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.
Thomas Kono, Sean Festemaker, Kevin Silverstein, Nathan Carlson, Phil Pardey
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.
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!
Researchers that are transitioning from doing analysis locally on their laptop to remote or large scale analysis often fall into a few common traps. Moving from local analysis isn’t necessarily difficult, but there are some good habits to develop that will make your transition more productive.
Establish a small test set
The set should be representative of the variety of data in the full data set if possible, but doesn’t necessarily need to produce results similar to that of the full analysis. There are two key wins that come from having a small test set predefined.
Development loop speed
There is a temptation, especially for those who are used to working on smaller datasets, to write code and then run it against the complete dataset. However, code is almost never perfect on the first, second, or even third attempt. If running a test takes several minutes or more that can force you to focus your attention elsewhere while you wait, reducing your efficiency and causing even longer delays. It is best to have a small, even if nonsensical, test set that can run in 10 seconds or less. Rapid iteration is key, especially during early development phases.
Validating changes before large runs
The point of this set is to establish a set of unit tests that exercise the code which can be used to verify core functionality. There is little worse than starting a long running task, getting most of the way through the task, and then encountering an error due to a typo or other simple oversight that requires starting the process over again. Having tests that check for basic functionality at the edges of expected input will save a lot of time by preventing waiting for executions that can never successfully complete.
Additionally, as new errors are encountered based on real data make sure to extend both the code and the tests to appropriately handle the new cases that were improperly handled. This does not necessarily mean to solve data problems during execution, but when faults or exceptions occur due to type errors, etc, catch the error and log it in a way that can be presented to the user as part of a list of data cleaning tasks to perform before the next attempt. In a large dataset, only returning the first encountered error is a sure way to make the task take far too long. Instead, log each case as they are encountered, while ensuring that the program keeps going all of the way to the end generating an easy-to-understand error log.
Execution environment
One of the keys to ensuring reproducible behavior is tracking which external libraries are used in a program and more specifically which versions. In modern software development we are very dependent on others and their contributions to make our own development processes tractable. As with everything there are pros and cons to the way software is currently being developed. Further challenges and opportunities will arise as AI generated code, or AI assisted development becomes more common.
For now, ensure familiarity with the concept of semantic versioning. In brief, the first number is the major version, the next version is the minor version, and the third version is the patch version. For most purposes, when setting up an execution environment a good rule of thumb is to use the minor version of a library as the one to base an environment on. This should allow for fixes to be applied at the patch level, but more substantive changes can be adopted as needed.
Required libraries
In this case, required libraries only refer to the libraries that are directly imported and invoked by the code under development. Each required library may also include subsequent libraries, but trying to enumerate these or their versions will make building an execution environment much more challenging. The tradeoff is that these dependencies of dependencies can introduce unexpected changes.
Packaging systems
Once a requirements list has been established, most programming environments have tools that can be used to create an environment that includes the contents of the requirements list. In Python, this can be achieved with the pip command. However, it is important to note that some libraries will be C or C++ based and require an appropriate compiler to build.
This is where a tool like conda comes into play. Where a tool like pip will install dependencies from their source code, conda maintains repositories with prebuilt binaries instead. Conda also supports multiple languages. The tradeoff is that conda adds a significant amount of disk usage to an environment.
If the intent is to rebuild an environment on every system where the code will be executed this is not much of a problem. If, however, ensuring the exact same version of required libraries are available and the environment itself will be packaged for distribution the additional gigabytes of storage can be more of a disadvantage.
Container Images w/Docker, Apptainer, or Kubernetes
Over the last decade Docker images and other container infrastructure providers have become more popular as a way to provide a lightweight way to build a frozen, all inclusive, execution environment. This allows a researcher to deploy an identical execution environment and code from their laptop to a High Performance Computing or Cloud Computing environment.
With some planning, a researcher could develop a good test suite to ensure that their code produces expected results, create a reproducible environment to the degree of their choice, and get the best of both rapid development locally using a minimal data set as well as the ability to then run the full data set on appropriately scaled hardware elsewhere. Each case will have unique circumstances that are difficult to provide a standard solution for. However, hopefully this introduction to some of the possibilities will help researchers choose a path that will help them develop a robust approach to working with big data.
Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing
Virtually everyone in Minnesota and bordering states whose profession has a direct stake in Water Conservation assembled in Downtown St. Paul’s RiverCentre October 17-18 for the annual Water Resources Conference. I had the honor of chairing a Special Session at the conference titled “Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing.” The session featured two very foundational efforts that we are attempting to bridge together in partnered research at GEMS: (1) The Minnesota Department of Agriculture’s (MDA) Minnesota Agricultural Water Quality Certification Program (MAWQCP), directed by Brad Jordahl Redlin, and (2) the Department of Forest Resources’ remote-sensed Water Quality assessment for our 10,000+ lakes, led by Leif Olmanson.
