Maize is the most important food crop in Sub-Saharan Africa and Latin America, and is a key Asian crop. By 2050 the demand for maize in the developing world is expected to double, while yields are expected to decline due to climate change, primarily driven by drought stress.
In a study recently published in in silico Plants, Dr. Carlos Messina, Research Fellow at Corteva Agriscience, and his colleagues are the first to develop a quantitative synthesis model of maize reproductive physiology, which captures the stages during which maize yield is most sensitive to drought.
According to Dr. Messina, “This work was only possible because of a strong partnership between Industry and Academia. It was the diversity of thought and integration of knowledge in breeding, genetics, physiology and advanced field-based phenotyping that led to the concepts that ultimately enabled predicting emergent behavior such as the dynamic relationship between growth, partitioning, anthesis-silking interval and kernel set”
The authors evaluated the model by means of simulation and experimentation under controlled patterns of water deficit. It was found to accurately simulate the dynamics of silk initiation, elongation, fertilization, and kernel growth, and was able to generate well-known emergent phenotypes such as the relationship between plant growth, anthesis-silking interval, kernel number and yield, as well as ear phenotypes under drought.
The results presented in this paper demonstrate that it is possible to predict functional relations and phenotypic responses as emergent properties of the plant system based on the interplay of the physiological processes formalized in the underpinning set of equations and integrated in crop growth models.
According to Professor Mark Cooper, Chair of Prediction Based Crop Improvement at The University of Queensland, “In addition to providing improved modelling capability to study the reproductive development and yield determination of maize this opens exciting new genomic selection opportunities for yield potential and reproductive resiliency to accelerate genetic gain by breeding.”
This quantitative dynamic framework is a significant advance from previous descriptive methods and can be used to guide the development of drought tolerance in maize.
The software and model used in this research is freely available.