Print version ISSN 1982-5676
Trop. plant pathol. vol.36 no.3 Brasília May/June 2011
RESEARCH ARTICLE ARTIGO
José A. OrnaghiI; Guillermo J. MarchII; Ricardo C. MoschiniIII; Malvina I. MartínezIII; Graciela T. BoitoI
IFacultad de Agronomía y Veterinaria, Universidad Nacional de Río Cuarto, 5800 Río Cuarto, Córdoba, Argentina
IIInstituto Nacional de Tecnología Agropecuaria - INTA, Instituto de Fitopatología y Fisiología Vegetal, X5020ICA Córdoba, Argentina
IIIInstituto Nacional de Tecnología Agropecuaria - INTA, Instituto de Clima y Agua, Pdo. de Hurlingham, Buenos Aires, Argentina
The planthopper Delphacodes kuscheli Fennah (Hemiptera: Delphacidae), principal insect vector of Mal de Río Cuarto virus on corn in Argentina, produces severe disease outbreaks when migration of large macropterous populations coincides with early corn growth stages. Linear models based on winter environmental variables were developed to explain variation of macropterous populations accumulated on oat until November 30 (1993-2001) in La Aguada (department of Río Cuarto, Córdoba, Argentina). Using daily records of maximum and minimum temperatures and precipitation, variables were generated and processed in different periods from June 1 to September 20. The best bivariate model (R2=0.97) had the lowest mean square error, was selected by Stepwise procedure, and its validation was highly satisfactory. It included the variable DDTxn, which accumulates mean temperature values exceeding 10°C on days with maximum and minimum temperature >24.5°C and 11°C, respectively, and DPr, which counts days with precipitation (>0mm). These variables were processed from July 1 to September 19. Using this model (correctly validated against independent observations in Chaján, Sol de Mayo and Espinillo -department of Río Cuarto- for 2, 3 and 7 years, respectively), climate risk in the Pampas region was evaluated relative to insect population levels in the endemic area.
Key words: Mal de Río Cuarto virus, corn disease, management strategies, predictive models, vector population dynamics.
The Mal de Río Cuarto virus-MRCV (genus Fijivirus, family Reoviridae) causes the most important viral disease on corn (Zea mays L.) in Argentina (Lenardon et al., 1998; March et al., 1995). Corn crops in the department of Río Cuarto (province of Córdoba) and neighboring growing areas in San Luis, La Pampa and Buenos Aires provinces - usually identified as the Mal de Río Cuarto (MRC) endemic area - are affected with different intensity every year. The first outbreak occurred in the 1981/82 growing season, affecting corn crops in the endemic area and producing losses of about 40 million US dollars (March et al., 1997); 15 years later, in the 1996/97 growing season, an outbreak spread outside the endemic area for the first time, affecting crops in the most important growing areas of Argentina: in the Pampas region to the east of Córdoba, and Santa Fe and Buenos Aires provinces, with losses estimated at about 120 million US dollars (Lenardon et al., 1998). Recently, in the 2006/07 growing season, another epidemic occurred that spread to different agricultural areas of these provinces, with losses estimated at about 70 million US dollars by the Secretaría de Agricultura y Alimentación of the province of Córdoba (Lenardon et al., 2007).
Population dynamics of insect vectors and their role as epidemic agents are important components of epidemic structures of viral diseases (Ferris & Berger 1993; Jeger et al., 2004; Madden et al., 1990, 2000; Raccah & Irwin, 1988). Different studies have associated insect vector populations with climatic variables and some predictive models of disease intensity have been developed (Heathcote, 1986; Madden et al., 1983; Mora-Aguilera et al., 1993; Ochoa-Martinez et al., 1999; Plumb et al., 1986).
The planthopper Delphacodes kuscheli Fennah (Hemiptera: Delphacidae) is the main insect vector of MRCV in the endemic area. While this planthopper has been recorded in different growing areas (Remes Lenicov & Virla 1999), its population dynamics has been studied in the department of Río Cuarto (Boito, 2005; Ornaghi et al., 1993; Remes Lenicov et al., 1991; Tesón et al., 1986). Also, studies of the influence of agroecosystem on abundance and spatial distribution of D. kuscheli have demonstrated that the insect population present in the endemic area strongly decreases towards non-endemic regions (Grilli & Gorla, 2002).