The 90-minute session was organized so that each of the three speakers (one had dropped out last minute for logistical reasons) had 20 minutes to speak plus two minutes for burning questions. At the end the audience had 15 minutes for questions with the entire panel of speakers. There were about 30 very inquisitive people in the audience, who peppered the speakers with questions throughout.
Updates on the MDA’s certification program
Brad Jordahl Redlin kicked off the session with an outstanding talk that outlined the principles on which the certification program he founded and directs was created, and highlighted the progress that has been made. Literally over a million acres have now been certified, spread across over 1400 producers. In exchange for regulatory certainty for a period of time, these producers allow his government agency to (1) assess their farm practices, (2) suggest new management practices (sometimes with additional incentives such as subsidized equipment from a grant), and (3) carry out audits of their new practices. The program, which has been operational for nearly a decade, is well beyond the stage of corralling early-adopters. New participants span the spectrum of producer demographics in age, farm size, and more.
Scaling lake quality measurement
Leif Olmanson stepped up to review an activity that he first spearheaded twenty years ago – using remote sensing to measure the water quality of Minnesota lakes. In the interim, he has overseen steady progress using new generations of satellites (from Landsat to Sentinel), multiple metrics (clarity, chlorophyll, organic matter), and the frequency of data reporting (from once every 5 years to ~weekly lake snapshot and monthly pixel-level composites of all 10,000+ lakes). Data is made available to the casual user via a friendly Lake Browser interface, and in numeric form pixel-by-pixel for data wizards on GEMS Exchange.
GEMS bridging the two programs
David Porter rounded out the session discussing the improvements he has made in creating algorithms that properly remove clouds and aerosols from the satellite images. This is a prerequisite for feeding all of the regularly spaced (~5 day) images of a field through a growing season into a sophisticated Recurrent Neural Network (RNN, a type of Machine Learning algorithm) that his colleague Anubha Agrawal has been preparing. As described in this case study, David, Anubha, and I intend to use the RNN trained on known, certified management practices (e.g., strip till / no till, cover crops) from the MAWQCP to make a prediction of what practices growers are making on uncertified farmland. We then will trace these producer fields to the downstream lakes in each watershed, and see what covariates (e.g., slope, soil type, distance to waterway) affect the correlation of % farm practice adoption vs. downstream lake quality in a watershed.
In 2019, we began working on a project in partnership with the Centre for Agricultural Transformationto accelerate the transformation of Malawian agricultural production away from tobacco to other sustainable agri-food value chains that can provide positive economic and livelihood outcomes. Growing agri-food markets requires farmers to have more affordable and timely access to the right farm inputs, financial services, and expanding fresh and processed commodity markets at home and abroad. The obvious initial questions were which value chains to prioritize, and where in the country does one target interventions to best meet growing urban and export opportunities? But where does one find the requisite market intelligence?
While there are plenty of one-off, glossy, consultancy reports available for specific value chains in Malawi, they left us frustrated. The types of data reported are useful in providing broad contextual information but make it difficult to reach actionable conclusions. By their very nature, these reports provide static snapshots that don’t lend themselves to more user-driven questions.
Not All Farmers Are the Same
Farmers, and the farms they manage, vary in a myriad of ways that have important market development implications. Many Malawian farmers are smallholders, but there are mid-to-larger sized farms as well. They grow different crops, in different locations, with different degrees of input and output market participation, and vary in their resilience to climate and market risks. Identifying and then targeting appropriate technology, innovations and other investments towards the right market segment is key to agricultural transformation.
The Market Segmentation Tool (otherwise known as MST) is a user-driven, digital “market intelligence” tool to better inform value-chain investments. It is designed for all sorts of agri-food market participants in Malawi, notably farmer-based organizations, non-profit agencies, for-profit firms, startups and established enterprises, NGOs, government, and research organizations. This first-in-class tool was designed by the GEMS Informaticsteam at the University of Minnesota to fill an important agricultural development void. It allows users to better tailor their products and services to the unique needs of the particular on- and post-farm market segments they seek to serve.
Intuitive Interactive Intelligence
Available through a publicly available URL, MST uses sophisticated on-the-fly analytics to allow non-technical users to quickly interrogate large, complex, and disparate data on the environment, income, market accessibility and agricultural production. MST draws on more than 100 relevant variables in a spatially explicit way and presents insights through simple charts, graphs and mapped representations. These customizable figures can be saved via a screenshot and the underlying data are available for download in a .csv format at a district-level.
