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Hemp Descriptors version 1, archived 2023-02-16headline bar  USDA Hemp Descriptor and Phenotyping Handbook Zachary Stansell, Anya Osatuke 30-Sep-2021
  ABOUT The USDA Hemp Descriptor and Phenotyping Handbook was undertaken with the following objectives: To assist breeders and researchers in identifying accessions with specific traits to facilitate germplasm selection within hemp (*Cannabis sativa* L.) improvement programs.
To identify gaps in the existing hemp collections and help formulate strategies for future collection and conservation efforts.
To designate and maintain a core collection of critical materials.
To increase NPGS user utility and accessibility to hemp germplasm and associated data.
To identify duplicate accessions and reduce costs of hemp genetic resource conservation.
 The methods and protocols are based on peer-reviewed literature and/or crowd-sourced from the hemp community. The robust, reliable, and high-dimensional data generated from these phenotyping efforts will empower conservation of hemp genetic diversity and aid selection of materials with unique trait combinations for breeding programs. We have attempted to compile a list of standardized characterization and evaluation methods to capture passport information and to quantify morphology, horticultural and agronomic quality, pathogen resistance, and metabolic profile. This document can be used a reference to standardize phenotypic data collection across the broader pool of hemp germplasm and will be updated periodically as better methodologies become available. The information gained from these phenotyping efforts will be digitally stored and made publicly available within GRIN-Global alongside the hemp germplasm held within the Plant Genetic Resources Unit (PGRU) in Geneva, NY. PGRU coordinates hemp germplasm collection and exchanges from domestic and foreign sources. Information related to plant genetic resources increases usefulness to diverse stakeholders. Phenotypic data can be collected either by the curator during routine multiplication or by collaborators during collection, germplasm screening, or breeding experiments. PGRU asks germplasm recipients or donors to provide as much data associated with these materials **as possible**. Collected data can be stored in a spreadsheet using the **`trait_name`** as column headings and **`PUID`** as row names. This document can then be emailed to zachary.stansell@usda.gov for inclusion into GRIN-Global. The authors gratefully acknowledges the critical review, editing, and the numerous suggestions for improvements made by Olivia Aldin, Masoume Amirkhani, Anthony Barraco, Gary Bergstrom, Mark Berhow, Peter Bretting, Mark Bridgen, Kadie Britt, Zachary Brym, Carlyn Buckler, Ali Cali, Brian Campbell, Craig Carlson, Jeffrey Carstens, Ernst Cebert, David Chalkley, Chengci Chen, Alyssa Collins, Whitney Cranshaw, Randy Crowl, Heather Darby, David Dierig, Jorge de Silva, Chris Delhom, Sadanand Dhekney, Shelby Ellison, David Fang, David Gang, Nicholas Genna, Heather Grab, Jason Griffin, Kelly Gude, Joshua Havill, Yu Jiang, Nick Kaczmar, Joanne Labate, Michael Loos, Jessica Lubell-Brand, Tyler Mark, Victoria Meakem, Virginia Moore, Jay Noller, Bear Reel, Andrew Ristvey, Savanna Shelnutt, Chris Smart, Lawrence Smart, George Stack, Jeffrey Steiner, Conor Stephen, Alan Taylor, Jacob Toth, Daniela Vergara, and Don Viands. The drawing on the front cover is used with permission by Anya Osatuke. Kadie Britt provided many primary source images and text for the invertebrate section. Craig Carlson provided original figures, methods, and many ideas. Jacob Toth, Joshua Havill, Savanna Shelnutt, Brian Campbell, Shelby Ellison, and Jeffrey Carstens provided many helpful comments, references, protocols, and edits. We have tried to acknowledge everyone who’s helped with this work, but any omissions are solely Zachary Stansell’s fault. This work has drawn heavily on input from the Cornell Hemp Stakeholder Survey. Please take the survey if you have not already done so. Please contact zachary.stansell@usda.gov with any questions, comments, remarks, or ideas. The United States Department of Agriculture (USDA) prohibits discrimination in its programs on the basis of race, color, national origin, sex, religion, age, disability, political beliefs, and marital or familial status. Not all prohibited bases apply to all programs. Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact the USDA Office of Communications at (202)720-5881 (voice) or (202)720-7808 (TOO). To file a complaint, write to the Secretary of Agriculture, U.S. Department of Agriculture, Washington, DC 0250, or call (202)720-7327 (voice) or (202)720- 1127 (TOO). USDA is an equal employment opportunity employer. Editors Zachary Stansell (Hemp Curator, USDA-ARS, Plant Genetic Resources Unit)
Anya Osatuke (Cornell Cooperative Extension)
 Boxes This document uses several special colored text boxes: **Phenotype/Descriptor**
 **`trait_name`** [datatype; units] **`elevation_meters`** [decimal; m]
Elevation of collecting site above sea level.
 🧪**Phenotyping Protocol**🧪
 **Seed germination** 10 waterproof trays
Sterile water-holding material (cotton wool, paper towels)
200 seeds, stored between 4 to 56 weeks...
 📐**Equation**📐
 Percent moisture may be calculated as: \(\frac{(wet - dry)}{wet} \times 100\%\)
 📜**List**📜
 **Invertebrate pests** *Acherontia atropos*
*Aculops cannabicolus*
*Aecidium cannabis*
...
 📚**Additional References**📚
 One of the earliest publications lauding hemp was “The Praise Of Hemp Seed” by John Taylor (1620).
 Keywords hemp
*Cannabis sativa* L.
germplasm
phenotype
trait
characterization
evaluation
USDA
ARS
NPGS
PGRU
 Language GRIN-Global supports displaying data in multiple languages for system-level data. That is, if the system requires text to be displayed that is not actual GRIN-Global data, that text should be in the appropriate language for the current user. This is accomplished by using a table ending with **`_lang`** as a child table.
 Data types & units **`datetime`**
A datetime data type that can handle time in nanoseconds and has a year range extending from the year “0001” to “9999.” **`decimal`**
The decimal data type can store a maximum of 38 digits, all of which can be to the right of the decimal point. The decimal data type stores an exact representation of the number; there is no approximation of the stored value. **`int`**
The integer data type is stored as a 4-byte integer; numeric values can range from \(-2^{31}\) through \(2^{31} – 1\). **`nvarchar`**
An nvarchar field can store a string of text characters (maximum 4,000). The “n” in nvarchar means uNicode. “varchar” is an abbreviation for variablelength character string. Essentially, nvarchar is variable text field that supports two-byte characters, therefore capable of handling non-English symbols. **Units**
All units are SI unless otherwise indicated.
 PASSPORT An accession consists of seed or plant material representing a sample of a single species, collected at a single time and location. An accession may be a sample of multiple plants found at the same location at the same time, or it may be collected from a single individual. By default, NPGS will retain different samples of a putative cultivar/population as discrete inventories nested within the Plant Introduction accession. Accession **`taxonomy_species_id`** [nvarchar]
Scientific name of accession linking the accession record to its taxonomy parent (genus / species). Modified from GRIN-Global. Subtaxon may be included: ‘subsp.’ (subspecies)
‘var.’ (variety; not the same as the breeder’s named variety [uniform & stable product of breeding] or cultivar.)
