Ahuja, Lajpat R
Supervisory Soil Scientist
(970) 492-7315
USDA-ARS-NPA-NRRC, GREAT PLAINS SYSTEMS RES. UNIT
2150 CENTRE AVENUE, BUILDING D, SUITE 200
Fort Collins CO 80526

UNIT VISION

Provide leadership in systems research for developing sustainable and adaptive integrated agricultural systems.

UNIT MISSION

Enhance economic and environmental sustainability of agricultural production systems by:

• Synthesizing and quantifying biological, chemical and physical processes at the whole-system level.
• Conducting cooperative field research to fill knowledge gaps.
• Developing computer models of agricultural systems to support field research and analysis of major issues, emphasizing water quality and water conservation, production, precision farming, and climate change. Providing farm-level computerized decision support technology and information system packages to farmers, ranchers, agricultural consultants and action agencies for evaluating sustainability of alternative farming/ranching options. Creating an Object Modeling System with a library of science and related tools to assemble customized modular models. Collaborating nationally and internationally to evaluate and improve knowledge and products.

HISTORY, ACCOMPLISHMENTS, AND CURRENT STATE OF THE GPSR UNIT

The Great Plains Systems Research (GPSR) Unit was created in 1985 by merging two units, Soil-Plant-Water Research Unit and Forage & Range Research Unit, with Dr. Al Grable as the Research Leader (RL). The driving force was the realization that the solution of real-world problems in the Great Plains and elsewhere requires whole-system approaches. The new GPSR Unit was charged with the mission to develop such approaches, and then transfer the technologies to users. Following Dr. Grable’s retirement in 1988, Dr. Charles Townsend became Acting RL, and Dr. Jim Welch became RL in 1990. In 1991, the GPSR and other natural resources research units in Colorado and Wyoming were reorganized into a Natural Resources Research Center (NRRC) with Dr. Jim Welch as the Director of NRRC and RL of GPSR. The GPSR Unit was reorganized by combining some members of the former GPSR and Hydro-Ecosystems Research Units. Dr. Laj Ahuja became RL in 1991.

For the first few years after the establishment of Unit in 1985, its research primarily dealt with rangelands. Charles Townsend was working on breeding Cicer Milkvetch as a potential legume for rangelands. Marv Shoop was studying grazing management in short grass prairie at the CPER (Central Plains Experimental Range) in Northern Colorado. Bill McGinnis did range work and revegetation of minelands. Jon Hanson worked on extending the SPUR (Simulating Production and Utilization of Rangelands) model by adding the cow-calf model, application of the model to study potential global change effects on the livestock production, and on remote sensing of rangelands. Jack Morgan conducted physiological studies on winter wheat under drought, and modeling of these effects. He also started the studies on the effects of increased carbon dioxide and temperature on photosynthesis and transpiration responses of some range species. Vern Cole worked on soil phosphorous as well as participated with Colorado State University on dryland cropping system project and in the enhancement of the Century model for evaluating changes in soil organic matter in agricultural systems. Rudy Bowman was working on soil organic matter in cropping systems. Marv Shaffer was hired to initiate crop modeling and take a major part in developing the NLEAP model. Greg McMaster developed the SHOOTGRO model for wheat. In honor of Al Grable’s retirement, a symposium was held on “Sustainable Agriculture for the Great Plains” in 1989. Subsequent to the conference, a report entitled Great Plains Agrosystem Project outlined the basic components and key institutions needed in a regional project that would tie together the research across the Plains. The Project incorporated the conceptual basis and the recognized the need for a systems approach and networking of scientists for agricultural research and technology development.

