SKU: 7015177087
tiger lily vs orange lily

tiger lily vs orange lily Tiger Lily Species Lily – The Lily Pad Bulb Farm

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Description

tiger lily vs orange lily Tiger Lily Species Lily – The Lily Pad Bulb FarmLilium Tigrinum (lancifolium) is an heirloom favorite, quite properly known as the "Tiger Lily" of "Grandma's garden" fame. It has large unscented pendant orange flowers with black spots and recurved tips. It is a notoriously vigorous grower and naturalizes easily, producing a big crop of stem "bulblets" every year and aerial bulbils (resembling black beads thickly arranged along the stem). Once they develop rootlets, these bulbils can be easily

Lilium Tigrinum (lancifolium) is an heirloom favorite, quite properly known as the "Tiger Lily" of "Grandma's garden" fame. It has large unscented pendant orange flowers with black spots and recurved tips. It is a notoriously vigorous grower and naturalizes easily, producing a big crop of stem "bulblets" every year and aerial bulbils (resembling black beads thickly arranged along the stem).

Once they develop rootlets, these bulbils can be easily raised to blooming size, in fact will plant themselves if allowed to drop to the ground. If you seek a rapidly-propagating lily, this one's FOR YOU! In the Orient this species is raised for its edible bulbs (delicious in veggie stir-fry dishes. Very enduring and virus-resistant. We barely keep up with the revival of demand for this hard-to-get species. 16-18 cm bulb size. Choose between 1 bulb or a 3-pack.

Botanical name: Lilium lancifolium 'Splendens' 
Form: Perennial
Hardiness Zone: 3-9
Flower Color: Orange/black spots
Foliage Type: Linear, green leaves arranged in whorls or spirals up the stems
Bloom time: July-August
Height: 3-4 ft 
Spread: 12 inches
Light requirements: Full or partial sun 
Plant depth: 6 inches of soil over top of bulb 

See "Growing Tips" for more detailed instructions
Tips and Growing Instructions

Visit our How-To Cultivation Library for more growing tips.

Species lilies, also known as Tiger Lilies, are wild lilies, native to North America, Europe, and Asia. Delicate and full of charm, there are 80 - 100 accredited Species. Most species lilies do not require specialized care and will naturalize themselves very well. They are very adaptable to most soil conditions and environmental conditions, and they do quite well in warm, moist climates with fertile soil. Tiger Lily bulbs for the most part are smaller than hybrid varieties, but will produce good quality stems and a lot of blooms. Their delicate, beautiful flowers are quite showy and most will bloom for long periods compared to other lilies.

Lilies are incredibly easy to grow and few garden pests trouble them. The most natural location for lilies is on sloping ground with excellent drainage. Lilies prefer to have their blooms in the sun and their roots in the shade. Try planting them among annuals or perennials that will keep their roots cool. 

The general rule of thumb for planting spring bulbs is to plant two to three times as deep as the bulb is tall. This means that some of our larger Bulb-Zilla lily bulbs will need to be planted 6-8 inches deeps. Plant with the roots downward and the scales upward. After planting, water well two or three times. Lilies are most effective when planted in groups of three or more. Space them about a foot apart – they will spread and fill this space in no time! 

Always allow the leaves on the stalk to turn yellow and fall off as part of the lily’s natural growth process. This ensures that the bulbous underground part of the plant has gotten enough nourishment and will mean greater growth next year. Each year watch their beauty increase as they multiply! 


Shipping

Sorry, we do not ship outside the U.S. or to Hawaii due to agricultural restrictions.

We guarantee safe arrival of healthy bulbs, that varieties will be true-to-name, and will grow if planted as instructed, subject to the limitations described in our Shipping and Returns Policy.

We begin shipping our bulbs in mid-February through Spring. Despite what you may have heard, Spring planting of bulbs is not a bad thing, and in fact, there are many advantages to planting in Spring. Visit our Spring Planting page to learn more. Upon arriving, your bulbs and most of your perennials may show signs of growth such as green leaves and sprouts - this is okay and transportation during this time should not harm the plants.

All bulbs and perennials should be planted in your garden as soon as possible to ensure the best success. If you must delay planting, open the bags to permit air circulation and place them out of direct sunlight in a cool, well-ventilated room. Do not place the bulbs where they can freeze. If you have the space, storing the bulbs in the refrigerator is another excellent way to “hold them” prior to planting. If you choose this method of storing your bulbs, be sure not to store fruit with them, as this is detrimental to bulbs.

Plant outdoors when the ground is no longer frozen and the conditions have improved for your zone.

If you have any questions or concerns our customer service team would be glad to help you at any time. Please use our contact form, email us at [email protected], or call us at 541-671-3196.
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Phoenix, 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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Carnegie, 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
Lowell, 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
Charlottesville, 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
Whiting, 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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