Simulation Model and Scenario to Increase Corn Farmers’ Profitability

Corn demand in Indonesia is quite high, this commodity is useful as food, animal feed ingredients, and as industrial raw materials. The main problem in corn farm-ing is not enough production to meet the demand as staple foods and industry. It is necessary to increase the amount of corn production to meet demand as well as to increase farmers income and profits. Therefore, in this research we propose to develop a simulation model and scenario to increase farmers income through land productivity improvement. As a method use for model development, we utilize system dynamics framework based on consideration that system dynamics is a scientific framework for addressing complex and nonlinear feedback systems. System dynamics can use both qualitative and quantitative techniques such as computer simulations. It also facilitates the adoption of nonlinear mental models so that they can search and describe the feedback process of problem dynamics. In particular, system dynamics has proven useful in overcoming agricultural problems. Simulation results show that increasing farmers’ income can be done through increasing land productivity. With the increase in land productivity, corn production will increase, so that the income of corn farmers will also increase. In-creased productivity can be done by carrying out structural and non-structural ap-proaches. Structural approach can be carried out through rehabilitation of water-sheds and irrigation networks. Meanwhile non-structural approach can be carried out through the application of new technologies, strict land conversion rules, dy-namic planting calendars, dissemination of climate information, and the develop-ment of climate field schools

The role of radiation-dynamics interaction in regional numerical weather prediction

The role of radiation-dynamics interaction in regional numerical weather prediction of severe storm environment and mesoscale convective systems over the United States is researched. Based upon the earlier numerical model simulation experiments, it is believed that such interaction can have a profound impact on the dynamics and thermodynamics of regional weather systems. The research will be carried out using real-data model forecast experiments performed on the Cray-X/MP computer. The forecasting system to be used is a comprehensive mesoscale prediction system which includes analysis and initialization, the dynamic model, and the post-forecast diagnosis codes. The model physics are currently undergoing many improvements in parameterizing radiation processes in the model atmosphere. The forecast experiments in conjunction with in-depth model verification and diagnosis are aimed at a quantitative understanding of the interaction between atmospheric radiation and regional dynamical processes in mesoscale models as well as in nature. Thus, significant advances in regional numerical weather prediction can be made. Results shall also provide valuable information for observational designs in the area of remote sensing techniques to study the characteristics of air-land thermal interaction and moist processes under various atmospheric conditions