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san pedro's cactus

san pedro's cactus Buy San Pedro Cactus Phoenix, AZ | Echinopsis pachanoi

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san pedro's cactus Buy San Pedro Cactus Phoenix, AZ | Echinopsis pachanoiA Towering Columnar Cactus for Phoenix Desert Gardens San Pedro The San Pedro Cactus (Echinopsis pachanoi) is one of the fastest growing columnar cacti available for Phoenix landscapes. Native to the Andes Mountains, this striking blue green cactus grows tall, ribbed columns that branch with age into dramatic multi stemmed specimens. San Pedro can reach 1020 feet tall in the Phoenix Valley, adding bold vertical structure to xeriscape gardens,

A Towering Columnar Cactus for Phoenix Desert Gardens — San Pedro

The San Pedro Cactus (Echinopsis pachanoi) is one of the fastest-growing columnar cacti available for Phoenix landscapes. Native to the Andes Mountains, this striking blue-green cactus grows tall, ribbed columns that branch with age into dramatic multi-stemmed specimens. San Pedro can reach 10–20 feet tall in the Phoenix Valley, adding bold vertical structure to xeriscape gardens, courtyard plantings, and modern desert designs. It produces spectacular large white flowers that bloom at night during summer — a rare treat for any garden. Whether you’re creating a sculptural cactus garden in Scottsdale, anchoring a Chandler desert border, or adding architectural drama to a Mesa backyard — San Pedro delivers fast growth and jaw-dropping form.

San Pedro Cactus Plant Details

Attribute Detail
Scientific Name Echinopsis pachanoi (syn. Trichocereus pachanoi)
Common Names San Pedro Cactus, Saint Peter Cactus
Mature Height 10–20 feet
Mature Width 4–6 feet (multi-branched clump)
Growth Rate Fast for a cactus — 1–2 feet per year in Phoenix
Sun Full sun to light shade. Handles reflected heat well.
Water Low once established. Drought-tolerant but appreciates occasional deep watering.
USDA Zones 9–11 (Phoenix is Zone 9b–10a)
Soil Well-draining required. Thrives in sandy, rocky Arizona soils and handles caliche with drainage.
Foliage Evergreen — blue-green ribbed columns year-round
Bloom Large white nocturnal flowers in summer — fragrant and spectacular

San Pedro Cactus Uses in Phoenix Landscapes

Sculptural Focal Point & Cactus Gardens

San Pedro’s tall, ribbed columns create dramatic vertical architecture in any desert garden. Plant a single specimen as a living sculpture in a Scottsdale courtyard, or group 3–5 for a columnar cactus grove effect. Pair with Golden Barrel, Totem Pole Cactus, and Mexican Fencepost for an all-columnar desert statement garden.

Modern Desert Borders & Property Screens

Because San Pedro branches and fills in with age, it makes an effective living screen or border plant. Space 3–4 feet apart along a Chandler property line or Gilbert fence to create a striking green wall. The columns grow fast enough to provide meaningful screening within 3–5 years.

Pool-Friendly & Low-Litter Plantings

San Pedro is an excellent pool-adjacent plant — it produces virtually no leaf litter, requires minimal trimming, and its smooth columns and minimal spines make it safer than many cacti. Plant along Tempe and Mesa pool perimeters for a clean, architectural look with zero maintenance debris.

Best Time to Plant San Pedro Cactus in Phoenix

Spring (March–May) is the ideal planting window. Warm soil and rising temperatures promote fast root establishment and active growth. Fall (October–November) is the second-best option. Avoid planting in winter — San Pedro is slightly frost-sensitive and roots best in warm soil.

How to Plant San Pedro Cactus

  1. Dig wide, not deep — excavate 2x the root ball width at the same depth. Cacti have shallow root systems.
  2. Ensure excellent drainage — break through any caliche layer. San Pedro will rot in standing water.
  3. Backfill with native soil — no amendments needed. Sandy, rocky Arizona soil is ideal.
  4. Spacing — 3–4 feet apart for a border or screen; 5+ feet for standalone specimens.
  5. Let the cut callus — if transplanting a cutting, let the cut end dry and callus for 1–2 weeks before planting.
  6. Gravel mulch — 2–3 inches of decomposed granite or gravel. Never use organic mulch that retains moisture.

