seed dispersal experiment

Teachers are Terrific

How to Make Seed Dispersal Hands-On

April 20, 2018 by Carol Davis

This hands-on seed dispersal project had an interesting beginning…

Once upon a time, our third graders planted seeds every spring and we watched the  Brassica  plant change through its entire life cycle- including pollinating them with dried bees. This was always my favorite day of the year!  

I would hint to my third graders that the “bees were coming” and they just assumed the bees were alive. On Bee Day I would have my door closed and a note on it saying to be quiet because the bees had arrived.  

They would come in whispering and looking around for the bees. Later I would bring out the dried bees. I loved it! Fooled them every time.  So, how can we add some of this excitement to the STEM Lab….hmmmm…..

A Little Background

What if we could learn more about seed dispersal after the seeds have been created in the plant? BAM! This challenge was born!

I drew a fabulous anchor chart showing the methods of seed dispersal and we had a great discussion including ideas of how we could build something for each method.      

I mean, seriously, it’s a great anchor chart. I am not one bit artistic, but I can look at photos and sketch a little and colored pencils create the rest.

After our discussion, we also watched several short videos about dispersal. Then I passed out the assignment cards. I chose six different dispersal methods (the ones I thought we could actually recreate as a STEM project). Each group had a different assignment card. Then they had some decisions to make!  

How would they build a model and what materials would they need? I created a list of materials and they could choose what was needed for the dispersal method they were designing. This turned out to be challenging!

Dispersal by Animals

ANIMAL DISPERSAL – Basically, animals move seeds by eating the fruit of a plant and then expelling the seeds. They might also move seeds by taking the seeds back to the homes. Kids really enjoyed thinking about this one- mostly because they like to say the word ‘poop’! The cute little bird is made of tissue paper and craft sticks. The group explained that the bird had seeds in its mouth and after eating he would poop them out!

Dispersal by Gravity 

GRAVITY DISPERSAL – Gravity is a form of dispersal, but generally results in dropping the seeds right near the parent plant. This is really not what you want to happen. So, teams were challenged with dropping the seeds, but maybe moving them away from the plant, too. Some tried a bouncing method for this or a balloon that exploded. Some just had the seeds drop straight down. The trees to the left worked really well. On the right, you can see the hanging fruit that is ready to fall.

Dispersal by Attachment 

ATTACHMENT DISPERSAL – Attachment is the dispersal method that involves plant parts clinging to an animal. This one has an interesting story that involves the invention of velcro! We watched a short video about velcro and that helped kids understand how to make a model of this form. The photo is showing a dog with seeds clinging to his “cotton ball” fur!

Dispersal by Water

WATER DISPERSAL – Did you know that most seeds float? So, what would you build to show this dispersal method? Most of my groups built a form of a boat that carried the seeds away. The one in the photo floated and held the seeds and the team explained that rough water would make the seeds pop out.

Dispersal by Explosion

EXPLOSION DISPERSAL – This dispersal method was, by far, the one all the kids wanted. They just loved thinking about placing seeds inside a balloon and then popping the balloon to create the exploding and seed dispersal. We had a lot of fun watching the testing of those seed filled balloons. Every single time a balloon popped someone would scream!

Did We Have Problems with This Challenge?

Of course! We always have things happen that create problems to solve- or funny things to happen! Take a look!

This team above made a boat of cotton balls. They were truly perplexed when it sank very quickly. This STEM Challenge was all about choosing the right materials to build a model!

Above- a team picked a balloon as the building material for their boat. Well, of course, it floated. But was this the best choice for a building item? Absolutely not! The seeds placed on top of the boat made the boat twist with that added weight and turn over. It was not what they expected.

Above – I had these little gloves from the Dollar Tree that had a very stringy and coarse texture. I thought they would be great for making the attachment model, but the team wanted it to make an animal model. They created the animal above which we thought was a bird. They declared it was an insect and it does look like a winged insect. Was the glove the best choice?

Parachutes are fun to make and kids love trying them. Above the team made a tiny parachute and the weight of the cup made it crash to the ground. It did scatter the seeds, though.

And, finally, this model. This one was puzzling for me and I could not decide what the team had built. It’s a mess, I thought.

Ah, but you should never underestimate kids! Here’s the explanation:

“It’s a dog. When you put a seed inside the white cup on the left it will travel all the way through and come out the other end. Why can’t you see the dog’s head? Because that dog has just been to the vet and it’s wearing a cone!”

You have to admit that is really funny!

You need to try this challenge! It is such a fabulous learning experience! Click on any of the images to see the details in my store!

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Gone with the Wind: Plant Seed Dispersal

A science activity from Science Buddies, based on a project from the Botanical Society of America

By Science Buddies

Scattering seeds! Learn how some of the most fun seeds of summer travel so well on the breeze. Can you design a seed that scatters well with the wind?

George Retseck

Key concepts Biology Plants Evolution Aerodynamics

Introduction Have you ever looked outside on a windy day and seen "helicopter" seeds spinning through the air? Or picked up a dandelion and blown on it, sending the tiny, fluffy seeds flying all over the place? Wind is very important for dispersing seeds to help plants reproduce. In this project you will design some of your own "seeds" and see which ones work best when they are blown across the room by a fan.

Background Dispersal of seeds is very important for the survival of plant species. If plants grow too closely together, they have to compete for light, water and nutrients from the soil. Seed dispersal allows plants to spread out from a wide area and avoid competing with one another for the same resources.

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Seeds are dispersed in several different ways. In some plants seeds are housed within a fruit (such as apples or oranges). These fruits, including the seeds, are eaten by animals who then disperse the seeds when they defecate. Some fruits can be carried by water, such as a floating coconut. Some seeds have little hooks that can stick on to an animal's furry coat. (You may have gotten them stuck on your clothing if you ever went hiking in the woods or tall grass.)

Other seeds are dispersed by the wind—such as the "winged" seeds from a maple tree that spin and "helicopter" through the air as they fall or the light feathery seeds from a dandelion that can catch on the breeze. The longer a seed stays in the air, the farther it can be blown by the wind, helping the plant species widely scatter its offspring. In this project you will make your own artificial "seeds" from craft materials. Can you design seeds that will stay in the air for a long time?

Examples of different seeds that are dispersed by the wind (Depending on where you live, you may be able to find some of these seeds outside. If you have access to the Internet, you can also do a Web search for maple seeds, dandelion seeds and other types of wind-dispersed seeds to help get ideas.)

Small, uniform, lightweight objects that you can use as "seeds" (For example, you could use small paper clips or small binder clips; or purchase a bag of real seeds—such as sunflower seeds—at the supermarket.)

Craft supplies to build dispersal mechanisms for your seeds (These could be as simple as paper and tape or you could also use things such as streamers, cotton balls or even items you find outside, such as blades of grass.)

Scissors, tape and glue for cutting and attaching your craft supplies to your seeds (Be careful when using scissors.)

A window fan or large box fan (Use with caution and appropriate supervision.)

Stopwatch or timer (optional)

Measuring tape or ruler (optional)

Preparation

Clear an open area in the room where you will do the seed-testing activity.

Place the fan on a table or chair, aimed across the room. You can also do the experiment outside on a windy day.

Design and build several—at least four—dispersal mechanisms for your seeds. The activity works best if you can create at least two similar dispersal mechanisms to test against one another (see examples below). You can use your imagination and come up with your own ideas but here are a few to get you started (using a paper clip as an example "seed"):

Attach a paper clip to a small, square piece of paper, about the size of a Sticky Note, without making any changes to the paper.

Attach a paper clip to another small piece of paper, but make a several parallel cuts in one side of the paper to give it "frills," and bend them outward.

Attach a paper clip to a cotton ball.

Attach a paper clip to a cotton ball that you have pulled on to expand it a bit and make it wispier.

Cut out some paper in the shape of a maple seed and attach a paper clip.

Which seed dispersal mechanism or mechanisms do you think will travel the farthest when dropped in front of the fan? Why?

Turn on the fan. Standing in the same place, try dropping your seeds one at a time in front of the fan. Also try dropping a plain "seed" (for example, a regular paper clip with nothing attached) to see what happens.

How far do the seeds get blown by the fan? Do certain seeds take longer to reach the ground than others?

Think about your results. Did some of your designs not work at all (fall straight down, without blowing forward)? Did some work better than others? What can you do to improve your designs? Can you make changes to your seeds to make them blow even farther?

Extra: Have a friend use a stopwatch to time how long it takes the seeds to hit the ground. This might be easier if you drop the seeds from a higher location. (Have a tall adult drop them, carefully stand on a chair or drop them from the top of stairs.)

Extra: Use a tape measure to record how far the seeds travel horizontally from where you drop them to where they hit the ground. Which seeds go the farthest?

Extra: How do your results change if you change the speed of the fan?