‘f.’ (form)
‘group’ (botanical variety not cultivar name ) **`PUID`** [nvarchar]
If persistent, unique identifier has been previously assigned, report. Assigned to one accession to be unambiguously referenced at the global level, with associated information aggregated via automated means. Genebanks not applying a true PUID should use a combination of Institute Code, Accession Number, and the Genus as a globally unique identifier. Modified from Bioversity International, FAO (2015). **`improvement_status`** [nvarchar] Short paragraph. If known, elaborate on material improvement status, e.g., wild, landrace, breeding material, hybrid, founder stock, colonal selection, mutant, polyploid, mapping population, transgenic, etc. **`plant_name`** [nvarchar] Top name assigned to display (sometimes referred to as the top name), typically given by farmer, breeder, seed-saver. Cultivar name is a possible type of top name. If in non-Latin alphabet, provide original spelling alongside a Latin-alphabet transliteration in remarks. Modified from GRIN-Global and Bioversity International, FAO (2015). **`accession_pedigree`** [nvarchar] Description of plant pedigree, if known, e.g.: “Selection from ‘Carmagnola’”
‘Beniko’/‘Carmagnola’//‘Futura 75’///‘Carmaleonte’/‘Felina 32’//‘Futura 75’ * “Mutation found in ‘Beniko’” **`ploidy`** [int]
Record ploidy if known. If mixoploid or other, elaborate in **`passport_remarks`**. See Adriel Garay and Sabry Elias (1998). **`accession_ipr`** [nvarchar]
State PVP registration status, if applicable. U.S. Link. Varieties may also be protected by a U.S. Plant Patent (e.g. `CW2A`) or Utility Patent and/or Plant Breeder’s Right from a UPOV country; e.g., Canada. US patents search
UPOV requires account to search Variety Database.
Canada **`crop_use`** [nvarchar] Explain crop use(s); e.g., oil, fiber, secondary metabolite, ecosystem services.
 Germplasm source **`source_cooperator_id`** [nvarchar]
Field associating the cooperator (person or organization) who was the source of the germplasm. **`collector_cooperator_id`** [nvarchar]
Indicating the individual collecting sample. **`developer`** [nvarchar]
List the name of the organization (or person) that bred the material.
 Sampling & location Modified from S-1084 Collection Protocols, GRIN-Global, personal conversations with hemp researchers, and Bioversity International, FAO (2015) standards. **`number_plants_sampled`** [int]
Number of plants sampled to collect the accession material (S-1084 Collection Protocols). **`source_date`** [datetime]
Date when germplasm is collected from source material (S-1084 Collection Protocols). **`geography_id`** [nvarchar]
The internal geographic identifier indicating the cooperator’s country and state (S-1084 Collection Protocols). **`elevation_meters`** [decimal; m]
Elevation of collecting site above sea level (S-1084 Collection Protocols). **`latitude`** & **`longitude`** [decimal]
Latitude and longitude in decimal degree format. The format is 10 integers and 8 decimals. Positive values are east of the Greenwich Meridian; negative values are west of the Greenwich Meridian (S-1084 Collection Protocols). **`coordinate_method`** [nvarchar]
Georeferencing method used (e.g.; GPS, map, estimated). Modified from Bioversity International, FAO (2015). **`uncertainty`** [decimal; m]
Maximum coordinate uncertainty radius. **`georeference_datum`** [nvarchar]
Geodetic datum/spatial reference system; WGS84 datum is preferred. **`accession_inv_voucher_note`** [nvarchar]
If applicable, include additional voucher information.
 ARCHITECTURE Unless stated otherwise, measure plant architecture traits as the mean of 10 unpruned plants during week of harvest. Samples submitted to NPGS will be evaluated by a USDA-ARS laboratory using similar protocols as described below. Morphology **Architectural traits** modified from Carlson et al. (2021).
 **`ht`** [decimal; cm]
Height of the stem from the ground to tip apical inflorescence, modified from Carlson et al. (2021). **`mcd`** [decimal; cm]
Maximum canopy diameter (**`mcd`**) as width of plant at widest set of branches, see Carlson et al. (2021). Measured from widest tip to tip without stretching branches. Include flowering tissue in measurement. **`mcdh`** [decimal; cm]
Height evaluated at maximum canopy diameter (**`mcdh`**) from ground to max canopy diameter, see Carlson et al. (2021). **`trkl`** [decimal; cm]
Trunk length (**`trkl`**) is evaluated as distance from ground to first branch. See Carlson et al. (2021). **`inl`** [decimal; cm]
Average internode length (**`inl`**) is calculated between internodes along the primary stem (50 cm max, see diagram). See Carlson et al. (2021). **`kite_hypot`** [decimal; cm]
See Carlson et al. (2021). **`kite_perimeter`** [decimal; cm]
See Carlson et al. (2021). **`kite_area`** [decimal; \(m^2\)]
See Carlson et al. (2021). **`ba`** [decimal; (0°-180°)]
Kite branch angle **`ba`** is calculated from the lower kite triangle, using the difference of maximum canopy diameter height and trunk length. See Carlson et al. (2021). **`branches`** [int]
Number of **`branches`** per plant. When grown from seed, branching is initially opposite, transitioning to alternate as the plant matures. Plants propagated from cuttings generally have alternate branching in the whole plant Stack et al. (2021). Modified Carlson et al. (2021). *In the above figure, **`branches = 24`** for this plant*
 **`kite.circularity`** [categorical]
A continuous scale of apical dominance can be derived (2021): \(kite.circularity = \frac{4\pi\cdot kite.area}{kite.perimeter^2}\)
 Stems **`dia`** [decimal; mm]
Diameter of the stem at soil level using forestry measuring tape or fabric measuring tape, modified from Carlson et al. (2021). **`pith_diameter`** [decimal; mm]
Diameter of the pith in the stem cross section at stem midpoint , modified from International Union for the Protection of New Varieties of Plants (2012).
 Uncrewed aerial vehicle evaluation **`uav_xxx`** [TBA]
See Carlson et al. (2021).
 Remarks **`architecture_remarks`** [nvarchar]
If possible, report date of measurement [days from sowing], sex average, minimum, and maximum height and width observed in a planting (cm).
 📚**Additional References**📚
 Anderson (1980)
Werf, Haasken, and Wijlhuizen (1994)
Werf et al. (1995)
Meijer and Keizer (1996)
Ranalli (1999)
Mishchenko and Lajko (2016)
Magagnini, Grassi, and Kotiranta (2018)
Backer et al. (2018)
Spitzer-Rimon et al. (2019)
Carlson et al. (2021)
Danziger and Bernstein (2021)
Stack et al. (2021)
Vergara et al. (2021)
 LEAF Unless otherwise noted, gather leaf data from the uppermost set of mature leaves, as mean of 5 leaves gathered from each of 10 different plants immediately before onset of flowering. Morphology **`petiole_length`** [decimal; cm]**`central_leaflet_length`** [decimal; cm] **`central_leaflet_width`** [decimal; cm] Leaf is flattened and measured from tip until start of rachis; petiole is flattened and measured from base of rachis until petiole base, modified from (2012) and (1980). (**A**) **`central_leaflet_length`** and (**B**) **`petiole_length`** measurement.
 Imaging **`leaf_color_L`** [decimal]**`leaf_color_a`** [decimal]**`leaf_color_b`** [decimal] The average color of uppermost set of mature leaves, collected before flowering, measured with a colorimeter, modified from (2012). A RHS color chart may also be used, but values should be converted to (L*a*b*) before addition to GRIN. There are many programmatic solutions to convert colors (I use R for everything: 1,2,3) as well as many online tools (e.g., Colormine). Consider printing a label to include in the scan as well. PGRU germplasm imaging and scans typically include accession ID, species, and plant id name (e.g. ‘FIN-314’). PGRU uses a small color wheel in the corner of our templates (DOWNLOAD), but that might not be necessary for you since you are measuring color with a colorimeter. **`leaf_variegation`** [nvarchar; Y/N] Indicate whether not variegation has been noted or is present.
Variegated leaves are most likely *not* virus based, but it might be worth investigating. **`leaf_scan_protocol`** [.jpg or .png] 🧪**Scan protocol**🧪
 **Equipment** Flatbed scanner
Desktop monitor
Black cloth, ideally velvet, of equal dimensions to scanner bed. **Protocol** Gather one mature leaf from a representative sample of 10 plants a week before the onset of flowering. Retain petioles. Keep on ice if wilting is a concern.
Scan leaves within the hour of collection using a scanner with the lid open, draping black fabric over the leaves to absorb background light. Include scale or ruler (cm) and **`puid`**.
