Progress 08/06/03 to 08/04/08
Outputs
Progress Report Objectives (from AD-416) 1)Develop a national integrated Farm And Ranch Decision Support System (iFARM DSS) based on GPFARM (Great Plains Framework for Agricultural Resource Management) for both strategic and tactical management to maximize economic returns and minimize environmental impacts on farms and ranches. 2)Extend iFARM DSS to: a) include risk assessment and management for variable and extreme weather, production and marketing uncertainty, and insect, disease, and weed problems; and b) target global climate change issues in the areas of carbon sequestration/credits and greenhouse gas emissions. 3)Apply DSS on selected farms and ranches for whole-farm strategic and tactical management, risk assessment of alternative management practices, and global climate change issues such as carbon sequestration/credits and greenhouse gas emissions to demonstrate feasibility of approach. Transfer technology to potential users. Approach (from AD-416) The Great Plains Systems Research Unit team recently delivered the whole farm/ranch Great Plains Framework for Agricultural Resource Management (GPFARM) DSS for long-term strategic planning in the central Great Plains. GPFARM cooperators requested an enhanced DSS to allow seasonal tactical planning and management of their operations based on climatic and price fluctuations, and other risk factors. The new CRIS project will extend and expand GPFARM into a regional Integrated Farm and Ranch DSS (iFARM DSS) for both strategic and tactical planning/management that will include climate, market, and natural-hazard risks, as well as carbon sequestration and greenhouse gas emissions. New science modules and technologies will be introduced into the DSS for this purpose (e.g., a crop module that responds better to water/N/heat stresses and a module to account for carbon and greenhouse gases). Other new modules will be developed for management effects on soil, economic and environmental risk assessment, and estimation of field-scale model parameters. New crops and managements will be added for expansion into the northern and southern Great Plains, and the Midwest. DSS components will be tested on experimental data and the whole DSS evaluated across selected farms and ranches. The outcome of this research will be a validated iFARM DSS for use by farmers, ranchers, agri-businesses, and NRCS in strategic/tactical management and by scientists in research. Significant Activities that Support Special Target Populations During this the final year for this project, the on-going research was completed to accomplish all the revised milestones for this year as detailed below. Previous annual reports have listed accomplishments related to the project plan. During five years of the project, some objectives were revised due to retirements and redirections, and in response to an external review panel report. All the revised objectives have been accomplished. We delivered an improved version of GPFARM Decision Support system for strategic planning to Colorado Wheat and Corn growers, NRCS, industry, and other users. We have enhanced and improved the Unit�s Root Zone Model (RZWQM2) to develop simpler tools for planning and guiding tactical management, with improved crop models, new crops, and improved routines for residue cover effects, rainfall interception, water stress response, soil nitrogen, and carbon sequestration. Using the model results, we have created a graphical guide for managing limited water, scheduling irrigations, showing the role of soil water content at planting, and optimal N application. We have added an economic and environmental risk assessment and management tool for the crop model results. We have validated an improved range-livestock model to estimate forage production and livestock weight gains and forecast these to manage stocking rates and drought. We have developed a spreadsheet for based economic analysis tools to help producers find the optimum balance between crops, insurance type and level of coverage, inputs, and lease options, and used it to derive cost-benefit analyses for several options. From cooperative research with Colorado State University, we have established that no-till allows more intensive and flexible dryland cropping systems in the Central Great Plains and improves soil quality. Cooperative agreements with various farm and ranch organizations were initiated to assist in understanding farm and ranch problems, decision support tool development, and to facilitate technology transfer. These accomplishments contribute to NP216- Agricultural System Competitiveness and Sustainability, Component 1. Agronomic Crop Production Systems, Objective 1A1. Develop economic risk averting management strategies that improve soil productivity, enhance soil and water conservation and nutrient cycling, and reduce fuel and pesticide use while enhancing the natural resource base. Technology Transfer Number of New CRADAS: 1 Number of Active CRADAS: 2
Impacts
(N/A)
Publications
- Ascough Ii, J.C., Mcmaster, G.S., Andales, A.A., Hansen, N.C., Ahuja, L.R., Sherrod, L.A. 2007. Evaluating GPFARM for No-Tillage Dryland Experimental Sites in Eastern Colorado. Transactions of the ASABE, 50(5):1565-1578.
- Anapalli, S.S., Ahuja, L.R., Nielsen, D.C., Trout, T.J., Ma, L. 2008. Use of Crop Simulation Models to Evaluate Limited Irrigation Management Options for Corn in Semi-Arid Environment. Water Resources Research. Water Resources Research, 44,W00E02, doi10.1029/2007WR006181.
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Progress 10/01/06 to 09/30/07
Outputs
Progress Report Objectives (from AD-416) 1)Develop a national integrated Farm And Ranch Decision Support System (iFARM DSS) based on GPFARM (Great Plains Framework for Agricultural Resource Management) for both strategic and tactical management to maximize economic returns and minimize environmental impacts on farms and ranches. 2)Extend iFARM DSS to: a) include risk assessment and management for variable and extreme weather, production and marketing uncertainty, and insect, disease, and weed problems; and b) target global climate change issues in the areas of carbon sequestration/credits and greenhouse gas emissions. 3)Apply DSS on selected farms and ranches for whole-farm strategic and tactical management, risk assessment of alternative management practices, and global climate change issues such as carbon sequestration/credits and greenhouse gas emissions to demonstrate feasibility of approach. Transfer technology to potential users. Approach (from AD-416) The Great Plains Systems Research Unit team recently delivered the whole farm/ranch Great Plains Framework for Agricultural Resource Management (GPFARM) DSS for long-term strategic planning in the central Great Plains. GPFARM cooperators requested an enhanced DSS to allow seasonal tactical planning and management of their operations based on climatic and price fluctuations, and other risk factors. The new CRIS project will extend and expand GPFARM into a regional Integrated Farm and Ranch DSS (iFARM DSS) for both strategic and tactical planning/management that will include climate, market, and natural-hazard risks, as well as carbon sequestration and greenhouse gas emissions. New science modules and technologies will be introduced into the DSS for this purpose (e.g., a crop module that responds better to water/N/heat stresses and a module to account for carbon and greenhouse gases). Other new modules will be developed for management effects on soil, economic and environmental risk assessment, and estimation of field-scale model parameters. New crops and managements will be added for expansion into the northern and southern Great Plains, and the Midwest. DSS components will be tested on experimental data and the whole DSS evaluated across selected farms and ranches. The outcome of this research will be a validated iFARM DSS for use by farmers, ranchers, agri-businesses, and NRCS in strategic/tactical management and by scientists in research. Significant Activities that Support Special Target Populations 3.1.1 Develop on-farm management scenarios; collect producer data (field and climate) for crop and livestock systems During the past year on-farm management scenarios involving different crop rotations and land cost scenarios were developed for a cross-section of Colorado farms. The analysis is on-going but an early summary of the data indicate that some common crop rotations are less economically sustainable that others given the producers land costs. A final analysis and write up for a fact sheet and popular press publication are the goals in the coming months. We continue to collect detailed producer data including management, yield, marketing, and climate information from 4 farmer cooperators. In addition we have recently begun work with Mountain View Harvest Cooperative to collect detailed production data from Cooperative members. Mountain View Harvest Cooperative will use the database and iFARM tools to look for management practices that result in the desired quality of wheat. As part of the database design we will also provide MVHC with a �virtual elevator� an unexpected but potentially very useful tool to add to the iFARM suite of DSS tools. As part of the iFARM data collection process and in conjunction with the RMA drought calculator project, rangeland forage and livestock data were collected from 5 research locations in the Central Great Plains. In addition, meetings with ranchers in Nevada, North and South Dakota, Kansas, Wyoming, and Utah were held to demonstrate an early version of the iFARM/RMA strategic and tactical drought planning tool. 3.2.1 Administer producer surveys In September 2006 a survey of the iFARM focus group was conducted. We received a good return on the surveys and the results of the survey have provided the basis for future work on iFARM and indicate that the project is appropriate and on-track to meet our customers� needs. We followed up with a customer focus meeting in December 2006 at which time we discussed the findings with the customers and sought additional input or refinement to the survey results. A similar survey is planned for the fall of 2007. Another producer survey with ties to iFARM was conducted by Ms. Tia McDonald, Master�s Candidate, as part of a cooperative effort with Colorado State University to assess the use and understanding of farmers with respect to crop insurance. This project is scheduled for completion in 2007 and will provide the unit with information on how crop insurance impacts management decisions both strategically and tactically. 