Grand Challenge Fellowships
The School of Integrative Plant Science (SIPS) is offering three two-year fellowships to graduate students for research tackling our Grand Challenges.
The program seeks to:
- Recruit and support a diverse cohort of SIPS graduate students.
- Catalyze engagement with the four SIPS Grand Challenges.
- Stimulate new inter-sectional collaborations within SIPS.
Contact associated faculty for more information about each fellowship.
2025 Fellowships
Understanding factors limiting recombination in interspecific grape hybrids
Faculty in the Graduate Fields of Horticulture and Plant Biology are collaborating to understand factors that limit recombination in grape hybrids to inform development of improved breeding methods.
New York is the nation’s third leading grape producing state. Developing grape varieties that will thrive in our changing climate is critical to the sustainability of the state’s viticulture and winemaking industry.
Interspecific and intergeneric hybridization offers valuable opportunities for developing valuable traits, such as novel flavors and aromas and disease resistance. But in interspecific grape hybrids, large genome regions are recombination deprived, limiting trait introgression. The exact extent of these regions, their genomic characteristics and the source of the recombination suppression are not well understood.
This project will seek to better understand where and why recombination is suppressed in grape hybrids by:
- Investigating the meiotic recombination landscape in interspecific grape hybrids.
- Exploring how these recombination patterns correlate with known introgression regions.
- Identifying avenues to improve success of wide-cross introgression.
These objectives will be accomplished through investigation of existing hybrids in the Cornell Grape Breeding program, such as exploration of the region surrounding the RUN1 locus introgressed from Muscadine grapes (Muscadinia rotundifolia) and through the development and investigation of novel interspecific and intergeneric grape crosses.
Collaborating labs:
- Madeline Oravec, Horticulture
- Wojtek Pawlowski, Plant Biology
Investigating crop and weed cold tolerance in a changing climate
Faculty in the Graduate Fields of Soil and Crop Sciences, Plant Biology, and Plant Breeding and Genetics are collaborating to learn more about how changes in winter weather due to climate change affect the survival and range of winter pea (Pisum sativum, and important crop grown for grain, forage and as a cover crop) and Johnsongrass (Sorghum halepense, one of the planet’s worst invasive weeds).
This research will generate fundamental knowledge of the mechanisms of freeze tolerance and cold acclimation in crop and weed species and apply this knowledge to enhance sustainability of crop production in the context of climate change.
Working with faculty mentors, the fellow will have autonomy to design specific projects that may include (but are not limited to):
- Identifying genetic mechanisms associated with freezing tolerance in P. sativum.
- Evaluating genetic diversity of cold tolerance of S. halepense across Hardiness Zone 4 (northern New York) to Zone 7 (Long Island), and in controlled environments.
- Developing growth chamber protocols with improved predictive ability for winter survival across diverse field conditions.
Collaborating labs:
- Antonio DiTommaso, Soil and Crop Sciences (DiTommaso Lab website)
- Jian Hua, Plant Biology (Hua Lab website)
- Virginia Moore, Plant Breeding & Genetics (Moore Lab website)
Two birds with one stone: Building scalable predictive analytics to advance plant breeding and yield forecast with chlorophyll fluorescence remote sensing
Faculty in the Graduate Fields of Soil and Crop Sciences, and Plant Breeding and Genetics are collaborating to refine a remote sensing technique that better predicts crop yields, aids plant breeding and improves food security.
When remotely sensed, Solar-Induced chlorophyll Fluorescence (SIF), via its mechanistic linkage to electron transport rate (ETR), offers promise to work effectively at both the field and regional scales.
A model developed by the Sun Lab using SIF has proven to be a robust and scalable approach to estimating crop yield in the U.S. Corn Belt and India’s Indo-Gangetic Plain -- even outperforming AI models, especially under stress. But the broader potential of using the model to accelerate crop breeding in the developing countries that are most food insecure and vulnerable to climate changes has yet to be explored.
The fellow in this project will engage in two primary research tasks. They will:
- Use the model to forecast county-level yield in South Africa and Malawi, where there is sufficient high-quality historical ground data for validation.
- Collect SIF measurements at Cornell’s Musgrave Farm (30 miles north of the Ithaca campus) and apply the model to compute yield and infer other phenotypical traits (e.g., chlorophyll content, rubisco activities).
For both tasks, we will compare the model’s performance against AI and crop growth models.
Collaborating labs:
- Ying Sun, Soil and Crop Science (Sun Lab website)
- Kelly Robbins, Plant Breeding and Genetics (Robbins Lab website)