After its reorganization in 1991, the GPSR Unit inherited the development of the ARS Root Zone Water Quality Model from the Hydro-Ecosystem Unit. Four of the seven CAT-1 scientists in the Unit, Donn DeCoursey, Laj Ahuja, Marv Shaffer, and Jon Hanson, were already members of the RZWQM team. Work on other projects also continued. Dave Hartley and Carlos Alonso were working on a SWAM, a small watershed model, Vern Cole on the dryland cropping systems and the Century model, Marv Shaffer on NLEAP, and Jon Hanson on SPUR2. In 1992, Donn, Carlos, and Dave left the Unit for other positions, and in 1993 Vern Cole retired. These positions were filled by current scientists of the Unit, Greg McMaster, Jim Ascough, Tim Green, and Liwang Ma, over time. In 1993, the Unit started work on the development of GPFARM, a decision support system for farmers and ranchers in the Great Plains. All CAT-1 scientists participated in the development of RZWQM and GPFARM as team members. The tested and improved RZWQM version 2.0 was released in 2000 and GPFARM version 2.0 completed in 2002, and we are still supporting the users of these models. Over this time period, the Unit also refined and enhanced its Vision and Mission, in concert with our Customer Focus Group, stakeholders, and collaborators. The current CRIS projects described in the next section were the natural successors to the above major model development projects to meet the needs of our customers. Jon Hanson moved to Mandan, ND in 1999; Mark Weltz replaced him in 2000, then became a National Program Leader in 2002; and Marv Shaffer retired in 2003. We took this opportunity to create a new novel position of a Technology Transfer Coordinator for the Unit that was filled by Gale Dunn, who also now functions as the Project Manager for the GPFARM and its successor the iFARM project. We have Allan Andales, a post-doctoral scientist, working on modeling of rangeland forage and livestock systems, and we are looking for a new CAT-1 scientist to focus on modeling the effects of water stress on plant processes in field cropping systems, a weak link in agricultural system modeling. We brought Olaf David, a computer scientist, from Jena, Germany in to work with us as a CSU Collaborator to help us with building the Object Modeling System (OMS).

The Unit’s accomplishments thus far are summarized below:

A. Contributions to New Knowledge:

1. Quantification, modeling, and evaluation of the effects of agricultural management practices on soil physical properties and processes that affect water, water quality, and plant growth—tillage and reconsolidation effects on bulk density, porosity, and soil water properties; wheel track effects; preferential flow of water and chemicals into the soil through macropores created in no-tillage soils; residue cover effects on infiltration, evaporation, soil temperature, and crop emergence, development and yield; how long-term no-tillage cropping and residue systems change surface organic matter, aggregation and soil water properties, and how surface aggregates hold back an agricultural chemical near the surface, reduce leaching through the soil, but increase transport in macropores; and how grazing intensity and micro-topography affect rangeland rain infiltration and production in the long run.
2. Quantification, modeling, and field evaluation of the effects of soil properties on two-dimensional root growth; effects of two-dimensional root growth on water and chemical movement; two-dimensional infiltration of water from furrows-every furrow versus alternate furrows; effects of banding nitrogen in crop rows on its movement and availability to plants with furrow irrigation; leaching of chemicals in crop rows versus interrows.
3. Through a cooperative research agreement with Colorado State University, contributed to the development of new dryland cropping systems for the Great Plains under no-tillage that improves the capture and retention of incident precipitation; established that 3 or 4 year rotations (wheat-corn-fallow, wheat-corn-millet-fallow) give higher annualized yields than the common 2 year rotation (wheat-fallow); and established that increased crop residues at the surface lead to increased soil organic matter, and improved aggregation and physical properties. Area under new rotations in Central Great Plans has increased about 20 fold in the last 18 years.
4. Development sequences for simulating crop phenology under optimal and water-stressed conditions for winter wheat, barley, and corn; phytomers, phyllochrons, phenology, and temperate cereal development, field evaluation of the simulations.
5. Developed and evaluated new simpler techniques to estimate model parameters--saturated hydraulic conductivity (Ksat) and water retention curve of soils and their spatial variability, from soil bulk density and 1/3-bar water content; new intrinsic scaling relationships, based on the pore-size distribution index, for soil hydraulic properties across different soil textural classes, and extended to relate infiltration, soil water storage, evaporation, and transpiration in different soils. This scaling is a major breakthrough for site-specific management and estimating parameters and results for large scales.
6. Evaluated and improved numerous processes in our computer models with national and international collaborations.
7. Identified common patterns of crop yield, soil water content, and certain topographic attributes, where all of these variables can be characterized using fractal geometry; developed relationships between yield and topographic attributes.

B. Computer Models Delivered for Problem Evaluation, Policy Assessments and Decision Support:

1. NLEAP model for assessment of nitrate leaching, 1991.
2. SPUR2 model for rangeland-livestock systems, 1992.
3. SHOOTGRO model for winter wheat, 1993.
4. Contributed to Daily Version of the Century Model—Day Century, 1993.
5. 2-DROOT--A two-dimensional model of root growth, 1995.
6. RZWQM for simulating management effects on water quality and crop production, Version 1.0 1992; Version 4.0 2000; Version 5.0 2004.
7. GPFARM, a farm/ranch decision support system for strategic planning, Version 1.0 1998; Version 2.0 2002; Version 2.5 2003; Version 2.6 2004.
8. Just completed the development of AgSimGIS, a spatial modeling prototype for spatially variable fields and farms to assist with development of site-specific management (precision agriculture).

