- Email: [email protected]
The effects of global temperature change on rice leaf blast epidemics: a simulation study in three agroecological zones
Agriculture, Ecosystems and Environment 68 Ž1998. 187–196
The effects of global temperature change on rice leaf blast epidemics: a simulation study in three agroecological zones Y. Luo a
a,),1
, P.S. Teng a , N.G. Fabellar a , D.O. TeBeest
b
DiÕision of Entomology and Plant Pathology, International Rice Research Institute, P.O. Box 933, Manila 1099, Philippines b Department of Plant Pathology, UniÕersity of Arkansas, 217 Plant Science, FayetteÕille, AR 72701, USA Accepted 2 June 1997
Abstract A combined simulation model ŽCERES-Rice coupled with BLASTSIM. was used to study the effects of global temperature change on rice leaf blast epidemics in several agroecological zones in Asia. At least five years of historical daily weather data were collected from each of 53 locations in five Asian countries ŽJapan, Korea, China, Thailand and Philippines.. Two weather generators, WGEN and WMAK, from the Decision Support System for Agrotechnology Transfer ŽDSSAT., were utilized to produce estimated daily weather data for each location. Thirty years of daily weather data produced by one of the generators for each location were used as input to the combined model to simulate blast epidemics for each temperature change. Maximum blast severity and the area under the disease progress curve ŽAUDPC. caused by leaf blast resulted from 30-yr simulations were statistically analyzed for each temperature change and for each location. Simulations suggest that temperature changes had significant effects on disease development at most locations. However, the effect varied in different agroecological zones. In the cool subtropics such as Japan and northern China, elevation of temperature above normal temperature resulted in more severe blast epidemics. In warmrcool humid subtropics, elevation of temperature caused significantly less blast epidemics. However, lower temperature caused insignificant difference in disease epidemics compared with that in normal temperature. Conditions in the humid tropics were opposite to those in cool areas, where daily temperature changes by y18C and y38C resulted in significantly more severe blast epidemics, and temperature changes by q18C and q38C caused less severe blast. Scenarios showing blast intensity as affected by temperature change in different agroecological zones were generated with a geographic information system ŽGIS.. q 1998 Elsevier Science B.V. Keywords: Rice blast; Simulations; Global climate change; GIS
1. Introduction Rice production has been affected by changes in global climate brought about by human activities in )
Corresponding author. Current address: Department of Plant, Soil and General Agriculture, Southern Illinois University, Carbondale, IL 62901-4415, USA. 1
the last few decades ŽSolomon and Leemans, 1990; Matthews et al., 1995.. Rice blast Žcaused by Pyricularia oryzae Cav.., an important disease of rice worldwide, is known to cause severe yield losses in rice production area where high inputs of nitrogen fertilizer and favourable climatic conditions occur. This pathogen can cause symptoms of brown chlorotic lesions on leaf, stem, collar, neck and panicle of rice at different growing stages. Severe
0167-8809r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved. PII S 0 1 6 7 - 8 8 0 9 Ž 9 7 . 0 0 0 8 2 - 0
188
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
blast can cause significant yield losses ŽPinnschmidt, 1989.. Leaf blast lesions reduces the net photosynthetic rate of individual leaves to an extent far beyond the visible diseased leaf fraction ŽBastiaan, 1991.. Yield loss caused by blast vary depending on cultural system, growing season and production level. For instance, in southern China, the yield loss caused by blast was estimated of about 39.4% of total loss from disease during early season and 19.8% during late season ŽShen and Lin, 1994.. Blast epidemics are commonly known to be affected by climate, varietal susceptibility and crop management practices such as nitrogen inputs and availability of water supply ŽSuzuki, 1975; Ou, 1985.. Air and dew temperature significantly affect blast infection processes including infection, latency, lesion growth and sporulation, and play important roles in blast epidemics ŽOu, 1985; Teng, 1994.. Estimation of effects of global temperature change on rice blast epidemics is therefore important to predict future disease development and global rice production. This information can help in the assessment of new strategies for blast management specifically for rice production ŽTeng and Savary, 1992.. A model is a useful tool to analyze and predict the intensity of disease development through simulations of crop growth and plant infection processes ŽTeng, 1985.. Weather is an important factor in causing variability in disease development. The effect can be studied by incorporating weather factors into a disease simulation model. The effects of global temperature change on blast epidemics could then be studied by comparing simulated disease intensity caused by global temperature change with that under normal temperature condition Ždesigned by historical probability distributions.. Monte Carlo simulation ŽHammersley and Handscombe, 1964. is a useful modelling technique in which the probability of an uncertain event can be obtained ŽTeng and Yuen, 1991.. In this study, weather factors were considered to cause uncertainty in blast epidemics. The distribution of rice blast epidemics produced by simulations contains the information about the risk of future blast epidemics caused by global temperature change. The objective of the study was to determine how global temperature change may affect rice leaf blast epidemics in different agroecological zones. Simula-
tions were conducted for 53 locations in different agroecological zones. Outputs were analyzed statistically for each agroecological zone.
2. Materials and methods
2.1. Model description The CERES-Rice model is a rice growth simulation model constructed by the International Benchmark Sites Network for Agrotechnology Transfer ŽIBSNAT, 1989.. The model can simulate rice growth based on physiological development of root, stem, leaf, and grain formation under weather conditions as model inputs. Biomass production is simulated with photosynthesis and subsequent distribution of assimilates into different plant parts. The effects of weather, nitrogen, water and crop management practices on rice growth are also considered in the model. Standardized input and output data formats for the model provides collaborators with the ability to use the model after fundamental site data are obtained. The model has been validated for tropical lowland and upland rice ŽAlocilja and Ritchie, 1988.. The BLASTSIM model simulates the leaf blast monocycle based on crop growth and weather conditions. The infection cycle of blast is described by subroutines including spore production, dispersion, deposition and infection, latent period, lesion formation, lesion development, and infectious process ŽCalvero and Teng, 1992.. The relationship between process rates of the components of the infection cycle and environmental factors was determined from experiments and incorporated into the model. The model was validated with experimental data from dryland and paddy, wetland conditions in the Philippines ŽCalvero and Teng, 1992.. The two models were combined, and the main routine of BLASTSIM was modified to become a subroutine of CERES-Rice ŽLuo et al., 1997.. The main consideration in coupling the models was that leaf photosynthesis was affected by blast severity ŽBastiaan, 1991.. A quantitative relationship between reduction of photosynthesis and blast severity was used ŽLuo et al., 1997..