Convert the scanned leaf image into .png file and save. *Leaf imagaging setup at PGRU*
 📚**Additional References**📚
 Haney and Kutscheid (1975)
Dayanandan and Kaufman (1976)
Anderson (1980)
Meijer and Keizer (1996)
Amaducci et al. (2008)
Hall, Bhattarai, and Midmore (2012)
Mishchenko and Lajko (2016)
Magagnini, Grassi, and Kotiranta (2018)
Spitzer-Rimon et al. (2019)
Carlson et al. (2021)
Stack et al. (2021)
Vergara et al. (2021)
 SEX & INFLORESCENCE Unless otherwise noted, record mean of 10 plants at harvest. Specify field or greenhouse conditions in **`sex_remarks`**, as well as photoperiod or day length. Sex ratio **`sex_ratio`** [nvarchar]
Sex ratio during flowering.
Target a sample of 100 individuals in the field before flowering begins. Label and number sample plants clearly. Record F:M:O; as the number of female, male, and monoecious (male + female flowers) individuals, respectively.
If plants are wild-collected, 100 plants may not be available; measure as many as possible (See S-1084 Collection Protocols).
 Phenology **`days_to_flower_female`** [integer]**`days_to_flower_male`** [integer]**`days_to_flower_monoecious`** [integer] Pre-terminal date when axial flowers with shortening internodes and terminal pistils (clusters of flowers at shoot termini) were observed Carlson et al. (2021) as days from sowing to first observed open female and male inflorescence. See also Faux et al. (2013); Shams et al. (2020) **`maturity`** [int; d]
Days from germination to commercial maturity. **`day_neutrality`** [nvarchar]
Describe flowering behavior as critical day length and/or day neutral response. **A more precise definition is required to define this phenotype quantitatively**.
 Inflorescence **`inflorescence_length`** [decimal; cm]
Length of inflorescence cluster (cola) at the uppermost branch, excluding leaves. Remove leaves with more than 1 leaflet. Retain stems and seeds; inflorescence may be retained as clusters. **`inflorescence_weight`** [decimal; cm]
Measure weight after drying at room temperature for >48 hr. **`inflorescence_yield`** [decimal; \(kg\cdot ha^{-1}\)]
Combine inflorescence dry weight data with planting density to calculate kg/ha. **`inflorescence_color`** [nvarchar; L*a*b*]
Measured with a colorimeter at commercial maturity. A RHS color chart may also be used, but values should be converted to (L*a*b*) before addition to GRIN.
 Remarks **`sex_remarks`** [nvarchar]
Short paragraph, if possible/applicable include: If plants were grown in field, greenhouse, or growth chamber.
Planting density (e.g., \(\frac{plants}{100 cm^{2}}\)) if applicable.
Female and male bract, stigma, and flower color.
Date of collection.
Day length (hh:mm) on the date of the first observed open female flower.
 📚**Additional References**📚
 Schaffner (1921)
Grishko, NN and Levchenko, VI and Seletski, VI (1937)
Werf, Haasken, and Wijlhuizen (1994)
Mandolino et al. (1999)
Ranalli (1999)
Lisson, Mendham, and Carberry (2000)
Shao, Song, and Clarke (2003)
Pahkala, Pahkala, and Syrjälä (2008)
Cosentino et al. (2012)
Hall, Bhattarai, and Midmore (2012)
Faux et al. (2013)
Razumova (2014)
Lynch et al. (2016)
Razumova et al. (2016)
Vergara et al. (2016)
Punja, Rodriguez, and Chen (2017)
Zhang et al. (2018)
Eichhorn Bilodeau et al. (2019)
Salentijn, Petit, and Trindade (2019)
Kovalchuk et al. (2020)
Punja and Holmes (2020)
Toth et al. (2020)
Adal et al. (2021)
Danziger and Bernstein (2021)
Dowling, Melzer, and Schilling (2021)
Hurgobin et al. (2021)
Stack et al. (2021)
 SEED Samples submitted to NPGS will be evaluated by a USDA-ARS laboratory using similar protocols as described below. General **`hundred_seed_weight`** [decimal; g]
Record mass of 100 seeds. **`seed_image`** [.jpg or .png] 🧪**SEED IMAGE**🧪
 **Equipment** Flatbed scanner
Desktop monitor
Black cloth, ideally velvet, of equal dimensions to scanner bed.
Transparent, flat-bottomed tray or transparency film to protect scanner bed from scratches. **Protocol** Gather a sample of 20 grains.
Scatter samples across tray or transparency film.
Convert the scanned seed image into .PNG file and save. *Seed image. Photo credit Alan Taylor,Michael Loos, and Masoume Amirkhani*
 **`seed_size_length`** [nvarchar]**`seed_size_width`** [nvarchar] Size of the largest, smallest, and median seed in the lot (mm) as L:S:M using using Tomato Analyzer, Smart Grain, Photoshop, GNU Image Manipulation Program, or other imaging software to make these calculations based on a scanned image of 20 seeds. **`seed_moisture`** [%]
Dry seeds in a single layer in a constant temperature oven held at 105  ̊C for 20 h. 📐% Moisture📐 \(\frac{(wet - dry)}{wet} \times 100\%\)
 Germination/viability **`percent_germ`** [%] 🧪**SPROUT TEST**🧪
 **Equipment** 10 waterproof trays
Sterile water-holding material (cotton wool, paper towels)
Water source
200 seeds, stored between 4 to 56 weeks after harvest in dry conditions between 16 - 25 ̊C. **Protocol** 4 sub-samples of 50 seeds are isolated.
Each sub-sample is spread evenly on trays lined with water-holding material. Seeds may be placed on top of water holding material and in between rolls.
Each tray is saturated with water and placed in a temperature-controlled room: 16 hours of dark at 20 ̊C, 8 hours of light at 30 ̊C. Trays are kept moist.
Live, normal, sprouted seeds are counted by hand, sprouts are then removed from the germination tray.
After 7, 14, and 21, days the number of live, normal, sprouted seeds is counted by hand. The duration of experiment may be extended if dormancy is an issue (record in **`germ_remarks`**)
Germination percentage for each tray is calculated as: \(\frac{ sprouted}{total} \times 100\%\) and averaged per tray.
Consider executing TZ tests for lots with high dormancy to verify they truly are dormant and not dead. *Seed germ testing (photo credit: Alan Taylor, Michael Loos, and Masoume Amirkhani).*
 Modified from Seed Laboratory Oregon State University (2018), NPGS protocols, and communication with Alan Taylor, Jeffrey Carstens, and Joshua Havill.
 **`germ_remarks`** [nvarchar]
Record any dormancy issues and stratification methods used. Note: dormancy in feral/naturalized populations may also be important to characterize.
Cold stratification may be the norm in feral specimens with a temperate origin.
 Yield **`grain_yield`** [decimal; kg/Ha]
Subsample larger strip trials (transect method) at seed maturity. Subsamples are chosen depending on the size of the field/plot to estimate kg/hectare. **`shattering`** [nvarchar; L,M,H]
Retain subsample of field planting for shattering evaluation and report as low, medium, or high. **A more precise definition is required to define this phenotype quantitatively**. See Planteome ontology.
 Oil **`oil_content`** [decimal; %]
The percentage of oil constituting the whole, unhulled seed mass calculated as: \(\frac{extracted.oil}{ground.sample} \times 100\%\) Indicate whether measurement was made using Nuclear Magnetic Resonance (NMR) imaging, or using a Soxhlet extractor in `seed_remarks`. 🧪**Soxhlet evaluation**🧪
 Seeds are ground to a particle size of 0.5 mm ± 0.1.
Place 10 g of ground seed into an extractor thimble, extracting with 100 mL hexane for 24 h at 70  ̊C. If a different protocol is used, record the sample weight (g), solvent name (IUPAC) and volume (mL), extraction time (h) and extraction temperature ( ̊C). Adapted from Devi and Khanam (2018).
 🧪**NMR evaluation**🧪
 To ensure accurate NMR evaluation of hemp seed, seed moisture content must be below 8%.
Subsample 10-15 seeds after evaluation of moisture content.
The sample is evaluated with a 40 mm diameter sample probe.
Prior to being placed in a sample box, seeds are to be further temperature-conditioned in the drying oven or a heating block at 40  ̊C for 90 min.
Instrument settings:
5 mHZ operating frequency
4 scans
1 second re-cycle delay
40  ̊C magnetic box temperature.
