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Control strategy for maize wallaby ear symptom based on a quantitative forecasting model for the maize orange leafhopper, Cicadulina bipunctata
Crop Protection 75 (2015) 139e143
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Control strategy for maize wallaby ear symptom based on a quantitative forecasting model for the maize orange leafhopper, Cicadulina bipunctata Keiichiro Matsukura a, b, *, Kazuhiro Yoshida a, Masaya Matsumura a a b
NARO Kyushu Okinawa Agricultural Research Center, Suya 2421, Koshi, Kumamoto 861-1192, Japan Pacific Biosciences Research Center, University of Hawaii, 3050 Maile Way, Gilmore 408, Honolulu, HI 96822, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 March 2015 Received in revised form 29 May 2015 Accepted 31 May 2015 Available online 8 June 2015
The maize orange leafhopper (Cicadulina bipunctata) induces maize wallaby ear symptom (MWES) in forage maize in temperate southwest Japan. Using 10 years of field data (2004e2013), we developed a forecasting model for C. bipunctata based on climate and density factors that can guide the minimum level of control required to prevent MWES. The multiple regression analysis selected both temperature and precipitation from the previous winter to summer and maximum C. bipunctata density in the previous August as determinant factors in the model for forecasting occurrence of C. bipunctata in summer. This forecasting model could predict C. bipunctata occurrence with an accuracy of <1 averaged standardized residuals, supporting the validity of the model. Economic levels of injury due to MWES in susceptible and tolerant (resistant) maize varieties were sustained at densities of 21 and 74 adults/m2 respectively, derived from the results of suction and light trap monitoring of C. bipunctata in the field. Combining these economic injury levels and the forecast from the model can guide choice of crops/ varieties and seeding period in the coming summer to prevent MWES injury. This forecasting system is expected to be implemented in southwest Japan after consideration of the adaptability of the forecasting model to the entire target area. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Cicadellidae Forage maize Global warming Leave-one-out cross-validation Pest forecasting Regression analysis
1. Introduction Maize wallaby ear symptom (MWES), which is characterized by stunted growth together with swelling of leaf veins, is one of the most serious growth defects of maize (Zea mays) in the AsiaOceania region. Occurrence of MWES was restricted to tropical regions of Australia and the Philippines until the early 1980s (Grylls, 1975; Agati and Calica, 1949); however in recent years, outbreaks have been reported in subtropical and temperate east Asia (Li and Liu, 2004; Matsumura et al., 2006). The current northern limit of MWES occurrence is an area of plains in Kyushu Island, southwest Japan. MWES is induced by the feeding behavior of the maize orange leafhopper (Cicadulina bipunctata) (Matsukura et al., 2009a, 2010). Certain chemicals injected into the plants from the salivary glands
* Corresponding author. NARO Kyushu Okinawa Agricultural Research Center, Suya 2421, Koshi, Kumamoto 861-1192, Japan. E-mail address: [email protected] (K. Matsukura). http://dx.doi.org/10.1016/j.cropro.2015.05.019 0261-2194/© 2015 Elsevier Ltd. All rights reserved.
of C. bipunctata induce MWES on the leaves that emerge after infestation. The degree of MWES depends on both the density of the C. bipunctata infestation and the length of the infestation period, as well as the developmental stage of the maize (Matsukura et al., 2009a; Matsukura and Matsumura, 2010). MWES becomes serious when young maize plants are exposed to long term infestation by large numbers of C. bipunctata. Some other poaceous plants such as rice (Oryza sativa), wheat (Triticum aestivum) and Italian ryegrass (Lolium multiflorum) show stunted growth together with swelling of leaf veins, similar to the symptoms of MWES on maize, when C. bipunctata feeds on them (Maramorosch et al., 1961; Li et al., 2004; Matsukura and Matsumura, 2013). Two cultural control methods for MWES on maize have been developed in Kyushu Island, where damage by MWES on summer cropping of forage maize has been serious since around 2000. Tolerant (resistant) varieties are effective in preventing MWES occurrence because they exhibit hardly any MWES up to certain levels of infestation (Matsukura and Matsumura, 2010; Tokuda et al., 2013), although even the tolerant varieties exhibit MWES when they are exposed to large numbers of C. bipunctata at the
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seedling stage (Matsukura and Matsumura, 2010). Early seeding is another effective strategy to suppress yield loss by MWES. In Kyushu Island, the summer cropping of forage maize usually starts from late June to mid August, while the density of C. bipunctata begins to increase after mid July (Matsukura et al., 2009b). This means that maize seeded later (i.e., early to mid August) is exposed to higher densities of C. bipunctata at the seedling stage than that seeded earlier. Our previous field study demonstrated that seeding in late July resulted in the highest yield, and the yields gradually decreased depending on seeding dates (Matsukura and Matsumura, 2010). The loss of yield was correlated with adult C. bipunctata density during the first 9 days after seeding (Matsukura and Matsumura, 2010). The abundance of C. bipunctata fluctuates greatly among years depending on climatic conditions (Matsukura et al., 2009b, 2012). C. bipunctata was originally a tropical species (Webb, 1987), and therefore, the appropriate range of temperature for its survival and reproduction is higher than that in common temperate region insects. For example, the maximum reproductive rate of C. bipunctata occurs at 31 C (Tokuda and Matsumura, 2005), a temperature at which most common temperate region leafhoppers show arrested development or high mortality (Kiritani, 2012). On the other hand, the overwintering rate of adult C. bipunctata in Kyushu Island is extremely low (<1%) (Matsukura et al., 2014). A multiple regression model detected significant positive contributions of both winter and summer temperature and a significant negative contribution of precipitation during winter to abundance of C. bipunctata in summer (Matsukura et al., 2012). Accurate prediction of C. bipunctata occurrence in early summer is important in order to implement cultural control methods (i.e., tolerant variety and early seeding) most effectively. All current tolerant varieties are late ripening, and they sometimes cannot attain full maturity before harvest because of insufficient growing degree days in Kyushu. Furthermore, the tolerant varieties are not inherently tolerant to other serious diseases such as southern rust. Varieties susceptible to MWES, therefore, still have an advantage in terms of better forage production unless MWES occurs. A prediction of C. bipunctata occurrence in early summer also helps farmers to adjust seeding schedules to prevent MWES occurrence. The purpose of this study is to establish a control strategy for MWES based on a forecasting C. bipunctata occurrence in early summer. Our previous study revealed that the occurrence of C. bipunctata in early summer can be predicted with some accuracy by climatic factors (Matsukura et al., 2012). In this study, we developed a forecasting model from the previous prediction model by considering the level of occurrence in the previous year as well as climatic factors. 