Modelling risk, trade, agricultural and environmental policies to assess trade-offs between water quality and welfare in the hog industry

Savard, Marielle

doi:10.1016/s0304-3800(99)00173-8

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…Phosphorous (P) being less soluble compared with Nitrogen (N), is mainly lost through runoff and erosion, thus affecting surface water. In comparison, N is lost through both runoff and leaching, thereby contaminating surface and subsurface waters (Savard 2000). Runoff from urban and agricultural land contain nutrients that stimulate the growth of algae and other aquatic vegetation, which later decompose and deprive waters of oxygen creating hypoxic zones that kill aquatic habitat, or causing eutrophication (Uriarte et al 2011).…”

Section: Review: Agricultural Land Management and Water Quality Issuesmentioning

confidence: 99%

Assessing the Accuracy of Soil and Water Quality Characterization Using Remote Sensing

Obade

Lal

Moore

2014

Water Resour Manage

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Assessing the risks of agricultural management practices on agro-ecosystem sustainability has special relevance in Ohio, USA due to the states prominence in agricultural production. However, identifying detrimental management practices remains controversial, a situation that may explain the inability to halt the recurring harmful algal blooms in inland waters, or the build-up of nutrients in the agricultural soils. Thus, detailed and accurate information is required to identify soils and water susceptible to degradation, and to support counteractive remedial measures. In this study soil and water spectral reflectance data were acquired with an Analytical Spectral Device, and modeled with laboratory measured physical and chemical properties using the Analysis of Variance (ANOVA) and decision trees. Results reveal no site differences in pH for the water, but the differences in electrical conductivity (EC) were significant. Similarly, the pH for soils did not vary significantly with depth increments. However, the no till (NT) managed soils had significantly higher pH. EC varied with depth of the water, whereas the soil carbon: nitrogen (C/N) ratio varied with management in 4 out of 5 sites. Finally, this study shows that remotely sensed data can be utilized to effectively characterize agricultural management practices based on inherent soil and water properties, thus providing information critical for assessing the efficacy of Water Quality Trading initiatives.

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Section: Review: Agricultural Land Management and Water Quality Issuesmentioning

confidence: 99%

Assessing the Accuracy of Soil and Water Quality Characterization Using Remote Sensing

Obade

Lal

Moore

2014

Water Resour Manage

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“…, 2006). Finally, it should be noted that some diffuse pollution models have been coupled with socio‐economic treatments (e.g., Savard, 2000; Priess et al. , 2001; Herrmann et al.…”

Section: Diffuse Pollution: Moving the Search For The Causes Of Wamentioning

confidence: 99%

“…There is a very well‐established tradition of interdisciplinarity at the interface between agricultural economics and environmental modelling, in relation to nutrient management (e.g., Donaldson et al. , 1995; O'Callaghan, 1996; Savard, 2000; Priess et al. , 2001; Herrmann et al.…”

Section: Scimap and Interdisciplinaritymentioning

confidence: 99%

“…The level of sophistication in this last class of models is very variable, but in diffuse pollution models, the tendency remains either to treat hydrological structuring in a simplified way or to lose much of the spatial detail of what we know about which areas of the landscape are most likely to be critical source areas (e.g., Adams et al, 1995). A good example of this simplified approach is the Soil Conservation Service Curve Number method as used in various models (e.g., Beasley et al, 1980;Young et al, 1989;Savard, 2000;Weber et al, 2001;Galbiati et al, 2006). Finally, it should be noted that some diffuse pollution models have been coupled with socio-economic treatments (e.g., Savard, 2000;Priess et al, 2001;Herrmann et al 2003;Matthews, 2006;Vatn et al, 2006) that allow assessment of social and economic impacts as well as water quality impacts.…”

Section: The Nature Of Diffuse Pollutionmentioning

confidence: 99%

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Surveillant Science: Challenges for the Management of Rural Environments Emerging from the New Generation Diffuse Pollution Models

Lane

Brookes

Heathwaite

et al. 2006

J Agricultural Economics

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Current models of diffuse pollution are characterised by a progressive engagement with remotely-sensed data coupled with more elegant modelling approaches. Central to these new models is the concept of connectivity, which leads to the identification and prioritisation of those landscape units (e.g., fields) where the consequences of land management activities are most readily transmitted to watercourses. The practice of diffuse pollution modelling using such models encounters certain problems. Following Brewer (1999) we argue that interdisciplinarity offers the opportunity to overcome these problems through: (1) its explicit recognition of the framing implicit in model development; (2) an emphasis on context in problem-solving; (3) methodological pluralism; and (4) following from these other factors, the possibility of a different sort of engagement between land managers and modellers. Hence, the case for developing interdisciplinary approaches goes beyond the conventional dictates of problem-led research and points to very different ways of conducting diffuse pollution research, taking on board the full dimensions of interdisciplinarity, with its emphasis on reflexivity, contextuality, substance and engagement. Copyright 2006 Blackwell Publishing Ltd.

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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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Modelling risk, trade, agricultural and environmental policies to assess trade-offs between water quality and welfare in the hog industry

Cited by 15 publications

References 14 publications

Assessing the Accuracy of Soil and Water Quality Characterization Using Remote Sensing

Assessing the Accuracy of Soil and Water Quality Characterization Using Remote Sensing

Surveillant Science: Challenges for the Management of Rural Environments Emerging from the New Generation Diffuse Pollution Models

Historical Development and Applications of the EPIC and APEX models

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