[break] Observations and results

You should find that adding light materials to the "seed" can make it fall more slowly and blow farther—however, the shape of the materials is also very important. For example, a paper clip attached to a crumpled-up piece of paper will still fall very fast. A piece of paper with a "wing" design (similar to that of a maple seed) or a bunch of individual streamers (like a dandelion seed), however, will fall more slowly and be blown farther by the fan. Exactly how far the seeds blow will depend on the strength of your fan but you should definitely see a difference in the horizontal distance traveled between a "plain" seed and one with a dispersal mechanism. When you take your best designs and try to improve on them, you mimic the process of evolution—because the "best" seed designs in nature are the ones most likely to reproduce!

More to explore Gone With the Wind: An Experiment on Seed and Fruit Dispersal , from Science Buddies Sailing Seeds: An Experiment in Wind Dispersal , original project from the Botanical Society of America Make a Whirlybird from Paper , from Scientific American Science Activities for All Ages! , from Science Buddies

This activity brought to you in partnership with Science Buddies

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Last updated by Linda Kamp on December 9, 2022 • Leave a Comment

Animal Attachment Seed Dispersal STEM Activity

During our Plants, Animals & Life Cycles science unit, we discuss how animals help plants by moving their seeds to other areas so the plants can grow there. This easy animal seed dispersal activity is a great way for students to design and build models and simulate how animals help plants grow.

What is animal attachment seed dispersal?

One way plants survive is to spread their seeds to new places. Plants spread, or disperse, their seeds in a variety of ways, including with the help of animals.

Animals like birds and squirrels carry seeds from place to place on purpose. They often bury them to eat later, but when they forget about those buried seeds, the seeds grow into new plants.

On the other hand, some animals accidentally carry seeds from place to place. Some plants let seeds drop to the ground and animals pick them up on their fur and move them to different areas. Some plants even have special seeds that are barb-shaped or have sticky substances on them to help them attach to animals’ fur.

In this lab, students will design and build a model that shows how seeds are dispersed by attaching to an animal. Students will use a variety of materials to create a model animal. Then, they will test out how seeds stick to their model animal.

Introduce this lab by posing the question: How do animals move seeds?

Animal attachment seed dispersal lab experiment

• black Velcro strips
• cotton balls or bag of polyfill
• pipe cleaners
• wiggly eyes
• construction paper
• toilet paper rolls
• paper plates

1. Place students into groups and have them form a plan for building their model animal. Present the options for materials and have students discuss and record the materials they will use on their lab sheets. I cut Velcro into squares, then cut them in half diagonally to represent hooked seeds.

2. Students draw a sketch of their design on their lab sheets and explain their plan. Make sure to ask students what each material they are using represents. For example, cotton balls represent fuzzy animal fur.

3. Students build their model animal with the materials. 

How to make the animal models

Place the toilet paper tube in the middle of the polly fill. Roll the poly fill around the tube and tuck it into each end. Students then used the pipe cleaners to make legs by folding them over the tube and twisting the pipe cleaners to hold them in place.

Next, we place a small wad of paper in the middle of a square piece of polly fill and twisted the end to make a head. Tuck the head inside one end of the tube. Lastly, students glued on wiggly eyes, paper ears and a nose.

4. Pour bird seed and any other seeds you are using onto a paper plate. I had students add the Velcro pieces to their plate of seeds as well.

5. Students use their model animal to act out an animal walking, laying down, and rolling in a field or in the woods by moving their animal in the plate of seeds. Guide students to discuss how the shape and texture of a seed helps it stick.

6. Students record observations and come up with an explanation on their lab sheets.

7. Finally, I have different groups pair up to explain their models to another group. They share what they have observed about the seeds and how this relates to animals helping plants with seed dispersal.

Plants, Animals, & Life Cycles experiments and lesson plans

The animal attachment seed dispersal lab is part of a complete Plants, Animals, & Life Cycles unit for 2 nd grade that is also available in a digital format.

Click HERE for the complete printable unit and HERE for the digital version. Or, save by purchasing the Print & Digital Bundle .

Click here for the yearlong 2 nd grade science units.

Don’t forget to pin this animal attachment seed dispersal activity for later! Visit these posts for more science activities for teaching about plants, adaptations and seed dispersal:

Exploding Seed Pods Seed Dispersal Lab

Making Models of Plant Adaptations

Life Cycle of Plants Science Activitites & Experiments

Happy teaching!

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I’m Linda Kamp, a 20 year primary grade teacher with a passion for creating educational materials that excite students and make learning fun! I'm so glad you're here!

Seed dispersal: practical

I can explain why it is important for a plant that its seeds are dispersed over a wide area, and carry out an experiment to measure seed dispersal.

Lesson details

Key learning points.

• Seeds are dispersed by different mechanisms to increase the chance of survival.
• Seeds are adapted to match the mechanism for dispersal; e.g. wind dispersed are light with a large surface area.
• Variables are factors that affect the process being investigated, the dependant variable is distance the seed travels.
• Practical investigation of how far model seeds disperse with changes to their template.
• Repeat measurements for each value to identify anomalous results and calculate a mean.

Common misconception

That means are always divide by three.

Show examples where the number of repeats varies.

Seed dispersal - Mechanism by which seeds are transported away from the parent plant.

Adaptations - Features of living organisms that help them survive in their environment.

Variable - Factor in a scientific investigation which may have an effect on the process being studied.

Repeats - Measurements of the same value allows anomalous results to be identified.

Mean - A measure of average where all values are added together and divided by the number of repeat measurements that have been taken.

Content guidance

• Risk assessment required - equipment

Supervision

Adult supervision required

This content is © Oak National Academy Limited ( 2024 ), licensed on Open Government Licence version 3.0 except where otherwise stated. See Oak's terms & conditions (Collection 2).

Starter quiz

6 questions.

independent -  

variables that are chosen to be changed

dependent -  

variables that are measured

control -  

variables that are kept the same

independent variable -  

width of wings

dependent variable -  

distance the seed travels

control variable -  

length of wings

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Seed dispersal.

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Plants make seeds that can grow into new plants, but if the seeds just fall to the ground under the parent plant, they might not get enough sun, water or nutrients from the soil. Because plants cannot walk around and take their seeds to other places, they have developed other methods to disperse (move) their seeds. The most common methods are wind, water, animals, explosion and fire.

Dandelion seeds

Dandelion seeds float away in the wind. To make sure at least some of the seeds land in a suitable growing place, the plant has to produce lots of seeds.

Wind dispersal

Have you ever blown on a dandelion head and watched the seeds float away? This is wind dispersal. Seeds from plants like dandelions, swan plants and cottonwood trees are light and have feathery bristles and can be carried long distances by the wind. Some plants, like kauri and maple trees, have ‘winged’ seeds. They don’t float away but flutter to the ground. With wind dispersal, the seeds are simply blown about and land in all kinds of places. To help their chances that at least some of the seeds land in a place suitable for growth, these plants have to produce lots of seeds.

Water dispersal

Many plants have seeds that use water as a means of dispersal. The seeds float away from the parent plant. Mangrove trees live in estuaries. If a mangrove seed falls during low tide, it can begin to root in the soil. If the seeds fall in the water, they are carried away by the tide to grow somewhere else. Kōwhai trees also use water dispersal. They have a hard seed coat that allows them to float down streams and rivers. That is one of the reasons kōwhai trees are commonly found on stream banks.

Animal dispersal

Over 70% of plants in our woody forests in New Zealand have fleshy fruit that is eaten by birds. Chemicals in our native birds’ digestive systems help to weaken the tough coats around these seeds. Birds often fly far away from the parent plant and disperse the seeds in their droppings. The kererū , tūī and bellbird play an important role in seed dispersal. Trees that produce the largest fruit – miro, pūriri, tawa and taraire – rely on the kererū because it has such a large, wide beak to eat the fruit.

Kererū feeding on karaka berry

The kererū is important in the seed dispersal of large native berries in forest ecosystems. Kererū are the only birds left (all others are now extinct) big enough to swallow the large fruits of the karaka tree.

Some seeds have hooks or barbs that catch onto an animal’s fur, feathers or skin. Plants like pittosporum have sticky seeds that can be carried away by birds. Humans can also spread seeds if they get stuck to our clothing or shoes – and if we throw fruit pips and stones out of the car window!

This method of seed dispersal isn’t quite as exciting as it may sound. Some plants, like peas, gorse and flax, have seedpods that dry out once the seeds are ripe. When dry, the pods split open and the seeds scatter. If you’re lucky, on a hot summer day when you walk by a gorse bush, you will hear the gorse seedpods popping open.

Plants cannot run away from a fire so some plants have developed a way to help their seeds survive. There are some species of pine tree that require the heat from a fire before their cones will open and release seeds. Banksias, eucalypts and other Australian plants also rely on fire. The intensity and timing of the fire is important. It needs to be hot enough to trigger the cones to open, but if fires are too frequent, there is not enough time for the plants to grow big enough to make new seeds.

