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Description
tarp gardening Silage TarpReversible Silage Tarp for Weed Control & Bed Prep This 6 mil black and white silage tarp is a must have for no till, regenerative, and organic farmers. Designed to block light, suppress weeds, and protect soil structure, this reversible tarp helps you prepare clean planting beds without disturbing your soil biology. The black side provides full blackout for weed seed termination or photo period control, while the reflective white side helps regulate
Reversible Silage Tarp for Weed Control & Bed Prep
This 6 mil black and white silage tarp is a must-have for no-till, regenerative, and organic farmers. Designed to block light, suppress weeds, and protect soil structure, this reversible tarp helps you prepare clean planting beds without disturbing your soil biology. The black side provides full blackout for weed seed termination or photo-period control, while the reflective white side helps regulate soil temperature in tunnels and covered spaces.
Available in a range of sizes to fit your operation. Need something more specific? Contact us for custom-cut options.
Features
- 100% light-blocking when used black side up
- Reflective white layer helps keep covered areas cooler
- Excellent for stale seedbed prep and organic weed suppression
- Custom cut to your needed length
- UV-resistant and rated for up to 3 years of exposure
- Withstands foot traffic and repeated field use
Note: This item is not eligible for returns.
A Proven Tool for No-Till Bed Prep
Kill weeds without disturbing your soil. By using silage tarps to fallow beds or prepare planting zones, growers can build soil health, reduce labor, and get a head start on the next planting—all without reaching for the tiller. Read more on this process.
Tips for Using Silage Tarps Effectively
- Weed Suppression: Lay tarp black side up for at least 3–6 weeks to smother weed seeds before planting.
- Moisture Retention: Use tarp as a mulch layer to keep soil moist during dry periods.
- Temperature Control: Use white side up in high tunnels to reflect heat and protect soil biology.
- Storage: Fold and store dry to extend tarp life between seasons.
- Safety Note: Secure edges with sandbags or weights to prevent wind lift.
Specifications
|
Film Usage |
Silage/Ground and Greenhouse Cover |
|
| Width | Up to 54 ft | |
| Thickness | 6 mil | |
| Length | As Required | |
| Tensile Strength at break | MD/ ASTM D-882 23.0 MPa | TD/ ASTM D-882 25.0 MPa |
| Elongation at break | MD/ ASTM D-882 550% | TD/ ASTM D-882 650% |
| Tear Resistance | MD/ ASTM D1922 6000 Gr./mm. | TD/ASTM D1922 9000 Gr./mm. |
| Falling dart drop impact | ASTM D-1709 750 Gram |
|
| Light transmissions- PAR | ASTM D-1003 | 0 |
| Reflection- PAR | ASTM D-1003 | 70 |
Warranty
This plastic includes a 3-year Manufacturers warranty (when used as a fixed cover) and 1- Year Manufacturer's warranty (when used as a mulch) against premature UV degradation.
SHIPPING VIA MOTOR FREIGHT (SEMI)
Rolls over 120 lbs. must ship via motor freight. We will notify you if your order requires this shipping method. Click here for information on freight delivery.
Because each order is custom cut, we can not accept returns once the item has shipped.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.8 ★★★★★
Based on 22 reviews
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Product Reviews
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 14, 2019
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 24, 2019
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 1, 2019
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
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