3.2.2 Conduct hard- and soft-tests of whole enterprise data The tool known as iFARM Economic Assessment Tool (iFEAT) has been tested with cooperators and met with their approval of design and function. We have used iFEAT to analyze several whole enterprises to determine what lease arrangements work for the model-predicted yields for several crop rotations. The tool is now in the hands of approximately 20 on-farm users who will be polled to determine whether the tool was used to make strategic or tactical decisions and if so, how well it functioned. Accomplishments Tactical Planning Spreadsheet Tools Guides In-season Management Decisions Farmers and ranchers in the Central Great Plains face a multitude of management planning decisions at the beginning of each season, such as: crops and crop rotations, land ownership/lease arrangement, government programs, crop insurance options, commodity prices, and input costs including petroleum products. These management options are difficult to analyze because they have complex interactions with each other and the choice of what management optimizes economic returns and stability are not always straightforward, further complicated by the recurrent drought. As part of the iFARM suite of tools a tactical field planning tool known as iFEAT has been developed and released. iFEAT has completed beta and version 1.0 testing with ASR cooperators and is currently available as a downloadable spreadsheet tool. iFEAT allow the user to look at current management options then adjust one or more of the options to see the projected effect on net economic returns. The tool can be used in conjunction with GPFARM to evaluate the yield potential of new crop rotations or the user can input yield estimates of their own. With the yield data the user can adjust land-lease arrangements, input costs, and insurance premiums among other things to see what the combined effect is on economic returns. iFEAT generates economic reports that the producer can use in applying for production loans, negotiating lease agreements, etc. iFEAT was developed in collaboration with the Central Great Plains Experiment Station in Akron, CO. The tool is also available on the Colorado Wheat Growers homepage, and after a recent article was published by the No-till Farmer journal it is available on the journal�s homepage. Because farmers and ranchers have only limited time to �play� with computer based tools iFEAT is simple and easy to understand, but has the potential to take much of the guess work out of tactical decision making experienced by almost all farmers in the Central Great Plains. This accomplishment addresses the attribute statements of the Integrated Agricultural Systems NP-207. Economic and Risk Analysis at the Farm Level Farmers face the challenging question of choosing between different management alternatives while simultaneously considering economic maximization and risk minimization. Plot- and farm-level data were collected from Iowa, Colorado, Kansas, and Nebraska; Iowa plot data were analyzed using multiple stochastic decision analysis techniques [e.g., stochastic dominance with respect to a function (SDRF) and stochastic efficiency with respect to a function (SERF)] in order to rank various farm management choices under economic risk. Research was also initiated on generalizing the stochastic decision analysis results to Colorado, Kansas, and Nebraska using a minimum data approach at the farm level (as opposed to the plot level), and on producing stand-alone web-based economic/environmental risk analysis tools. The outputs of this research include draft manuscripts summarizing the continuum of risk analysis methods for screening multiple economic choices and the sharing of these results with the Colorado Corn Growers Association. This study offers state-of-the-science advances in conclusively demonstrating that simple statistical analysis approaches may not be adequate for analyzing and ranking various farm management practices due to the stochastic nature of on-farm data as well as the multiple management factors typically involved in the farm decision-making process. However, using methods such as SERF, accompanied with other analysis/visualization tools (e.g., the Stoplight visualization method), produce credible results which are simple to convey and understand. This accomplishment contributes to Attribute 10 of NP 207. A System model Can Help Maximize Production with limited Water CERES-maize model was applied and validated on all the available data for dryland and irrigated corn (Zea mays L.) production at Akron, CO, and then used to evaluate production in response to different levels of limited irrigation, divided between vegetative and reproductive growth stages to different degrees, over 92 years of historical weather records. The study established: 1) mean and variations of production functions (production vs. amount of irrigation) over 92 years; 2) that 20% of limited water during vegetative stage and 80% during reproductive stage of corn was the best recipe overall; 3) quantitative effect of initial soil water content on Corn yields; 4) that scheduling irrigation when 80% of available soil water has been depleted saved water with no effect on yield for the Akron soil; and 5) data for optimizing N applications at different irrigation levels. This study provided evidence that a system model can help develop location-specific agronomic practices to maximize water use efficiency (WUE) under limited water conditions. Concepts developed in the study will be adapted to other locations, climates, and crops. This accomplishment contributes to Attribute 8 of the NP207 and NP201 problem areas of irrigation water management and water quality protection. A Range-Livestock Model Can Help Determine the best Stocking Rate Adjustment of stocking rate is the primary management tool available to ranchers that has the biggest impact on vegetation and livestock production on semi-arid rangelands. Profitability and sustainability can be achieved by appropriately stocking cattle herds on native rangelands. Scientists from the Agricultural Systems Research Unit (ASRU) and the Rangeland Resources Research Unit (RRRU) collaborated in using the improved USDA-ARS GPFARM rangeland-cattle model to simulate interactions between stocking rate, forage production, and steer weights on semiarid northern mixed-grass prairie in southeast Wyoming. Comparisons of simulated peak standing crop (PSC) (1991 � 2001) and average grazing season (mid-June to mid-October) weights of Hereford yearling steers (1982 � 2001) to experimentally-obtained values from three stocking rates (light: 16 steers on 198 acres; moderate: 4 steers on 30 acres; heavy: 4 steers on 22 acres) indicated that the model could explain 65% to 74% of the variability in PSC and 96% to 99% of the variability in steer weights. These simulation results provide further evidence that the recommended moderate stocking rate is sustainable and does not adversely affect rangeland productive capacity or animal performance. The model can be used to explore the interactions among the environment (especially precipitation), stocking rate, rangeland productive capacity, and steer performance. Simulation results can then be used to recommend appropriate stocking rates on native rangelands. Improved Rangeland Model for Simulating Environmental Effects The GPFARM rangeland model needed an improved plant model that is more responsive to water stress. Furthermore, the model did not include the effects of nitrogen (N) availability on forage production and quality. The ASRU added into the model a method of estimating daily plant growth based on transpiration, which is the water consumption of the plant. The method considers relative humidity, capacity of the plants to take up water from the soil and CO2 from the air, and available water in the soil to obtain a better estimate of plant growth. The ASRU is also collaborating with scientists from the Agroscope Reckenholz-T�nikon Research Station, Zurich, Switzerland to add a carbon (C) and nitrogen simulation module for tracking C and N cycling through the plants and the soil. The plant model has been improved to account for C and N in plant shoots and roots. After testing against available data, the improved rangeland model can be used to simulate environmental and climate change effects on forage production and quality (N content). Improving Water Stress Response of Agricultural System Models Application of models to help optimize limited water use requires that the models respond well to water stress. Several of the common system models (DSSAT, RZWQM, CropSyst, APSIM, and GLYCIM) were reviewed on their simulations of water stress. This review provides the current status of crop simulation models on water stress and needs for enhancements. This information is critically important for developing next generation of plant models and it led to the much-needed collaboration among Agricultural Systems Research Unit in Fort Collins, CO; Crop Systems and Global Change Laboratory in Beltsville, MD; University of Florida; and University of Georgia. This accomplishment contributes to Attribute 8 of the NP207 and NP201 problem areas of irrigation water management and water quality protection. Enhanced surface energy balance simulation in RZWQM-2 Energy balance is important to simulate water stress response of crops as well as evapotranspiration and soil temperature. A linkage between RZWQM and SHAW was tested using field measured heat fluxes and canopy temperature. This is the first time a plant growth model was linked to SHAW to simulate energy balance for an entire growing season. This study not only enhances RZWQM for surface energy balance simulation, but also extended SHAW model to simulate energy balance for a growing season with simulated plant parameters. The study enhanced collaboration among Agricultural Systems Research Unit in Fort Collins, CO; Northwest Watershed Research Center in Boise, ID; and Chinese Academy of Sciences. This accomplishment contributes to Attribute 8 of the NP207 and NP201 problem areas of irrigation water management and water quality protection. Technology Transfer Number of Active CRADAS and MTAS: 1 Number of Web Sites managed: 2 Number of Non-Peer Reviewed Presentations and Proceedings: 8 Number of Newspaper Articles,Presentations for NonScience Audiences: 16
Impacts
(N/A)
Publications
- Andales, A.A., Derner, J.D., Ahuja, L.R., Hart, R.H. 2006. Strategic and Tactical Prediction of Forage Production in Northern Mixed-Grass Prairie. Rangeland Ecology and Management. 59(6), 59:576-584/ November 2006. DOI: 10.2111/06-001R1.1.
- Campbell, C.A., Janzen, H.H., Paustian, K., Gregorich, E.G., Sherrod, L.A., Liang, B.C., Zentner, R.P. 2005. Carbon storage in soils of the north american great plains: effect of cropping frequency. Agronomy Journal 97:349-363. Mar/April 2005.
- Cameira, M.R., Fernando, R.M., Ahuja, L.R., Ma, L. 2007. Simulating the Fate of Nitrogen in Field Soil - Crop Environment in the Mediterranean Region. Agricultural Water Management. April 2, 2007 (0378-3774), doi:10. 1016/j.agwat.2007.03.002.
- Ahuja, L.R., Andales, A.A., Ma, L., Saseendran, S.A. 2007. Whole System Integration and Modeling Essential to Agricultural Science and Technology for the 21st Century. Crop Management. Vol.19, No 1/2, April 2007.
- Mcmaster, G.S., Wilhelm, W.W. 2006. Canopy development of wheat. Book Chapter.
- Kozak, J.A., Ahuja, L.R., Green, T.R., Ma, L. 2007. Crop canopy and residue rainfall interception effects on water and crop growth. Hydrological Processes. 21:229-241, March 2007.
- Kozak, J.A., Aiken, R.M., Flerchinger, G.N., Nielsen, D.C., Ma, L., and Ahuja, L.R. 2007, Comparison of modeling approaches to quantify residue architecture effects on soil temperature and water. International Journal of Soil and Tillage Research.95:84-96.
