Winter damage is one of the most difficult things to manage on a golf course because, by the time you can see it, much of the evidence for what caused it is gone. 

You walk by a green in April and find dead turf. Was it crusted snow and ice that formed during a February thaw and refreeze? Was it saturated soils in a low spot of the green? Or was it exposure to wind where snow blew clear in January? Each is a different winter, and while we can currently make educated guesses about what caused the damage, we've never had the data to pinpoint what exactly happened.

It's this problem - teasing apart the causes of winter injury - that the WinterTurf project is solving.

Data for Predicting Winter Damage

Over the past six years, you've partnered with us to collect data that we never had before: 327 golf courses, 23 winters of historical surveying, on-course sensors, global satellite data, and over 1,600 course-season observations of what the winter did and whether the turf survived (Figure 1).

Figure 1: Damage observations combining historical damage surveys and end-of-season surveys. 

What this data has allowed us to do is build first-of-a-kind machine learning models that can accurately identify the locations where winter damage happened and forecast where it might show up in the spring.

Helping Machines Learn Agronomy

The major challenge we've faced now that we have a robust dataset has been that machine learning models don't understand plants well. Typically, these models are built using purely "data-driven" approaches, where all of the data is fed directly into the model, and then the model figures out what does and doesn't matter. 

This didn't work for winter damage though because it's caused by both the winter conditions that a turf stand is exposed to and the hardiness of the turf itself.

To overcome this problem, we've built models that incorporate the best of machine learning with knowledge of how a plant responds to its environment and acclimates during the fall in preparation for winter. We also built into the models what we know about the mechanisms driving winter damage to explicitly account for freeze-thaw cycles, saturated soils, high light and low temperature, among others.

Models to Forecast Winter Damage

The results from all of this data and machine learning are encouraging. Tested against winters the model had never seen, it ranks damaged course-seasons above undamaged ones about 75% of the time.

We also asked whether all of this work with the in-green sensors was worth it, or could we have only used satellite data? And what we found was that the in-green sensors improved classification and forecast skill to over 81%, but because we had fewer observations, that skill was unstable across model configurations.

What is causing damage?

In addition to classifying courses and forecasting damage, we also built a model to infer the mechanisms causing it. Going back to the early work of James Beard out of Michigan State University, we've known for a long time that winter damage is caused by a mixture of abiotic and biotic factors. Our attribution models currently reflect this for abiotic factors alone (see Figure 2), and while preliminary, illustrates where future work is headed.

Figure 2. Inferred dominant mechanism of winter damage for A) North America and B) Europe. These are not able to be directly ground truthed with the datasets we currently have acquired and will be a focus of future research.

What comes next?

As scientists, we'll of course always say we need more data, but in this case, the models show that's true. We need more observations of winter damage at the same sites we put in-green sensors so that we can continue to increase our forecasting skill. Our current modeling work shows that in-green sensors are the way to get accurate in-winter forecasts that will be able to reliably recommend specific actions to mitigate damage before it happens.

Right now, a particular weakness is that we don't have the data to fully validate our attribution model (displayed for a course in Figure 3). We're further expanding our remote sensing pipelines to help with this and doing more analysis of the weekly surveys, but without more in-green direct measurement, we're limited in what we can do. 

Figure 3. Damage risk graph through a winter for a single course.

Sign Up

If you're interested in helping make these systems better, there are three ways to join.

First, you can sign up for our pilot that will send regular reports to you about your golf courses' winter risk: https://z.umn.edu/interest-survey

We will be providing regular forecasts sent to peoples' emails about your current winter injury risk and recommendations for what you can do. See an example in Figure 4.

Figure 4. Example of WinterTurf regular report that superintendents can sign up at this link: https://z.umn.edu/interest-survey.

Second, you can request to host a sensor and donate to help us maintain and expand our fleet. Our large USDA grant funding this work ends in August of 2026, so we're running on a shoestring budget while we write grants for additional federal funding. Each in-green sensor costs roughly $4,500 to manufacture along with staff time to maintain the fleet and generate reports. 

Every bit helps, and those interested can contact Eric Watkins at [email protected] about how to make a contribution.

Lastly, we'd like to thank the National Institute of Food and Agriculture, U.S. Department of Agriculture, Specialty Crop Research Initiative under award number 2021-51181-35861 and the Minnesota Golf Course Superintendents Association who have supported this work in the past. Without those past investments, we wouldn't be to this point and every winter we're one more closer to consistent forecasting of winter damage.