C. Technology Transfers Accomplished:

1. Conducted workshops and provided training on using the ARS Root Zone Water Quality Model (RZWQM) to 15+ groups composed of scientists, students, NRCS professionals and private industry in the U.S., Portugal, China, India, Switzerland and Canada.  In addition, answer more than 100 questions from RZWQM users each year.  The Unit took extra steps to transfer ARS research results to customers. When RZWQM model was developed in 1992, the Unit actively worked with scientists of the MSEA project to synthesize the multimillion-dollar-MSEA data. The RZWQM application results were published as a package of 8 papers in Agronomy J. (91:169-227). The RZWQM book and software were commercialized through Water Resources Publications who sold 500+ copies worldwide. Using systems approach, the Unit also helped several ARS scientists to design the right type of experiments to fill knowledge gaps and collect the right type of data. Mentored over 80+ visiting scientists, students, post-doctorals, and young scientists in the development and application of models.
2. The U.S. Geological Survey have selected RZWQM for process-based analysis of the water quality data from agricultural areas of their National Water Quality Assessment (NAWQA) Project 2004.
3. The U.S. EPA has selected this model for future use in assessing environmental impacts of new pesticide candidates for registration by the industry in 2004.
4. GPFARM was officially released to central Great Plains farmers and ranchers two years ago. The GPSR/GPFARM team has made numerous presentations on GPFARM to various farm and ranch organizations over the last three years.  In particular, the Colorado Conservation Tillage Association asked the team to use GPFARM to answer specific questions and provide the results at their annual meeting.  The team made several presentations to the group over the last three years with attendance at sessions going from 5 producers to over 40.
5. Successes with the Colorado Conservation Tillage Association lead to an agreement (MOU) with the Colorado Association of Wheat Growers (CAWG) to provide GPFARM in its membership packet. Approximately 600 copies of GPFARM have been distributed by CAWG. The team facilitated this activity and conducted several training sessions for the membership. As a result of the team's success with CAWG, the Kansas Association of Wheat Growers asked if they could develop a similar agreement with ARS. The agreement will result in distribution of over 2000 copies of GPFARM. The team is planning to conduct several training sessions to support this distribution.
   In recognition of the GPFARM technology transfer achievements GPSR was awarded the “Outstanding Laboratory Award” by the Federal Laboratory Consortium Mid-Continent Region. The award was accepted by Gale Dunn, our Technology Transfer Coordinator, and Laj Ahuja, Research Leader.
6. In 2002, the GPSR team gave demonstrations of RZWQM and GPFARM to 60 scientists in China.
7. In 2004, Dr. Gale Dunn, the GPSR Technology Transfer Coordinator, was invited to Tunisia where he made several presentations on the use of GPFARM.
8. Tim Green – Integrative Land Use Modeling cooperative agreement with ETHZ, 2005. The Institute of Terrestrial Ecology at the Swiss Federal Institute of Technology (ETH) in Zürich, Switzerland has invited and funded Dr. Green to be a visiting scientist for six months in 2005.  The purpose of this visit is to further our collaboration on agricultural hydrology, scaling, and landscape modeling.  Research ideas from our GPSR CRIS project plan (Scaling and Modeling Space-Time Variabilities of Landscape Processes to Enhance Management) are the basis for ETH’s interest and financial support (approx. $24,000).
9. August-December 2004, Dr. Liwang Ma conducted a collaborative research with CSRIO-APSRU in Toowoomba, Australia, under an OECD fellowship. The tile subsurface drainage component of RZWQM was incorporated into the APSIM model and tested using data from Nashua, Iowa. This new feature was released in APSIM version 4.0. He also incorporated transpiration efficiency approach into RZWQM to improve water stress on photosynthesis. The APSIM model was further tested for water and fertilizer management, and crop rotation effects on water use efficiency under Colorado conditions.
10. The GPSR Unit’s program has been the subject of the following articles written in popular magazines/press: Nitrates can leach, but they can’t hide—High levels of nitrate-nitrogen have plagued groundwater in Weld County for some years. Now computer maps peg how it ties to farming. Colorado Rancher and Farmer, November 1992, p. 6-8; Forecasting Livestock Production - Climate change in the Great Plains. Agricultural Research, p. 12-13, Feb. 1993; Computing Pollution. Agricultural Research, April, 1996; RZWQM - Modeling effects of farm decisions. Agricultural Research, p. 18-19, July 1997; Computer Model will help farmers project yields and water quality. ARS News Service, July 18, 1997; New software could help on farm and ranch decisions. ARS News Service, Jan. 28, 1998; Farmers get high-tech help: Software helps out plans for planting. Fort Collins Coloradoan (Daily Newspaper), Feb. 7, 1998; Fractals - a bridge to future for soil science.  Agricultural Research, p. 10-13, April, 1998; New computer model aims to keep farms and ranches productive. ARS Press Release, September 1998; Farm analysis and comparison tool - designed for world-wide web. AGRIFACTS, USDA-ARS, Akron, CO, Oct., 1998; GPFARM software foresees the future.  Agricultural Research, p. 22, Nov. 1998; User-Friendly farm Planning—Software helps you find the best option for your operation. Western Farmer-Stockman, July 2002, p. M6/10; More proof of the effectiveness of no-till farming. ARS News Service. May 2003; Moving away from wheat-fallow in the Great Plains, models are playing a role. Agricultural Research, p. 14-17, June 2005; Conservation: Are we getting the money’s worth? GPSR research and development of Object Modeling System will be key to nationwide computer modeling effort that forms CEAP’s backbone. Agricultural Research, p. 4-5, Dec. 2005; and Evaluating nitrogen and water management in a double-cropping system using RZWQM. Science in Action. ASA-CSSA-SSSA. 2006.