A calibration curve is constructed by comparing several varieties with a range of oil concentrations.
A linear calibration curve is constructed against the peak area of the NMR resonance signal normalized against sample mass.
The y-axis of the plot reports normalized peak area, and the x-axis reports % oil concentration.
Validate oil concentration from seeds of the same batch using Soxhlet extraction .
Indicate the % oil concentration of seed and the R2 value of the linear equation.
If a different NMR setting is used, list the weight of seeds sampled (g), seed temperature conditioning ( ̊C), operating frequency (mHZ) and line equation. Adapted from Hutton, Garbow, and Hayes (1999). See also Shams et al. (2020); Yadav and Murthy (2016).
 **`seed_fatty_acid`** See ISO 12966-1:2014 gas chromatography protocols for determination of fatty acids, free and bound, in animal and vegetable fats and oils following their conversion to fatty acid methyl esters (FAMEs).
 Protein **`combustion_analysis`** [decimal] 🧪**Protein combustion analysis**🧪
 150 mg seeds are frozen in liquid nitrogen and ground.
Seeds are placed into a combustion analyzer.
Combustion tube is at 960 °C, and oxygen is dosed for 80 s at 170 mL. Adapted from Schultz et al. (2020).
 🧪**Kjeldahl method**🧪
 Based on an adaption of AOAC Official Method 991.20 by Thiex et al. (2002), where **`Kjeldahl N %`** = ([`std.acid sample (mL)`] - [`std.acid blank (mL)`]) x (`[HCl]`) x 14.01) / (`weight (g)` x 10) **`Crude protein %`** = `% Kjeldahl N` x 6.25
 📚**Additional References**📚
 Boyce (1900)
Mah (1923)
Tuma (1972)
Ribnicky et al. (2000)
Vaknin et al. (2011)
Sera et al. (2012)
Small and Brookes (2012)
Serkov (2015)
Suriyong et al. (2015)
Yadav and Murthy (2016)
Citti, Pacchetti, et al. (2018)
Hu, Liu, and Liu (2018)
Devi and Khanam (2018)
Jian et al. (2018)
Williams (2019)
He et al. (2020)
Moon et al. (2020)
Punja and Holmes (2020)
Schultz et al. (2020)
Serkov et al. (2020)
 FIBER Samples submitted to NPGS will be evaluated by a USDA-ARS laboratory using similar protocols as described below. Yield **Hemp stem cross section**. ***A** = epidermis; **B** = bast and cortex; **C** = pith, also known as hurd, **D** = inner core, usually hollow but not at joints.*
 **`fiber_yield`** [decimal; \(\frac{kg}{Ha}\)]
Fiber yield; modified from Serkov (2015). See also Backer et al. (2018). Harvest 10 plants, remove top and bottom 15 cm, defoliate run sample through a chipper, and measure mass. Calculate **`fiber_yield`** based on planting density. **`wet_fiber_biomass`** [decimal; g]
Harvest stems 15 cm from base, remove top 15 cm, and defoliate. Wet biomass is average weight per stem without inflorescences from a sample of 10 plants Modified from Carlson et al. (2021). **`dry_fiber_biomass`** [decimal; g]
Use sample from **`wet_fiber_biomass`** measurements. Dry biomass is average weight per stem dried at 65  ̊C until brittle from a sample of 10 plants. Modified from Carlson et al. (2021). **`bast`** [decimal; %]
Stems collected from a sample of 10 plants, dried until brittle, and separated into bast (bark) and hurd (core) using a flax breaker, or by hand. Record as fraction of oven-dried mass. **`hurd`** [decimal; %]
Stems collected from a sample of 10 plants, dried until brittle, and separated into bast (bark) and hurd (core) using a flax breaker, or by hand. Record as fraction of oven-dried mass. 📐Bast & hurd content📐 `bast` = \(\frac{bast}{dry} \times 100\%\) `hurd` = \(100\% - bast\)
 **`bast_soluble`** & **`fiber_solubility`** [decimal; %]
Soluble materials in bark after evaluation with sodium hydroxide evaluation. 🧪**Sodium hydroxide evaluation**🧪
 **Protocol** 10 - 15 g isolated hurd samples isolated from dry stems, or 10 - 15 g isolated bast samples from dry stems.
Pulp dispersion apparatus, consisting of a variable speed motor and a stainless steel stirrer with a shell.
Thermometer
Vacuum source
Filtering flasks, 100 mL
Graduated cylinder, 100 mL
Watch glasses
Stirring rods
Tall, 200 mL beakers
Water bath with cover and holes to securely submerge the bottoms of tall, 200 mL beakers.
Filtering crucibles, 30 mL, 10 - 15 μm maximum pore size.
1000 mL sodium hydroxide solution, 1.0 % ± 0.1 NaOH (0.25 N)
1000 mL acetic acid, 10 %. Grind hurd or bast sample into a fine meal.
Dry filtering crucibles before use in an oven at 105° ± 3 °C for 60 min, cool in a desiccator, and record weight.
Heat water bath to 97 - 100  ̊C
Adjust speed of the motor and the angle of the blades so that no air is drawn into the pulp suspension during stirring.
Place 10 g meal into 200 mL beaker and add 100 mL 1 % NaOH and stir with glass rod.
Cover beaker with watch glass and place into water bath for 1 h. Keep water level above level of alkali solution in the beaker at all times.
Stir the meal with a rod for about 5 s at 10, 15, and 25 min after placing into the bath.
At the end of 1 h, transfer the material to a tared filtering crucible and wash with 100 mL of hot water.
Add 25 mL of 10 /% acetic acid and allow to soak for 1 min before removal. Repeat this step once again.
Wash material with 100 mL hot water three times to remove acid.
Dry crucible and contents in the oven at 105 - 108  ̊C until constant weight.
Cool in desiccator.
 📐% Soluble materials📐 \(\frac{meal.before - meal.after}{meal.before} \times 100\%\)
 **`non_fiber`** [TBD]
Protocol for evaluating stem non-fiber components not determined.
 Quality There is little modern research on hemp fiber quality evaluation protocols. This work will be conducted in collaboration with the Southern Regional Research Center (SRRC). 📜**Candidate fiber traits**📜
 fiber length
fiber strength
fiber flexibility; see Werf, Haasken, and Wijlhuizen (1994)
fiber length/diameter ratio; see Ranalli (1999)
tensile strength; see Ranalli (1999)
brittleness; see Ranalli (1999)
crystallization/cellulose crystallinity Rongpipi et al. (2019)
cross linking
elasticity; see Ranalli (1999)
ease of decortication
mechanical vs microbial retting
cellulose:lignin ratio
 **`fiber_remarks`** [nvarchar] Remarks are used to add notes or to elaborate on fiber descriptors
 📚**Additional References**📚
 Charles (1708)
Unknown (1720)
David (1729)
Slator (1735)
Marcandier (1764)
Gee (1767)
Farmer (1775)
Wissett (1808)
Humphrey (1919)
Vavilov (1957)
Werf et al. (1995)
Daryl T. Ehrensing (1998)
Rustichelli et al. (1998)
Hampton (2000)
Struik et al. (1999)
Hillig (2005)
Pahkala, Pahkala, and Syrjälä (2008)
Zatta, Monti, and Venturi (2012)
Piluzza et al. (2013)
Salentijn et al. (2015)
Serkov (2015)
Mishchenko and Lajko (2016)
Grassi and McPartland (2017)
Tang et al. (2016)
Weijde et al. (2016)
Johnson (2018)
Musio, Müssig, and Amaducci (2018)
Salentijn, Petit, and Trindade (2019)
Williams (2019)
Petit et al. (2020)
 SECONDARY METABOLITES Unless otherwise stated, collect from a sample of 10 plants at harvest. Samples submitted to NPGS will be evaluated by a USDA-ARS laboratory using similar protocols as described below. Chemotype **`chemotype`** [int; 1-6]
Note that living tissues synthesize acid forms of most cannabinoids (i.e. THC-a, CBD-a) and these are often decarboxylated to non-acid forms during evaluation (i.e. THC, CBD). Chemotype is largely driven by segregation of alleles at the \(B\) or \(O\) loci Toth et al. (2020). 1 = Mostly THC(A)
2 = ~1.5:1 CBD(A):THC(A)
3 = Mostly CBD(A)
4 = Mostly CBG(A)
5 = Low overall cannabinoid content
6 = Other **`chemotype_segregation`** [nvarchar] To not mask chemotype, we recommend measuring cannabinoids from 10 individual plants rather than pooling samples. If multiple individuals are evaluated for chemotype and differing chemotypes are observed in the same population, indicate the percentage of each chemotype using the `chemotype` scale (1:#;2:#;3:#,4:#,5:#).
