Abstract: Abstract: New urbanization planning and new countryside construction planning are significant tasks in China. How to make proper planning in the context of increasing uncertainty will be a great challenge for the policymakers. We need to find a way to get more future information for planning. The trend extrapolation method, for example LUCC, CLUES, and CA-Markov, is a traditional approach to predict land use change. These methods perhaps solve some certain problems, but the results may be invalidated if some relevant factors change drastically. With the purpose of avoiding this situation, we introduced a new method normative landscape scenario. The normative landscape scenario can allow us to merge science into policy and explore scientific questions in simulated landscape patterns. The normative landscape scenario is distinctive in: integrating multiple disciplinary perspectives; the emphasis on public participation; the iterative design process; being plausible, replicable, and imaginative; and allowing investigation, comparison, and evaluation. In our study, the scenario building process was based on the following steps: 1) Describing the present and defining the focal questions; 2) Formulating hypotheses describing scenarios with qualitative storylines based on knowledge of multidisciplinary, stakeholder perceptions and policy; 3) Generating new landscape patterns by extracting quantitative mapping rules from qualitative narratives; 4) Repeating step 2 and 3 until fulfilling the specific target; and 5) Assessing scenario outcomes and inspiring policy. In order to examine the application, we took Jinjing Town as a case study, and successfully developed two alternative scenarios: I maximizing agricultural production and II improving water quality. Meanwhile we also simulated scenario III: rural landscape pattern by the Life cycle method. Land cover maps of alternative futures were created to provide visual insight into landscape change for managers. The effects of each landscape scenario were calculated across a range of environmental, biodiversity, economic, and social indicators that were compared with a baseline from 2010. Using the model of LPI and water quality variables, we evaluated the water quality under three scenarios. Obviously, scenario I indicated some improvement in water quality, as the values were slightly lower than the baseline; the water quality in scenario II was very well controlled l and kept at a very low concentration level; scenario III indicated the bad results. The export of TN in the three scenarios was 343 t, 175 t, and 575 t per year, respectively. About the aspect of agricultural value, the maximal total value was unquestionably scenario I which was 672.35×105 yuan, and it increased by 65% relative to the baseline. Scenario II had a larger ecosystem service value than the other two scenarios which was up to 2033.58×105 yuan. In other words, scenario II might supply more ecological functions. With the help of FRAGSTATS 4.2, the species richness density in scenario II was the highest, followed by scenario I, and Scenario III was the same as the baseline. Compared with the baseline, biodiversity including plant and animal diversity was of the best quality under the landscape resulting from scenario II. Scenario I was the next best. Farmers are likely to prefer Scenario I because it increased economic returns. However, this scenario may contradict the farmers' perception of an appealing landscape, a friendly environment, and a beautiful rural landscape, which can be achieved in scenario II. Therefore an array of policy implications for production prices, agricultural subsidies, ecological compensation, and agricultural services were proposed to support sustainable agricultural development and water quality improvements. Evidence from the case studies suggests that this method is feasible and presented some advantages over the traditional trend extrapolation method. The normative landscape scenario is not only a suitable method in new countryside construction planning, but also can provide available policy choices for policymakers. It is meaningful for nature resources management, environmental protection, and agricultural policy-making.