Current State:

Currently, the GPSR Unit is very well positioned for applying agricultural system models to solve problems of the 21st Century. It is a leading ARS Unit working on the synthesis and modeling of the whole agricultural system. It utilizes these models to create decision support technology, including economics, for use by farmers, ranchers, and action agencies in planning and management. It is unique in modeling of the integrated cropping-range-livestock systems. The Unit developed strong collaborations with a number of leading experimental Labs and Units in ARS and university partners during evaluation and improvement of its models, especially the National Soil Tilth Lab at Ames, Iowa; Central Great Plains Research Station at Akron, CO; Rangeland Resources Research Unit at Fort Collins, CO/Cheyenne, WY; Iowa State University; Colorado State University; and the University of Nebraska. These collaborations have now expanded (see charts). The Unit’s RZWQM model has been or is being used extensively in the U.S. and other countries—Canada, Portugal, Germany, China, India, Pakistan, Thailand, Uzbakiston, Italy, and more (see chart). The Unit has also developed collaborations with other leading systems modeling groups in ARS and internationally, including the Crop Systems and Global Climate Change Lab in Beltsville; Peanut Lab in Dawson, GA; the DSSAT crop modeling groups at the universities of Georgia and Florida; the N modeling groups at Taastrup, Denmark; APSIM modeling group at Toowoomba, Australia; the range-livestock modeling group at Canberra, Australia; and the Century Modeling group at Colorado State University. We are now developing collaborations with UNESCO, the Swiss Federal Institute of Terresterial Ecology, and the Agrophysical Research Institute, Russia. The Unit has a dedicated group of
farmer/rancher collaborators in the Great Plains who participated in the development and evaluation of GPFARM and who are helping us define a tactical planning and management decision tool, iFARM. The Unit is leading the ARS-NRCS-USGS efforts in developing an Object Modeling System for generating and updating customized system models of the future more easily and quickly.

This program of the ARS needs to be strengthened to fully realize the great potentials of the agricultural system models in research and technology transfer.

STRATEGIC ISSUES/PROBLEM AREAS

A.  Whole System Integration and Modeling—Essential to Agricultural Science and Technology in the 21st Century

In the 20th Century, we made tremendous advances in discovering fundamental principles in different scientific disciplines that created major breakthroughs in management and technology for agricultural systems, mostly by empirical means. However, as we enter the 21st Century, agricultural research has more difficult and complex problems to solve. The environmental consciousness of the general public requires us 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 products is challenging economic viability of the traditional agricultural systems, and requires the development of new and dynamic production systems. Frequency of extreme climatic events such as droughts has recently increased, possibly due to global climate change, requiring changes in management. Our customers, the agricultural communities, are asking for more timely transfer of research results in an integrated usable form to aid them in meeting these challenges through improved cropping and management. Fortunately, the new electronic technologies and remote sensing can provide us a vast amount of real-time information about crop conditions, near-term weather, and global markets, that can be utilized to develop a whole new level of management tools. However, we need the means to capture and make sense of this vast amount of site-specific data.

To address all the above needs requires us to better understand the whole system. Agricultural systems involve highly complex interactions among soil, plant, weather, and management components that are beyond a human brain to comprehend quantitatively. Modern computer technology can complement and assist the human brain in this process. The scientists need to improve on how to present their research results in the context of the whole agricultural system, this requires synthesis and quantification of experimental data based on fundamental principles and laws. The system models are indeed the product of this synthesis and quantification of current knowledge based on fundamental principles and laws. These models then form the basis of decision support systems for transferring research findings to producers under different soil and climatic conditions for making complex management decisions.

