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Highlights • Proposed framework identifies the similarity among the weather condition over years. • A powerful domain specific weather-based decision support system (DSS) can be built using this framework. • Significant reuse of the developed content in a weather-based DSS can be achieved using this framework. Abstract Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather condition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse , we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have analyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions. [ABSTRACT FROM AUTHOR]

Abstract: Traditionally the agriculture expert''s knowledge is descriptive and experimental, therefore, it is difficult to describe it mathematically and subsequently build agriculture Decision Support Systems (DSS). Furthermore, the corresponding data may be in such a raw form that it cannot be used in a DSS. The Agriculture Decision Support System (ADSS) is a 26-month project based on the Agro-met data from 2001 to 2006 of Punjab (the bread-basket of Pakistan), its ADSS-OLAP, i.e. Online Analytical Processing tool (www.agroict-olap.org) allows for quick analysis of all possible interesting aggregates of the ADSS data by employing drag-drop and mouse-click and is used in this paper to identify the effective pesticide groups related to the mealybug incidence. Pakistan is the world''s fifth-largest producer of cotton, but the emergence of the mealybug as a new cotton pest is likely to reduce the cotton yield by 1.3 million bales. The research work reported in this paper is based on the detailed pest-scouting data of 2300+ farmers of District Multan (cotton hub of Pakistan) for the years 2005 and 2006. This paper will also provide guidelines for the design and development of similar complex systems/tools and discusses the issues of agricultural data-quality management, particularly in the field of insect-pest management. [Copyright &y& Elsevier]