 Cannabinoids **`cannabinoids`** [decimal; \(\frac{μg}{mg}\)]
Cannabinoid content is evaluated either by UPLC or HPLC. THC is a ‘’sticky’’ compound and residues will persist on laboratory equipment and analytical vessels following cleaning. THC carryover onto subsequent samples may erroneously increase the reported THC content. We strongly advise against re-using sample vials for UPLC and HPLC cannabinoid evaluation. UPLC uses a shorter run time per sample, however HPLC provides better resolution for minor cannabinoids. Both methods are sufficient for quantifying total THC in compliance with the 2020 Hemp Final Rule.
10 samples collected from 10 individual plants. Each sample should be analysed in triplicate. Cannabinoid content is variable across the height of the plant. THC-a decarboxylates into THC following exposure to heat. We advise against decarboxylating cannabis samples prior to instrument evaluation, as this process introduces error through the volatilization of a variable percentage of the total cannabinoid content. Instead, we recommend combining the THC-a and THC content of unheated plant samples to calculate the total THC content. The formula for this is: 📐**THC & Cannabinoid analytes**📐 \(THC_{total.potential}(μg/mg) = THC + 0.877 \cdot THC_a\) \(Cannabinoid.analyte = C_e\frac{V_f}{W_s}\) where: \(C_e\): Conc of the sample in the extraction solution (\(\frac{μg}{μL}\))\(V_f\): Final volume of the sample (\(μl\))\(W_s\): Weight of the sample (\(mg\))
 🧪**UPLC cannabinoid evaluation**🧪
 *Sample run time: 15 min.* **Equipment** Sonicator
Thermo Scientific UltiMate 3000 controlled by Chromeleon software
IntertSustain C18 3 μm chromatographic column (2.1 x 100 mm)
5 mM aqueous ammonium formate
0.1 % formic acid
95 % formic acid
Acetonitrile
Volumetric flasks: 1.5 L, 50 mL.
0.45 μm membrane filter. **Standards:** Cannabicyclolic acid (CBLA)
Δ9 Tetrahydrocannabinolic acid A (THCA)
Cannabidiolic acid (CBDA)
Cannabinolic acid (CBNA)
Cannabigerolic acid (CBGA)
Cannabidivarinic acid (CBDVA)
Tetrahydrocannabivarinic acid (THCVA)
Cannabichromene acid (CBCA)
Analyte mixture of the 8 main decarboxylated cannabinoids:
Δ9 Tetrahydrocannabinol (THC-d9)
Δ8 Tetrahydrocannabinol (THC-d8)
Cannabidiol (CBD)
Cannabinol (CBN)
Cannabigerol (CBG)
Cannabivarin (CBDV)
Tetrahydrocannabivarin (THCV)
Cannabichromene (CBC)
Potentially add CBGVA, THCP, CBGM, others? **Standard preparation** All standard solutions are to be prepared at a concentration of 100 μg/mL.
Cannabinoid acid standards are prepared in acetonitrile. Decarboxylated cannabinoids are prepared in methanol. **Reagent preparation** 1 L reagent for Mobile Phase A: Combine 0.21 mL 95 % formic acid with 5 mM ammonium hydroxide (0.62 mL 30 % sodium hydroxide solution) with millipore water to 999 mL. Add 1 mL 1 % formic acid.
1 L reagent for Mobile Phase B: Combine acetonitrile with formic acid to 0.1 % formic acid. **Protocol** Dry Inflorescences at room temperature (20  ̊C) to < 10 % moisture.
Grind samples.
Combine 0.1 g sample with 10 mL acetonitrile and 0.1 % formic acid
Sonicate sample at 230 V for 30 min at room temperature, then rest sample for 6 h.
Filter supernatant two times through the 45 μm membrane filter.
Dilute filtrate to 200x, then inject 1 μL sample volume into the ULPC system.
Set flow rate to 0.55 mL/min.
Set mobile phase to 2.5 min equilibrating at 60 % B between injections, with the gradient elution: 0-2 min, 70 % B;
2-8 min, 75 % B;
8-9 min, 100 % B;
9-10 min, 100 % B;
10-10.5 min, 60 % B;
10.5-11 min, 60 % B. Quantify peaks at 228 nm. Methods provided by Berhow and Gude (2021).
 🧪**HPLC cannbinoid evaluation**🧪
 *Cannaflavin A & B is evaluated on the 342 nm range using the HPLC protocol over 27 min* **Equipment** Sonicator
Shimadzu LC20 AT HLPC controlled by LabSolutions Software
Intersil ODS3 5 μm column (4.6 x 250 mm)
5 mM aqueous ammonium formate
0.1 % formic acid
Acetonitrile
Methanol
Volumetric flasks: 1.5 L, 50 mL.
0.45 μm membrane filter.
Standards: See above UPLC standards. **Reagent preparation** All standard solutions are to be prepared at a concentration of 100 μg/mL.
Cannabinoid acid standards are prepared in acetonitrile.
Decarboxylated cannabinoids are prepared in methanol.
1 L reagent for Mobile Phase A: Combine 5 mM aqueous ammonium formate with 0.1 % formic acid.
1 L reagent for Mobile Phase B: Combine an equal amount of acetonitrile with methanol, then bring to 0.1 % formic acid. **Protocol** Dry Inflorescences at room temperature (20  ̊C) to >10 % moisture.
Grind samples.
Combine 0.1 g sample with 10 mL acetonitrile and 0.1 % formic acid
Sonicate sample at 230 V for 30 min at room temperature, then rest sample for 6 h.
Filter supernatant two times through the 45 μm membrane filter.
Dilute filtrate to 200x, then inject 20 μL sample volume into the HLPC. 7.Set flow rate to 1.2 mL/min.
Set column to 50  ̊C.
Set mobile phase to 2.5 min equilibrating at 60 % B between injections, with the gradient elution:
0 min, 70 % B;
3-5 min, 85 % B;
5-10 min, 95 % B;
10-15 min, 100 % B;
15-25, 100 % B;
25-26, 70 % B;
26-27, 60 % B. Methods provided by Berhow and Gude (2021).
 Other metabolites **`terpenes`** [decimal] 🧪**Terpene evaluation**🧪
 **Equipment** GC/MS (Agilent 7890B GC, Agilent 5977B MSD, PAL 3) controlled by LabSolutions software
HP-5MS UI, 30 m × 0.25 mm, film 0.25 μm column
0.45 μm filter
Hexane **Standards** β-bisabolol
Bulnesol
m-Camphorene
p-Camphorene
Δ3-Carene
β-Caryophyllene
10-epi-γ-Eudesmol
α-eudesmol
β-eudesmol
α-humulene
Limonene
Linalool
β-Myrcene
Plastochromanol-8
α-Phellandrene
α-Pinene
cis-Sabinene hydrate
γ-Selmene
Selna-3,7(11)-diene
α-Tocopherol
β-Tocopherol
δ-Tocopherol
γ-Tocopherol
β-Pinene
Nerolidol
Camphene
Terpinolene
Ocimene
α-Terpinene
γ-Terpinene **Protocol** Dilute essential oil standards with hexane to a concentration of 0.1 mg/mL.
Dry inflorescences at room temperature (20  ̊C) to > 10 % moisture.
Grind samples.
Combine 20 mg plant material with hexane.
Pass through the 0.45 μm filter to extract terpenes.