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
2.2. Weather data generation The DSSAT system ŽDecision Support System for Agrotechnology Transfer; IBSNAT, 1989. is an integrated computer system that provides collaborators with access to crop models for model validation under local conditions. WMAK and WGEN are two weather generator programs in the DSSAT system. These generators need at least five years of daily weather data including solar radiation, maximum temperature, minimum temperature, and rainfall as inputs to generate weather data for many years. The generators produce the relevant coefficients for each
189
weather variable from the historical data based on the principle of time series, Markov chain, and gamma distribution. Historical daily weather data from 53 locations in five Asian rice-growing countries including Japan, Korea, China, the Philippines and Thailand were collected by the climatology unit of the International Rice Research Institute ŽIRRI. ŽFig. 1.. Date were collected from different ricegrowing ecological zones such as warm humid tropics, warm arid subtropics, warm subhumid subtropics, warmrcool humid subtropics and cool subtropics ŽFig. 1.. Estimation methods ŽLuo et al., 1997. were used to calculate weather variables that were
Fig. 1. Weather stations Žmarked with dark dots. in different agroecological zones in southern and southeastern Asian rice growing countries for which weather data are available at the International Rice Research Institute ŽIRRI.. Fifty-three locations from five countries among these locations were studied in the simulations.
190
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
not available from the above weather database such as relative humidity and dew temperature. The two weather generators and the estimation methods were applied to produce a complete set of estimated weather data required by the combined model including minimum and maximum daily temperature, solar radiation, humidity, wind speed, rainfall, dew period, cloudiness and soil temperature. Thirty-year estimated weather data for each location were generated based on the Monte Carlo method ŽHammersley and Handscombe, 1964.. Some methods were used to treat the missing data in the IRRI weather database. Locations with a larger amounts of missing data were omitted from the study. For locations with a few missing data, we interpolated or averaged the data using the time series method. For example, in Thailand, all 20 locations had no daily wind speed data. To estimate wind speed, we classified these locations into four different regions—northern, northeastern, central, and southern Thailand—according to their geographic locations in the country. Average monthly wind speed data from several years at each location were collected from the FAO data base ŽFood and Agriculture Organization, 1987.. The corresponding standard deviation in a certain region was calculated from averages of locations in the region, and was then used as the coefficient parameter in the weather generator to generate wind speeds. We assumed that the locations in this region had the same standard deviations in wind speed. 2.3. Simulation experimental design Simulation experiments were designed to provide information on how global temperature changes affect rice blast epidemics in different Asian ricegrowing ecological zones. Global temperature changes were considered by increasing or decreasing daily temperature on estimated weather data in simulations. Different temperature changes were used in different agroecological zones ŽFig. 1. because the same temperature change may have different effects on rice growth in different agroecological zones. For instance, we found that decreasing daily temperature by 28C may shorten the rice growth season in most locations of Japan and northern China, and application of current varieties and even cultural systems
would not be suitable for rice production. For the locations in Japan and Korea, the cool subtropics, treatments of daily temperature change were selected as four levels, 08C to q38C, which were added to the normal daily temperature. Since China covers a wide range of climate types including warmrcool humid subtropics and warm subhumid subtropics ŽFig. 1., the temperature change levels were selected as y28C, y18C, 08C, q18C and q28C added to normal daily temperature. For the locations in warm humid tropical countries such as Philippines and Thailand, the temperature changes were set at y38C, y18C, 08C, q18C and q38C. For each location, thirty years of estimated weather data were recalculated to fit the requirement of daily temperature change in simulations. Simulated outputs of blast epidemics included severity Žpercentage of lesion area covering on whole leaf area. and the area under the disease progress curve ŽAUDPC.. The AUDPC was calculated by accumulating daily disease index Žincidence = severity. for the whole growing season. It provided the information about the dynamics of disease development and could be used to assess the disease intensity and correlate the yield loss. The simulated disease severity and AUDPC were used to fit certain distributions from which the information about average and deviation of blast epidemics were obtained. 2.4. Initialization of simulations Initializations of simulation conditions for each location Žincluding sowing date, variety, fertilization and irrigation management, initial disease intensity, and relevant crop data needed to estimate dew period. were defined ŽTable 1.. Variety IR36 was assumed to grow in the tropical countries and variety LABELLE in warm and cool subtropics. These varieties were assumed to have a moderate resistance to leaf blast equivalent to that of the variety IR50. Three disease developmental stages Žinputs required by BLASTSIM. including the date of initial inoculation, the date of peak blast epidemic, and the date of end of blast development were defined as 21 days after planting, the date of heading, and the date of initial panicle filling, respectively. Water and nitrogen application were assumed to be nonlimiting in all simulations. Initial incidence at the beginning of
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
191
Table 1 Initial simulation conditions for each location Growing season and sowing date Defined for each location based on the data from World Rice Statistics, 1990 ŽIRRI, 1990. Initial condition of blast 1. Initial lesion number observed per hill Ž3–5 plants.: 30.0 2. Initial severity Ž%. observed per hill: 3.0 3. Crop age at which the above severity was observed Žday after sowing, DAS.: 20 days after emergence 4. Crop age when peak severity was observed ŽDAS.: the date of panicle initialization 5. Crop age when severity was zero ŽDAS.: the date of heading 6. Date of simulation termination ŽDAS.: the date of physical maturity Crop data file Crop heightrhill, leaf arearhill, leaf widthrleaf Varieties IR36 or LABELLE Žfor simulating rice growth. IR50 Žfor parameters of receptivity to blast isolate. Weather condition changes See text
epidemics for all simulations was defined as 3% per hill Ž3–5 plantsrhill. of rice crop. Each simulation run was for 120 days of the growing season. 2.5. Data analysis Simulations were conducted for each rice growing season in each location Žthere are more than one rice growing seasons in some locations in the warm subtropics and humid tropics.. Complete simulation experiments were executed on a VAX minicomputer. The mean and standard deviation of outputs from 30 years of simulations in terms of AUDPC and severity for each temperature treatment were calculated by analysis of variance ŽANOVA. using SAS ŽSAS
Fig. 2. Schematic diagram of the simulation process. The historical weather data were used to drive one of two weather generators to produce the weather coefficient file. The coefficients were then used by the weather generators to generate 30 years of synthetic weather data. The data were reformated to drive the combined model ŽLuo et al., 1997. for simulating rice growth and blast epidemics. The outputs from 30-yr simulations could fit a distribution graphically depicted as disease intensity Ž x axis. and its frequency Ž y axis..
