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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Soil Management and Sugarbeet Research » Research » Research Project #433170

Research Project: Increase Long-term Productivity and Economic Returns of Great Plains Agriculture

Location: Soil Management and Sugarbeet Research

Project Number: 3012-12210-001-003-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Aug 2, 2017
End Date: Aug 1, 2022

Agricultural management practices can have profound effects, both positive and negative, on the soil resource that cumulatively affect crop productivity, impacting net economic returns for farmers. Long-term field research is essential to fully evaluating these economic impacts. One of the main research approaches to achieving this goal is the use of our ARS long-term research plots that have been monitored intensively since 2001 at the Agricultural Research, Development and Education Center (ARDEC) located near Fort Collins, Colorado, to conduct an economic analysis of best management practices considering the effects of traditional and best-management/new agricultural practices on ecosystem services (e.g., nitrogen fertilizer sources [manure, controlled release fertilizer, urea], nitrogen use efficiencies, GHGs, crop residue management, carbon sequestration, soil biology, and other factors). A second approach is to repeat similar studies to test the previous findings about crop rotations and best management practices from the studies at ARDEC, at other Virginia Tech cooperators’ sites (e.g., at farmer fields or research centers) to compare how these new management practices and cropping systems perform in other regions. The goal is to evaluate the impact of current and improved land-use, agricultural management, and crop production systems to develop sustainable production systems that can adapt to and/or mitigate climate change so we could develop sustainable economic systems that increase productivity to contribute to U.S. and global food security. Research is needed to determine the economic, environmental and production tradeoffs of improved BMPs, by evaluating the production and economic performance of new BMPs that produce ecosystem service outcomes of intensive agricultural production systems that are sustainable.

This work will continue to evaluate the long-term research being conducted at ARDEC to increase long-term productivity and economic returns. ARS SMSBRU data sets collected at ARDEC related to increasing NUEs and yields, reducing GHG emissions, increasing C-Seq, and improving soil biology will be used to assess the economic viability of best management practices that contribute to sustainable systems. These datasets will be used to determine economic, environmental and production tradeoffs of improved soil management practices in the Great Plains, by using field studies to evaluate the production and economic performance of new N fertilizer, cropping system, tillage, and manure BMPs that produce ecosystem service outcomes in intensive agricultural production systems. Methods will require conducting field research and linking field research with economic analysis and simulation modeling to include effects of spatial and temporal variability, and long-term dynamics using production and natural resource economics, econometrics, statistics, simulation modeling, computer programming, and mathematical programming approaches. Relevant variables such as inputs and environmental conditions are controlled or randomly assigned as a part of the statistical design; estimation of the production functions is straightforward (no problems of endogeneity). We will employ a transcendental logarithmic specification (all variables in logs and interacting) and estimate using ordinary least squares: Where Y is the quantity of output, X is a vector of inputs, and A, a and ß are parameters to be estimated. The error term is e ~(0, s2). As in most production function estimations, different technologies (such as the BMP package) will be captured by A, a technology shifter. In the simplest model, we will test whether ABMP'A Existing Technology. We will then test whether input substitution varies by technology, such that ßij(BMP)' ßij(Existing Technology). This estimation will provide the basic coefficients of the production relation; other measures of long-term benefits will also be incorporated. Prior to their incorporation, standard statistical tests will be conducted of differences in outcomes (e.g. GHG emissions) between the management practices.