Australian bush fire

Banksias, eucalypts and other Australian plants rely on fire to help spread their seeds.

Adaptation and seed dispersal

Adaptation is an evolutionary process that helps an organism make the most of its habitat. Seed dispersal is an example of adaptation. Fires are common in Australia, so some plants have a dapted and become well suited to make the most of it. Mangrove trees have seeds that float, making the most of their watery environment.

Nature of science

Science is an attempt to explain the natural world. Evolution explores how groups of living things have changed over long periods of time, for example, how plants have developed different ways to disperse their seeds.

Activity ideas

Use Plant reproduction – literacy and numeracy learning links to record and deepen student understanding of key science ideas.

Looking at seeds and fruits is a ready-to-use cross-curricular teaching resource. Intended for NZC levels 2–3, this worksheet-based activity does not require internet access and has multiple literacy activities.

Try these 'hands-on' activities:

• Seed dispersal puppet play uses stick puppets to explain how plants disperse their seeds.
• Woolly sock walk – meander through long grass to experience seed dispersal.
• Matching seeds and fruits uses activity cards to match seeds with the fruits from which they grow.

Use this suite of activities to collect , observe and plant kōwhai seeds.

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Flying Helicopter Seeds – science investigation

September 27, 2023 By Emma Vanstone Leave a Comment

Helicopter seeds are a type of seed that spin as they fall from a tree. The spinning movement and the fact that they are light and can catch the wind allows them to travel further from the parent tree than if they just dropped to the floor.

This is one method of seed dispersal used by plants and trees to reduce the amount of competition around the parent plant for light, water and nutrients.

This activity uses simple paper spinners to demonstrate how helicopter seeds fall from a tree.

Which trees have helicopter seeds?

Ash Trees – these helicopter seeds have one wing and hang in bunches from the tree.

Norway Maple

Sycamore – symmetrical wings in a V shape

Make your own seed helicopter

You’ll recognise these as simple paper spinner s from a previous science activity, but they work really well for demonstrating this kind of seed dispersal.

You’ll need

Paper clip or hair clip

How to make a helicopter spinner

Cut out a rectangle from paper like the image below using scissors. Any size works well.

Cut down the dotted lines and attach the two ends together with a paper clip.

Hold the spinner as high as you can and drop.

Watch as the helicopter spins to the ground just like a seed.

Investigation ideas

Try dropping different-sized helicopters from the same point and measure how far they travel from the drop point.

Drop the helicopters inside and outside. Design an investigation to find out If the wind makes a difference to how far they travel.

Add extra weight to the helicopter and try the investigation again.

Do smaller helicopters spin faster than large helicopters?

Print the experiment instructions

Investigation sheet

Another idea for learning about a different type of seed dispersal is to make a sticky seed pod !

If you need a bit of help identifying different trees, try this Tree Identification app from The Woodland Trust.

I have lots more science experiments for autumn you might like and some autumn STEM challenges too!

Last Updated on September 27, 2023 by Emma Vanstone

Safety Notice

Science Sparks ( Wild Sparks Enterprises Ltd ) are not liable for the actions of activity of any person who uses the information in this resource or in any of the suggested further resources. Science Sparks assume no liability with regard to injuries or damage to property that may occur as a result of using the information and carrying out the practical activities contained in this resource or in any of the suggested further resources.

These activities are designed to be carried out by children working with a parent, guardian or other appropriate adult. The adult involved is fully responsible for ensuring that the activities are carried out safely.

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Seed Dispersal – physics and biology at Key Stage 3

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Age Ranges:

This collection of resources for 11-14 students uses the topic of plants to address important scientific ideas in biology and physics.

While investigating how plants have evolved to disperse their seeds in different environments, the students can cover topics about forces, pressure in fluids, and forces and motion.

The resources involve a mixture of independent investigations, engaging and hands-on practicals, and activities to identify misconceptions and strengthen understanding. Teachers can opt to include as many or as few of the resources from the collection as they like.

Seed dispersal is an important aspect of plant reproduction. If not suitably dispersed, seeds will germinate very close to their parent plant. This means that the seedlings must compete with one another and, of course, the parent plant for space, light, water and nutrients.

Studying wind dispersal can bring together important scientific ideas in biology and physics.

These include forces, motion and evolution.

Evolution of seed dispersal

Plants have evolved clever ways of having their seed-containing fruits carried away, sometimes over long distances. They produce fruit that, for example

• are attractive to animals who eat them, carry them away and release the seeds in their droppings
• have hooks or barbs that can attach to the fur or wool of an animal
• explode and burst open when ripe, propelling the seeds away from the plant
• have structures that allow them to be carried by the wind away from the plan.

This group of sheets is about wind dispersal :

• Falling from trees
• Parachuting fruits
• Gliding fruits
• Spinning fruits
• One-winged fruits
• Two-winged fruits

Key scientific ideas

Studying wind dispersal can bring together important scientific ideas in biology and physics. These are summarised by statements in the National curriculum for science in England at key stage 3:

In  biology  pupils should be taught about Reproduction

• reproduction in plants, including flower structure, wind and insect pollination, fertilisation, seed and fruit formation and dispersal, including quantitative investigation of some dispersal mechanisms

In  physics  pupils should be taught about Forces, Pressure in fluids and Forces and motion

• forces as pushes or pulls, arising from the interaction between two objects
• using force arrows in diagrams, adding forces in one dimension, balanced and unbalanced forces
• moment as the turning effect of a force
• forces: with pushing things out of the way; resistance to motion of air and water
• non-contact forces: gravity forces acting at a distance on Earth and in space
• atmospheric pressure, decreases with increase of height as weight of air above decreases with height
• pressure in liquids, increasing with depth; upthrust effects, floating and sinking
• pressure measured by ratio of force over area – acting normal to any surface
• forces being needed to cause objects to stop or start moving, or to change their speed or direction of motion (qualitative only)
• change depending on direction of force and its size

Tackling common misconceptions

The activities described in the sheets also provide an opportunity to tackle some of the common misconceptions, including

• Gravity = ‘downness’;
• When an object is stationary, no forces are acting on it;
• Heavy objects fall faster than light objects;
• Something stops moving because the force has run out;
• Air doesn’t weigh anything;
• Particles are the same as visible grains as in rocks, for example;
• Microbes, cells and particles are much the same.

This resource links to and follows from the KS2 resource:  Fruits, seeds and their dispersal

What's included?

• SAPS - 1 Falling from trees - Student Sheet
• SAPS - 1 Falling from trees - Technical and Teaching Notes
• SAPS - 2 Coconuts - Technical and Teaching Notes
• SAPS - 2 Coconuts - Student Sheet
• SAPS - 3 Parachuting fruits - Technical and Teaching Notes
• SAPS - 3 Parachuting fruits - Student Sheet
• SAPS - 4 Gliding fruits - Student Sheet
• SAPS - 4 Gliding fruits - Technical and Teaching Notes
• SAPS - 5 Spinning fruits - Student Sheet
• SAPS - 5 Spinning fruits - Technical and Teaching Notes
• SAPS - 6 One-winged fruits - Student Sheet
• SAPS - 6 One-winged fruits - Technical and Teaching Notes
• SAPS - 7 Two-winged fruits - Student Sheet
• SAPS - 7 Two-winged fruits - Technical and Teaching Notes
• Particulate nature of matter
• Physical changes
• Plant growth
• Plant reproduction

Related content

Teaching resources.

• Fruits, Seeds and their Dispersal
• Plants and Science for 11-14 Year Olds
• Ins and Outs of Water - biology, chemistry and physics for 11-14 students
• Plant Needs - biology, chemistry and physics for 11-14 students
• Archive Space biology - teaching resources to inspire from Rocket Science

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Seed Dispersal - An Investigation

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Try this Seed Dispersal Investigation to see for yourself some of the interesting adaptations plants have developed to spread their seeds.

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Prepare for Experiment:

Read Seed Dispersal .

Do the Seed Dispersal Matching.

Prepare for Experiment: Read the: Plants - Read Aloud Study the: Spreading the Seeds Mini-Poster Try the: Spreading the Seeds - Matching Objective: Learn about seed dispersal by performing an experiment mimicking a dispersal technique found in nature. Materials:  scissors, old white socks, plant mister, flower pot full of damp potting soil, uncut grassy field in June-September. Procedures: 1. Students take turns putting a sock on one hand and walking through the tall grass, sweeping the sock-covered hand through the tall grass. 2. After all the students have had a chance to “collect seeds”, cut the sock up one side and spread over the pot full of damp soil. 3. Mist the sock until it is damp every day (not soaking wet). Place pot in a sunny spot. 4. Over the next couple of weeks, mist the sock every day and note if there is any green growth. 5. After a few weeks, look at the plant growth on the sock and talk about how the seeds got there. Conclusions: Talk about the physical structures seeds might have to help them be spread from one place to another - hooks, sticky fluid, fruit, nut, etc. Talk about how burrs stick to your clothes (and animals) when they are touched.

