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)

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

Cover crops provide many benefits such as improving soil health, sequestering carbon, and potentially providing nitrogen credits. Accurately measuring these benefits has traditionally required labor-intensive sampling, expensive instrumentation, and technical expertise.

Recent research in the Runck Lab by Rosen et al. (2024) investigates how consumer-grade cameras and RGB (Red-Green-Blue) imaging can offer a low-cost and scalable alternative for estimating cover crop biomass and biochemical composition. The findings suggest that common digital and smartphone cameras can provide good estimates of vegetative ground cover, nitrogen content, and carbon-to-nitrogen (C:N) ratios.

Using RGB Indices to Monitor Cover Crops

In this study, different RGB color indices were tested using off-the-shelf cameras on medium red clover (Trifolium pratense L.), a common cover crop, to classify vegetation pixels and estimate biomass The four indices included were Excess Green (ExG), Excess Green minus Red (ExGR), Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARI). The ExGR index with a preset threshold of zero was the most effective at correctly identifying plant pixels from the background 86.25% of the time. The research findings also included strong correlations between plant canopy coverage and biomass (R² = 0.554, RMSE = 219.29 kg ha⁻¹), as well as between vegetation index values and nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and C:N ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). This method remained stable across varying lighting conditions, making it practical for field applications.

Practical Applications and Future Potential

This study highlights the potential of RGB-based sensing to provide accurate estimates of biomass and nitrogen content. By integrating these indices into digital agriculture platforms or mobile applications, farmers and researchers could better manage soil health, use less fertilizers, and scale up research efforts with less specialized equipment.
While further validation across different crops and environments is needed, this approach represents a promising addition to the growing suite of precision agriculture tools. By using low-cost and widely available technology RGB-based indices have the potential to make data-driven farming more accessible and sustainable.

Acknowledgments

This work was funded by the United States Department of Agriculture, GEMS Informatics Center’s Real-time Geoinformation Systems Lab, and the University of Minnesota MnDRIVE Global Food Ventures Faculty Scholars program.

Photo Credit: Wikimedia Commons

As part of our ongoing work to improve the use of environmental data in agricultural research, we recently published a prototype tool that integrates multiple agrometeorological data sources into a unified access and querying system. This tool demonstrates how general-purpose extract-transform-load (ETL) systems can reduce overhead and improve data usability for digital agriculture workflows.

Researchers often spend a lot of time downloading, cleaning, and reformatting the same climate datasets from multiple providers. Each data source has its own format, API conventions, and spatial-temporal structures. Our prototype simplifies this by offering a standardized, open-source interface that harmonizes disparate sources and automates many of the most common processing tasks.

Built with extensibility and usability in mind, the system includes separate ETL managers for each dataset and supports spatial and temporal aggregation across user-defined parameters. Outputs can be downloaded as CSV or JSON, and users can access the tool through either a graphical user interface or a RESTful API. The system currently runs on Windows, Mac, and Linux and is designed to be lightweight and usable by researchers without extensive programming backgrounds.

We used Minnesota as the case study for this prototype. We were able to explore how researchers might customize queries for specific cropping systems, field experiments, or landscape-scale assessments. We believe this kind of data interface is especially important in the context of changing climates, where the ability to quickly assemble regionally and temporally relevant data can support adjustments to cropping calendars, irrigation schedules, and other time-sensitive decisions.

The project was supported by the Minnesota Environment and Natural Resources Trust Fund and the Legislative-Citizen Commission on Minnesota Resources. We hope this prototype serves not only as a useful tool for others but also as an example of how modular ETL architectures can be applied more broadly to agricultural and environmental research challenges.

The code is available under an open-source license. 

To see related activities by Bryan Runck, check out my Lab Page.

​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

Revolutionizing Agriculture

In recent years, the fusion of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) has opened up unprecedented opportunities for agricultural innovation. One groundbreaking development in this arena is the implementation of real-time geoinformation systems, which promise to revolutionize agri-environment research by enhancing data quality, scalability, and cost-efficiency.

The Rise of Spatial IoT in Agriculture

As the agricultural sector increasingly relies on AI and ML for knowledge discovery, the need for large, high-quality datasets has become paramount. Spatial IoT technologies, which involve deploying internet-connected sensors throughout agricultural environments, have emerged as a crucial tool in this data-driven landscape. These sensors collect real-time, high-resolution geospatial and temporal data, enabling researchers to monitor and analyze agricultural systems with unprecedented precision.

