Forewarning Rice Blast in India

More documents

Recommendations

Info

Forewarning Rice Blast in India management programme in the northern region of Kerala. Kapoor et al (2000) developed rice leaf blast rules for Kangra district of Himachal Pradesh based on weather parameters. Models, validation and operational use for rice blast Several computer simulation models have been developed (Ota, 1982; Hashimoto et al., 1984; Takai et al., 1985; Gunther, 1986; Ishiguro, 1986). Surin et al (1991) stated that leaf blast incidence can be estimated by disease severity or by incidence on the top four leaves. However, disease severity on leaf 3 was found to be most representative of average severity at all leaf positions and therefore disease severity on a single leaf rather than on the whole plant is easier to do and saves a lot of time. Hence these models cannot sufficiently quantify the dispersion and deposition of the blast fungus spores in the rice canopy; quantitative data on the distribution of susceptible / non-susceptible tissue and inoculum are still lacking (Gunther, 1986). The first leaf blast model, BLASTL, a systemic analytic model that simulates the epidemic was developed in Japan (Hashimoto et al., 1982, 1984). This model has been verified under field experiments for disease progress and used to predict exact time for the fungicide application. A polycyclic model PYRICULARIA, was developed by Gunther (1986) using information from the literature. PYRICULARIA was modified by Tastra et al (1987) for upland rice farming in Indonesia and was subsequently called PYRNEW. The BLASTAM system (Koshimizu, 1983, 1988; Hayashi and Koshimizu, 1988) predicted weather conditions favourable for infection using weather data of the ‘automated meteorological data acquisition system’ (A Me DAS) via telephone modem in real time. BLASTAM can be used to predict the onset of leaf blast epidemic and the time for the first fungicidal application. Later, LEAFBLAST was developed (Choi et al., 1988) using data from the growth chamber experiments and the literature. This model consists of modules that compute spore germination, infection, latent period, lesion growth and spore production, dispersal and deposition, as affected by weather factors. Another model YYJM, was prepared to simulate a leaf blast (Magnoporthe grisea) epidemic under the conditions of Jilin Province in China, in order to forecast out breaks and requirements for chemical control (Anonymous, 1990). Ishiguro and Hashimoto (1991) developed computer based forecasting model of rice blast epidemics in Japan. The disease severity of leaf blast on photosynthesis and crop growth of rice crop was modeled by Basitaans (1991). Teng et al (1991) analysed blast pathosystem viz. BLASTL, BLASTCAST, simulation model PYRICULARIA and panicle blast pathosystem simulation model PBLAST to guide modeling and forecasting rice blast. Later, Kim and Kim (1993) developed a dynamic simulation model EPIBLAST for quantitative forecasting of the incidence of the leaf blast disease. Two other simulation models, CERES-RICE (a rice growth simulation model) and BLASTSIM (a rice leaf blast epidemic simulation model), were coupled by considering the effects of leaf blast on rice leaf photosynthesis and biomass production 12
Forewarning Rice Blast in India (Luo and Teng, 1994 and Luo et al., 1997). Regression equations used as empirical models to predict rice blast caused by Pyricularia grisea on cv. Jinheung at Icheon, South Korea, and on cvs. IR50 and C22 at Cavinti, Philippines, were generated, using weather factors identified by the WINDOW PANE program which showed that several weather factors were highly correlated with the disease variables (Calvero et al., 1996, 1997). Huang et al (1999) studied a decision model for the integrated control of rice blast based on the data of its occurrence, climatic factors, yield loss, and the economic benefit of fungicide in China. The first developed model in Japan for panicle blast was from Takahashi (1958). He treated a spore deposition and penetration as a stochastic process and subdivided each panicle into small infection site units. This model does not account for secondary infections. Hori (1963) and Kim (1982) attempted the forecasting of neck or panicle blast based on the number of diseased leaves per hill. The most comprehensive stochastic simulation model-panicle blast (PBLAST) was developed and verified by Ishiguro (1986), and Ishiguro and Hashimoto (1988, 1990). A Me DAS weather data, data on host development, time of cultivation practices, and number of spores formed on leaf lesion were used as input for PBLAST. It provides useful information for understanding patho-systems of panicle blast and improving the method of fungicidal application. A simple method was devised for forecasting the epidemic outbreak of blast in plains. With the help of ‘trap’ plots of susceptible varieties, successful warnings were given at least 10 to 15 days in advance of the outbreak in farmers’ fields (Muralidharan and Venkatrao, 1980). Manibhushan Rao and Krishnan (1991) developed a simulation model with computer based forewarning system (EPIBLA) to simulate the incidence and progress of rice leaf blast in the fields. EPIBLA (EPIdemiology of BLAst) is designed to forecast leaf blast progress over a period of seven days in advance. 13
Page 1 and 2: Forewarning Rice Blast in India C.
Page 3 and 4: PREFACE Rice, the staple food crop
Page 5 and 6: CONTENTS CONTENTS CONTENTS I. Intro
Page 7 and 8: I. I. INTRODUCTION INTRODUCTION For
Page 9 and 10: the infected node die. If infection
Page 11 and 12: III. BLAST HOT SPOT AREAS AND EXTEN
Page 13 and 14: Table 2. Blast locations, ecosystem
Page 15 and 16: IV. WORK CARRIED OUT ALREADY ON BLA
Page 17: Forewarning Rice Blast in India Lea
Page 21 and 22: 1. HISTORICAL DATA ON BLAST AND WEA
Page 23 and 24: 2. MODELS DEVELOPED BY DRR, HYDERAB
Page 25 and 26: Table 6.Average daily weather condi
Page 27 and 28: Farmers’ fields, Medchal Progress
Page 29 and 30: Table 9. Average daily weather cond
Page 31 and 32: CSK HPKV, Palampur, Malan and farme
Page 33 and 34: Forewarning Rice Blast in India In
Page 35 and 36: 3. VALIDATION OF MODELS DEVELOPED A
Page 37 and 38: Forewarning Rice Blast in India Thi
Page 39 and 40: Table 16. Fungicidal spray schedule
Page 41 and 42: VI. STRATEGIES FOR THE CONTROL OF B
Page 43 and 44: VII. FUTURE WORK Historical hypothe
Page 45 and 46: EXECUTIVE SUMMARY Forewarning Rice
Page 47 and 48: Forewarning Rice Blast in India The
Page 49 and 50: Forewarning Rice Blast in India Gun
Page 51 and 52: Lee, J.T., Yun, S.H., Kim, C.K., Im

Forewarning Rice Blast in India

Create successful ePaper yourself

Delete template?

Save as template?