Institute, Cary, NC.. Comparisons of AUDPC and severity among temperature treatments were conducted for each growing season in each location using the LSD approach of SAS. The simulation process is summarized in Fig. 2. Geographic information system ŽGIS. software was used to synthesize results for different ecological zones in terms of different countries. The graphics were generated by the GIS Laboratory, IRRI, using IDRISI, and converted to gray maps.
3. Results The analyses showed that changes in temperature had significant effects on blast epidemics at most locations. However, temperature effects varied in agroecological zones. Figs. 3–5 show examples of scenarios of blast epidemics in terms of simulated average AUDPC caused by global temperature change for different countries. In northern Japan and northern China, decreases in temperature by 18C or 28C caused extension of such a large rice growing period that rice would not be mature before winter. In these areas, a variety requiring a shorter growing period should be used under such temperature-change conditions. 3.1. Cool subtropics In cool subtropical zones, an increase in temperature at most locations, especially in Japan and northern China, caused significant increase in AUDPC or severity. For instance, in six of eight locations of
192
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
Fig. 3. A GIS output-scenario of simulated AUDPC Žarea under the disease progress curve. caused by rice leaf blast in five Asian countries under current temperature Žtemperature changes 0.0. condition.
Japan, increasing temperature by 18C and 28C caused more severe blast epidemics. The effect of enhanced temperature on blast epidemics seemed to be greater in northern than in southern Japan. Under normal conditions Žtemperature changes 0., the AUDPC was usually in a range of 50–150 in northern Japan compared with locations in southern Japan where the AUDPC was greater than 150. Although higher temperature could cause more severe blast epidemics in northern Japan, the risk of the epidemics in southern Japan is still higher than in the north. In northern China such as the Beijing area, blast epidemics with all temperature changes were not
severe. Temperature change had no effect on disease development. The situation in Korea was slightly different from those in Japan and northern China. In general, normal temperature seemed to be more favourable to blast epidemics compared with higher temperature. Elevation of temperature caused less severe blast epidemics in most locations. AUDPC was commonly in the range of 50–170 in all temperature changes. Maximum severities were from 5 to 15% in most locations. In 80% of locations, increasing temperature by 18C has no significant effect on decreasing AUDPC and severity based on LSD analysis from
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
193
Fig. 4. Scenario of simulated AUDPC of leaf blast epidemics in China caused by a temperature change of y28C, and in Thailand and Philippines caused by a temperature change of y38C.
SAS. However, further elevation in temperature caused significantly less severe blast epidemics. Blast severities were lower than those in locations of Japan of similar latitudes. 3.2. Subhumid and warm humid subtropics Most locations in China are located in these agroecological zones. Some locations in southern China have two rice growing seasons, and simulations were therefore conducted based on these conventional growing systems. In general, decreasing temperatures by 18C and 28C did not cause significantly different blast epidemics compared with normal temperature at most locations. For instance, the AUDPCs were in the range of 50–240 under normal temperatures, and 80–250 with temperature change of y28C at all locations. However, the lower temperature caused
significantly higher severity of blast in some locations such as Hangzhou and Chengdu regions. Increasing temperature by 28C caused significantly less intensity of blast at most locations except in the Fuzhou region. The AUDPCs were in the range of 16–160 under higher temperature conditions compared with 50–240 under normal temperature condition. Thus, elevation of temperature has significant effects on inhibiting blast epidemics in this agoecological zone. For the locations where there are two growing seasons Že.g., regions of Wuhan, Hangzhou, Changsha, Fuzhou and Guangzhou., blast was generally more severe in the early growing season than in the later season with normal temperature. However, the effect of temperature change on blast epidemics was greater in the later season than in the early season. For instance, decreasing temperature by 28C in Fuzhou region caused AUDPC changes from 116 to 114 in early season, and from 90 to 124 in later
194
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
Fig. 5. Scenario of simulated AUDPC of leaf blast in China caused by a temperature change of q28C, and in Thailand and Philippines caused by temperature change of q38C.
season. Similar conclusions could also be made in Guangzhou and Changsha regions. Blast epidemics in southern China Žareas around Fuzhou and Guangzhou. seemed to be more severe than in other regions. The areas were considered the higher risk regions of blast epidemics in China that could be identified from GIS maps ŽFigs. 3–5.. 3.3. Humid tropics The effect of temperature change on blast epidemics in the humid tropics and warm humid subtropics ŽPhilippines and Thailand. on rice leaf blast was opposite to that in the cool subtropics. At most locations, lower temperature led to more severe blast epidemics. Elevation of temperature could inhibit blast development in this ecological zone. All locations in Philippines selected for the study have three growing seasons. There were small differences in blast severity among growing seasons at any location. Each temperature change had significant effect on blast epidemics in most locations. An increase in
temperature led to a significant decrease in blast severity. Moreover, temperature change by y38C led to more severe blast epidemics and resulted in higher AUDPC compared with other temperature changes for most locations ŽFigs. 3–5.. Standard deviations of AUDPC and severity in the temperature change by y38C at most locations were higher than those in other temperature changes. The values of AUDPC were in the range of 200–500 in the temperature change by y38C, 100–300 in normal condition, and 60–230 in the temperature change by q38C ŽFigs. 3–5.. The values are much higher than those in Japan, Korea and China. The effect of temperature change on blast epidemics in Thailand was similar. The increase in temperature resulted in less blast severity. In most locations, decreasing temperature by 38C resulted in much higher AUDPC than other temperature changes did. In this situation, blast epidemics in northern Thailand Že.g., Chiang Rai, Chiang Mai and Loei. were more severe than those in other parts of the country ŽFigs. 3–5..
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
The decrease in temperature by 18C also resulted in significant increase in blast epidemics, compared with normal condition in most locations of eastern and northeastern Thailand. A decrease in temperature change by 18C resulted in less AUDPC than did a decrease in temperature by 38C. In eastern and northeastern Thailand, the increase in temperature did not lead to significant difference in blast severity compared with other temperature changes. Blast epidemics in northeastern Thailand were relatively stable as shown by a small standard deviation obtained from the study.
