THE ROLE OF COLLABORATIVE SOFTWARE AND DECISION SUPPORT SYSTEMS IN THE SMARTER CITIES

The transition from the traditional city to the smart city is made by supported efforts regarding the achievement of a more steady, more efficient, more responsible city, through convergent strategies that deal with Smart Transportation Systems, Energy and Utilities Management, Water Management, Smart Public Safety, Healthcare Systems, Environmental Management, Educational Systems, Telecommunications (ITC Support),etc. and Positive Thinking. Service Oriented Architecture (SOA) meets the customers’ needs and the administration, the management of data, information, knowledge and decisions through Collaborative Systems and Decision Support Systems have a major impact both at the level of the smart city and the level of subsystems/services, and the information technology within smart cities becomes a major direction of research in the field of ITC.Smart City, Collaborative Systems, Decision Support Systems (DSS), Service Oriented Architecture (SOA), Portal technology

Use of traditional knowledge by the United States Bureau of Ocean Energy Management to support resource management

Professionals who collect and use traditional knowledge to support resource management decisions often are preoccupied with concerns over how and if traditional knowledge should be integrated with science. To move beyond the integration dilemma, we treat traditional knowledge and science as distinct and complementary knowledge systems. We focus on applying traditional knowledge within the decision-making process. We present succinct examples of how the Bureau of Ocean Energy Management has used traditional knowledge in decision making in the North Slope Borough, Alaska: 1) using traditional knowledge in designing, planning, and conducting scientific research; 2) applying information from both knowledge systems at the earliest opportunity in the process; 3) using traditional knowledge in environmental impacts assessment; 4) consulting with indigenous leaders at key decision points; and 5) applying traditional knowledge at a programmatic decision level. Clearly articulating, early in the process, how best to use traditional knowledge and science can allow for more complete and inclusive use of available and pertinent information

Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers

Probability modelling to reduce decision uncertainty in environmental niche identification and driving factor analysis: CaNaSTA case studies

Hillside agro-ecosystems have a complex spatial and temporal distribution of natural resources. Farmers generally possess a vast body of knowledge about environmental resources on their farms but this knowledge is largely based on locally observable features rather than generalized knowledge. The lack of process-based knowledge concerning agro-ecosystem function creates uncertainty that obstructs sound decision-making under conditions of rising economic and ecologic pressure in many developing countries. Since the past decade, Precision Agriculture provides tools to reduce uncertainty caused by environmental variation. By describing spatial and temporal variation of the environment, Geographic Information Systems help to detect suitable crops for specific environmental niches and support farmers to find optimal management practices for their plot of land. Hence Precision Agriculture helps to raise the economic benefits of farming, ensures consistent product quality and reduces negative environmental impacts caused by inappropriate management practices. A spatial decision support system called CaNaSTA was developed to aid the decision making process of crop adoption in tropical agriculture. Using Bayesian probability statistics, CaNaSTA integrates trial data, spatial data and expert knowledge and provides maps, tables and graphs analyzing and interpreting the probability distributions of spatial phenomena. The International Centre for Tropical Agriculture (CIAT) has applied CaNaSTA to three case studies related to tropical agriculture. The first case study identifies niches for specialty coffee production, the second analyses the potential of cowpea (Vigna unguiculata (L.) Walp.) for tropical hillside environments in Colombia. Finally, Canasta was applied to a non-crop related area by performing a study of carbon concentration in tropical soils.

Sustaining Working Rangelands: Insights from Rancher Decision Making

Grazed rangeland ecosystems encompass diverse global land resources and are complex social-ecological systems from which society demands both goods (e.g., livestock and forage production) and services (e.g., abundant and high-quality water). Including the ranching community's perceptions, knowledge, and decision-making is essential to advancing the ongoing dialogue to define sustainable working rangelands. We surveyed 507 (33% response rate) California ranchers to gain insight into key factors shaping their decision-making, perspectives on effective management practices and ranching information sources, as well as their concerns. First, we found that variation in ranch structure, management goals, and decision making across California's ranching operations aligns with the call from sustainability science to maintain flexibility at multiple scales to support the suite of economic and ecological services they can provide. The diversity in ranching operations highlights why single-policy and management "panaceas" often fail. Second, the information resources ranchers rely on suggest that sustaining working rangelands will require collaborative, trust-based partnerships focused on achieving both economic and ecological goals. Third, ranchers perceive environmental regulations and government policies-rather than environmental drivers-as the major threats to the future of their operations