In the department of Río Cuarto, D. kuscheli populations develop in the winter season, mainly on oat (Avena sativa L.) and wheat (Triticum aestivum L.) crops subjected to grazing production practices. The insect acquires the MRCV in these crops; then the macropterous forms migrate to corn from September-October to December (Boito, 2005; March et al., 1997; Ornaghi et al., 1993; Remes Lenicov et al., 1991; Tesón et al., 1986). Given that corn is not a host colonized by D. kuscheli and infectivity of field populations is relatively low, one of the main causes of outbreaks is the migration of D. kuscheli macropterous forms in high population densities coinciding with the early stages of corn growth (March et al., 2002; Ornaghi et al., 1993, 1999).
Based on historical data of MRC intensity (1981-1993) and on winter meteorological variables, a predictive model of disease intensity was developed (March et al., 1995). This pre-seeding forecasting system (September 1) and monitoring of D. kuscheli populations from August to December are the basis for outlining disease management strategies (Lenardon et al., 2004). No published studies (Lenardon and March, pers. com.) showed a significant (p=0.021-0.0008) and strong (R2=0.60-0.92) correlation between macropter population density (monitored in direct grazing oat commercial fields) and MRC incidence, evaluated in corn experimental plots (weekly sowing dates) for 10 years. Given the importance of D. kuscheli in the spread of MRCV, the objectives of this study were to develop and to validate winter meteorological-based models for predicting population density of macropterous vector in the endemic area, and to estimate the associated climate risk in the principal corn growing areas of Argentina.
MATERIALS AND METHODS
Development of predictive models
D. kuscheli macropters were counted at approximately 7-day intervals on an oat field located in La Aguada (32º58'S, 64º47'W), department of Río Cuarto, from August to December of the 1993-2001 growing seasons (N=9 years), following the method developed by Ornaghi et al. (1993). On each sampling date four samples were taken using a 38cm diameter sweep net. Each sample unit consisted of the material collected in 25 consecutive insect net sweeps. Specimens in the samples were frozen with carbon dioxide in the field, and taken to the laboratory for sorting and identification. Delphacids were identified (99% of D. kuscheli) and categorized as nymphs and adults, and these were classified as brachypters, macropters, females and males. All the sampling points were commercial oat fields managed under direct grazing by cattle throughout the winter-spring period and without the use of insecticides.
D. kuscheli macropter weekly values were gradually accumulated every year from August to December (different end sampling dates). The dependent variable was the total annual macropters accumulated from August until November 30 (fixed end sampling date). The number of macropters collected until November 30 was annually calculated by linear interpolation of the values accumulated until the immediately previous and following sampling dates (Table 1).
Daily records of maximum (Tx) and minimum (Tn) temperatures and precipitation (Pr) were provided by the meteorological station of the Facultad de Agronomía y Veterinaria, Universidad Nacional de Río Cuarto, located in La Aguada. Daily mean temperature (Td) was calculated as half the sum of the daily minimum and maximum temperatures. From these daily weather records, the following predictive meteorological variables were calculated: DDTxn: total accumulation of degree-days (°C day) above different base Td that ranged from 10 to 14°C, when Tx and Tn are greater than threshold values (range: Tx: 22-25°C; Tn: 10-14°C); DDTd: total accumulation of degree-days (°C day) above different base Td (range analyzed: 10-14°C), if daily precipitation (Pr)=0 mm; DTn: total days with Tn lower than thresholds that ranged from 10 to 13°C; MTn: mean of daily Tn (ºC); MTx: mean of daily Tx (ºC); DPr: number of days with Pr greater than different thresholds (range: 0-5 mm); TPr: cumulative daily Pr (mm) greater than different thresholds (range: 0-5 mm).