4. Discussion In this study, we found that compared with current temperature, a decrease in temperature by 18C or 38C caused significantly more severe blast epidemics, and an increase in temperature caused significantly less severe blast epidemics in the warm humid tropics. In the warmrcool subtropics, decreasing temperature did not cause significant difference in blast epidemics compared with normal temperature. However, elevation of temperature caused a significantly lower intensity of blast. However, increasing temperature caused significantly more severe blast epidemics in the cool subtropics. Therefore, temperature changes have different effects on blast epidemics in different agroecological zones. Several similar studies on the effects of climate change on rice production for different agroecological zones in Asia using modelling approaches have also been conducted ŽMatthews et al., 1995.. Rice yield affected by climate changes, especially by temperature and CO 2 changes for different Asian ricegrowing countries, was studied. However, the potential impact of diseases on rice production has not been considered. As an extensive work, this study focused on the effect of global temperature changes on rice blast epidemics. The information could be useful in decision-making for rice production and blast control for different agroecological zones. This study attempted to estimate how global temperature change may affect blast epidemics in different agroecological zones. The accuracy and usefulness of the predictions depend on Ž1. the validity of the simulation model, Ž2. reliability of the parame-
195
ters of rice growth and blast infection in the model and, Ž3. representativeness of selected locations and estimated weather data driving the model. Although the parameters of the model and model validation were obtained and performed from greenhouse and field experiments ŽBastiaan, 1991; Calvero and Teng, 1992., simulation results still need to be further evaluated by experiments, expert panels and analysis of historical events. The results could serve as a reference for predicting the risk of blast epidemics in future situations and for decision-making on blast management on a macroecological level. Although specific interpolation approaches had been used in the generation of GIS graphics, the number of locations that have available weather data may still be not enough to represent the overall situations in whole agroecological zones. We also still face the challenge of prediction accuracy by the GIS graphics. Some limitations in the use of the two weather generators to estimate weather data were encountered in this study. WMAK can be used to estimate solar radiation, temperature and rainfall for tropical areas, except for the cool subtropics like Japan, Korea, and northern China. Moreover, it can be used to simulate vapor pressure and wind speed. WGEN can be used to estimate radiation, temperature, and rainfall in any ecological zone but cannot be used to simulate vapor pressure and wind speed. The methodology for assessment of plant disease epidemics still requires improvement ŽTeng and Yang, 1993.. To compare blast epidemics at locations from different ecological zones, an index that integrates information about the average intensity and its deviation under a certain condition is desirable. For example, the index could be generated by combining a probability value of epidemics with weather influence such as quantitative effects of temperature on critical periods of rice growth and blast epidemics. Locations in a country can then be classified by means of risks from blast epidemics according to the index values. The index could be utilized likewise in decision-making on strategies of disease management. GIS graphics showing scenarios of blast epidemics for a country were produced based on simulated information for several locations of the country using spatial interpolation methods in GIS software.
196
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
The graphics could only be referenced for rice-growing areas, and the values shown in non-rice-growing areas have no meaning for rice blast epidemics. For instance, the information about possible AUDPC of rice blast epidemics shown in GIS graphics covering in non-rice-growing locations of China Žnortheast and southeast China. or northern Korea have no reference meaning regarding the possible blast epidemics in these areas. The study only provided the information for rice-growing areas in the five Asian countries.
Acknowledgements The authors appreciate the climatology unit of the International Rice Research Institute ŽIRRI. for providing us with the historical weather data. Although the research described in this article has been funded wholly or in part by the US Environmental Protection Agency under cooperative agreement number 817426 to the International Rice Research Institute, it has not been subject to the Agency’s review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.
References Alocilja, E.C., Ritchie J.T., 1988. Upland rice simulation and its use in multicriteria optimization. Univ. of Hawaii and Michigan State Univ. IBSNAT. 95 pp. Bastiaan, L., 1991. Ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology 81, 611–615. Calvero, S.B., Teng, P.S., 1992. Validation of BLASTSIM.2 model in IRRI blast ŽB1. nursery and Cavinti, Laguna, Philippines. Int. Rice Res. Newsl. 17 Ž5., 20–21.
Food and Agriculture Organization, United Nations, 1987. Agroclimatological Data Asia 2, Vol. 2 ŽK–Z., Rome. Hammersley, J.M., Handscombe, D.C., 1964. Monte Carlo methods. Methuen, London. International Benchmark Sites Network for Agrotechnology Transfer ŽIBSNAT., 1989. Decision support system for agrotechnology transfer DSSAT user’s guide. IBSNAT Project, Department of Agronomy and Soil Science, Univ. of Hawaii. International Rice Research Institute, 1990. World rice statistics, 1990. IRRI, p. 320. Luo, Y., Teng, P.S., Fabellar, N.G., TeBeest, D.O., 1997. A rice–leaf blast combined model for simulation of epidemics and yield loss. Agricultural Systems 53, 27–39. Matthews, R.B., Kropff, M.J., Bachelet, D., Van Laar, H.H., 1995. Modeling the impact of climate change on rice production in Asia. IRRI, CAB Int., 289 pp. Ou, S.H., 1985. Rice Diseases, 2nd edn. Commonwealth Mycological Inst., Kew, Surrey, England, 380 pp. Pinnschmidt, H.O., 1989. Freilanduntersuchungen zur Epidemiology of Pyricularia oryzae Cav. an Trockenreis ŽField studies on the epidemiology of P. oryzae Cav. on upland rice.. PhD Thesis, Univ. of Giessen, Germany, 268 pp. Shen, M.G., Lin, J.Y., 1994. The economic impact of rice blast disease in China. In: Zeigler, R.S., Leong, S.A., Teng, P.S. ŽEds.., Rice Blast Disease. IRRI, CAB Int., pp. 321–331 Ž626 pp.. Solomon, A.M., Leemans, R., 1990. Climatic change and landscape ecological response: issues and analysis. In: Boer, M.M., de Groot, R.S. ŽEds.., Landscape Ecological Impact of Climatic Change. IOS Press, Amsterdam, pp. 293–316. Suzuki, H., 1975. Meteorological factors in the epidemiology of rice blast. Annu. Rev. Phytopathol. 13, 239–256. Teng, P.S., 1985. A comparison of simulation approaches to epidemic modeling. Annu. Rev. Phytopathol. 23, 351–379. Teng, P.S., Yuen, J.E., 1991. Epidemic models: lesions from plant pathology. In: M.A. Levin, H.S. Strauss, Risk Assessment in Genetic Engineering. McGraw-Hill, NY, pp. 272–96. Teng, P.S., Savary, S., 1992. Implementing the systems approach in pest management. Agricultural Systems 40, 237–264. Teng, P.S., Yang, X.B., 1993. Biological impact and risk assessment in plant pathology. Annu. Rev. Phytopathol. 31, 495–521. Teng, P.S., 1994. The epidemiological basis for blast management. In: Zeigler, R.S., Leong S.A., Teng, P.S. ŽEds.., Rice Blast Disease. IRRI, CAB Int., pp. 409–433 Ž626 pp..
