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Strategies for Non-GM Segregation in the U.S. Grain and Feed Supply Chain.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Strategies for Non-GM Segregation in the U.S. Grain and Feed Supply Chain./
作者:	Gupta, Priyanka.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	142 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Agricultural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28645609
ISBN:	9798544278627

Strategies for Non-GM Segregation in the U.S. Grain and Feed Supply Chain.
Gupta, Priyanka.

Strategies for Non-GM Segregation in the U.S. Grain and Feed Supply Chain. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 142 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Iowa State University, 2021.

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

Segregation of non-genetically modified (GM) corn and soybeans in the U.S. grain and feed supply chain is challenging. Adventitious presence (AP) or accidental commingling of GM material in the non-GM product can potentially occur at every stage in the supply chain. The challenge is to segregate at low AP tolerance limits, usually ranging from 0.9% - 5%, in the products intended to be sold as non-GM. The current study adopted a holistic approach to evaluate the U.S. grain and feed supply chain, with stages farm, grain elevator, grain processor, and feed mill, to develop a comprehensive understanding of non-GM segregation. A system-failure analysis was conducted to identify how and when the supply chain might fail to segregate. The study adopted a novel methodology based on the combination of fault tree analysis (FTA) and failure mode and effects analysis (FMEA) to systematically evaluate the supply chain stages and identify factors that can potentially contribute to AP if the supply chain handles both GM and non-GM streams. A total of twenty-seven factors potentially contributing to AP were identified using FTA, out of which fourteen were prioritized as 'critical factors' using FMEA by assessing their effects, probability of occurrence, severity of impact, and FMEA ranking score (multiplication of probability and severity). Prioritized critical factors included seed impurity (AP of GM traits in non-GM seed lots), cross-pollination (between GM and non-GM cornfields), transportation vehicles (residual GM grain in transporting or shipping vehicles), grain impurity (GM impurity in incoming grain lots), and equipment residue (residual or carryover GM grain in unloading, conveying, and processing equipment). Critical factors were found across the supply chain, indicating that the onus of successful segregation lies on all supply chain participants; strategies evidenced to reduce the likelihood of AP due to identified factors were presented. The next objective was to quantitatively assess identified AP-contributing factors and test the feasibility of the supply chain to achieve common AP tolerance limits of 0.9% - 5%. A probabilistic model was developed to stochastically model AP-levels contributed by the factors and evaluate using the Monte Carlo simulation method. The model's findings provide useful insights into the capability of the commodity grain and feed supply chain, as it exists in the current state, to achieve specified tolerance limits. The model revealed low probabilities of achieving tolerance limits of 0.9%, 1.5%, and 3%, but a high probability of achieving a 5% tolerance limit in most scenarios. For example, the probability of meeting a 0.9% tolerance limit was just over 10% at the farm stage and 0.1% at the grain elevator stage, but the probability of meeting a 5% tolerance limit was 100% at both stages. The model emphasized the need for segregation measures to achieve low AP levels, and therefore, the next objective was to evaluate segregation strategies (dedication, spatial, and temporal segregation) at the grain elevator and feed mill. Probabilistic models were developed based on segregation strategies, facility configurations, handling and processing operations, and AP-contributing factors. According to the model, dedication and spatial segregation were best-suited strategies for non-GM segregation; the probabilities of meeting a 0.9% tolerance limit were 96% - 100% in the dedication scenarios and 7.1% - 83.8% in the spatial segregation scenarios. The temporal segregation scenarios with cleaning between loads effectively achieved 3% and 5% tolerance limits but not 0.9% and 1.5% tolerance limits. Overall, this study elucidates the impact of various AP-contributing factors on non-GM segregation in the supply chain and demonstrates the effectiveness of segregation strategies in achieving low AP-tolerance limits in different practical scenarios.

ISBN: 9798544278627Subjects--Topical Terms:

3168406
Agricultural engineering.
Subjects--Index Terms:

Adventitious presence

Strategies for Non-GM Segregation in the U.S. Grain and Feed Supply Chain.
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520 $a Segregation of non-genetically modified (GM) corn and soybeans in the U.S. grain and feed supply chain is challenging. Adventitious presence (AP) or accidental commingling of GM material in the non-GM product can potentially occur at every stage in the supply chain. The challenge is to segregate at low AP tolerance limits, usually ranging from 0.9% - 5%, in the products intended to be sold as non-GM. The current study adopted a holistic approach to evaluate the U.S. grain and feed supply chain, with stages farm, grain elevator, grain processor, and feed mill, to develop a comprehensive understanding of non-GM segregation. A system-failure analysis was conducted to identify how and when the supply chain might fail to segregate. The study adopted a novel methodology based on the combination of fault tree analysis (FTA) and failure mode and effects analysis (FMEA) to systematically evaluate the supply chain stages and identify factors that can potentially contribute to AP if the supply chain handles both GM and non-GM streams. A total of twenty-seven factors potentially contributing to AP were identified using FTA, out of which fourteen were prioritized as 'critical factors' using FMEA by assessing their effects, probability of occurrence, severity of impact, and FMEA ranking score (multiplication of probability and severity). Prioritized critical factors included seed impurity (AP of GM traits in non-GM seed lots), cross-pollination (between GM and non-GM cornfields), transportation vehicles (residual GM grain in transporting or shipping vehicles), grain impurity (GM impurity in incoming grain lots), and equipment residue (residual or carryover GM grain in unloading, conveying, and processing equipment). Critical factors were found across the supply chain, indicating that the onus of successful segregation lies on all supply chain participants; strategies evidenced to reduce the likelihood of AP due to identified factors were presented. The next objective was to quantitatively assess identified AP-contributing factors and test the feasibility of the supply chain to achieve common AP tolerance limits of 0.9% - 5%. A probabilistic model was developed to stochastically model AP-levels contributed by the factors and evaluate using the Monte Carlo simulation method. The model's findings provide useful insights into the capability of the commodity grain and feed supply chain, as it exists in the current state, to achieve specified tolerance limits. The model revealed low probabilities of achieving tolerance limits of 0.9%, 1.5%, and 3%, but a high probability of achieving a 5% tolerance limit in most scenarios. For example, the probability of meeting a 0.9% tolerance limit was just over 10% at the farm stage and 0.1% at the grain elevator stage, but the probability of meeting a 5% tolerance limit was 100% at both stages. The model emphasized the need for segregation measures to achieve low AP levels, and therefore, the next objective was to evaluate segregation strategies (dedication, spatial, and temporal segregation) at the grain elevator and feed mill. Probabilistic models were developed based on segregation strategies, facility configurations, handling and processing operations, and AP-contributing factors. According to the model, dedication and spatial segregation were best-suited strategies for non-GM segregation; the probabilities of meeting a 0.9% tolerance limit were 96% - 100% in the dedication scenarios and 7.1% - 83.8% in the spatial segregation scenarios. The temporal segregation scenarios with cleaning between loads effectively achieved 3% and 5% tolerance limits but not 0.9% and 1.5% tolerance limits. Overall, this study elucidates the impact of various AP-contributing factors on non-GM segregation in the supply chain and demonstrates the effectiveness of segregation strategies in achieving low AP-tolerance limits in different practical scenarios.
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653 $a Non-GM grain and feed
653 $a Non-GM segregation
653 $a Genetically Modified foods
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