 Banner photo: "Frost on Alderley Edge Golf Course" by Colin Park, via Geograph / Wikimedia Commons, CC BY-SA 2.0 (cropped)

Who would have ever guessed that 6 years after the pandemic middle-schoolers would still be crazed about sourdough!! 

Sure enough, on February 9 at 8 am, registration for our new “Science of Bread Making” 4-day summer science event went online with 16 slots open, and within 30 minutes we were full with a 17-person waiting list. So we opened another 16 slots, creating an AM group and a PM group. The pressure was on for our team of 30 volunteers to create an unforgettable summer science experience June 15-18, 2026 for this crowd – the first offering of this new event on top of the third delivery of the Food Ag & U experience that followed the week afterwards.

As the WinterTurf 2024–2025 data collection season comes to a close, the WinterTurf sensing nodes are being removed to make way for spring maintenance and regular golf course operations. This winter marked our largest data collection effort yet, with 75 sensing nodes deployed across the northern hemisphere at golf courses and research sites. These nodes collected 702,905 data packets and 16,713,955 sensor readings, capturing the daily changes that influence turf health during the harshest months of the year.

By refining our technology and pursuing collaborative research, we aim to equip superintendents with the knowledge they need to protect and maintain their greens throughout the winter.

Technological advancements in WinterTurf sensing

This season, our team introduced sensing improvements to our data collection and sensor monitoring efforts. Our new Command Execution (CommandExe) support tool for our v3 data logger enables a remote command function to check connectivity and fine-tune functionality. Additionally, our updated dashboards provide real-time diagnostics, improving our daily monitoring capabilities.

With every season comes challenges. Some courses experienced poor cellular signal or quality, not allowing the node to send data in real time and limiting our remote access for diagnostics. To address this issue, our system is designed to store all data locally on an internal microSD card, which we can download once the node comes back to the lab in the spring. Another challenge in winter is the limited sunlight – our system relies on incoming solar energy with a battery backup. During the darkest months, some nodes still require manual battery charging by course superintendents, ensuring continued operation in very low-light conditions.

Exploring data through a multidisciplinary hackathon

Recently, we had an exciting two-day intensive hackathon event that included researchers, data scientists, and turfgrass experts. The meeting aimed to generate research questions and uncover patterns at a fast-paced tempo using our growing and extensive dataset. The group explored questions such as:

• How do CO2 accumulation rates differ between these three cover conditions: ice, impermeable covers, and impermeable covers and ice (Figure 1)?
• Which combination of fall practices correlates most strongly with reduced winterkill damage?
• How do light intensity levels under different covers correlate with turfgrass recovery rates?

Figure 1. Exploratory bar graph showing weekly average CO2 levels under cover types: impermeable covers, ice, both ice and impermeable covers, or other cover type. Credit: Majid Farhadloo.

While the hackathon was mainly exploratory, it identified new directions for future research. The collaborative meeting highlighted the value of multidisciplinary analysis in understanding complex environmental data.

A banner image representing WinterTurf data collection efforts on golf courses across the northern hemisphere during winter.

Final thoughts

Our goal remains the same: to provide golf course managers with research-based knowledge and tools for winter turf management. By refining our technology and pursuing collaborative research, we aim to equip superintendents with the knowledge they need to protect and maintain their greens throughout the winter. As we reflect on another successful season, we look forward to further advancements. Stay tuned for more updates as we continue to dig into the data.

​This activity supported in part by: MnDRIVE Global Food Ventures, University of Minnesota

Potatoes are a pervasive staple and specialty crop the world over, but so too are the pests and diseases that affect potato yields, tuber quality and farmer profitability. However, like the productive part of the crop itself, many potato diseases develop below ground, incurring costly damage well before the farmer becomes aware of the problem. Getting a better handle on the spatial extent, depth and temporal variation of soil temperature, moisture and other environmental variables that affect the development of potato diseases is key to modeling the field-scale risks posed by these threats. This is especially so if the aim is to model disease development in ways that provide farmers with actionable (real-time) information to mitigate or manage the crop production and profitability outcomes of these diseases.