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Progress 10/01/05 to 09/30/06
Outputs
Progress Report 1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? Why does it matter? Agriculture has become a highly complex enterprise in the 21st century due to environmental and resource sustainability concerns, global market competition, and variable weather with frequent floods and droughts. Decision aids based on the synthesis of current research knowledge for agricultural production systems can help farmers manage the above complexities. A Great Plains Systems Research Unit team recently developed a computerized whole farm/ranch Decision Support System (DSS), Great Plains Framework for Agricultural Resource Management (GPFARM), for long-term strategic planning for farmers and ranchers in the central Great Plains. The main objective of GPFARM is to address long-term sustainability for production, economics, and the environment. Farm and ranch cooperators of GPFARM
are requesting an enhanced DSS to allow seasonal tactical planning and management of their operations, based on climatic and price fluctuations and other risk factors. The proposed project will extend and expand GPFARM into a national Integrated Farm and Ranch DSS (iFARM DSS) for both strategic and tactical planning/management that will include climate, market, and natural-hazard risks, as well as carbon sequestration. New science modules and technologies will be introduced into the DSS for this purpose, such as a crop module that responds better to water/N/heat stresses; accounting of carbon and greenhouse gases; management effects on soil, precipitation capture, and water storage; soil temperature; economic and environmental risk assessment; estimation of field-scale model parameters; rule-based management; and Internet connections to national soil and climate databases. New crops and management systems will be added for expansion into the northern and southern Great Plains, and
the Midwest. DSS components will be tested on experimental data and the whole DSS evaluated across selected farms and ranches. The outcome of this research will be a validated iFARM DSS for use by farmers, ranchers, agri- businesses, and NRCS in strategic/tactical management, and by scientists and educators in research and teaching. This research is aligned with ARS National Program 207 - Integrated Farming Systems. 2. List by year the currently approved milestones (indicators of research progress) Objective. Hypothesis - Milestone - Initials of the responsible scientist(s) are given in parentheses for each milestone: Laj Ahuja (LA), Allan Andales (AA), Jim Ascough (JA), Gale Dunn (GD), Tim Green (TG), Liwang Ma (LM) Greg McMaster (GM). The project was initiated in August 2003; hence, FY 2005 was the second year of the project. 1.1 - Design Overall DSS Framework and Delivery Mechanisms): Year 1. 1.2 - Develop Science Components: Years 1-3. 1.2.1 - Assess crop growth module needs and
make required improvements (GM, LM). 1.2.2 - Develop phenology and emergence module; tie phenology module to grazing of live crop biomass (GM, RS). 1.2.3 - Enhance soil physical process module (soil properties, water storage, soil temperature, wind and water erosion (LA, TG, JA). 1.2.4 - Add CO2, to existing carbon/nutrient cycling (OMNI) module (LM plus new scientist). 1.2.5 - Improve range vegetation and animal components (AA, LA). 1.3 - Obtain Data and Test Science Modules: Years 1-4. 1.3.1 - Obtain field data from ARS collaborators and ARS, NRCS, and NASS databases (GD, LA, JA). 1.3.2 - Identify cropping and range management systems for evaluation (AA, GM, GD). 1.3.3 - Test soil, crop growth, nutrient, erosion, and global change process- based science modules against obtained data (All Scientists) 2.1 - Risk Experimental Design: Years 1-2. 2.1.1 - Develop theoretical framework for tactical and strategic risk assessment identify and evaluate nonparametric empirical
distribution methods, efficiency criteria approaches, and hydrologic frequency analysis techniques (JA, LM plus new scientist, AA). 2.2 - Risk Methodology and Component Testing: Years 2-4. 2.2.1 - Develop and test numerical, discrete-state stochastic simulation module (JA). 2.2.2 - Simulate gross revenues and their probabilities; implement contract- based risk management strategies, generate kernel-smoothed cumulative distribution functions and display graphically (JA) . 2.2.3 - Program and test efficiency criteria methods for system applications (JA). 2.2.4 - Generate non-exceedance probability (NEP) curves of key environmental variables; assign qualitative risk ratings to NEP values (JA). 2.3 - Carbon Sequestration, and Nutrient Cycling: Years 2-4. 2.3.1 - Add CO2 effects to plant growth and carbon (organic and inorganic) pool tracking modules (LM plus new scientist, GM). 2.3.2 - Enhance and evaluate crop N uptake equations; develop and refine C/N/P pool structures (LM plus new
scientist, GM). 2.3.3 - Extend existing rule-based system for management events for fertilizer and manure applications (LM plus new scientist). 2.4 - Nutrient Module and Global Change Component Testing: Years 2-4. 2.4.1- Calibrate and validate nutrient module (LM plus new scientist). 2.4.2 -Develop probability density functions for nutrient model coefficients (LM plus new scientist, AA). 3.1 - On-Farm Testing Experimental Design: Years 1-4. 3.1.1 - Develop on-farm management scenarios; collect producer data (field and climate) for crop and livestock systems (GD and all scientists). 3.2- Apply and Evaluate iFARM DSS for Strategic and Tactical Planning: Years 4-5. 3.2.1 - Administer producer surveys (GD, JA). 3.2.2 - Conduct hard- and soft-tests of whole enterprise data (GD and all scientists). 3.2.3- Describe and analyze differences in economic and environmental risk among tactical and strategic management strategies (JA, GD, GM). 3.2.4 - Perform cost-benefit analysis for alternative,
in-season management scenarios (GD, LM, JA, LA). 3.2.5 - Assess utility of iFARM DSS for carbon credits (LM plus new scientist, GM). 4a List the single most significant research accomplishment during FY 2006. On-Farm Research Yields Simple Spreadsheet Planning Tools Farmers and ranchers on the central Great Plains have had to adjust to regular and cyclic drought, lower commodity prices, increased input costs, and new crops. These factors coupled with government programs and crop insurance options make optimal decision making difficult. ASRU initiated a program of on-farm research for testing GPFARM and related technologies, with over 100 visits made with on-farm cooperators to collect whole farm information. The detailed economic, environmental, and management information collected has been invaluable in the development of ASRUs new iFARM decision support tools. In particular the Unit has released in beta version several spreadsheet based economic analysis tools to help producers
find the optimum balance between crops, insurance type and level of coverage, inputs, and lease options. The spreadsheets have been well received and the Colorado Association of Wheat Growers has made the spreadsheets available on their members website. This accomplishment is aligned with ARS National Program 207 - Integrated Farming Systems, Component I: Attributes of Integrated Agricultural Systems and Associated Projects. 4b List other significant research accomplishment(s), if any. Strategic and Tactical Prediction of Rangeland Forage Production helps Drought Management: This accomplishment addressed the need to develop and demonstrate methods for using the iFARM rangeland model in making strategic (long-term) and tactical (within-season) decisions on appropriate stocking rates of cattle on native rangelands. The rangeland forage model was used to simulate annual peak standing crop (PSC) of northern mixed-grass prairie for a 20-year period to estimate the long- term chances
(probabilities) of getting various amounts of PSC, based on historical weather. Also, the forage model was used to predict within- season forage production based on knowledge of previous weather (observed weather in previous months), beginning soil moisture in spring, and expected normal as well as extreme (wet and dry) weather. Model- estimated PSC values and their chances of occurrence can guide ranchers in selecting an appropriate stocking rate based on long-term expected availability of forage. Within-season predictions of forage production with 1 to 5 months lead time can guide ranchers in making adjustments in stocking rate (e.g., destocking during drought) based on predicted availability of forage. Both the strategic and tactical applications of the forage model can potentially reduce the risk associated with variability in available forage caused by weather variability. This accomplishment is aligned with ARS National Program 207 - Integrated Farming Systems, Component I:
Attributes of Integrated Agricultural Systems and Associated Projects. Agricultural system-level models used to help direct field research and Management. The agricultural system model Root Zone Water Quality Model (RZWQM) provides a systems approach for field scientists to simulate various producer management practices. Over the last five years, several improvements have been made linking RZWQM with DSSAT (CERES and CROPGRO) plant growth models to provide users more options in plant growth simulation. Also, RZWQM and SHAW have been integrated to simulate no- tillage effects on soil moisture and temperature, and with the GIS application RZWQM2-GIS to extend applications to spatially distribute field results. RZWQM has been used to simulate the effects of weather variability, controlled drainage, and cover crop components on soil and water quality. Model use for North China Plain showed that water and nitrogen applications there could be reduced by 50% without affecting production.
This accomplishment is aligned with ARS National Program 207 - Integrated Farming Systems, Component I: Attributes of Integrated Agricultural Systems and Associated Projects. 4d Progress report. 1. A prototype Unified Plant Growth Model (UPGM) has been developed based on the standalone plant growth model derived from the Wind Erosion Prediction System (WEPS), which is based on the EPIC plant growth model. Ongoing research that should improve the plant growth modeling technology includes: 1) incorporating modifications from work done to other models that are based on the EPIC plant growth model (e.g., GPFARM; Water Erosion Prediction Project, WEPP; ALMANAC; and Soil and Water Assessment Tool, SWAT), and 2) thoroughly evaluating how the plant processes are represented in these models. High priority needs identified to date include: 1) seedling emergence, 2) phenology, 3) biomass generation, 4) biomass partitioning, 5) root growth, and 6) plant stress factors. Therefore, initial work has
focused on creating stand-alone submodels for predicting seedling emergence (as a function of soil water and thermal time) and phenology (by predicting specific growth stages and responses to different levels of soil water availability). Evaluation of alternative approaches for generating biomass (e.g., radiation use efficiency, transpiration use efficiency, plant growth analysis), biomass partitioning (e.g., modifications to generating LAI and partitioning coefficients partly based on better phenology prediction), and stress factors (e.g., single-most limiting, additive, multiplicative) is underway. We envision that these modifications and enhancements should improve model responses to varying levels of soil water availability. 2. Work has continued on a previously developed ten-step risk management program for agricultural producers. The program combines strategic planning with decision analysis for risk to create a user friendly and powerful economic risk management framework.
A set of user-friendly Excel tools have been developed or to assist producers with strategic, tactical, and operation farm management. In particular, we have developed Excel spreadsheet tools containing Northern Colorado farm/county crop yields, animal production history, and local and national futures prices for crops grown or animals produced on farms/ranches. The data has been analyzed to derive the gross revenue probability distribution for alternative farm or ranch risk management strategies. Excel VBA macros have been developed to estimate the expected joint yield and price revenue distribution, and different classes of pricing (e.g., premiums paid, strike, futures, and forward prices) and insurance (e.g., price, yield, and revenue guarantees) risk management contracts can evaluated. We are working to expand the Excel spreadsheets to further simulate revenues and their probabilities under the above contract-based risk management strategy. In addition, we have started
development of an Excel-based strategic risk component that employs efficiency criteria methods [i.e., expected-variance (E-V) and stochastic dominance techniques] to segregate long-term (10 to 20 years) historical net returns from the farm or ranch into efficient (non-dominated) and non- efficient sets for alternative management scenarios. To this end, we are working developing a new quadratic programming modeling approach (using the Excel-based Whats Best software) to model risk-return tradeoffs for the efficient combinations of crop and livestock enterprises. The analysis results are displayed so that optimal sets of management alternatives are easily distinguishable by producer collaborators. 3. A new multiple land unit hillslope erosion standalone soil erosion component for the Object Modeling System (OMS) has been developed. The component has links to continuous infiltration and overland flow across spatial land units, and contains many enhancements to the Water Erosion