2. Materials and methods 2.1. Developing a forecasting model for adult C. bipunctata 2.1.1. Field sites Occurrence of C. bipunctata was monitored in a forage cropping area in Kikuchi, Kumamoto, Japan (32.57 N, 130.50 E) in July and August from 2004 to 2013. In this area, most farmers practice double-cropping of forage maize (spring crop: April to late July, summer crop: late July to November), and MWES has been serious in the summer crop since around 2000 (Matsumura et al., 2006). 2.1.2. Monitoring of C. bipunctata density in fields Adult C. bipunctata were collected from poaceous weeds, mainly southern crabgrass (Digitaria ciliaris) and goosegrass (Eleusine
indica), usually around the forage crop field site (220 m2), and occasionally from the same poaceous weeds within 300 m of the main field when the weeds around the main field were not appropriate for sampling because of weed control. Sampling was conducted, usually weekly or biweekly, with a suction machine (inlet diameter 11.5 cm, Proforce, Tanaka, Japan) following the methods of Matsukura et al. (2012). One sample consisted of insects collected from ten suction points (approximately 103.8 cm2 10 points) from the weed vegetation. The collected insects were dried at 55 C or frozen at 20 C for one day and the numbers of adult C. bipunctata were counted under a binocular microscope. Adult density per area was calculated from the average number of adult C. bipunctata collected from several samples (numbers of samples per day were from 5 to 20, average 6.9) and the area of the suction machine inlet. 2.1.3. Modeling A model forecasting adult C. bipunctata density from July to August was developed from the prediction model of Matsukura et al. (2012). In the prediction model, changes in adult C. bipunctata density during summer were fit to an exponential function as follows,
NðtÞ ¼ N0 expðrtÞ where, t is a number of days passed from 2 July (t ¼ 1 means 2 July and t ¼ 61 means 31 August, for example), N(t) is an estimated density of adult C. bipunctata on day t, N0 is an initial density of adult C. bipunctata, and r is the intrinsic rate of natural increase. Changes in N(t) in each year can be predicted by calculating N0 and r from temperature and precipitation from the previous December to August of the current year. To improve the prediction accuracy, the present study considered maximum adult C. bipunctata density in August of the previous year (in fact on 31 August because density in August is monotonic) as well as the climatic factors considered by Matsukura et al. (2012). The maximum density in August in 2004 was taken as log2.5 (z316.2 adults/m2) because no data were available for 2003. For the other years, maximum densities in August were estimated by fitting C. bipunctata density data from each year into the above exponential equation, as done by Matsukura et al. (2012). The climatic data were obtained from the Automated Meteorological Data Acquisition System (AMeDAS) point at Kikuchi, Kumamoto Prefecture (32.9 N, 130.8 E, approximately 5 km from the field site area) operated by the Japan Meteorological Agency. The climatic and density factors determining N0 and r were selected with a forward stepwise approach based upon leave-one-out cross validation using JMP ver. 11.0.0 (SAS Institute Inc., USA). This approach selects an appropriate model that can be used for unknown data (i.e., data that were not included in establishing of the model). Unlike in Matsukura et al. (2012), ln(N0) was used as the dependent variable instead of N0 to avoid an estimated value of negative infinity. 2.2. Verification of the forecasting model The validity of the forecasting model developed was assessed with absolute standardized residuals. Data beyond 2 absolute standardized residuals are usually regarded as outliers (Maxwell et al., 2003; Vergara-Torres et al., 2010), and therefore we used this value as a criterion for evaluating our forecasting model. After estimating exponential curves of C. bipunctata density from 2004 to 2013 using the forecasting model, residuals between the predicted densities and actual field densities were calculated.
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2.3. Establishment of control strategy for MWES
3.3. Control strategy based on the forecasting model
To establish a control strategy for MEWS based on a forecasting model of C. bipunctata, the relationship between adult C. bipunctata density on poaceous weeds and the level of damage from MWES was examined. The levels of C. bipunctata causing economic injury due to MWES were defined as the lowest numbers of C. bipunctata that cause more than 20% yield loss stem and leaf dry matter. These are 5 and 16 adult C. bipunctata trapped in a light trap per day during the first 9 days after seeding, for susceptible and tolerant varieties, respectively (Matsukura and Matsumura, 2010). To relate levels of C. bipunctata occurrence in the forecasting model to numbers obtained by light trap monitoring, the correlation between adult density on poaceous weeds and numbers of adults trapped in the light trap was examined experimentally. An automatic daily monitoring trap (MT-7-N2, Ikeda Rika, Tokyo) fitted with a light bulb (60 W) was located at a site 300 m from the main crop field from 2007 to 2013. After numbers of trapped adult C. bipunctata in the light were averaged into pentads (5 days), correlation between the averaged data (adults/day) and density data on poaceous weeds (adults/m2) were examined by Pearson productemoment correlation analysis and linear regression analysis. All the data from 2007 to 2013 were pooled for the analyses (n ¼ 43).
