Charlie Messina is a professor of predictive breeding in the Department of Horticultural Sciences. Charlie works with breeders to improve the nutritional value of Florida produce and to reimagine agriculture as a solution to climate change. He also specializes in developing AI for plant breeding, which he believes will enable society to harmonize crop improvement efforts for regenerative agricultural systems that improve human health, nutrient security and adaptation to climate change.
Areas of Expertise (11)
Symbolic Artificial Intelligence
Media Appearances (3)
UF professor set to lead next generation of plant breeders
The Independent Florida Alligator online
A UF alumnus who altered the agricultural world has returned to teach students how to combat the effects of climate change on crops.
New UF/IFAS professor to combine AI and plant breeding; teach tomorrow’s AI innovators
As a child in Buenos Aires, Argentina, Carlos “Charlie” Messina knew he wanted to help people produce food worldwide, while also preserving the environment. But learning about agriculture in a big city wasn’t easy.
Plant Water Management Critical for Climate Change
Seed World online
The bourgeoning world population is challenging the agricultural industry to produce ever-increasing quantities of harvestable crops for food and feed. The ability to annually produce greater crop yields is aggravated by a warming of the global climate that results in more frequent and persistent droughts.
Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functionsIn Silico Plants
M Cooper, et. al
Plant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies.
Towards a multiscale crop modelling framework for climate change adaptation assessmentNature Plants
Bin Peng, et. al
Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships.
Integrating genetic gain and gap analysis to predict improvements in crop productivityCrop Science
Mark Cooper, et. al
A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for predicting grain yield outcomes for Genotype by Environment by Management (G×E×M) scenarios. This required development of a CGM to encode contributions of genetic and environmental determinants of biophysical processes that influence key resource (radiation, water, nutrients) use and yield-productivity within the context of the target agricultural system. Prediction of water-driven yield-productivity of maize for a wide range of G×E×M scenarios in the U.S. corn-belt is used as a case study to demonstrate applications of the framework. Three experimental evaluations are conducted to test predictions of G×E×M yield expectations derived from the framework: (1) A maize hybrid genetic gain study, (2) A maize yield potential study, and (3) A maize drought study.
Crop science: A foundation for advancing predictive agricultureCrop Science
Carlos D. Messina, et. al
This special issue in Crop Science provides a diverse cross section of views from prior and current efforts to enable prediction in agriculture. The contributions discuss and demonstrate how current advances in phenomics, genomics and artificial intelligence are being combined to explore new modeling paradigms and prediction frameworks to advance crop science and improve decision making in agriculture. The synthesis of these views can motivate a transdisciplinary dialogue to define predictive agriculture as a discipline and guide future research efforts for the integration of data-driven and science-based methodologies. Collectively, these methods can provide the needed foundation for design in agricultural and food systems (National Academies of Sciences, Engineering, and Medicine, 2019).