Critical Thinking: 1. Which human inventions may have mimicked these adaptive structures in plants?

2. Explain the irony in this Seed Dispersal Comic .

National Science Standard Addressed

Performance Expectations: 2-LS2 Ecosystems: Interactions, Energy, and Dynamics Plan and conduct an investigation to determine if plants need sunlight and water to grow. 2-LS2-1 Develop a simple model that mimics the function of an animal in dispersing seeds or pollinating plants. 2-LS2-2

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Tag: Seed dispersal activities

Plant science: seed dispersal.

The majority of plants can’t move from where they are planted once they start growing, yet we see plants almost everywhere. How do they get there?

Many plants travel as seeds, which have many different ways to spread and scatter. Some seeds are carried by animals, some float on the wind, others float on water, some simply roll down hill due to gravity, and still others have ways to shoot out of their seed pods. The ways that seeds move from place to place is called “seed dispersal.”

See for example, these spectacular examples of seed dispersal in this video from the Smithsonian Channel.

Activity 1. Investigate seed structure and movement through observation.

Take a look at some of seeds and guess how they might be transported from place to place.

How do the Chinese elm seeds (samara) look? How do you think they move around?

For the answers, see the bottom of this post.

Go outside and look for seeds, particularly in the fall. Observe them and try to figure out how their structure helps them get from place to place. Look at them through a hand lens. Toss them in the air. Blow on them. Put the seeds in a puddle. See if they will stick to your sleeve. Think about where you see seeds and how they got there.

Once you have made your observations, research what others have found out about how those particular seeds disperse. If little is known, design and conduct your own experiments.

Activity 2. Floating Seeds

Seeds – like the sea bean – can float from place to place. They don’t have to be in a big body of water like the ocean, either. A small trickle created by a downpour of rain may be enough to float seeds away.

• Large bowl, sink, tub or aquarium to fill with water
• Seeds or fruits to test for ability to float:   coconuts, cranberries, a pinto bean or other dried bean, etc.

Predict what will happen to each item and then test each item. Let the seeds or fruit float as long as possible to show that they might reach land without sinking. You might want to cut open a cranberry to show the seeds inside.  (Remember that cranberries are harvested by floating them in ponds). Is a cranberry that has been cut open still able to float?

More advanced activity:

Scientists in Hawaii needed to know how plants arrived on the islands in order to protect native species and prevent introductions of invasive species. A scientist named Henry Guppy placed different seeds in jars of seawater for several months to see how long they could float.

Design your own experiment to test which seeds float in your area and investigate how they do it.

Have you ever gone to the beach or the shore of a lake? Look for seeds on the shore that were carried there by water.

Wayne’s World has an extensive discussion of the botany of drift seeds and drift fruit (those that float).

Activity 3. Flying Seeds

Most of us have seen seeds flying in the air at one time or another. Dandelions, milkweeds, maple keys and cottonwoods are just a few examples of trees with seeds that ride the wind.

Dandelion and oleander seeds fly with structures that are like tiny parachutes. If you are interested, try investigating parachutes .

Design an experiment to test how far a dandelion seed can fly. How would you measure it?

Science Buddies has suggestions for how to carry out a seed dispersal experiment called Gone with The Wind (based on a similar experiment at Scientific American ).

Maple keys are so interesting that scientists take high speed movies of them to discover the secrets of their movements. According to this study, the keys produce swirling air like mini-tornadoes while they spin. Here the seed has been dropped in oil to make the whirls easier to see.

Do you see the tiny swirls that form over the end of the “tail” part of the key? Cool!

For more about maple key science, try these links:

Whirling Wonders

NASA Maple Seed Aeronautics

• Also, make a paper whirlybird .

If you want to learn more details about the botany behind wind dispersal, try Wayne’s World .

To see our complete plant science lessons, either visit the plant science category (newest posts to oldest posts) or the plant science section of our experiment archive page (links to posts in order).

For more information about plants and seeds, try our Seed of the Week archive or the mystery seed tag and Seed of the Week category .

Seed dispersal answers:

• Tickseed sunflower seeds have barbs that stick to clothing and fur. They are carried by animals.
• The wings on the Chinese elm seeds help them float on the wind.
• The red and orange structures on the willow acacia seeds are eaten by birds and other animals. The animals carry away the seeds, eat the red part and discard the hard, slippery seeds.
• Filaree seeds have an interesting ability to twist themselves into the soil. They are like tiny drills.
• Nuts, like hickory nuts, are often carried away and buried by animals.

Weekend Science Fun: How Seeds Get Around

This week our science fun has been inspired by a book that just came out, Planting the Wild Garden by Kathryn O. Galbraith and illustrated by Wendy Anderson Halperin. See Wrapped in Foil blog for a full review.

Planting the Wild Garden is a beautifully illustrated picture book that is a delightful introduction to the ways wild seeds move around (are dispersed).

Plants can’t move once they start growing, yet we see plants almost everywhere. How did they get there? Most travel as seeds. Seeds have many different ways to spread and scatter.

In this video from the Life of Plants by David Attenborough we get to see some marvelous footage of the amazing ways seeds move.

Here at Growing with Science we have a regular feature called Seed of the Week . Take a look at some of the seeds and guess how they might be transported from place to place. For example, check the Chinese elm seeds (samara) with their tiny wings. Don’t they look like they could fly?

Activity 2. Floaters

Seeds like the sea bean can float from place to place. They don’t have to be in a big body of water like the ocean either. A small trickle created by a downpour of rain may be enough to float seeds away.

Predict what will happen to each item and then test each item. Let the seeds or fruit float as long as possible to show that they might reach land without sinking. You might want to cut open a cranberry to show the seeds inside.  (Remember that cranberries are harvested by floating them in ponds). Does a cut cranberry float?

Scientists in Hawaii needed to know how plants arrived on the islands in order to protect native species and prevent introductions of invasive species. A scientist named Henry Guppy placed different seeds in jars of seawater for several months to see how long they could float. Design your own experiment to test which seeds float in your area and investigate how they do it.

Dandelion and cattail seeds fly with structures that are like tiny parachutes. If you are interested, try investigating parachutes .

Advanced: Design an experiment to test how far a dandelion seed can fly. How would you measure it?

Maple keys are so interesting that scientists take high speed movies of them to discover the secrets of their movements. According to one study, the keys produce swirling air like mini-tornadoes while they spin.

Animals also transport a lot of different kinds of seeds. Whenever an animal, such as a bird, eats a juicy bit of fruit like this pyracantha berry, it ingests the seeds. The seeds end up on the ground later on. Other animals, like squirrels, may bury seeds and forget where they are.

Some seeds, like burdocks, hitch a ride by being sticky or latching on the fur of mammals.

We often think of big animals moving seeds, but tiny ones move a lot of seeds, too. Check for a related post at Wild About Ants for information about ants and seed dispersal .

Finally, by far the coolest are the seeds that pop out of the pods and shoot away. Plants with this kind of dispersal include jewelweed, lupines and Scotch broom. See if you can find a plant that does this and try it out.

And don’t forget to pick up a book about seed dispersal, such as Planting the Wild Garden , to learn more and inspire your own investigations.

Reading level: Ages 4-8 Hardcover: 32 pages Publisher: Peachtree Publishers (April 1, 2011) ISBN-10: 1561455636 ISBN-13: 978-1561455638

Part of our growing list of Children’s Books about Seeds at Science Books for Kids.

Disclosures: The book was provided for review purposes. Also, I am an affiliate for Amazon. If you click through the linked titles or ads and make a purchase, I will receive a small commission at no extra charge to you. Proceeds will be used to maintain this self-hosted blog.

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• Published: 04 September 2024

Cannabis pollen dispersal across the United States

• Manu Nimmala 1 ,
• Shane D. Ross 2   na1 &
• Hosein Foroutan 3   na1  

Scientific Reports volume  14 , Article number:  20605 ( 2024 ) Cite this article

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For the recently legalized US hemp industry ( Cannabis sativa ), cross-pollination between neighboring fields has become a significant challenge, leading to contaminated seeds, reduced oil yields, and in some cases, mandated crop destruction. As a step towards assessing hemp cross-pollination risk, this study characterizes the seasonal and spatial patterns in windborne hemp pollen dispersal spanning the conterminous United States (CONUS). By leveraging meteorological data obtained through mesoscale model simulations, we have driven Lagrangian Stochastic models to simulate wind-borne hemp pollen dispersion across CONUS on a county-by-county basis for five months from July to November, encompassing the potential flowering season for industrial hemp. Our findings reveal that pollen deposition rates escalate from summer to autumn due to the reduction in convective activity during daytime and the increase in wind shear at night as the season progresses. We find diurnal variations in pollen dispersion: nighttime conditions favor deposition in proximity to the source, while daytime conditions facilitate broader dispersal albeit with reduced deposition rates. These shifting weather patterns give rise to specific regions of CONUS more vulnerable to hemp cross-pollination.