Numbers of adult C. bipunctata in the light trap (Ntrap, adults/ day) and adult C. bipunctata density on poaceous weeds (Nweeds, adults/m2) showed a significant positive correlation (Pearson productemoment correlation analysis, df ¼ 41, r ¼ 0.633, P < 0.001) with the following relationship (Fig. 2),
3. Results 3.1. Factors selected for the forecasting model The multiple regression analyses determined several climatic and density factors that were associated with the yearly fluctuation of adult C. bipunctata density from July to August. Monthly average temperature in the previous December, January, March and June, total precipitation in the previous winter (December to March) and that in the previous spring (April to June) were selected as independent variables contributing to prediction of N0 (Table 1). Monthly average temperature in July and August and the maximum density of adult C. bipunctata in the previous summer were selected as independent variables contributing to the prediction of r (Table 2). 3.2. Validity of the forecasting model A comparison of population densities predicted by the forecasting model and actual C. bipunctata density in the field resulted in average standardized residuals of 0.536 ± 0.648 (Fig. 1, Table 3). Of all 57 data points from 2004 to 2013, only 2 were >2 standardized residuals from the mean with a maximum of 2.528 on 24 July 2012, and 45 were <1 standardized residuals from the mean.
Ntrap ¼ 0:209 Nweeds þ 0:659 Estimates of C. bipunctata densities causing economic injury levels due to MWES based on this relationship were 21 and 74 adults/m2 for susceptible and tolerant varieties, respectively. These economic injury levels together with the forecasting model permit proposal of recommended crops/varieties and seeding period to prevent MWES injury according to a strategy such as shown in Fig. 3. In this example, both susceptible and tolerant varieties can be seeded before 26 June because C. bipunctata density was less 21. Tolerant varieties can still be seeded until 17 August whereas seeding of susceptible varieties in this period would cause yield loss. After 17 August, neither tolerant nor susceptible varieties should be cropped. 4. Discussion The model developed for forecasting C. bipunctata levels in early summer enabled us to select a minimum required control method to prevent MWES occurrence on forage maize in Kyushu Island (Fig. 3). This forecasting system would contribute to better forage maize production in this area. Cultivation of the current tolerant varieties involves the risk of harvesting immature grain and occurrence of serious plant diseases because of late ripening and poor disease tolerance of the tolerant varieties. Cultivation of susceptible varieties based on the forecasting model would reduce these risks. Prediction of appropriate seeding period is also a merit of the forecasting model. Earlier seeding is not always practicable for farmers despite its effectiveness because seeding date depends on weather conditions and progress to maturation of the spring crop from April to late July. Forecasting of C. bipunctata could guide farmers in deciding on a seeding schedule. When the level of C. bipunctata is predicted to be too high to avoid MWES occurrence even by the tolerant varieties (after 17 August in the example in Fig. 3), autumn cropping of oats, barley and Italian ryegrass as alternative crops is recommended. These crops are less affected than maize by infestation of C. bipunctata (Matsukura and Matsumura, 2013). Accuracy of the forecasting model is required for it to be useful in practice. Averages of absolute standardized residuals calculated from the forecasting model were not remarkably improved from those from the prediction model previously proposed (Matsukura et al., 2012); however, in terms of prediction of unknown data
Table 1 Factors and their contribution to ln(N0) as estimated by multiple linear regression analysis based on the data from 2004 to 2012. Factor and intercepta Intercept Average temperature in the previous Average temperature in the previous Average temperature in the previous Average temperature in the previous Total precipitation from Dec. to Mar. Total precipitation from Apr. to Jun. a b
Dec. Jan. Mar. Jun.
Contribution
SE
P
71.457 0.717 0.446 1.514 2.300 7.168 103 2.014 103
5.220 0.044 0.061 0.078 0.185 0.785 103 0.447 103
<0.001 <0.001 0.005 <0.001 0.001 0.003 0.021
Units of temperature, precipitation and density of Cicadulina bipunctata are C, mm and adults/m2, respectively. R2 is the coefficient of determination of the estimated multiple linear function.
R2b 0.998
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Table 2 Factors and their contribution to r as estimated by multiple linear regression analysis based on the data from 2004 to 2012. Factors and intercepta
Contribution
SE
P
R2b
Intercept Average temperature in Jul. Average temperature in Aug. Maximum density of adults in the previous August
1.366 2.613 102 2.128 102 5.852 102
0.291 0.490 102 0.664 102 0.6941 102
0.003 0.002 0.019 <0.001
0.941
a b
Units of temperature and density of Cicadulina bipunctata are C and adults/m2, respectively. R2 is the coefficient of determination of the estimated multiple linear function.
