Climate change is expected to drastically affect future crop yields. Fortunately, scientists can develop computational models that predict how crops will respond to climate change and identify the molecular machinery in control of that response. This information can be used to bioengineer crops that are adapted to climate change and help meet growing food demand.
Many crop models are empirical. This means that their predictions are based on a wealth of data collected across years and geographical locations. They are therefore limited in their ability to predict crop response to future climate scenarios, which will include interacting environmental changes. These include higher atmospheric CO2 interacting with changes in temperature, precipitation, and other soil and climate factors.
Alternatively, mechanistic models use equations representing a crop’s physiological responses to environmental variables to predict what will happen in the real world. This allows extrapolation beyond the experimental conditions, including complex interactions.
Dr. Megan Matthews, Assistant Professor of Civil and Environmental Engineering at the University of Illinois, recently published an article in in silico Plants that describes how they were able to predict soybean growth under elevated atmospheric CO2.
To do this, they modified BioCro, a modular, semi-mechanistic dynamic crop growth model framework built on underlying biophysical and biochemical photosynthesis mechanisms. BioCro was previously parameterized for bioenergy crops. To predict soybean growth, the authors used the existing canopy photosynthesis, canopy energy balance, and soil-water processes modules and incorporated two new modules describing the rate of soybean development and carbon partitioning and senescence.
The resulting model, Soybean-BioCro, was parameterized using field measurements of soybean growing under ambient CO2. Simulations were then run to predict soybean growth using actual measurements of temperature, relative humidity, wind speed, and photosynthetically active radiation (PAR) over multiple years.
Soybean-BioCro successfully predicted how elevated CO2 impacted field-grown soybean growth, partitioning, and yield under ambient and elevated CO2 with the exception of one unusually cool growing season (see figure).
Remarkably, the model made these correct predictions without requiring field measurements of soybean growing under elevated CO2 for re-parameterization.
According to Matthews, “this outcome demonstrated that BioCro’s existing C3 photosynthesis and multilayer canopy module accurately describe the response of the C3 photosynthetic machinery at the biochemical and biophysical levels to elevated CO2.” A previous version of BioCro was found to accurately predict leaf photosynthetic rates of poplar trees.
As more mechanistic models of crop processes are developed, they can be added to Soybean-BioCro to shift it from a semi-mechanistic towards a mechanistic model. Additionally, Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.
Explains Matthews, “Soybean-BioCro is a collection of modules describing different plant processes. With this modularity, models of other metabolic processes, regulatory mechanisms, and feedback effects can be incorporated into the main Soybean-BioCro framework as these models are developed. Incorporating these types of models would help us to explore methods of engineering crops for future climates.”
READ THE ARTICLE
Megan L Matthews, Amy Marshall-Colón, Justin M McGrath, Edward B Lochocki, Stephen P Long, Soybean-BioCro: a semi-mechanistic model of soybean growth, in silico Plants, Volume 4, Issue 1, 2022, diab032, https://doi.org/10.1093/insilicoplants/diab032