Prediction Project (WEPP) hillslope erosion model. This component will be very useful for the iFARM modeling effort. In addition, a prototype hillslope hydrology and erosion model was created in OMS to demonstrate the potential of the framework in assembling science components from various model sources. The OMS-based prototype hillslope hydrology and erosion model delivers the water erosion component information from other system modules including Green-Ampt infiltration, kinematic wave runoff routing, snowmelt, PET, etc. in continuous simulation mode across multiple land units. Module code was restructured and added to the OMS library repository. 5. Describe the major accomplishments to date and their predicted or actual impact. This new project is being built upon its GPFARM predecessor project, while we are still supporting GPFARM. The GPFARM was developed for farmers and ranchers for strategic planning of their cropping and range- livestock systems. It was delivered to Colorado
Association of Wheat Growers (CAWG). Under a cooperation agreement with ARS-GPSR, CAWG is providing GPFARM DSS in its membership packet. Approximately 600 copies of GPFARM have been distributed by CAWG. The GPSR scientists facilitated this activity and conducted several training sessions for the membership. Further improvements in GPFARM were made by way of providing standardized management scenarios for the central Great Plains that save farmers considerable time in setting up GPFARM for their site-specific conditions. With GPFARM, CAWG users can evaluate alternative management strategies for cropping and range-livestock systems, and view the results of the strategies in both economic and environmental terms. We are continuing to work with farmer groups to utilize this technology that is having a great deal of impact (please see under Question #6). GPFARM was a major milestone in the action plans for the NP 207. GPFARM was evaluated for its performance in simulating field
management over time on the Alternative Crop Rotation Study at the Central Great Plains Research Station, Akron, Co. GPFARM reproduced the trend in crop production for most crops with the exception of replant operations and complete yield failures. GPFARM predicted within 20% of the observed annualized yields for 7 of 11 rotations. GPFARM predicted within 1 standard deviation of observed data for all rotations except WMF. Recommendations for IFARM include improved interface data entry and improved algorithms to simulate effects of poor stand establishment, effects of precipitation timing on yield, and complete yield failures. The GPFARM forage and cow-calf modules were re-parameterized and tested against experimental data from northeastern Colorado and southeastern Wyoming. It was demonstrated that GPFARM could accurately simulate forage production and average cow and calf weights when properly calibrated. The work has been published in the Journal of Range Ecology and
Management. This work provides examples of how a model could benefit field research. No-tillage allows more intensive and flexible dryland cropping systems and improves soil quality. Dryland cropping systems on the Central Great Plains are subject to frequent and cyclic drought spells. Even during periods of normal precipitation, available soil moisture is marginally adequate for most crop production that has wheat-fallow as the established crop rotation. A long-term cooperative research project has focused on investigating no-tillage as a way to conserve soil moisture and grow more intensive crop rotations, such as wheat-corn-fallow, wheat- corn-millet-fallow, or wheat-corn-sunflower-fallow. This research has established that no-tillage conserves more soil moisture and allows more intensive and flexible cropping choices. It also improves soil organic matter in the surface layer that helps enhance rainfall infiltration. These and similar research results from the ARS Central Great
Plains Experiment Station, have convinced the farmers to convert more than half a million acres of wheat-fallow to more intensive cropping systems, and this conversion is still increasing. Predicting multi-crop and rangeland forage phenology: A computer program has been developed that simulates the phenology of crop (e.g., wheat, barley, maize, proso millet, hay millet, sorghum, sunflower) and rangeland forage species that responds to water stress. This program is needed for 1) more accurate simulations of crop and rangelands in the iFARM project, and 2) as part of the tactical planning component of iFARM. This program summarized the developmental sequence of these species, quantified the changes expected to water stress, and makes the information readily available to producers, consultants, agribusiness, extension personal, and scientists. Improved Range-Forage and livestock Growth Model: This product addressed the need for a reliable rangeland model as a basis for creating tactical
range planning and management tools, identified by the USDA- Risk Management Agency (RMA) and our ranch cooperators, especially for drought conditions. The GPFARM forage and cow-calf modules were re-programmed in object-oriented Java for easier code improvement, maintenance, and debugging, with the following improvements: 1) A simple phenology submodel based on heat units was added to simulate different growth stages, especially to estimate green-up shoot biomass in Spring from live roots; 2) To simulate plant competition for soil water, root growth, potential transpiration, and root water uptake are now simulated separately for each functional group; 3) Also, leaf area index (LAI) of each functional group is now simulated to account for differences in light interception and subsequent transpiration; and 4) A steer class was added to the animal model for simulation of stockers. Using this improved range model, simpler information systems will be developed for use by range managers,
and this new model will also be used to aid range research for synthesis and quantification of management and climate change effects on forage and animal growth. Residue Rainfall Interception Effects on Soil Water and Crop Growth Quantified: A residue interception component was added to the Root Zone Water Quality Model (RZWQM) and examined with respect to soil water balance effects in a cropping system. Residues have been shown to intercept a significant amount of rainfall but have not been readily accounted for in modeling and management efforts. The model was tested with respect to a hypothetical plot in Akron, Colorado: a corn residue-covered soil. Interception was shown to decrease infiltration, runoff, ET, deep seepage, macropore flow, soil water storage, leaf area index, and crop/grain yield. Knowledge of plant residue effects on soil water can guide farm and regional assessment of residue management alternatives for soil, water, and nutrient conservation. Improved modeling of
Residue architecture effects on soil temperature and water: A new hybrid model (RZ-SHAW) that extends the applications of the Root Zone Water Quality Model (RZWQM) to conditions of different residue types and architectures affecting heat and water transfer at the soil surface was developed and examined with respect to soil water and temperature under different residue managements. Residue layering and architecture effects on the surface energy balance and subsequent heat and water flux have not been fully explored. RZ-SHAW allows different methods of surface energy flux evaluation to be used: (1) The Simultaneous Heat and Water (SHAW) method; (2) the Shuttleworth-Wallace (S-W) method; and (3) the PENFLUX method. The model and different surface energy balance methods were tested with respect to two plots in Akron, Colorado: a wheat residue- covered soil. Based on a statistical analysis, SHAW and PENFLUX simulation results agreed with measured soil temperature and water storage data
much better than S-W. Knowledge of plant residue effects on the soil energy balance can guide farm and regional assessment of residue management alternatives for soil, water, and nutrient conservation; pest management; and plant development processes. Models used to determine optimum planting date for corn: Planting date can greatly affect production and some information on the effect of planting dates on production will be useful to the farmers. Calibrated and validated Root Zone Water Quality Model (RZWQM) and CERES- Maize model were used with longterm weather data at Akron, CO. Optimum planting dates for the three corn hybrids in the Eastern Colorado region were determined. This information was disseminated to the customer focus group at Akron. Models used to develop best N management options: RZWQM and RZWQM-CERES hybrid models were calibrated and tested for modeling crop rotations in Nashua, Iowa and Akron, CO. In addition, the CERES-wheat model was calibrated and validated for
nitrogen (N) management effects of winter wheat in the Central Great Plains. Utilizing both the validated models, best N management options (e.g.: rate, dose, and method of application) for optimizing yield return and environmental quality in Eastern Colorado were developed. This information is being provided to farmers in the region. Model use for North China Plain showed that water and nitrogen applications could be reduced by 50% without affecting production. Accomplishments given under Question 4a and 4b above are the major accomplishments made this past year in developing the needed components for the iFARM decision support system. All new components are designed to be standalone, exchangeable, components in the Object Modeling System (OMS). We have learned that this process of making standalone reusable components is more complex and requires manual effort, and thus time- consuming. However, this will be highly cost-effective for the longterm maintenance and updates, and the new
iFARM model will be flexible and customizable to different conditions and problems. Based on Expert Panel Review recommendations, the iFARM will be targeted for use by agricultural advisors, ag-businesses , field scientists, and advanced farmers and ranchers for year to year planning of cropping and livestock herds and as guide to management. The iFARM will also be used by scientists to generate simple management guidelines for farmers and ranchers, and in teaching. While the iFARM-OMS is being made ready, we have also incorporated the improved component in the new enhanced RZWQM- based model called RZWQM2 and RZWQM2-GIS. Until the iFARM-OMS is ready, we will use RZWQM2 in its place for above applications. The above accomplishments dealing with applications of GPFARM and development of iFARM (Range and Crop) models are major contributions to the National Program 207, Integrated Agricultural Systems (which is not divided into components). They also contribute to the following
components of National Program 205, Rangeland, Pasture and Forage: Ecosystems and their Sustainable Management (Problem Area - Decision Support Systems); and Grazing Management: Livestock Production and Environment (all problem areas). The accomplishments address the ARS Strategic Plan Goal #5: Protect and Enhance Nation's Natural Resource Base and Environment, and Goal #1: Enhance Economic Opportunities for Agricultural Producers. 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? i. Focus Group Meetings In September the Unit will survey by e- and surface mail members of its focus group as requested by National Program Leaders for NP-207. The results of the survey will be presented in Atlanta in October at the NP- 207 meeting. ii. Field Days and/or
Farmer Organized Sessions The Unit had presentations on GPFARM and iFARM at two (2) major customer- organized conferences. Eight (8) presentations were made to the Colorado Association of Wheat Growers and Colorado Corn Growers Association. Unit scientists made two (2) guest lecturer appearances at CSU for both classroom and laboratory instruction. iii. Meetings with Cooperators and Collaborators For the purpose of concept testing and user feedback thirty-three (33) on-farm visits to approximately 15 farmer cooperators were made. These sessions have shaped our approach and design for tactical farm planning tools. For our collaborators in the science community, the Unit hosted two (2) internationally attended symposia. iv. Meetings with NFCA and CRADA Partners The interface code for GPFARM was transferred to our CRADA partner Decision Commerce Group for the next phase of product development. An NFCA was signed with the Colorado Corn Growers Association for the transfer of GPFARM to
CCGA. CCGA will modify the GPFARM interface for their purposes. A CRADA with CCGA is pending and should be signed by the end of the FY. The existing NFCA with the Colorado Association of Wheat Growers was extended for another 5 years. CAWG provides GPFARM version 2.6 as a members benefit and has provided two iFARM tools to its members for testing. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List your peer reviewed publications below). High Plains Journal January 24, 2006 http://www.hpj.com/dtnnewstable.cfm?type=story&sid=16054 Ag Professional - January 24, 2006 http://www.agprofessional.com/show_story.php?id=37806 South Dakota Cattlemen - January 24, 2006 http://www.sdcattlemen.org/index.cfm?show=4&id=16054 Oregon Wheat Growers League - January 24, 2006 http://www.owgl.org/index.cfm?show=4&id=16054
Impacts
(N/A)
Publications
- Chander, S., Ahuja, L.R., Peairs, F.B., Aggarwal, P.K., Kalra, N. 2006. Modeling the effect of russian wheat aphid, diuraphis noxia (mordvilko) and weeds in winter wheat as guide to management. Agricultural Systems. Agricultural Systems (Journal) June 2006. 88(2-3):494-513. 2006.
- Sherrod, L.A., Peterson, G.A., Westfall, D.G., Ahuja, L.R. 2005. Soil organic carbon pools after 12 years in no-till dryland agroecosystems. Soil Science Society of America Journal. Vol. 69:1600-1608. 2005.
- Cantero-Martinez, C., Westfall, D.G., Sherrod, L.A., Peterson, G.A. 2006. Long-Term Crop Residue Dynamics in No-Till Cropping Systems Under Semi- Arid Conditions. Journal of Soil and Water Conservation. Vol 61:2 (84-95).
- Andales, A.A., Derner, J.D., Bartling, P.N., Ahuja, L.R., Dunn, G.H., Hart, R.H., Hanson, J.D. 2005. Evaluation of GPFARM for simulation of forage production and cow-calf weights. Rangeland Ecology and Management.58:247- 255.
- Derner, J.D., Andales, A.A., Morgan, J.A. 2006. Tactical and strategic difficulties in managing water-limited rangelands of the Western Great Plains. In: Soil and Water Conservation Society Proceedings, Rocky Mountain Rendezvous II. p. 10.