(i.e., data that are not included in construction of the model), the forecasting model showed lower absolute standardized residuals (0.655 on average) than the former model (1.604, Matsukura et al., 2012), indicating the greater validity of the forecasting model. Climatic factors and their contributions selected in the forecasting model basically coincide with those used in the prediction model previously proposed (Matsukura et al., 2012). These models include temperatures from the previous winter to early summer as positive factors and precipitation in the previous winter as a negative factor determining C. bipunctata occurrence. In addition to these factors, the forecasting model contains a positive contribution of precipitation from the previous spring to early summer (Table 1). This positive contribution can be explained by the relationship between rainfall and occurrence of herbivorous insects. Large amounts of rainfall may result in increased population
growth of herbivorous insects through enhancement of plant growth (Price and Clancy, 1986; Masters et al., 1998). Heavy rainfall from spring to early summer, when C. bipunctata begins to reproduce, resulted in enhanced growth of poaceous weeds and crops, which might enhance C. bipunctata abundance. Maximum adult C. bipunctata density in the previous August was also included in the forecasting model, contributing positively to r (i.e., intrinsic rate of natural increase after 1 July). This positive relationship between two continuous years means that the risk of MWES becomes high
Fig. 2. Relationship between adult Cicadulina bipunctata density on poaceous weeds and numbers of trapped C. bipunctata in a light trap in the forage maize cropping area. Regression of C. bipunctata numbers in the light trap of densities on poaceous weeds was calculated from pooled data from 2007 to 2013 (n ¼ 43).
Fig. 1. Actual field density of C. bipunctata (shown with symbols) and occurrence estimated from the forecasting model (shown with lines) from 2004 to 2013.
Table 3 Mean (±SD) absolute standardized residuals between actual C. bipunctata density in fields and values estimated from the forecasting model. Year
n
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 average
3 6 3 8 7 6 5 7 6 6 5.7
Standardized residuals (Mean ± SD) 0.608 0.181 0.080 0.629 0.482 0.249 0.913 0.626 1.070 0.351 0.536
± ± ± ± ± ± ± ± ± ± ±
0.380 0.312 0.121 0.556 0.633 0.342 0.912 0.896 0.891 0.401 0.648
Fig. 3. Strategy for control of maize wallaby ear symptom (MWES) in forage maize based on a forecasting model of adult Cicadulina bipunctata. The exponential curve is an example of changes in adult C. bipunctata density. When numbers of C. bipunctata are less than 21 (adults/m2), any variety can be seeded at little risk of MWES. Only tolerant varieties can be used when numbers of C. bipunctata are from 21 to 74. When C. bipunctata density reaches more than 74, forage maize should not be seeded because damage from MWES would become economically serious; autumn cropping of other forage crops such as barley, oats and Italian ryegrass is recommended as an alternative under such conditions.
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when occurrence of C. bipunctata in last year is high. The forecasting model could be improved in two ways. The model, which is expected to be appropriate for southwest temperate Japan, is constructed from field data from one area only. The economic injury level caused by MWES is supposed not to vary among regions because the MWES-inducing ability of C. bipunctata does not differ significantly among geographical populations (Kumashiro et al., 2014). In addition, a previous study reported a similar pattern of occurrence of adult C. bipunctata in two cities in this region (Matsukura et al., 2009b). Nevertheless, adaptability of the forecasting model in the target areas should be considered before using it as a basis for planting decisions because some regional factors such as flora, natural enemies, and cropping system may influence on C. bipunctata occurrence in the region. Long-term fluctuation in C. bipunctata occurrence is another issue to be considered. Some ecological factors such as occurrence of natural enemies and global warming have great impacts on the population dynamics of insect pests (Landis et al., 2000; Diffenbaugh et al., 2008; Roos et al., 2011; Martin et al., 2013). Occurrence of C. bipunctata may be affected by these ecological factors in the future, although no important natural enemy of C. bipunctata has yet been reported in Japan. Further monitoring of C. bipunctata occurrence is the only way to address this issue. Prevention of further expansion of C. bipunctata is important in any effort to reduce MWES occurrence in temperate Japan. Since C. bipunctata was first collected in Kumamoto prefecture in Kyushu Island in 1914 (Matsumura, 1914) it had not been reported again until the 1980s. Nowadays, it has been found in all 7 prefectures of Kyushu Island, as well as two prefectures of Shikoku Island to the east (Kumashiro et al., 2014), indicating that it is gradually expanding its range. It may not spread rapidly because of its poor adaptability to low temperature during winter (Matsukura et al., 2014), but it may do so as winter temperature in these area increases as a result of further global warming. Improvement of the tolerant variety is another effective way to reduce MWES occurrence. A comparative study using barley chromosome disomic addition lines of wheat suggested that the tolerance to MWES is a quantitative trait controlled by multiple genes (Kumashiro et al., 2011). Such a quantitative difference in the tolerance would exist in maize although any differences in the degree of tolerance to MWES among present tolerant varieties has not been reported. Molecular biological approaches such as QTL analysis and RNA sequencing are effective to create better tolerant maize varieties to MWES. The economic injury level in the tolerant varieties (74 adults/m2 at present) can be changed when the tolerant varieties are improved. Acknowledgments We thank Robert Cowie of the University of Hawaii for improving our manuscript. This study was supported by a grant for development of mitigation and adaptation techniques to global warming in the sectors of agriculture, forestry, and fisheries (44130) from the Ministry of Agriculture, Forestry, and Fisheries of Japan. References Agati, J.A., Calica, C., 1949. The leaf-gall disease of rice and corn in the Philippines. Philipp. J. Agric. 14, 31e40. Diffenbaugh, N.S., Krupke, C.H., White, M.A., Alexander, C.E., 2008. Global warming presents new challenges for maize pest management. Environ. Res. Lett. 3,