Verticillium wilt is a long standing scourge of potato farmers. In related work, we estimate this particular soil borne fungi is a threat to almost 72% of the world’s potato growing area. V. wilt infections first become evident above ground when the plant’s lower leaves wither and die. Symptoms progress upwards until the entire plant yellows and wilts. The disease causes early senescence of the plant, which results in economically significant yield losses and tuber discoloration. In some instances, costly fumigation can be an effective mitigation strategy, while long rotations (3 years or more) with other crops can reduce the inoculum load of this long-lived disease at a particular site.

 
Creating fit-for-purpose biotic threat models that reveal the potential risks associated with V. wilt and other crop diseases at field scale and beyond is a core research focus of the GEMS Biotic Threat Analytics Lab. Pest risk prediction models and timely access to the targeted information products they enable helps farmers and others prioritize disease intervention on local (and neighboring) farms, informs a host of post-farm supply-chain decisions that rely on prospective crop production outcomes, feeds valuable information into early warning systems, and informs crop breeding strategies.

Digging Deeper into Above- and Below-Ground Environmental Data

Appropriately scaled environmental data both above and below ground data are required to informatively model the field-level risks posed by V. wilt (and other crop pests and diseases). While there are  several relevant gridded environmental datasets to hand, most are at coarser resolutions that extend well beyond the area extent of a typical potato field or farm. Moreover, these datasets often lack relevant below ground variables (e.g., soil moisture and temperature, at variable depths) that in combination with other variables are required to develop and deploy actionable pest prediction models of soil-born biotic threats. To rectify these two shortcomings, we turned to our GEMS Sensing team to provide real-time sensing of the needed environmental data. 
 

To best align our environmental sensing efforts with incidence and severity information on V. wilt, we also paired up with Dr. Ashish Ranjan’s Lab in the University of Minnesota’s (UMN) Department of Plant Pathology. Ashish conducts extensive V. wilt trials at UMN’s potato disease nursery located at the U’s Sand Plains Research Center in Becker, Minnesota. 

Siting Sensors to Reap the Biggest Predictive Bang for the Buck!

In 2023 we ran a test deployment of two GEMS sensing systems in the V. wilt resistance screening blocks at Becker, MN. Each system was configured with 3 above ground sensors (temperature, barometric pressure, and relative humidity) and 5 below ground sensors (soil moisture, temperature, permittivity, bulk soil electrical conductivity, and porosity). The above ground sensors were deployed in 3 replicates, and the below ground sensors at 3 depths. Our statistical assessment of these real-time data indicated that one set of above ground sensors coupled with below ground sensors at two depths yielded the optimal sensor configuration. 
 

For the 2024 growing season we scaled up our sensing efforts to 17 sensing stations, each with 3 above ground sensors and 5 below ground sensors. Fifteen sensor systems were deployed in the research plots where select potato varieties are screened for V. wilt by the Ashish Lab, plus 2 sensing systems for benchmarking in the (disease free) potato breeding plots at Becker managed by Dr. Laura Shannon in UMN’s Department of Horticultural Science. 

The precise placement of each sensing system was informed by an environmental profiling exercise prior to field deployment. First we digitized the boundaries of each of the 16 blocks used in the V. wilt screening nursery then overlaid that on gridded data we accessed from GEMS Exchange on 10 variables of potential relevance for disease risk modeling; including elevation, slope, available water storage (AWS) and soil organic carbon stock estimate (both at 3 depths throughout the rootzone). Our aim was to sense as much environmental variation from within the study area as possible in the process of generating our targeted below (and above) ground environmental variables.
 

The deployed location of each sensor is marked by the red dot in image #3, where in this instance each disease nursery block is overlaid on just one (i.e., elevation) of the 10 environmental variables we used to select a site for each sensor. 
 

The wealth of high-resolution, real-time (every 15 minutes) environmental data generated by this deployment is now being analyzed and integrated with correspondingly geo-tagged V wilt field data from the Ashish Lab. Field-scale predictive pest models are also being prototyped drawing directly on these novel, environment-linked-to-disease data sets to both develop and ground truth our modeling results. Working with our industry partners, PepsiCo, we look forward to further refining and then geographically scaling up the deployment of these predictive models to provide real-time, fit-for-purpose insights into dealing with this (and other) pesky potato diseases.     