For example, if a user was interested in exploring the production practices of smallholders, rather than simply looking at farmers who cultivate less than 1 hectare of land, users can create a more nuanced smallholder definition. Within MST agricultural households can be readily categorized based on up to 6 segmentation variables: poverty, cropland assets, livestock assets, output market orientation, input market orientation, and income diversity. For instance, users could characterize a market cohort defined by agricultural households that cultivate between 0.5 and 1 hectare and have an average per capita consumption expenditure between 400 and 800 Kwacha per day.
Once the market cohorts are defined, the user then navigates through the dashboard to explore the historic production, market orientation and income diversification practices of households that meet the criteria defined by our poverty and cropland thresholds. Data are displayed in multi-level geographical maps as well as infographic charts, many of which are interactive, and all of which can be filtered by farm type, gender, and tobacco producers across three World Bank LSMSsurvey waves (2010-11, 2015-16, and 2019-20).
Spatially explicit market intelligence is further enhanced with grid-based data on market access and agro-ecological information such as time to market, road density, population density, agro-ecological zones, climate variables (including temperature and precipitation) and soil types, which can be accessed through the environment and market access modules.
Bottom Line. Our tool enables users to identify which farmers, for which markets, in which locales, make the most sense for their particular value-chain investments.
Feel free to contact usif you have any questions or comments on the tool, which has been designed to be extensible to additional variables and other countries with the ability to accommodate open and private data.
This posting was produced as part of the Centre for Agricultural Transformation, an effort led by Land O’Lakes Venture37 and funded with a grant from the Foundation for a Smoke-Free World, Inc. (“FSFW”), a US nonprofit 501(c)(3) private foundation. The contents, selection and presentation of facts, as well as any opinions expressed herein, are the sole responsibility of the authors and under no circumstances should they be regarded as reflecting the positions of FSFW.
Making Sense of Agriculture from Minnesota to Malawi
GEMS Sensing has coupled world-class engineering design and build expertise with first-rate agricultural science smarts to realize a robust, extensible sensing system for agri-food research anywhere in the world.
Since early 2020, GEMS Sensing has partnered with the Centre for Agricultural Transformation(CAT) in Malawi to pilot the local deployment of GEMS research-grade weather stations and its associated data visualization and sharing tool. Piloting a network of weather stations across Malawi involves much more than working through the logistics of manufacturing, delivering and deploying the necessary hardware and software. It also involves identifying partners willing to trial the weather stations on their research stations/farms and training local CAT personnel to enable the deployment, particularly in remote rural areas with uncertain cellular connectivity. Working through the CAT provides a unique opportunity to engage with private and public agribusiness throughout Malawi to test GEMS Sensing technologies in a low-income, smallholder farmer, tropical environment.
GEMS weather stations across Malawi
Over the past five years, the GEMS Sensing team has used feedback from our field partners to develop ever-improving scientific-grade, plug-and-play sensing devices coupled with a secure web-based informatics service. Weather stations aren’t new, but GEMS Sensing has created a highly customizable system that is built specifically for the varied sensing needs of researchers and their collaborators. The system's core logger box can be used for scientists with vastly different sensing requirements, from a full-stack meteorological station supporting a host of on-station or in-field crop performance studies to more specialized applications aimed at monitoring the below ground conditions that affect plant growth and health. The system's configurable and easy-to-use dashboard enables the integration, exploration, and sharing of real-time sensor data from multiple, perhaps distant, sites through a single portal.
Tracking the real-time, micro-level weather and edaphic patterns impacting agricultural production is crucial for a whole host of reasons, including advancements in breeding, agronomy, and extension efforts and the livelihoods of the farmers responsible for this production. These types of data are used for modeling weather events over large areas (e.g., rainfall and temperature) or informing management decisions like when and how much to irrigate a crop. However, being able to collect, store, clean, explore, share, and analyze these data from multiple sites, isn’t always straightforward. With this motivation in mind, the team has coupled world-class engineering design and build expertise with first-rate agricultural science smarts to realize a robust, extensible sensing system for agri-food research anywhere in the world.
GEMS Sensing in sub-Saharan Africa
For the Malawi pilot project, we worked with a South African-based manufacturer to produce the GEMS Sensing stations. The full-stack weather stations are configured to collect eight parameters every 15 minutes: air temperature, humidity, barometric pressure, rainfall, soil moisture, soil temperature, wind speed, and solar radiation. Data are sent via cellular network to a cloud-based database, processed for quality, and made accessible to partners via a user-friendly, web-based portal.