Introduction

The 2014 and 2018 US Farm Bills legalized the production of industrial hemp ( Cannabis sativa ) for cannabidiols, seed, and fiber 1 . This nascent industry has been challenged by wind-blown cross-pollination between neighboring hemp fields, leading to contaminated seeds, reduced oil yields, and in some cases, mandated crop destruction 2 , 3 . Financial impacts reported in a 2022 Colorado survey 2 ranged from $12,000 to millions of dollars, with an Oregon lawsuit alleging damages of over $8 million 4 . Economic modeling 3 shows that the industry will transition away from cannabidiol hemp production entirely without effective cross-pollination mitigation strategies.

As hemp production has only recently been legalized 1 , there is a deficit in hemp dispersal research. The only study quantifying hemp pollen dispersal as a function of distance from a known source is an experiment by Small and Antle 5 . They sampled hemp pollen for three weeks at distances of up to 400 meters from a source field and observed significant deposition even at the edge of their domain, 17,000 pollen grains/ \(\hbox {m}^2\) /day, enough to “achieve excellent seed set”, i.e., successfully cross-pollinate. The authors noted that due to its small size ( \(\sim\) 30 microns) hemp pollen travels farther and deposits in greater quantities than other wind-pollinated crops, and that it is prolific—each male flower can release up to 350,000 pollen grains, and there are potentially hundreds of flowers on larger plants 6 . A single male plant can therefore release about 100 million pollen grains. Recommended isolation distances are far greater than the experimental domain, typically varying between 1 and 5 km 7 , 8 , but there have been reports of cross-pollination up to 20 km 9 and even 48–96 km away 2 . Two back-trajectory studies have demonstrated that Cannabis pollen likely travelled over 200 km, from Northern Africa to Spain 10 , 11 . This indicates that hemp pollen has great potential for long-distance transport, and that the ‘fat tail’ of the hemp pollen dispersal kernel could play an outsized role in cross-pollination between fields.

Dispersal modeling studies show that the fat tail in wind-borne dispersal is highly sensitive to changes in meteorological conditions, particularly the combined effects of shear and convective turbulence. During the day, solar heating of the surface induces a positive heat flux that creates large-scale convective updrafts. Shear-driven turbulence arises as horizontal wind passes over rough surfaces. One study found that rising temperatures, correlated with increasing heat flux, led to a greater proportion of seeds traveling beyond 100 meters in simulations 12 . Another found that sustained updrafts caused dandelion seeds to disperse further, while horizontal wind speed did not play a factor 13 . In contrast, Soons et al. 14 found that horizontal wind velocity was the primary driver of downwind transport, and heat flux only played a role when wind velocity was low ( \(< 4\) m/s). Understanding such patterns in variation of the tail would help inform cross-pollination mitigation strategies.

Two dispersal modeling studies have identified seasonal and diurnal patterns in the variation of wind-borne dispersal kernels. Oneto et al. 15 used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate fungal spores released at ten North American locations in January, April, July, and October, 2014. They found a strong diurnal pattern in average flight times, with spores staying in the air longer during the day than at night. They also observed seasonal changes, with the longest flight times in July and lowest in January. Savage et al. 16 simulated spore dispersal using hourly meteorological inputs from a large-scale weather model at two towns in Western Australia for June and September 2007, early winter and early spring, respectively. They found seasonal and diurnal changes in the number of spores travelling past 10 km, and differences between the two towns, aligning with seasonal and diurnal changes in temperature and wind velocity. These studies suggest contiguous spatial patterns in dispersal on a country-wide scale.

In this study, we seek seasonal and spatial patterns in pollen dispersal spanning the conterminous United States (CONUS), revealing regions more prone to cross-pollination. We extend the methodology of Savage et al. 16 , using meteorological data provided by a mesoscale model simulation to drive Lagrangian Stochastic (LS) models of pollen dispersion for each county in the United States over five months. The LS model is ideal for examining the sensitivity of dispersal due to shear and convection, as it more naturally captures the variations of turbulent flow using stochasticity. It is an application of Brownian motion to turbulent diffusion, in which the trajectories of many particles through the air are modeled as random walks. By releasing thousands of particles and computing an ensemble average of their trajectories, we can determine the relative concentration at any point in the domain and the mean shape of the plume. Therefore, they require a fraction of the computational resources of more resolved Eulerian models like Large Eddy Simulations. Although conventional Gaussian plume models are computationally lighter than LS models, their treatment of turbulence is more prescribed. Modifications have been made to incorporate effects like convection in Gaussian plume models (for example, the AERMOD model 17 ), but these require more parameters and increase complexity 18 .

We used two LS model formulations: a convective boundary layer model 19 , 20 , 21 for unstable (typically day) conditions and a surface layer model 22 for stable (night) conditions. To drive the LS model, we used meteorological fields obtained from a Weather Research and Forecasting (WRF) model simulation over CONUS for the entire year of 2016 23 . This high-resolution meteorological dataset, developed by the U.S. Environmental Protection Agency to support modeling applications, comprises an hourly time series of weather conditions on a 12 km-square horizontal grid and has been extensively validated 24 . For each county, we extracted the weather data at the grid point nearest to its centroid and averaged across local noon and midnight hours for each month from July to November, to represent average “day” and “night” conditions respectively. We performed LS simulations for day and night conditions, for five months from July to November, for each of 3,107 counties in the CONUS, totalling to 31,070 simulations. In this study, we used 2D LS models, in which we simulate pollen travelling in the downwind and vertical directions. From each simulation, we compute a dispersal kernel by counting the number of particles which have deposited in the simulation domain within 250 meter-wide bins up to 50 km downwind of the source. The meteorological conditions are assumed to be statistically stationary and horizontally homogeneous for each simulation.

To the best of our knowledge, this is the first simulation study of hemp pollen dispersal. It is also the first large-scale simulation study of the inhomogeneity of pollen dispersal across regions and seasons.

Results and discussion

Simulation of day and night pollen dispersion over five months reveals significant seasonal and spatial variations, particularly in the tail of the dispersal kernel. Each simulation yielded a dispersal kernel, or number of particles deposited downwind from the source in 250 m wide bins, normalized by the number of particles released. Figure  1 a,b show median day and night dispersal kernels on a log scale by month for each of nine US climate divisions 25 , in order to compare between climatically different regions. We observe depositions up to 50 km downwind, the edge of our domain, which is the limit of applicability of our LS model.

Median dispersal kernels for each month during ( a ) daytime and ( b ) nighttime, separated by US climate region: Northeast (NE), Upper Midwest (UM), Ohio Valley (OV), Southeast (SE), Northern Rockies and Plains (NRP), South (S), Southwest (SW), Northwest (NW), and West (W). Dispersal kernels are formed by counting depositions within 250 meter-wide bins up to 50 km downwind of the source, normalized by the amount released. Shading represents data between the 10th and 90th percentiles. Note that the vertical axis is a log scale.

The tail of the dispersal kernel varies seasonally and spatially

Simulations of day and night pollen dispersion over five months yields variation only in the tail of the dispersal kernel. For all climate regions, in both day and night conditions, Fig.  1 shows a steep decline in depositions by two orders of magnitude within the first few kilometers of the source. Approximately 70% of simulated pollen is deposited in the first bin alone for all cases. Figure  2 a shows that across all simulations, dispersal kernels decreased to 1% of released particles within 3 km of the source. Although there is a slight increase in distance for nighttime conditions, this region of steep decline is indistinguishable across counties regardless of region and seasonal weather changes.

Distances at which dispersal kernels first fall below a threshold: ( a ) 1%, ( b ) 0.1%, and ( c ) 0.01%. Red represents day simulations, while blue represents night.

Heat map of 0.01%-distances averaged over all day and night simulations from July to November for each county.

While this steep decline in depositions appears to support commonly-used hemp isolation distances (< 5 km 7 , 8 ), even 1% of 100 million pollen grains would result in 1 million pollen depositing at that distance. In Fig. 2 b, lowering the threshold to 0.1% of released particles results in far more spread, 1–10 km during the day, and 10–15 km at night. Further decreasing the threshold to 0.01% results in distances varying throughout the entire domain, as shown in Fig.  2 c. This fat tailed deposition kernel is common for wind-dispersed species 26 , 27 , and poses challenges when computing the risk of rare events in the tail, e.g., burning embers from a wildfire 28 or cross-pollination. For hemp in particular, the Small and Antle experiment 5 provides evidence that even reduced depositions at the tail of the distribution can result in effective cross-pollination. Given the prolific nature of hemp pollen, potentially massive fields, and reports of hemp pollen travelling well beyond established isolation distances, the fat tail of the dispersal kernel becomes necessary to assess cross-pollination risk 27 , 29 .

