What led you to crop modeling?
“As a student majoring in Environmental Engineering, I was introduced to mathematical modeling during my Master’s degree in India. I learned my mathematical modeling instructor had worked with Dr. Stephen Hawking at Cambridge, so I tried very hard to learn from him beyond the classroom lectures. It was my instructor, Dr. Sat Ghosh, who ignited my curiosity in systems analysis and modeling.”
“My first independent modeling project was on why the marble on the Taj Mahal was becoming yellow. It was challenging, but the results from the acid rain modeling were very meaningful. Another important milestone in my student life was working on the Gaussian plume modeling on air pollution. Our research results helped engineers make decisions on where safe areas were constructing factories and how to consider the extent of dispersion of pollutants in the environment. I also worked on biogeochemical models for my Ph.D. It was during my stint as a postdoctoral researcher that I began to work with crop models.”
“You know that feeling when you love what you do? I felt like that every time I took up a process model project. The surprise element in modeling is what keeps me going.”
What is your favorite thing about being a scientist?
“I get to work with real world problems. Our research helps in developing decision support tools for farmers. I work with very smart data scientists, statisticians, agricultural scientists and engineers. It gives us a platform to think together, work together and solve problems. The diversity in our background and specialization is my favorite part of working as a scientist.”
What is one thing that would surprise people about your field of work in agriculture and data?
“We work with many different complex mathematical, statistical and systems analysis methods. The crop models need a fair understanding of linear algebra. I don’t think people always relate agriculture to complex mathematics. The buzzword “AgTech” involves many computer programming, remote sensing, mathematics and statistics involved than most people presume it to be. As a process modeler, I integrate mathematics into cropping systems and soil processes and help in better decision making. Predictive modeling in agriculture is challenging yet very interesting.”
What kinds of things do you think we should be doing to encourage more people to pursue a career in science?
“We should introduce computer coding right from middle school. You may disagree with me by saying that coding might not be of interest to every student but it opens up a plethora of opportunities for students. Today, multiple disciplines need coding for better probing into their research areas. A wildlife ecologist, an agronomist or a hydrologist all need to have a fair grip on coding. Academic institutions and industry can partner together and help in curriculum design where kids get hands-on coding experiences.”
Have you won any awards?
“I have won several performance-based scholarships throughout my Ph.D. including The Grinter Fellowship, awarded to outstanding international students and the Office of Research fellowship at the University of Florida. I’m also a member in the Gamma Sigma Delta honor society of agriculture for my academic performance.”
Chatterjee, Nilovna, et al. “Simulating Winter Rye Cover Crop Production under Alternative Management in a Corn‐Soybean Rotation.” Agronomy Journal, 26 July 2020, doi:10.1002/agj2.20377.
Chatterjee, Nilovna, et al. “Changes in Soil Carbon Stocks across the Forest-Agroforest-Agriculture/Pasture Continuum in Various Agroecological Regions: A Meta-Analysis.” Agriculture, Ecosystems and Environment, vol. 266, Nov. 2018, pp. 55–67.
Chatterjee, Nilovna, et al. “Biochar in the Agroecosystem–Climate-Change–Sustainability Nexus.” Frontiers in Plant Science, vol. 8, Dec. 2017, doi:https://www.frontiersin.org/articles/10.3389/fpls.2017.02051/full.
Chatterjee, Nilovna, et al. “Depth-Wise Distribution of Soil-Carbon Stock in Aggregate-Sized Fractions under Shaded-Perennial Agroforestry Systems in the Western Ghats of Karnataka, India.” Agroforestry Systems, vol. 94, no. 2, 2019, pp. 341–358., doi:10.1007/s10457-019-00399-z.
Chatterjee, Nilovna, et al. “Decadal Changes in Shoreline Patterns in Sundarbans, India.” Journal of Coastal Sciences, 15 July 2015, drs.nio.org/drs/bitstream/handle/2264/4847/J_Coast_Sci_2_54.pdf?sequence=1.
View Nilovna Chatterjee’s full Google Scholar profile: https://scholar.google.com.br/citations?user=9jh6uIEAAAAJ&hl=en
What brought you to CIBO?
“CIBO’s technology. Very few companies amalgamate technology, agricultural science and engineering at a large scale as smoothly as CIBO. As a crop modeler, I was aware of CIBO’s modeling platform and how they scaled it up to a whole new level. I joined CIBO because their work in the modeling teams aligned with my interest. It makes work easier when you love what you do.”
What are your primary responsibilities at CIBO?
“I am a crop modeling scientist with the Data Science and Modeling team. My primary responsibilities include developing new crop models, improving model performance, and testing and validating crop model performances. A significant part of my work involves integrating data science techniques to improve the capabilities of our models.”
What do you find the most rewarding about your work here?
“I get to work on issues in agriculture that I deeply care about at CIBO. Engineers and scientists work together to solve a common problem. My coworkers are very friendly and helpful. It makes a huge difference when you work in a healthy and happening environment such as CIBO’s.”
About Nilovna Chatterjee
Nilovna Chatterjee is a Crop Modeling Scientist at CIBO Technologies, a science-driven software startup. Prior to CIBO, she worked for the University of Nebraska-Lincoln, department of Agronomy and Horticulture as a postdoctoral research associate. She holds a Bachelor of Engineering, Master of Engineering from Vellore Institute of Technology, Vellore, India and a Ph.D. in Forest Resources Conservation and Soil Science from the University of Florida, Gainesville.