in their biology and ecology. This was made possible by capturing this diversity with representation of epidemics at different hierarchical levels, from leaf tissue to plant. We further show that the disease-parameterized models can be linked to a GIS platform to simulate rice disease epidemics spatially at a global scale. These results thus represent a proof of concept, and provide a first step toward the exploration of the relative importance of a range of diseases under current or future contexts of agricultural systems. The approach is generic, and can be used to address diseases of other crops, or other plant species. Simulated epidemics of individual plant diseases, regionally (Luo et al., 1998) or globally (Hijmans et al., 2000) have been reported. To our knowledge, this is however the first report of simulated epidemics of a range of different diseases that are mapped globally for the same crop using a single generic process-based model. Depending on environmental and host-pathogen compatibility, disease progress curves may show a near infinite range of disease

intensities. The overall shapes of actual dynamics are however retained in the literature, irrespective of maximum intensity (Fig. 1, left hand side). Such a range of possible intensities renders a numerical, quantitative evaluation of the model questionable, since our emphasis is on potential epidemics. The evaluation rules proposed by Teng (1981; intercept not significantly different from 0 and slope not significantly different from 1) were met only in the case of leaf blast. In the other four examples, there was some departure from these rather stringent rules. Nevertheless, based on the overall shapes of the simulated curves, on the results of Chisquare tests, on the possibility to further adjust model parameters to the specific characteristics of a given disease, and on possible improvement of the structure of the model, one may consider EPIRICE as robust enough for the proof of concept intended in this work. The comparison of simulated and observed epidemics (Fig. 1) showed that EPIRICE correctly captured the main epidemiological characteristics of the five diseases addressed. This includes: the progressive exponential increase of brown spot, the rapid increase and then decrease of leaf blast, the slightly delayed bacterial blight increase, and its decrease toward the end of the cropping season, the sigmoid shape of a sheath blight epidemic, and the delayed establishment, exponential increase, and tapering-off of tungro. Again, it is important to stress that different levels of hierarchy were considered (sites on leaves for the two first diseases; and incidences on leaves in the third, on tillers in the fourth, and on entire plants in the fifth). These differences contribute to explaining the different shapes of disease progress curves. There is certainly scope for model improvement. This includes a better (earlier and faster) development of brown spot epidemics, and a faster increase and decrease of leaf blast. The distribution of simulated potential epidemics of the five diseases in general corresponds well to their observed distribution (CABI, 2010). High levels of potential brown spot epidemics were simulated in several areas where the disease is actually considered as an important problem for rice, e.g., in Brazil (Carvalho et al., 2010), and Eastern India, where the disease is considered to be one of the causes of the great Bengal famine in 1943 (Chakrabarti, 2001). The range of high levels of simulated blast epidemics corresponded in general to tropical areas with semi-elevated altitudes, or to foot hills, e.g. Northern India, South East China, where leaf blast epidemics have been regularly reported (Zeigler et al., 1994), and to temperate areas such as Japan and Korea, where leaf blast is also very frequent (Ou, 1987; Zeigler et al., 1994). High levels of potential epidemics of bacterial blight were simulated in Southeast and South Asia. Epidemics were indeed observed in the area in the 1970s after the release of high-yielding highly susceptible varieties (Mew, 1987), such as TN1 in Bihar, North-Eastern India (Kauffman and Kannaiyan, 1987). Incorporation of resistance genes in breeding programs allowed later on to restrain the disease to low levels (Savary et al., 2006). High potential bacterial blight epidemics were also simulated in Western Africa, where epidemics have been reported (Mew, 1987). Sheath blight is observed in all rice growing areas, and simulated potential epidemics were at the highest levels where the disease is reported as important e.g., in Brazil (Rodrigues et al., 2003), and in South and South-East Asia (Dasgupta, 1992). The highest simulated potential levels of tungro (Fig. 2j) were mapped in South and South-East Asia, that is, at locations where indeed tungro epidemics have long been reported (Teng, 1990; Azzam and Chancellor, 2002). One striking output is that tungro is potentially mapped also in West and South-West Africa, and Madagascar, where the disease has not been reported. This can be explained by the reported absence of the vector at these locations (CABI, 2010). This result highlights a potential risk for tungro occurring elsewhere than Asia, would the vector establish in new areas.

