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The Agricultural Policy/Environmental eXtender (APEX) model was developed by the Blackland Research and Extension Center in Temple, Texas. APEX is a flexible and dynamic tool that is capable of simulating a wide array of management practices, cropping systems, and other land uses across a broad range of agricultural landscapes, including whole farms and small watersheds. The model can be configured for novel land management strategies, such as filter strip impacts on pollutant losses from upslope crop fields, intensive rotational grazing scenarios depicting movement of cows between paddocks, vegetated grassed waterways in combination with filter strip impacts, and land application of manure removed from livestock feedlots or waste storage ponds. A description of the APEX model is provided, including an overview of all the major components in the model. Applications of the model are then reviewed, starting with livestock manure and other management scenarios performed for the National Pilot Project for Livestock and the Environment (NPP), and then continuing with feedlot, pesticide, forestry, buffer strip, conservation practice, and other management or land use scenarios performed at the plot, field, watershed, or regional scale. The application descriptions include a summary of calibration and/or validation results obtained for the different NPP assessments as well as for other APEX simulation studies. Available APEX GIS-based or Windows-based interfaces are also described, as are forthcoming improvements and additional research needs for the model.

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 R 2 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.

Marginal lands have received wide attention for their potential to improve food security and support bioenergy production. However, environmental issues, ecosystem services, and sustainability have been widely raised over the use of marginal land. Knowledge of the extent, location, and quality of marginal lands as well as their assessment and management are limited and diverse. There are many perceptions about what constitutes marginal lands and so clear definitions are needed. This paper provides a review of the historical development of marginal concept, its application and assessment. Challenges and priority research needs of marginal land assessment and management were also discussed.