From June 1 to September 20, several time subperiods were analyzed and predictive variables were processed. The Rsquare routine from SAS was selected to identify the periods with the strongest associations between annual total number of macropters collected until November 30 and winter meteorological variables, by analyzing the coefficients of determination (R2). A final selection of predictive models was based on the following criteria: R2 values, mean square error (MSE), Stepwise procedure (significance level for entry into the model: 0.05), and validation behaviour using independent data of D. kuscheli macropterous populations collected during 2, 3 and 7 years in Chaján (33º33'S, 65º01'W), Sol de Mayo (33º07´S, 64º09´W) and Espinillo (33º01'S, 64º21'W), respectively, and meteorological data from Chaján and Servicio Meteorológico Nacional (SMN) station in Río Cuarto for Sol de Mayo and Espinillo. The three sites are located in the department of Río Cuarto.
Climate risk in the Pampas region relative to population level of Delphacodes kuscheli
Daily meteorological records of Tx, Tn and Pr for the 1971-2006 series were available from 37 meteorological stations (SMN-INTA: Instituto Nacional de Tecnología Agropecuaria) in the Argentine Pampas region. The macropter population level accumulated until November 30 was estimated for each meteorological station and for each one of the 36 years of the series, using the selected and validated predictive model. Then the percentage of years with the population level of collected macropters predicted as "High" (H: >818), "Intermediate" (I: <=818 and >493) and "Low-Nil" (L:<=493 macropters) was calculated (threshold values corresponded to the 60 % and 40 % percentiles, respectively, and were obtained from the series of 36 annual values predicted by the selected model in Río Cuarto). The "Very high" (VH) population level was consistent with values higher than 80% (1204 wing forms). Percent values of years with "High" and "Intermediate" macropter population levels were spatially displayed (map) by means of a grid with linear variogram (Kriging procedure) of the entire Pampas region.
To analyze the possible influence of precipitation on oat and wheat crop growth, on which D. kuscheli populations developed, and on corn planting date in the three growing seasons when the most important outbreaks occurred, rainfall data from June to November were considered in Río Cuarto (1981, 2006) and La Aguada (1996), as well as rainfalls occurring in the closest years (1982, 1997, 2004) with similar D. kuscheli population characteristics and when no outbreaks occurred.
The strongest associations between winter meteorological variables and the total number of macropters collected until November 30 were found between July 1 and September 19 (81 days). The best fitted models (simple and bivariate ones) were the following:
Dkma = 322.7+17.15 DDTxn (R2=0.94; MSE=158.5) Model A
Dkma = 528.2+16.98 DDTxn-34.49 DPr (R2=0.97; MSE=0108.4) Model B
where Dkma: number of Delphacodes kuscheli macropters accumulated until November 30; DDTxn: total accumulation of degree-days above 10°C (base daily mean temperature), when daily Tx (maximum temperature) and Tn (minimum temperature) are greater than 24.5°C and 11°C, respectively; DPr: number of days with precipitation (Pr>0mm).
Validation of models
Total numbers of macropters recorded in 2, 3 and 7 growing seasons in Chaján, Sol de Mayo and Espinillo, respectively, were used to validate models A and B (Table 2). The simple model including the variable DDTxn had the highest R2 value (0.94); however, it exhibits a trend towards overestimation, thus failing to predict the low population levels observed in 1988 and 1991 in Espinillo. The variable DDTd (R2 =0.63) only works with threshold values of daily mean temperature (base Td) and fails to express insect dependence on days with wide range of temperatures, but with high daily minimum and maximum values. These conditions were observed in 1996, when MRCV caused the most widespread epidemic. The variable DDTxn explains this situation correctly.
The simple model including DPr (negative slope) only accounts for 5% of variation in the vector population level until November 30, but the variable makes a significant contribution when included in a bivariate model along with DDTxn. The resulting model B was selected as the most appropriate by Stepwise procedure. It also exhibited the highest coefficient of determination, lowest estimation error and optimal performance when it was validated against independent data. Hence, it was selected for application in the subsequent analyses.
