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Modeling carbon dynamics and social ...

Hunt, Natalie D.

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Modeling carbon dynamics and social drivers of bioenergy agroecosystems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling carbon dynamics and social drivers of bioenergy agroecosystems./
作者:	Hunt, Natalie D.
面頁冊數:	212 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
標題:	Environmental management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3624550
ISBN:	9781303981791

Modeling carbon dynamics and social drivers of bioenergy agroecosystems.
Hunt, Natalie D.

Modeling carbon dynamics and social drivers of bioenergy agroecosystems. - 212 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2014.

This item must not be sold to any third party vendors.

Meeting society's energy needs through bioenergy feedstock production presents a significant and urgent challenge, as it can aid in achieving energy independence goals and mitigating climate change. With federal biofuel production standards to be met within the next decade, and with no commercial scale production or markets currently in place, many questions regarding the sustainability and social feasibility of bioenergy still persist. Clarifying these uncertainties requires the incorporation of biogeochemical, biophysical, and socioeconomic modeling tools.

ISBN: 9781303981791Subjects--Topical Terms:

535182
Environmental management.

Modeling carbon dynamics and social drivers of bioenergy agroecosystems.
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245 1 0 $a Modeling carbon dynamics and social drivers of bioenergy agroecosystems.
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500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Advisers: Stephen J. Ventura; Stith T. Gower.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Meeting society's energy needs through bioenergy feedstock production presents a significant and urgent challenge, as it can aid in achieving energy independence goals and mitigating climate change. With federal biofuel production standards to be met within the next decade, and with no commercial scale production or markets currently in place, many questions regarding the sustainability and social feasibility of bioenergy still persist. Clarifying these uncertainties requires the incorporation of biogeochemical, biophysical, and socioeconomic modeling tools.
520 $a Chapter 2 validated the biogeochemical cycling model AGRO-BGC by comparing model estimates with empirical observations from corn and perennial C 4 grass systems across Wisconsin and Illinois. AGRO-BGC, in its first application to an annual cropping system, was found to be a robust model for simulating carbon dynamics of an annual cropping system.
520 $a Chapter 3 investigated the long-term implications of bioenergy feedstock harvest on soil productivity and erosion in annual corn and perennial switchgrass agroecosystems using AGRO-BGC and the soil erosion model RUSLE2. Modeling environments included biophysical landscape characteristics and management practices of bioenergy feedstock production systems. This study found that intensifying aboveground residue harvest reduces soil productivity over time, and the magnitude of these losses is greater in corn than in switchgrass systems. Results of this study will aid in the design of sustainable bioenergy feedstock management practices.
520 $a Chapter 4 provided evidence that combining biophysical crop canopy characteristics with satellite-derived vegetation indices offers suitable estimates of crop canopy phenology for corn and soybeans in Southwest Wisconsin farms. LANDSAT based vegetation indices, when combined with a light use efficiency model, provide yield estimates in agreement with farmer reports, providing an efficient and accurate means of estimating crop yields from satellite data.
520 $a The most important stakeholder in bioenergy sustainability and feasibility research is the farmer. Chapter 5 identified and measured the influence of bioenergy feedstock choice drivers using logistic regression choice models constructed from survey and geospatial data. The strongest choice drivers among farmers willing to participate in a proposed bioenergy feedstock production program included socioeconomic, biophysical, and environmental attitudes. Outcomes of this research will be useful in designing further bioenergy policy and economic incentives.
590 $a School code: 0262.
650 4 $a Environmental management. $3 535182
650 4 $a Plant sciences. $3 3173832
650 4 $a Alternative Energy. $3 1035473
650 4 $a Remote sensing. $3 535394
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710 2 $a The University of Wisconsin - Madison. $b Environment & Resources. $3 2106144
773 0 $t Dissertation Abstracts International $g 75-10B(E).
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791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3624550