June 28, 2017
Bruno Basso is a Foundation Professor of earth and environmental sciences in the College of Natural Science. His research falls broadly in the area of sustainable agriculture.
I was interested in becoming an agricultural scientist because I wanted to make an impact in the world and be able to contribute to the ambitious goal of making our planet a better place to live. We are only leasing our land, and ideally, we are supposed to leave it in better conditions than when we received it. Are we doing this? Well, this is what my work is about – trying to help produce more food from a healthy environment across the globe.
Extreme weather events provide challenges for agriculture. Big data can help farmers improve management strategies for water, nutrients and evaluate the economics of smart agriculture technologies and practices.
There is a limit to the amount of land we can use to grow food and, as the world’s population continues to grow, farmers will need to produce more food without increasing the amount of land they cultivate.
The pressure to increase food production can sometimes lead to loss of fertilizers through run-off and leaching, which can harm the environment. In addition, fertilizer that is lost from their fields lowers profits. Limiting over-fertilization while still maintaining or even increasing production is not an easy task.
Our $4.9 million Coordinated Agricultural Project, funded by United States Department of Agriculture’s National Institute of Food and Agriculture, is trying to do just that through the use of new technology designed to help farmers maximize crop growth and limit the loss of nutrients. This project aims to addresses the “four Rs”–placing the right kind of fertilizer at the right rate in the right place at the right time across the U.S. corn acreage.
Our project includes the use of state-of-the-art technology to identify different management zones on cultivated fields. 
We use drones equipped with cameras and sensors to evaluate crop growth throughout the growing season and determine locations within fields where growth varies. Even if a farmer applies fertilizers at a uniform rate throughout a field, the amount may be too much for one area of the field, but too little for another.
Understanding variability is one of the core goals of the project in order to help farmers make better decisions with water and nutrient management. The combination of remotely sensed data and new algorithms has allowed us to quantify nitrogen losses from U.S. corn fields. In addition, we have developed modeling software that farmers can use to digitally evaluate various fertilizer application strategies in response to the observed variability. The crop growth model includes detailed calculations on nutrient, water and carbon balances of the crop system and is coupled with other databases.
Farmers will be able to use this model to simulate crop growth under a variety of nitrogen management levels to understand the risk associated with any given nitrogen rate and the potential impact of that choice on yield, profit and the environment. The scalability of this approach will allow any farmer in the country to better manage their field and to keep nitrogen and phosphorus in the soil rather than run off into rivers.