We find that the tail of the dispersal kernel below the 0.1% and 0.01% thresholds and beyond 3 km, shows considerable variability. Figure  2 b,c show stark differences between day and night simulations, driven by diurnal differences in wind conditions. For more detail, see Supplementary Fig. S4 . Below the 0.01% threshold, we observe a large spread in nighttime threshold distances and two peaks for day simulations, which point to large-scale regional and seasonal shifts in wind conditions.

Daytime seasonal and spatial patterns

In Fig. 1 , daytime dispersal kernels for all climate regions exhibit a steady rise from July to November. This increase is responsible for the second peak in daytime 0.01% threshold distances, which is dominated by simulations later in the season. Although all regions experience increase over the season, the Southwest region maintains the least depositions throughout. In the peak summer months of July and August, the Southwest region experiences the lowest depositions, as do the Northwest, Northern Rockies & Plains, and Northeast. By October and November these latter three regions exhibit an almost 10-fold increase, shifting from relatively low depositions to the highest, on par with the Upper Midwest and Ohio Valley.

Seasonal shifts are most apparent between 5 and 10 km downwind, where overall depositions increase by nearly an order of magnitude. At this distance, Fig.  1 shows a distinctive local minimum near the source for nearly all simulations. The daytime dispersal dip in an otherwise monotonically decreasing curve is due to updrafts from convective turbulence 30 , 31 , and can be interpreted as a region of relatively less deposition, or a “pollen shadow”, in the near-field downwind of the source. Beyond the pollen shadow, there is relatively less seasonal and regional variation in depositions, indicating that in daytime, these downwind distances are not as strongly tied to patterns in underlying meteorological parameters.

Mapping out daytime deposition values in Fig. 4 a at 5 km, 10 km, 20 km, and 35 km downwind reveals contiguous, large-scale seasonal and spatial patterns. Within the pollen shadow, at 5 km downwind, Northern counties are the first to experience increases in deposition. From September, we see a region of higher depositions in California and the Upper Midwest. That region extends to the northernmost counties by October, coalescing into a band above about \(40^{\circ }\) N latitude in November. Further downwind, beyond the pollen shadow, this pattern of northern seasonal increase is not as apparent; only the Southwest stands out with the lowest depositions throughout the season.

Percent of particles deposited in 250 m-wide bins at downwind distances of 5, 10, 25, and 35 km for each county: ( a ) daytime simulations, ( b ) nighttime simulations. Note that the colorbar is a log scale.

We observe the lowest depositions in simulations with higher boundary layer height, \(z_i\) , and greater convective velocity, \(w^*\) . High \(w^*\) and \(z_i\) together indicate greater buoyancy associated with the surface heat flux and more convective turbulence 32 . Scatter plots and correlation values between daytime depositions and these meteorological parameters are provided in Supplementary Fig. S2 and the monthly heatmaps are shown in Supplementary Fig. S6 . High convective conditions in summer lead to more pollen uplifting and less deposition, particularly in the pollen shadow. More pollen is uplifted, carried far from the source, before descending in small quantities at great distances. A reduction in convective conditions from summer to fall explains the pattern of deposition increase for northern regions, particularly within the pollen shadow. It is also why the Southwest exhibits low depositions throughout the season. Greater convective conditions make long-distance transport of pollen more likely 12 , 15 , 16 , but results in fewer depositions within the domain.

Our results align with other dispersal studies, which show that greater sensible heat flux and warming temperatures during the day led to greater transport distances 12 , 13 , 16 , particularly in combination with increased wind speed 14 . In our results, however, neither the 10-m wind speed (estimated roughly as \(10 u^*\) ) nor the Monin-Obukhov length, L , influenced deposition counts, indicating that shear-driven turbulence did not play a major role in daytime dispersal patterns. This could be due to the monthly averaging of the meteorological input parameters. For example, monthly-averaged \(u^*\) only varied between 0.45 and 0.65 m/s, or maximum variations in 10-m wind speed of 2 m/s. It is likely that averaging resulted in less variation, allowing convective conditions to govern deposition patterns within the domain.

In summary, during the day, we identify large-scale contiguous spatial patterns that shift from summer to fall. The Southwest maintains the lowest depositions throughout the season because it experiences greater convective conditions than all other regions. On the other hand, northern counties shifted from comparatively low to high depositions relative to other climate regions due to a decrease in convective conditions in the fall months. This is consistent with typical CONUS weather trends; Northern climate regions experience changing seasons more strongly, and daytime dispersal is particularly dependent on these seasonal factors.

Nighttime seasonal and spatial patterns

Unlike the daytime curves, night-time dispersal kernels for each month show a monotonic decrease with downwind distance, as shown in Fig. 1 b. Within the first 10 km, depositions at night are ten times greater than during the day. Relative to these large values, spatial patterns and seasonal differences only become clear beyond about 10 km. Beyond this distance, we observe slight overall increase in deposition primarily in October and November.

While we do not see a major seasonal increase at night, shifting spatial patterns are discernible in both the heat maps and dispersion kernels. Figure  4 b shows night-time depositions by county at 5, 10, 20, and 35 km downwind of the source. Observing heatmaps at 10 km and beyond, in July and August, there is a swathe of high depositions in the center of the country, beginning with the South region and extending into the Northern Rockies & Plains (NRP). By September, the South region is no longer as prominent, and by October, the swathe of high depositions has extended into the Upper Midwest (UM) and NRP. The Northeast (NE) region also progressively increases in depositions over the season. By November, the regions with the greatest deposition include the UM, NRP, and NE, while the least deposition occur in the Southeast and West regions.

We find that regions of least deposition correspond to high friction velocity, \(u^*\) , high boundary layer height, \(z_i\) , lower roughness length, \(z_0\) , and high Monin-Obukhov length | L |. Scatter plots and correlation values between night-time depositions and these meteorological parameters are provided in Supplementary Fig. S3 . These parameters indicate more neutral conditions and greater wind shear, resulting in pollen travelling further from the source and depositing in greater amounts 14 . Our results show that greater \(u^*\) , i.e., greater horizontal wind speed, is primarily responsible for variations in night time dispersal, and the slight increase in depositions in the cooler months of October and November. This aligns with previous dispersal studies, which show that particles travel further 16 and remain airborne for longer 15 in winter than in summer months.

Overall, we find that night-time dispersal kernels are dictated by wind speed, or shear-driven turbulence. This results in more depositions further downwind in cooler months, where depositions increased with greater wind speeds.

Reconciling day and night patterns

We observe strong diurnal patterns and find that night-time dispersal dominates consideration of cross-pollination risk near the source. Within approximately 20 km of the source, night-time depositions are one to two orders of magnitude greater than during the day, as shown in Figs. 1 and  4 . Nearly all released particles are deposited by 20 km at night—an average of 97% across night-time cases, compared to only 81% during the day. Cumulative depositions are shown in Supplementary Fig. S5 . This results in a stark difference in cross-pollination risk between day and night, showing that nighttime dispersal is more important to consider within the domain and within 20 km.

Beyond this distance, nighttime dispersal kernels experience a steep decline in depositions, while daytime kernels possess a fatter tail. We can see this at 35 km in Fig.  1 , where the daytime kernels have a shallower slope than and in Fig. 4 , where most regions during the day are greater than at night. At night, almost all pollen is deposited near the source, but convective uplifting during the day allows for pollen to deposit in low quantities at the furthest reaches of the domain and even beyond it. Oneto et al. 15 found that spores released during the day had much longer flight times than at night, on the order of several days rather than a few hours and escaped into the stratosphere in greater numbers, while spores at night had flight times on the order of hours. For longer day flight times, pollen viability may become a factor for risk of cross-pollination 15 . Choudhary et al. found that viability of Cannabis pollen only decreased substantially three days after release from the anther 33 . In our study, we are only considering dispersal within 50 km of the source. Even with a slow wind speed of 1 m/s, it would only take a pollen grain 14 h to traverse the 50 km domain, thus viability need not be taken into account. Within the domain, viability has little impact on cross-pollination risk, and so daytime dispersal patterns impact risk at the furthest reaches of our domain.

It is possible that hemp pollen only disperses during the day, as is common for many wind-dispersed species 27 . One study observed that male Cannabis anthers open and release pollen in the morning hours 33 . However, Cannabis pollen measurement studies found only slight diurnal changes in concentration 10 , 34 , indicating that Cannabis pollen remains in the air throughout the day. As Cannabis production has only recently been legalized, there is minimal research on the diurnal timings of Cannabis pollen release. For these reasons, we consider both day and night dispersal in this study for risk assessment.