Integration of system models with field research has the potential to raise agricultural research to a higher plateau. It is also an essential first step to improve model usability and make a significant impact on the agricultural community. This integration will benefit field research and models in the following ways:

• Promote a systems approach to field research.
• Facilitate better understanding and quantification of research results.
• Promote quick and accurate transfer of results to different soil and weather conditions, and different cropping and management systems outside of experimental plots.
• Help research to focus on the identified fundamental knowledge gaps and make field research more efficient, i.e., get more out of research per dollar spent.
• Provide the needed field tests of the models before delivery to other potential users—agricultural consultants, farmers/ranchers, state extension agencies, and federal action agencies (NRCS, EPA, and others).

The National Science and Technology Council, Committee on Environmental and Natural Resources, 1997 report entitled, “Integrating the Nations Environmental Monitoring and Research Network and Programs: A Proposed Framework”, recommended a national framework that links systematic observations and monitoring with predictive modeling and process research.  The 1999 report of the National Academy of Sciences and Engineering on “New Strategies for America’s Watersheds” identified several critical support functions; one of these is the integration of theory, data, simulation models, and expert judgment to solve practical problems and provide a scientific basis for decision-making.

Field-tested models can be used as decision aids or guides for best management practices, including site-specific management or precision agriculture [Ahuja and Ma, 2002, Encyclopedia of Soil Science. Lal, R. (ed.). Marcel Dekker, Inc., NY. Pp 218-222] as tools for in-depth analysis of problems in management, environmental quality, global climate change effects, and other new emerging issues, and as guides for planning and policy making. The most desirable vision for agricultural research and technology transfer is to have a continual two-way interaction among the field research, process-based models of agricultural systems, and management decision support systems (Figure 1). Field research can certainly benefit from the process models as described above, but also a great deal from the feedback from the management decision support systems (DSSs), to be cutting-edge. On the other hand, field research forms the pivotal basis for models and DSS. The DSSs generally have models as their cores (simple or complex).

Figure 1.  Our vision of the role of system models in agricultural research and technology transfer system modeling has been a vital step in many scientific disciplines. We would not have gone to the moon successfully without the combined use of good data and models. In automobile designing, computer models of the system have replaced the scaled physical models of the past. Models have also been used extensively in designing and managing water resource reservoirs and distribution systems, and in analyzing waste disposal sites. Although agricultural system models have shown promise (Ahuja et al., 2002, Agricultural System Models in Field Research and Technology Transfer. CRC Press. Boca Raton, FL.) more work is needed to realize their full potential benefits to field research and technology transfer. We have been and are attempting to do this by working cooperatively with ARS field scientists of several Units in evaluation and application of models, providing training in models’ use to many groups, and in giving guest lectures on models to a number of classes at the Colorado State University.

A need for a whole-system approach does not mean that we will always need a whole-system model as a guide to management. After an initial analysis of the system has shown that only a certain component, such as soil erosion, or a certain insect or disease, is the main problem that needs to be corrected, a model or a technology guide that addresses only this component of the system may be adequate.  An important requirement, however, is that there is a consistency in the science used among components and whole-system models.

B.  Models to Evaluate Agricultural Management Effects on Water Quality, Water   Conservation, and Crop Production

Since the 1970s, water quality impairment of both surface and ground waters from agricultural sources has been recognized as a major national issue in the U.S. As a part of the new research to ameliorate water quality, the ARS also recognized the need for computer models to evaluate water quality impacts of cropping systems and the associated management practices, and to guide a change in management to minimize these impacts. In the mid 1980s, two ARS teams were set up to develop two major models—NLEAP (Nitrate Leaching and Economic Assessment Package) and RZWQM (Root Zone Water Quality Model). Along with the Soil Plant Nutrients Unit, the GPSR Unit had a major part in developing NLEAP. It was designed as a screening model for quick estimation of nitrate leaching hot spots in cropping systems, and has been used extensively by NRCS and scientists all over the world. On the other hand, under leadership of the GPSR Unit, the RZWQM was developed as a comprehensive model of root zone processes that influence water quality, as well as soil water storage and efficient water use. It is an integrated physical, chemical, and biological process-based model that simulates the effect of management on plant growth and the movement of water, nutrients, and pesticides in runoff and through the root zone of a cropping system to shallow groundwater. The model allows simulation and evaluation of a wide spectrum of management practices and scenarios on water and water quality, such as no-tillage and residue cover vs. conventional tillage; rates, methods, and timing of application of water, fertilizers, manures, and various pesticide formulations; and different crop rotations up to 100 years. The model contains the special features of rapid transport of surface-applied chemicals through soil macropores to deeper depths, groundwater and tile flow. The first version of the model was released in 1992, after which it underwent extensive evaluation, refinement, and validation in cooperation with numerous ARS and university users. A new Windows user interface was developed to make it easier to use. RZWQM development was essentially completed in 1999; the model is now being used nationally and internationally, with the Unit providing some user support. The model as a whole or its parts are also being used in the 3-Location CRIS and iFARM CRIS. For this purpose, crop growth models developed by the DSSAT group and used all over the world, and improved routines for water and temperature simulations in residue-covered soil from SHAW model, were linked recently to RZWQM. At the request of U.S. Geological Survey and EPA, the model application has been extended from just the root zone to 30 meters depth.

