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)

2025 World Food Prize: Panel Summary

The global agri-food system is under unprecedented pressure from climate change and shifting economic landscapes. At a recent panel during the 2025 Borlaug Dialogue, leaders from the public and private sectors converged on a singular truth: the traditional R&D model must evolve into a more integrated, mutual value-driven ecosystem.  Read on for the speaker perspectives and short video clips of their discussion.

Speaker Perspectives: Bridging the Gap from Discovery to Impact

1. The Shifting Geography of R&D

Phil Pardey | University of Minnesota (GEMS Informatics) Phil highlights a "seismic shift" in global agri-food R&D spending: middle-income countries now account for half of the world's agri-food R&D and the private sector role is becoming more prominent. However, low-income countries are being left behind, spending less than $1 on agri-food R&D for every $100 spent by the rest of the world. Phil calls for innovating the way we innovate and the benefits of partnerships where data and analytical tools are created and made accessible in IP- and market-aware ways that incentivize public and private investment while maintaining competitive value.

2. Beyond Core Crops: Cooperation & Community

Ty Vaughn | Bayer Crop Science Ty outlines Bayer’s "three-pronged" approach: Collaboration (working with groups like 2Blades), Cooperation (providing free IP and sequencing for crops like TR4-resistant bananas), and Market Enablement. He highlights a new $32M facility in Zambia that doesn't just process corn—it trains local workers and attracts secondary investments from partners like John Deere and Mastercard.

3. Purposefully Derisking Change

Ian Puddephat | PepsiCo Ian argues a resilient supply chain is impossible without a resilient farming community. He emphasizes that the real bottleneck isn’t just a lack of technology, but the transfer of knowledge required to apply it. He argues that the key to adoption is making change "easy" by sharing the journey of de-risking new technologies across the entire value chain.

4. Aligning Research with Actual Market Needs

Juan Lucas Restrepo | Alliance of Bioversity International and CIAT Juan Lucas discusses market-responsible collaboration, arguing for a sharper “ideation-to-deployment’ continuum where scientists develop solutions firmly rooted in actual market needs,  in addition to looking at "market pulls” such as shifting consumer habits or public policies to create a complementary dynamic between research in the public and  private sector, especially in the pre-competitive parts of the agri-food value chains.  

5. Bridging Gaps Through Aligned Interests

Diana Horvath | 2Blades Diana highlights that effective partnerships aren't just about "doing good"—they are about aligning incentive structures to create a win-win for everyone involved. By acknowledging that the private sector requires a return on investment while the public sector seeks social impact, 2Blades acts as a translational bridge. They facilitate models where private partners gain exclusive rights in their core markets while "carving out" and reserving those same technological benefits for smallholders in non-competing geographies. This "enlightened self-interest" ensures that every partner remains fully vested in the success of the project.

To hear more from these leaders in the Agri-food space, watch the entire panel discussion. 
Full length version of the panel discussion

University of Minnesota alum and Nobel Peace Prize laureate Norman Borlaug often implored agricultural scientists to look beyond their lab benches and “take it to the farmer.” As economies develop, farmers become embedded in ever-more complex and interconnected agri-food value chains (AVCs). A 21st century variant of Borlaug’s challenge is “take it to the farmer, and beyond!” Like the value chains being served, innovation in the agri-food sciences must now recognize the close connectivity between the pre-, on- and post-farm links within rapidly changing AVCs to sustainably improve their economic and environmental performance.

Notably, the private sector now plays a much larger and expanding role in research, development and innovation (RDI) for AVCs worldwide. While this opens new opportunities to spur innovation along AVCs, it also requires the public sector to rethink and reposition their R&D roles, cognizant of the potential for productive public-private innovation partnerships while also prioritizing the types of R&D for which the public sector has comparative advantages.

GEMS Informatics has teamed up with 2Blades to host a Deep Dive Session at the 2025 Borlaug Dialogue, convened by the World Food Prize Foundation in Des Moines Iowa, October 21-24.

This in-person session will be held on Wednesday, October 22 from 3:15 – 4:15pm at the Iowa Event Center in Des Moines. Entitled “Reimagining Partnerships to Transform the Agri-food Innovation Chain,” the session will explore how innovative public-private R&D partnerships can (and must) accelerate innovations that tackle, and help solve, the myriad challenges facing agri-food value chains now and in the decades ahead.  

The session will cover the following topics:

• The changing global realities of agri-food R&D
• Lessons from successful public-private innovation partnerships
• Efficiency drivers across crops, regions, and stages of the innovation chain
• Pathways to equitable, sustainable, and resilient food systems

Summer Science Fun!

Last  year a dedicated team of nearly 30 scientists from CFANS, MSI, UMGC and Macalester College teamed up to create and deliver a fun-filled weeklong summer science event for middle school students. Another year has come and gone, and so too has this year’s delivery of this event. But this wasn’t simply a rinse and repeat in this second year. We analyzed reactions to last year’s delivery and met monthly all year long with the goal of improving what was already a very successful endeavor.

