But new research shows large-scale land acquisitions (LSLAs) in the region could threaten water resources downstream to the local farmers and Indigenous populations living along the Omo — just one example of how a decades-long “global land rush” could intensify water scarcity around the world.

Researchers at the University of Notre Dame, the Polytechnic University of Milan, the University of California, Berkeley, Colorado State University, the University of Delaware and Vrije University Amsterdam studied 160 land deals made between 2005 and 2015 across Europe, South America, Africa and Asia for the study, published in Nature Communications. Hydrological models used to simulate future cultivation of those deals found close to two-thirds of those deals are unsustainable.

“The idea of this study was to quantify how many of these deals, how much of this land acquisition would cause competition and water grabbing — and how much is actually beneficial. We found that the majority of those land acquisitions actually cause competition,” said Marc Müller, assistant professor in the Department of Civil and Environmental Engineering and Earth Sciences at Notre Dame and co-author of the study. “We looked at the crops being planted and the water needed for those crops, and what we found is that if the acquisitions go as planned, if these crops and the area of irrigated land changes the way investors have said they plan to change it — there’s not enough water for everybody.”

More than 220 million acres of land in middle-income and developing countries were purchased through LSLAs to increase production of local staple crops and to produce export-bound crops such as wheat and those that could be utilized for biofuel, such as palm oil and sugar cane.

“You need a lot of water to produce these foods,” said Müller. Some of that need is met by rainfall, or green water. When green water isn’t enough to meet the needs of the crop, the next step is supplemental irrigation using blue water, or water that can be redirected or taken from one source and used elsewhere. But supplemental irrigation is unsustainable when it depletes blue water resources — leaving independently owned farmlands and local populations hurting.

“If you have blue water scarcity — if there is not enough water to irrigate after the land acquisition — that creates competition with local water users,” Müller said. “All the communities that rely on the same water — now they don’t have enough for their own crops.”

An outcome those living along the Omo River could very well face.

As part of the study, Müller and his co-authors closely examined development of two land deals, simulating the need for surface water irrigation in Western Ethiopia, and found increases in water scarcity downstream. “That was surprising,” Müller said. “If you withdraw the water from the system upstream, it clearly affects how much stream flow is available downstream. All the people along the river that rely on that water — they will feel it. There will be less water available for use. You can see the broad effects downstream. It goes way further than I actually expected.”

The study highlights how the globalization of land and food systems can put additional stress on water resources and limit water access at the local level.

“By increasing how much water is necessary at any given locations, these deals shift the balance and water becomes very limited locally,” said Müller. “One of the arguments of this project is, are these agricultural transitions from small-holder, traditional farming to large, mechanized agriculture beneficial? You want to determine when evaluating the impact of an impending deal whether the benefits are going to outweigh the costs.”

The research is part of a series of studies the team has done to examine the impact of LSLAs on a broad scale, identifying global patterns and observations. A previous study focused on the impacts on food security.

Müller is an affiliated faculty member of Notre Dame’s Environmental Change Initiative. Co-authors of the study include Gopal Penny, then a postdoctoral student at Notre Dame and currently assistant professor at the National University of Singapore; Davide Danilo Chiarelli and Maria Cristina Rulli at the Polytechnic University of Milan; Paolo D’Odorico at the University of California, Berkeley; Nathaniel D. Mueller at Colorado State University; Kyle Frankel Davis at the University of Delaware; and Jampel Dell’Angelo at Vrije University Amsterdam.

The National Science Foundation partially funded the study.

Story Source:

Materials provided by University of Notre Dame. Original written by Jessica Sieff. Note: Content may be edited for style and length.

In the study, the team analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry.By applying a contrast pattern mining algorithm developed at the MU College of Engineering, the team was able to identify major differences in health outcomes among people living with Type 1 diabetes who do or do not have an immediate family history of the disease.

Chi-Ren Shyu, the director of the MU Institute for Data Science and Informatics (MUIDSI), led the AI approach used in the study, and said the technique is exploratory in nature.

