Due to the scarcity of land for cultivation especially in Asian countries, the lack of knowledge about rice straw management and its environmental consequences, as well as low opportunities for income, large amounts of biomass residues are being burnt by farmers on-site after harvesting. Such "Rice straw open burning" (RSOB) wastes nutrients like Nitrogen and Phosphorus, and emits pollutants causing environmental and health problems. RSOB is also contributing to declining soil fertility resulting in rather low yields in paddy rice fields. The goal of this thesis is to develop a methodology for simulating the economic and environmental effectiveness of rice straw management considering knowledge and financial limitations of small farm holders. To reach the objectives, the concepts of Material Flow Analysis (MFA), Substance Flow Analysis (SFA), Scenario Analysis, and Economic Analysis (EA) are applied for assessing straw management on a hectare of an exemplary farm in view of resource management practice, environmental consequences, and economic advantages. Data and statistics for describing the farm by the software STAN are collected from national and international organizations, including data by satellite imageries and from personal interviews. Based on stoichiometric equations and mass balances, process equations for Status Quo and four scenarios are developed. The scenario results serve to design an optimized scenario, a combination of simple technologies for straw management allowing farmers to utilize straw for producing food, feedstock, energy, and construction material. By optimizing straw management, emissions of 800 kg CO2e/y.ha, of 110 kg/y.ha CO, and of 11 kg/y.ha particulate matter (PM) affecting climate change and public health are eliminated. In addition, substances previously released to the environment are transformed into food and feed products, in biogas, and in straw bricks. At the same time, economic profits for farmers increase 4.7 times, motivating stakeholders to change their straw management. This research shows the potential of combining MFA (STAN), SFA, EA, and scenario analysis to improve resource management, environmental management, and human health, and at the same time to increase farming profits.