Dilute filtrate 1:20 with hexane.
Inject samples into split injection port (1:5).
Instrument settings as follows:
Injection port held at 100  ̊C with an initial time of 4 min.
Inlet temperature fixed at 250  ̊C.
Detector temperature fixed at 280  ̊C.
Column held at 35 °C for 5 min, then raise to 150 °C at 5 °C/min. Then raise to 250  ̊C at 15 ̊ C/min. Hold time 90 min.
Helium serves as carrier gas, with flow rate 1 mL/min. Adapted from Hanuš and Hod (2020). See also hops methods
 **`flavonol_index`** [decimal] 🧪**Flavonol evaluation**🧪
 A handheld MPM-100 meter by ADC BioScientific can do this measurement instantly. (See) **Flavonoids**: Spectroscopic measurement of fluorescence at different wavelengths (F660 nm and F325 nm):
**Flavonols**: (F660 nm/F325 nm).
 **`anthocyanins`** [decimal] 🧪**Anthocyanin evaluation**🧪
 **Sample prep and extraction** Samples are ground into a fine powder with a coffee mill and passed through a 60 mesh filter to collect the fine ground fraction.
Batches of 250 grams of ground sample are extracted with 1 % HCl in methanol with stirring overnight at room temperature.
The liquid is decanted and filtered through Whatman 54 filter paper.
The remaining solid material is extracted with only methanol a second time with stirring overnight and decanted and filtered.
The extracts are pooled, and approximately 400 mL of water is added, and allowed to evaporate over 72 hours in the hood or rotovaped at 45 °C to remove the methanol.
The concentrated extract is filtered gain to remove any remaining solid material. **Method A: Preparative Chromatography** A Buchi (Newcastle, DE) Sepacore flash chromatography system with dual C-605 pump modules, C-615 pump manager, C-660 fraction collector, C-635 UV photometer, with SepacoreRecord 1.2 chromatography software is used.
A 40 x 150 mm flash cartridge column with approximately 90 grams of preparative C18 reverse phase end capped bulk packing material (Silicycle SiliSep BUC18 (17%) 60 Angstroms, 40-63 um, Silicycle, Quebec, Canada) is used for the preparative separation.
The columns are installed in the flash chromatography system, and new dry columns are initially washed with methanol and then equilibrated with 0.2% acetic acid in water for five minutes at a flow rate of 50 mL per min.
After samples (50-100 mL) are loaded on the column, the column is developed with a binary gradient to 40% methanol over 10 minutes, then to 100% methanol over an additional 5 minutes. For anthocyanins, the effluent is monitored at 520 nm and 50 mL fractions are collected in the fraction collector by the software program.
Fractions are concentrated by evaporation in the hood at room temperature or methanol is removed by rotovap at 45 °C.
The fractions containing the DGA are pooled and freeze dried over 2-5 days to dryness. **Method B: HPLC Analysis** Samples are run on a stand-alone Shimadzu 10A HPLC system (SCL-10A system controller, two LC-10A pumps, CTO-10A column oven, and SIL-10A autoinjector).
Peaks are monitored using a Hewlett-Packard 1040A photodiode array detector running under the HP Chemstation software version A.02.05.
The column used is an Inertsil ODS-3 reverse phase C-18 column (5 μM, 250 x 4.6 mm, with a Metaguard column, from Varian).
The initial conditions are 2% acetonitrile and 0.5% acetic acid in water, at a flow rate of 1 ml per minute.
The effluent is monitored at 520 nm on the PDA.
After injection (typically 25 μL), the column is held at the initial conditions for 2 minutes, and then developed to 100% acetonitrile in a linear gradient over 60 minutes.
Standard curves based on nanomoles injected are prepared from a pure standard of delphnidin-3-O-glucoside purchased from Chromadex (Irvine, CA).
Extinction coefficients are calculated from a linear regression formula based on four different nanomole concentrations of anthocyanin standards (purchased from Chromadex) injected and their respective mAbs areas.
The extinction coefficient for each anthocyanin is then used to calculate respective anthocyanin glycoside concentration in the samples by the following formula: \[ mAbs(area) \cdot extc.coef(\frac{nM}{mAbs}) \cdot \frac{1}{inj.vol(μL)}  \cdot vol.extract(mL) \cdot MW_{anth.glucoside}(\frac{μg}{nM}) \cdot \frac{1}{sample(g)}\] Addition of a glycosyl groups to the anthocyanin has little effect on its absorption profile, so anthocyanin aglycones can be used to prepare standard curves for anthocyanin glycosides on a molar basis (Berhow 2002, Mabry 1970, Markham, 1982). **Method C: LC-ESI-MS Analysis of Anthocyanins** Samples are run on an Thermo Electron LTQ Orbitrap Discovery Mass Spectrometer – a linear ion trap (LTQ XL) MS, coupled to a high precision electrostatic ion trap (Orbitrap) MS with a higher energy C-trap dissociation (HCD) cell attached – with an Ion Max electrospray ionization (ESI) source; a Thermo Scientific ACCELA series HPLC system (ACCELA 1250 UHPLC pump; ACCELA1 HTC cool stack autoinjector; and a ACCELA 80 Hz PDA detector); all running under Thermo Scientific Xcalibur 2.1.0.1140 LC-MS software.
HPLC conditions: The column is a 3 mm x 150 mm Inertsil reverse phase C-18, ODS 3, 3 μ column (Metachem, Torrance, CA).
For anthocyanin analysis, the initial solvent system is 10% methanol verses water with 0.1% formic acid at a flow rate of 0.25 mL per minute.
After injection (1 μl or less) the column is held at the initial conditions for 2 minutes then developed with a linear gradient to 100% methanol and 0.1% formic acid over 50 additional min.
The column effluent is monitored at 520 nm by the PDA detector.
The MS is run with the ESI probe in the positive mode.
The source inlet temperature is set to 300  ̊C, the sheath gas rate is set at 50 arbitrary units, the auxiliary gas rate is set at 5 arbitrary units and the sweep gas rate is set at 2 arbitrary units.
The maximal mass resolution is set at 30,000, the spray voltage is set at 3.0 kV, the tube lens is set at 100 V.
The MS is typically calibrated at least weekly with a standard calibration mixture recommended by Thermo Scientific and the signal detection optimized by running the autotune software feature as needed.
Other parameters are determined and set by the calibration and tuning process.
The software package will usually be set to collect mass data between 100-2000 AMUs.
Generally the most significant sample ions generated under these conditions are [M]+. Methods provided by Berhow and Gude (2021), see also Giusti and Wrolstad (2001).
 **`phenolics`** [decimal] 🧪**Phenolic evaluation**🧪
 **Sample prep and extraction** Freeze-dry samples overnight and grind each to a fine powder.
Weigh samples ~0.25 g and place in vial with 3 mL methanol:DMSO (1:1) solvent.
Sonicate for 30 min, allow to stand overnight at room temperature.
Filter extract through a 0.45 μM nylon 66 filter **Methodology** We use a Shimadzu LC-20 HPLC system (LC-20AT quaternary pump, DGU-20A5 degasser, SIL-20A HT autosampler, and a SPD M20A photodiode array detector, running under Shimadzu LCSolutions version 1.22 chromatography software, Columbia, MD, USA) and an Inertsil ODS-3 reverse phase C-18 column (5 μm, 250 x 4.6 mm, GL Sciences, Torrance, CA).
For phenolic compound analysis, the initial conditions are 10% methanol (or acetonitrile) with 0.25% trifluroacetic acid and 90% water with 0.25% trifluroacetic acid, at a flow rate of 1 ml per minute.
The effluent is monitored at 280 and 340 nm on the VWD
After injection (typically 25 μL), the column is held at the initial conditions for 2 minutes, then developed to 100% methanol with 0.25% trifluroacetic acid in a linear gradient over 50 additional minutes.