What were some of the changes for the second year of this program? In a major change, we carved out 20-30 minutes each morning for Tex Ostvig, leader of CFANS’s Office of Inclusive Excellence, to provide inspirational instruction to the students on leadership development activities. Students responded remarkably well to reflective exercises on how to be a good listener, what are my core values, what are the different forms of group governance and more. These highly interactive activities served to boost students' self image and really anchored them each day as they moved to each new space for instruction. In a program that changed daily by design, Tex was a constant presence each day. And once this initial activity was done, students were emotionally ready to engage with the scientific activities that followed. Check out pictures of Tex with the students in each of the 5 days in the gallery below.

We also had a new sponsor for this year’s event, Forever Green Initiative. Many thanks to them for a $10,000 grant that allowed us to provide 7 students-in-need scholarships to attend, snacks, lab supplies, porta-potties, and other incidentals, along with carry-over funds from PepsiCo and Cargill from last year.

And once again, many thanks to all the dedicated volunteers that planned this event and delivered an incredible experience for these kids!! Your nimbleness, especially when it rained necessitating plans B and C, were amazing. There is no question in my mind that we have convinced several kids to consider college, consider science, and consider the U of M as a place they want to be in the future. Way to go!

Check out a sampling of the photos from this year’s event below.

Tex highlights: Leadership activities.

Day 1. Food Science and Nutrition featuring fresh ice cream and cheese (voted most memorable!)

Day 2. DNA extraction, sequencing and Mutant fruit flies

Day 3. Wheat breeding, Plant Pathology, Kernza and the Conservatory 

Day 4. Dairy barn activities and a walk with the calves

Day 5. Supercomputers and computing activities

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

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

Cassava, a root crop, is a critical staple food crop planted on 32 million acres worldwide. It is widely grown throughout sub-Saharan Africa, notably in Nigeria, DR Congo, Ghana, Angola, and Mozambique, but with large acreages in Vietnam, Brazil, Indonesia and India. Besides being a critical source of calories and dietary fiber for many poorer households throughout Africa it is also a very versatile crop, serving as an important source of animal feed and starch with uses in foods, glues, biodegradable products and drugs. It has a global market value of $48.7 billion and is also a priority crop for the Vision for Adapted Crops and Soils (VACS) program, led by the U.S. State Department, which aims to create resilient food systems in Africa by growing nutritious, climate-adapted crops in healthy soils.

Unlocking Cassava’s Potential for Food Security and Climate Resilience

Conventional breeding is a painstaking laborious process that takes many crop generations to develop the first in a stream of varieties that adapt to ever-evolving market and climate conditions. To speed up this process, the International Center for Tropical Agriculture (CIAT) sequenced thousands of cassava varieties to reveal genetic markers of adaptive and harmful traits. GEMS colleagues Nathan Carlson, Tom Kono, and Kevin Silverstein in the Minnesota Supercomputing Institute (MSI), developed a queryable genomics database to enhance cassava breeding and improvement efforts at CIAT and elsewhere. To do so they drew on the whole genome resequencing data spanning 3,673 accessions of cassava provided by CIAT and identified short DNA sequence variants among them–totalying over 9 million sequence variants! More specifically, the MSI team identified nonsynonymous variants, a subset of the sequence variants that change the amino acid sequence of the plants’ proteins from the reference genome sequence. The functional impact of the nonsynonymous variants was then predicted using a sequence constraint model called BAD_Mutations (Chun and Fay 2009, Kono et al. 2018) to identify sequence variants with potential impact on cassava trait variation.

Tackling Deleterious Mutations

CIAT breeders, led by Sean Fenstemaker, are excited at the possibilities these data provide for them. Deleterious mutations can significantly reduce crop yield and quality. CIAT’s breeding program now incorporates BAD_Mutations, an innovative SNP annotation tool designed to identify harmful genetic variants in cassava. This tool employs a likelihood ratio test based on alignments of publicly available angiosperm genomes, allowing for improved detection of deleterious mutations. 

Why Use BAD_Mutations?

Jonathon Newby, Cassava Program Leader, CIAT noted that  “While smallholder cassava farmers are faced with a range of new threats, there are also many untapped opportunities for this formerly neglected crop to address food security and nutrition,and still be a globally competitive product in industrial and food application. The cassava variant database addresses challenges and explores new opportunities for cassava breeding to unlock this potential. By comparing genetic variants with their ancestral origins, it provides insights into diversity and traits conserved in plants, aiding in the identification of key genetic variations. The database also supports molecular marker development, parent selection, and breeding strategy refinement.”

