Submitted to: Proceedings of ARS/INIFAP Binational Symposium on Modeling and Remote Sensing in Agriculture
Publication Type: Proceedings
Publication Acceptance Date: 1/1/2004
Publication Date: 3/1/2006
Citation: Richardson, C.W., Baez-Gonzalez, A.D., Tiscareno-Lopez, M. 2006. Modeling and remote sensing applied to agriculture (U.S. and Mexico). Proceedings of ARS/INIFAP Binational Symposium on Modeling and Remote Sensing in Agriculture, June 2-6, 2003, Aguascalientes, Mexico. 250 p. Interpretive Summary: Modern remote sensing technology can be used to identify agricultural crops on specific areas and the stage of crop development. In like manner, computer models of agricultural processes are capable of simulating crop growth and yield. When used together, these two areas of technology have tremendous potential to be used within growing seasons to predict crop yields. This book summarizes progress in these areas with application to the United States and Mexico
Technical Abstract: The simulation of hydrologic and agricultural processes began over 30 years ago as independent endeavors. Early hydrologic process models focused on the distribution and movement of water across the earth's surface and through the soil mantel with little attention to plants, the primary user of water. The earliest agricultural process models simulated crop growth and yield with little attention to the dynamics of hydrologic processes and their influence on plant growth. Eventually, these two types of models came together, resulting in integrated systems capable of simulating hydrologic and crop growth processes in a continuum of soils, crops, and weather. Modern integrated systems can be used to make within-season management decisions and end-of-season yield estimates. In like manner, remote sensing technology has progressed over the last 30 years from simply identifying crops on specific areas to being capable of quantifying crop development. These two areas of technology, simulation models and remote sensing can be used together to provide a powerful tool for managing crop production and estimating crop yield. The U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS) and the Mexican National Research Institute for Forestry, Agriculture, and Livestock Production (INIFAP) conducted a joint project to evaluate the potential of using remote sensing and crop simulation models for agriculture management and crop yield predictions. This book describes some results of this collaborative effort involving ARS, INIFAP and other ogranizations.