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Control strategy for maize wallaby ear symptom based on a quantitative forecasting model for the maize orange leafhopper, Cicadulina bipunctata Keiichiro Matsukura a, b, *, Kazuhiro Yoshida a, Masaya Matsumura a a b
NARO Kyushu Okinawa Agricultural Research Center, Suya 2421, Koshi, Kumamoto 861-1192, Japan Pacific Biosciences Research Center, University of Hawaii, 3050 Maile Way, Gilmore 408, Honolulu, HI 96822, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 March 2015 Received in revised form 29 May 2015 Accepted 31 May 2015 Available online 8 June 2015
The maize orange leafhopper (Cicadulina bipunctata) induces maize wallaby ear symptom (MWES) in forage maize in temperate southwest Japan. Using 10 years of field data (2004e2013), we developed a forecasting model for C. bipunctata based on climate and density factors that can guide the minimum level of control required to prevent MWES. The multiple regression analysis selected both temperature and precipitation from the previous winter to summer and maximum C. bipunctata density in the previous August as determinant factors in the model for forecasting occurrence of C. bipunctata in summer. This forecasting model could predict C. bipunctata occurrence with an accuracy of <1 averaged standardized residuals, supporting the validity of the model. Economic levels of injury due to MWES in susceptible and tolerant (resistant) maize varieties were sustained at densities of 21 and 74 adults/m2 respectively, derived from the results of suction and light trap monitoring of C. bipunctata in the field. Combining these economic injury levels and the forecast from the model can guide choice of crops/ varieties and seeding period in the coming summer to prevent MWES injury. This forecasting system is expected to be implemented in southwest Japan after consideration of the adaptability of the forecasting model to the entire target area. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Cicadellidae Forage maize Global warming Leave-one-out cross-validation Pest forecasting Regression analysis
1. Introduction Maize wallaby ear symptom (MWES), which is characterized by stunted growth together with swelling of leaf veins, is one of the most serious growth defects of maize (Zea mays) in the AsiaOceania region. Occurrence of MWES was restricted to tropical regions of Australia and the Philippines until the early 1980s (Grylls, 1975; Agati and Calica, 1949); however in recent years, outbreaks have been reported in subtropical and temperate east Asia (Li and Liu, 2004; Matsumura et al., 2006). The current northern limit of MWES occurrence is an area of plains in Kyushu Island, southwest Japan. MWES is induced by the feeding behavior of the maize orange leafhopper (Cicadulina bipunctata) (Matsukura et al., 2009a, 2010). Certain chemicals injected into the plants from the salivary glands
* Corresponding author. NARO Kyushu Okinawa Agricultural Research Center, Suya 2421, Koshi, Kumamoto 861-1192, Japan. E-mail address: [email protected] (K. Matsukura). http://dx.doi.org/10.1016/j.cropro.2015.05.019 0261-2194/© 2015 Elsevier Ltd. All rights reserved.
of C. bipunctata induce MWES on the leaves that emerge after infestation. The degree of MWES depends on both the density of the C. bipunctata infestation and the length of the infestation period, as well as the developmental stage of the maize (Matsukura et al., 2009a; Matsukura and Matsumura, 2010). MWES becomes serious when young maize plants are exposed to long term infestation by large numbers of C. bipunctata. Some other poaceous plants such as rice (Oryza sativa), wheat (Triticum aestivum) and Italian ryegrass (Lolium multiflorum) show stunted growth together with swelling of leaf veins, similar to the symptoms of MWES on maize, when C. bipunctata feeds on them (Maramorosch et al., 1961; Li et al., 2004; Matsukura and Matsumura, 2013). Two cultural control methods for MWES on maize have been developed in Kyushu Island, where damage by MWES on summer cropping of forage maize has been serious since around 2000. Tolerant (resistant) varieties are effective in preventing MWES occurrence because they exhibit hardly any MWES up to certain levels of infestation (Matsukura and Matsumura, 2010; Tokuda et al., 2013), although even the tolerant varieties exhibit MWES when they are exposed to large numbers of C. bipunctata at the
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seedling stage (Matsukura and Matsumura, 2010). Early seeding is another effective strategy to suppress yield loss by MWES. In Kyushu Island, the summer cropping of forage maize usually starts from late June to mid August, while the density of C. bipunctata begins to increase after mid July (Matsukura et al., 2009b). This means that maize seeded later (i.e., early to mid August) is exposed to higher densities of C. bipunctata at the seedling stage than that seeded earlier. Our previous field study demonstrated that seeding in late July resulted in the highest yield, and the yields gradually decreased depending on seeding dates (Matsukura and Matsumura, 2010). The loss of yield was correlated with adult C. bipunctata density during the first 9 days after seeding (Matsukura and Matsumura, 2010). The abundance of C. bipunctata fluctuates greatly among years depending on climatic conditions (Matsukura et al., 2009b, 2012). C. bipunctata was originally a tropical species (Webb, 1987), and therefore, the appropriate range of temperature for its survival and reproduction is higher than that in common temperate region insects. For example, the maximum reproductive rate of C. bipunctata occurs at 31 C (Tokuda and Matsumura, 2005), a temperature at which most common temperate region leafhoppers show arrested development or high mortality (Kiritani, 2012). On the other hand, the overwintering rate of adult C. bipunctata in Kyushu Island is extremely low (<1%) (Matsukura et al., 2014). A multiple regression model detected significant positive contributions of both winter and summer temperature and a significant negative contribution of precipitation during winter to abundance of C. bipunctata in summer (Matsukura et al., 2012). Accurate prediction of C. bipunctata occurrence in early summer is important in order to implement cultural control methods (i.e., tolerant variety and early seeding) most effectively. All current tolerant varieties are late ripening, and they sometimes cannot attain full maturity before harvest because of insufficient growing degree days in Kyushu. Furthermore, the tolerant varieties are not inherently tolerant to other serious diseases such as southern rust. Varieties susceptible to MWES, therefore, still have an advantage in terms of better forage production unless MWES occurs. A prediction of C. bipunctata occurrence in early summer also helps farmers to adjust seeding schedules to prevent MWES occurrence. The purpose of this study is to establish a control strategy for MWES based on a forecasting C. bipunctata occurrence in early summer. Our previous study revealed that the occurrence of C. bipunctata in early summer can be predicted with some accuracy by climatic factors (Matsukura et al., 2012). In this study, we developed a forecasting model from the previous prediction model by considering the level of occurrence in the previous year as well as climatic factors. 