Verticillium wilt Image credit: Utah State University

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

Fun for everyone at interdisciplinary Food Agriculture and U summer science camp

Imagine going to an agriculturally-themed summer camp where you got to see and taste protein bars, puffed cereal, ice cream and cheese being made at an industrial grade facility; did experiments where you pulled iron out of fortified breakfast cereal; extracted and sequenced DNA from your food; used a supercomputer to decipher that DNA; simulate a wheat breeding experiment and identify diseased plants in the field; stick your arm inside a dairy cow’s stomach, and lead your own dairy calf in a field by the barns! Wow, that’s a lot to experience in one week. But that’s precisely what 15 brave, inquisitive and incredibly bright 11, 12 and 13-year olds did at the inaugural launch of the ‘Food, Agriculture and U’ summer science camp, held in collaboration with the University of Minnesota’s Youth Programs June 24-28, 2024.

The camp was initially conceived by Kevin Silverstein at the Minnesota Supercomputing Institute and at GEMS Informatics, George Annor at the CFANS Department of Food Science and Nutrition, and Getiria Onsongo, at Macalester College and GEMS. But they soon found that people from all over the University loved the idea and joined the effort, volunteering their time. In all, more than 25 professionals ended up making significant contributions to the camp, nearly all interacting directly with the kids on one or more of the 5 days. Special thanks to co-organizing leaders Shea Anderson and Daryl Gohl at the UMGC, Emily Conley and Becca Hall at Agronomy & Plant Genetics and Plant Pathology, and Tony Seykora and Isaac Salfer in Animal Science for their extensive planning and staff recruitment.

In recruiting students for the camp, significant effort was made to reach the Native American community. An outstanding liaison at each of two institutions, Migizi in Minneapolis and the American Indian Magnet School in St. Paul, helped us recruit students that normally miss out on these opportunities. We also attended two powwows and appealed to families there directly.

Corporate sponsors were also amenable to the concept. We are very grateful for contributions from PepsiCo, Cargill, and Fairbault Foods for allowing us to provide a free camp experience for 6 of our 15 students. And they also allowed us to purchase healthy snacks (which the kids greatly appreciated and kept talking about) for all participants each day. Thanks also go to NSF and ACCESS for an allocation that enabled the students to decode their DNA sequence on the Nations' supercomputing infrastructure, and to the Digital Science Initiative for helping us to manage donations. 

Please check out the gallery which has 4 photos from each day, highlighting the diversity of activities. Miraculously, even though this was the first time giving this camp, all 5 days went forward smoothly and were a great hit. There are a few tweaks to make next year for sure, but the response was overwhelmingly positive and we are all delighted!

Day 1

Exploring Food Science and Nutrition

Day 2

DNA sequencing, extraction and mutants

Day 3

Linking ag data via supercomputers

Day 4

Visiting agricultural fields on campus with breeders and plant doctors

Day 5

Out in the barns with the dairy cattle

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

APIs: Now well-documented and much easier to use

One of the big frustrations in using computing to solve large, multidisciplinary challenges is managing data sets from different disciplines. Often, you have to go to each individual site and download the entire dataset. Then you have to parse out the subset of data fields you want within the geographies, spatial resolutions and time periods you care about. It is still the case that a few groups provide their data in the form of an Application Programmer Interface (API), where the data are served in a structured form with clear metadata documentation. Data can be sliced and diced how you like, selecting subsets of geography, time, and variables of interest. Once you sign up and obtain an API key, it just takes a few lines of code in Python or R to establish a connection and query at will!

GEMS has been building out a portfolio of APIs since 2021 across a range of useful datasets seeking to span the full Genetics x Environment x Management x Socioeconomic data landscape. Those who tried GEMS Exchange before will know that we used to have a middle layer managed by RapidAPI. Users found that cumbersome and confusing, so we are now using our own Apache APISIX server within our own web pages to serve you your key and monitor usage. We’re confident that your experience will be super easy this time around. Let’s get you started!

First, check out which APIs might interest you at our GEMS Exchange page. To obtain your API key simply click here for key. (Note you will need to have a Globus.org account, which is free – or you can connect via your academic institution, Google account, or ORCID). Once you know which APIs interest you, explore our collection of Jupyter notebooks in Github that give you practical guidance on how to use them. Many of the APIs we offer use the GEMS Grid which help ensure they are interoperable. And the GEMS Grid itself has recently been made open source, so you can place your own data sets on the Grid and interoperate with the community.

