Issues in Development and Adoption of Genetically Modified (Gm) Wheats

Wilson, William W.; Janzen, Edward L.; Dahl, Bruce L.; Wachenheim, Cheryl J.

doi:10.22004/ag.econ.23497

Cited by 12 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Increase in AP levels in non-GM crop 2 2 • P: Unlikely; Likelihood declines with the increase in year gap (Wilson et al, 2003).…”

Section: Volunteer Gm Plantsmentioning

confidence: 99%

“…The P and S of each factor were assessed on criteria making up a 5-point scale as shown in Tables 1 and 2. The scales were created, and factors were rated by the first author, referring to quantitative data derived from previous studies (Bradford, 2006;Devos et al, 2005;Dolphin et al, 2020;Hanna & Jarboe, 2011;Hurburgh, 2000;Kershen & McHughen, 2005;Ingles et al, 2003Ingles et al, , 2006Lecroart et al, 2012;Palaudelmàs et al, 2009;Wilson et al, 2003) and practical observations related to grain and feed handling. A user may change these scales to better model a specific situation.…”

Section: Fmeamentioning

confidence: 99%

See 1 more Smart Citation

Application of fault tree analysis: Failure mode and effect analysis to evaluate critical factors influencing non‐GM segregation in the US grain and feed supply chain

et al. 2022

View full text Add to dashboard Cite

Background and Objective Adventitious presence (AP) or unintentional commingling of genetically modified (GM) grain in non‐GM grain lots is a concern for stakeholders who wish to produce non‐GM grain and grain‐based products. AP that exceeds tolerance levels results in the loss of product marketability as “non‐GM” and subsequent loss of a premium market price. The objective of this study was to evaluate the US commodity corn supply chain using Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) to assess factors contributing to AP in supply chains that handle both GM and non‐GM products. Findings Systematic analysis of the farm‐to‐feed supply chain identified key processes and actors involved. FTA identified 27 factors potentially contributing to AP along with their root causes. Ranking of factors using FMEA prioritized 14 factors as “critical” with a comparatively higher likelihood of occurrence and impact on AP levels. Conclusions AP contributing factors were found across the supply chain, indicating that successful segregation relies on all supply chain participants. Identification and prioritization of critical factors make it possible to target resources to a small number of significant factors to attain low AP levels rather than distributing resources across the entire set of factors. Significance and Novelty This study is an initial attempt to provide insights into the assessment and management of AP in the grain and feed supply chain with a novel application of FTA and FMEA techniques.

show abstract

“…Increase in AP levels in non-GM crop 2 2 • P: Unlikely; Likelihood declines with the increase in year gap (Wilson et al, 2003).…”

Section: Volunteer Gm Plantsmentioning

confidence: 99%

Section: Fmeamentioning

confidence: 99%

Application of fault tree analysis: Failure mode and effect analysis to evaluate critical factors influencing non‐GM segregation in the US grain and feed supply chain

et al. 2022

View full text Add to dashboard Cite

show abstract

“…If the cleanout is absent or insufficient, the transportation vehicle may carry over residual GM grain from previous loads and commingle it with non-GM loads, contributing to AP. The level of AP contributed by transportation vehicles was modeled using a triangular distribution to simulate three possible values: 1% for the maximum level, 0.3% for the most likely level, and 0.01% for the minimum level, by referring to data by Wilson et al (2003).…”

Section: Farm Stagementioning

confidence: 99%

Assessing the Feasibility of Meeting Tolerance Limits for GM Adventitious Presence in Corn Supply Chain Using Probabilistic Modeling

Gupta,

Hurburgh,

Bowers

et al. 2023

Applied Engineering in Agriculture

View full text Add to dashboard Cite

Highlights Most simulated scenarios showed low probabilities of meeting with 0.9% and 1.5% tolerance limits. 3.0% and 5.0% tolerance limits were achievable at some supply chain stages under specific conditions. Feasible tolerance limits at individual supply chain stages ranged from 2.25% to 6.25%. Seed purity and cross-pollination were key factors affecting the probability of meeting AP tolerance limits. Abstract. Tolerance limits for the adventitious presence (AP) of GM material in non-GM grain, food, and feed vary worldwide from 0.9% to 5.0%. This research analyzed the likelihood of meeting four common trade tolerance limits for AP (0.9%, 1.5%, 3.0%, and 5.0%) in the U.S. commodity corn supply chain. A model was developed to evaluate existing practices and patterns for bulk corn production, handling, and processing in an open-market supply chain that concurrently handles GM and non-GM products. Monte Carlo simulation was used to test 50,000 iterations of supply chain scenarios to determine the likelihood of successfully meeting specified tolerance limits. The model revealed that the supply chain, as it exists today, does not effectively facilitate the concurrent handling of GM and non-GM streams at 0.9% and 1.5% tolerance limits in most cases. At individual supply chain stages, some tolerance limits were reasonably achievable, such as 3.0% and 5.0% at the farm stage. The probabilities of complying with 0.9% and 1.5% tolerance limits at the farm stage were just over 10% and 67%, respectively, while the probabilities of complying with 3.0% and 5.0% tolerance limits were more than 90%. The grain elevator and grain processor could achieve 3.0% and 5.0% tolerance limits with reasonable likelihood. At the feed mill, a 5.0% tolerance limit was achievable but only when bypassing some supply chain stages. The 99% feasible tolerance limits at individual supply chain stages ranged from 2.25% to 6.25%. Significant factors influencing the ability to meet AP tolerances were identified using sensitivity analysis. These factors included seed impurity, cross-pollination, and transportation vehicles. Keywords: Adventitious presence, Corn, Feed, Genetically modified grain, Monte Carlo simulation, Segregation, Supply chain.

show abstract

Implications of biotech traits with segregation costs and market segments: the case of Roundup Ready(R) Wheat

Wilson

DeVuyst

Taylor

et al. 2008

European Review of Agricultural Economics

View full text Add to dashboard Cite

Issues in Development and Adoption of Genetically Modified (Gm) Wheats

Cited by 12 publications

References 25 publications

Application of fault tree analysis: Failure mode and effect analysis to evaluate critical factors influencing non‐GM segregation in the US grain and feed supply chain

Application of fault tree analysis: Failure mode and effect analysis to evaluate critical factors influencing non‐GM segregation in the US grain and feed supply chain

Assessing the Feasibility of Meeting Tolerance Limits for GM Adventitious Presence in Corn Supply Chain Using Probabilistic Modeling

Implications of biotech traits with segregation costs and market segments: the case of Roundup Ready(R) Wheat

Contact Info

Product

Resources

About