“Here we let the computer do the work of connecting millions of dots in the data to identify only major contrasting patterns between individuals with and without a family history of Type 1 diabetes, and to do the statistical testing to make sure we are confident in our results,” said Shyu, the Paul K. and Dianne Shumaker Professor in the MU College of Engineering.

Erin Tallon, a graduate student in the MUIDSI and the lead author on the study, said the team’s analysis resulted in some unfamiliar findings.

“For instance, we found individuals in the registry who had an immediate family member with Type 1 diabetes were more frequently diagnosed with hypertension, as well as diabetes-related nerve disease, eye disease and kidney disease,” Tallon said. “We also found a more frequent co-occurrence of these conditions in individuals who had an immediate family history of Type 1 diabetes. Additionally, individuals who had an immediate family history of Type 1 diabetes also more frequently had certain demographic characteristics.”

Tallon’s passion for this project began with a personal connection, and quickly grew as a result of her experience working as a nurse in an intensive critical care unit (ICU). She would often see patients with Type 1 diabetes who were also dealing with other co-existing conditions such as kidney disease and high blood pressure. Knowing that a person’s Type 1 diabetes diagnosis often occurs only when the disease is already very advanced, she wanted to find better ways for prevention and diagnosis, starting with finding a way to analyze the large amounts of publicly available data already collected about the disease.

In 2019, Mark Clements, who is a pediatric endocrinologist at Children’s Mercy Kansas City, professor of pediatrics at University of Missouri-Kansas City and corresponding author on the study, was invited to speak at the Midwest Bioinformatics Conference hosted by BioNexus KC. While Tallon wasn’t able to attend Clements’ presentation, she followed up with a phone call to share her proposal for helping people better understand Type 1 diabetes. He was intrigued. Eventually, Tallon introduced Clements to Shyu, and an ongoing research collaboration was born.

Tallon said the results of the collaboration speak to the power and value of using real-world data.

“Type 1 diabetes is not a single disease that looks the same for everybody — it looks different for different people — and we’re working on the cutting-edge to address that issue,” Tallon said. “By analyzing real-world data, we can better understand risk factors that may cause someone to be at higher risk for developing poor health outcomes.”

While the results are promising, Tallon said researchers were limited by not having a population-based data set to work with.

“It is important to note here that our findings do have a limitation that we hope to address in the future by using larger, population-based data sets,” Tallon said. “We’re looking to build larger patient cohorts, analyze more data and use these algorithms to help us do that.”

Personalizing medicine

Clements hopes the approach can be adopted as a way to help develop personalized treatment options for people with diabetes.

“In order to get the right treatment to the right patient at the right time, we first need to understand how to identify the patients who are at a higher risk for the disease and its complications — by asking questions such as if there are characteristics early in someone’s life that can help identify an individual with high risk for an outcome years down the road,” Clements said. “Having all of this information could one day help us establish a more complete picture of a person’s risk, and we can use that information to develop a more personalized approach for both prevention and treatment.”

“Contrast pattern mining with the T1D Exchange Clinic Registry reveals complex phenotypic factors and comorbidity patterns associated with familial versus sporadic Type 1 diabetes,” was published in Diabetes Care, a journal of the American Diabetes Association. MU graduate students Danlu Liu and Katrina Boles, and Maria Redondo at Texas Children’s Hospital, also contributed to the study.

The study’s authors would like to thank the funding agency of the T1D Exchange Clinic Registry, the Helmsley Charitable Trust, the investigators located across the country who drove the data collection for the registry, as well as all of the registry’s participants and their families who were willing to share their medical information.

The researchers would also like to acknowledge the support provided by grants from the National Institutes of Health (5T32LM012410) and the National Science Foundation (CNS-1429294). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Potential conflicts of interest are also noted by two of the study’s authors — Clements and Shyu. Clements is the chief medical officer at Glooko, and receives support from Dexcom and Abbot Diabetes Care. Shyu is a consultant for Curant Health.