Cross-pollination vulnerability

While we cannot directly estimate risk of cross-pollination, as these are 2D models that do not take into account lateral spread, we can evaluate counties based on total counts of particles reaching certain distances downwind. In Fig.  3 , we plot the 0.01%-distances averaged over all day and night simulations from July to November for each county as a heat map. This figure shows that across all months and time periods, the Upper Midwest, Ohio Valley, and Northeast regions have the greatest average 0.01% threshold distances—they experience the most depositions at the farthest distances. Thus, according to simulation results alone, these regions are most vulnerable to cross pollination.

However, when county-specific information such as hemp acreage and land area are incorporated, vulnerability does not necessarily reflect the same contiguous spatial patterns demonstrated in Fig. 3 . In Eq. ( 1 ) below, we incorporate this information to compute a novel, dimensionless “vulnerability” metric for each county. We first normalize the dispersion area, \(A_{\text{disp}}\) , i.e., the area of a circle with radius equal to the average 0.01% threshold distance, by the land area of each county, \(A_{\text{land}}\) . This yields the fraction of a county that falls within its theoretical area of risk. We then normalize the number of acres of planted hemp in 2023 per county 35 , \(A_{\text{hemp}}\) , by the land area of each county, \(A_{\text{land}}\) . This yields the proportion of land used for hemp cultivation for each county. See Supplementary Fig. S7 for heat maps of the components of the vulnerability metric. We then multiply these two factors to produce a rudimentary measure of how vulnerable a county is to cross-pollination,

Figure  5 shows a heat map of the vulnerability metric for all counties with nonzero hemp acreage in 2023 35 . The five states with the most land area with vulnerability greater than \(10\times 10^{-6}\) —Montana, South Dakota, Idaho, Wisconsin, and Kentucky—are enlarged to illustrate vulnerable counties in more detail.

In counties with high vulnerability, large isolation distances may not be sufficient to prevent cross-pollination, as the combination of more hemp acreage and larger 0.01% threshold distances result in a greater likelihood of pollen transport across the entire county. Instead, a more comprehensive approach is necessary. A 2022 Colorado report exploring hemp cross-pollination suggested a voluntary pinning system to track where hemp is planted in a region 2 . Rather than mandating specific isolation distances, we recommend a pinning system which includes location of outdoor plants, time of planting, and anticipated flowering dates. This could be combined with an awareness of when and where pollen transport is greatest, as demonstrated in this study, to produce a dynamic time-dependent map of high-risk areas within a county.

Vulnerability to hemp cross-pollination across the conterminous United States. The counties with non-zero planted hemp acreage as of 2023 are shown with darker shades showing greater vulnerability. The five states with the most land area with vulnerability greater than \(10\times 10^{-6}\) are shown with stars.

The dependence of the vulnerability metric on dispersal distances and meteorological conditions tends to vary by regional meteorological differences on a country-wide scale. Within a state, variation in the vulnerability metric is more dependent on hemp acreage within a county. These country-wide spatial patterns and local variations could be useful for potentially insuring farmers in the event of financial losses due to cross-pollination—another form of risk management, with insurance coverage and premiums varying based on region and local risk.

Weather forecasting, combined with dispersal modeling, could provide a way to predict when and where pollen will tend to travel further, rather than relying on historical weather patterns as done in this study. This would enable individual farmers to plan their crops strategically, incorporating dispersal patterns when evaluating the risks of growing one crop over another. It would also allow for voluntary community-level planning, where stakeholders make decisions together regarding when and where certain varieties should be planted in each season. Finally, local government could require sharing of crop timing and location so that more informed decisions could be made.

Cannabis is typically photosensitive, flowering as day lengths shorten below a threshold (typically 10–12 hours) 36 , 37 . However, this varies depending on the cultivar and planting location. A strain adapted to northern latitudes may flower in an entirely different month when planted further south 38 , and there are also non-photosensitive cultivars 39 . It may be possible to strategically plan and plant crops so that flowering times between fiber/grain growers and floral hemp do not overlap. A three-year Cannabis pollen sampling study 34 in Tetouan, Morocco, observed that the main pollen season, when concentrations peaked, began almost a month late due to rainfalls that caused delays in planting. Strategic planting and community coordination could shift the dates of regional pollen concentration significantly. In fact, artificially reducing the day-lengths by covering crops has also previously been suggested to induce earlier flowering 40 .

Strategic community planning for hemp growers would alleviate many of the challenges facing the US hemp industry today. This industry is extremely new, and is still developing the infrastructure to balance production with supply-chain capacity and consumer demand 41 . For example, in North Carolina, there was a crash in cannabidiol hemp production following a massive grower rush which exceeded demand 38 . There are also insufficient fiber processors for the state to bounce back to growing for fiber. It has been suggested that hemp grown for fiber, cannabidiols, and seed should be grown near their respective processing facilities in order to optimize production and prevent such problems 41 . These kinds of risks, in addition to the cross-pollination risk, can be managed with more intensive community planning.

Limitations and future directions

Currently, there is no single LS model that addresses both stable and unstable conditions effectively across our entire domain. Therefore, to model dispersal both during the day (typically unstable) and the night (typically stable), we chose two separate LS model formulations. Although this choice of different models for day and night might influence the observed diurnal patterns in this study, our results qualitatively align with the literature in terms of day and night differences and seasonal variation 15 , 16 . In addition, the LS model we use for stable conditions incorporates only shear-generated turbulence produced at the surface. In reality, turbulence in the nocturnal boundary layer is complex, involving physics such as decoupling from the surface layer, the low-level nocturnal jet, and slope effects 32 , 42 . Future work to identify night-time dispersal patterns might include more nuanced modeling in stable conditions. In general, more resolved, albeit more computationally expensive models, would greatly improve risk prediction. These models could incorporate more detailed physics such as release of pollen from the anthers, dispersal within a canopy, wet deposition, and even conditions specific to a farm’s location like topography.

The models used in this study were shown to perform reasonably when compared to experimental results, described further in the “ Methodology ” section. However, we have not found previous experimental Cannabis pollen dispersal studies with enough information to validate the model. Experimental evidence suggests that airborne Cannabis pollen is ubiquitous 43 , in part because of its long flight times due to its small size compared to other pollen 5 . Therefore, validation of dispersal from a known source is difficult. One approach is to use a source made of genetically engineered (GE) plants which produce pollen with fluorescent markers 44 , enabling accurate source attribution. Our group is currently pursuing this in collaboration with co-workers. However, making GE Cannabis has proven difficult, and a study was performed instead with GE switchgrass, which produces pollen of a similar small size 45 . A paper on this combined experimental and modeling study is forthcoming.

The present study was performed using meteorological data only from 2016. This data has been validated with an extensive measurement network in the US 24 , which was deemed appropriate for this proof-of-concept study. Performing this same study over multiple years could increase the robustness of our results and provide insight into possible yearly variation. For example, warming temperatures could cause changes to these seasonal and spatial patterns. Kuparinen et al. 12 demonstrated greater seed dispersal distances achieved in simulations when using increasing temperatures.

In this study, averaging meteorological data across months reduces the occurrence of extreme weather patterns and does not take into account frequency of certain conditions. Incorporating wind-direction frequency would provide directionality to cross-pollination risk assessment. For example, the Small and Antle experiment 5 measured six times more pollen deposition downwind than upwind at their source field over a period of two weeks. For future studies, a better measure of cross-pollination risk would include frequency of weather conditions and directional variability in deposition.

Furthermore, incorporating the distance between farms would provide a more sophisticated measure of county vulnerability, as was demonstrated theoretically for hemp farms in Kentucky counties 46 . Our vulnerability metric assumes one source of hemp per county, as data for the locations of individual farms are not currently available. When averaging the 0.01%-threshold distances, we weighted day and night dispersal equally, as literature describing diurnal Cannabis emission rates is lacking. However, including temporally varying rates of pollen emission would increase accuracy.

This investigation represents a pioneering effort to assess the potential risks associated with windborne hemp cross-pollination, emphasizing the variability in risk across different seasons and geographic regions. By leveraging meteorological data for an entire year, obtained through mesoscale model simulations, we have driven Lagrangian Stochastic models to simulate wind-borne pollen dispersion across the conterminous United States on a county-by-county basis. Our findings reveal that pollen deposition rates generally escalate from summer to autumn, attributed to the reduction in convective activity during daytime and the increase in wind shear at night as the season progresses. Notably, we detected pronounced diurnal variations in pollen dispersion: nighttime conditions favor deposition in proximity to the source, while daytime conditions facilitate broader dispersal albeit with reduced deposition rates. Such variability complicates the establishment of uniform isolation distances, suggesting the superiority of adaptive risk management strategies. These strategies could incorporate weather pattern considerations to mitigate cross-pollination risks more effectively and could include measures like intertemporal zoning, farm quotas, cross-pollination damage insurance, and regulatory policies.

