Sensing what plants sense to predict crop performance

The results could lead to more accurate and faster models that will allow plant breeders to develop crop varieties with desirable traits. Jianming Yu, a professor of agronomy and the Pioneer Distinguished Chair in Maize Breeding, said the study sheds light on phenotypic plasticity, or the ability of crops to adapt to environmental changes. This could help plant breeders get a better understanding of how “shapable” plant species are, or how much potential they have to perform well in different environments.

Corn, wheat and oats

The dataset included 282 inbred lines of corn evaluated in the United States and Puerto Rico; 288 inbred lines of wheat evaluated in Africa, India and Middle Eastern countries; and 433 inbred populations of oats evaluated in the United States and Canada.

Yields, plant height and flowering time

The data included environmental conditions such as temperature and availability of sunlight. The phenotypic data analyzed in the study included yields, plant height and flowering time, or the window of time during which the plant reaches the reproductive stage.

Advanced data analytics allowed the researchers to develop an environmental index, extracting the major differentiating pattern among the studied natural field conditions. With this explicit environmental dimension defined, how individual genes respond to external signals and collectively lead to the varied final performance of an organism can be systematically evaluated.

Accurate performance predictions and forecasting

The study “presents an integrated framework that not only reveals the genetic effect dynamics along an identified environmental index but also enables accurate performance predictions and forecasting,” the authors wrote in the paper yhat was published recently in the peer-reviewed academic journal Molecular Plant.

Predictions for yields more difficult

The study found the integrated framework predicted flowering time and plant height accurately, while predictions for yields were more difficult. Li said that’s most likely due to how many different environmental parameters, beyond just temperature and sunlight, affect yield at different growth stages. The research team will continue refining its methods to account for more environmental factors in an effort to better predict yields.

Yu and his collaborators first developed their initial data analytics in sorghum but have since expanded their research to include other major global crops. This could help plant scientists design a better plan for finding varieties to test.

Yu said applying advanced data analytics to all the available genomic, phenotypic and environmental data can help breeders zero in on varieties they‘re interested in much faster and more efficiently.