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Modeling of Bacterial Leaf Blight Disease Assessment and Yield Losses of Rice

MONITORING, MAPPING, AND SPATIAL – MODELING OF BACTERIAL LEAF BLIGHT DISEASE ASSESSMENT AND YIELD LOSSES OF RICE

TABLE OF CONTENTS
  • INTRODUCTION
  • OBJECTIVES
  • REVIEW OF LITERATURE
  • SIGNIFICANCE OF STUDY
  • MATERIALS AND METHODS
  • PLAN OF WORK
  • LITERATURE SITED

  

ABSTRACT

Rice is the seed of the grass species Oryza sativa. It is the most widely consumed staple food for a large part of the world’s human population, especially in Asia. It is the agricultural commodity with the third-highest worldwide production, after sugarcane and maize. Approximately 90% of the world’s rice is grown in Asia continent and constitutes a staple food for 2.7 billion people worldwide. Rice is attacked by many diseases including Bacterial leaf Blight (BLB) caused by Xanthomonasoryzaepv. oryzae. Bacterial leaf blight of rice is one of the most caustic diseases of rice throughout the world. It was first noticed by the farmers in Fukuoka prefecture Kyushu Island, Japan, as early as in 1884-85. Bacterial Leaf Blight is a vascular disease. In Pakistan it can reduce the yield up to 20 to 30% so to come out from this problem many plant protection measurements used among which use of GPS and GIS for mapping out diseased portions in rice growing area is new sort of research in agriculture.The objective of this study will quantify the yield loss due to bacterial leaf blight by using EPIRICE and RICEPEST model. And a base map of the district will prepare by using Geographical Information System (GIS) to indicate the sampling sites and physical features of the area.Present piece of work will help the scientists and managers to predict those areas, potentially subjectedto disease appearance. By coupling GPS and GIS technologies with BLB disease detection tools and survey data, prevalence and incidence maps can be generated that depict well-defined geographical areas with high or low BLB risk. Moreover, the integration of GPS and GIS technologies with disease survey data will allows researchers to quantify (and better understand) the spatial and temporal dynamics of plant disease epidemics, and to examine how spatial disease dynamics are influenced by biotic and abiotic BLB risk factors.

INTRODUCTION:

Rice (Oryzae sativa L.), is a member of family Poaceae and is one of the major food crop of the world especially of the most Asian countries like Pakistan, Bangladesh,China, Vietnam and Korea. Rice is placed on second position in cereal cultivation around the globe and occupies an important position in the economy of Pakistan as an exportitem as well as staple food (Zahidet al., 2005).  It is the predominant dietary energy source for 17 countries in Asia and the Pacific, 9 countries in North and South America and 8 countries in Africa. Rice provides 20% of the world’s dietary energy supply, while wheat supplies 19% and maize (corn) 5%(Juliano and Bienvenido, 1993). Approximately 90% of the world’s rice isgrown in Asia (Salimet al., 2003).  In Pakistan, it is the second staple food after wheat and it’s the second largest earner of foreign exchange after cotton. Pakistan is the 11th largest producer and 5th largest exporter of rice. Rice accounts 4.9% percent of the value added in agriculture and 1% percent of GDP of Pakistan. During July March 2013-14, rice export earned foreign exchange of US$ 1.667 billion. During 2013-14, rice is cultivated on an area of 2789 thousand hectares and production stood at 6798 thousands tones(PARC, 2015).

It is however unfortunate that such an important crop is attacked by many kinds of diseases including Bacterial leaf Blight (BLB) caused by Xanthomonasoryzaepv. Oryzae.The causal bacterium Xanthomonasoryzaepv. oryzae is a gram negative non-spore forming rod shaped bacterium which is motile by single polar flagellum. Colonies on culture media are round and yellow in color due to production of a pigment called ‘xanthomonadin’(Ishiyama, 1922). Bacterial leaf blight of rice is one of the most destructive diseases of rice in many Asian countries. This disease become serious because many improved, high yielding varieties, when managed with high nitrogen levels and close spacing, have inadequate resistance to the pathogen. It starts as water soaked lesion on the tip of the leaves, increases in length downwards and turns into yellow to straw colored stripes with wavy margins. Lesions may be developed at one or both edges of the leaves or along the mid rib. In humid areas, on the surface of the young lesions, yellowish, opaque and turbid drops of bacterial ooze may be observed in the early morning (Eamchit& Mew, 1982). In Pakistan the disease was first time observed by Mew &Majid (1977), and after that Ahmad & Majid (1980)observed it on different rice varieties at Rice Research Institute, KalaShah Kaku and farmer’s fields.