We are always happy to hear of useful datasets that could be added to the GEMS gridded collection in Exchange, so by all means reach out with suggestions or queries here.

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

We are delighted to announce that we have just released the GEMS Grid code library, where the code is under the open source Apache 2.0 license, which allows anyone to use the code for commercial or non-commercial purposes – you simply need to provide attribution to GEMS Informatics when you use or modify it.

Just before we at GEMS Informatics started developing Application Programmer Interfaces (APIs) in agriculture for GEMS Exchange, GEMS geospatial expert Jeffery Thompson worked with others in the GEMS team and colleagues at NSIDC to develop the GEMS Grid, a hierarchical discrete global gridding system. This Grid has allowed us to provide data sets at different resolutions ranging from 36 km to 1 m, and still have them remain functionally interoperable. The interoperability is possible because we have written the code to allow users to project data onto the grid, aggregate data to coarser resolutions, and, notably, also disaggregate data to finer resolutions. The latter operation is ordinarily a difficult problem, but is made easier, as I discuss below, since we enable the users of our code to thoughtfully address it in a standardized, replicable way.

Many problems in agriculture (e.g., understanding the spatial location of crop production) require equal area parcels of land to do proper calculations. Working with strict lat-lon coordinates won’t suffice as areas near the equator are significantly different in size as areas near the poles. The GEMS Grid preserves equal-area assumptions as it divides land, so you can do these calculations with confidence, and preserve aggregation-disaggregation consistency in the data, even if you are not a GIS expert.

So let’s look at the 5 options for disaggregation that GEMS geospatial developer Olena Boiko included in the GEMS grid toolbox, schematically described in the figure she developed above.

Option 1. Value transference. 
In this case, if you were to subdivide a 3 km² resolution grid cell into 9 x 1 km² cells, this option would be appropriate for any value that is deemed roughly constant throughout the area applied. Examples would be rainfall in inches or grain yield in bushels / acre.

Option 2. Even value division. 
Sometimes the quantity measured in a cell represents a cumulative value for the area in which it is reported. In this case, if the parent cell is homogenous, then splitting it up into 9 equal-area pieces would require that you divide the value in each equivalent cell by a factor of 9. Examples where this selection makes sense include grain production in bushels, crop acreage, and population.

Option 3. Value transference with a mask. 
This one is similar to Option 1 except we are no longer making the assumption that the distribution of values in the parent cell is spatially homogeneous. For example suppose you were measuring grain yield, but you knew that 3 of your nine cells had buildings occupying them (see white areas in the Figure). In this case you only transfer your values to 6 remaining cells (colored peach) that have arable land. Cells are binary with this option (i.e., either allowed a value or not).

Option 4. Even value division with a mask. 
Analogously, you can mask out cells in the value division case when you know that your daughters cells are not all equal. This is just like the case in Option 3, except you divide your parent-cell value evenly by the number of viable daughter cells. In this pictorial example, there are 6 viable daughter cells, so each gets a value of 900/6 = 150. This would be appropriate if you were computing grain production in bushels and you had a total value that needed to be split up across the 6 arable daughter parcels.

Option 5. Flexible division with a mask. 
This scenario is the most flexible, and allows the user to create a master mask with arbitrary weights at each daughter cell. It allows you to block off daughter cells entirely, and prescribe the relative weights of all remaining daughter cells. This is ideal for situations where you are allocating crop distributions and you want to avoid certain land use features (e.g., lakes, forests, housing) and probabilistically distribute the remaining crop areas (e.g., with higher probability near soils with a high SSURGO National Commodity Crop Productivity Index).

I’m confident these flexible disaggregation tools will provide much easier, more accurate, and replicable solutions for your particular spatial analytic problem. So please give them a try!

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

We’re thrilled to announce that GEMS is leading a novel 2-year project funded by the Minnesota Invasive Terrestrial Plants and Pests Center (MITPPC) with support from the Minnesota Environment and Natural Resources Trust Fund.  "Linking soybean aphid losses to technology investment decisions"  a transformative project aimed at developing a flexible, evidence-based, bio-economic evaluation workflow to characterize the long-term, probabilistic extent of soybean aphid damage throughout Minnesota.

