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United States Department of Agriculture

Agricultural Research Service


Location: Agricultural Systems Research Unit

2010 Annual Report

1a.Objectives (from AD-416)
Objective 1.Develop, implement, enhance, and maintain an object modeling system (OMS) and a library of modules for building agricultural system models at field to watershed scales for a variety of applications. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 of the new National Program (NP) 201 Action Plan (FY 2006 - 2010)] Objective 2.Develop, verify, and evaluate field to watershed modeling tools and techniques that quantify environmental outcomes of conservation practices in major agricultural regions, including modeling and decision aids for drainage water management systems. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 and Problem Area #3, Drainage Water Management Systems, Product #4 of the new NP 201 Action Plan (FY 2006 - 2010)] Objective 3.Develop improved space-time scaling and model parameterization approaches for landscape processes in new agricultural system models from field to watershed scales. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 of the new NP 201 Action Plan (FY 2006 - 2010), and to Goal 1.7.2 of NP 201 to develop methods to determine input model parameters, values, and state variables for multiple scales to account for the effect of management practices].

1b.Approach (from AD-416)
Objective 1. Hypothesis: The OMS framework can be used to develop customized, modular field to watershed ag system models with interchangeable components for assessing the effects of conservation practices. Experimental Design: OMS represents an ARS-led effort in partnership with the NRCS, USGS, and university collaborators (e.g. CO State University). Enhancing OMS functionality includes the development of improved capabilities for: .
1)model building .
2)code testing, data connectivity, and database integration; .
3)geospatial output visualization and model parallelization; and .
4)uncertainty, sensitivity analysis and parameter estimation. Objective 2 Hypothesis 2-1: A new prototype regionalized model can provide improved estimates of the effects of conservation practices on environmental responses at the field to watershed scales. Experimental Design: The overall goal is to develop an OMS-based modular simulation model with interchangeable components that can address regional soil and water conservation and water quality need from field to watershed scales. Specific task areas for Objective 2 are: .
1) Identify regions and define process modules for a selected regional area; .
2) Obtain needed scientific model components; .
3) Develop a prototype regionalized watershed model and perform a preliminary evaluation; .
4)Modify existing modules or identify and develop additional modules; .
5) Evaluate the prototype watershed model with various conservation practices; and .
6) Transfer the prototype model to NRCS. Hypothesis 2-2: An agricultural systems model, RZWQM2, can simulate and quantify the effects of BMPs under tile drainage for different Midwest climate and soil conditions. Experimental Design: In a collaborative research effort with the National Soil Tilth Laboratory (Ames, IA). Field experiments will be conducted in Iowa. RZWQM2 will be used to quantify controlled drainage and cover crop effects on drainage volumes, nitrate losses in drainage flow, and crop growth. Objective 3 Hypothesis: Soil, water and plant properties can be scaled over space and time to identify scale-appropriate behaviors and model parameters across agricultural landscapes. The resulting perameters can be used to improve the modeling of spatial interactions between land areas containing differential management and conservation practices. Experimental Design: The prototype regionalized watershed model will be used to assess the propogation of uncertainty in model structure, parameter values, and inputs to water quantity and quality effects up to watershed scales. Scale-dependence and uncertainty of model parameters will be evaluated as follows: .
1) Characterize the spatial and temporal variability of measured system variables in the prototype watershed model; .
2) Relate key model parameters to spatial surrogates; .
3) Generate high resolution inputs to detailed process modules and upscale the results; determine effective parameter values over the range of scales of interest; and.
4) Quantify parameter uncertainty and its impacts on model output uncertainty using a suite of object-based tools developed for parameter estimation.

3.Progress Report
The Object Modeling System Version 3.0 (OMS3) was released (see Accomplishments below). The OMS3 toolset was extended by addition of:.
1)the Fourier Amplitude Sensitivity Test (FAST) sensitivity analysis method; and.
2)the Dynamically Dimensioned Search (DDS) optimization algorithm for autocalibration of watershed simulation models. In addition, component testing techniques (e.g., code analyzer tools) were added to the OMS3 toolset to enable fully automated testing against observed data. GIS-based model output visualization and watershed delineation components were developed and are being evaluated.

The Conservation Effects Assessment Program (CEAP) Prototype Watershed model developed under OMS 2.2 was:.
1)enhanced with new Java components for erosion, nitrogen (N) cycling, and multi-flow direction routing;.
2)restructured to run under OMS3; and.
3)renamed as the AgES-W (AgroEcoSystem-Watershed) component-based model. AgES-W is currently being evaluated for N and sediment transport. Additional Java scientific components for water table depth and tile drainage were developed and are undergoing verification before incorporation into AgES-W. AgES-W has a fully-distributed simulation capability to better quantify conservation impacts on water quality at field to watershed scales, incorporating the most effective approaches for CEAP regionalized model customization.

The component-based Unified Plant Growth Model (UPGM) was enhanced to improve crop growth simulation responses to varying water deficits. New modules for seedling emergence, crop phenology, and canopy height that responded better to varying levels of soil water were developed and are being modified for incorporation into AgES-W.

A large field or a Hydrologic Response Unit (HRU) in a watershed usually contains many soil types; however, soil properties are typically assumed to be homogeneous. Accurate field- or unit-average infiltration requires an effective average saturated hydraulic conductivity for all component soil types, and a weighted average parameter for soil moisture redistribution. A new method to obtain effective hydraulic conductivities was developed, and a science module component written for incorporation into AgES-W. This approach will be compared with the dominant (single) soil approach for field data in Colorado, Indiana, and Iowa watersheds, and then made available for incorporation in other watershed models.