The primary objectives of the pilot exercise were to better understand:
Tropical environmental impacts on the sensing hardware;
Total cost of ownership from a user perspective (i.e., costs of acquisition, installation, and maintenance) involved in deploying digital ag weather sensors in a low-income context with limited infrastructure support; and
Malawian agribusiness’ demand, willingness to pay and potential use cases for networked weather stations (or related real-time sensing needs).
To date, the CAT team has deployed nearly 40 solar-powered weather stations across the spatially and temporally variable agro-ecologies that affect agricultural outcomes throughout the country. The stations continue to stream real-time data from experimental research stations, commercial farms, and smallholder farms. The pilot project is set to wrap-up in December 2023, though the CAT may continue to operate some of the weather stations beyond that time.
Learnings and Lessons
Scaling up a pilot project in Malawi from our operations based in Minnesota during the height of the global pandemic was not without its challenges. The original game plan was to travel with a handful of sensors to Malawi so that our technical team could work closely with CAT personnel and deployment partners to scope out the entire constellation of technical, human capital, cell connectivity, local logistics, device assembly and deployment partner protocols and other factors that could likely affect the odds of a successful outcome. Based on this test deployment we had envisaged tweaking the technical specs, revising our training and related deployment protocols before scaling up deployment throughout the country.
COVID-19 had other plans. It shut down all travel to Malawi, disrupted supply chains for computer chips that were critical to fabricating the GEMS designed sensors, and complicated shipping logistics. Undeterred, we pivoted our whole operation to enable a remote deployment, which, despite our best efforts, was not without its own complications.
Persistence ultimately paid. As we began scaling field deployment in April 2022, our technical team became adept at diagnosing issues long distance, ably supported by a great ground team in Malawi. Fluky cell connectivity issues seemed to be associated with variable soil properties that were vastly improved by simply raising the logger box further off the ground. Unexpected down-time issues were eventually tracked to battery depletion problems that arose from locally assembled devices that sat turned on for lengthy time before travel resumption enabled field deployment to proceed. The fix, change out the run-down batteries.
All told, our GEMS Sensing devices exceeded our expectations, even withstanding Tropical Cyclone Freddy that hit Malawi in February 2023!
Our team visited Malawi in May and traveled with the local CAT sensing team throughout the Southern and Central regions to get first-hand feedback from a host of public and private partners who participated in the GEMS Sensing pilot project. We made site visits with DARS research scientists, commercial agrifood businesses such as Bayer, Pxyus and Global Seeds, and One Acre Fund which services smallholder farmers directly. These partners were uniformly enthusiastic about the ready access to automatically streamed and easily accessible localized weather data. They also highlighted numerous opportunities for leveraging the streaming weather data streams with various backend analytics to tailor informatics solutions to the particular and varied agri-food production problems they faced.
GEMS sensors are currently being deployed with success across four continents. However, this represented our first widespread test of the system in a logistically challenged, low-income African agricultural setting. Prior to the pilot we had all sorts of questions as to whether or not the system would even work, and if so, how reliably and securely. Importantly, would anyone find practical value in such a sophisticated, real-time sensing system in a country dealing with many infrastructure and technological constraints.
Weather matters a lot for agriculture, whether you are farming in Minnesota or Malawi. While Minnesota farmers have access to all sorts of weather related data products, locally accurate, real-time data is still relatively scarce. In Malawi, rurally-relevant weather data of any sort is largely absent. The last time we checked, NOAA (National Oceanic and Atmospheric Administration) live stream weather data from just one site in Malawi, the Blantyre International airport! This pilot demonstrated the potential for changing this reality for Malawian farmers and the numerous public and private agencies that support their operations.
Even with its many hurdles and holdups, this pilot exercise revealed the feasibility and the latent demand for access to locally sensed weather data. With the lessons learned from our pilot deployment we are excited about the possibility of using GEMS Sensing’s data generation and analytical capabilities to support data-driven decisions that strengthen existing and emerging value chains in Malawi, as well as unlock new agri-food value-chains throughout the world.
This posting was produced as part of the Centre for Agricultural Transformation, an effort led by Land O’Lakes Venture37 and funded with a grant from the Foundation for a Smoke-Free World, Inc. (“FSFW”), a US nonprofit 501(c)(3) private foundation. The contents, selection and presentation of facts, as well as any opinions expressed herein, are the sole responsibility of the authors and under no circumstances should they be regarded as reflecting the positions of FSFW.