Our Mission: A Repeatable and Extensible Pest Risk Assessment Tool to Inform Decision Making

Soybean aphids have emerged as a significant arthropod pest impacting soybeans in North America since 2000. However, a systematic effort to collect, analyze, and report on yield losses caused by this pest in farmers' fields has been notably absent, particularly concerning the longer-term, state-wide perspectives that are crucial for strategic R&D and policy decisions.

Our goal is to comprehensively assess the risk posed by the soybean aphid for the state of Minnesota to help inform decisions regarding research investment and pest mitigation strategies. Our evaluation framework breaks new ground by factoring in both the spatially- and temporally-variable pest risk elements that many prior efforts have overlooked. Our flexible evaluation approach is designed to deal with either data-poor or data-rich scenarios while taking into account the geographical extent, frequency, and severity of damage to undertake regional and meso-scale risk assessments. 

Our project, 'Linking Soybean Aphid Losses to Technology Investment Decisions,' is breaking new ground by developing a flexible, evidence-based framework for estimating crop losses that account for the dynamic nature of pest risks. Through interdisciplinary collaboration, we're forging a path to innovative solutions in agriculture.

-Yuan Chai

Forward-Looking Impact: Shaping Tomorrow's Risk Management Strategies

This project entails close collaboration between the GEMS Informatics Center (PI Dr. Philip Pardey and co-PI Dr. Yuan Chai) and the Department of Entomology (co-PI Dr. Robert Koch), leveraging a spectrum of expertise in pest, crop, and socio-economics. With cutting-edge data and analytics support, we're developing a data-driven, replicable, and extensible framework for meso-scale ex-ante pest risk evaluation of soybean aphids. The project's impact stretches far beyond the laboratory, resonating with stakeholders invested in the field of crop pest management. Our findings will empower MITPPC, state government agencies, academic units, and crop commodity groups to strategically allocate resources for targeted investments in pest management strategies. Moreover, our approach is designed to be extendable to a wide range of crop pest and disease challenges in Minnesota and beyond.

Join Us on this Journey! 

Join us on this transformative journey, where our collaborative effort is poised to shape the future of agricultural resilience and resource optimization decisions. Stay tuned for updates on findings, methodologies, and the broader implications of our work by signing up for the MITPPC newsletter and by visiting our project page. Together, let's revolutionize the landscape of agricultural research and pest management! ??

Photo Attribution: Christina DiFonzo, Michigan State University / © Bugwood.org

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing

Virtually everyone in Minnesota and bordering states whose profession has a direct stake in Water Conservation assembled in Downtown St. Paul’s RiverCentre October 17-18 for the annual Water Resources Conference. I had the honor of chairing a Special Session at the conference titled “Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing.” The session featured two very foundational efforts that we are attempting to bridge together in partnered research at GEMS: (1) The Minnesota Department of Agriculture’s (MDA) Minnesota Agricultural Water Quality Certification Program (MAWQCP), directed by Brad Jordahl Redlin, and (2) the Department of Forest Resources’ remote-sensed Water Quality assessment for our 10,000+ lakes, led by Leif Olmanson.

The 90-minute session was organized so that each of the three speakers (one had dropped out last minute for logistical reasons) had 20 minutes to speak plus two minutes for burning questions. At the end the audience had 15 minutes for questions with the entire panel of speakers. There were about 30 very inquisitive people in the audience, who peppered the speakers with questions throughout.

Updates on the MDA’s certification program

Brad Jordahl Redlin kicked off the session with an outstanding talk that outlined the principles on which the certification program he founded and directs was created, and highlighted the progress that has been made. Literally over a million acres have now been certified, spread across over 1400 producers. In exchange for regulatory certainty for a period of time, these producers allow his government agency to (1) assess their farm practices, (2) suggest new management practices (sometimes with additional incentives such as subsidized equipment from a grant), and (3) carry out audits of their new practices. The program, which has been operational for nearly a decade, is well beyond the stage of corralling early-adopters. New participants span the spectrum of producer demographics in age, farm size, and more.
 

Scaling lake quality measurement

Leif Olmanson stepped up to review an activity that he first spearheaded twenty years ago – using remote sensing to measure the water quality of Minnesota lakes. In the interim, he has overseen steady progress using new generations of satellites (from Landsat to Sentinel), multiple metrics (clarity, chlorophyll, organic matter), and the frequency of data reporting (from once every 5 years to ~weekly lake snapshot and monthly pixel-level composites of all 10,000+ lakes). Data is made available to the casual user via a friendly Lake Browser interface, and in numeric form pixel-by-pixel for data wizards on GEMS Exchange.
 