The effects of global temperature change on rice leaf blast epidemics: a simulation study in three agroecological zones Y. Luo a
a,),1
, P.S. Teng a , N.G. Fabellar a , D.O. TeBeest
b
DiÕision of Entomology and Plant Pathology, International Rice Research Institute, P.O. Box 933, Manila 1099, Philippines b Department of Plant Pathology, UniÕersity of Arkansas, 217 Plant Science, FayetteÕille, AR 72701, USA Accepted 2 June 1997
Abstract A combined simulation model ŽCERES-Rice coupled with BLASTSIM. was used to study the effects of global temperature change on rice leaf blast epidemics in several agroecological zones in Asia. At least five years of historical daily weather data were collected from each of 53 locations in five Asian countries ŽJapan, Korea, China, Thailand and Philippines.. Two weather generators, WGEN and WMAK, from the Decision Support System for Agrotechnology Transfer ŽDSSAT., were utilized to produce estimated daily weather data for each location. Thirty years of daily weather data produced by one of the generators for each location were used as input to the combined model to simulate blast epidemics for each temperature change. Maximum blast severity and the area under the disease progress curve ŽAUDPC. caused by leaf blast resulted from 30-yr simulations were statistically analyzed for each temperature change and for each location. Simulations suggest that temperature changes had significant effects on disease development at most locations. However, the effect varied in different agroecological zones. In the cool subtropics such as Japan and northern China, elevation of temperature above normal temperature resulted in more severe blast epidemics. In warmrcool humid subtropics, elevation of temperature caused significantly less blast epidemics. However, lower temperature caused insignificant difference in disease epidemics compared with that in normal temperature. Conditions in the humid tropics were opposite to those in cool areas, where daily temperature changes by y18C and y38C resulted in significantly more severe blast epidemics, and temperature changes by q18C and q38C caused less severe blast. Scenarios showing blast intensity as affected by temperature change in different agroecological zones were generated with a geographic information system ŽGIS.. q 1998 Elsevier Science B.V. Keywords: Rice blast; Simulations; Global climate change; GIS
1. Introduction Rice production has been affected by changes in global climate brought about by human activities in )
Corresponding author. Current address: Department of Plant, Soil and General Agriculture, Southern Illinois University, Carbondale, IL 62901-4415, USA. 1
the last few decades ŽSolomon and Leemans, 1990; Matthews et al., 1995.. Rice blast Žcaused by Pyricularia oryzae Cav.., an important disease of rice worldwide, is known to cause severe yield losses in rice production area where high inputs of nitrogen fertilizer and favourable climatic conditions occur. This pathogen can cause symptoms of brown chlorotic lesions on leaf, stem, collar, neck and panicle of rice at different growing stages. Severe
0167-8809r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved. PII S 0 1 6 7 - 8 8 0 9 Ž 9 7 . 0 0 0 8 2 - 0
188
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
blast can cause significant yield losses ŽPinnschmidt, 1989.. Leaf blast lesions reduces the net photosynthetic rate of individual leaves to an extent far beyond the visible diseased leaf fraction ŽBastiaan, 1991.. Yield loss caused by blast vary depending on cultural system, growing season and production level. For instance, in southern China, the yield loss caused by blast was estimated of about 39.4% of total loss from disease during early season and 19.8% during late season ŽShen and Lin, 1994.. Blast epidemics are commonly known to be affected by climate, varietal susceptibility and crop management practices such as nitrogen inputs and availability of water supply ŽSuzuki, 1975; Ou, 1985.. Air and dew temperature significantly affect blast infection processes including infection, latency, lesion growth and sporulation, and play important roles in blast epidemics ŽOu, 1985; Teng, 1994.. Estimation of effects of global temperature change on rice blast epidemics is therefore important to predict future disease development and global rice production. This information can help in the assessment of new strategies for blast management specifically for rice production ŽTeng and Savary, 1992.. A model is a useful tool to analyze and predict the intensity of disease development through simulations of crop growth and plant infection processes ŽTeng, 1985.. Weather is an important factor in causing variability in disease development. The effect can be studied by incorporating weather factors into a disease simulation model. The effects of global temperature change on blast epidemics could then be studied by comparing simulated disease intensity caused by global temperature change with that under normal temperature condition Ždesigned by historical probability distributions.. Monte Carlo simulation ŽHammersley and Handscombe, 1964. is a useful modelling technique in which the probability of an uncertain event can be obtained ŽTeng and Yuen, 1991.. In this study, weather factors were considered to cause uncertainty in blast epidemics. The distribution of rice blast epidemics produced by simulations contains the information about the risk of future blast epidemics caused by global temperature change. The objective of the study was to determine how global temperature change may affect rice leaf blast epidemics in different agroecological zones. Simula-
tions were conducted for 53 locations in different agroecological zones. Outputs were analyzed statistically for each agroecological zone.
2. Materials and methods
2.1. Model description The CERES-Rice model is a rice growth simulation model constructed by the International Benchmark Sites Network for Agrotechnology Transfer ŽIBSNAT, 1989.. The model can simulate rice growth based on physiological development of root, stem, leaf, and grain formation under weather conditions as model inputs. Biomass production is simulated with photosynthesis and subsequent distribution of assimilates into different plant parts. The effects of weather, nitrogen, water and crop management practices on rice growth are also considered in the model. Standardized input and output data formats for the model provides collaborators with the ability to use the model after fundamental site data are obtained. The model has been validated for tropical lowland and upland rice ŽAlocilja and Ritchie, 1988.. The BLASTSIM model simulates the leaf blast monocycle based on crop growth and weather conditions. The infection cycle of blast is described by subroutines including spore production, dispersion, deposition and infection, latent period, lesion formation, lesion development, and infectious process ŽCalvero and Teng, 1992.. The relationship between process rates of the components of the infection cycle and environmental factors was determined from experiments and incorporated into the model. The model was validated with experimental data from dryland and paddy, wetland conditions in the Philippines ŽCalvero and Teng, 1992.. The two models were combined, and the main routine of BLASTSIM was modified to become a subroutine of CERES-Rice ŽLuo et al., 1997.. The main consideration in coupling the models was that leaf photosynthesis was affected by blast severity ŽBastiaan, 1991.. A quantitative relationship between reduction of photosynthesis and blast severity was used ŽLuo et al., 1997..