Enhancing Breeding Strategies

BAD_Mutations helps identify and select against genetic variants that negatively impact traits of interest. By using this tool, breeders can enhance phenotypic variation, leading to the development of robust and high-yielding cassava varieties. This genomic precision is vital for adapting to changing environmental conditions and meeting market demands. Additionally, breeders may use BAD_Mutations as a strategy for in silico validation of trait-linked markers, further ensuring the accuracy and effectiveness of molecular breeding efforts.

CIAT is using BAD_Mutations in tandem with other advanced technologies such as flower-inducing and doubled haploid techniques. These methods, combined with the University of Minnesota’s genomic tools, facilitate backcrossing-based trait introgression and systematic exploration of heterosis, significantly improving breeding efficiency of CIAT and its partners.

While cassava genetics was the focus of this project, the resulting queryable database framework has much broader applications within agriculture. Identification of genetic variants of potentially large effect is a technique that is useful for general crop and animal improvement, especially for complex traits (e.g., yield) which are typically under the influence of many genetic loci. Construction of an efficient, query-ready database allows for the genetic variation data to be rapidly assessed with standard input and output formats, making it easier for researchers to interpret the data.

This project demonstrates how cross-institution collaborations can accelerate applied research efforts. By partnering, CIAT and UMN crunched through a very large set of genomics data (requiring continuous compute cycles on hundreds of supercomputer processors for a month) into a format that can easily be used by geneticists and breeders to improve an important staple crop.

Global Collaboration for Food Security

These advanced genomic tools are pivotal in enhancing crop resilience and productivity by addressing the challenge of deleterious genetic mutations. CIAT invites researchers and global partners to collaborate in using this new cassava variant effect database. There is a real urgency to accelerate crop breeding to address global food security and poverty reduction concerns in the face of consequential changes in climate worldwide. Novel partnerships that pool complementary resources are key to making significant strides that result in timely and climate-resilient improvements in cassava and other crops.

​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

GEMS Informatics and Water Quality Management

Fertilizers are essential for boosting crop yields and increasing farm productivity. However, excessive use of fertilizers not only incurs high costs for farmers but also leads to harmful runoff that pollutes waterways. To address these challenges, GEMS Informatics is collaborating with the Minnesota Department of Agriculture’s Water Quality Certification Program and colleagues from the University of Minnesota. Together, they are leveraging AI and data science, in combination with ground-truth and remote-sensed data, to inform farmers of best management practices and track the water quality outcomes of those practices at scale.

The Problem with Over-Fertilization

While fertilizers play a crucial role in modern farming, their overuse can be detrimental. Excess fertilizers often wash into nearby water bodies, causing environmental pollution and financial waste for farmers. The key to effective fertilizer management lies in precise measurement and informed decision-making.

Harnessing Data for Sustainable Practices

Brad Jordahl Redlin, Water Quality Certification Program Manager at the Minnesota Department of Agriculture, emphasizes the importance of GEMS Informatics' work: “GEMS is building the analytical backend, linking farmer fields to watersheds and monitoring ongoing water quality. This will enable us to measure the positive effects of our certification program.”

Kevin Silverstein, Operations Manager at GEMS Informatics, adds: “We use satellite imagery to support government policy. Partnering with the Department of Agriculture, we train machine learning programs to recognize sustainable practices like strip-till or no-till farming, and cover crops and buffer strips that prevent water runoff. We can then correlate these practices with their impact on water quality and provide recommendations to the certification program.”

Advanced Monitoring with Satellite Technology

In collaboration with CFANS colleague and remote sensing water quality expert Leif Olmanson, GEMS Informatics employs satellite technology and supercomputers to monitor water quality. Monthly averages of satellite signals provide comprehensive measurements across Minnesota’s lakes, even those seldom exposed to direct sunlight.

Machine learning algorithms developed by GEMS require regular, unobstructed satellite signals. To ensure accuracy, GEMS imputes missing data using adjacent pixels in time and space. This approach turns vast amounts of data into actionable insights, empowering policymakers and farmers while safeguarding privacy.

Jim Wilgenbusch, Director of Research Computing at the University of Minnesota, praises GEMS Informatics' innovative approach: “It’s extremely exciting that they push at the boundaries of what researchers traditionally considered possible. GEMS sits at the intersection of a database where you store, catalog, and organize information, and modeling that information on a massive scale. It has huge potential to support researchers in their work at the cutting edge.”

A Collaborative Effort

GEMS Informatics integrates efforts from various University of Minnesota schools and institutions, including U-Spatial, Research Computing, the Data Science Initiative, the Minnesota Supercomputing Institute and the College of Food, Agricultural, and Natural Resource Sciences. Additionally, GEMS partners with state institutions like the Minnesota Department of Agriculture and multinational companies to drive data-driven innovation in the agri-food sector.

By combining AI, data science, and collaborative efforts, GEMS Informatics is paving the way for sustainable farming practices that benefit both the environment and the farming community.

​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