Economic and environmental evaluation of alternative pollution-reducing nitrogen management practices in central Illinois

Rejesus, Roderick M.; Hornbaker, Robert H.

doi:10.1016/s0167-8809(99)00058-4

Cited by 62 publications

(27 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, at field level, more details have to be considered and for this reason, soil-plantenvironment models are often equipped with additional modules which allow simultaneous evaluations of the economical and environmental impact of crop management decisions (e.g. Hughes et al 1995;Lindgren and Elmquist 2005;Rejesus and Hornbaker 1999;Vatn et al 1999). In most cases, the simulated total above-ground crop dry matter (TDM) is used to calculate economic returns, which is feasible for many agricultural systems.…”

Section: Introductionmentioning

confidence: 99%

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

et al. 2009

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

et al. 2009

View full text Add to dashboard Cite

“…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

View full text Add to dashboard Cite

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.

show abstract

“…This is commonly referred to as Precision Agriculture (PA). Many have suggested that PA offers the potential of reducing negative environmental impacts and increasing farm profitability (Pierce and Nowak, 1999;Rejesus and Hornbaker, 1999), however it is uncertain if the benefits of PA are enough to pay for the PA technology investment Khanna and Epouhe, 1998;Plant, 2001). We propose that current practice of PA suffers from an inadequate understanding of the major factors that affect plant performance at a field scale.…”

Section: Introductionmentioning

confidence: 99%

Associations between field characteristics and soybean plant performance using canonical correlation analysis

2005

View full text Add to dashboard Cite

The main objective of this study is to find associations between site characteristics (topographic, and soil physical and chemical properties) and soybean [Glycine max (L.) Merr.] plant performance (e.g. yield, canopy development) occurring at a field scale. The study took place in an Illinois production field in the 2000 and 2001 seasons. These associations were studied with canonical correlation analysis (CCA) followed by a spatial analysis of the resulting canonical variables with semivariography. The CCA discovered several significant associations between site characteristics. The first pair of canonical variables had a correlation coefficient of 0.76. The site characteristics most consistently correlated with the first pair of canonical variables were organic matter (OM) (r ¼ 0.64 and 0.51 for the 2000 and 2001 seasons, respectively), pH (r ¼ 0.39 and 0.51 for the 2000 and 2001 seasons, respectively), and deep electrical conductivity (EC D ) (r ¼ 0.53 and 0.49 for the 2000 and 2001 seasons, respectively). Site variables soil phosphorous (P) and soil potassium (K) were inconsistently correlated with the site characteristics canonical variable. These results indicate that site variables related to soil water retention are more consistently associated with soybean performance than site variables related to soil fertility. The plant performance characteristic most correlated with the soybean performance canonical variable were NDVI N (r ¼ 0.76 and 0.72 for the 2000 and 2001 seasons, respectively), SPAD (r ¼ 0.70 and 0.47 for the 2000 and 2001 seasons, respectively), and yield (r ¼ 0.44 and 0.58 for the 2000 and 2001 seasons, respectively).The variables NDVI N , yield, EC D are obtained with sensors and thus they can be easily used at a production field scale. The common spatial structures in pairs of the canonical variables confirm the relationship between site properties and soybean performance, proving their potential in the demarcation of uniform areas within production fields. This approach can be used to explore soil plant relationships in other field studies.Abbreviations: Band 825 -reflectance in the band between 824 and 826 nm; CCA -canonical correlation analysis; GIS -geographic information systems; GPS -global positioning system; LDB -leaf dry biomass; NIR 727 -near infrared reflectance centered in 727 nm; nd R 752 -Red reflectance centered in 752 nm; NIR 795 near infrared reflectance centered in 795 nm; R 650 -red reflectance centered in 650 nm; NIR 795 -near infrared reflectance centered in 795 nm; NIR 757 -near infrared reflectance centered in 757 nm; PA -precision agriculture; PC -carbon concentration in plant; R 650 -red reflectance in the portion of the spectrum between 630 and 680 nm; R 732 -Red reflectance in the portion of the spectrum between 727 and 737 nm.

show abstract

Economic and environmental evaluation of alternative pollution-reducing nitrogen management practices in central Illinois

Cited by 62 publications

References 12 publications

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Historical Development and Applications of the EPIC and APEX models

Associations between field characteristics and soybean plant performance using canonical correlation analysis

Contact Info

Product

Resources

About