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
2.2. Weather data generation The DSSAT system ŽDecision Support System for Agrotechnology Transfer; IBSNAT, 1989. is an integrated computer system that provides collaborators with access to crop models for model validation under local conditions. WMAK and WGEN are two weather generator programs in the DSSAT system. These generators need at least five years of daily weather data including solar radiation, maximum temperature, minimum temperature, and rainfall as inputs to generate weather data for many years. The generators produce the relevant coefficients for each
189
weather variable from the historical data based on the principle of time series, Markov chain, and gamma distribution. Historical daily weather data from 53 locations in five Asian rice-growing countries including Japan, Korea, China, the Philippines and Thailand were collected by the climatology unit of the International Rice Research Institute ŽIRRI. ŽFig. 1.. Date were collected from different ricegrowing ecological zones such as warm humid tropics, warm arid subtropics, warm subhumid subtropics, warmrcool humid subtropics and cool subtropics ŽFig. 1.. Estimation methods ŽLuo et al., 1997. were used to calculate weather variables that were
Fig. 1. Weather stations Žmarked with dark dots. in different agroecological zones in southern and southeastern Asian rice growing countries for which weather data are available at the International Rice Research Institute ŽIRRI.. Fifty-three locations from five countries among these locations were studied in the simulations.
190
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
not available from the above weather database such as relative humidity and dew temperature. The two weather generators and the estimation methods were applied to produce a complete set of estimated weather data required by the combined model including minimum and maximum daily temperature, solar radiation, humidity, wind speed, rainfall, dew period, cloudiness and soil temperature. Thirty-year estimated weather data for each location were generated based on the Monte Carlo method ŽHammersley and Handscombe, 1964.. Some methods were used to treat the missing data in the IRRI weather database. Locations with a larger amounts of missing data were omitted from the study. For locations with a few missing data, we interpolated or averaged the data using the time series method. For example, in Thailand, all 20 locations had no daily wind speed data. To estimate wind speed, we classified these locations into four different regions—northern, northeastern, central, and southern Thailand—according to their geographic locations in the country. Average monthly wind speed data from several years at each location were collected from the FAO data base ŽFood and Agriculture Organization, 1987.. The corresponding standard deviation in a certain region was calculated from averages of locations in the region, and was then used as the coefficient parameter in the weather generator to generate wind speeds. We assumed that the locations in this region had the same standard deviations in wind speed. 2.3. Simulation experimental design Simulation experiments were designed to provide information on how global temperature changes affect rice blast epidemics in different Asian ricegrowing ecological zones. Global temperature changes were considered by increasing or decreasing daily temperature on estimated weather data in simulations. Different temperature changes were used in different agroecological zones ŽFig. 1. because the same temperature change may have different effects on rice growth in different agroecological zones. For instance, we found that decreasing daily temperature by 28C may shorten the rice growth season in most locations of Japan and northern China, and application of current varieties and even cultural systems
would not be suitable for rice production. For the locations in Japan and Korea, the cool subtropics, treatments of daily temperature change were selected as four levels, 08C to q38C, which were added to the normal daily temperature. Since China covers a wide range of climate types including warmrcool humid subtropics and warm subhumid subtropics ŽFig. 1., the temperature change levels were selected as y28C, y18C, 08C, q18C and q28C added to normal daily temperature. For the locations in warm humid tropical countries such as Philippines and Thailand, the temperature changes were set at y38C, y18C, 08C, q18C and q38C. For each location, thirty years of estimated weather data were recalculated to fit the requirement of daily temperature change in simulations. Simulated outputs of blast epidemics included severity Žpercentage of lesion area covering on whole leaf area. and the area under the disease progress curve ŽAUDPC.. The AUDPC was calculated by accumulating daily disease index Žincidence = severity. for the whole growing season. It provided the information about the dynamics of disease development and could be used to assess the disease intensity and correlate the yield loss. The simulated disease severity and AUDPC were used to fit certain distributions from which the information about average and deviation of blast epidemics were obtained. 2.4. Initialization of simulations Initializations of simulation conditions for each location Žincluding sowing date, variety, fertilization and irrigation management, initial disease intensity, and relevant crop data needed to estimate dew period. were defined ŽTable 1.. Variety IR36 was assumed to grow in the tropical countries and variety LABELLE in warm and cool subtropics. These varieties were assumed to have a moderate resistance to leaf blast equivalent to that of the variety IR50. Three disease developmental stages Žinputs required by BLASTSIM. including the date of initial inoculation, the date of peak blast epidemic, and the date of end of blast development were defined as 21 days after planting, the date of heading, and the date of initial panicle filling, respectively. Water and nitrogen application were assumed to be nonlimiting in all simulations. Initial incidence at the beginning of
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
191
Table 1 Initial simulation conditions for each location Growing season and sowing date Defined for each location based on the data from World Rice Statistics, 1990 ŽIRRI, 1990. Initial condition of blast 1. Initial lesion number observed per hill Ž3–5 plants.: 30.0 2. Initial severity Ž%. observed per hill: 3.0 3. Crop age at which the above severity was observed Žday after sowing, DAS.: 20 days after emergence 4. Crop age when peak severity was observed ŽDAS.: the date of panicle initialization 5. Crop age when severity was zero ŽDAS.: the date of heading 6. Date of simulation termination ŽDAS.: the date of physical maturity Crop data file Crop heightrhill, leaf arearhill, leaf widthrleaf Varieties IR36 or LABELLE Žfor simulating rice growth. IR50 Žfor parameters of receptivity to blast isolate. Weather condition changes See text
epidemics for all simulations was defined as 3% per hill Ž3–5 plantsrhill. of rice crop. Each simulation run was for 120 days of the growing season. 2.5. Data analysis Simulations were conducted for each rice growing season in each location Žthere are more than one rice growing seasons in some locations in the warm subtropics and humid tropics.. Complete simulation experiments were executed on a VAX minicomputer. The mean and standard deviation of outputs from 30 years of simulations in terms of AUDPC and severity for each temperature treatment were calculated by analysis of variance ŽANOVA. using SAS ŽSAS
Fig. 2. Schematic diagram of the simulation process. The historical weather data were used to drive one of two weather generators to produce the weather coefficient file. The coefficients were then used by the weather generators to generate 30 years of synthetic weather data. The data were reformated to drive the combined model ŽLuo et al., 1997. for simulating rice growth and blast epidemics. The outputs from 30-yr simulations could fit a distribution graphically depicted as disease intensity Ž x axis. and its frequency Ž y axis..
Institute, Cary, NC.. Comparisons of AUDPC and severity among temperature treatments were conducted for each growing season in each location using the LSD approach of SAS. The simulation process is summarized in Fig. 2. Geographic information system ŽGIS. software was used to synthesize results for different ecological zones in terms of different countries. The graphics were generated by the GIS Laboratory, IRRI, using IDRISI, and converted to gray maps.