2. Materials and methods 2.1. Developing a forecasting model for adult C. bipunctata 2.1.1. Field sites Occurrence of C. bipunctata was monitored in a forage cropping area in Kikuchi, Kumamoto, Japan (32.57 N, 130.50 E) in July and August from 2004 to 2013. In this area, most farmers practice double-cropping of forage maize (spring crop: April to late July, summer crop: late July to November), and MWES has been serious in the summer crop since around 2000 (Matsumura et al., 2006). 2.1.2. Monitoring of C. bipunctata density in fields Adult C. bipunctata were collected from poaceous weeds, mainly southern crabgrass (Digitaria ciliaris) and goosegrass (Eleusine
indica), usually around the forage crop field site (220 m2), and occasionally from the same poaceous weeds within 300 m of the main field when the weeds around the main field were not appropriate for sampling because of weed control. Sampling was conducted, usually weekly or biweekly, with a suction machine (inlet diameter 11.5 cm, Proforce, Tanaka, Japan) following the methods of Matsukura et al. (2012). One sample consisted of insects collected from ten suction points (approximately 103.8 cm2 10 points) from the weed vegetation. The collected insects were dried at 55 C or frozen at 20 C for one day and the numbers of adult C. bipunctata were counted under a binocular microscope. Adult density per area was calculated from the average number of adult C. bipunctata collected from several samples (numbers of samples per day were from 5 to 20, average 6.9) and the area of the suction machine inlet. 2.1.3. Modeling A model forecasting adult C. bipunctata density from July to August was developed from the prediction model of Matsukura et al. (2012). In the prediction model, changes in adult C. bipunctata density during summer were fit to an exponential function as follows,
NðtÞ ¼ N0 expðrtÞ where, t is a number of days passed from 2 July (t ¼ 1 means 2 July and t ¼ 61 means 31 August, for example), N(t) is an estimated density of adult C. bipunctata on day t, N0 is an initial density of adult C. bipunctata, and r is the intrinsic rate of natural increase. Changes in N(t) in each year can be predicted by calculating N0 and r from temperature and precipitation from the previous December to August of the current year. To improve the prediction accuracy, the present study considered maximum adult C. bipunctata density in August of the previous year (in fact on 31 August because density in August is monotonic) as well as the climatic factors considered by Matsukura et al. (2012). The maximum density in August in 2004 was taken as log2.5 (z316.2 adults/m2) because no data were available for 2003. For the other years, maximum densities in August were estimated by fitting C. bipunctata density data from each year into the above exponential equation, as done by Matsukura et al. (2012). The climatic data were obtained from the Automated Meteorological Data Acquisition System (AMeDAS) point at Kikuchi, Kumamoto Prefecture (32.9 N, 130.8 E, approximately 5 km from the field site area) operated by the Japan Meteorological Agency. The climatic and density factors determining N0 and r were selected with a forward stepwise approach based upon leave-one-out cross validation using JMP ver. 11.0.0 (SAS Institute Inc., USA). This approach selects an appropriate model that can be used for unknown data (i.e., data that were not included in establishing of the model). Unlike in Matsukura et al. (2012), ln(N0) was used as the dependent variable instead of N0 to avoid an estimated value of negative infinity. 2.2. Verification of the forecasting model The validity of the forecasting model developed was assessed with absolute standardized residuals. Data beyond 2 absolute standardized residuals are usually regarded as outliers (Maxwell et al., 2003; Vergara-Torres et al., 2010), and therefore we used this value as a criterion for evaluating our forecasting model. After estimating exponential curves of C. bipunctata density from 2004 to 2013 using the forecasting model, residuals between the predicted densities and actual field densities were calculated.
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2.3. Establishment of control strategy for MWES
3.3. Control strategy based on the forecasting model
To establish a control strategy for MEWS based on a forecasting model of C. bipunctata, the relationship between adult C. bipunctata density on poaceous weeds and the level of damage from MWES was examined. The levels of C. bipunctata causing economic injury due to MWES were defined as the lowest numbers of C. bipunctata that cause more than 20% yield loss stem and leaf dry matter. These are 5 and 16 adult C. bipunctata trapped in a light trap per day during the first 9 days after seeding, for susceptible and tolerant varieties, respectively (Matsukura and Matsumura, 2010). To relate levels of C. bipunctata occurrence in the forecasting model to numbers obtained by light trap monitoring, the correlation between adult density on poaceous weeds and numbers of adults trapped in the light trap was examined experimentally. An automatic daily monitoring trap (MT-7-N2, Ikeda Rika, Tokyo) fitted with a light bulb (60 W) was located at a site 300 m from the main crop field from 2007 to 2013. After numbers of trapped adult C. bipunctata in the light were averaged into pentads (5 days), correlation between the averaged data (adults/day) and density data on poaceous weeds (adults/m2) were examined by Pearson productemoment correlation analysis and linear regression analysis. All the data from 2007 to 2013 were pooled for the analyses (n ¼ 43).