Challenges in IoT Implementation

Despite its potential, the implementation of IoT in agriculture presents significant challenges. Managing large fleets of devices while maintaining data quality is a complex task. Scientists often start with one-off prototypes, but scaling these to thousands of internet-connected devices requires overcoming numerous technical and logistical hurdles.

Case Studies in IoT System Development

The University of Minnesota’s Real-Time GeoInformation Systems Lab has been at the forefront of addressing these challenges. Since 2019, the lab has developed and deployed over 2,727 IoT devices across four continents. This extensive deployment has provided valuable insights into creating a generalizable, open-source spatial IoT system tailored for agricultural research. This work was summarized in a recent pre-print on Arxiv.com (Runck et al. 2024).
One key aspect of the lab's work has been the iterative development of the IoT system, progressing through three major and fourteen minor versions. Each iteration has refined the system's capabilities, from improving sensor accuracy to enhancing data transmission reliability. The current version of the system is designed to be scalable, ensuring that it can be deployed widely while maintaining high data quality.

Practical Applications

The applications of these IoT systems are diverse and impactful. For instance, in irrigation management, real-time data on soil moisture and temperature help optimize water usage, crucial in regions facing water scarcity. Similarly, in plant winterkill research, sensors monitor microclimates to understand the conditions leading to crop damage in cold environments. These insights enable farmers to adopt preventive measures, safeguarding crop yields.
Another notable application is in meteorological observations. Deploying IoT systems for weather monitoring provides granular data that enhance the accuracy of weather forecasts, which is vital for agricultural planning and risk management. For example, in Minnesota and Malawi, extensive networks of weather stations equipped with IoT sensors collect data that support both local farmers and broader agricultural research initiatives. However, paying attention to data quality, access and interoperability matters, often coupled with fit-for-purpose analytic pipelines, is key to ensuring real-time, geo-sensed data lead to actionable, data-driven informatics products.

The Role of Open Source in Scaling IoT

Open-source technology plays a crucial role in the scalability of IoT systems. By making design files and code publicly available, researchers can build on existing work, ensuring broader adoption and continuous improvement. This collaborative approach aligns with the scientific principles of transparency and reproducibility, fostering innovation across the agricultural research community.

Moving Forward: GEMS Sensing Service

To support the ongoing development and deployment of IoT systems, the University of Minnesota has established GEMS Sensing, a service organization within its GEMS Informatics Center. This initiative aims to provide turnkey IoT solutions for researchers, ensuring that the technology is accessible and sustainable. By offering both internal and external sales models, GEMS Sensing facilitates public-private partnerships, driving further advancements in digital agriculture.

Conclusion

The integration of real-time geoinformation systems into agricultural research marks a significant leap towards smarter, more sustainable farming practices. By harnessing the power of spatial IoT, researchers can collect and analyze data at an unprecedented scale and resolution, paving the way for innovative solutions to some of agriculture's most pressing challenges. As these technologies continue to evolve, the future of agriculture looks increasingly data-driven and resilient, promising enhanced productivity and sustainability for the global food system.

Image: Generated with Firefly. of A modern agricultural field with IoT sensors placed at various points.

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

Making Sense of Agriculture from Minnesota to Malawi

GEMS Sensing has coupled world-class engineering design and build expertise with first-rate agricultural science smarts to realize a robust, extensible sensing system for agri-food research anywhere in the world.

Since early 2020, GEMS Sensing has partnered with the Centre for Agricultural Transformation (CAT) in Malawi to pilot the local deployment of GEMS research-grade weather stations and its associated data visualization and sharing tool. Piloting a network of weather stations across Malawi involves much more than working through the logistics of manufacturing, delivering and deploying the necessary hardware and software. It also involves identifying partners willing to trial the weather stations on their research stations/farms and training local CAT personnel to enable the deployment, particularly in remote rural areas with uncertain cellular connectivity. Working through the CAT provides a unique opportunity to engage with private and public agribusiness throughout Malawi to test GEMS Sensing technologies in a low-income, smallholder farmer, tropical environment. 