In Pakistan it can reduce the yield up to 20 to 30% (Ou, 1985). To reduce this problem many plant disease management practices are use. The new tactic GPS and GIS for spatial and temporal model analyses that  used in agriculture as well as in the field of plant disease on a variety of scales, from single field to large agricultural area, to assess the connections between host, pathogens (Nelson et al.,1994). GIS has been applied in agriculture for the spatial analysis of insect pests (Everittet al., 1994), weeds (Wilson et al., 1993), and plant diseases(Orumet al., 1997). Weltzien, 1988 introduced the term “Geophytopathology” to describe studies of spatial patterns of plant diseases, the causal understanding of these patterns, and the geographic aspects of disease control. It will give a detailed description of the use of maps to illustrate the spread of plant diseases over regional or even continental scales and classified the types of maps that will be used for these purposes.

The study of spatial distribution of diseases provides important information on where a disease is occurring and effects of environmental factors on plant disease epidemics. Based on that, preventive and control measures can be taken. Several spatial statistical techniques have been employed to characterize the distribution of plant pathogens and diseased plants (Wu etal., 2001). Geographic information systems (GIS) can describe, manipulate, analyze, and display the data of most variables referenced by geographic coordinates (Star and Estes, 1990). GIS can be adapted to any size operation, and data can be incorporated at any scale from a single field to an agricultural region to describe the spatial relationships and interactions between pathogens, hosts and environmental variables (i.e., soil type, temperature) in relation to plant disease epidemics (Nelson et al., 1999). Various analyses can be performed in GIS environment and maps can be derived for an effective and comprehensive management of plant disease.

GIS have been applied in plant pathology for the spatial analysis of plant diseases epidemics (Nutter et al., 1995; Orumet al., 1997) and most extensively for mapping distributions of disease or specific genotypes of plant pathogen (Nelson et al., 1994). It has also been used in the plant disease epidemiology and management (Nelson etal., 1999). Thomas et al. (2002) geo referenced ground-based weather, plant stage measurements, and remote imagery in GIS software using an integrated approach to determine 6 insect pests and 12 disease risk map of various crops in northern California and Washington. Jaime-Garcia etal. (2001) spatially analyzed genetic structure of Phytophthorainfestans, the causal agent of late blight in a mixed potato and tomato production area in Mexico. Furthermore, GIS has been applied to determine the spatial relationship among soil texture, crop rotation and Aspergillus community structure (Jaime-Garcia and Cotty, 2006). In order to compare the effect of planting density on the distribution of Basal Stem Rot of oil palm, a GIS-based study was done on distribution pattern of the disease in an area about 10.88 ha in Malaysia from 1993 to 2005 (Azaharet al., 2011). Information management system will be playing pivotal role to enhancing agriculture production in the upcoming decades. Biological and physical aspects of agricultural systems produce spatial heterogeneity and as a result, patchiness is the rule in the occurrence and distribution of plant pathogens and disease (Campbell and madden, 1990). By application geographic information system (GIS) plant disease management practices can be improved. GIS is a computer base system capable of manipulating, and displaying data by geographic coordinates. GIS program is the best tool for disease forecasting. By coupling GPS and GIS technologies with BLB disease detection tools and survey data, prevalence and incidence maps can be generated that will depict well-defined geographical areas with high or low BLB risk (Star  and Estes, 1990). The other objective of this study will to forecast the rice yield loss as result of BLB. To carry out this study we will link two existing models, EPIRICE and RICEPEST and we will apply them using spatially and temporally down-scaled climate change data to generate predictions. Linking RICE-PEST with GIS will allow us to map yield losses.

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