Numbers of adult C. bipunctata in the light trap (Ntrap, adults/ day) and adult C. bipunctata density on poaceous weeds (Nweeds, adults/m2) showed a significant positive correlation (Pearson productemoment correlation analysis, df ¼ 41, r ¼ 0.633, P < 0.001) with the following relationship (Fig. 2),
3. Results 3.1. Factors selected for the forecasting model The multiple regression analyses determined several climatic and density factors that were associated with the yearly fluctuation of adult C. bipunctata density from July to August. Monthly average temperature in the previous December, January, March and June, total precipitation in the previous winter (December to March) and that in the previous spring (April to June) were selected as independent variables contributing to prediction of N0 (Table 1). Monthly average temperature in July and August and the maximum density of adult C. bipunctata in the previous summer were selected as independent variables contributing to the prediction of r (Table 2). 3.2. Validity of the forecasting model A comparison of population densities predicted by the forecasting model and actual C. bipunctata density in the field resulted in average standardized residuals of 0.536 ± 0.648 (Fig. 1, Table 3). Of all 57 data points from 2004 to 2013, only 2 were >2 standardized residuals from the mean with a maximum of 2.528 on 24 July 2012, and 45 were <1 standardized residuals from the mean.
Ntrap ¼ 0:209 Nweeds þ 0:659 Estimates of C. bipunctata densities causing economic injury levels due to MWES based on this relationship were 21 and 74 adults/m2 for susceptible and tolerant varieties, respectively. These economic injury levels together with the forecasting model permit proposal of recommended crops/varieties and seeding period to prevent MWES injury according to a strategy such as shown in Fig. 3. In this example, both susceptible and tolerant varieties can be seeded before 26 June because C. bipunctata density was less 21. Tolerant varieties can still be seeded until 17 August whereas seeding of susceptible varieties in this period would cause yield loss. After 17 August, neither tolerant nor susceptible varieties should be cropped. 4. Discussion The model developed for forecasting C. bipunctata levels in early summer enabled us to select a minimum required control method to prevent MWES occurrence on forage maize in Kyushu Island (Fig. 3). This forecasting system would contribute to better forage maize production in this area. Cultivation of the current tolerant varieties involves the risk of harvesting immature grain and occurrence of serious plant diseases because of late ripening and poor disease tolerance of the tolerant varieties. Cultivation of susceptible varieties based on the forecasting model would reduce these risks. Prediction of appropriate seeding period is also a merit of the forecasting model. Earlier seeding is not always practicable for farmers despite its effectiveness because seeding date depends on weather conditions and progress to maturation of the spring crop from April to late July. Forecasting of C. bipunctata could guide farmers in deciding on a seeding schedule. When the level of C. bipunctata is predicted to be too high to avoid MWES occurrence even by the tolerant varieties (after 17 August in the example in Fig. 3), autumn cropping of oats, barley and Italian ryegrass as alternative crops is recommended. These crops are less affected than maize by infestation of C. bipunctata (Matsukura and Matsumura, 2013). Accuracy of the forecasting model is required for it to be useful in practice. Averages of absolute standardized residuals calculated from the forecasting model were not remarkably improved from those from the prediction model previously proposed (Matsukura et al., 2012); however, in terms of prediction of unknown data
Table 1 Factors and their contribution to ln(N0) as estimated by multiple linear regression analysis based on the data from 2004 to 2012. Factor and intercepta Intercept Average temperature in the previous Average temperature in the previous Average temperature in the previous Average temperature in the previous Total precipitation from Dec. to Mar. Total precipitation from Apr. to Jun. a b
Dec. Jan. Mar. Jun.
Contribution
SE
P
71.457 0.717 0.446 1.514 2.300 7.168 103 2.014 103
5.220 0.044 0.061 0.078 0.185 0.785 103 0.447 103
<0.001 <0.001 0.005 <0.001 0.001 0.003 0.021
Units of temperature, precipitation and density of Cicadulina bipunctata are C, mm and adults/m2, respectively. R2 is the coefficient of determination of the estimated multiple linear function.
R2b 0.998
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Table 2 Factors and their contribution to r as estimated by multiple linear regression analysis based on the data from 2004 to 2012. Factors and intercepta
Contribution
SE
P
R2b
Intercept Average temperature in Jul. Average temperature in Aug. Maximum density of adults in the previous August
1.366 2.613 102 2.128 102 5.852 102
0.291 0.490 102 0.664 102 0.6941 102
0.003 0.002 0.019 <0.001
0.941
a b
Units of temperature and density of Cicadulina bipunctata are C and adults/m2, respectively. R2 is the coefficient of determination of the estimated multiple linear function.