Over the past five years, the GEMS Sensing team has used feedback from our field partners to develop ever-improving scientific-grade, plug-and-play sensing devices coupled with a secure web-based informatics service. Weather stations aren’t new, but GEMS Sensing has created a highly customizable system that is built specifically for the varied sensing needs of researchers and their collaborators. The system's core logger box can be used for scientists with vastly different sensing requirements, from a full-stack meteorological station supporting a host of on-station or in-field crop performance studies to more specialized applications aimed at monitoring the below ground conditions that affect plant growth and health. The system's configurable and easy-to-use dashboard enables the integration, exploration, and sharing of real-time sensor data from multiple, perhaps distant, sites through a single portal.

Tracking the real-time, micro-level weather and edaphic patterns impacting agricultural production is crucial for a whole host of reasons, including advancements in breeding, agronomy, and extension efforts and the livelihoods of the farmers responsible for this production. These types of data are used for modeling weather events over large areas (e.g., rainfall and temperature) or informing management decisions like when and how much to irrigate a crop. However, being able to collect, store, clean, explore, share, and analyze these data from multiple sites, isn’t always straightforward. With this motivation in mind, the team has coupled world-class engineering design and build expertise with first-rate agricultural science smarts to realize a robust, extensible sensing system for agri-food research anywhere in the world.

GEMS Sensing in sub-Saharan Africa

For the Malawi pilot project, we worked with a South African-based manufacturer to produce the GEMS Sensing stations. The full-stack weather stations are configured to collect eight parameters every 15 minutes: air temperature, humidity, barometric pressure, rainfall, soil moisture, soil temperature, wind speed, and solar radiation. Data are sent via cellular network to a cloud-based database, processed for quality, and made accessible to partners via a user-friendly, web-based portal.

The primary objectives of the pilot exercise were to better understand:

1. Tropical environmental impacts on the sensing hardware;

2. Total cost of ownership from a user perspective (i.e., costs of acquisition, installation, and maintenance) involved in deploying digital ag weather sensors in a low-income context with limited infrastructure support; and

3. Malawian agribusiness’ demand, willingness to pay and potential use cases for networked weather stations (or related real-time sensing needs).

To date, the CAT team has deployed nearly 40 solar-powered weather stations across the spatially and temporally variable agro-ecologies that affect agricultural outcomes throughout the country. The stations continue to stream real-time data from experimental research stations, commercial farms, and smallholder farms. The pilot project is set to wrap-up in December 2023, though the CAT may continue to operate some of the weather stations beyond that time.

Learnings and Lessons

Scaling up a pilot project in Malawi from our operations based in Minnesota during the height of the global pandemic was not without its challenges. The original game plan was to travel with a handful of sensors to Malawi so that our technical team could work closely with CAT personnel and deployment partners to scope out the entire constellation of technical, human capital, cell connectivity, local logistics, device assembly and deployment partner protocols and other factors that could likely affect the odds of a successful outcome. Based on this test deployment we had envisaged tweaking the technical specs, revising our training and related deployment protocols before scaling up deployment throughout the country.

COVID-19 had other plans. It shut down all travel to Malawi, disrupted supply chains for computer chips that were critical to fabricating the GEMS designed sensors, and complicated shipping logistics. Undeterred, we pivoted our whole operation to enable a remote deployment, which, despite our best efforts, was not without its own complications.

Persistence ultimately paid. As we began scaling field deployment in April 2022, our technical team became adept at diagnosing issues long distance, ably supported by a great ground team in Malawi. Fluky cell connectivity issues seemed to be associated with variable soil properties that were vastly improved by simply raising the logger box further off the ground. Unexpected down-time issues were eventually tracked to battery depletion problems that arose from locally assembled devices that sat turned on for lengthy time before travel resumption enabled field deployment to proceed. The fix, change out the run-down batteries.

All told, our GEMS Sensing devices exceeded our expectations, even withstanding Tropical Cyclone Freddy that hit Malawi in February 2023!

Our team visited Malawi in May and traveled with the local CAT sensing team throughout the Southern and Central regions to get first-hand feedback from a host of public and private partners who participated in the GEMS Sensing pilot project. We made site visits with DARS research scientists, commercial agrifood businesses such as Bayer, Pxyus and Global Seeds, and One Acre Fund which services smallholder farmers directly. These partners were uniformly enthusiastic about the ready access to automatically streamed and easily accessible localized weather data. They also highlighted numerous opportunities for leveraging the streaming weather data streams with various backend analytics to tailor informatics solutions to the particular and varied agri-food production problems they faced.

