SKU: 25111087198
post-emergent pre emergent herbicide

post-emergent pre emergent herbicide Prodiamine 65 WDG (alternative - Barricade® 65WDG)

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Description

post-emergent pre emergent herbicide Prodiamine 65 WDG (alternative - Barricade® 65WDG)Important shipping restrictions to Alaska, Hawaii, and New York. Prodiamine 65 WG Pre Emergent Herbicide (generic Barricade) is a pre emergent weed control designed to stop weeds in the lawn, such as crabgrass, annual bluegrass (poa annua), bittercress, chickweed, and henbit. This is a dry product you mix in water to make a liquid to apply via backpack battery sprayer. One 5 lb bottle covers 4 5 acres, depending on mix rates 65% Prodiamine is the

Important shipping restrictions to Alaska, Hawaii, and New York.

Prodiamine 65 WG - Pre-Emergent Herbicide (generic Barricade) is a pre-emergent weed control designed to stop weeds in the lawn, such as crabgrass, annual bluegrass (poa annua), bittercress, chickweed, and henbit. This is a dry product you mix in water to make a liquid to apply via backpack/battery sprayer.

  • One 5 lb bottle covers 4-5 acres, depending on mix rates
  • 65% Prodiamine is the active ingredient
  • Great for preventing crabgrass, poa annua (annual bluegrass), and more

Product Description:
Prodiamine 65 WG is a professionally formulated pre-emergent that is mixed in water and sprayed out as a liquid. 

Most DIYers use this product in spring to stop crabgrass. Crabgrass is an annual grassy weed that germinates in spring, grows lime green and large during summer, then, towards the later fall, drops seeds. The following spring, the process starts all over again. This is why crabgrass is called an “annual” grassy weed. The good news is that it can be stopped very effectively using this product.

The best time to apply Prodiamine is in the spring or fall, as soil temperatures are between 55F to 70F. This will give you approximately 3 months of pre-emergence protection.

For more details, you can check out the FREE Pre-Emergent Guide. If you download our FREE app it will tell you exactly when your soil temps cross these milestones.
For cool and warm-season lawns, our recommendation is two applications in spring if the target is crabgrass.

Application Information:
DO NOT use Prodiamine if you are seeding or reseeding, as it will keep your seed from germinating.

This product should be applied via a pump sprayer or battery-powered hand can, NOT recommended to be applied via a hose-end sprayer. Always read the label and follow safety equipment recommendations.

We recommend weighing this product on a scale to be accurate, and grams will be the easiest. See the following table for the application rates we recommend. The product must be watered in within 48 hours; the sooner, the better with ½” of water from irrigation or rainfall.

  • 1 oz = 28.35 grams
Grass Type Application Rate
Kentucky Bluegrass, Perennial Ryegrass
  • 5.2g/1000 sq ft, Early Spring
  • 5.2g/1000 sq ft, Mid Spring
  • 5.2g/1000 sq ft, Early Fall (if not seeding)
  • Total 15.6g yearly

Following this yearly plan, (1) 5 lb bottle will cover 145,385 sq ft for a year's worth of applications. 
Fine Fescue
  • 11.9 g per calendar year

(1) 5 lb bottle will cover190,588 sq ft for a year's worth of applications
Bermudagrass, Bahiagrass, Centipedegrass, St Augustinegrass, Tall Fescue, Zoysia
  • 5.2g/1000 sq ft, Early Spring
  • 5.2g/1000 sq ft, Mid Spring
  • 13g/1000 sq ft, Early Fall
  • Total 23.5g/1000 sq ft, yearly

Following this yearly plan, (1) 5 lb bottle will cover 96,923 sq ft for a year's worth of applications. 

 

Prodiamine WG can be used on established turfgrasses, golf courses (excluding putting greens), sod farms, and lawns.

 Coverage/Gallon

(1) 5 lb bottle covers 4-5 acres, depending on mix rates

SEE TABLE ABOVE
Application  Details See the table above for specific application details and instructions
Water-In  Instructions

Water into the soil within 24 hours with 1/2" of rainfall or irrigation
Manufactured by

EPA Reg. No. 100-834
EPA Est. No. 37429-GA-001
  • Label/Specs/SDS:  Resolute Prodiamine Info
  • Package Includes: (1) 5-lb bottle
  • Storage: Store in dry conditions away from moisture. Can be stored for up to 3 years.

Shipping Information:

Shipping Costs

Included for most states within the U.S.
NO P.O. Boxes
State Restrictions
 Alaska (AK), Hawaii (HI), New York (NY)
Stickers

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Typical Delivery Time

4 - 7 business days
Product Type Water Dispersible Granular
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SKU: 25111087198

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Hashi Hanta
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★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
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Carol
Phoenix, US
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Natrona Heights, 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
Boise, 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
Port Orchard, US
★★★★★ 5
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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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