Five-point standard curves are used for the evaluation of the concentration of the identified phenolics for the determination of extinction coefficients at 280 and 340 nm. **LC-ESI-MS Confirmation** Samples are run on an Thermo Electron LTQ Orbitrap Discovery Mass Spectrometer – a linear ion trap (LTQ XL) MS, coupled to a high precision electrostatic ion trap (Orbitrap) MS with a high energy collision (HCD) cell – with an Ion Max electrospray ionization (ESI) source, and a Thermo Scientific ACCELA series HPLC system (ACCELA 1250 UHPLC pump, ACCELA1 HTC cool stack autoinjector, and a ACCELA 80 Hz PDA detector) all running under Thermo Scientific Xcalibur 2.1.0.1140 LC-MS software.
The MS is typically calibrated at least weekly with a standard calibration mixture recommended by Thermo Scientific and the signal detection optimized by running the autotune software feature as needed.
The MS is run with the ESI probe in the negative mode.
The source inlet temperature is 300  ̊C, the sheath gas rate is typically set at 50 arbitrary units, the auxiliary gas rate is usually set at 5 arbitrary units and the sweep gas rate is set at 2 arbitrary units.
The maximal mass resolution is set at 30,000, the spray voltage is set at 3.0 kV, the tube lens is set at -100 V.
Other parameters are determined and set by the calibration and tuning process.
For phenolic analysis, the initial solvent system is 10% methanol verses water with 0.25% formic acid at a flow rate of 0.25 mL per minute.
After injection (5 μl or less) the column is developed with a linear gradient to 100% methanol over 50 to 60 min.
The column effluent is monitored at 280 nm and 340 nm in the PDA detector. * The software package is set to collect mass data between 100-2000 AMUs. Generally, the most significant sample ions generated under these conditions are [M-1]- and [M+HCOO]-.
Six mass spec “events” are programmed to run in sequence in the MS detection scheme.
LTQ(IT)-MS full scan m/z 150 to 2000.
LTQ(IT)-MS set to trap the most abundant ion above a threshold of 500 units and perform CID at 35% energy, with the resulting ions being detected by the IT-MS.
FT-MS (Orbitrap) full scan m/z 150 to 2000.
Mass-dependent MS/MS on the most abundant ion trapped by the IT-MS in Event 1 and perform HCD at 25% energy with the resulting fragmentation ions being detected by the FT-MS.
Mass-dependent MS3 on the most abundant fragment ion generated from Event 2 and perform HCD at 25% energy with the resulting fragmentation ions being detected by FT-MS.
Mass-dependent MS3 on the most abundant fragmentation ion generated from Event 2 and perform CID at 35% energy with the resulting ions being detected by IT-MS.
For the evaluation of Xcalibur accurate mass data by the Cerno BioScience LLC MassWorks 5.0.0.0 software the FTMS is set to collect spectra at a resolution of 7500 and a range of m/z of 100 to 2000 and then evaluated by sCLIPS (self Calibrating Line-shape Isotope Profile Search) which enhances formula ID accuracy.
 Remarks **`metabolite_remarks`** [nvarchar] Identification of other metabolites may be provided in the `metabolite_remarks` field.
 📚**Additional References**📚
 Turner, Hemphill, and Mahlberg (1978)
Rustichelli et al. (1998)
Meijer and Hammond (2005)
De Backer et al. (2009)
USDA (2009)
Casano et al. (2011)
Russo (2011)
Hazekamp and Fischedick (2012)
Pertwee (2014)
Pandohee et al. (2015)
Lynch et al. (2016)
Weijde et al. (2016)
Brighenti et al. (2017)
Dufresnes et al. (2017)
Jin et al. (2017)
Patel, Wene, and Fan (2017)
Ciolino, Ranieri, and Taylor (2018)
Citti, Braghiroli, et al. (2018)
Citti, Pacchetti, et al. (2018)
Palmieri et al. (2018)
Pellati et al. (2018)
Pollastro, Minassi, and Fresu (2018)
Wang et al. (2018)
Zivovinovic et al. (2018)
Booth and Bohlmann (2018)
Comeau et al. (2018)
Hädener, König, and Weinmann (2018)
Laverty et al. (2018)
Mandrioli et al. (2018)
Protti et al. (2018)
Reimann-Philipp et al. (2018)
Pavlovic et al. (2019)
Nahar, Onder, and Sarker (2020)
Križman (2020)
Danziger and Bernstein (2021)
Hurgobin et al. (2021)
Stack et al. (2021)
 PATHOGEN/PEST Diseases are scored by indicating % area of planting affected and whether crop failure occurred in conjunction with infection. If one data score is provided for multiple plantings, provide the highest area coverage observed. Ideally the hemp community should decide on a set of check-varieties to include in every trial to compare between seasons. For consistent scoring of disease and stress data, it is recommended that the same individual be responsible for rating the entire planting.
Refrain from capturing data for a small set or singular accessions, instead prefer an actual growout of a replicated trial that contains numerous accessions in a single season. Record other relevant information in the `disease_remarks` field. Specifically, it will be useful to distinguish between hop powdery mildew, caused by *P. macularis*, and cannabis powdery mildew, caused by *Golovinomyces ambrosiae*. These appear to possess varying degrees of aggressiveness and phenotyping remarks should include information about which pathogen species was used for phenotyping. Fungal and oomycetes **`fungal_xxx`** [decimal; %]
Known or suspected fungal/oomycete pathogens: *Alternaria* spp
*Ascochyta* spp
*Athelia rolfsii* or *Sclerotium rolfsii*
*Bipolaris spp*
*Bipolaris gigantea*
*Boeremia*
*Botrytis cinerea*
*Botrytis pseudocinerea*
*Botrytis porri*
*Cercospora cf. flagellaris*
*Chaetomium globosum*
*Cladosporium* spp
*Colletotrichum* spp
*Curvularia* spp
*Diaporthe eres/ D. subordinaria*
*Exserohilum* spp
*Fusarium avenaceum*
*Fusarium brachygibbosum*
*Fusarium chlamydosporum*
*Fusarium equiseti*
*Fusarium graminearum*
*Fusarium lichenicola*
*Fusarium oxysporum*
*Fusarium proliferatum*
*Fusarium solani*
*Fusarium sporotrichiodes*
*Fusarium tricinctum*
*Globisporangium irregulare*
*Globisporangium ultimum*
*Golovinomyces ambrosiae*
*Golovinomyces cichoracearum*
*Golovinomyces spadiceus*
*Lasiodiplodia theobromae*
*Leveillula taurica*
*Leptosphaeria*
*Neofusicoccum parvum*
*Penicillium* spp
*Phoma* spp
*Phoma multirostrata*
*Phomopsis* spp
*Phytophthora* spp
*Podosphaera macularis* (syn. *Sphaerotheca macularis*)
*Pseudocercospora* spp
*Pythium aphanidermatum*
*Pythium catenulatum*
*Pythium dissotocum*
*Pythium myriotylum*
*Rhizoctonia solani*
*Sclerotinia sclerotiorum*
*Sclerotinia minor*
*Sclerotium rolfsii*
*Septoria* spp
*Stemphylium* spp
*Thielaviopsis basicola*
*Uredo kriegeriana*
*Verticillium dahliae*
 Bacterial **`bacterial_xxx`** [decimal; %]
Known or suspected bacterial pathogens: *Agrobacterium tumefaciens*
*Pseudomonas koreensis*
*Pseudomonas syringae*
*Serratia marcescens*
*Sphingomonas yanoikuyae*
*Xanthomonas campestris* pv. *cannabis*
 Virus/Viroid **`virus_xxx`** [decimal; %]
Known or suspected virus/viroid/phytoplasma pathogens: Alfalfa mosaic virus (AMV)
Arabis mosaic virus (ArMV)
Beet curly top virus
Cannabis sativa mitovirus 1
Cannabis cryptic virus
Citrus yellow-vein associated virus
Cucumber mosaic virus (CMV)
Curly top virus
Lettuce chlorosis virus (LCV)
Tobacco ringspot virus (TRSV)
Tomato ringspot virus (ToRSV)
Tobacco streak virus (TSV)
Tomato mosaic virus (ToMV) **`viroid_xxx`** [decimal; %] Hop latent viroid (HLVd) **phytoplasma_xxx** *Candidatus phytoplasma trifolii* **nematode_xxx** *Meloidogyne incognita* (Root knot nematodes)
 Remarks **`disease_remarks`** [nvarchar] In a short paragraph, describe the disease. Please include the anatomy of the plant affected, the growth stage of the plant affected, symptoms of infection, patterns of spread in field or greenhouse, evidence leading to conclusion of the nature of the pathogen, and any other relevant observations.
