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

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

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
 

APIs: Now well-documented and much easier to use

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

-Yuan Chai

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

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

Join Us on this Journey! 

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

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

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