RZWQM2 was calibrated with measured monthly tile flow, N loss, flow weighted nitrate-N concentration, plant N uptake and grain yield under conventional drainage management in Iowa. Similar data with controlled drainage management (CDM) were used to evaluate the model. Simulations suggest that CDM reduced N loss by 23% with an N rate decrease of 200 to 150 kg N ha-1 and less than 3% corn yield reduction. CDM effects on N loss were increasingly pronounced with increasing N application rates and rainfall. Overall, CDM in combination with reduced N application rates can substantially reduce N loss to the environment by about 40% without reducing corn yield greatly.

1. Release of Object Modeling System (OMS) Version 3.0. ARS scientists and collaborators at Fort Collins, Colorado released OMS3 as a new framework for computer model development and broad application. OMS3 provides an open-source approach for object- and component-based modeling, and contains multi-threading capability for high performance computing on multi-CPU desktops (in particular for complex spatially-distributed models). OMS3 also supports Natural Resources Conservation Service (NRCS) infrastructure for information technology and the use of existing model code. On-demand documentation of simulation projects, including model variables and parameter sets, has been implemented to produce indexed PDF technical documents. Simulation projects currently being implemented in OMS3 by ARS, NRCS, United States Geological Survey (USGS) and university partners will result in large cost reductions in terms of model development, deployment, maintenance, and ongoing application.

2. Computer simulations extend experimental field data on fertilizer effects over time. Soil nitrate tests to determine N fertilizer rates vary from year to year mostly because of annual differences in early season precipitation and temperature. In a collaboration between ARS scientists in Fort Collins, CO and Ames, IA, the Root Zone Water Quality Model (RZWQM2) was tested on field data and then used to simulate long-term N fertilizer rates. The results show that RZWQM2 simulates significantly lower nitrate concentration in discharge from late-spring nitrogen test (LSNT) treatments compared to fall N fertilizer applications within the tile drained Walnut Creek Iowa watershed. A statistical model using simulations from 1970-2005 showed that early season precipitation and early season temperature account for 90% of the interannual variation in LSNT rates. This research will help agricultural scientists more thoroughly understand the effect of weather patterns on spring fertilizer application rates and develop simple tools to estimate optimum N application rates, all of which facilitates the design of more effective systems that maintain crop production while protecting the environment.

3. Automated parameter optimization improves simulations of soil water dynamics. Root Zone Water Quality Model 2 (RZWQM2) and other complex agricultural system models are usually calibrated manually, which is time-consuming and inconsistent. Global search analysis using Latin Hypercube Sampling (LHS) and local parameter optimization (PEST) methods were explored to calibrate soil hydraulic parameters in RZWQM2. Six methods of estimating soil parameters of the soil water retention curve and saturated hydraulic conductivity were evaluated to simulate daily soil water dynamics under fallow conditions in eastern Colorado. Errors in simulated soil water contents were reduced by using LHS to initialize and constrain the PEST parameter space, which also stabilized the cross-validation results. Calibration results using water content measurements at four depths were similar to results using ten depths, indicating that 30 cm depth increments used at most sites were sufficient for water balance computations. ARS scientists in Fort Collins, Colorado and international collaborators published the methodology to aid researchers who apply such models to water and nutrient management issues.

4. Linking genetic and physiological information facilitates determining plant parameters in simulation models. Determining plant parameters is essential in producing accurate simulation results for crop growth models, yet thousands of varieties exist for each crop and new varieties are constantly emerging. Furthermore, plant parameters are a function of the specific genotype (both species and variety) and how genotype interacts with the environment. ARS scientists in Fort Collins, Colorado used well understood genes controlling wheat plant height as a model system for exploring how to use rapidly emerging genotype information in determining plant parameters a priori, and most importantly, how gene expression is altered over varying environments. This work was captured in a model component available for use in existing crop growth models, and will improve prediction of plant height and yield in varying environments for soil and water conservation and crop management.

5.Significant Activities that Support Special Target Populations
On-farm spatial data collection and transfer of information to “small farms” are enabled under the MOU with a family owned and operated farm near Ault, Colorado. Information from this cooperative work has also been shared with other owner/operators of small farms through meetings of the Young Farmers Association.

Review Publications
Weiss, A., Baenziger, P.S., Mcmaster, G.S., Wilhelm, W.W., Al-Ajlouni, Z.I. 2009. Quantifying Phenotypic Plasticity Using Genetic Information for Simulating Plant Height in Winter Wheat. Wageningen Journal of Life Sciences. 57:59-64. Available:

Mcmaster, G.S., Hargreaves, J.G. 2009. CANON in D(esign): Composing Scales of Plant Canopies From Phytomers to Whole-Plants Using the Composite Design Pattern. Wageningen Journal of Life Sciences. 57:39-51. Available:

Ascough II, J.C., Fathelrahman, E.M., Mcmaster, G.S. 2008. Insect Pest Models and Insecticide Application. In: Jorgensen, S.E., Faith, B.D. editors. Ecological Models. Vol. (3) Encyclopedia of Ecology., 5 vols. Oxford, Elsevier. p. 1978-1985.

Ascough II, J.C., Ahuja, L.R., Mcmaster, G.S., Ma, L., Andales, A.A. 2008. Agricultural Models. In: Jorgensen, S.E., Faith, B.D.editors. Ecological Models. Vol. (1) Encyclopedia of Ecology., 5 vols. Oxford, Elsevier. p. 85-95.

Green, T.R., Yu, Q., Ma, L., Wang, T. 2010. Crop Water Use Efficiency at Multiple Scales. Agricultural Water Management. 97(8):1099-1101. Available:

Last Modified: 4/23/2014
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