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

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Trans-Disciplinary Soil Physics Research Critical to Synthesis and Modeling of Agricultural Systems Published online February 2, 2006 Trans-Disciplinary Soil Physics Research Critical to Synthesis and Modeling of Agricultural Systems Lajpat R. Ahuja,* Liwang Ma, and Dennis J. Timlin ABSTRACT global climate change are threatening economic viability Synthesis and quantification of disciplinary knowledge at the whole of the traditional agricultural systems, and require the system level, via the process models of agricultural systems, are critical development of new and dynamic production systems. to achieving improved and dynamic management and production Site-specific, optimal management of spatially variable systems that address the environmental concerns and global issues of soil, appropriately selected crops, and available water the 21st century. Soil physicists have made significant contributions in resources on the landscape can help achieve both envi- this area in the past, and are uniquely capable of making the much- ronmental and production objectives. Fortunately, the new needed and exciting new contributions. Most of the exciting new re- electronic technologies can provide a vast amount of real- search opportunities are trans-disciplinary, that is, lie on the interfacial time information about soil and crop conditions via re- boundaries of soil physics and other disciplines, especially in quanti- mote sensing with satellites or ground-based instruments, fying interactions among soil physical processes, plant and atmospheric processes, and agricultural management practices. Some important which, combined with near-term weather, can be utilized knowledge-gap and cutting-edge areas of such research are: (1) quan- to develop a whole new level of site-specific management. tification and modeling the effects of various management practices However, we need the means to assimilate this vast (e.g., tillage, no-tillage, crop residues, and rooting patterns) on soil amount of data. A synthesis and quantification of disci- properties and soil–plant–atmosphere processes; (2) the dynamics of plinary knowledge at the whole-system level, via process- soil structure, especially soil cracks and biochannels, and their effects based modeling of agricultural systems, is essential to on surface runoff of water and mass, and preferential water and chem- develop such means and the management systems that can ical transport to subsurface waters; (3) biophysics of changes in proper- be adapted to continual change. Interactions among dis- ties and processes at the soil–plant and plant–atmosphere interfaces; ciplinary components of the agricultural systems are gen- (4) modeling contributions of agricultural soils to climate change and erally very important. Models are the only way to find and effects of climate change on soil environment and agriculture; and (5) physical (cause-effect) quantification of spatial variability of soil understand these interactions in a system, integrate var- properties and their outcomes, new methods of parameterizing a ious experimental results and observations for different variable field for field-scale modeling, and new innovative methods conditions, and extrapolate limited experimental results to of aggregating output results from plots to fields to larger scales. The other soil and climate conditions. current status of the various aspects of these research areas is re- An agricultural system is complex (Fig. 1; also see viewed briefly. The future challenges are identified that will re- Fig. 3) and needs interdisciplinary field research and quire both experimental research and development of new concepts, quantification. Integration of system models with field theories, and models. 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 NDERSTANDING REAL-WORLD SITUATIONS and solving integration will benefit both field research and models in Usignificant agronomic, engineering, and environmen- the following ways: tal problems require process-based synthesis and quanti- fication of knowledge at the whole system level. In the 20th . Promote a systems approach to field research, which Century, we made tremendous advances in discovering looks at all component interactions fundamental principles in different scientific disciplines . Facilitate better understanding and quantification of using reduction methods, which created major break- research results throughs in management and technology for agricultural . Promote efficient and effective transfer of field re- systems. However, as we enter the 21st Century, agricul- search results to different soil and weather condi- tural research has more difficult and complex problems to tions, and to different cropping and management solve. The environmental consciousness of the general systems outside the experimental plots public is challenging producers to modify farm manage- . Help field researchers to focus on the identified ment to protect water, air, and soil quality, while staying fundamental knowledge gaps and make field re- economically profitable. At the same time, market-based search more efficient global competition in agricultural production and the . Provide the needed field test and improvement of the models before delivery to other potential L.R. Ahuja and L. Ma, USDA-ARS, Great Plains Systems Research users—agricultural consultants, farmers, ranchers, Unit, Fort Collins, CO 80526, USA; D.J. Timlin, USDA-ARS, Crop extension agencies, and action agencies (NRCS, Systems and Global Change Lab., Beltsville, MD 20705. Received 29 EPA, and others). June 2005. *Corresponding author ([email protected]). Field-tested models can be used as decision aids or Published in Soil Sci. Soc. Am. J. 70:311–326 (2006). Reproduced from Soil Science Society of America Journal.Reviews Published by Soil Science Society of America. All copyrights reserved. and Analyses and Soil Physics guides for best management practices, including site- doi:10.2136/sssaj2005.0207 specific management or precision agriculture (Ahuja ª Soil Science Society of America 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: DSS, decision support system. 311 312 SOIL SCI. SOC. AM. J., VOL. 70, MARCH–APRIL 2006 a lot more work is needed to bring them to the level of physics, engineering, and hydraulic system models. In particular, we need to improve on the methods and structure of building models so that: (1) the models are modular, with each model component (module) clearly defined, documented, and assigned a degree of uncer- tainty; (2) each model component can be independently tested and improved, and can be easily substituted; (3) the whole world community can contribute to developing, testing, and improving components; (4) the components may vary with the scale of application; (5) hierarchical parameter estimation from varying degree of input infor- mation is a component of the model; (6) the assembled models of the system are kept compact and easy to use by Fig. 1. Complex nature of an agriculture system, illustrated by the C-N customizing them to agroecosystem regions; (7) a user- cycle. Also see Fig. 3. friendly interface is provided for easy input of data and output of results; and (8) a well-illustrated user manual and Ma, 2002), as tools for in-depth analyses of problems is provided to illustrate a step-by-step procedure for run- in management, environmental quality, global climate ning the model and some examples of model application change effects, and other emerging issues, and as guides that demonstrate the benefits of using the model as well as for planning and policymaking (Ahuja et al., 2002). the uncertainty in results. An Object Modeling System, Models can be used to explore new ideas and strategies with a library of components (modules), being developed under different weather and climatic conditions before by the USDA-ARS (Ahuja et al., 2005), will hopefully in- testing them in expensive field experiments. The most corporate all the above features. desirable vision for agricultural research and technology transfer is to have a continual two-way interaction among SOIL PHYSICISTS CAN MAKE the cutting-edge field research, process-based models of A GREAT IMPACT agricultural systems, and management decision support systems (Fig. 2). Field research can certainly benefit from Variation of soil physical properties and their effects process models as described above, but also a great deal on soil water movement and retention, heat movement, from the feedbacks from the management decision soil temperature, chemical transport, root activity, and support systems (DSSs). On the other hand, field research soil biota functioning, involving both measurements and forms the basis for models and DSSs. theory, have been the core of soil physics research. System modeling has been a vital step in many sci- However, the scope of soil physics has gradually ex- entific disciplines. We would not have gone to the moon panded toward interdisciplinary boundaries (trans- successfully without the combined use of good data and disciplinary) because of the central role of soil water in models. In automobile designing, computer models of agricultural systems (Fig. 3). Examples of soil physics the system are increasingly replacing the scaled physical research in the interfacial areas include: modeling the models of the past. Models have also been used ex- linkage of transpiration and photosynthesis (Tanner, tensively in designing and managing water resource 1981; Tanner and Sinclaire,
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