Watering San Pedro Cactus in Phoenix

First Year Watering Schedule

  • Weeks 1–2: Every 5–7 days, light watering to settle soil
  • Months 1–2: Every 7–10 days
  • Months 3–6: Every 10–14 days
  • After Year 1: Every 2–3 weeks in summer; monthly or less in winter

Drip Irrigation

Place 1 emitter (1–2 GPH) 12–18 inches from the base. San Pedro appreciates more water than most columnar cacti, which helps it maintain its fast growth rate. However, always let the soil dry completely between waterings. Overwatering causes root rot.

How fast does San Pedro grow in Phoenix?
San Pedro is one of the fastest-growing columnar cacti, adding 1–2 feet per year in Phoenix with regular summer watering. A 5-gallon plant can reach 6–8 feet within 3–4 years.

Is San Pedro frost-hardy in Phoenix?
San Pedro handles most Phoenix winters well, tolerating temps down to about 25°F. During rare hard freezes, drape frost cloth over the plant. Established specimens are more cold-hardy than young ones.

Does San Pedro bloom?
Yes — mature San Pedro cacti produce large, spectacular white flowers that open at night during summer. The blooms are fragrant and typically last one night, attracting moths and bats. Plants usually begin blooming once they reach 4–6 feet tall.

How does San Pedro compare to Totem Pole Cactus?
Both are tall columnar cacti, but San Pedro has visible ribs and small spines, while Totem Pole (Pachycereus schottii ‘Monstrosus’) is smooth and spineless with a knobby texture. San Pedro grows faster and produces showy flowers. Both are excellent choices for Phoenix desert gardens.

You May Also Like

  • Totem Pole Cactus — a smooth, spineless columnar cactus with a unique sculptural form.
  • Mexican Fence Post — a tall, columnar cactus often used as a living fence in desert landscapes.
  • Golden Barrel Cactus — a round, golden-spined cactus that contrasts beautifully with tall columnar species.
  • Ocotillo — a spindly desert native with fiery red spring blooms, perfect for adding movement to cactus gardens.

How Many San Pedro Cactus Do I Need?

San Pedro works two ways: as a single sculptural specimen, or branched together into a fast-growing columnar screen. For a focal point, plant one and give it 5 to 6 feet of clear space so the multi-stemmed form can spread. For a living screen along a wall or property line, space the columns 3 to 4 feet apart:

Run length Plants at 3.5 ft spacing
10 ft 3 plants
20 ft 6 plants
30 ft 9 plants
40 ft 11 plants

For a grove effect, group 3 to 5 columns in odd numbers, each 3 to 4 feet apart, so the ribbed stems read as one bold cluster.

San Pedro Cactus Season-by-Season in Phoenix

  • Spring (Feb-Apr): Prime planting window. Warm soil drives fast root establishment and the first flush of new column growth.
  • Summer (May-Sep): Peak growth season, adding 1 to 2 feet with regular deep watering. Large fragrant white flowers open at night and draw moths and bats. Handles full reflected heat off walls and pavement.
  • Fall (Oct-Nov): Second-best planting window and continued growth before cooling. Taper watering as temperatures drop.
  • Winter (Dec-Jan): Evergreen blue-green structure holds all winter. Hardy to about 25°F: during a hard freeze, drape frost cloth over the columns, especially on young plants.

At a Glance

✔ Heat-Loving (Reflected-Heat Tolerant)   ✔ Drought-Tolerant   ✔ Pollinator-Friendly   ✔ Pool-Friendly (Low-Litter)   ✔ Evergreen   ✔ Low-Maintenance   ✔ Deer & Rabbit-Resistant   ✔ Cold-Hardy to 25°F

Plant It With

Is San Pedro Cactus Right for Your Yard?

San Pedro thrives in full sun to light shade with fast-draining soil, and it tolerates reflected heat off walls and pavement better than most columnar cacti. Give it room to branch and break through any caliche layer so water never pools at the roots. It is not a fit if your spot stays wet or shaded, or if you cannot cover it during a rare hard freeze while it is young.

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Kirsten
Bozeman, US
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it. For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would. The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is. This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
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Reviewed in the United States on March 17, 2026
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Lowell, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
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Zygerian99
Battle Creek, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
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Shannon
Natrona Heights, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
Chelsea, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017

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