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Record W2560469163 · doi:10.2134/csa2016-61-6-4

Industrial Hemp Meeting to Address Research Gaps

2016· article· hu· W2560469163 on OpenAlexaboutno aff
Susan V. Fisk

Bibliographic record

VenueCSA News · 2016
Typearticle
Languagehu
FieldAgricultural and Biological Sciences
TopicPeanut Plant Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProduct (mathematics)Agricultural economicsAgricultural scienceBusinessPrincipal (computer security)AgricultureIndustrial productionEngineeringGeographyMathematicsEconomicsEnvironmental scienceComputer scienceArchaeology

Abstract

fetched live from OpenAlex

The lights have been off for industrial hemp in the U.S. for more than 80 years. The USDA stopped keeping tabs on hemp seeds in the 1940s, and research publications within our Societies dropped off about the same time. There is no recorded production of hemp in the U.S. after the late 1950s.1 But ever since the 2014 farm bill allowed research institutions to grow industrial hemp, the future for this crop seems bright once again. Even so, there are numerous research gaps and other issues that need to be addressed as a result of not producing industrial hemp for all of these years. Who will certify seeds? How will we breed hemp for the various uses that the marketplace demands? Filling these gaps in research and scientific knowledge for the U.S. hemp industry is the goal of CSSA's conference The Science of Industrial Hemp, to be held in Denver, CO, 28–29 July 2016. ASA is a co-sponsor of the meeting. Hemp seeds. Photo by Blake Romney. According to Hemp Biz Journal, U.S. hemp-based product sales will nearly triple in the period of 2012 to 2016.2 Projecting out to 2020, the industry will reach $1.5 billion in sales—contrast that to $5 billion for the cotton industry in 2015.3 Europeans have been developing hemp as multi-purpose crops for more than a decade, according to Stefano Amaducci. He is one of the conference speakers, and the principal investigator for EU Multihemp, a project that “aims at developing hemp genotypes with enhanced traits suitable for diverse cultivation environments and to provide improved feedstock for a wide array of innovative end products generated within an integrated biorefinery.” Research will continue to provide varieties of hemp that provide long fibers for clothing, strong fibers for the auto industry, higher oil contents for food and personal products, and high-protein hemp for food and feed. Many EU countries lifted their ban on growing industrial hemp in the 1990s, giving them more knowledge of breeding and production than the U.S. Ernie Small, principal research scientist for Agriculture and Agri-Food Canada, agrees that scientific advancements in hemp will be led by the marketplace's needs. “Domesticated plant evolution is based on ‘artificial’ selection in contrast to ‘natural selection,’ which guides the evolution of groups in nature. Virtually all of the evolution of Cannabis is domestication—artificial selection. Historically, the major kinds of Cannabis plants (fiber, oilseed, and marijuana) are the result of human domestication by farmers. Today, more sophisticated plant breeding is producing improved cultivars to satisfy current market forces.” Following a 60-year ban, Canada began to allow commercial growing of hemp in 1998. Challenges of breeding are unique to hemp as well. “Cannabis is wind-pollinated, and very large distances are needed to prevent pollen contamination during breeding,” Small says. “Very few other crops are faced with as difficult a need for long-distance isolation.” In addition, “THC is controlled, so breeding for agronomic characteristics cannot be done without also simultaneously controlling for this.” The USDA typically keeps track of the genetic traits of plants species, but they only kept a bank of hemp seed up to the 1940s, according to CSSA member Stephanie Greene, lead scientist with the Seed Preservation Program within the USDA. At the conference, Greene will discuss “the genetic resources of cannabis, including what collections are available internationally, and how we can rebuild the U.S. germplasm collection to support industrial hemp breeders.” “Plant breeders use valuable traits found in old varieties, farmers’ seeds, and wild species,” Greene says. “Because industrial hemp has been a Schedule I controlled substance, the USDA gene bank has not been able to maintain seed collections or distribute seed to breeders. Now is the time to determine what type of seed collection will support the efforts of industrial hemp breeders.” Looking to international scientists to report on their research may be part of the answers for the U.S., with CSSA and ASA leading the way in hemp meetings and scientific information. Creating spaces where discussions between researchers can happen is another, and the Science of Industrial Hemp meeting will have plenty of time for networking. Why is now the time for a meeting on the science of hemp? “There have been multiple meetings that have focused more on the fledgling business side of hemp products,” says Ellen Bergfeld, CEO of the Societies. “Yet, there has not been anything that has focused on the science of production here in the U.S. As perceptions are changing about hemp as a legitimate product, separate from marijuana, and there is growing interest in and support for the products themselves, we feel that it is very important to focus on the science of industrial hemp production to help lift it out of obscurity as well.” According to Small, restarting the hemp industry in Canada didn't happen overnight, and, so it won't in the U.S., either. “As with any crop that hasn't been grown for many years, progress was slow because of a lack of knowledge on the part of producers, product developers, marketers, and regulators,” Small says. The Science of Industrial Hemp conference will hopefully increase the speed with which knowledge is shared, connections are made, and the industry rebuilds itself. For more information about the meeting, visit www.crops.org/meetings/hemp-meeting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.309
GPT teacher head0.384
Teacher spread0.074 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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