 Invertebrate **Key pests**
Many insects and mites can be observed in hemp and the pest complex can differ depending on whether the crop is cultivated indoors or outdoors. While many arthropods can be found in hemp, some of the most often-seen pests include corn earworm, twospotted spider mite, cannabis aphid, and hemp russet mite. Invertebrate pests are scored by indicating % area of planting affected and whether crop failure occurred in conjunction with infestation. Refrain from capturing data for a small set or singular accessions, instead prefer an actual growout of a replicated trial that contains numerous accessions in a single season. Ideally the hemp community should decide on a set of check-varieties to include in every trial to compare between seasons. Images and text were very generously provided by Kadie Britt (2021). We highly recommend McPartland, Clarke, and Watson (2000), Cranshaw et al. (2019), and Britt (2021) as excellent overviews. Corn earworm **`helicoverpa_zea`** [decimal; %]*Helicoverpa zea* or corn earworm is the most damaging pest of hemp grown in outdoor environments as it targets marketable portions of hemp plants – floral regions of CBD and seeds of grain varieties (2021). *Young corn earworm larva. (Photo credit: Kadie Britt)*
 *Bud rot in floral hemp bud from corn earworm feeding. (Photo credit: Kadie Britt*
 *Adult corn earworm moth on hemp plant. (Photo credit: Katlyn Catron)*
 Hemp russet mite **`aculops_cannabicola`** [decimal; %]*Aculops cannabicola* or hemp russet mite is a microscopic, cannabis-specific mite that can be found in indoor and outdoor hemp. Mites have four legs on their white- to beige-colored, cigar shaped bodies and are not visible without the use of magnification Kadie Britt (2021) and McPartland and Hillig (2003). *Hemp russet mites on underside of hemp leaf. (Photo credit: Kadie Britt)*
 *Upward curling of hemp leaves. Can sometimes be a symptom of hemp russet mite feeding injury to hemp but depends on cultivar. (Photo credit: Kadie Britt)*
 Twospotted spider mite **`tetranychus_urticae`** [decimal; %]*Tetranychus urticae* or twospotted spider mite is a generalist mite pest that can be found indoors and outdoors. Feeding injury causes stippling marks on leaves (Figure 1.7) and webbing can sometimes be observed in apical portions of plants (Figure 1.8). This mite is small and oval in shape, has 8 legs, and can be orange/red or brown with two distinct dark spots on the body (2021). *Twospotted spider mite on hemp leaf. (Photo Credit: Kadie Britt)*
 *Stippling on leaves due to twospotted spider mite feeding. (Photo credit: Kadie Britt)*
 *Webbing in apical portion of plant due to twospotted spider mite populations. (Photo credit: Kadie Britt)*
 Cannabis aphid **`phorodon_cannabis`** [decimal; %] *Phorodon cannabis* or cannabis aphid is a specialist, piercing-sucking insect that feeds exclusively on hemp. Populations can rapidly increase in favorable environments (Figure 1.10) as aphids can reproduce via asexual reproduction Kadie Britt (2021) and Cranshaw et al. (2018). *Cannabis aphid infestation on indoor hemp plant. (Photo credit: Kadie Britt)*
 *Cannabis aphid skins caught in honeydew on surface of hemp leaves. (Photo credit: Kadie Britt)*
 Other pests Known or suspected pests: *Acherontia atropos*
*Aculops cannabicola*
*Aecidium cannabis*
*Agallia constricta*
*Aglais urticae*
*Agromyza reptans*
*Aphis fabae*
*Aphis gossypii*
*Camnula pellucida*
*Ceutorhynchus assimilis*
*Chinavia hilaris*
*Chloealtis conspersa*
*Chlorochroa ligata*
*Chlorochroa uhleri*
*Chromatomyia horticola*
*Cosmopepla lintneriana*
*Bemisia tabaci*
*Diabrotica undecimpunctata howardi*
*Ditylenchus dipsaci*
*Empoasca fabae*
*Estigmene acrea*
*Euschistus servus*
*Frankliniella fusca*
*Frankliniella occidentalis*
*Graphocephala versuta*
*Grapholita delineana*
*Halyomorpha halys*
*Helicoverpa zea*
*Heterodera humuli*
*Hysteroneura setariae*
*Liorhyssus hyalinus*
*Liriomyza cannabis*
*Liriomyza strigata*
*Loxostege sticticalis*
*Mamestra configurata*
*Melanchra picta*
*Melanoplus bivittatus*
*Melanoplus femurrubrum*
*Melanoplus lakinus*
*Melanoplus differentialis*
*Microtechnites bractatus*
*Micrutalis calva*
*Miridae*
*Nezara viridula*
*Oebalus pugnax*
*Ostrinia nubilalis*
*Pentatomidae*
*Peridroma saucia*
*Phorodon cannabis*
*Phyllophaga tristis*
*Phyllotreta pusilla*
*Podosphaera macularis*
*Polyphagotarsonemus latus*
*Popillia japonica*
*Prionus*
*Pseudoperonospora cannabina*
*Pseudoperonospora humuli*
*Psylliodes attenuata*
*Rhopalidae*
*Rhopalosiphum abdominalis*
*Spilosoma virginica*
*Spissistilus festinus*
*Spodoptera exigua*
*Spodoptera ornithigalli*
*Strymon melinus*
*Systena blanda*
*Systena elongata*
*Tetramorium caespitum*
*Tetranychus urticae*
*Thamnurgus caucasicus*
*Thrips tabaci*
*Thyanta custator*
*Trichiocampus cannabis*
*Uredo kriegeriana*
*Vanessa cardui*
Known European species
 📚**Additional References**📚
 H M G Van der Werf, W C A van Geel, and M Wijlhuizen (1995)
Punja, Rodriguez, and Chen (2017)
Cranshaw et al. (2019)
Eric Anderson (2019)
Campbell et al. (2019)
McKernan et al. (2020)
Szarka et al. (2020)
Farinas and Peduto (2020)
Thiessen et al. (2020)
Hu, Masson, and Dickey (2020)
Stack et al. (2021)
 COLLECTION 🧪**Feral hemp collection protocol**🧪
 This feral hemp collection protocol was very generously provided by Shelby Ellison via work with the S-1084 USDA Hatch Multistate Research Project: Industrial Hemp Production, Processing, and Marketing in the U.S.. Secure state licensing to possess industrial hemp in your jurisdiction.
Secure written authorization from your regulatory agency (state department of agriculture) and/or University counsel to collect and hold feral hemp that has not been certified as < 0.3% ∆9-THC.
Collect GPS coordinates (record Google Maps point on a smartphone), and record a thorough description of the site – drainage patterns, surrounding vegetation, elevation, and soils classification from Web Soil Survey. Contact Zachary Stansell for a collection template.
Preferred population size up to 50 random female individuals
Populations should be separated by at least 5 miles (8 km) to assure pollen isolation.
Give each population an ID code in this scheme: state (WI), year (2022), five-digit sequential accession number for your program (00001), and plant number (01) – i.e. WI-2022-00001-01. If there are multiple programs collecting in a state, the state identifier should be modified (WI-M) for Wisconsin-Madison, for example.
Secure verbal and/or written permission from the landowner to collect plant material.
Using sharp pruning shears, cut down up to 50 random female plants. Label a large brown paper bag for each plant with ID code. Thresh each plant individually, place seed into the separate and individually-labeled large paper bag.
Dry all material at room temp (or <100F°) with a fan providing moving air for at least one week. Thresh and sieve seeds if possible or clean on a Clipper seed cleaner or similar.
First contact then send cleaned seed to Zachary Stansell at zachary.stansell@usda.gov at **21 Crabapple Dr. Geneva NY, 14456**.
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