SKU: 11738983708
garden seed mats

garden seed mats Wildflower Seed Mat Kit – Chimney Sheep

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Description

garden seed mats Wildflower Seed Mat Kit – Chimney SheepWhat does the Wildflower seed mat kit contain? The kit contains a generous sized piece of our garden felt (60cm x 35cm), a packet of annual and perennial wildflower seeds, a bag of Sheepwool compost, and eight bamboo skewers so you can peg it down as a whole or cut it into two. How do you use the Wildflower seed mat kit? The great thing about this mat is that you dont need to prepare a bed or anything. No weeding or raking or anything first. Just peg

What does the Wildflower  seed mat kit contain?

The kit contains a generous sized piece of our garden felt (60cm x 35cm), a packet of annual and perennial wildflower seeds, a bag of Sheepwool compost, and eight bamboo skewers so you can peg it down as a whole or cut it into two.

 

How do you use the Wildflower seed mat kit?

The great thing about this mat is that you don’t need to prepare a bed or anything. No weeding or raking or anything first. Just peg the mat down firmly in the place you want the flowers to grow. Make sure it’s nice and wet, then sprinkle the seeds all over. Then lightly sprinkle over the sheep wool compost. Keep it moist and the seeds will start growing in a week or two.

 

What’s the best time of year for planting the Wildflower seed mat kit?

It’s best to sow them in the spring or early summer. However, if you want to give them a head start you can cut them into two and put them in seed trays, to get them started indoors. Once the seeds start sprouting you can cut the felt into bits and put the wool into pots, out on the bed where you want them to grow, or even in hanging baskets. You can get imaginative and plant them in any kind of container you like, even old boots or watering cans!

 

What’s so great about wildflowers?

Insects are attracted to all kinds of flowers, but our native insects have evolved to work with our native wildflowers. Some insects are multi-taskers and some are specialists, relying on just a limited range of species. So it’s good to use native wildflowers for native insects.

 

Are cultivated flowers as good for insects as wildflowers?

Our gardens provide a wide range of flowers, extending beyond the natural growing season of our native wildflowers. These can provide valuable food sources for insects, especially at the beginning and end of the season. Sadly the number of wildflowers has decreased over recent years so by having them in the garden or by having exotic flowers that flower earlier or later than native ones, there is more of a food source for our insects.

The Royal Horticultural Society did a study to see whether native or non-native plants were best for pollinating insects. The conclusion was that a mix of both was beneficial, with a greater proportion of native ones. Read more about it here.

Look out for their Perfect for Pollinators logo for the best plants for our pollinating insects.

 

What seeds are in the packet?

Perfect for Pollinators seeds of course! A mixture of annual, biennial and perennial plants. These are:

Common Agrimony, Borage, Wild Clary, Red Clover, White Clover, Corn Cockle, Cornflower, Ox-eye Daisy, Wild Foxglove, Common Knapweed, Greater Knapweed, Purple Loosestrife, Wild Marjoram, Meadow Cranesbill, Musk Mallow, Common Pooppy, Ragged Robin, Sainfoin, Field Scabious, Teasel, Birds-foot Trefoil, Kidney Vetch, Viper’s Bugloss, Yarrow, Yellow Rattle. The species included in this mixture create an attractive display from May to October and is suitable for creating habitats for bees, butterflies and other pollinating insects. It contains a wide range of species to create a diverse environment and range of food to support local wildlife.

 

Why are bees and butterflies getting more scarce? And why does it matter?

Here, let Professor Dave Goulson explain it.

 

Do we have to like all insects?

It’s easy to like the “good” insects like bees and butterflies, and harder to like the ones that bite or sting or chew your clothes. But they’ve all got a role to play. Our clothes moths products are to control clothes moths in the home, but out in the wild they do a great job of cleaning up fur, feathers, skin, stuff that would sit around for a long time if the clothes moth larva wasn’t there to chew it up and then hatch into bat food.

 

How long will the wildflower wool seedmat last for?

The wool will biodegrade and provide nutrients for the plants over about a year. The annuals / biennials will bloom once, produce seeds which will be eaten by birds or re-seed if they can. The perennials  should keep going indefinitely.

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Waukegan, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Par
Pawtucket, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
Alexandria, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
Alexandria, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Fort Morgan, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026

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