![apsim developer professor apsim developer professor](https://www.researchgate.net/profile/Julien-Morel-3/publication/351628018/figure/fig2/AS:1028340601344002@1622186913983/Changes-in-crops-yields-for-the-five-locations-of-the-study-Brown-colors-indicate-a_Q320.jpg)
![apsim developer professor apsim developer professor](https://www.researchgate.net/publication/332289044/figure/fig2/AS:745640933994506@1554786060886/Simulated-diurnal-responses-of-C3-and-C4-CO2-assimilation-A-hourly-transpiration-Thr_Q320.jpg)
Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments. Hu, Pengcheng, Chapman, Scott C., Dreisigacker, Susanne, Sukumaran, Sivakumar, Reynolds, Matthew and Zheng, Bangyou (2021). Journal Article: Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments With > 8500 citations, Prof Chapman is currently in the top 1% of authors cited in the ESI fields of Plant and Animal Sciences and in Agricultural Sciences. Current projects include a US DoE project with Purdue University, and multiple projects with CSIRO, U Adelaide, La Trobe, INRA (France) and U Tokyo. He has led numerous research projects that impact local and global public and private breeding programs in wheat, sorghum, sunflower and sugarcane led a national research program on research in ‘Climate-Ready Cereals’ in the early 2010s and was one of the first researchers to deploy UAV technologies to monitor plant breeding programs. Building on an almost continuous collaboration with UQ over that time, including as an Adjunct Professor to QAAFI, Prof Chapman was jointly appointed (50%) as a Professor in Crop Physiology in the UQ School of Agriculture and Food Sciences from 2017 to 2020, and at 100% with UQ from Sep 2020. Much of this research was undertaken with CSIRO since 1996.In recent years, this work has expanded more generally into various applications in digital agriculture from work on canopy temperature sensing for irrigation decisions through to applications of deep-learning to imagery to assist breeding programs. Application of quantitative approaches (crop simulation and statistical methods) and phenotyping (aerial imaging, canopy monitoring) to integrate the understanding of interactions of genetics, growth and development and the bio-physical environment on crop yield. Genetic and environment effects on physiology of field crops, particularly where drought dominates.