Climate risk in the Pampas region relative to Delphacodes kuscheli population level
Figure 1 illustrates the spatial distribution of climate risk level (% of years) with "High" (a) and "Intermediate" (b) population level of D. kuscheli macropters accumulated until November 30, predicted by bivariate model B. Annual population values predicted were categorized as "High" (more than 818 macropters) and "Intermediate" (between <=818 and >493). For the disease endemic area, a "High" vector population level is estimated in 2-3 years out of 10, climate risk increasing towards the NE of this core area. The greatest frequency of intermediate vector levels is concentrated in the Central-Eastern portion of Córdoba. Both maps show the very low climate risk in Central-Southern Buenos Aires with respect to the possible detection of high population levels.
Figure 2 shows population levels of D. kuscheli macropters accumulated until November 15, until November 30 and until December 30, and values of the variable DDTxn in the nine-year period evaluated in La Aguada. To predict populations accumulated until November 15 and to estimate possible insect vector migrations, the variables DDTxn and DPr were analyzed, both processed from July 1 to different dates: September 9, September 19, October 1 and October 15. The best regression model (selected by Stepwise, R2=0.99, optimal performance when validated with independent data from Chaján, Sol de Mayo and Espinillo) was the following:
Dkma=379.2+12.8 DDTxn - 20.27 DPr (Model C)
where DDTxn is processed from July 1 to September 19, and DPr from July 1 to October 15.
Population values above 80% estimated by model C (813 wing forms) and model B (1204 wing forms) might be used for early warnings of probable migrations of the vector insect from the endemic area. For Río Cuarto, these threshold values were only reached or exceeded in 1971, 1982, 1986, 1990, 1996, 1997, 2004, and 2006 (Figure 3).
Precipitation records from June through November in Río Cuarto (1981, 1982, 2004, 2006) and La Aguada (1996, 1997) are shown in Figure 4. Precipitation from June through September (oat and wheat growth period) in 1981, 1996 and 2006, when the main epidemics occurred, amounted to 18, 41 and 11 mm, respectively; whereas during the same period in the years 1982, 1997 and 2004, rainfall amounted to 119, 107 and 69 mm, respectively.
Understanding the relationship between virus insect vector populations and their movements is necessary to determine the vector's role in virus spread (Raccah & Irwin 1988). This is especially important in the case of MRCV, because its main insect vector, D. kuscheli, migrates from oat or wheat to corn crops, which it does not colonize because corn is not a suitable host for feeding, oviposition or breeding (Virla & Remes Lenicov, 1991).
Population dynamics of planthoppers is influenced by several factors, such as crowding, temperature, host conditions and photoperiod (Denno et al., 1986; Iwanaga et al., 1987). The present work demonstrates the importance of temperature (DDTxn) and precipitation (DPr) in population dynamics of D. kuscheli. On the basis of both variables, the best linear bivariate model obtained (Model C, R2=0.99) allows us to predict total macropters accumulated until November 15 by October 15, usually when most corn crops are still to be planted in the endemic area of the disease; the second best bivariate model (Model B, R2=0.97) allows us to predict total macropters accumulated until November 30, 26 days earlier (September 19), often when corn planting has not started in this area. The statistical method applied for developing these models has often been used in the generation of disease forecasting systems in Argentina (March et al., 1995; Moschini & Fortugno 1996; Moschini et al., 2006).
The climate risk maps generated in this work show that in the endemic area of the disease and in the main growing areas of Argentina (east of Córdoba, south of Santa Fe, north of Buenos Aires), climate risk values are similar. However, the agroecosystem structure favors the development of high population densities in the endemic region and low densities in the growing areas mentioned, because D. kuscheli populations are closely associated with their preferred host crops, wheat and oats (Grilli & Gorla, 2002).