C.  Quantifying and Modeling Management Effects on Soil and Plant Properties and Processes, and Improving Modeling of the Response of Crops under Water Stress Conditions

For models to guide precision agriculture and precision management of our water resources, it is crucial to be able to quantify the effects of agricultural management and cropping practices on soil properties and soil-plant-atmosphere processes. The Unit initiated new pioneering research in this critical knowledge-gap area during the development of RZWQM, such as the effects of tillage and reconsolidation, no-tillage, and residue cover. This research is continuing for the ongoing and future projects. Further improvement in models and parameters are needed for the effects of tillage on the soil water retention curve and hydraulic conductivity; effect of wheel tracks on these properties; effects of long-term no-tillage cropping and residue systems on surface aggregation and soil water properties, infiltration, soil water storage, evaporation, soil temperature, and crop emergence, development, and yield; and how grazing intensity affects rangeland water storage and production in the long run. Additional improvements are needed in modeling preferential transport to groundwater through no-till caused decayed root channels and earth worm holes (macropores), field methods for measuring macropore flow parameters, and the role of surface soil aggregates in holding back applied chemical and increasing macropore transport.

Within the plant component of systems, the GPSR Unit has pioneered research in improving the simulation of plant growth across widely varying environmental conditions and levels of resource availability.   Much of the work centered on better quantification of plant development, phenology, seedling emergence, and early growth, and incorporating this into simulation models such as SHOOTGRO and Phenology MMS.  Further improvements are needed in better integration between developmental and functional physiology, improvements in linkage among the soil-plant-atmosphere components of systems, creating better response surfaces for interacting stresses, and recognition of the reality of the genotype by environment interaction, possibly by better incorporation of new knowledge from functional genomics.

Most of the existing crop growth models do not adequately respond to water stress conditions. Based on earlier work described above, the Unit is developing new improved models of crop phenology and development under water stress. These new models will then be linked to the advanced knowledge of plant physiological processes of leaf growth, transpiration, photosynthesis, and carbon allocation to different plant parts under stress conditions at different growth stages. The new models should also include how increased carbon dioxide and temperature will interact with water stress and effect plant growth and yield in crop and rangeland systems.

Modeling root growth is still a black box. How do root growth and active root distribution with depth change with soil conditions? How do they affect the root water uptake, nutrient uptake, and soil water, chemical, and heat movement? Is this movement still one-dimensional--perhaps for closely planted crops like wheat, but what about row crops? We either need a 2-dimensional model of root growth, water uptake, and water flow and chemical leaching in crop row vs. interrow zones and from bands of N in the cropped ridges, or ingenious 1-D simplifications. The processes of water and nutrient uptake, passive and active, also need improvements.

D.  Scaling and Modeling Space-Time Variability of Landscape Processes for Precision Management

Agricultural land management has to deal with a high degree of spatial variability of land and temporal variability of weather. There has been broad interest in precision agriculture, which consists of site-specific, spatially variable, management to optimize production and minimize both on-site and off-site adverse effects on water quality and quantity. However, no comprehensive methods and tools are available to guide such management of landscape and climate variability. For this purpose and to aggregate expected results from plots to field, farm, and watershed scales, we need a framework for integrated spatial analysis, distributed modeling, and up- and down-scaling for different land areas. A scientifically robust scaling framework is the greatest need for making breakthroughs in transferring research knowledge across scales, and to understand and manage large areas (National Research Council, 1991: Opportunities in Hydrologic Sciences).

