Meeting Environmental Goals Efficiently on a Farm-Level Basis

Teague, Mark L.; Bernardo, Daniel J.; Mapp, Harry P.

doi:10.2307/1349653

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…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;.…”

Section: Economic and Environmental Studiesmentioning

confidence: 99%

Historical Development and Applications of the EPIC and APEX models

Gassman

Williams²,

Benson³

et al. 2004

2004, Ottawa, Canada August 1 - 4, 2004

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The development of the field-scale Erosion Productivity Impact Calculator (EPIC) model was initiated in 1981 to support assessments of soil erosion impacts on soil productivity for soil, climate, and cropping conditions representative of a broad spectrum of U.S. agricultural production regions. The first major application of EPIC was a national analysis performed in support of the 1985 Resources Conservation Act (RCA) assessment. The model has continuously evolved since that time and has been applied for a wide range of field, regional, and national studies both in the U.S. and in other countries. The range of EPIC applications has also expanded greatly over that time, including studies of (1) surface runoff and leaching estimates of nitrogen and phosphorus losses from fertilizer and manure applications, (2) leaching and runoff from simulated pesticide applications, (3) soil erosion losses from wind erosion, (4) climate change impacts on crop yield and erosion, and (5) soil carbon sequestration assessments. The EPIC acronym now stands for Erosion Policy Impact Climate, to reflect the greater diversity of problems to which the model is currently applied. The Agricultural Policy EXtender (APEX) model is essentially a multi-field version of EPIC that was developed in the late 1990s to address environmental problems associated with livestock and other agricultural production systems on a whole-farm or small watershed basis. The APEX model also continues to evolve and to be utilized for a wide variety of environmental assessments. The historical development for both models will be presented, as well as example applications on several different scales.

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Section: Economic and Environmental Studiesmentioning

confidence: 99%

Historical Development and Applications of the EPIC and APEX models

Gassman

Williams²,

Benson³

et al. 2004

2004, Ottawa, Canada August 1 - 4, 2004

View full text Add to dashboard Cite

show abstract

“…Various researchers have shown that the distributional assumptions employed in CCP models have a significant impact on the estimated trade-offs (Zhu et al 1994;Qiu et al 2001;Kampas and White 2003;Kataria et al 2010) and may not hold for all situations as a result of the site-specific nature of agricultural NPS pollution (Wang et al 2016;Qiu et al 2001). To overcome the problem, techniques like the Environmental Target-MOTAD model (Teague et al 1995) were developed to estimate economic-environmental tradeoffs while making use of empirical distributions. Qiu et al (1998) scrutinized the use of the Environmental Target-MOTAD model and argued that it would be difficult to apply because the scientific basis for the selection of a reasonable environmental risk level is weak.…”

mentioning

confidence: 99%

“…Such an environmental outcome could be achieved by determining states of nature with deviations above g and then restricting the number of states to 25% of the number of total states of nature. Teague et al (1995) have demonstrated that states with deviations above g could easily be identified using an Environmental Target-MOTAD framework.…”

mentioning

confidence: 99%

Economic-environmental trade-offs and the conservativeness of the upper partial moment

Matthews

Grové

2016

Stoch Environ Res Risk Assess

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Accounting for the stochastic nature of environmental outcomes when quantifying economic environmental trade-offs with mathematical programming models requires the use of probabilistic programming approaches like the upper partial moment (UPM) method. Application of the UPM model may result in overregulation and losses in farm profit because the probabilistic constraint is satisfied at a higher level than the specified compliance probability, resulting in conservative responses from polluters. The main objective of this article was to present the upper frequency method as an alternative to enforce a probabilistic constraint with a close bound to the actual compliance probability. The UFM uses binary variables in a linear programming framework to enforce the probability bound on an empirically distributed outcome variable. Results showed that the UPM model was very conservative in the estimation of the upper probability bound, which resulted in an overestimation of abatement costs and an underestimation of the average amount of pollution above the environmental goal. Inconsistencies also exist between the ranking of alternatives when comparing the UPM and UFM methods. The UFM is general enough to ensure that the technique can be applied to any problem where the researcher is concerned with the risk of exceeding a specified target level.

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Modelling Environmental Risk Using the Upper Partial Moment: a Safety-First Approach

Matthews

Grové

2017

Environ Model Assess

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Meeting Environmental Goals Efficiently on a Farm-Level Basis

Cited by 10 publications

References 8 publications

Historical Development and Applications of the EPIC and APEX models

Historical Development and Applications of the EPIC and APEX models

Economic-environmental trade-offs and the conservativeness of the upper partial moment

Modelling Environmental Risk Using the Upper Partial Moment: a Safety-First Approach

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