Trans-Disciplinary Soil Physics Research Critical to Synthesis and Modeling of Agricultural Systems

Synthesis and quantification of disciplinary knowledge at the whole system level, via the process models of agricultural systems, are critical to achieving improved and dynamic management and production systems that address the environmental concerns and global issues of the 21st century. Soil physicists have made significant contributions in this area in the past, and are uniquely capable of making the muchneeded and exciting new contributions. Most of the exciting new research opportunities are trans-disciplinary, that is, lie on the interfacial boundaries of soil physics and other disciplines, especially in quantifying interactions among soil physical processes, plant and atmospheric processes, and agricultural management practices. Some important knowledge-gap and cutting-edge areas of such research are: (1) quantification and modeling the effects of various management practices (e.g., tillage, no-tillage, crop residues, and rooting patterns) on soil properties and soil–plant–atmosphere processes; (2) the dynamics of soil structure, especially soil cracks and biochannels, and their effects on surface runoff of water and mass, and preferential water and chemical transport to subsurface waters; (3) biophysics of changes in properties and processes at the soil–plant and plant–atmosphere interfaces; (4) modeling contributions of agricultural soils to climate change and effects of climate change on soil environment and agriculture; and (5) physical (cause-effect) quantification of spatial variability of soil properties and their outcomes, new methods of parameterizing a variable field for field-scale modeling, and new innovative methods of aggregating output results from plots to fields to larger scales. The current status of the various aspects of these research areas is reviewed briefly. The future challenges are identified that will require both experimental research and development of new concepts, theories, and models. UNDERSTANDING REAL-WORLD SITUATIONS and solving significant agronomic, engineering, and environmental problems require process-based synthesis and quantificationof knowledgeat thewhole system level. In the 20th Century, we made tremendous advances in discovering fundamental principles in different scientific disciplines using reduction methods, which created major breakthroughs in management and technology for agricultural systems. However, as we enter the 21st Century, agricultural research has more difficult and complex problems to solve. The environmental consciousness of the general public is challenging producers to modify farm management to protect water, air, and soil quality, while staying economically profitable. At the same time, market-based global competition in agricultural production and the global climate change are threatening economic viability of the traditional agricultural systems, and require the development of new and dynamic production systems. Site-specific, optimal management of spatially variable soil, appropriately selected crops, and available water resources on the landscape can help achieve both environmental and production objectives. Fortunately, the new electronic technologies can provide a vast amount of realtime information about soil and crop conditions via remote sensing with satellites or ground-based instruments, which, combined with near-term weather, can be utilized to develop a whole new level of site-specific management. However, we need the means to assimilate this vast amount of data. A synthesis and quantification of disciplinary knowledge at the whole-system level, via processbased modeling of agricultural systems, is essential to develop suchmeans and themanagement systems that can be adapted to continual change. Interactions among disciplinary components of the agricultural systems are generally very important.Models are the only way to find and understand these interactions in a system, integrate various experimental results and observations for different conditions, and extrapolate limited experimental results to other soil and climate conditions. An agricultural system is complex (Fig. 1; also see Fig. 3) and needs interdisciplinary field research and quantification. Integration of system models with field research has the potential to raise agricultural research to the next higher level. It is also an essential first step to improve model integrity, reliability, and usability. The integration will benefit both field research and models in the following ways: . Promote a systems approach to field research, which looks at all component interactions . Facilitate better understanding and quantification of research results . Promote efficient and effective transfer of field research results to different soil and weather conditions, and to different cropping and management systems outside the experimental plots . Help field researchers to focus on the identified fundamental knowledge gaps and make field research more efficient . Provide the needed field test and improvement of the models before delivery to other potential users—agricultural consultants, farmers, ranchers, extension agencies, and action agencies (NRCS, EPA, and others). Field-tested models can be used as decision aids or guides for best management practices, including sitespecific management or precision agriculture (Ahuja L.R. Ahuja and L. Ma, USDA-ARS, Great Plains Systems Research Unit, Fort Collins, CO 80526, USA; D.J. Timlin, USDA-ARS, Crop Systems and Global Change Lab., Beltsville, MD 20705. Received 29 June 2005. *Corresponding author ([email protected]). Published in Soil Sci. Soc. Am. J. 70:311–326 (2006). Reviews and Analyses and Soil Physics doi:10.2136/sssaj2005.0207 a Soil Science Society of America 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: DSS, decision support system. R e p ro d u c e d fr o m S o il S c ie n c e S o c ie ty o f A m e ri c a J o u rn a l. P u b lis h e d b y S o il S c ie n c e S o c ie ty o f A m e ri c a . A ll c o p y ri g h ts re s e rv e d . 311 Published online February 2, 2006