Measurement of agricultural processes across multiple scales has become more feasible in recent years due to the improvement of automated data collection technology. Tremendous amounts of site-specific data (e.g., soil properties and crop yield) are being collected; unfortunately, scientific analysis capability lags behind the technological advances driving data collection efforts. Models for predicting plant growth/development and flow/transport in agricultural landscapes are simultaneously improving, but nearly all of these models are designed for homogeneous areas of undefined scale, or represent soil-plant-water processes in a very simplistic manner at large scales. Improvements in water quality and production using precision agriculture require thoughtful research considering real-world landscapes and climate variability across field, farm, and watershed scales. Such research efforts must: 1) quantify spatial variability using causal factors, such as topographic attributes, and develop new physical and statistical methods of scaling; 2) improve spatial data analyses and model parameterization through application of these scaling methods; 3) provide improved spatial modeling tools, including surface and subsurface interactions among different spatial units, for enhancing site-specific management; and 4) simulate management effects on soil and plant processes and the transmission of these effects on water, water quality, and crop growth across to field, farm, and watershed scales. 

E. Integrated Farm and Ranch Management Decision Support Systems to Help Producers in Planning and Management

Agriculture has become a highly complex socio-economic-environmental enterprise. The U.S. agricultural community is facing severe hardships at the farm and ranch levels from depressed commodity prices, weather fluctuations ranging from droughts to flooding, and foreign competition. Concerns from the environmental community about agricultural impacts on water quality and greenhouse gas emissions (e.g., global climate change) have further challenged farmers and ranchers. Decision aids based on the synthesis of current research knowledge for the production systems can help farmers deal with complexities. The GPSR Unit team developed a whole farm/ranch Decision Support System (DSS), Great Plains Framework for Agricultural Resource Management (GPFARM), for long-term strategic planning in the central Great Plains. GPFARM allows evaluation of different cropping systems, range-livestock systems, and integrated cropping-range-livestock systems, and associated management practices, for their economic, environmental, and resource sustainability on a long-term basis (15-20 years+). Integrated farm/ranch enterprise management is a unique feature of GPFARM. Farm/ranch cooperators of GPFARM have asked for an enhanced DSS to allow yearly or seasonal tactical planning and management of their operations based on climatic (especially rainfall) conditions and price fluctuations, and other risk factors. For example, the producers want a tactical tool to decide what crop to plant in Spring based on the known soil moisture, expected climate, and price conditions, and the risk associated with the decision. Ranchers would like to predict forage production in the coming season, especially during ongoing droughts, to decide the number of cattle to keep. The tool may also be used to guide within-season management, such as irrigation, fertilizer, and herbicide applications. There is also a national interest in developing a tool for evaluating carbon sequestration and greenhouse gas emissions in different agricultural systems. Further, the GPFARM is currently limited to six crops common in a four-state region of the central Great Plains, and to cow-calf animal operations on rangeland. Actual and potential GPFARM users have requested a broader geographic scope extending to a national level, with enhanced capability for modeling a variety of crops and range/animal species and addition of an environmental and economic risk assessment component for seasonal cropping and animal management. This expanded DSS would provide nationwide assistance to farmers and ranchers, and would help Federal and State conservation and regulatory agencies to make better management decisions across the country.

F.  Integration of Research Information into a Database for Decision Support for Resource Conservation Planning and Water Quality Protection by NRCS

Decision-making about the appropriate conservation practices to place on the landscape for protecting or enhancing water quality requires the integration of data, expert opinion, and knowledge. Evaluation of best management practices for water quality in the past has not accounted for the differences in efficacy of these practices due to soil, landscape, climate, and crop or agronomic characteristics. The problems being considered were often single factor (e.g., erosion) that did not require the interaction of several management components to make decisions about the effect of various options on water quality criteria and production. These options may include changes in crop rotations, tillage, nutrients, pesticides, and conservation practices. All of these considerations include some aspect of type, rate, or timing. This type of multiple decision-making is central to the improvement of water quality and it requires the integration of several interacting factors that include weather, position in the watershed, management history, soils, and geology. Producers and action agencies, e.g., Natural Resources Conservation Service (NRCS), do not have a system tool available in which comparisons among different system configurations can be easily made for a range of conditions and no formal method of comparing among systems for their water quality or economic impact. Development and implementation of best management practices for impacting water quality and production at field and watershed scales will require demonstrated procedures that can identify the best combination of practices before producers will alter their management systems.