- Yu, Q., Kozak, J.A., Xu, S., Flerchinger, G.N., Ma, L., Ahuja, L.R. 2005. Energy balance and water and heat transfer simulated by shaw and rz-shaw. ASA-CSSA-SSSA Annual 2005 Meeting Abstracts. Salt Lake City, UT. Nov. 6-10, 2005.
- Ma, L., Sherrod, L.A., Peterson, G., Hansen, N., Ahuja, L.R. 2006. Soil organic carbon pool changes under long-term no-till and cropping intensity regimes across an evapotranspiration gradient in Eastern Colorado, USA. International Soil Tillage Research Organization Proceedings. KIel, Germany. 8/28 - 9/3/2006. (Abstract)
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Progress 10/01/04 to 09/30/05
Outputs
1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? What does it matter? Agriculture has become a highly complex enterprise in the 21st century due to environmental and resource sustainability concerns, global market competition, and variable weather with frequent floods and droughts. Decision aids based on the synthesis of current research knowledge for agricultural production systems can help farmers manage the above complexities. A Great Plains Systems Research Unit team recently developed a computerized whole farm/ranch Decision Support System (DSS), Great Plains Framework for Agricultural Resource Management (GPFARM), for long-term strategic planning for farmers and ranchers in the central Great Plains. The main objective of GPFARM is to address long-term sustainability for production, economics, and the environment. Farm and ranch cooperators of GPFARM are
requesting an enhanced DSS to allow seasonal tactical planning and management of their operations, based on climatic and price fluctuations and other risk factors. The proposed project will extend and expand GPFARM into a national Integrated Farm and Ranch DSS (iFARM DSS) for both strategic and tactical planning/management that will include climate, market, and natural-hazard risks, as well as carbon sequestration. New science modules and technologies will be introduced into the DSS for this purpose, such as a crop module that responds better to water/N/heat stresses; accounting of carbon and greenhouse gases; management effects on soil, precipitation capture, and water storage; soil temperature; economic and environmental risk assessment; estimation of field-scale model parameters; rule-based management; and Internet connections to national soil and climate databases. New crops and management systems will be added for expansion into the northern and southern Great Plains, and the
Midwest. DSS components will be tested on experimental data and the whole DSS evaluated across selected farms and ranches. The outcome of this research will be a validated iFARM DSS for use by farmers, ranchers, agri- businesses, and NRCS in strategic/tactical management, and by scientists and educators in research and teaching. 2. List the milestones (indicators of progress) from your Project Plan. Objective. Hypothesis - Milestone - Initials of the responsible scientist(s) are given in parentheses for each milestone: Laj Ahuja (LA), Allan Andales (AA), Jim Ascough (JA), Gale Dunn (GD), Tim Green (TG), Liwang Ma (LM) Greg McMaster (GM). The project was initiated in August 2003; hence, FY 2005 was the second year of the project. 1.1 - Design Overall DSS Framework and Delivery Mechanisms): Year 1. 1.2 - Develop Science Components: Years 1-3. 1.2.1 - Assess crop growth module needs and make required improvements (GM, LM). 1.2.2 - Develop phenology and emergence module; tie phenology
module to grazing of live crop biomass (GM, RS). 1.2.3 - Enhance soil physical process module (soil properties, water storage, soil temperature, wind and water erosion (LA, TG, JA). 1.2.4 - Add CO2, to existing carbon/nutrient cycling (OMNI) module (LM plus new scientist). 1.2.5 - Improve range vegetation and animal components (AA, LA). 1.3 - Obtain Data and Test Science Modules: Years 1-4. 1.3.1 - Obtain field data from ARS collaborators and ARS, NRCS, and NASS databases (GD, LA, JA). 1.3.2 - Identify cropping and range management systems for evaluation (AA, GM, GD). 1.3.3 - Test soil, crop growth, nutrient, erosion, and global change process- based science modules against obtained data (All Scientists) 2.1 - Risk Experimental Design: Years 1-2. 2.1.1 - Develop theoretical framework for tactical and strategic risk assessment identify and evaluate nonparametric empirical distribution methods, efficiency criteria approaches, and hydrologic frequency analysis techniques (JA,
LM plus new scientist, AA). 2.2 - Risk Methodology and Component Testing: Years 2-4. 2.2.1 - Develop and test numerical, discrete-state stochastic simulation module (JA). 2.2.2 - Simulate gross revenues and their probabilities; implement contract- based risk management strategies, generate kernel-smoothed cumulative distribution functions and display graphically (JA) . 2.2.3 - Program and test efficiency criteria methods for system applications (JA). 2.2.4 - Generate non-exceedance probability (NEP) curves of key environmental variables; assign qualitative risk ratings to NEP values (JA). 2.3 - Carbon Sequestration, and Nutrient Cycling: Years 2-4. 2.3.1 - Add CO2 effects to plant growth and carbon (organic and inorganic) pool tracking modules (LM plus new scientist, GM). 2.3.2 - Enhance and evaluate crop N uptake equations; develop and refine C/N/P pool structures (LM plus new scientist, GM). 2.3.3 - Extend existing rule-based system for management events for fertilizer and manure
applications (LM plus new scientist). 2.4 - Nutrient Module and Global Change Component Testing: Years 2-4. 2.4.1- Calibrate and validate nutrient module (LM plus new scientist). 2.4.2 -Develop probability density functions for nutrient model coefficients (LM plus new scientist, AA). 3.1 - On-Farm Testing Experimental Design: Years 1-4. 3.1.1 - Develop on-farm management scenarios; collect producer data (field and climate) for crop and livestock systems (GD and all scientists). 3.2- Apply and Evaluate iFARM DSS for Strategic and Tactical Planning: Years 4-5. 3.2.1 - Administer producer surveys (GD, JA). 3.2.2 - Conduct hard- and soft-tests of whole enterprise data (GD and all scientists). 3.2.3- Describe and analyze differences in economic and environmental risk among tactical and strategic management strategies (JA, GD, GM). 3.2.4 - Perform cost-benefit analysis for alternative, in-season management scenarios (GD, LM, JA, LA). 3.2.5 - Assess utility of iFARM DSS for carbon credits
(LM plus new scientist, GM). 3a List the milestones that were scheduled to be addressed in FY 2005. For each milestone, indicate the status: fully met, substantially met, or not met. If not met, why. 1. 1.2.2 - Develop phenology and emergence module; tie phenology module to grazing of live crop biomass (GM, AA). Milestone Substantially Met 2. 1.2.3 - Enhance soil physical process module (soil properties, water storage, soil temperature, wind and water erosion (LA, TG, JA). Milestone Substantially Met 3. 1.2.5 - Improve range vegetation and animal components (AA, LA). Milestone Fully Met 4. 1.3.1 - Obtain field data from ARS collaborators and ARS, NRCS, and NASS databases (GD, LA, JA). Milestone Substantially Met 5. 2.1.1 - Develop theoretical framework for tactical and strategic risk assessment - identify and evaluate nonparametric empirical distribution methods, efficiency criteria approaches, and hydrologic frequency analysis techniques (JA, LM plus new scientist, AA).
Milestone Substantially Met 6. 2.4.1- Calibrate and validate nutrient module (LM plus new scientist). Milestone Substantially Met 7. 3.1.1 - Develop on-farm management scenarios; collect producer data (field and climate) for crop and livestock systems (GD and all scientists) . Milestone Substantially Met 3b List the milestones that you expect to address over the next 3 years (FY 2006, 2007, and 2008). What do you expect to accomplish, year by year, over the next 3 years under each milestone? FY 2006: 1.2.1 - Assess crop growth module needs and make required improvements (GM,LM). 2.2.1 - Develop and test numerical, discrete-state stochastic simulation module (JA). 2.2.3 - Program and test efficiency criteria methods for system applications (JA). 2.2.4 - Generate non-exceedance probability (NEP) curves of key environmental variables; assign qualitative risk ratings to NEP values (JA). 2.3.1 - Add CO2 effects to plant growth and carbon (organic and inorganic) pool tracking modules (LM
plus new scientist, GM). 2.3.2 - Enhance and evaluate crop N uptake equations; develop and refine C/N/P pool structures (LM plus new scientist, GM). 2.3.3 - Extend existing rule-based system for management events for fertilizer and manure applications (LM plus new scientist). FY 2007: 1.3.3 - Test soil, crop growth, nutrient, erosion, and global change process- based science modules against obtained data (All Scientists) 2.2.2 - Simulate gross revenues and their probabilities; implement contract- based risk management strategies, generate kernel-smoothed cumulative distribution functions and display graphically (JA). 2.4.1- Calibrate and validate nutrient module (LM plus new scientist) . 2.4.2 - Develop probability density functions for nutrient model coefficients (LM plus new scientist, AA). 3.1.1 - Develop on-farm management scenarios; collect producer data (field and climate) for crop and livestock systems (GD and all scientists). FY 2008: 3.2.1 - Administer producer surveys (GD,
JA). 3.2.2 - Conduct hard- and soft-tests of whole enterprise data (GD and all scientists). 3.2.3- Describe and analyze differences in economic and environmental risk among tactical and strategic management strategies (JA, GD, GM). 3.2.4 - Perform cost-benefit analysis for alternative, in-season management scenarios (GD, LM, JA, LA). 3.2.5 - Assess utility of iFARM DSS for carbon credits (LM plus new scientist, GM). 4a What was the single most significant accomplishment this past year? Improved Range-Forage and livestock Growth Model: This product addressed the need for a reliable rangeland model as a basis for creating tactical range planning and management tools, identified by the USDA- Risk Management Agency (RMA) and our ranch cooperators, especially for drought conditions. The GPFARM forage and cow-calf modules were re-programmed in object-oriented Java for easier code improvement, maintenance, and debugging, with the following improvements: 1) A simple phenology submodel based
on heat units was added to simulate different growth stages, especially to estimate green-up shoot biomass in Spring from live roots; 2) To simulate plant competition for soil water, root growth, potential transpiration, and root water uptake are now simulated separately for each functional group; 3) Also, leaf area index (LAI) of each functional group is now simulated to account for differences in light interception and subsequent transpiration; and 4) A steer class was added to the animal model for simulation of stockers. Using this improved range model, simpler information systems will be developed for use by range managers, and this new model will also be used to aid range research for synthesis and quantification of management and climate change effects on forage and animal growth. 4b List other significant accomplishments, if any. Residue Rainfall Interception Effects on Soil Water and Crop Growth Quantified: A residue interception component was added to the Root Zone Water
Quality Model (RZWQM) and examined with respect to soil water balance effects in a cropping system. Residues have been shown to intercept a significant amount of rainfall but have not been readily accounted for in modeling and management efforts. The model was tested with respect to a hypothetical plot in Akron, Colorado: a corn residue-covered soil. Interception was shown to decrease infiltration, runoff, ET, deep seepage, macropore flow, soil water storage, leaf area index, and crop/grain yield. Knowledge of plant residue effects on soil water can guide farm and regional assessment of residue management alternatives for soil, water, and nutrient conservation. Improved modeling of Residue architecture effects on soil temperature and water: A new hybrid model (RZ-SHAW) that extends the applications of the Root Zone Water Quality Model (RZWQM) to conditions of different residue types and architectures affecting heat and water transfer at the soil surface was developed and examined with
