Elsevier

Geoderma

Volume 140, Issue 3, 15 July 2007, Pages 217-222
Geoderma

Preface
Integrating soil and crop research with system models in the midwest USA: Purpose and overview of the Special Issue

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Goal of this Special Issue

The overall goal of this Special Issue was to illustrate the advantages of integrating field research data with whole-system models for better understanding, quantifying, and extending the experimental results for the effects of a variety of management practices on crop production and quality of drainage water. This is accomplished through a case study and analysis of a long-term database from the Northeast Research Center of the Iowa State University near Nashua, Iowa, USA, involving a series

Background of the Nashua experimental study, Iowa, USA

The Nashua experimental site was one of the USDA Management Systems Evaluations (MSEA) sites in the 1990s to evaluate agricultural systems and management effects on water quality and crop production in the Midwestern U.S. This site has been used to evaluate several different agricultural management practices and treatments. The experimental site consists of 36 one-acre plots established in 1977. Tile drains were installed on the site in 1979. The site has gone through three experimental periods

Objectives and approaches for analyses presented

The analyses presented here in a set of papers were part of a project of the USDA, Agricultural Research Service (ARS). Three ARS units were involved: the Agricultural (formerly the Great Plains) Systems Research Unit in Fort Collins, CO; National Soil Tilth Laboratory in Ames, IA; and Southwest Watershed Research Center in Tucson, AZ. The objectives and approaches employed in these analyses were:

  • 1.

    Explore the possibilities of simpler, met-data based, multi-variable regression, models for the

An overview of the papers

  • Paper 1

    As a first step of synthesizing the massive Nashua database for planning purposes, Malone et al. explored the use of multi-variable regression analyses to identify major causative variables and their contributions to tile flow, N load in tile flow, and crop production. They derived a multivariable regression model from selected 1994–2003 data to predict crop yield, tile flow, and N in tile flow. The independent variables were: yearly rainfall, N source, N rate, timing of N application, and

Synthesis and future directions

One important lesson learnt from the above papers is that the unknown complexity of the subsurface groundwater flow at the Nashua site was a major factor that greatly impacted the simulations. This complexity undoubtedly influenced the experimental results, which implies that the observed data are highly site specific and will not apply in general to other situations. The complexity reduces the value of the experimental studies, making it harder or impossible to quantify the results adequately.

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