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.

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

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

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

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

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