respect to soil water and temperature under different residue managements. Residue layering and architecture effects on the surface energy balance and subsequent heat and water flux have not been fully explored. RZ-SHAW allows different methods of surface energy flux evaluation to be used: (1) The Simultaneous Heat and Water (SHAW) method; (2) the Shuttleworth-Wallace (S-W) method; and (3) the PENFLUX method. The model and different surface energy balance methods were tested with respect to two plots in Akron, Colorado: a wheat residue- covered soil. Based on a statistical analysis, SHAW and PENFLUX simulation results agreed with measured soil temperature and water storage data much better than S-W. Knowledge of plant residue effects on the soil energy balance can guide farm and regional assessment of residue management alternatives for soil, water, and nutrient conservation; pest management; and plant development processes. Models used to determine optimum planting date for corn:
Planting date can greatly affect production and some information on the effect of planting dates on production will be useful to the farmers. Calibrated and validated Root Zone Water Quality Model (RZWQM) and CERES- Maize model were used with longterm weather data at Akron, CO. Optimum planting dates for the three corn hybrids in the Eastern Colorado region were determined. This information was disseminated to the customer focus group at Akron. Models used to develop best N management options: RZWQM and RZWQM-CERES hybrid models were calibrated and tested for modeling crop rotations in Nashua, Iowa and Akron, CO. In addition, the CERES-wheat model was calibrated and validated for nitrogen (N) management effects of winter wheat in the Central Great Plains. Utilizing both the validated models, best N management options (e.g.: rate, dose, and method of application) for optimizing yield return and environmental quality in Eastern Colorado were developed. This information is being
provided to farmers in the region. Predicting multi-crop and rangeland forage phenology: A computer program has been developed that simulates the phenology of crop (e.g., wheat, barley, maize, proso millet, hay millet, sorghum, sunflower) and rangeland forage species that responds to water stress. This program is needed for 1) more accurate simulations of crop and rangelands in the iFARM project, and 2) as part of the tactical planning component of iFARM. This program summarized the developmental sequence of these species, quantified the changes expected to water stress, and makes the information readily available to producers, consultants, agribusiness, extension personal, and scientists. 4d Progress report. 1. Explored plant parameters for crop simulation models using gene-based approaches (e.g., information derived in various ways such as from genome mapping and functional genomics) with particular emphasis on photoperiod and gibberelin-insensitive genes. Dr. McMaster
co-organized the 2003 CSSA symposium and served as a Guest Editor of a special issue of Field Crops Research. 2. Managing risk is an important issue in farming and ranching. A ten- step program for risk management was developed for agricultural producers based on academic risk (theoretical) literature and applied extension (applied) literature using off-the-shelf risk software (e.g., @RISK and SIMETAR). The program combines strategic planning with decision analysis for risk to create a user friendly and more powerful economic risk management framework. This product will help agricultural producers improve risk management 5. Describe the major accomplishments over the life of the project, including their predicted or actual impact. This new project is being built upon its GPFARM predecessor project, while we are still supporting GPFARM. The GPFARM was developed for farmers and ranchers for strategic planning of their cropping and range- livestock systems. It was delivered to Colorado
Association of Wheat Growers (CAWG). Under a cooperation agreement with ARS-GPSR, CAWG is providing GPFARM DSS in its membership packet. Approximately 600 copies of GPFARM have been distributed by CAWG. The GPSR scientists facilitated this activity and conducted several training sessions for the membership. Further improvements in GPFARM were made by way of providing standardized management scenarios for the central Great Plains that save farmers considerable time in setting up GPFARM for their site-specific conditions. With GPFARM, CAWG users can evaluate alternative management strategies for cropping and range-livestock systems, and view the results of the strategies in both economic and environmental terms. We are continuing to work with farmer groups to utilize this technology that is having a great deal of impact (please see under Question #6). GPFARM was a major milestone in the action plans for the NP 207. GPFARM was evaluated for its performance in simulating field
management over time on the Alternative Crop Rotation Study at the Central Great Plains Research Station, Akron, Co. GPFARM reproduced the trend in crop production for most crops with the exception of replant operations and complete yield failures. GPFARM predicted within 20% of the observed annualized yields for 7 of 11 rotations. GPFARM predicted within 1 standard deviation of observed data for all rotations except WMF. Recommendations for IFARM include improved interface data entry and improved algorithms to simulate effects of poor stand establishment, effects of precipitation timing on yield, and complete yield failures. The GPFARM forage and cow-calf modules were re-parameterized and tested against experimental data from northeastern Colorado and southeastern Wyoming. It was demonstrated that GPFARM could accurately simulate forage production and average cow and calf weights when properly calibrated. The work has been published in the Journal of Range Ecology and
Management. This work provides examples of how a model could benefit field research. Finally, a great deal of progress has been made in developing new flexible cropping systems for the Great Plains and transferring this technology to the farmers in the Central Great Plains, through a SCA with Colorado State University (see Progress Report under the SCA CRIS # 5402- 66000-005-06S). Accomplishments given under Question 4a and 4b above are the major accomplishments made this past year in developing the needed components for the iFARM decision support system. All new components are designed to be standalone, exchangeable, components in the Object Modeling System (OMS). We have learned that this process of making standalone reusable components is more complex and requires manual effort, and thus time- consuming. However, this will be highly cost-effective for the longterm maintenance and updates, and the new iFARM model will be flexible and customizable to different conditions and
problems. Based on Expert Panel Review recommendations, the iFARM will be targeted for use by agricultural advisors, ag-businesses , field scientists, and advanced farmers and ranchers for year to year planning of cropping and livestock herds and as guide to management. The iFARM will also be used by scientists to generate simple management guidelines for farmers and ranchers, and in teaching. While the iFARM-OMS is being made ready, we have also incorporated the improved component in the new enhanced RZWQM- based model called MARIA. Until the iFARM-OMS is ready, we will use MARIA in its place for above applications. The above accomplishments dealing with applications of GPFARM and development of iFARM (Range and Crop) models are major contributions to the National Program 207, Integrated Agricultural Systems (which is not divided into components). They also contribute to the following components of National Program 205, Rangeland, Pasture and Forage: Ecosystems and their
Sustainable Management (Problem Area - Decision Support Systems); and Grazing Management: Livestock Production and Environment (all problem areas). The accomplishments address the ARS Strategic Plan Goal #5: Protect and Enhance Nation's Natural Resource Base and Environment, and Goal #1: Enhance Economic Opportunities for Agricultural Producers. 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? GPFARM was officially released to central Great Plains farmers and ranchers two years ago. The GPSR/GPFARM team has continued to make presentations on GPFARM to various farm and ranch organizations this past year. The technologies from the iFARM project are still under development although several are now being tested. The rangeland forage growth module of
iFARM has been improved and should respond better to environmental stress. A grant from the USDA- Risk Management Agency has been received and will help develop and disseminate technology within the next three years to ranchers. A tactical economic module has been developed for evaluating the advantages or disadvantages of making a management decision or combination of decisions including crop insurance, lease, and other options. This module has been demonstrated on-farm with very favorable results. Further testing and refinement is necessary but the module, which can be used with GPFARM output, should be available in the next year. A CRADA with Decision Commerce Group (DCG) of Billings, MT has been signed. The CRADA calls for the parallel development of iFARM tactical decision support technology by GPSR and marketing/end-usage software developed by DCG. DCG plans to provide "blueprints" of tactical strategies for agricultural management as a free service and will use the science
in iFARM to provide site-specific management advice with a fee for service. A meeting with DCG in July 2005 was very productive and the next meeting is scheduled for October in Billings. The enhanced RZWQM and MARIA models are being used by EPA, and Bayer and other and Chemical industries for Tier-2 evaluation of the transport of new pesticides to groundwater and surface water. They are also being used by USGS for deep groundwater contamination from ag chemicals in their National Water Quality Assessment Project. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List your peer reviewed publications below). Presentations at Customer Meetings - 2004 and 2005: i. Customer Focus Meetings iFARM Focus Group Meeting December 9, 2004 Fort Collins, CO ii. Field Days and/or Farmer Organized Sessions Colorado Precision Agriculture August 3, 2005 Kersey, CO InfoAg 2005 July 19 -
21, 2005 Springfield, IL iii.Meetings with Individual Cooperators Individual Meetings with Key Cooperators at their farms. Between Oct. 2004 and now over 20 separate meetings in three states were held with cooperators. iv. Meetings with Potential Partners National Panel Review of Range Land Drought Planning Tools Sponsored by Agren and RMA August 17, 2004 Nebraska City, NE Gordon Crop Insurance Company March 21, 2005 Limon, CO ZedEx Inc. Agricultural Software July 21, 2005 Springfield, IL developers and manufacturers v. Meetings with CRADA Partners Decision Commerce Group July 26 - 28, 2005 Fort Collins, CO vi. The following publications from subordinate CRIS #5402-66000-005-06S make important contribution to objectives of this parent CRIS. Other publications are given in the Report for this subordinate CRIS. Campbell, C.A., H.H. Janzen, K. Paustian, E.G. Gregorich, L. Sherrod, B. C. Liang, and R.P. Zentner. 2005. Carbon Storage in Soils of the North
American Great Plains: Effect of Cropping Frequency. Agron. J. 97:349- 363. Peterson, G.A. and Westfall. D.G. 2004 Managing precipitation use in sustainable dryland agroecosystems. Annals Applied Biology 144:127-138. Peterson, G.A. 2005. Water conservation principles and no-till practices. In: The second international conference on sustainable and effective agriculture using no-till systems approach. Majskoye, Dnipropetrovsk, Ukraine O. David, I.W. Schneider, G.H. Leavesley: "Metadata and Modeling Frameworks: the Object Modeling System Example." International Environmental Modeling and Software Society (IEMSS) 2004 Conference - Complexity and Integrated Resources Management, Osnabruck, Germany. June 14-17. Vol 1. p. 439-444
Impacts
(N/A)
Publications
- Baenziger, P., Mcmaster, G.S., Wilhelm, W.W., Weiss, A., Hays, C.J. 2004. Putting genes into genetic coefficients. Field Crops Research. Oct. 2004. Volume 90, pp. 133-143.