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4.2. Standard deviation to mean relationships In all five cases, a parabolic shape in the standard deviationemean relationships (Fig. 3) is observed, from no disease (and 0 variance) to high disease (and low or 0 variance too). These graphs thus show the range of prevalence of the diseases, from absence, to increasingly chronic, and to endemicity (Savary et al., 2011a,b). The intermediate dots (moderate mean, variable variance) indicate locations where epidemics are simulated at given locations, and reach some level of intensity in all or some of the 12 years considered. In the case of the first four diseases (brown spot, leaf blast, bacterial blight, and sheath blight), the observed pattern appears fairly regular. In the case of the fifth disease (tungro), this pattern is overlaid by series of mean location-years, where the variance increases with the mean, of which about eight series are observed (Fig. 3e). These successive series correspond to the frequency at which rainfall enables early initiation of epidemics. For instance, a highly visible group (right bottom corner, Fig. 3e) is associated with very large mean and very small variance, that is, with disease endemicity. A second group (relatively high mean, medium variance) corresponds to sites where epidemics usually do take place, but where, in one year out of 12 years, rainfall started too late to initiate a strong epidemic, thus making the variance larger. The last (less visible) series correspond to groups of sites where rainfall infrequently occurred out of 12 years, enabling epidemic initiation or not. As a result, the mean of these series is low, and the variance is very large. However, as in the other four diseases, there is a large number of sites where rainfall (or other factors) never enabled epidemics to occur over the 12 years, leading to null variance and mean. Interestingly, the general shape of the standard deviation to mean graphs for the five diseases is similar to variance-mean relationships in disease incidences over the course of epidemics at the individual field scale. Higher variance corresponds to higher spatial heterogeneity (aggregation) of the disease. A binomial distribution corresponds to a random disease distribution, and a beta-binomial distribution corresponds to disease aggregation (Madden and Hughes, 1995). Such relationships have also been shown using spatio-temporal epidemiological simulation models (Yang, 1995), and have been observed from disease incidence monitoring over the course of epidemics (e.g., Savary et al., 2001). In our case, dots located in the upper part of the graphs correspond to locations where heterogeneity of potential disease intensity simulated across the 12 years was large, and thus to higher uncertainty. Dots located on the lower left and right corners of the figures correspond to locations where the simulated level of disease was low and high, respectively, and thus associated with low standard deviations. The graph obtained for tungro also fits to this general pattern, although in a discrete, patterned, manner.

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EPIRICE in its ability to address such a range of pathosystems is further supported by the fact that it meaningfully describes the variance-to-mean relationships (Fig. 3), using time (i.e., climatic conditions over years) as a source of variance. Three areas for improvements stand foremost. First is the treatment of the spatial structure of disease epidemics, on which much progress has been accomplished in botanical epidemiology recently. The spatial structure at the crop stand or field level can have major bearing at higher levels of geographical scale. Another area for progress is our handling of epidemiological processes in vector-borne diseases, and the dilemma of whether to include the vector (healthy, viruliferous/infected) population in EPIRICE. We did not, because we wanted to adhere to simplicity. Future research will inevitably show that this is insufficient when addressing viral diseases with specific questions. A third area is associated to the dearth of published disease progress curves. Rice, an international model crop, and its diseases, would be expected to be one of the best-documented examples worldwide. It is not quite the case, and this paper further stresses the need for such basic information to be made publicly available: no progress, whichever the sophistication of methods available today, can be contemplated without such basic information. The maps of Fig. 2 nevertheless provide a starting point for pondering where major epidemics are to be expected, where their variability is the highest, and thus where research efforts have to focus, or continue. Technology targeting is not about deploying host plant resistance only; it also incorporates a number of other plant protection tools (Zadoks and Schein, 1979; Teng and Savary, 1992), which may enable to adapt crop health management strategies to locations where diseases are chronic, acute, or emerging. Fig. 3e (tungro) also demonstrates that the approach may have value in anticipating emerging epidemics. One development of this work would be to consider yield losses caused by diseases (and generally pests), and yield gains accrued by crop health management. RICEPEST (Willocquet et al., 2004) might be linked to a GIS framework for that purpose, and provide a powerful tool to map the importance of diseases (pests) in isolation or in combination, and identify where yield gains could be maximized through technology targeting. This would require more spatially detailed daily agroclimatology data; whether one-degree resolution is a sufficient scale for geographic targeting in plant protection remains to be seen. Farmers actually seldom deal with one crop health problem at a time. Future progress should also consider crop health syndromes (Savary et al., 2011a,b), that can be linked to production contexts. Mapping risks associated with crop health as a whole might prove to be a very rewarding effort in enabling technology targeting for crop health management strategies. Again, a challenging issue will be the collection, access, and mobilization of actual ground truth, rather than the technological aspects such development will entail.

5. Conclusions and perspectives This work represents a proof of concept that enables to progress in steps across a hierarchy of plant structures, from individual organs, to crop stands, entire regions, and global distribution of potential epidemics. It demonstrates that it is possible to model the status of crop health globally in absence of any disease control. It also raises a number of questions and indicates several avenues for future investigation. The model used in this work was actually developed in 1971 (Zadoks, 1971), with cereal rusts as systems models. EPIRICE only includes a few additional elements (crop growth, plant senescence, spatial disease aggregation). This suggests that the structure of the model has a generic value, which can be used to model and map epidemics of any polycyclic disease of any crop. The general value of

Acknowledgements We wish to thank R. Hijmans, UC Davis, USA, for his support in this research, and S. Hobbs, CABI, for granting access to the CPC during this study. Agroclimatology data were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program. References Adhikari, T.B., 1991. Effects of rice genotype and environment on bacterial blight progression. PhD thesis, University of the Philippines, Los Baños. Adhikari, T.B., Shrestha, A., Basnyat, R.C., Mew, T.W., 1999. Use of partial host resistance in the management of bacterial blight of rice. Plant Dis. 83, 896e901.

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