GEMS bridging the two programs

David Porter rounded out the session discussing the improvements he has made in creating algorithms that properly remove clouds and aerosols from the satellite images. This is a prerequisite for feeding all of the regularly spaced (~5 day) images of a field through a growing season into a sophisticated Recurrent Neural Network (RNN, a type of Machine Learning algorithm) that his colleague Anubha Agrawal has been preparing. As described in this case study, David, Anubha, and I intend to use the RNN trained on known, certified management practices (e.g., strip till / no till, cover crops) from the MAWQCP to make a prediction of what practices growers are making on uncertified farmland. We then will trace these producer fields to the downstream lakes in each watershed, and see what covariates (e.g., slope, soil type, distance to waterway) affect the correlation of % farm practice adoption vs. downstream lake quality in a watershed.

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota

GEMS’s new NSF award

Training ag and food scientists how to make the most out of supercomputers

Do you write code (maybe in R, maybe in Python)? Maybe you’re an agri-food scientist in a lab, or in a similar field in industry. Perhaps you’ve tried to write some code to get your work done, figuring you can run it on your laptop. But when you churn on that data you find after two weeks of keeping your laptop open to run the code that it’s probably not going to finish any time soon… Epiphany. You need to do this in a smarter way! Well you’re in luck – GEMS was just awarded a grant from NSF titled “Cyber Training: Pilot -- Breaking the Compute Barrier, Upskilling Agri-Food Researchers to Utilize HPC Resources” and you are exactly the audience we wish to reach with the courses we are developing.

The Team

The course material is being developed by an interdisciplinary team including:

• Instructor Joe Axberg, who works on supercomputing infrastructure by day but is also a part-time instructor after hours in the IT Infrastructure Program in the College of Continuing and Professional Studies.
• Project mastermind, Ali Joglekar, an agricultural economist by trade with real-world experience working with smallholder farmers in East Africa
• Kevin Silverstein, a cofounder of GEMS Informatics and expert bioinformaticist with specialties in molecular plant-microbe interactions and large-scale informatics problems
• Jesse Erdmann, systems architect and guiding force behind much of the inner workings of GEMS infrastructure.
• Ben Lynch, Director of MSI, with a long track record of innovating in the scientific computing domain

Scope

The proposal identifies 7 separate course modules that each contain a significant number of concepts, representing a broad swath of content to help up-skill agri-food researchers to use HPC:

1. Introduction to High-Performance Computing (HPC) for Agri-Food Researchers
2. Introduction to Cloud Computing for Agri-Food Researchers
3. Hands-On: Use HPC to Analyze More Data and Faster
4. Leveling-Up I: Expanding HPC Skills for Agri-Food Research
5. Computer Science for the Agri-Food Researcher
6. Leveling-Up II: Advanced HPC Concepts
7. High Performance Workstations and Servers for Agri-Food Researchers

It is expected that the number of modules proposed and the content within each module may change as we build out the course and modules during the development phase(s). The goal of our iterative course development process is to refine this broad set of concepts and craft cohesive modules each with a duration of 2-6 hours of lecture materials (depending on the module). Some modules will span multiple weeks. Several rounds of purposeful learner feedback is integral to our course development process. 

The course modules will also be “stackable” allowing learners to have some ability to “pick and choose” modules in line with their competencies and interests.

Timeline for delivery

As the diagram indicates, we are planning to offer each HPC for Agri-Food Researchers course over a 10-12 week period of time. The proposed course structure, in line with our other GEMS Learning offerings, includes asynchronous and synchronous instructional elements. Lecture materials will be delivered via videos and readings, which students are expected to review ahead of the weekly 1.5 hour class session. This instructor-led class time is primarily dedicated to synthesizing and reviewing the content being taught. To solidify learning objectives, students will be expected to engage in regular assessments and hands-on learning exercises outside of the weekly class session. We estimate that students will be responsible for 6+ hours/week of self-directed learning activities, depending on their skill-levels.

You can influence our content!

The good news is we are still in development, so you can influence these course offerings. Please fill out this survey now to make your voice heard!

​This activity supported in part by MnDRIVE Global Food Ventures, University of Minnesota