- Ma, L., Selim, H.M. 2005. Predicting pesticide transport in mulch amended soils: a two-compartment model. Soil Science Society of America Journal. 69:318-327. 2005.
- White, J.W., Mcmaster, G.S., Edmeades, G. 2004. Genomics and crop response to global change: what have we learned?. Field Crops Research. 90:165-169.
- Chunsheng, H., Delgado, J.A., Xiying, Z., Ma, L. 2005. Assessment of groundwater use by wheat (triticum acestivum l.) in the luancheng xian region and potential implications for water conservation in the northwestern north china plain. Journal of Soil and Water Conservation. 60:80-88.
- Timlin, D.J., Williams, R.D., Ahuja, L.R., Heathman, G.C. 2004. Simple parametric methods to estimate soil water retention and hydraulic conductivity. In: Development of Pedotransfer Functions in Soil Hydrology. Pachepsky, Y. and Rawls, W., editors. Amsterdam: Elsevier. 30:71-93.
- Andales, A.A., David, O., Ahuja, L.R. 2005. Development of a forage growth component in the object modeling system. American Society of Agricultural Engineers Meetings Papers. 2005 ASAE Annual International Meeting, Tampa, FL, July 17-20, 2005. Paper #053011.2005. Extended Abstract.
- Hoag, D.L., Ascough Ii, J.C., Keske-Handley, C.G., Koontz, L.R. 2005. Decision making with environmental indices. Book Chapter. In: Burk, A.R. (Ed), New Trends in Ecology Research. Nova Science Publishers, Hauppauge, NY. Chapter 7, pg. 159-182. 2005.
- Ma, Q.L., Rahman, A., James, T.K., Holland, P.T., McNaughton, D.E., Rojas, K.W., Ahuja, L.R. 2004. Modeling the fate of acetochlor and terbuthylazine in the field using the Root Zone Water Quality Model. Soil Science Society of America Journal.68:1491-1500.
- Westfall, D.G., Peterson, G.A., Peairs, F.B., Sherrod, L.A., Poss, D.J., Shaver, T., Larson, K., Thompson, D.L., Ahuja, L.R., Koch, M.D., Walker, C. B. 2004. Sustainable dryland agroecosystem management. Experiment Station Technical Bulletin TB04-05 Nov. 2004.
- Ahuja, L.R., Ascough Ii, J.C., David, O. 2005. Developing natural resource models using the object modeling system: feasibility and challenges. Advances in Geosciences. 3:1-8, 2005.
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Progress 10/01/03 to 09/30/04
Outputs
1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? What does it matter? Agriculture has become a highly complex enterprise in the 21st century due to environmental and resource sustainability concerns, global market competition, and variable weather with frequent floods and droughts. Decision aids based on the synthesis of current research knowledge for agricultural production systems can help farmers manage the above complexities. A Great Plains Systems Research Unit team recently developed a computerized whole farm/ranch Decision Support System (DSS), Great Plains Framework for Agricultural Resource Management (GPFARM), for long-term strategic planning for farmers and ranchers in the central Great Plains. The main objective of GPFARM is to address long-term sustainability for production, economics, and the environment. Farm and ranch cooperators of GPFARM are
requesting an enhanced DSS to allow seasonal tactical planning and management of their operations, based on climatic and price fluctuations and other risk factors. The proposed project will extend and expand GPFARM into a national Integrated Farm and Ranch DSS (iFARM DSS) for both strategic and tactical planning/management that will include climate, market, and natural-hazard risks, as well as carbon sequestration. New science modules and technologies will be introduced into the DSS for this purpose, such as a crop module that responds better to water/N/heat stresses; accounting of carbon and greenhouse gases; management effects on soil, precipitation capture, and water storage; soil temperature; economic and environmental risk assessment; estimation of field-scale model parameters; rule-based management; and Internet connections to national soil and climate databases. New crops and management systems will be added for expansion into the northern and southern Great Plains, and the
Midwest. DSS components will be tested on experimental data and the whole DSS evaluated across selected farms and ranches. The outcome of this research will be a validated iFARM DSS for use by farmers, ranchers, agri- businesses, and NRCS in strategic/tactical management, and by scientists and educators in research and teaching. 2. List the milestones (indicators of progress) from your Project Plan. Objective. Hypothesis - Milestone - Initials of the responsible scientist(s) are given in parentheses for each milestone: Laj Ahuja (LA), Allan Andales (AA), Jim Ascough (JA), Gale Dunn (GD), Tim Green (TG), Liwang Ma (LM) Greg McMaster (GM). 1.1 - Overall Design (Design Overall DSS Framework and Delivery Mechanisms): Year 1. 1.2 - Develop Science Components: Years 1-3. 1.2.1 - Assess crop growth module needs and make required improvements (GM, LM). 1.2.2 - Develop phenology and emergence module; tie phenology module to grazing of live crop biomass (GM, RS). 1.2.3 - Enhance soil physical
process module (soil properties, water storage, soil temperature, wind and water erosion (LA, TG, JA). 1.2.4 - Add CO2, to existing carbon/nutrient cycling (OMNI) module (LM plus new scientist). 1.2.5 - Improve range vegetation and animal components (RS, LM). 1.3 - Obtain Data and Test Science Modules: Years 1-4. 1.3.1 - Obtain field data from ARS collaborators and ARS, NRCS, and NASS databases (GD, LA, JA). 1.3.2 - Identify cropping and range management systems for evaluation (RS, GM, GD). 1.3.3 - Test soil, crop growth, nutrient, erosion, and global change process-based science modules against obtained data (All Scientists) 2.1 - Risk Experimental Design: Years 1-2. 2.1.1 - Develop theoretical framework for tactical and strategic risk assessment - identify and evaluate nonparametric empirical distribution methods, efficiency criteria approaches, and hydrologic frequency analysis techniques (JA, LM plus new scientist, RS). 2.2 - Risk Methodology and Component Testing: Years 2-4.
2.2.1 - Develop and test numerical, discrete-state stochastic simulation module (JA). 2.2.2 - Simulate gross revenues and their probabilities; implement contract-based risk management strategies, generate kernel-smoothed cumulative distribution functions and display graphically (JA). 2.2.3 - Program and test efficiency criteria methods for system applications (JA). 2.2.4 - Generate non-exceedance probability (NEP) curves of key environmental variables; assign qualitative risk ratings to NEP values (JA). 2.3 - Carbon Sequestration, and Nutrient Cycling: Years 2-4. 2.3.1 - Add CO2 effects to plant growth and carbon (organic and inorganic) pool tracking modules (LM plus new scientist, GM). 2.3.2 - Enhance and evaluate crop N uptake equations; develop and refine C/N/P pool structures (LM plus new scientist, GM). 2.3.3 - Extend existing rule-based system for management events for fertilizer and manure applications (LM plus new scientist). 2.4 - Nutrient Module and Global Change Component
Testing: Years 2-4. 2.4.1- Calibrate and validate nutrient module (LM plus new scientist). 2.4.2 - Develop probability density functions for nutrient model coefficients (LM plus new scientist,RS). 3.1 - On-Farm Testing Experimental Design: Years 1-4. 3.1.1 - Develop on-farm management scenarios; collect producer data (field and climate) for crop and livestock systems (GD and all scientists) . 3.2- Apply and Evaluate iFARM DSS for Strategic and Tactical Planning: Years 4-5. 3.2.1 - Administer producer surveys (GD, JA). 3.2.2 - Conduct hard- and soft-tests of whole enterprise data (GD and all scientists). 3.2.3- Describe and analyze differences in economic and environmental risk among tactical and strategic management strategies (JA, GD, GM). 3.2.4 - Perform cost-benefit analysis for alternative, in-season management scenarios (GD, LM, JA, LA). 3.2.5 - Assess utility of iFARM DSS for carbon credits (LM plus new scientist, GM). 3. Milestones: A. The chart under Question 2 shows the
milestones that were scheduled to be addressed in FY 2004 (Year 1). The two milestones scheduled to be completed in Year 1 are 1.1 and 1.3.2. These two milestones have been substantially met. For milestones # 1.2, 1.3, 2.1, and 3.1, work was scheduled to start in Year 1 on several of the sub-components and continued in later years. We have started work on all of these sub- components. Substantial progress has been made on enhancing soil properties and water storage modules (sub-component #1.2.3) to account for rainfall interception by crop canopy and residues, surface depression storage of rain water due to surface roughness, and methods to obtain average parameters for a field with variable soil types. An improved module has been developed and tested for simulating rangeland biomass and animal weight gain (sub-component #1.2.5). The widely used DSSAT plant growth submodels have been integrated with our RZWQM model and tested. This hybrid plant growth model provides an option for
use in iFARM. A new phenology submodel consisting of a Java interface and Fortran simulation model has been developed for use in the iFARM crop growth model (#1.2.2). The phenology submodel prototype allows simulation of detailed developmental sequences for winter and spring wheat, winter and spring barley, maize, sorghum, and sunflower. B. The chart under Question 2 gives the milestones and their sub- components to be addressed during the next three fiscal years (Years 2, 3, and 4). In FY 2005, we aim to have all the existing as well as the improved (GPFARM and new) science modules in OMS (Object Modeling System) library. In FY 2006, we will develop new modules for risk assessment and carbon sequestration, and add those to the OMS library. We will then test the individual science modules against experimental datasets wherever available. In FY 2007, we will assemble the modules into a comprehensive iFARM science package, revise the existing user interface from GPFARM, and connect
the two to complete a prototype iFARM DSS. We will then test and evaluate this DSS against experimental data and improve it as necessary. In FY 2008, we will test the iFARM DSS package on collaborator farms and ranches. This DSS software will be available for making year-to-year cropping and ranch management tactical decisions, with estimates of risk and soil carbon, in addition to use as a long-term strategic planning tool by farmers, ranchers, and NRCS. The OMS library will be available for creating new models for other applications, such for the CEAP project, and for evaluating the effects of global climate change. 4. What were the most significant accomplishments this past year? A. The GPFARM DSS was delivered to Colorado Association of Wheat Growers (CAWG). Under a cooperation agreement with ARS-GPSR, CAWG is providing GPFARM DSS in its membership packet. Approximately 600 copies of GPFARM have been distributed by CAWG. The GPSR scientists facilitated this activity and
