Jay D. Atwood

Sensitivity and uncertainty analyses of crop yields and soil organic carbon simulated with epic

Modeling biophysical processes is a complex endeavor because of large data requirements and uncertainty in model parameters. Model predictions should incorporate, when possible, analyses of their uncertainty and sensitivity. The study incorporated uncertainty analysis on EPIC (Environmental Policy Impact Calculator) predictions of corn (Zea mays L.) yield and soil organic carbon (SOC) using generalized likelihood uncertainty estimation (GLUE). An automatic parameter optimization procedure was developed at the conclusion of sensitivity analysis, which was conducted using the extended Fourier amplitude sensitivity test (FAST). The analyses were based on an experimental field under 34-year continuous corn with five N treatments at the Arlington Agricultural Research Station in Wisconsin. The observed average annual yields per treatment during 1958 to 1991 fell well within the 90% confidence interval (CI) of the annually averaged predictions. The width of the 90% CI bands of predicted average yields ranged from 0.31 to 1.6 Mg ha-1. The predicted means per treatment over simulations were 3.26 to 6.37 Mg ha-1, with observations from 3.28 to 6.4 Mg ha-1. The predicted means of yearly yield over simulations were 1.77 to 9.22 Mg ha-1, with observations from 1.35 to 10.22 Mg ha-1. The 90% confidence width for predicted yearly SOC in the top 0.2 m soil was 285 to 625 g C m-2, while predicted means were 5122 to 6564 g C m-2 and observations were 5645 to 6733 g C m-2. The optimal parameter set identified through the automatic parameter optimization procedure gave an R2 of 0.96 for average corn yield predictions and 0.89 for yearly SOC. EPIC was dependable, from a statistical point of view, in predicting average yield and SOC dynamics.

Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone

Significance Hypoxic (low-oxygen) zones threaten an increasing number of marine ecosystems. Hypoxia in the Gulf of Mexico is the second largest in the world. The United States has a policy goal of reducing the average zone to 5,000 km2. Reductions in nutrients from cropland in the Mississippi-Atchafalaya River Basin are needed to achieve this goal. We use an integrated assessment model coupled with optimization to identify the cost-effective locations to target cropland conservation investments across the Basin’s 550 agricultural subwatersheds and to identify the nature of tradeoffs between hypoxia and costs of conservation investments. Targeted conservation practice investments are estimated to achieve the hypoxia reduction goal at the cost of

SENSITIVITY ANALYSIS OF APEX FOR NATIONAL ASSESSMENT

2.7 billion annually. A seasonally occurring summer hypoxic (low oxygen) zone in the northern Gulf of Mexico is the second largest in the world. Reductions in nutrients from agricultural cropland in its watershed are needed to reduce the hypoxic zone size to the national policy goal of 5,000 km2 (as a 5-y running average) set by the national Gulf of Mexico Task Force’s Action Plan. We develop an integrated assessment model linking the water quality effects of cropland conservation investment decisions on the more than 550 agricultural subwatersheds that deliver nutrients into the Gulf with a hypoxic zone model. We use this integrated assessment model to identify the most cost-effective subwatersheds to target for cropland conservation investments. We consider targeting of the location (which subwatersheds to treat) and the extent of conservation investment to undertake (how much cropland within a subwatershed to treat). We use process models to simulate the dynamics of the effects of cropland conservation investments on nutrient delivery to the Gulf and use an evolutionary algorithm to solve the optimization problem. Model results suggest that by targeting cropland conservation investments to the most cost-effective location and extent of coverage, the Action Plan goal of 5,000 km2 can be achieved at a cost of

An Approach for Estimating Water Quality Benefits of Conservation Practices at the National Level

2.7 billion annually. A large set of cost-hypoxia tradeoffs is developed, ranging from the baseline to the nontargeted adoption of the most aggressive cropland conservation investments in all subwatersheds (estimated to reduce the hypoxic zone to less than 3,000 km2 at a cost of

Evaluation of Two Maize Models for Nine U.S. Locations

5.6 billion annually).

Regional Estimation of Soil Carbon and Other Environmental Indicators Using EPIC and i_EPIC

Sensitivity analysis for mathematical simulation models is helpful in identifying influential parameters for model outputs. Representative sets of APEX (Agricultural Policy/Environmental eXtender) model data from across the U.S. were used for sensitivity analysis to identify influential parameters for APEX outputs of crop grain yields, runoff/water yield, water and wind erosion, nutrient loss, and soil carbon change for a national assessment project: the Conservation Effects Assessment Project (CEAP). The analysis was based on global sensitivity analysis techniques. A test case, randomly selected from the representative sets of APEX model data, was first analyzed using both the variance-based sensitivity analysis technique and the enhanced Morris method. The analysis confirmed the reliability of the enhanced Morris measure in screening subsets of influential and non-influential parameters. Therefore, the enhanced Morris method was used for the national assessment, where the cost of applying variance-based techniques would be excessive. Although sensitivities are dynamic in both temporal and spatial dimensions, the very influential parameters (ranking 1st and 2nd) appear very influential in most cases. Statistical analyses identified that the NRCS curve number index coefficient is very influential for runoff and water-related output variables, such as soil loss by water, N and P losses in runoff. The Hargreaves PET equation exponent, moisture fraction required for seed germination, RUSLE C factor coefficient, and the potential heat units are influential for more than two APEX outputs studied.

Historical Development and Applications of the EPIC and APEX Models

The United States Department of Agriculture has initiated the Conservation Effects Assessment Project (CEAP) to quantify the environmental benefits of conservation practices at the national scale. This paper provides an overview of the analytical approach being used in the CEAP national assessment to estimate off-site water quality benefits. For the assessment, a sampling and modeling approach is used. The farm-scale model Agricultural Policy/Environmental EXtender (APEX) is used to simulate conservation practices for cultivated cropland. Farmer surveys conducted on a subset of National Resource Inventory sample points provide information on current farming activities and conservation practices for APEX. Output from APEX will be input into the watershed scale model, Soil and Water Assessment Tool (SWAT) in the HUMUS (Hydrologic Unit Modeling for the United States) system for routing the pollutants to the 8-digit watershed outlet. SWAT will be calibrated and validated using the United States Geological Survey’s SPAtially Referenced Regressions On Watershed attributes (SPARROW) model output, streamflow and pollutant data. The HUMUS system simulates in-stream effects for (a) a baseline scenario with conservation practices and (b) an alternative scenario without conservation practices. The off-site water quality benefits of conservation practices currently in use will be determined by comparing outputs for the alternative scenario to the baseline outputs at each 8-digit watershed. Benefits will be reported as reductions in in-stream concentrations and loadings of sediment, nutrients and pesticides, and reductions in the number of days that concentrations exceed human health and ecological thresholds.