Hello there!
Welcome to our March Newsletter! In this issue, we are bringing you the latest updates and insights from the NASA Acres Consortium.
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Seeking your feedback on our Draft 13 Core Principles for Agriculture Data Use
The NASA Acres Consortium recognizes the need for researchers to work closely with farmers to integrate Ag Data into the development and assessment of models for the mutual benefit of both communities. The agreements that govern data sharing should be simple and transparent to all parties involved. Taking inspiration and learning from Ag Data Transparent's Core Principles for Agriculture Data Use, we have developed a draft of NASA Acres Core Principles for Agricultural Data Use which is intended to help establish clear and transparent Ag Data Use Agreements between researchers and agriculture technologists generally – and NASA Acres Collaborators specifically - and farmers.
There are 13 Core Principles each of which contains both General Guidance (what belongs in all Ag Data Use Agreements) as well as Comments from the NASA Acres Leadership which provides specific recommendations on good-practices for data governance.
We seek your feedback on the 13 Core Principles, our definition of "Ag Data," and generally on our approach. Your feedback will be anonymous unless you provide your email address. Once the Core Principles are agreed upon, we will develop Model Data Use Agreements which will be made available to all to tailor to the specific needs of their project or relationship.
Feedback is due by April 12th
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In our efforts to better understand how best to serve farmers and ranchers in the U.S., NASA Acres and Ag Data Transparent have launched a survey focused on farmer perspectives on sharing their data in research collaborations geared toward improving their operations.
If you are a farmer or rancher in the U.S., please fill it out! And either way, please forward to farmers and ranchers in your network!
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Map of sub-field level soybean yield for 2022 across the U.S. Midwest |
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Data source: Landsat and GridMET
Map production by Drs. Yuchi Ma and David Lobell of Stanford University
The above map displays the estimated yield for soybean in 2022 across the U.S. Midwest, ranging from 1 ton per hectare (blue) to 2 tons per hectare (pink) up to 3 tons per hectare (yellow). The map was generated by Drs. Yuchi Ma and David Lobell of Stanford University using Landsat and Gridded Surface Meteorological (gridMET) weather data. Their methods are based on a scale transfer framework called Quantile Domain Adversarial Neural Networks (QDANN), which leverages the yield information from the public county-level yield statistics and accurately maps yield at the subfield level with a spatial resolution of 30m based on time-series Landsat observations and gridMET weather variables. The map can reveal information about the magnitudes and causes of yield gaps which, in turn, can support farmers who wish to implement precision agricultural practices and inform commodity market decisions. Under NASA Acres, the Stanford group is will conduct a series of related studies on the effects of new practices on cropping system performance in the U.S. The main practices of interest are cover cropping, no-till, and adoption of new hybrids. The main outcomes of interest will be average yields of key crops (corn, soybean, possibly wheat), yield losses in drought conditions, and yield losses from early season ponding.
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Cheers to Dr. Katie Gold, Cornell University, for being honored with New York Wine and Grape Foundation’s 2024 Research Award |
Dr. Katie Gold of Cornell University has received the New York Wine and Grape Foundation’s Research Award, which recognizes major contributions in the area of grape growing processing, and wine research. Dr. Gold accepted the award with guest of honor - 7 week old Mimi! “Being a grape pathologist at Cornell is truly my dream job and it has been such a pleasure to engage with and learn from NY grape growers over the past 4 years in this position,” says Dr. Gold, “I love being a part of the NY wine and grape industry and am grateful for the acknowledgment of the value of the research the Gold Lab conducts.” Under NASA Acres, Dr. Gold & Dr. Yu Jiang are co-leading research on wine grape pest and disease detection with satellite Earth observations, we look forward to what's to come from this valuable research!
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NASA and the USGS's Landsat Next is on the horizon |
The new mission, which is expected to launch in late 2030, will not only ensure the continuity of the longest space-based record of Earth’s land surface, but it will fundamentally transform the breadth and depth of actionable information freely available to end users. With two to three times the temporal, spatial, and spectral resolution, Landsat Next will build upon the Landsat legacy of observing, managing, and adapting to change on Earth and provide expanded capabilities to support evolving and emerging applications in land, water, and climate science. We are excited for how this next-generation technology will expand our capabilities for agriculture in the U.S.!
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Executive Director, Dr. Alyssa Whitcraft, shares insights on unlocking the value of satellite data to Canadian agriculture value chain leaders
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The Canadian Crops Convention is put on by both the Canola Council of Canada and the Canada Grains Council, the audience represents a wide breadth of crops beyond canola, including wheat, barley, oats, pulses, soybeans, corn and flax. Last week, Dr. Alyssa Whitcraft, Executive Director at NASA Acres, attended the conference where she presented to an audience of more than 300 influential leaders from across the Canadian agriculture value chains on the potential of Earth observations for agriculture and how we can get there.
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The Hyperspectral Remote Sensing and Agriculture Intelligence Lab at Colorado State University Fort Collins is seeking applicants for a postdoctoral scholar focused on remote sensing to study plant-disturbance interactions. The postdoctoral scholar will conduct research using drone/satellite hyperspectral (HySpex drone sensor with 490 bands from 400-2500 nm and SVC HR-2024i with over 1000 bands from 350-2500 nm) remote sensing and machine learning in plant and soil health research.
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