conducted several training sessions for the membership. As a result of this success with CAWG, the Kansas Association of Wheat Growers (KAWG) asked if they could develop a similar agreement with ARS. The agreement will result in distribution of over 2000 copies of GPFARM. The GPSR team is planning to conduct several training sessions to support this distribution. With GPFARM, CAWG and KAWG users can evaluate alternative management strategies for cropping and range-livestock systems, and view the results of the strategies in both economic and environmental terms. B. None. C. None. D. GPFARM was evaluated for its performance in simulating field management over time on the Alternative Crop Rotation Study at the Central Great Plains Research Station, Akron, Co. The objective was to determine the accuracy of yield and total aboveground biomass predictions for 11 dryland crop rotations (WF with 3 tillage systems, WCF, WMF, WSF, WCMF, WCSF, WM, WCM, WMSF, WSF) containing winter wheat,
corn, sunflower, and proso millet crops throughout 10 study years. Without any model calibration, GPFARM reproduced the trend in crop production for most crops with the exception of replant operations and complete yield failures. GPFARM predicted within 20% of the observed annualized yields for 7 of 11 rotations. GPFARM predicted within 1 standard deviation of observed data for all rotations except WMF. Simulating corn yield failure was particularly important in the WCM rotation. Recommendations for IFARM include improved interface data entry and improved algorithms to simulate effects of poor stand establishment, effects of precipitation timing on yield, and complete yield failures. The GPFARM forage and cow-calf modules were re-parameterized and tested against experimental data from northeastern Colorado and southeastern Wyoming. It was demonstrated that GPFARM could accurately simulate forage production and average cow and calf weights when properly calibrated. The Journal of
Range Management is currently reviewing a manuscript describing this work*. Existing rangeland/forage models (GPFARM, SPUR2, GRAZPLAN, and Hurley Pasture Model) were reviewed to identify components that may be integrated into iFARM to develop an improved plant growth model that correctly responds to environmental stresses. The SPUR2 model was identified as the best starting point for development of the iFARM forage model. The existing GPFARM forage module has been converted into an Object Modeling System (OMS) module for future use in that platform. From this, enhancements from SPUR2 will be gradually incorporated to develop the iFARM forage module. Also, work has started on improving the GPFARM animal module and converting it into an OMS module. In addition to cow-calf operations, stocker management systems will be included for evaluation in iFARM and variable stocking will be evaluated for both cow-calf and stocker operations. Finally, a great deal of progress has been made
in developing new flexible cropping systems for the Great Plains and transferring this technology to the farmers in the Central Great Plains, through a SCA with Colorado State University (see Progress Report under the SCA CRIS # 5402-66000-005-06S). *Andales, A. A., J. Derner, P. N. S. Bartling, J. D. Hanson, L. R. Ahuja, G. H. Dunn, and R. H. Hart. Evaluation of GPFARM for simulation of forage production and cow-calf weights. Submitted to J. Range Management (in review). 5. Describe the major accomplishments over the life of the project, including their predicted or actual impact. This is a new project started last year, and the accomplishments given above for FY 2004 are the accomplishments thus far. However, this new project is being built upon its GPFARM predecessor project. We completed the development of GPFARM DSS last year and successfully delivered it to farmers and ranchers in the central Great Plains. This past year, we made further improvements in GPFARM; specifically, we
standardized management scenarios for the central Great Plains that save farmers considerable time in setting up GPFARM for their site-specific conditions. We are continuing to work with farmer groups to transfer this technology that is having a great deal of impact (please see under Question #6). 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? 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. 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. Because of the grassroots approach to transfer of GPFARM, evidence of impact can only be gathered directly from the users. Many positive comments have been received such as: (1) "GPFARM is a great tool that has allowed us to
help producers and banks evaluate the potential success of future rotations using specific data, including climate and soils." (2) "I compared historic and yield monitor yields with simulated model output. I potentially saved $2-5/acre of unnecessary input costs (seed and fertilizer) on non-productive fields based on simulated model output with my soil types." (1800 acres = $3600 to $9000 per year). (3) "Under low precipitation conditions my standard wheat-corn-fallow and wheat-sunflower-fallow rotations were struggling. We used GPFARM to look at the economics and feasibility of a continuous wheat-millet rotation with no fallow. The model output confirmed my thinking both with the economics and productivity of the rotation." (4) "My standard rotation is a wheat-sunflower-fallow rotation. With the consecutive dry years my APH for sunflowers has been decreasing. I used GPFARM to look at cropping alternatives, specifically wheat-corn- fallow. The model confirmed my decision to plant
corn." Technology Transfer Internationally: A. In 2002, the GPSR team gave demonstrations of GPFARM to 60 scientists in China. B. In 2004, Dr. Gale Dunn, the GPSR Technology Transfer Coordinator, was invited to Tunisia where he made several presentations on the use of GPFARM. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. Peterson, G.A. and Westfall, D.G. 2004. Managing precipitation use in sustainable dryland agroecosystems. Annals of Applied Biology 144: 127- 138. Peterson, G.A. and Westfall, D.G. 2003. Landscapes, soil and water conservation, and diversity. p. 20-28. In: Hanson, J.D. and J.M. Krupinsky (eds.), Proceedings of the Dynamic Cropping Systems: Principles, Processes, and Challenges. 4-7 August 2003, Bismarck, ND. Halvorson, A.D., Del Grosso, S.J., Mosier, A.R., Parton, W.J., Peterson, G.A., and Robertson, G.P. 2003. Measurement and modeling of soil atmosphere N2O and CO2 exchange for
global warming potential in agroecosystems. Agron. Abs. Amer. Soc. of Agron., Madison, WI. Denver, CO 2-6 Nov. 2003. Johnson, C.K., Eskridge K.M., Wienhold, B.J., Doran, J.W., Peterson, G.A. , Buchleiter, G.W. and Corwin, D.L. 2003. Designing field scale experiments using apparent soil electrical conductivity. Agron. Abs. Amer. Soc. of Agron., Madison, WI. Denver, CO 2-6 Nov. 2003. Koch, M.D., Peairs, F.B., and Peterson, G.A. 2003. Integrating pest management with dryland cropping rotations. Agron. Abs. Amer. Soc. of Agron., Madison, WI. Denver, CO 2-6 Nov. 2003. Poss, D.J., Peterson, G.A., and Peairs, F.B. 2003. Dryland cropping systems in a low precipitation high evapotranspiration environment. Agron. Abs. Amer. Soc. of Agron., Madison, WI. Denver, CO 2-6 Nov. 2003. Theses Completed: Sorge, G. 2003. Legume replacement of fallow phase in semiarid agroecosystems: Soil quality effects. M.S. Thesis. p. 69. Colorado State University Fort Collins, CO. Sherrod, L.A. 2003. Soil organic
carbon pools after 12 years in dryland no-till agroecosystems. M.S. Thesis. p. 79. Colorado State University Fort Collins, CO.
Impacts
(N/A)
Publications
- Mcmaster, G.S. 2004. Crop growth modeling and decision support systems: selected perspectives from gpfarm. Workshop Proceedings. Intl. Workshop on Applications, Enhancements, and Collaboration of ARS RZWQM and GPFARM Models, April 20-22, 2004. Fort Collins, CO.
- MCMASTER, G.S., ASCOUGH II, J.C., SHAFFER, M.J., DEER ASCOUGH, L.A., BYRNE, P.F., NIELSEN, D.C., HALEY, S.D., ANDALES, A.A., DUNN, G.H. GPFARM PLANT MODEL PARAMETERS: COMPLICATIONS OF VARIETIES AND THE GENOTYPE X ENVIRONMENT INTERACTION IN WHEAT. TRANSACTIONS OF THE AMERICAN SOCIETY OF AGRICULTURAL ENGINEERS. 2003.
- Ahuja, L.R. 2003. Quantifying agricultural management effects on soil properties and processes. Geoderma Special Issue. Vol. 116, pp. 1-2. 2003.
- GREEN, T.R., AHUJA, L.R., BENJAMIN, J.G. ADVANCES AND CHALLENGES IN PREDICTING AGRICULTURAL MANAGEMENT EFFECTS ON SOIL HYDRAULIC PROPERTIES. GEODERMA. 2003. Vol. 116, pp. 3-27.
- Sherrod, L.A., Peterson, G.A., Westfall, D.G., Ahuja, L.R. 2003. Cropping intensity enhances soil organic carbon and nitrogen in a no-till agroecosystem. Soil Science Society of America Journal. Vol 67, pp 1533- 1543.
- Sherrod, L.A., Peterson, G.A., Westfall, D.G., Ahuja, L.R. 2004. Carbon budget in dryland agroecosystems after 12 years in no-till as affected by climate gradient, slope position, and cropping intensity. Meeting Abstract. Great Plains Fertility Conf. Denver, CO. Marcy 2-3, 2004.
- Shaffer, M.J., Bartling, P.N., Mcmaster, G.S. 2004. Gpfarm modeling of corn yield and residual soil nitrate-n. Computers and Electronics in Agriculture. May 2004. Volume 43, Issue 2, Pages 87-107.
- Ma, L., Ahuja, L.R. 2004. Book review: mathematical modeling for system analysis in agricultural research by karel d. vohnout. Agricultural Systems. Vol 81, pp. 273-274.
- Andales, A.A., Ahuja, L.R., Peterson, G.A. 2003. Evaluation of gpfarm for dryland cropping systems in eastern colorado. Agronomy Journal. Vol. 95, pp. 1510-1524.
- Sherrod, L.A., Peterson, G.A., Westfall, D.G., Ahuja, L.R. 2003. Soil organic carbon pools after 12 years in no-till agroecosystem as impacted by cropping intensity. Meeting Abstract. Amer. Soc. of Agronomy. Nov. 2-6, 2003. Denver, CO.
- Andales, A.A., Ahuja, L.R., Green, T.R., Erskine, R.H., Peterson, G. 2003. Spatial and temporal correlations among dryland grain yeild and soil-water content in a sloping field. Agronomy Abstracts. Amer. Soc. of Agronomy, Denver, CO. Nov. 2-6, 2003.
- Saseendran, S.A., Ahuja, L.R., Ma, L. 2003. Book review: crop-soil simulation models: applications in developing countries. Journal of Environmental Quality. Vol 32, pp. 2445-2446.
- Ahuja, L.R. 2003. Special issue: quantifying agricultural management effects on soil properties and processes. Geoderma, Special Issue. Vol. 116, No. 1-2, pp. 1-248.
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