Indian Cartographer, Vol. XXXIII, 2013
GIS Based Decision Support System of Spodoptera Litura (Fabricius) (Noctuidae: Lepidoptera) in Soybean Amaresh Mishra, D. K. Das*, Jitendra Singh, A. K. Jain and R. Singh Division of Agricultural Physics, Indian Agricultural Research Institute New Delhi – 110012 *E-mail:
[email protected]
Keywords: Forewarning, DSS, GIS, Spodoptera litura (Fabricius), Soybean Abstract: The GIS based decision support system (DSS) enables farmers to follow the proper management practices when the conditions are favorable for pest infestation. Thus the growers can protect their crops by taking action in time in one hand and avoid unnecessary sprays on the other which minimizes the cost of crop protection and protect the environment. A new computerized decision support system has been developed to predict the pest infestation of Spodoptera litura (Fabricius) (Noctuidae: Lepidoptera) in soybean using weather based prediction rule. The weather parameters used were maximum temperature (25-28oC), minimum temperature (>19.5), morning relative humidity (>90), evening relative humidity (78-83%) and rainfall (400male moth/trap/week.
the optimal values/ranges of 5 weather parameters were found to be congenial for high infestation of S.litura on Soybean. These weather parameters with their optimal ranges are given below:
The most of the research has shown that the study of plant disease begins with a discussion of the plant disease triangle .The three important factors (host, disease and environment) are responsible for any plant disease occurrence (Stevens,1960). Similar principal also works for insect pest infestation. Environment consists of weather parameters and a modified pest infestation triangle is presented in Fig 1. First, Spodoptera litura ((Fabricius) (Noctuidae: Lepidoptera)) must be present in the environment. After that a pest must come in contact with a host (soybean).The most important factor weather condition has a greater impact on pest population.
1. Weekly mean maximum temperature (2528°C), 2. Weekly mean (>19.5°C),
minimum
temperature
3. Weekly mean morning relative humidity (>90%), 4. Weekly mean evening relative humidity (78-83%) 5. Weekly total rainfall (