Furthermore, the growth stage of oat and wheat crops, mainly determined by rains, influences age structure of D. kuscheli populations and consequent migration of macropters (Ornaghi et al., 1993). The two bivariate models selected include the variable DPr with negative slope, indicating that with a lower number of days with precipitation a greater population of macropters accumulated until November 30 is predicted. The absence of rains in winter would have a double effect: it would produce the deterioration of oat and wheat crops, favoring development of D. kuscheli macropters, and it would delay corn planting dates; thus, the most susceptible crop stages would coincide with insect vector migrations. Several authors indicate that system instability induced either by the end of the host cycle or by host quality decline due to adverse environmental factors, favor migration of delphacid macropters (Denno & Grissell 1979; Denno et al., 1980; Fernández-Badillo & Clavijo, 1990; Grilli & Gorla, 2002). Interestingly, it has been stated that dispersal of D. kuscheli would be determined by a mechanism that is independent of population density (Grilli & Gorla, 1999).
While habitat instability due to host natural senescence or decline has been indicated as favorable to production and migration of D. kuscheli macropters (Boito, 2005; Grilli & Gorla, 2002; Ornaghi et al., 1993; Remes Lenicov et al., 1991; Tesón et al., 1986), the key factor that would trigger this process has not been accurately identified. Nevertheless, because of the high losses produced by this insect vector, the most important epidemics (1981/82, 1996/97, 2006/07) are worth analysis. In the first epidemics (1981) D. kuscheli population level estimated by the bivariate model was "High", whereas in the following year (1982) it was estimated as "Very high"; however, while losses were high in the 1981/82 growing season, disease intensity in commercial crops in the following growing season was low. The absence of rainfall in the 1981 winter season jeopardized oat and wheat crops, favoring macropter migration to corn crops that had been planted as late as November, after the occurrence of rains in late October-early November. Thus, MRC average incidence was 60% for the entire Río Cuarto department (March et al., 1993). In contrast, in 1982, rains in September favored the development of winter cereals and early planting of corn. Thus, the large D. kuscheli populations would have found more suitable conditions for developing on oat and wheat, and gradually migrated to corn crops that were at advanced growth stages, since most of the crops had been planted in October; hence, the disease did not reach epidemic characteristics (Lenardon et al., 1986; March et al., 1993).
Similar situations were observed with epidemics that occurred in the 1996/97 and 2006/07 growing seasons, which spread outside the endemic area. While in 1996 and 1997 population levels of D. kuscheli macropters - actual and estimated by the model - were "Very High" due to optimum temperature conditions (DDTxn=93.2 and 104.7 degree days, respectively), the absence of winter rains in 1996 (DPr=3) favored oat and wheat deterioration, and consequently the migration of macropters to corn crops not only in the endemic area but also in the provinces of Santa Fe and Buenos Aires. In contrast, winter rains in 1997 (DPr=10) promoted growth of winter cereal; thus, migration of macropters was gradual and did not produce important losses, since corn crops were at advanced growth stages (Lenardon et al., 1998; Ornaghi et al., 2001). Also, as there had been a recent epidemic, growers in the endemic area included tolerant hybrids at planting in 1997.
Furthermore, while the bivariate B model predicted a "High" macropterous population level for Chaján (994 macropters), it underestimated the 2570 individuals collected to November 30, 1996. For this site and growing season, the predictive variable DDTxn reached 37.6 degree days, a lower value than that obtained in La Aguada; this would be indicating the important role of the status of winter host of D. kuscheli populations in regulating migrations of macropters.
In 2004 and 2006, two years for which the predicted population was also "Very high", the situation would have been similar to those previously mentioned: migration of macropters due to deterioration of oat and wheat crops as a result of absence of winter rains in 2006 (DPr=2) with corn planted in November; and gradual migration in 2004 due to the good cereal growth in winter, to corn crops that had been sown in September-October because of the occurrence of rains (DPr=8). Most of the recently introduced hybrids are of high production potential, but often susceptible to the disease (Lenardon et al., 2007). The results of this work contribute to understanding the influence of temperature and precipitation on population dynamics of D. kuscheli. The predictive models generated are useful tools to be included in disease management strategies.
This work was supported by the Instituto Nacional de Tecnología Agropecuaria - INTA and the Universidad Nacional de Río Cuarto, Córdoba, Argentina.
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Received 23 November 2010
Accepted 27 June 2011
Author for correspondence: Ricardo C. Moschini, e-mail: firstname.lastname@example.org
Section Editor: Alice K. Inoue Nagata