3. Results The analyses showed that changes in temperature had significant effects on blast epidemics at most locations. However, temperature effects varied in agroecological zones. Figs. 3–5 show examples of scenarios of blast epidemics in terms of simulated average AUDPC caused by global temperature change for different countries. In northern Japan and northern China, decreases in temperature by 18C or 28C caused extension of such a large rice growing period that rice would not be mature before winter. In these areas, a variety requiring a shorter growing period should be used under such temperature-change conditions. 3.1. Cool subtropics In cool subtropical zones, an increase in temperature at most locations, especially in Japan and northern China, caused significant increase in AUDPC or severity. For instance, in six of eight locations of
192
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
Fig. 3. A GIS output-scenario of simulated AUDPC Žarea under the disease progress curve. caused by rice leaf blast in five Asian countries under current temperature Žtemperature changes 0.0. condition.
Japan, increasing temperature by 18C and 28C caused more severe blast epidemics. The effect of enhanced temperature on blast epidemics seemed to be greater in northern than in southern Japan. Under normal conditions Žtemperature changes 0., the AUDPC was usually in a range of 50–150 in northern Japan compared with locations in southern Japan where the AUDPC was greater than 150. Although higher temperature could cause more severe blast epidemics in northern Japan, the risk of the epidemics in southern Japan is still higher than in the north. In northern China such as the Beijing area, blast epidemics with all temperature changes were not
severe. Temperature change had no effect on disease development. The situation in Korea was slightly different from those in Japan and northern China. In general, normal temperature seemed to be more favourable to blast epidemics compared with higher temperature. Elevation of temperature caused less severe blast epidemics in most locations. AUDPC was commonly in the range of 50–170 in all temperature changes. Maximum severities were from 5 to 15% in most locations. In 80% of locations, increasing temperature by 18C has no significant effect on decreasing AUDPC and severity based on LSD analysis from
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
193
Fig. 4. Scenario of simulated AUDPC of leaf blast epidemics in China caused by a temperature change of y28C, and in Thailand and Philippines caused by a temperature change of y38C.
SAS. However, further elevation in temperature caused significantly less severe blast epidemics. Blast severities were lower than those in locations of Japan of similar latitudes. 3.2. Subhumid and warm humid subtropics Most locations in China are located in these agroecological zones. Some locations in southern China have two rice growing seasons, and simulations were therefore conducted based on these conventional growing systems. In general, decreasing temperatures by 18C and 28C did not cause significantly different blast epidemics compared with normal temperature at most locations. For instance, the AUDPCs were in the range of 50–240 under normal temperatures, and 80–250 with temperature change of y28C at all locations. However, the lower temperature caused
significantly higher severity of blast in some locations such as Hangzhou and Chengdu regions. Increasing temperature by 28C caused significantly less intensity of blast at most locations except in the Fuzhou region. The AUDPCs were in the range of 16–160 under higher temperature conditions compared with 50–240 under normal temperature condition. Thus, elevation of temperature has significant effects on inhibiting blast epidemics in this agoecological zone. For the locations where there are two growing seasons Že.g., regions of Wuhan, Hangzhou, Changsha, Fuzhou and Guangzhou., blast was generally more severe in the early growing season than in the later season with normal temperature. However, the effect of temperature change on blast epidemics was greater in the later season than in the early season. For instance, decreasing temperature by 28C in Fuzhou region caused AUDPC changes from 116 to 114 in early season, and from 90 to 124 in later
194
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
Fig. 5. Scenario of simulated AUDPC of leaf blast in China caused by a temperature change of q28C, and in Thailand and Philippines caused by temperature change of q38C.
season. Similar conclusions could also be made in Guangzhou and Changsha regions. Blast epidemics in southern China Žareas around Fuzhou and Guangzhou. seemed to be more severe than in other regions. The areas were considered the higher risk regions of blast epidemics in China that could be identified from GIS maps ŽFigs. 3–5.. 3.3. Humid tropics The effect of temperature change on blast epidemics in the humid tropics and warm humid subtropics ŽPhilippines and Thailand. on rice leaf blast was opposite to that in the cool subtropics. At most locations, lower temperature led to more severe blast epidemics. Elevation of temperature could inhibit blast development in this ecological zone. All locations in Philippines selected for the study have three growing seasons. There were small differences in blast severity among growing seasons at any location. Each temperature change had significant effect on blast epidemics in most locations. An increase in
temperature led to a significant decrease in blast severity. Moreover, temperature change by y38C led to more severe blast epidemics and resulted in higher AUDPC compared with other temperature changes for most locations ŽFigs. 3–5.. Standard deviations of AUDPC and severity in the temperature change by y38C at most locations were higher than those in other temperature changes. The values of AUDPC were in the range of 200–500 in the temperature change by y38C, 100–300 in normal condition, and 60–230 in the temperature change by q38C ŽFigs. 3–5.. The values are much higher than those in Japan, Korea and China. The effect of temperature change on blast epidemics in Thailand was similar. The increase in temperature resulted in less blast severity. In most locations, decreasing temperature by 38C resulted in much higher AUDPC than other temperature changes did. In this situation, blast epidemics in northern Thailand Že.g., Chiang Rai, Chiang Mai and Loei. were more severe than those in other parts of the country ŽFigs. 3–5..
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
The decrease in temperature by 18C also resulted in significant increase in blast epidemics, compared with normal condition in most locations of eastern and northeastern Thailand. A decrease in temperature change by 18C resulted in less AUDPC than did a decrease in temperature by 38C. In eastern and northeastern Thailand, the increase in temperature did not lead to significant difference in blast severity compared with other temperature changes. Blast epidemics in northeastern Thailand were relatively stable as shown by a small standard deviation obtained from the study.