GEMS sensors are currently being deployed with success across four continents. However, this represented our first widespread test of the system in a logistically challenged, low-income African agricultural setting. Prior to the pilot we had all sorts of questions as to whether or not the system would even work, and if so, how reliably and securely. Importantly, would anyone find practical value in such a sophisticated, real-time sensing system in a country dealing with many infrastructure and technological constraints.

Weather matters a lot for agriculture, whether you are farming in Minnesota or Malawi. While Minnesota farmers have access to all sorts of weather related data products, locally accurate, real-time data is still relatively scarce. In Malawi, rurally-relevant weather data of any sort is largely absent. The last time we checked, NOAA (National Oceanic and Atmospheric Administration) live stream weather data from just one site in Malawi, the Blantyre International airport! This pilot demonstrated the potential for changing this reality for Malawian farmers and the numerous public and private agencies that support their operations.

Even with its many hurdles and holdups, this pilot exercise revealed the feasibility and the latent demand for access to locally sensed weather data. With the lessons learned from our pilot deployment we are excited about the possibility of using GEMS Sensing’s data generation and analytical capabilities to support data-driven decisions that strengthen existing and emerging value chains in Malawi, as well as unlock new agri-food value-chains throughout the world.

This posting was produced as part of the Centre for Agricultural Transformation, an effort led by Land O’Lakes Venture37 and funded with a grant from the Foundation for a Smoke-Free World, Inc. (“FSFW”), a US nonprofit 501(c)(3) private foundation. The contents, selection and presentation of facts, as well as any opinions expressed herein, are the sole responsibility of the authors and under no circumstances should they be regarded as reflecting the positions of FSFW.

• Centre for Agricultural Transformation
• Land O’Lakes Venture37
• Foundation for a Smoke-Free World

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

GEMS at the Intersection of Technology and Agriculture

Hi, I’m Joe Axberg, an Application Engineer at the Minnesota Supercomputing Institute (MSI).  I am part of the Development and Operations team that builds and maintains our GEMS applications and services. It is through that backend “IT” lens that I will be writing my first post on our blog site.  Others on the GEMS team will be blogging about all the great things being done with GEMS - in other words - how GEMS is being used and how it is making a difference.  In this post though, I’d like to talk a little bit about the technology that powers GEMS.

Here at the University of Minnesota, GEMS is a collaboration between the College of Food, Agricultural, and Natural Resource Sciences (CFANS) and Research Computing, including the Minnesota Supercomputer Institute (MSI) and U Spatial.  The MSI is responsible for providing a variety of high performance computing resources to researchers across the University and beyond. The kind of high performance computing power needed to solve the big problems and crunch the big data.

Utilizing high performance computing can be challenging, complex, and intimidating.  In the agri-food research space, adoption of high performance computing has historically not been very high. Agri-food researchers are not computer scientists - but neither should we need them to be.   Applications and tools should exist that lower the barrier to access high performance computing to the agri-food researcher.

Hence the development of the GEMS Informatics and GEMS Learning Platforms.

What is Informatics? 

A quick Google search revealed this definition:

“the science of processing data for storage and retrieval”

“...processing data for storage and retrieval” - that certainly does sum up what the GEMS platform does - in a perhaps over-simplified way.  At GEMS, we expand on that definition: “turning…data into actionable information for farmers, scientists, governments or companies…” (Check it out at our About Page)

These days it is all about the data and the amount of data is immense - especially in the agri-food sector.  Challenges abound in the form of sustainability, changing climate, distribution, and more.  The data is out there and there is a lot of it.

The volume and complexity of this data often means that traditional methods for gathering, cleaning, organizing, storing, and analyzing data run out of steam. 

The aim of the GEMS Informatics platform is to make the job of the agri-food researcher easier.  An application that allows the data to become actionable more quickly.  Present within GEMS are applications and services that can handle the entire lifecycle of agri-food data.

Coupled with the GEMS platform is GEMS Learning.  Provided are a series of training modules and courses for upskilling agri-food researchers in the use of technology.  GEMS Learning is constantly developing and looking at new training opportunities for agric-food researchers in the area of high performance computing.

What Powers GEMS Informatics?

At risk of sounding cliche’, it really is about the people. Please visit the GEMS website to learn more about the passionate group of researchers, professors, technologists, and administrators who make GEMS possible.

On the technical side, the GEMS platform is a thoroughly modern platform developed using some of the latest technologies and techniques.  The various components of the GEMS platform leverage both private and public cloud services. Docker containers form the basic infrastructure of the platform.  Modern web development technologies are used to implement the platform.

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