(i.e., data that are not included in construction of the model), the forecasting model showed lower absolute standardized residuals (0.655 on average) than the former model (1.604, Matsukura et al., 2012), indicating the greater validity of the forecasting model. Climatic factors and their contributions selected in the forecasting model basically coincide with those used in the prediction model previously proposed (Matsukura et al., 2012). These models include temperatures from the previous winter to early summer as positive factors and precipitation in the previous winter as a negative factor determining C. bipunctata occurrence. In addition to these factors, the forecasting model contains a positive contribution of precipitation from the previous spring to early summer (Table 1). This positive contribution can be explained by the relationship between rainfall and occurrence of herbivorous insects. Large amounts of rainfall may result in increased population
growth of herbivorous insects through enhancement of plant growth (Price and Clancy, 1986; Masters et al., 1998). Heavy rainfall from spring to early summer, when C. bipunctata begins to reproduce, resulted in enhanced growth of poaceous weeds and crops, which might enhance C. bipunctata abundance. Maximum adult C. bipunctata density in the previous August was also included in the forecasting model, contributing positively to r (i.e., intrinsic rate of natural increase after 1 July). This positive relationship between two continuous years means that the risk of MWES becomes high
Fig. 2. Relationship between adult Cicadulina bipunctata density on poaceous weeds and numbers of trapped C. bipunctata in a light trap in the forage maize cropping area. Regression of C. bipunctata numbers in the light trap of densities on poaceous weeds was calculated from pooled data from 2007 to 2013 (n ¼ 43).
Fig. 1. Actual field density of C. bipunctata (shown with symbols) and occurrence estimated from the forecasting model (shown with lines) from 2004 to 2013.
Table 3 Mean (±SD) absolute standardized residuals between actual C. bipunctata density in fields and values estimated from the forecasting model. Year
n
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 average
3 6 3 8 7 6 5 7 6 6 5.7
Standardized residuals (Mean ± SD) 0.608 0.181 0.080 0.629 0.482 0.249 0.913 0.626 1.070 0.351 0.536
± ± ± ± ± ± ± ± ± ± ±
0.380 0.312 0.121 0.556 0.633 0.342 0.912 0.896 0.891 0.401 0.648
Fig. 3. Strategy for control of maize wallaby ear symptom (MWES) in forage maize based on a forecasting model of adult Cicadulina bipunctata. The exponential curve is an example of changes in adult C. bipunctata density. When numbers of C. bipunctata are less than 21 (adults/m2), any variety can be seeded at little risk of MWES. Only tolerant varieties can be used when numbers of C. bipunctata are from 21 to 74. When C. bipunctata density reaches more than 74, forage maize should not be seeded because damage from MWES would become economically serious; autumn cropping of other forage crops such as barley, oats and Italian ryegrass is recommended as an alternative under such conditions.
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when occurrence of C. bipunctata in last year is high. The forecasting model could be improved in two ways. The model, which is expected to be appropriate for southwest temperate Japan, is constructed from field data from one area only. The economic injury level caused by MWES is supposed not to vary among regions because the MWES-inducing ability of C. bipunctata does not differ significantly among geographical populations (Kumashiro et al., 2014). In addition, a previous study reported a similar pattern of occurrence of adult C. bipunctata in two cities in this region (Matsukura et al., 2009b). Nevertheless, adaptability of the forecasting model in the target areas should be considered before using it as a basis for planting decisions because some regional factors such as flora, natural enemies, and cropping system may influence on C. bipunctata occurrence in the region. Long-term fluctuation in C. bipunctata occurrence is another issue to be considered. Some ecological factors such as occurrence of natural enemies and global warming have great impacts on the population dynamics of insect pests (Landis et al., 2000; Diffenbaugh et al., 2008; Roos et al., 2011; Martin et al., 2013). Occurrence of C. bipunctata may be affected by these ecological factors in the future, although no important natural enemy of C. bipunctata has yet been reported in Japan. Further monitoring of C. bipunctata occurrence is the only way to address this issue. Prevention of further expansion of C. bipunctata is important in any effort to reduce MWES occurrence in temperate Japan. Since C. bipunctata was first collected in Kumamoto prefecture in Kyushu Island in 1914 (Matsumura, 1914) it had not been reported again until the 1980s. Nowadays, it has been found in all 7 prefectures of Kyushu Island, as well as two prefectures of Shikoku Island to the east (Kumashiro et al., 2014), indicating that it is gradually expanding its range. It may not spread rapidly because of its poor adaptability to low temperature during winter (Matsukura et al., 2014), but it may do so as winter temperature in these area increases as a result of further global warming. Improvement of the tolerant variety is another effective way to reduce MWES occurrence. A comparative study using barley chromosome disomic addition lines of wheat suggested that the tolerance to MWES is a quantitative trait controlled by multiple genes (Kumashiro et al., 2011). Such a quantitative difference in the tolerance would exist in maize although any differences in the degree of tolerance to MWES among present tolerant varieties has not been reported. Molecular biological approaches such as QTL analysis and RNA sequencing are effective to create better tolerant maize varieties to MWES. The economic injury level in the tolerant varieties (74 adults/m2 at present) can be changed when the tolerant varieties are improved. Acknowledgments We thank Robert Cowie of the University of Hawaii for improving our manuscript. This study was supported by a grant for development of mitigation and adaptation techniques to global warming in the sectors of agriculture, forestry, and fisheries (44130) from the Ministry of Agriculture, Forestry, and Fisheries of Japan. References Agati, J.A., Calica, C., 1949. The leaf-gall disease of rice and corn in the Philippines. Philipp. J. Agric. 14, 31e40. Diffenbaugh, N.S., Krupke, C.H., White, M.A., Alexander, C.E., 2008. Global warming presents new challenges for maize pest management. Environ. Res. Lett. 3,
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