4. Discussion In this study, we found that compared with current temperature, a decrease in temperature by 18C or 38C caused significantly more severe blast epidemics, and an increase in temperature caused significantly less severe blast epidemics in the warm humid tropics. In the warmrcool subtropics, decreasing temperature did not cause significant difference in blast epidemics compared with normal temperature. However, elevation of temperature caused a significantly lower intensity of blast. However, increasing temperature caused significantly more severe blast epidemics in the cool subtropics. Therefore, temperature changes have different effects on blast epidemics in different agroecological zones. Several similar studies on the effects of climate change on rice production for different agroecological zones in Asia using modelling approaches have also been conducted ŽMatthews et al., 1995.. Rice yield affected by climate changes, especially by temperature and CO 2 changes for different Asian ricegrowing countries, was studied. However, the potential impact of diseases on rice production has not been considered. As an extensive work, this study focused on the effect of global temperature changes on rice blast epidemics. The information could be useful in decision-making for rice production and blast control for different agroecological zones. This study attempted to estimate how global temperature change may affect blast epidemics in different agroecological zones. The accuracy and usefulness of the predictions depend on Ž1. the validity of the simulation model, Ž2. reliability of the parame-
195
ters of rice growth and blast infection in the model and, Ž3. representativeness of selected locations and estimated weather data driving the model. Although the parameters of the model and model validation were obtained and performed from greenhouse and field experiments ŽBastiaan, 1991; Calvero and Teng, 1992., simulation results still need to be further evaluated by experiments, expert panels and analysis of historical events. The results could serve as a reference for predicting the risk of blast epidemics in future situations and for decision-making on blast management on a macroecological level. Although specific interpolation approaches had been used in the generation of GIS graphics, the number of locations that have available weather data may still be not enough to represent the overall situations in whole agroecological zones. We also still face the challenge of prediction accuracy by the GIS graphics. Some limitations in the use of the two weather generators to estimate weather data were encountered in this study. WMAK can be used to estimate solar radiation, temperature and rainfall for tropical areas, except for the cool subtropics like Japan, Korea, and northern China. Moreover, it can be used to simulate vapor pressure and wind speed. WGEN can be used to estimate radiation, temperature, and rainfall in any ecological zone but cannot be used to simulate vapor pressure and wind speed. The methodology for assessment of plant disease epidemics still requires improvement ŽTeng and Yang, 1993.. To compare blast epidemics at locations from different ecological zones, an index that integrates information about the average intensity and its deviation under a certain condition is desirable. For example, the index could be generated by combining a probability value of epidemics with weather influence such as quantitative effects of temperature on critical periods of rice growth and blast epidemics. Locations in a country can then be classified by means of risks from blast epidemics according to the index values. The index could be utilized likewise in decision-making on strategies of disease management. GIS graphics showing scenarios of blast epidemics for a country were produced based on simulated information for several locations of the country using spatial interpolation methods in GIS software.
196
Y. Luo et al.r Agriculture, Ecosystems and EnÕironment 68 (1998) 187–196
The graphics could only be referenced for rice-growing areas, and the values shown in non-rice-growing areas have no meaning for rice blast epidemics. For instance, the information about possible AUDPC of rice blast epidemics shown in GIS graphics covering in non-rice-growing locations of China Žnortheast and southeast China. or northern Korea have no reference meaning regarding the possible blast epidemics in these areas. The study only provided the information for rice-growing areas in the five Asian countries.
Acknowledgements The authors appreciate the climatology unit of the International Rice Research Institute ŽIRRI. for providing us with the historical weather data. Although the research described in this article has been funded wholly or in part by the US Environmental Protection Agency under cooperative agreement number 817426 to the International Rice Research Institute, it has not been subject to the Agency’s review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.
References Alocilja, E.C., Ritchie J.T., 1988. Upland rice simulation and its use in multicriteria optimization. Univ. of Hawaii and Michigan State Univ. IBSNAT. 95 pp. Bastiaan, L., 1991. Ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology 81, 611–615. Calvero, S.B., Teng, P.S., 1992. Validation of BLASTSIM.2 model in IRRI blast ŽB1. nursery and Cavinti, Laguna, Philippines. Int. Rice Res. Newsl. 17 Ž5., 20–21.
Food and Agriculture Organization, United Nations, 1987. Agroclimatological Data Asia 2, Vol. 2 ŽK–Z., Rome. Hammersley, J.M., Handscombe, D.C., 1964. Monte Carlo methods. Methuen, London. International Benchmark Sites Network for Agrotechnology Transfer ŽIBSNAT., 1989. Decision support system for agrotechnology transfer DSSAT user’s guide. IBSNAT Project, Department of Agronomy and Soil Science, Univ. of Hawaii. International Rice Research Institute, 1990. World rice statistics, 1990. IRRI, p. 320. Luo, Y., Teng, P.S., Fabellar, N.G., TeBeest, D.O., 1997. A rice–leaf blast combined model for simulation of epidemics and yield loss. Agricultural Systems 53, 27–39. Matthews, R.B., Kropff, M.J., Bachelet, D., Van Laar, H.H., 1995. Modeling the impact of climate change on rice production in Asia. IRRI, CAB Int., 289 pp. Ou, S.H., 1985. Rice Diseases, 2nd edn. Commonwealth Mycological Inst., Kew, Surrey, England, 380 pp. Pinnschmidt, H.O., 1989. Freilanduntersuchungen zur Epidemiology of Pyricularia oryzae Cav. an Trockenreis ŽField studies on the epidemiology of P. oryzae Cav. on upland rice.. PhD Thesis, Univ. of Giessen, Germany, 268 pp. Shen, M.G., Lin, J.Y., 1994. The economic impact of rice blast disease in China. In: Zeigler, R.S., Leong, S.A., Teng, P.S. ŽEds.., Rice Blast Disease. IRRI, CAB Int., pp. 321–331 Ž626 pp.. Solomon, A.M., Leemans, R., 1990. Climatic change and landscape ecological response: issues and analysis. In: Boer, M.M., de Groot, R.S. ŽEds.., Landscape Ecological Impact of Climatic Change. IOS Press, Amsterdam, pp. 293–316. Suzuki, H., 1975. Meteorological factors in the epidemiology of rice blast. Annu. Rev. Phytopathol. 13, 239–256. Teng, P.S., 1985. A comparison of simulation approaches to epidemic modeling. Annu. Rev. Phytopathol. 23, 351–379. Teng, P.S., Yuen, J.E., 1991. Epidemic models: lesions from plant pathology. In: M.A. Levin, H.S. Strauss, Risk Assessment in Genetic Engineering. McGraw-Hill, NY, pp. 272–96. Teng, P.S., Savary, S., 1992. Implementing the systems approach in pest management. Agricultural Systems 40, 237–264. Teng, P.S., Yang, X.B., 1993. Biological impact and risk assessment in plant pathology. Annu. Rev. Phytopathol. 31, 495–521. Teng, P.S., 1994. The epidemiological basis for blast management. In: Zeigler, R.S., Leong S.A., Teng, P.S. ŽEds.., Rice Blast Disease. IRRI, CAB Int., pp. 409–433 Ž626 pp..