Farm-Level Economic and Environmental Impacts of Eastern Corn Belt Cropping Systems

Abstract

Output from two process simulation models (EPIC and GLEAMS) was combined with a farm level linear programming model to assess the economic and environmental implications of selected eastern Corn Belt farming systems. The farm level economic results suggest that an alfalfa ( Medicago sativa L.)‐based cropping system is generally less profitable than a corn‐soybean ( Zea mays L.‐Glycine max L. [Merr.]) rotation produced under farm program prices or recent average market price conditions. Net returns are projected to decline by approximately 38% if alfalfa is included in an eastern Corn Belt cropping system. The adoption of a corn‐alfalfa cropping system could reduce annual soil erosion and the quantity of nitrates in surface water runoff relative to a conventional corn‐soybean rotation. The corn‐alfalfa system, however, leads to an increase in atrazine and alachlor runoff in surface water and an increase in nitrate percolation. A more environmentally desirable outcome may be attained by continuing farm programs that reduce acreage planted to corn. Yet to maintain farm income and encourage voluntary farmer participation, taxpayer costs may be unacceptably high. Thus, there are significant trade‐offs among environmental goals, federal budget reduction efforts, and income and employment in farming and agribusiness. Research Question Proponents of alternative agriculture suggest that certain sustainable cropping systems can reduce soil erosion and water pollution without adversely affecting farmers ’ incomes. This research evaluated the farm level environmental and economic impacts of cropping systems in the eastern Corn Belt, which include alfalfa in rotation with other conventional row crops. Literature Summary Most economic studies of alternative crop production systems have focused on the costs of adoption but have generally ignored the environmental impacts. Studies that have considered environmental impacts have typically focused on a single factor, for example soil loss or sedimentation. Study Description Two 500 acre farms were modelled: one on a Drummer silty clay loam, a high productivity soil commonly found in northwestern Indiana and eastern Illinois; and the second on a Clermont silt loam, a low productivity soil found in southern Indiana and Ohio. Three cases were analyzed: (i) a government program case with a 10% corn set‐aside, (ii) a market price scenario based on observed market prices for the crops, and (iii) a policy option requiring one‐fourth of the farm to produce alfalfa. Input use amounts can be found in Table . A farm‐level linear programming model was used for the economic analysis. The objective function of the model maximized gross revenue minus variable costs subject to constraints which included land, labor, and machinery resource requirements and availability during critical crop production periods. Two process simulation models were used to analyze the environmental impacts of the different cropping systems. The first of these was the Erosion Productivity Impact Calculator (EPIC). The s...

Validation of EPIC for Two Watersheds in Southwest Iowa

“…Integrated modeling systems range from farm-level (Foltz et al 1993;Taylor et al 1992;Wossink et al 1992), to watershed (Bouzaher et al 1990;Lakshminarayan et al 1991), and ultimately to regional (Bernardo et al 1993;Bouzaher et al 1995;Lakshminarayan et al 1996) applications. In each of these systems, functions and/or models are incorporated to predict environmental indicators for different combinations of landscape, soil, management, and climate conditions.…”

Economic Analysis of Sustainable Agricultural Cropping Systems for Mid-Atlantic States

“…Producers are urged to use a legume crop in a rotation with row crops such as corn and soybeans. Foltz et al (1993) assessed the economic and environmental implications of selected eastern Corn Belt farming systems using output from two process simulation models, EPIC and GLEAMS, combined with a farm level linear programming model. The results indicated that an alfalfa based cropping system was generally less profitable than a corn soybean rotation.…”

Historical Development and Applications of the EPIC and APEX models

“…For example, Kurkalova, Kling, and Zhao (2004) estimated the total sequestered carbon and nitrogen runoff, water erosion, and wind erosion reductions that would occur in Iowa in response to varying rates of conservation tillage adoption; the adoption rates were determined as a function of 40 different hypothetical budgets ranging from $2 to $80 million that could be potentially administered to Iowa farmers through the Conservation Security Mimouni, Zekri, and Flichman (2000) Program of the 2002 U.S. farm bill. Other examples of studies that incorporated both EPIC and an economic model are Shankar et al 2000;Lakshminarayan et al 1991;Bryant et al 1993;Bernardo et al 1993;Foltz, Lee, and Martin 1993;Chang et al 1994;Teague, Bernardo, and Mapp 1995;Kelly, Lu, and Teasdale 1996;Chowdhury and Lacewell 1996;Van Dyke et al 1999;Savard 2000;Rejesus and Hornbaker 1999;Pautsch et al 2001;Feng et al 2004;.…”

“…Example applications include assessment of sediment and nutrient losses as a function of different tillage systems, crop rotations, and fertilizer rates King, Richardson, and Williams 1996); nutrient losses from livestock manure applications (Edwards et al 1994;Pierson et al 2001); nitrate-nitrogen (NO 3 -N) losses through subsurface tile drainage (Chung et al 2001;Chung et al 2002); nutrient cycling as a function of cropping system (Cavero et al 1999;Bernardos et al 2001); soil loss due to wind erosion (Potter et al 1998;Bernardos et al 2001); climate change impacts on crop yield and/or soil erosion (Favis-Mortlock et al 1991;Brown and Rosenberg 1999); losses from field applications of pesticides (Williams, Richardson, and Griggs 1992;Sabbagh et al 1992); irrigation impacts on crop yields (Cabelguenne, Jones, and Williams 1995;Rinaldi 2001); estimation of soil temperature (Potter and Williams 1994;Roloff, de Jong, and Nolin 1998a); and soil carbon sequestration as a function of cropping and management systems (Lee, Phillips, and Liu 1993;Apezteguía, Izaurralde, and Sereno 2002). The flexibility of EPIC has also led to its adoption within several integrated economic and environmental modeling systems that have been used to evaluate agricultural policies at the farm, watershed, and/or regional scale (e.g., Taylor, Adams, and Miller 1992;Bernardo et al 1993;Foltz, Lee, and Martin 1993;Babcock et al 1997). …”