Technological advances in weather and climate forecasting and land surface and hydrology modeling have led to an increased ability to predict soil temperature, and soil moisture, near-surface weather elements. These variables are critical building blocks to the development of high-level agriculture-specific models such as pest models and crop yield models. The National Center for Atmospheric Research (NCAR) has developed a high-resolution agriculture-oriented land-data assimilation and forecast system (HRLDAS-ARG) targeted to the specific needs of agricultural user groups. This analysis and modeling system consists the following three core components that are modular to facilitate their integration and update: 1) Soil-condition analysis system: HRLDAS is based on land surface and 3-D hydrologic models that combines model analysis, satellite-derived vegetation conditions, land-cover, and soil texture data to constrain high-resolution (~km scale) evolution of soil moisture and temperature at various soil depths and vegetation root zones; 2) Soil-condition forecast system: realtime weather (up to 10 days) and seasonal climate forecast are used to drive HRLDAS to produce forecast of soil moisture and temperature at seasonal time scales; and 3) Crop-yield forecast system: HRLDAS is integrated with crop models to provide seasonal forecast of crop yield. The goal of such integration is to develop a forecast system HRLDAS-ARG that captures interactions between weather, climate, crop growth, and hydrology. HRLDAS-ARG contains, for instance, carbon allocation for crop roots, stems, and grains (fruits), calculation of maintenance respiration and growth respiration for specific agricultural land cover types (e.g., corn, soybean, wheat, rice, vegetables), and human-intervention processes influencing the crop yield such as seeding and irrigation. This paper will introduce different the integrated HRLDAS-ARG, assess its performance against field observations of soil conditions and crop phenology and yield, and discuss the uncertainties in realtime weather and climate forecast and in modeling crop yield.