To our knowledge, this study is unprecedented in its comprehensive simulation of pollen dispersal’s regional and seasonal inhomogeneities, specifically focusing on hemp. Although this study centers on Cannabis pollen, the methodologies employed are broadly applicable to the dispersion of any lightweight particles. This study lays the groundwork for developing sophisticated approaches to managing agricultural cross-pollination risks, potentially influencing both policy and practice.

Methodology

Lagrangian stochastic model formulations.

For this study, we required simulation of dispersal across a wide range of wind conditions, encompassing both the convection-driven unstable conditions typical of daytime and the shear-driven stable conditions of night. There is a surface-layer LS model that has been used effectively in both conditions 22 , 29 , 47 , but modeling the surface-layer alone is not sufficient in convective conditions and up to the 50 km scale we are interested in. In convective conditions in particular, we need to model the entire boundary layer, to capture both plume rise and descent. There is not currently a single LS model that addresses both conditions effectively across our entire domain. So we use two formulations: the surface-layer model (SL) for all stable conditions, and another model formulated for the convective boundary layer (CBL) for all unstable conditions.

Unstable formulation. For all unstable convective conditions, we employ a model formulated for the CBL, first introduced by Luhar et al. 19 , 48 . This model captures the skewed nature of the vertical wind velocity fluctuations, due to the convective updrafts and downdrafts, using the summation of two Gaussian probability distribution functions (PDFs), one representing updrafts and the other downdrafts. Luhar et al. 19 further introduced a new closure that enables the model to reduce to a single Gaussian distribution in the limit of zero skewness, typical of neutral and stable conditions, which expands the model’s applicability to neutral conditions. Boehm et al. 20 adapted the model to include heavy particles, and Boehm et al. 21 introduced wind statistics profiles which merge shear-generated turbulence at the surface with convective turbulence above. Here, CBL-SL wind statistics are merged in an effort to create a smooth transition from unstable to stable regimes. Results from the original CBL model aligned well with convective fluid tank experiments 19 . Predicted concentrations from the merged model were found to reasonably compare with measured aerial pollen concentrations 21 .

Stable formulation. For all stable conditions, we used the surface-layer model as described in Aylor 22 . It differs from the CBL model under neutral conditions only in that it uses a jointly Gaussian PDF in the u and w wind velocity components (downwind and vertical, respectively), resulting in better modeling at the surface. The CBL model assumes u and w wind velocity fluctuations are independent 49 . However, being a surface layer model, it incorporates only shear-generated turbulence produced at the surface. For the purpose of this study, including only the surface layer under stable conditions is sufficient, as species released in the stable boundary layer experience little vertical mixing 32 . In our simulations, pollen is released near the surface to represent release from a hemp field. Hence, we do not expect significant vertical transport above the surface layer. Results from this model have been previously compared favorably with measured pollen concentrations in stable conditions 22 . The complete model formulations for both stable and unstable conditions can be found in Supplementary Methods S1 online.

Wind statistics

LS models require wind statistics at every point in the domain, i.e., the mean, variances, covariances, and skewness. Both SL and CBL formulations assume horizontal homogeneity and stationarity, so that wind statistics vary only with height and remain constant for the duration of the simulation. Under these assumptions, we apply boundary layer scaling parameterizations to compute vertical profiles of the wind velocity statistics 21 , 42 , 50 , 51 , 52 as a function of five meteorological parameters: the friction velocity \(u^*\) , the Monin-Obukhov length L , the convective velocity scale \(w^*\) , the surface roughness length \(z_0\) , and boundary layer height \(z_i\) . Complete wind statistics profiles utilized in the models can be found in the Supplementary Methods S1 online.

Hemp pollen simulations

To simulate hemp pollen dispersal for each county in the CONUS, we release particles from a point source at a height of \(h_0 = 2\) m. Hemp height can vary between 1-5 meters, depending on its type and growing conditions 36 , 53 . A study examining hemp morphology found the mean height of 16 genotypes in the 1–2 m range 54 . We varied the release height by ± 0.5 m to test the sensitivity of our results to changes in release height. We found that although increasing the magnitude of depositions changed, qualitatively, the seasonal and spatial patterns we found remained the same. This sensitivity analysis can be found in the Supplementary Fig. S1 available online. For all simulations, we used a settling velocity of \(v_s = 0.027\) m/s, based on a typical hemp pollen diameter of 30 \(\mu\) m 5 , 36 , using Stokes’ law. As hemp pollen is nearly spherical 36 , Stokes’ law provides a good approximation of settling velocity 47 , 55 . To choose a simulation period encompassing typical pollen release, we considered that most hemp cultivars are photosensitive, flowering as day lengths shorten below a threshold (10–12 h), following the summer solstice 36 , 37  which varies with latitude. An allergen study measured airborne Cannabis pollen counts for 5 years (1992–1996) in Omaha, Nebraska, finding pollen starting in the last two weeks of July, peaking in late August, and ending in mid-September 43 . A 2022 Colorado survey reported cross-pollination events occuring between July to mid-October 2 . Therefore, we chose to simulate dispersion from July into November, to see the continuation as weather conditions change.

Meteorological input

To drive the LS model, we use meteorological fields obtained from a Weather Research and Forecasting (WRF) model simulation over the CONUS for calendar year 2016 23 . This dataset comprises an hourly time series of meteorological conditions on a 12 km-square horizontal grid, and has been evaluated extensively in previous studies 24 . At the grid-point nearest to the centroid of each county, we extracted meteorological parameters describing horizontal wind shear, convection, boundary layer height, and surface roughness, namely, the five variables mentioned above, \((u^*, L, w^*, z_0, z_i)\) . We averaged these parameters across local noon and midnight hours for each month from July to November to form county-specific monthly average “day” and “night” cases.

Model simulations and boundary conditions

In each LS simulation—a daytime and a nighttime simulation for each county and for each month—100,000 particles were released at a height of 2 m with initial velocity selected from the velocity PDF, minus a constant settling velocity. Particles were removed from the simulation when they travelled above the boundary layer height \(z_i\) , upwind 10 m, or downwind 50 km. Pollen traveling above the ABL were considered to be subject to transport far beyond the 50 km bounds of our model domain. Such long-distance transport was not considered, as this study is more focused on exploring risk of cross-pollination from nearby farms. The downwind extent of the domain was determined by computational constraints (resolution of depositions of 100,000 particles, and simulation time for this number of particles to traverse the domain), while considering cross-pollination distances of interest (5 km, 10 km, 20 km and greater). Particles were considered to have “deposited” at a height of 1 m and were removed from the simulation. This height was greater than the surface roughness length for the majority of counties, the lowest permissible bound for the model which allows for comparison between counties. In summary, particles are released at a 2 m height, advected by the wind model, and are considered deposited when they fall below 1 m. Each simulation yielded a dispersal kernel, or (normalized) number of particles deposited downwind from the source, in 250 m wide bins.

Simplifications

To facilitate a large-scale comparative model, the simulation conditions are simplified. We treated dispersion for every county as if pollen was travelling over a flat, rough plane. The following phenomena and conditions are not considered: canopy escape, deposition probability, precipitation, topology, ground-cover, or variable source. We chose these simplifications to compare the effects of weather conditions on model predictions of dispersion across counties and seasons. We are primarily interested in how the spatio-temporal distribution in the five meteorological input parameters, described above, yield geographic and seasonal patterns in pollen transport distances. To get a nationwide overview, we chose to vary only these five parameters. For a more accurate assessment of local dispersion from an individual field, the other phenomena and conditions listed above need to be taken into account.

Data and code availability

Simulation results, monthly-averaged meteorological input data, and all dispersal model code are made available in the Virginia Tech Data repository: https://doi.org/10.7294/25718625.v1 .

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Acknowledgements

We thank D.E. Aylor and A.K. Luhar for their communications and guidance as we implemented the dispersal models from their papers. We thank X. Huang for extracting the meteorological data for each county. This research was supported in part by the Biotechnology Risk Assessment program of the USDA NIFA, under grant number 2019-33522-29989. Open access publication of this article was partially supported by Virginia Tech's Open Access Subvention Fund (OASF).

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These authors contributed equally: Shane D. Ross and Hosein Foroutan.

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Engineering Science and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA

Manu Nimmala

Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA, 24061, USA

Shane D. Ross

Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061, USA

Hosein Foroutan

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M.N., S.R., and H.F. designed the study, interpreted the results, and contributed to the writing of the manuscript. M.N. implemented the models, conducted simulations, analyzed the data, and produced the figures. All authors reviewed the manuscript.

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Nimmala, M., Ross, S.D. & Foroutan, H. Cannabis pollen dispersal across the United States. Sci Rep 14 , 20605 (2024). https://doi.org/10.1038/s41598-024-70633-x

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