Resource conservation planning tools are primitive compared to current science and information technology. Recent development in agricultural system models, decision support tools, and computer technologies could provide an improved tool for NRCS conservation planning that would lead to increased adoption of practices enhancing water quality and profitability. However, use of models directly by NRCS field personnel is still not feasible at this time. A new approach towards a DSS is to create an integrated research information database as a core of the DSS in place of a model. A system model, validated against available experimental data, is used to generate production and environmental impacts of different management practices for all major soil types, weather conditions, and cropping systems outside the experimental limits. This model-generated information is then combined with available experimental data and the long-term experience of farmers and field professionals to create a database. The database can be combined with an economic analysis package. It may also be connected to a so-called “Multi-Objective Decision Support System” for determining trade-offs between conflicting objectives, such as economic return and environmental quality. It is also very flexible in generating site-specific recommendations.

G.  An Advanced Modular Model Building Framework for Agricultural System Models of the Future

At present, ARS and university scientists have more than a hundred small to large agricultural models or software packages to help evaluate or guide management practices; some of them are system-wide models whereas others are component models or software. Examples of large system models are: cotton and soybean models, Gossym and Glycim or Soygro; the crop models of CERES and CROPSGRO family; erosion models, WEPP and WEPS; water quality models, GLEAMS, RZWQM, AGNPS, and SWAT; and general purpose models, EPIC and CROPSYST. Examples of smaller, single-purpose, models are NLEAP and WEEDCOM. Unfortunately, these tools now exist as an array of individual disparate models. The large system models have been extremely expensive to develop ($15-30 million each).

At ARS customer workshops, the NRCS and other users of models or software have reported that different models or software tools do not give the same results for, say, soil erosion or crop yield, because these tools use different science in key areas like hydrology. There may also be conflicting data, scales, and methodologies.

Moreover, it is becoming extremely difficult to maintain and support such a large number of packages. In particular, yesterday’s monolithic models are very difficult to update, add to, or interface with other models, have diminishing technical support as the original developers retire, and lack the flexibility to meet today’s needs for more integrated analysis of changing resource issues. It is difficult to transfer the new knowledge to the customers quickly through the use of these old tools.

All of the above reasons indicate a need for a new framework of model development that allows building of models from a library of validated science modules in a computer. Such a system will integrate all existing models and all future models into a common, collaborative, and flexible system. Such a system will maintain modularity, reusability, and interoperabililty or compatibility of both science and auxiliary components. The system will also recognize the fact that different categories of applications may require different levels of scientific detail and comprehensiveness, as driven by problem objectives, scale of application, and data constraints. In other words, there can be alternate components for the same process. These functionalities of the system will be obtained by establishing standard libraries of inter-operable science and auxiliary components or modules that provide the building blocks for a number of similar applications. Module libraries have been successfully used in several domains, such as the manufacturing, transport, and other systems [see David et al. 2002 in Ahuja et al. (ed.) Agricultural System Models in Field Research and Technology Transfer, CRC Press, Boca Raton, FL]. One of the earliest modular model developments was done for SHE, the European Hydrologic System Model. Leavesley et al (1996, 2000, 2002 see David et al., 2002) reported the conversion of the Precipitation Runoff Modeling System (PRMS) to a Unix-based Modular Modeling System (MMS) for hydrologic modeling. Leavesley et al (2002) presented some successful applications of this concept.
In 1997, an ARS-NRCS-USGS Interagency Workshop reviewed the MMS and other similar approaches and unanimously endorsed the development of an advanced Object Modeling System (OMS) for Agricultural and Natural Resource Systems that will:

• reduce duplication of effort in agricultural and natural resource modeling;
• improve the quality and currency of model code;
• make simulation models much easier to build, access, understand and use;
• facilitate long term maintainability of existing and new models;
• lead to greater consistency of modeling for particular problems and scales;
• enhance response and delivery times in scientific modeling projects;
• ensure creditability and security of model implementations; and
• function on any major computing platform.

There is no software commercially available to build models from modules composed of existing source code. Some technology is available which uses a common interface for running different pre-built models, such as groundwater and surface water models. These systems cannot build models from modules.

Most current investments in model development programs by our university partners (e.g., University of Florida DSSAT Modeling Group), other government agencies (Corps of Engineers, Environmental Protection Agency), and foreign countries [e.g., the Cooperative Research Centre (CRC) for Catchment Hydrology in Australia; the APSIM group in Australia; the European Commission Framework Programme 5: Harmon IT] are in the area of modular or object modeling, all of which use a basic concept of providing proven and sound scientific modules of the past made available for use in custom-designed integrated analyses.

In 2001, several federal agencies – Nuclear Regulatory Commission, NOAA, EPS, COE, USGS, and USDA – entered into a Memorandum of Understanding to collaborate in developing and using models. These agencies enthusiastically endorsed the development and deployment of the Object Modeling System.