A Cross-Border Research Partnership with the University of Saskatchewan
Wheat Varietal Change in the US and Canada
The United States and Canada are both major wheat producers, with total harvested areas in 2021 respectively ranked 4th and 7th globally (FAOSTAT, 2023). Advancements in crop breeding and agricultural practices have led to steady productivity gains in the production of high-quality wheat varieties in both countries, positioning North America as a leading force in the world’s wheat market. Facing the challenges of climate change, increasing land and water scarcity, and emerging pest and disease threats, the resilience and productivity of wheat crops are of paramount importance in ensuring a stable and secure food supply for the continent and the world.
Using a century of data on commercial wheat crops in the United States, a recent paperpublished by researchers from the University's GEMS Informatics Center finds that the solution to sustainable wheat productivity growth lies in modern, scientifically-bred crop varieties. The study authors found that the increasingly intensive use of scientifically-selected crop varieties resulted in more biodiverse wheat production throughout the U.S., accompanied by a fourfold increase in average wheat yields over the past century.
Our Partnership with the University of Saskatchewan
To undertake a complementary, in-depth study of wheat varietal diversity and genetic improvements throughout Canada, the GEMS Informatics Center is partnering with Dr. Richard Gray’s team from the University of Saskatchewan to undertake joint research in support of the “4DWheat: Diversity, Discovery, Design and Delivery” project. The specific focus of this new study is to estimate the economic gains attributable to wheat varietal improvement in Canada and to identify the respective roles of landraces (farmer-bred varieties) along with Canadian and rest-of-world breeders in realizing these gains. In so doing, the study will quantify and characterize the shifting structure of varietal spill-ins to Canadian agriculture over the past half a century.
The first research objective of the GEMS-U Sask partnership is to evaluate the overall Genetic Gain (so-called G-gains) attributable to varietal improvement in Canadian wheat during the period 1970-2019. The study compiled comprehensive data on the areas planted to improved varieties coupled with detailed experimental data on the comparative yield performance of these varieties to assess the yield gains stemming from improved varieties relative to a chained counterfactual baseline of check varieties. To delve deeper into the different dimensions of the gains resulting from varietal improvement, we are dissecting the geography of genetic gains among the three major wheat-producing provinces (Alberta, Manitoba and Saskatchewan) and two specific classes of wheat (spring and durum).
Second, using a purpose-built data set developed by this project, we are also investigating the changing varietal diversity of Canadian wheat over the period 1970 to 2019, and partitioning the sources of yield/value gains into their various genetic parts using both phylogenetically-blind and phylogenetically-informed approaches. Our phylogenetically-informed approach utilizes the GEMS PedTools to map the lineage and evaluate the genetic relatedness among varieties when partitioning these gains. This analysis is designed to evaluate the economic value of the Canadian wheat crop that arises from various genetic sources, be that landraces, Canadian breeding programs, US breeding programs, and other international (e.g., CIMMYT/Mexico) research efforts. An additional feature of our approach is to identify the contributions coming from "dominant" varieties based on their prevalence and pedigree connections. Taken together, our findings will contribute to a better understanding of the dynamics and implications of varietal improvement in Canadian wheat crops.
Data-driven insights on the biodiversity and sustainability of wheat crops in Canada
Beginning in early 2020 our joint efforts have successfully compiled a novel Canadian wheat variety database that includes breeding passport information (both pedigree and phenotyping data) for over 310 commercial wheat varieties grown by Canadian farmers since 1970. Our initial results reveal that genetic improvements through breeding new wheat varieties are a dominant source of the overall yield gains achieved by commercial wheat farmers across the major wheat producing provinces in Canada. Furthermore, our new phylogenetically-informed partitioning procedures will help better align the costs and benefits associated with local R&D vis a vis research conducted elsewhere in the world. This work is also laying the quantitative foundations for practical, data-informed approaches to sharing the benefits arising from crop varietal change, a still often contentious aspect of the international agreements (e.g., the Nagoya Protocolembodied in the Convention on Biological Diversity) that shapes the access to and use of farmer versus scientifically bred crop varieties.
The key to sustainable agricultural productivity growth lies in embracing modern, scientifically-bred crop varieties that have demonstrated their ability to enhance biodiverse cropping practices and boost crop yields. Leveraging their respective expertise and data resources, the on-going partnership between the GEMS Informatics Center and the University of Saskatchewan is addressing the increasing challenges faced by cropping agriculture by providing data-driven insights on the biodiversity and sustainable productivity consequences of investments in modern crop breeding endeavors.
